Alderighi, T, Malomo, L, Giorgi, D, Pietroni, N, Bickel, B & Cignoni, P 2018, 'Metamolds: computational design of silicone molds', ACM Transactions on Graphics (TOG), vol. 37, pp. 136-136.
Alshehri, MD, Hussain, F & Hussain, O 2018, 'Clustering-Driven Intelligent Trust Management Methodologyfor the Internet of Things (CITM-IoT)', Mobile Networks and Applications, vol. 23, no. 3, pp. 419-431.
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The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.
Alzoubi, Y, Gill, AQ & Moulton, B 2018, 'A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development', Journal of Software Engineering Research and Development, vol. 6, no. 4.
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Anshar, M & Williams, MA 2018, 'Evolving robot empathy towards humans with motor disabilities through artificial pain generation', AIMS Neuroscience, vol. 5, no. 1, pp. 56-73.
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© 2018 the Author(s). In contact assistive robots, a prolonged physical engagement between robots and humans with motor disabilities due to shoulder injuries, for instance, may at times lead humans to experience pain. In this situation, robots will require sophisticated capabilities, such as the ability to recognize human pain in advance and generate counter-responses as follow up emphatic action. Hence, it is important for robots to acquire an appropriate pain concept that allows them to develop these capabilities. This paper conceptualizes empathy generation through the realization of synthetic pain classes integrated into a robot's self-awareness framework, and the implementation of fault detection on the robot body serves as a primary source of pain activation. Projection of human shoulder motion into the robot arm motion acts as a fusion process, which is used as a medium to gather information for analyses then to generate corresponding synthetic pain and emphatic responses. An experiment is designed to mirror a human peer's shoulder motion into an observer robot. The results demonstrate that the fusion takes place accurately whenever unified internal states are achieved, allowing accurate classification of synthetic pain categories and generation of empathy responses in a timely fashion. Future works will consider a pain activation mechanism development.
Babar, A, Bunker, D & Gill, AQ 2018, 'Investigating the Relationship between Business Analysts’ Competency and IS Requirements Elicitation: A Thematic-analysis Approach', Communications of the Association for Information Systems, vol. 42, no. 1, pp. 334-362.
Bai, L, Wang, J, Ma, X & Lu, H 2018, 'Air Pollution Forecasts: An Overview.', International journal of environmental research and public health, vol. 15, no. 4.
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Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.
Bano, M, Zowghi, D, Kearney, M, Schuck, S & Aubusson, P 2018, 'Mobile learning for science and mathematics school education: A systematic review of empirical evidence', Computers and Education, vol. 121, pp. 30-58.
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The ubiquity, flexibility, ease of access and diverse capabilities of mobile technologies make them valuable and a necessity in current times. However, they are under-utilized assets in mathematics and science school education. This article analyses the high quality empirical evidence on mobile learning in secondary school science and mathematics education. Our study employed a Systematic Literature Review (SLR) using well-accepted and robust guidelines. The SLR resulted in the detailed analysis of 49 studies (60 papers) published during 2003–2016. Content and thematic analyses were used to ascertain pedagogical approaches, methodological designs, foci, and intended and achieved outcomes of the studies. The apps and technologies used in these studies were further classified for domain, type and context of use. The review has highlighted gaps in existing literature on the topic and has provided insights that have implications for future research.
Belfin, R, Kanaga, GME & Brodka, P 2018, 'Overlapping community detection using superior seed set selection in social networks', COMPUTERS & ELECTRICAL ENGINEERING, vol. 70, pp. 1074-1083.
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Berta, M, Scholz, VB & Tomamichel, M 2018, 'Rényi Divergences as Weighted Non-commutative Vector-Valued Lp-Spaces', Annales Henri Poincaré, vol. 19, no. 6, pp. 1843-1867.
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© 2018, Springer International Publishing AG, part of Springer Nature. We show that Araki and Masuda’s weighted non-commutative vector-valued Lp-spaces (Araki and Masuda in Publ Res Inst Math Sci Kyoto Univ 18:339–411, 1982) correspond to an algebraic generalization of the sandwiched Rényi divergences with parameter α=p2. Using complex interpolation theory, we prove various fundamental properties of these divergences in the setup of von Neumann algebras, including a data-processing inequality and monotonicity in α. We thereby also give new proofs for the corresponding finite-dimensional properties. We discuss the limiting cases α→{12,1,∞} leading to minus the logarithm of Uhlmann’s fidelity, Umegaki’s relative entropy, and the max-relative entropy, respectively. As a contribution that might be of independent interest, we derive a Riesz–Thorin theorem for Araki–Masuda Lp-spaces and an Araki–Lieb–Thirring inequality for states on von Neumann algebras.
Bickel, B, Cignoni, P, Malomo, L & Pietroni, N 2018, 'State of the Art on Stylized Fabrication', Computer Graphics Forum, vol. 37, pp. 325-342.
Bluff, A, Johnston, A & Clarkson, D 2018, 'Interaction, Narrative and Animation in Live Theatre', IEEE Computer Graphics and Applications, vol. 38, no. 2, pp. 8-14.
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© 1981-2012 IEEE. Before embarking on their most ambitious project to date, a cosmically-themed participatory theatre event with interactive 3D visuals housed inside a bespoke dome structure, the interactive artists Andrew Johnston and Andrew Bluff joined the director of Stalker Theatre, David Clarkson in a round-table discussion. They reflected on what it was like to combine physical performance with interactive graphics in a childrens theatre show and discussed how the 360° format might be used to explore the cosmos in their upcoming Big Skies production.
Boixo, S, Isakov, SV, Smelyanskiy, VN, Babbush, R, Ding, N, Jiang, Z, Bremner, MJ, Martinis, JM & Neven, H 2018, 'Characterizing Quantum Supremacy in Near-Term Devices'.
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A critical question for the field of quantum computing in the near future is
whether quantum devices without error correction can perform a well-defined
computational task beyond the capabilities of state-of-the-art classical
computers, achieving so-called quantum supremacy. We study the task of sampling
from the output distributions of (pseudo-)random quantum circuits, a natural
task for benchmarking quantum computers. Crucially, sampling this distribution
classically requires a direct numerical simulation of the circuit, with
computational cost exponential in the number of qubits. This requirement is
typical of chaotic systems. We extend previous results in computational
complexity to argue more formally that this sampling task must take exponential
time in a classical computer. We study the convergence to the chaotic regime
using extensive supercomputer simulations, modeling circuits with up to 42
qubits - the largest quantum circuits simulated to date for a computational
task that approaches quantum supremacy. We argue that while chaotic states are
extremely sensitive to errors, quantum supremacy can be achieved in the
near-term with approximately fifty superconducting qubits. We introduce cross
entropy as a useful benchmark of quantum circuits which approximates the
circuit fidelity. We show that the cross entropy can be efficiently measured
when circuit simulations are available. Beyond the classically tractable
regime, the cross entropy can be extrapolated and compared with theoretical
estimates of circuit fidelity to define a practical quantum supremacy test.
Bown, O & Ferguson, S 2018, 'Creative Media + the Internet of Things = Media Multiplicities', Leonardo, vol. 51, no. 1, pp. 53-54.
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Bown, O & Ferguson, SJ 2018, 'Understanding media multiplicities', Entertainment Computing, vol. 25, pp. 62-70.
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Internet of Things (IoT) technologies enable new forms of media artworks. ‘Media multiplicities’ are defined here as creative media experiences made up of multiples of interacting and coordinated devices. In this paper, we review the state of the art of multiplicitous media artworks and provide a systematic analysis of the novel affordances and different forms such artworks can take, specifically that they are spatial, scalable, scatterable and sensing. We consider the analysis of media multiplicities from the point of view of both user experience and creative production. We offer three primary axes through which a categorisation of multiplicitous media forms can be framed: substrate versus object; composed versus self-organised, and homogeneous versus heterogeneous. We also analyse how the number of elements in the multiplicities (from tens to tens of thousands and beyond) affects the qualities of the experience.
Bracci, M, Tarini, M, Pietroni, N, Livesu, M & Cignoni, P 2018, 'HexaLab. net: an online viewer for hexahedral meshes', arXiv preprint arXiv:1806.06639.
Bródka, P, Chmiel, A, Magnani, M & Ragozini, G 2018, 'Quantifying layer similarity in multiplex networks: a systematic study.', Royal Society Open Science, vol. 5, no. 8, p. 171747.
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Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.
Cai, X, Han, J, Li, W, Zhang, R, Pan, S & Yang, L 2018, 'A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation.', IEEE transactions on neural networks and learning systems.
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Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher's needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach.
Cai, X, Han, J, Pan, S & Yang, L 2018, 'Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation', International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 591-599.
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© 2018, the Authors. Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, inter-relationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database.
Cai, Y, Pan, S, Wang, X, Chen, H, Cai, X & Zuo, M 2018, 'Measuring distance-based semantic similarity using meronymy and hyponymy relations', Neural Computing and Applications.
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© 2018, The Natural Computing Applications Forum. The assessment of semantic similarity between lexical terms plays a critical part in semantic-oriented applications for natural language processing and cognitive science. The optimization of calculation models is still a challenging issue for improving the performance of similarity measurement. In this paper, we investigate WordNet-based measures including distance-based, information-based, feature-based and hybrid. Among them, the distance-based measures are considered to have the lowest computational complexity due to simple distance calculation. However, most of existing works ignore the meronymy relation between concepts and the non-uniformity of path distances caused by various semantic relations, in which path distances are simply determined by conceptual hyponymy relation. To solve this problem, we propose a novel model to calculate the path distance between concepts, and also propose a similarity measure which nonlinearly transforms the distance to semantic similarity. In the proposed model, we assign different weights in accordance with various relations to edges that link different concepts. On basis of the distance model, we use five structure properties of WordNet for similarity measurement, which consist of multiple meanings, multiple inheritance, link type, depth and local density. Our similarity measure is compared against state-of-the-art WordNet-based measures on M&C dataset, R&G dataset and WS-353 dataset. According to experiment results, the proposed measure in this work outperforms others in terms of both Pearson and Spearman correlation coefficients, which indicates the effectiveness of our distance model. Besides, we construct six additional benchmarks to prove that the proposed measure maintains stable performance.
Cao, X, Qiu, B & Xu, G 2018, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, pp. 1-13.
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© 2018 Springer-Verlag London Ltd., part of Springer Nature We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
Cao, X, Qiu, B, Li, X, Shi, Z, Xu, G & Xu, J 2018, 'Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (kNNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DHBlan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm.
Cao, Z & Lin, CT 2018, 'Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 1032-1035.
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© 2017 IEEE. In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation.
Cao, Z, Lai, K-L, Lin, C-T, Chuang, C-H, Chou, C-C & Wang, S-J 2018, 'Exploring resting-state EEG complexity before migraine attacks.', Cephalalgia: an international journal of headache, pp. 333102417733953-333102417733953.
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Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or "normalization" of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.
Chapman, RJ, Karim, A, Huang, Z, Flammia, ST, Tomamichel, M & Peruzzo, A 2018, 'Beating the classical limits of information transmission using a quantum decoder', Physical Review A, vol. 97, no. 1.
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© 2018 American Physical Society. Encoding schemes and error-correcting codes are widely used in information technology to improve the reliability of data transmission over real-world communication channels. Quantum information protocols can further enhance the performance in data transmission by encoding a message in quantum states; however, most proposals to date have focused on the regime of a large number of uses of the noisy channel, which is unfeasible with current quantum technology. We experimentally demonstrate quantum enhanced communication over an amplitude damping noisy channel with only two uses of the channel per bit and a single entangling gate at the decoder. By simulating the channel using a photonic interferometric setup, we experimentally increase the reliability of transmitting a data bit by greater than 20% for a certain damping range over classically sending the message twice. We show how our methodology can be extended to larger systems by simulating the transmission of a single bit with up to eight uses of the channel and a two-bit message with three uses of the channel, predicting a quantum enhancement in all cases.
Chen, J, Tian, Z, Cui, X, Yin, L & Wang, X 2018, 'Trust architecture and reputation evaluation for internet of things', Journal of Ambient Intelligence and Humanized Computing, pp. 1-9.
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© 2018 The Author(s) Internet of Things (IoT) represents a fundamental infrastructure and set of techniques that support innovative services in various application domains. Trust management plays an important role in enabling the reliable data collection and mining, context-awareness, and enhanced user security in the IoT. The main tasks of trust management include trust architecture design and reputation evaluation. However, existing trust architectures and reputation evaluation solutions cannot be directly applied to the IoT, due to the large number of physical entities, the limited computation ability of physical entities, and the highly dynamic nature of the network. In comparison, it generally requires a general and flexible architecture to manage trust in such a dynamic environment as IoT. In this paper, we present IoTrust, a trust architecture that integrates Soft Defined Network (SDN) in IoT, and a cross-layer authorization protocol based on IoTrust. IoTrust and the protocol together provide a new insight for research on trust management in the IoT. For trust establishment, we further propose a Behavior-based Reputation Evaluation Scheme for the Node (BES) and an Organization Reputation Evaluation Scheme (ORES). Both our theoretical analysis and simulation results validate the efficiency of BES and ORES.
Chen, S, Wang, Z, Yuan, X & Liang, J 2018, 'Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data', Journal of Visual Languages and Computing.
Chen, Y, Dong, Y, Sun, Y & Liang, J 2018, 'A Multi-comparable visual analytic approach for complex hierarchical data', Journal of Visual Languages and Computing, vol. 47, pp. 19-30.
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© 2018 Elsevier Ltd Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in food safety. However, such standards which related to the characteristics of pesticides and the classification of agricultural products which organized into a hierarchical structure are complex and vary widely across different regions or countries. So it is a big challenge to compare multi-regional MRL standard data comprehensively. In this paper, we present a multi-comparable visual analytic approach for complex hierarchical data and a visual analytics system (McVA) to support multiple comparison and evaluation of MRL standard. With a cooperative multi-view visual design, our proposed approach links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for evaluating the completeness and strictness of MRL standards quantitatively. The case study of real problems and the positive feedback from domain experts demonstrate the effectiveness of this approach.
Chi, L, Li, B, Zhu, X, Pan, S & Chen, L 2018, 'Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts.', IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1591-1604.
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Many applications involve processing networked streaming data in a timely manner. Graph stream classification aims to learn a classification model from a stream of graphs with only one-pass of data, requiring real-time processing in training and prediction. This is a nontrivial task, as many existing methods require multipass of the graph stream to extract subgraph structures as features for graph classification which does not simultaneously satisfy "one-pass" and "real-time" requirements. In this paper, we propose an adaptive real-time graph stream classification method to address this challenge. We partition the unbounded graph stream data into consecutive graph chunks, each consisting of a fixed number of graphs and delivering a corresponding chunk-level classifier. We employ a random hashing function to compress the original node set of graphs in each chunk for fast feature detection when training chunk-level classifiers. Furthermore, a differential hashing strategy is applied to map unlimited increasing features (i.e., cliques) into a fixed-size feature space which is then used as a feature vector for stochastic learning. Finally, the chunk-level classifiers are weighted in an ensemble learning model for graph classification. The proposed method substantially speeds up the graph feature extraction and avoids unbounded graph feature growth. Moreover, it effectively offsets concept drifts in graph stream classification. Experiments on real-world and synthetic graph streams demonstrate that our method significantly outperforms existing methods in both classification accuracy and learning efficiency.
Chikara, RK, Chang, EC, Lu, Y-C, Lin, D-S, Lin, C-T & Ko, L-W 2018, 'Monetary Reward and Punishment to Response Inhibition Modulate Activation and Synchronization Within the Inhibitory Brain Network.', Frontiers in human neuroscience, vol. 12, p. 27.
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A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks.
Chotipant, S, Hussain, FK & Hussain, OK 2018, 'SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition', Concurrency and Computation: Practice and Experience, vol. 30, no. 1.
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Copyright © 2017 John Wiley & Sons, Ltd. Business service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi-automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi-automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic-based linking approaches, namely, Extended Case-based Reasoning, vector-based, and classification-based, that automatically annotate business services to relevant service concepts. Each approach assists in the single-label and multi-label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.
Chou, KP, Prasad, M, Wu, D, Sharma, N, Li, DL, Lin, YF, Blumenstein, M, Lin, WC & Lin, CT 2018, 'Robust Feature-Based Automated Multi-View Human Action Recognition System', IEEE Access, vol. 6, pp. 15283-15296.
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© 2013 IEEE. Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.
Chuang, C-H, Cao, Z, King, J-T, Wu, B-S, Wang, Y-K & Lin, C-T 2018, 'Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving.', Frontiers in neuroscience, vol. 12, p. 181.
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Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG) and 8-source/16-detector functional near-infrared spectroscopy (fNIRS). The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO) of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.
Cruz, CG, Naderpour, M & Ramezani, F 2018, 'Water resource selection and optimisation for shale gas developments in Australia: A combinatorial approach', Computers & Industrial Engineering, vol. 124, pp. 1-11.
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Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2018, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Dai, T, Gao, T, Zhu, L, Cai, X & Pan, S 2018, 'Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network', IEEE Access.
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OAPA With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike datasets.
Dai, T, Zhu, L, Cai, X, Pan, S & Yuan, S 2018, 'Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network', Journal of Ambient Intelligence and Humanized Computing, pp. 1-19.
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© 2017 Springer-Verlag Berlin Heidelberg Citation recommendation is the task of suggesting a list of references for an author given a manuscript. This is important for academic research for it provides an efficient and easy way to find relevant literatures. In this paper, we propose a novel probabilistic topic model to automatically recommend citations for researchers. The model considers not only text content similarity between papers but also community relevance among authors for effective citation recommendation. To fully utilize content and diversified link information in a bibliographic network, we extend LDA with matrix factorization, so that semantic topic learning and community detection are essentially reinforcing each other during parameter estimation. We also develop a flexible way to generate a family of citation link probability functions, which can substantially increase the model capacity. Experimental results on the ANN and DBLP dataset show that our model outperforms baseline algorithms for citation recommendation, and is capable of generating qualified author communities and topics.
Deng, K, Li, Y, Zeng, J, Yuan, M, Luo, J & Yu, JX 2018, 'User Preference Analysis for Most Frequent Peer/Dominator', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Given a set of objects O (e.g., hotels), each can be represented as a point in a multi-dimensional feature space where each dimension corresponds to one attribute of the objects (such as price). Given the preference of a customer, the objects in O not dominated by any other object (i.e., beat in all dimensions) are those worthy to be further considered. Such objects are known as skyline objects in database community. Suppose we have an object o in O. If o is a skyline point, other skyline objects are called peers of o. If o is not a skyline object, it must be dominated by some skyline objects which are called dominators of o. Given a large number of user preferences, an interesting problem is to identify the most frequent peer/dominator (MFP/MFD) of o. The MFP/MFD search has unique values in competitor analysis. However, it is a challenging task because of the complexity to process a large number of user preferences. In this work, we provide robust solutions including exact and approximate methods. The test resutls demonstrate the exact algorithms outperform various baseline algorithms significantly, and the approximate algorithms make further improvement by one order of magnitude with 90%-98% accuracy.
Ding, W, Lin, C-T & Cao, Z 2018, 'Deep Neuro-Cognitive Co-Evolution for Fuzzy Attribute Reduction by Quantum Leaping PSO With Nearest-Neighbor Memeplexes.', IEEE transactions on cybernetics.
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Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.
Ding, W, Lin, C-T & Pedrycz, W 2018, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce.', IEEE transactions on cybernetics.
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Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.
Ding, W, Lin, CT & Prasad, M 2018, 'Hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection and its application in neonatal cerebral cortex MRI', Expert Systems with Applications, vol. 101, pp. 243-257.
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© 2018 Elsevier Ltd A wide variety of feature selection methods have been developed as promising solutions to find the classification pattern inside increasing applications. But the exploring efficient, flexible and robust feature selection method to handle the rising big data is still an exciting challenge. This paper presents a novel hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection algorithm (CTFGES). It aims to select out the high-quality feature subsets, which can enrich the research of feature selection and classification in the heterogeneous big data. Firstly, we construct a flexible hierarchical co-evolutionary clustering tree model to speed up the process of feature selection, which can effectively extract the features from the parent and children branches of four-layer co-evolutionary clustering tree. Secondly, we design a mixed co-evolutionary game equilibrium scheme with adaptive dynamics to guide parent and children branch subtrees to approach the optimal equilibrium regions, and enable their feature sets to converge stably to the Nash equilibrium. So both noisy heterogeneous features and non-identified redundant ones can be further eliminated. Finally, the extensive experiments on various big datasets are conducted to demonstrate the more excellent performance of CTFGES, in terms of accuracy, efficiency and robustness, compared with the representative feature selection algorithms. In addition, the proposed CTFGES algorithm has been successfully applied into the feature segmentation of large-scale neonatal cerebral cortex MRI with varying noise ratios and intensity non-uniformity levels. The results indicate that it can be adaptive to derive from the cortical folding surfaces and achieves the satisfying consistency with medical experts, which will be potential significance for successfully assessing the impact of aberrant brain growth on the neurodevelopment of neonatal cerebrum.
Ding, W, Lin, CT, Chen, S, Zhang, X & Hu, B 2018, 'Multiagent-consensus-MapReduce-based attribute reduction using co-evolutionary quantum PSO for big data applications', Neurocomputing, vol. 272, pp. 136-153.
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© 2017 Elsevier B.V. The attribute reduction for big data applications has become an urgent challenge in pattern recognition, machine learning and data mining. In this paper, we introduce the multi-agent consensus MapReduce optimization model and co-evolutionary quantum PSO with self-adaptive memeplexes for designing the attribute reduction method, and propose a multiagent-consensus-MapReduce-based attribute reduction algorithm (MCMAR). Firstly, the co-evolutionary quantum PSO with self-adaptive memeplexes is designed for grouping particles into different memeplexes, which aims to explore the search space and locate the global best region during the attribute reduction of big datasets. Secondly, the four layers neighborhood radius framework with compensatory scheme is constructed to partition big attribute sets by exploiting the interdependency among multiple-relevant-attribute sets. Thirdly, a novel multi-agent consensus MapReduce optimization model is adopted to perform the multiple-relevance-attribute reduction, in which five kinds of agents are used to conduct the ensemble co-evolutionary optimization. So the uniform reduction framework of different agents' co-evolutionary game under the bounded rationality is further refined. Fourthly, the approximation MapReduce parallelism mechanism is permitted to formalize to the multi-agent co-evolutionary consensus structure, interaction and adaptation, which enhances different agents to share their solutions. Finally, extensive experimental studies substantiate the effectiveness and accuracy of MCMAR on some well-known benchmark datasets. Moreover, successful applications in big medical datasets are expected to dramatically scaling up MCMAR for complex infant brain MRI in terms of efficiency and feasibility.
Ding, W, Lin, CT, Prasad, M, Cao, Z & Wang, JD 2018, 'A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue', IEEE Transactions on Fuzzy Systems, vol. 26, no. 3.
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IEEE The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles & #x0027; states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered co-evolutionary model with multi-agent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependency among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR* has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise, compared with representative algorithms. Moreover, LCQ-ABR* can be successfully applied in the consistent segmentation for neonatal brain 3D-MRI, and the consistent segmentation results further demonstrate its stronger applicability.
Dong, F, Lu, J, Zhang, G & Li, K 2018, 'Active fuzzy weighting ensemble for dealing with concept drift', International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 438-450.
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© 2018, the Authors. The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles.
Dong, F, Zhang, G, Lu, J & Li, K 2018, 'Fuzzy competence model drift detection for data-driven decision support systems', Knowledge-Based Systems, vol. 143, pp. 284-294.
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© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust.
Dong, M, Yao, L, Wang, X, Benatallah, B, Huang, C & Ning, X 2018, 'Opinion fraud detection via neural autoencoder decision forest', Pattern Recognition Letters.
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© 2018 Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
Dong, X, Zheng, L, Ma, F, Yang, Y & Meng, D 2018, 'Few-Example Object Detection with Model Communication', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.
El-Sayed, H, Sankar, S, Daraghmi, YA, Tiwari, P, Rattagan, E, Mohanty, M, Puthal, D & Prasad, M 2018, 'Accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier', Sensors (Switzerland), vol. 18, no. 6.
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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.
El-Sayed, H, Sankar, S, Prasad, M, Puthal, D, Gupta, A, Mohanty, M & Lin, CT 2018, 'Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment', IEEE Access, vol. 6, pp. 1706-1717.
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© 2013 IEEE. A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network de vices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.
Fang, XS, Sheng, QZ, Wang, X, Chu, D & Ngu, AHH 2018, 'SmartVote: a full-fledged graph-based model for multi-valued truth discovery', World Wide Web.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. In the era of Big Data, truth discovery has emerged as a fundamental research topic, which estimates data veracity by determining the reliability of multiple, often conflicting data sources. Although considerable research efforts have been conducted on this topic, most current approaches assume only one true value for each object. In reality, objects with multiple true values widely exist and the existing approaches that cope with multi-valued objects still lack accuracy. In this paper, we propose a full-fledged graph-based model, SmartVote, which models two types of source relations with additional quantification to precisely estimate source reliability for effective multi-valued truth discovery. Two graphs are constructed and further used to derive different aspects of source reliability (i.e., positive precision and negative precision) via random walk computations. Our model incorporates four important implications, including two types of source relations, object popularity, loose mutual exclusion, and long-tail phenomenon on source coverage, to pursue better accuracy in truth discovery. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.
Gärdenfors, P 2018, 'Levels of communication and lexical semantics', Synthese, pp. 1-21.
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The meanings of words are not permanent but change over time. Some changes of meaning are quick, such as when a pronoun changes its reference; some are slower, as when two speakers find out that they are using the same word in different senses; and some are very slow, such as when the meaning of a word changes over historical time. A theory of semantics should account for these different time scales. In order to describe these different types of meaning changes, I present an analysis of three levels of communication: instruction, coordination of common ground and coordination of meaning. My first aim is to show that these levels must be considered when discussing lexical semantics. A second aim is to use the levels to identify the communicative roles of some of the main word classes, in particular nouns, adjectives, verbs, indexicals and quantifiers. I argue that the existence of word classes can, to a large extent, be explained by the communicative needs that arise on the different levels. © 2014 Springer Science+Business Media Dordrecht.
Gärdenfors, P & Brala-Vukanović, M 2018, 'Semantic domains of demonstratives and articles: A view of deictic referentiality explored on the paradigm of Croatian demonstratives', Lingua, vol. 201, pp. 102-118.
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© 2017 Elsevier B.V. We argue that the meanings of demonstratives and articles can be analysed in terms of a combination of the spatial domain and a small set of semantic domains. We propose that deictic terms have a 'fast semantics' in the sense that they get their meaning in the course of the communicative act, as a result of an interaction between the interlocutors, which ultimately leads to referential anchoring. We focus on the semantics of demonstratives, presenting a case study of the very rich paradigm of Croatian demonstratives. We submit that articles refer to an 'epistemic' domain that forms part of the semantic space of the common ground that is built up during a discoursive act. Finally, we compare the semantics of deictic expressions with the specificatory and predicative functions of adjectives.
Gheisari, S, Catchpoole, DR, Charlton, A & Kennedy, PJ 2018, 'Convolutional Deep Belief Network with Feature Encoding for Classification of Neuroblastoma Histological Images.', Journal of Pathology Informatics, vol. 9, p. 17.
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Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification.We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier.We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors.The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods.The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.
Gheisari, S, Catchpoole, DR, Charlton, A, Melegh, Z, Gradhand, E & Kennedy, PJ 2018, 'Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding.', Diagnostics (Basel, Switzerland), vol. 8, no. 3.
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Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.
Gill, AQ & Chew, E 2018, 'Configuration information system architecture: Insights from applied action design research', Information and Management.
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© 2018 Elsevier B.V. One of the critical information systems that enables service resilience is the service configuration information system (CiS). The fundamental challenge for organisations is the effective designing and implementation of the CiS architecture. This paper addresses this important research problem and reports insights from a completed applied action design research (ADR) project in an Australian financial services organisation. This paper aims to provide guidance to researchers and practitioners contemplating ADR, rooted in the organisational context, for practice-oriented academia-industry collaborative research. This research also contributes in terms of the CiS reference architecture design knowledge and demonstrates the applicability of the ADR method.
Gill, AQ, Henderson-Sellers, B & Niazi, M 2018, 'Scaling for agility: A reference model for hybrid traditional-agile software development methodologies', Information Systems Frontiers: a journal of research and innovation, vol. 20, no. 2, pp. 315-341.
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The adoption of agility at a large scale often requires the integration of agile and non-agile development elements for architecting a hybrid adaptive methodology. The challenge is ”which elements or components (agile or non-agile) are relevant to develop the context-aware hybrid adaptive methodology reference architecture?” This paper addresses this important challenge and develops a hybrid adaptive methodology reference architecture model using a qualitative constructive empirical research approach. In this way, we have uncovered the agility, abstraction, business value, business policy, rules, legal, context and facility elements or components that have not been explicitly modelled or discussed in International Standards (IS) such as the ISO/IEC 24744 metamodel. It is anticipated that a context-aware hybrid adaptive methodology can be architected by using the proposed context-aware hybrid adaptive methodology reference architecture elements for a particular situation when using a situational method engineering approach.
Gong, S, Wang, Z & Oberst, S 2018, 'Non-linear analysis of vibrating flip-flow screens', MATEC Web of Conferences, vol. 221, pp. 04007-04010.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2018, 'A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen.', Frontiers in Genetics, vol. 9.
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Over the last two decades, various in silico approaches have been developed and refined that attempt to identify protein and/or peptide vaccines candidates from informative signals encoded in protein sequences of a target pathogen. As to date, no signal has been identified that clearly indicates a protein will effectively contribute to a protective immune response in a host. The premise for this study is that proteins under positive selection from the immune system are more likely suitable vaccine candidates than proteins exposed to other selection pressures. Furthermore, our expectation is that protein sequence regions encoding major histocompatibility complexes (MHC) binding peptides will contain consecutive positive selection sites. Using freely available data and bioinformatic tools, we present a high-throughput approach through a pipeline that predicts positive selection sites, protein subcellular locations, and sequence locations of medium to high T-Cell MHC class I binding peptides. Positive selection sites are estimated from a sequence alignment by comparing rates of synonymous (dS) and non-synonymous (dN) substitutions among protein coding sequences of orthologous genes in a phylogeny. The main pipeline output is a list of protein vaccine candidates predicted to be naturally exposed to the immune system and containing sites under positive selection. Candidates are ranked with respect to the number of consecutive sites located on protein sequence regions encoding MHCI-binding peptides. Results are constrained by the reliability of prediction programs and quality of input data. Protein sequences from Toxoplasma gondii ME49 strain (TGME49) were used as a case study. Surface antigen (SAG), dense granules (GRA), microneme (MIC), and rhoptry (ROP) proteins are considered worthy T. gondii candidates. Given 8263 TGME49 protein sequences processed anonymously, the top 10 predicted candidates were all worthy candidates. In particular, the top ten included ROP5 and...
Graham, C, Smith, W, Moncur, W & van den Hoven, E 2018, 'Introduction: Mortality in design', Design Issues, vol. 34, no. 1, pp. 3-14.
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Gu, Y, Gu, M, Long, Y, Xu, G, Yang, Z, Zhou, J & Qu, W 2018, 'An enhanced short text categorization model with deep abundant representation', World Wide Web, pp. 1-15.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. Many researches focus on data sparsity and ambiguity issues in short text categorization. To tackle these issues, we propose a novel short text categorization strategy based on abundant representation, which utilizes Bi-directional Recurrent Neural Network(Bi-RNN) with Long Short-Term Memory(LSTM) and topic model to catch more contextual and semantic information. Bi-RNN enriches contextual information, and topic model discovers more latent semantic information for abundant text representation of short text. Experimental results demonstrate that the proposed model is comparable to state-of-the-art neural network models and method proposed is effective.
Guo, T, Pan, S, Zhu, X & Zhang, C 2018, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
Guo, X, Yu, JX, Li, RH & Miao, X 2018, 'Direction-based multiple views on data', World Wide Web, pp. 1-35.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature In this paper, we propose the direction heat maps to visualize the spreads of points w.r.t. different observers in the Euclidean space. It is a new way to look points. Traditionally, the spread of points is unique because it depends on the absolute coordinates of points w.r.t. the zero point. However, considering the relative directions of the points, the spread varies if the observers are different. To visualize the multiple views on the same set of points, we design a pie-shaped heat map. On a direction range the more points are, on the heat map the darker the corresponding sector is. Supporting by the heat map, we can visually know whether the points spread over evenly around or the points scatter only in small direction ranges. The heat map can be widely used in many decision-making applications where the tasks are direction sensitive. Typically, in bicycle sharing applications, the heat map can tell a user the general spread of the available bikes around, and the user may choose to walk on the deep coloured direction to meet more bikes. To generate the heat map, we propose the direction profile to store the direction intervals associated with the number of points in the intervals. We design an algorithm to build the direction profile incrementally. To measure a spread, we define the direction-based centrality which is a statistic value to reflect the uniform degree of the spread. The lower the centrality is, the more evenly the points spread. The observer with the minimum centrality is a direction-based center, while the observer with the maximum centrality is a direction-based edge. We design an algorithm to calculate the centralities. To find the center faster, we also design an algorithm to approximate the centralities. The experimental results show that the heat map can visualize different spreads effectively and the direction-based centralities can be calculated efficiently.
Han, B, Tsang, IW, Chen, L, Yu, CP & Fung, SF 2018, 'Progressive Stochastic Learning for Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 5136-5148.
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© 2018 IEEE. Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from "easy" tasks to "complex" tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from "reliable" labels to "noisy" labels. To realize POSTAL mechanism, we design a cluster of "screening losses," which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI1simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.1UCI is the abbreviation of University of California Irvine.
He, Q, Wang, J & Lu, H 2018, 'A hybrid system for short-term wind speed forecasting', Applied Energy, vol. 226, pp. 756-771.
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© 2018 Elsevier Ltd Wind speed forecasting is important for high-efficiency utilization of wind energy. Correspondingly, numerous researchers have always focused on the development of reliable forecasting models of wind speed, which is often noisy, unstable and irregular. Current approaches could adapt to various wind speed data. However, many of these usually ignore the importance of the selection of the modeling sample, which often results in poor forecasting performance. In this study, a hybrid forecasting system is proposed that contains three modules: data preprocessing, data clustering, and forecasting modules. In this system, the decomposing technique is applied to reduce the influence of noise within the raw data series to obtain a more stable sequence that is conducive to extract traits from the original data. To extract the characteristic of similarity within wind speed data, a kernel-based fuzzy c-means clustering algorithm is used in data clustering module. In the forecasting module, a sample with a highly similar fluctuation pattern is selected as training dataset, and which could reduce the training requirement of model to improve the forecasting accuracy. The experimental results indicate that the developed system outperforms the discussed traditional forecasting models with respect to forecasting accuracy.
He, T, Cai, L, Meng, T, Chen, L, Deng, Z & Cao, Z 2018, 'Parallel Community Detection Based on Distance Dynamics for Large-Scale Network', IEEE Access, vol. 6, pp. 42775-42789.
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© 2013 IEEE. Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007.
Hou, Z, Wang, Y, Sui, Y, Gu, J, Zhao, T & Zhou, X 2018, 'Managing high-performance computing applications as an on-demand service on federated clouds', Computers and Electrical Engineering, vol. 67, pp. 579-595.
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© 2018 Elsevier Ltd There are several challenges (e.g., imbalance between supply and demand of hardware resources and software licenses, and usability) under modern High-Performance Computing (HPC) environment. As a means of providing an on-demand service for end users, we propose a Software-as-a-Service (SaaS) approach for managing commercial HPC applications as a Web-based service deployed on top of federated clouds. Some inter-trusted private or public clouds are federated to create a unified service platform with a large amount of hardware resources. In addition, an on-demand, pay-per-use model for Web-service-enabled HPC applications is proposed. Further, we provide an economic analysis of the proposed approach from the perspective of end users, cloud service providers, and Independent Software Vendors (ISVs). We conduct a simulation using two HPC application services on three federated clouds. A combined Quality of Service (QoS) and economic evaluation demonstrates a better effect of the proposed approach comparing with existing HPC platforms.
Hu, S, Zou, L, Yu, JX, Wang, H & Zhao, D 2018, 'Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 824-837.
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© 1989-2012 IEEE. RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.
Hu, Y, Zheng, L, Yang, Y & Huang, Y 2018, 'Twitter100k: A Real-World Dataset for Weakly Supervised Cross-Media Retrieval', IEEE Transactions on Multimedia, vol. 20, no. 4, pp. 927-938.
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© 2017 IEEE. This paper contributes a new large-scale dataset for weakly supervised cross-media retrieval, named Twitter100k. Current datasets, such as Wikipedia, NUS Wide, and Flickr30k, have two major limitations. First, these datasets are lacking in content diversity, i.e., only some predefined classes are covered. Second, texts in these datasets are written in well-organized language, leading to inconsistency with realistic applications. To overcome these drawbacks, the proposed Twitter100k dataset is characterized by two aspects: it has 100 000 image-text pairs randomly crawled from Twitter, and thus, has no constraint in the image categories; and text in Twitter100k is written in informal language by the users. Since strongly supervised methods leverage the class labels that may be missing in practice, this paper focuses on weakly supervised learning for cross-media retrieval, in which only text-image pairs are exploited during training. We extensively benchmark the performance of four subspace learning methods and three variants of the correspondence AutoEncoder, along with various text features on Wikipedia, Flickr30k, and Twitter100k. As a minor contribution, we also design a deep neural network to learn cross-modal embeddings for Twitter100k. Inspired by the characteristic of Twitter100k, we propose a method to integrate optical character recognition into cross-media retrieval. The experiment results show that the proposed method improves the baseline performance.
Hua, J, Huang, M & Wang, G 2018, 'Graph Layout Performance Comparisons of Force-directed Algorithms', International Journal of Performability Engineering, vol. 14, no. 1, pp. 67-76.
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Due to force-directed algorithms’ capabilities of producing aesthetically pleasing graph layouts, which follow metrics for graph drawing aesthetics, these layouts have become the most common methods in the practical data visualization area. However, evaluating the performance of relevant algorithms remains a challenge, since graph layout quality is largely relying on aspects such as human intuition, personal judgment and methods’ pre-setting parameters. In addition, most aesthetics criteria of graph drawing conflict with each other. This study evaluated the performance measurements of four force-directed algorithms in terms of seven commonly applied aesthetic criteria based on practical raw data collected, and demonstrated the experimental framework. The early outcomes compared twenty final graph layouts and gave empirical evidences; the study may assist with future detailed force-directed algorithms selection based on users’ specific requirements.
Hua, J, Huang, M, Zreika, M & Wang, G 2018, 'Applying Data Visualization Techniques for Stock Relationship Analysis', Filomat, vol. 32, no. 5, pp. 137-144.
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Decision making in stock investment is often made based on current events in the market and the analysis of historical data on specific stocks. Besides, similar rates of price changing over a long-term period on different stocks may indicate potential connections between those listed corporations. The proposed methodology applies the force-directed algorithm and time-series chart to offer stakeholders capability to gain deeper insights initiative on potential relationships between stocks comes with less human interventions. Hence to assist in future decision making on stock investment via graph layouts.
Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2018, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters.
Huang, W, Yu, JX & Shang, Z 2018, 'Handling query skew in large indexes: a view based approach', Frontiers of Computer Science, vol. 12, no. 1, pp. 146-162.
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© 2018, Higher Education Press and Springer-Verlag GmbH Germany. Indexing is one of the most important techniques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is partially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a balanced tree-structured index in particular when datasets become larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a wellbalanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up manner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.
Huang, W, Zhang, Y, Shang, Z & Yu, JX 2018, 'To Meet or Not to Meet: Finding the Shortest Paths in Road Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 4, pp. 772-785.
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© 1989-2012 IEEE. Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. In this paper, we investigate a new problem for two users. Each user has his/her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. We model it as minimum path pair ( MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, we devise algorithms by enumerating node pairs. We adopt a location-based pruning strategy to reduce the number of node pairs for enumeration. An efficient algorithm based on point-To-point shortest path calculation is proposed to further improve query efficiency. We also give two fast approximate algorithms with approximation bounds. Extensive experiments are conducted to show the effectiveness and efficiency of our methods.
Hussain, W & Hussain, F 2018, 'Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs', Future Generation Computer Systems.
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In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider’s perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.
Hussain, W, Hussain, FK, Hussain, O, Bagia, R & Chang, E 2018, 'Risk-based framework for SLA violation abatement from the cloud service provider’s perspective', The Computer Journal, vol. 61, no. 9, pp. 1306-1322.
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The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties.
Jankowski, J, Szymanski, BK, Kazienko, P, Michalski, R & Bródka, P 2018, 'Probing Limits of Information Spread with Sequential Seeding.', Scientific reports, vol. 8, no. 1, p. 13996.
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We consider here information spread which propagates with certain probability from nodes just activated to their not yet activated neighbors. Diffusion cascades can be triggered by activation of even a small set of nodes. Such activation is commonly performed in a single stage. A novel approach based on sequential seeding is analyzed here resulting in three fundamental contributions. First, we propose a coordinated execution of randomized choices to enable precise comparison of different algorithms in general. We apply it here when the newly activated nodes at each stage of spreading attempt to activate their neighbors. Then, we present a formal proof that sequential seeding delivers at least as good spread coverage as the single stage seeding does. Moreover, we also show that, under modest assumptions, sequential seeding performs provably better than the single stage seeding using the same number of seeds and node ranking. Finally, we present experimental results comparing single stage and sequential approaches on directed and undirected graphs to the well-known greedy approach to provide the objective measure of the sequential seeding benefits. Surprisingly, applying sequential seeding to a simple degree-based selection leads to higher coverage than achieved by the computationally expensive greedy approach currently considered to be the best heuristic.
Jankowski, J, Waniek, M, Alshamsi, A, Bródka, P & Michalski, R 2018, 'Strategic distribution of seeds to support diffusion in complex networks.', PloS one, vol. 13, no. 10, p. e0205130.
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Usually, the launch of the diffusion process is triggered by a few early adopters-i.e., seeds of diffusion. Many studies have assumed that all seeds are activated once to initiate the diffusion process in social networks and therefore are focused on finding optimal ways of choosing these nodes according to a limited budget. Despite the advances in identifying influencing spreaders, the strategy of activating all seeds at the beginning might not be sufficient in accelerating and maximising the coverage of diffusion. Also, it does not capture real scenarios in which marketing campaigns continuously monitor and support the diffusion process by seeding more nodes. More recent studies investigate the possibility of activating additional seeds as the diffusion process goes forward. In this work, we further examine this approach and search for optimal ways of distributing seeds during the diffusion process according to a pre-allocated seeding budget. Theoretically, we show that a universally best solution does not exist, and we prove that finding an optimal distribution of supporting seeds over time for a particular network is an NP-hard problem. Numerically, we evaluate several seeding strategies on different networks regarding maximising the coverage and minimising the spreading time. We find that each network topology has a best strategy given some spreading parameters. Our findings can be crucial in identifying the best strategies for budget allocation in different scenarios such as marketing or political campaigns.
Jay, B 2018, 'Self-Quotation in a Typed, Intensional Lambda-Calculus', Electronic Notes in Theoretical Computer Science, vol. 336, pp. 207-222.
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© 2018 Intensional lambda-calculus adds intensional combinators to lambda-calculus to facilitate analysis. In particular, they are used to factorise data structures and programs into their components, which can be used to convert abstractions into combinators. This paper shows how to type an intensional lambda-calculus using no more than the types of System F. Even the quotation function used to access program syntax can be defined and typed, as can its inverse: the calculus supports typed self-quotation. Thus, one may freely alternate between program analysis and program execution. Proofs of all results have been verified in Coq.
Ji, K, Chen, Z, Sun, R, Ma, K, Yuan, Z & Xu, G 2018, 'GIST: A generative model with individual and subgroup-based topics for group recommendation', Expert Systems with Applications, vol. 94, pp. 81-93.
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© 2017 Elsevier Ltd In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.
Ji, S, Yu, CP, Fung, S-F, Pan, S & Long, G 2018, 'Supervised Learning for Suicidal Ideation Detection in Online User Content', COMPLEXITY.
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Jiang, W, Qi, J, Yu, JX, Huang, J & Zhang, R 2018, 'HyperX: A Scalable Hypergraph Framework', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Hypergraphs are generalizations of graphs where the hyperedges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion create excessive replicas and result in large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark. HyperX is based on the computation paradigm "Pregel", which is user-friendly and has been widely adopted by popular graph computation frameworks. To help create balanced workloads for distributed hypergraph processing, we investigate the hypergraph partitioning problem and propose a novel label propagation partitioning (LPP) algorithm. We conduct extensive experiments using both real and synthetic data. The result shows that HyperX achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption. For hypergraph partitioning, LPP outperforms the baseline algorithms significantly in these measures as well.
Jiang, X, Pan, S, Long, G, Xiong, F, Jiang, J & Zhang, C 2018, 'Cost-sensitive Parallel Learning Framework for Insurance Intelligence Operation', IEEE Transactions on Industrial Electronics.
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IEEE Recent advancements in artificial intelligence (AI) are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data is heterogeneous, and imbalanced class distribution with low frequency and high dimensions presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.
Kaiwartya, O, Abdullah, AH, Cao, Y, Lloret, J, Kumar, S, Shah, RR, Prasad, M & Prakash, S 2018, 'Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things', IEEE Internet of Things Journal, vol. 5, no. 2, pp. 571-580.
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IEEE Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for IoT. Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering fault tolerance and communication delay as two conflicting objectives. An adapted non-dominated sorting based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, fault tolerance and delay computation, crossover and mutation, and non-dominance based sorting. Analytical and simulation based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes fault tolerance for virtualization in WSNs.
Kang, G, Li, J & Tao, D 2018, 'Shakeout: A New Approach to Regularized Deep Neural Network Training', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1245-1258.
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© 1979-2012 IEEE. Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines L-{0} , L-{1} and L-{2} regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.
Kendall, S, Redshaw, S, Ward, S, Wayland, S & Sullivan, E 2018, 'Systematic review of qualitative evaluations of reentry programs addressing problematic drug use and mental health disorders amongst people transitioning from prison to communities.', Health & justice, vol. 6, no. 1, p. 4.
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The paper presents a systematic review and metasynthesis of findings from qualitative evaluations of community reentry programs. The programs sought to engage recently released adult prison inmates with either problematic drug use or a mental health disorder.Seven biomedical and social science databases, Cinahl, Pubmed, Scopus, Proquest, Medline, Sociological abstracts and Web of Science and publisher database Taylor and Francis were searched in 2016 resulting in 2373 potential papers. Abstract reviews left 140 papers of which 8 were included after detailed review. Major themes and subthemes were identified through grounded theory inductive analysis of results from the eight papers. Of the final eight papers the majority (6) were from the United States. In total, the papers covered 405 interviews and included 121 (30%) females and 284 (70%) males.Findings suggest that the interpersonal skills of case workers; access to social support and housing; and continuity of case worker relationships throughout the pre-release and post-release period are key social and structural factors in program success.Evaluation of community reentry programs requires qualitative data to contextualize statistical findings and identify social and structural factors that impact on reducing incarceration and improving participant health. These aspects of program efficacy have implications for reentry program development and staff training and broader social and health policy and services.
Kendrick, L, Musial, K & Gabrys, B 2018, 'Change point detection in social networksCritical review with experiments', Computer Science Review, vol. 29, pp. 1-13.
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© 2018 Elsevier Inc. Change point detection in social networks is an important element in developing the understanding of dynamic systems. This complex and growing area of research has no clear guidelines on what methods to use or in which circumstances. This paper critically discusses several possible network metrics to be used for a change point detection problem and conducts an experimental, comparative analysis using the Enron and MIT networks. Bayesian change point detection analysis is conducted on different global graph metrics (Size, Density, Average Clustering Coefficient, Average Shortest Path) as well as metrics derived from the Hierarchical and Block models (Entropy, Edge Probability, No. of Communities, Hierarchy Level Membership). The results produced the posterior probability of a change point at weekly time intervals that were analysed against ground truth change points using precision and recall measures. Results suggest that computationally heavy generative models offer only slightly better results compared to some of the global graph metrics. The simplest metrics used in the experiments, i.e. nodes and links numbers, are the recommended choice for detecting overall structural changes.
Kenning, G 2018, 'LAUGH: co-design for people living with advanced dementia', Australian Journal Of Dementia Care.
Kenning, G 2018, 'Reciprocal design: inclusive design approaches for people with late stage dementia', Design for Health, pp. 1-21.
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Kenning, G & Treadaway, C 2018, 'Designing for dementia: iterative grief and transitional objects', Mortality and Design in Design Issues, vol. 34, no. 1.
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King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2018, 'Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and ɣ band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.
Lenka, RK, Rath, AK, Tan, Z, Sharma, S, Puthal, D, Simha, NVR, Prasad, M, Raja, R & Tripathi, SS 2018, 'Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors', IEEE Access, vol. 6, pp. 30162-30173.
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© 2013 IEEE. Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.
Li, H, Wang, J, Lu, H & Guo, Z 2018, 'Research and application of a combined model based on variable weight for short term wind speed forecasting', Renewable Energy, vol. 116, pp. 669-684.
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© 2017 Elsevier Ltd Wind speed forecasting plays a prominent part in the operation of wind power plants and power systems. However, it is often difficult to obtain satisfactory prediction results because wind speed data comprise random nonlinear series. Current some statistical models are not proficient in predicting nonlinear time series, whereas artificial intelligence models often fall into local optima. For these reasons, a novel combined forecasting model, which combines hybrid models based on decomposed methods and optimization algorithms, is successfully developed with variable weighting combination theory for multi-step wind speed forecasting. In this model, three different hybrid models are proposed and to further improve the forecasting performance, a modified support vector regression is used to integrate all the results obtained by each hybrid model and obtain the final forecasting results. To verify the forecasting effectiveness of the proposed forecasting model, 10-min wind speed series from Penglai, China, are used as case studies. The experimental results indicate that the developed combined model not only outperforms other benchmark models but also can be satisfactorily used for planning for smart grids.
Li, X, Nie, L, Xu, H & Wang, X 2018, 'Collaborative Fall Detection Using Smart Phone and Kinect', Mobile Networks and Applications, vol. 23, no. 4, pp. 775-788.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Humanfall detection has attracted broad attentions as sensors and mobile devices are increasingly adopted in real-life scenarios such as smart homes. The complexity of activities in home environments pose severe challenges to the fall detection research with respect to the detection accuracy. We propose a collaborative detection platform that combines two subsystems: a threshold-based fall detection subsystem using mobile phones and a support vector machine (SVM)-based fall detection subsystem using Kinects. Both subsystems have their respective confidence models and the platform detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion theory-based methods. We have validated the two confidence models based on mobile phone and Kinect, which achieve the accuracy of 84.17% and 97.08%, respectively. Our collaborative fall detection approach achieves the best accuracy of 100%.
Li, Y, Qiao, Y, Wang, X & Duan, R 2018, 'Tripartite-to-Bipartite Entanglement Transformation by Stochastic Local Operations and Classical Communication and the Structure of Matrix Spaces', Communications in Mathematical Physics, vol. 358, no. 2, pp. 791-814.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. We study the problem of transforming a tripartite pure state to a bipartite one using stochastic local operations and classical communication (SLOCC). It is known that the tripartite-to-bipartite SLOCC convertibility is characterized by the maximal Schmidt rank of the given tripartite state, i.e. the largest Schmidt rank over those bipartite states lying in the support of the reduced density operator. In this paper, we further study this problem and exhibit novel results in both multi-copy and asymptotic settings, utilizing powerful results from the structure of matrix spaces. In the multi-copy regime, we observe that the maximal Schmidt rank is strictly super-multiplicative, i.e. the maximal Schmidt rank of the tensor product of two tripartite pure states can be strictly larger than the product of their maximal Schmidt ranks. We then provide a full characterization of those tripartite states whose maximal Schmidt rank is strictly super-multiplicative when taking tensor product with itself. Notice that such tripartite states admit strict advantages in tripartite-to-bipartite SLOCC transformation when multiple copies are provided. In the asymptotic setting, we focus on determining the tripartite-to-bipartite SLOCC entanglement transformation rate. Computing this rate turns out to be equivalent to computing the asymptotic maximal Schmidt rank of the tripartite state, defined as the regularization of its maximal Schmidt rank. Despite the difficulty caused by the super-multiplicative property, we provide explicit formulas for evaluating the asymptotic maximal Schmidt ranks of two important families of tripartite pure states by resorting to certain results of the structure of matrix spaces, including the study of matrix semi-invariants. These formulas turn out to be powerful enough to give a sufficient and necessary condition to determine whether a given tripartite pure state can be transformed to the bipa...
Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2018, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1.
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With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Lin, CT, Chiu, CY, Singh, AK, King, JT & Wang, YK 2018, 'A Wireless Multifunctional SSVEP-Based Brain Computer Interface Assistive System', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Lin, C-T, Chiu, T-C, Wang, Y-K, Chuang, C-H & Gramann, K 2018, 'Granger causal connectivity dissociates navigation networks that subserve allocentric and egocentric path integration.', Brain Research, vol. 1679, pp. 91-100.
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Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans.
Lin, CT, Hsieh, TY, Liu, YT, Lin, YY, Fang, CN, Wang, YK, Yen, G, Pal, NR & Chuang, CH 2018, 'Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 950-962.
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© 1989-2012 I EEE. The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.
Lin, CT, King, JT, Fan, JW, Appaji, A & Prasad, M 2018, 'The Influence of Acute Stress on Brain Dynamics during Task Switching Activities', IEEE Access, vol. 6, pp. 3249-3255.
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© 2013 IEEE. Task switching is a common method to investigate executive functions such as working memory and attention. This paper investigates the effect of acute stress on brain activity using task switching. Surprisingly few studies have been conducted in this area. There is behavioral and physiological evidence to indicate that acute stress makes the participants more tense which results in a better performance. In this current study, under stressful conditions, the participants gave quick responses with high accuracy. However, unexpected results were found in relation to salivary cortisol. Furthermore, the electroencephalogram results showed that acute stress was pronounced at the frontal and parietal midline cortex, especially on the theta, alpha, and gamma bands. One possible explanation for these results may be that the participants changed their strategy in relation to executive functions during stressful conditions by paying more attention which resulted in a higher working memory capacity which enhanced performance during the task switching.
Lin, CT, King, JT, Singh, AK, Prasad, M, Ma, Z, Lin, JW, Machado, AMC, Appaji, A & Gupta, A 2018, 'Voice Navigation Effects on Real-World Lane Change Driving Analysis using an Electroencephalogram', IEEE Access.
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OAPA Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this study, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.
Lin, CT, Nascimben, M, King, JT & Wang, YK 2018, 'Task-related EEG and HRV entropy factors under different real-world fatigue scenarios', Neurocomputing, vol. 311, pp. 24-31.
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© 2018 Elsevier B.V. We classified the alertness levels of 17 subjects in different experimental sessions in a six-month longitudinal study based on a daily sampling system and related alertness to performance on a psychomotor vigilance task (PVT). As to our best knowledge, this is the first EEG-based longitudinal study for real-world fatigue. Alertness and PVT performance showed a monotonically increasing relationship. Moreover, we identified two measures in the entropy domain from electroencephalography (EEG) and heart rate variability (HRV) signals that were able to identify the extreme classes of PVT performers. Wiener entropy on selected leads from the frontal-parietal axis was able to discriminate the group of best performers. Sample entropy from the HRV signal was able to identify the worst performers. This joint EEG-HRV quantification provides complementary indexes to indicate more reliable human performance.
Lin, CT, Prasad, M, Chung, CH, Puthal, D, El-Sayed, H, Sankar, S, Wang, YK, Singh, J & Sangaiah, AK 2018, 'IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring', IEEE Access, vol. 6, pp. 405-414.
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Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-Term tracing and research of personal sleep monitoring at home.
Liu, Q, Wu, R, Chen, E, Xu, G, Su, Y, Chen, Z & Hu, G 2018, 'Fuzzy cognitive diagnosis for modelling examinee performance', ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 4.
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© 2018 ACM. Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people's learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.
Liu, YT, Pal, NR, Marathe, AR & Lin, CT 2018, 'Weighted Fuzzy Dempster-Shafer Framework for Multimodal Information Integration', IEEE Transactions on Fuzzy Systems, vol. 26, no. 1, pp. 338-352.
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© 1993-2012 IEEE. This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidence obtained by different classification approaches, to realize a fusion system for integrating multimodal information. The Dempster-Shafer theory (D-S theory) of evidence enables us to integrate heterogeneous information from multiple sources to obtain collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, our system assigns beliefs and plausibilities to possible hypotheses of each decision maker and uses a combination rule to fuse multimodal information. For information fusion, an important step in D-S aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which remains an arduous and unsolved problem. Here, we propose a mathematical structure to aggregate weighted evidence extracted from two different types of approaches: fuzzy Naïve Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to address uncertainties between hypotheses/classes. Finally, 12 benchmark problems from the UCI machine learning repository for classification are employed to validate the proposed WFDSF-based scheme. In addition, an application of WFDSF to a practical brain-computer interface problem involving multimodal data fusion is demonstrated in this study. The experimental results show that the WFDSF is superior to several existing methods.
Lu, C, Yu, JX & Wei, H 2018, 'Exploring Triangle-Free Dense Structures', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 2, pp. 278-291.
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© 2017 IEEE. Triadic closure is ubiquitous in social networks, which refers to the property among three individuals, A, B, and C, such thatif there exist strong ties between A-B and A-C, then there must be a strong or weak tie between B-C. Related to triadic closure, thenumber of triangles has been extensively studied since it can be effectively used as a metric to analyze the structure and function of anetwork. In this paper, from a different viewpoint, we study triangle-free dense structures which have received little attention. We focuson K3;3 where there are two subsets of three vertices, a vertex in a subset has an edge connected to every vertex in another subsetwhile it does not have an edge to any other vertex in the same subset. Such Kn;n in general implies a philosophy contradiction: (a) Anytwo individuals are friends if they have no common friends, and (b) Any two individuals are not friends if they have common friends.However, we find such induced K3;3 does exist frequently, and they do not disappear over time over a real academic collaborationnetwork. In addition, in the real datasets tested, nearly all edges appearing in K3;3 appear in some triangles. We analyze the expectednumbers of induced K3;3 and triangles (D) in four representative random graph models, namely, Erdos-Renyi random graph model,Watts-Strogatz small-world model, Barabasi-Albert preferential attachment model, and configuration model, and give an algorithm toenumerate all distinct K3;3 in an undirected social network. We conduct extensive experiments on both real and synthetic datasets toconfirm our findings. As an application, such K3;3 found helps to find new stars collaborated by well-known figures who themselves donot collaborate.
Lyu, B, Qin, L, Lin, X, Chang, L & Yu, JX 2018, 'Supergraph Search in Graph Databases via Hierarchical Feature-Tree', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Most existing solutions for supergraph search follow the pruning-and-verification framework. However, they are not scalable due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationship to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the high scalability of our algorithms.
Manzoor, M, Hussain, W, Sohaib, O, Hussain, FK & Alkhalaf, S 2018, 'Methodological investigation for enhancing the usability of university websites', Journal of Ambient Intelligence and Humanized Computing, pp. 1-19.
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For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to covert prospective student inquiries into enrollment opportunities.
Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 2018, 'Extracting sentiment knowledge from pros/cons product reviews: Discovering features along with the polarity strength of their associated opinions', Expert Systems with Applications, vol. 114, pp. 267-288.
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© 2018 Elsevier Ltd Sentiment knowledge extraction is a growing area of research in the literature. It helps in analyzing users’ opinions about different entities or events, which can then be utilized by analysts for various purposes. Particularly, feature-based sentiment analysis is one of the challenging research areas that analyzes users’ opinions on various features of a product or service. Of the three formats for the product reviews, our focus in this paper is limited to analyzing the pros/cons type. Due to the nature of pros/cons reviews, they are mostly concise and follow a different structure from other review types. Therefore, specialized techniques are needed to analyze these reviews and extract the customers’ discussed product features along with their personal attitudes. In this paper, we propose the Pros/Cons Sentiment Analyzer (PCSA) framework that exploits dependency relations in extracting sentiment knowledge from pros/cons reviews. We also utilize two different lexicons to ascertain the polarity strength of the extracted features based on the customers’ opinions. Several experiments are conducted to evaluate the performance of PCSA in its different phases.
Nanda, P, Puthal, D, Obaidat, MS, Prasad, M, Mohanty, SP & Zomaya, AY 2018, 'Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing', IEEE Communications Magazine, vol. 56, no. 5, pp. 60-65.
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Fog computing is a recent research trend to bring cloud computing services to network edges. EDCs are deployed to decrease the latency and network congestion by processing data streams and user requests in near real time. EDC deployment is distributed in nature and positioned between cloud data centers and data sources. Load balancing is the process of redistributing the work load among EDCs to improve both resource utilization and job response time. Load balancing also avoids a situation where some EDCs are heavily loaded while others are in idle state or doing little data processing. In such scenarios, load balancing between the EDCs plays a vital role for user response and real-time event detection. As the EDCs are deployed in an unattended environment, secure authentication of EDCs is an important issue to address before performing load balancing. This article proposes a novel load balancing technique to authenticate the EDCs and find less loaded EDCs for task allocation. The proposed load balancing technique is more efficient than other existing approaches in finding less loaded EDCs for task allocation. The proposed approach not only improves efficiency of load balancing; it also strengthens the security by authenticating the destination EDCs.
Nawaz, F, Janjua, NK, Hussain, OK, Hussain, FK, Chang, E & Saberi, M 2018, 'Event-driven approach for predictive and proactive management of SLA violations in the Cloud of Things', Future Generation Computer Systems, vol. 84, pp. 78-97.
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© 2018 Elsevier B.V. In a dynamic environment such as the cloud-of-things, one of the most critical factors for successful service delivery is the QoS under defined constraints. Even though guarantees in the form of service level agreements (SLAs) are provided to users, many services exhibit dynamic Quality of Service (QoS) variations. This QoS variation as well as changes in the behavior and state of the service is caused by some internal events (such as varying loads) and external events (such as location and weather), which results in frequent SLA violations. Most of the existing violation prediction approaches use historic data to predict future QoS values. They do not consider dynamic changes and the events that cause these changes in QoS attributes. In this paper, we propose an event-driven-based proactive approach for predicting SLA violations by combining logic-based reasoning and probabilistic inferencing. The results show that our proposed approach is efficient and proactively identifies SLA violations under uncertain QoS observations.
Nguyen, Q, Arness, D, Sanderson, C, Simoff, S & Huang, M 2018, 'Enabling Effective Tree Exploration Using Visual Cues', Journal of Visual Languages and Computing, vol. 47, pp. 44-61.
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This article presents a new interactive visualization for exploring large hierarchical structures by providing visual cues on a node link tree visualization. Our technique provides topological previews of hidden substructures with three types of visual cues including simple cues, tree cues and treemap cues. We demonstrate the visual cues on Degree-of-Interest Tree (DOITree) due to its familiar mapping, its capability of providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. We conducted a usability study with 28 participants that measured completion time and accuracy across five different topology search tasks. The simple cues had the fastest completion time across three of the node identification tasks. The treemap cues had the highest rate of correct answers on four of the five tasks, although only reaching statistical significance for two of these. As predicted, user ratings demonstrated a preference for the easy to understand tree cues followed by the simple cue, despite this not consistently reflected in performance results.
Niazi, M, Mishra, A & Gill, AQ 2018, 'What Do Software Practitioners Really Think About Software Process Improvement Project Success? An Exploratory Study', Arabian Journal for Science and Engineering, pp. 1-17.
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Ning, X, Yac, L, Wang, X, Benatallah, B, Dong, M & Zhang, S 2018, 'Rating prediction via generative convolutional neural networks based regression', Pattern Recognition Letters.
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© 2018 Ratings are an essential criterion for evaluating the quality of movies and a critical indicator of whether a customer would watch a movie. Therefore, an important related research challenge is to predict the rating of a movie before it is released in cinema or even before it is produced. Many existing approaches fail to address this challenge because they predict movie ratings based on post-production factors such as review comments from social media. Consequently, they are generally inapplicable until a movie has been released for a certain period of time when a sufficient number of review comments have become available. In this paper, we propose a regression model based on generative convolutional neural networks for movie rating prediction. Instead of post-production factors widely used by previous work, this model learns from movies’ intrinsic pillars such as genres, budget, cast, director and plot information, which are obtainable before the production of movies. In particular, the model explores the correlations between the rating of a movie and its intrinsic attributes to predict its rating. The results can serve as a reference for investors and movie studios to determine an optimal portfolio for movie production and a guidance to the interested users to choose the movie to watch. Extensive experiments on a real dataset are benchmarked against a set of baselines and state of the art approaches. The results demonstrate the effectiveness of our approach. The proposed model is also general to be extended to handle other prediction tasks.
Niu, T, Wang, J, Lu, H & Du, P 2018, 'Uncertainty modeling for chaotic time series based on optimal multi-input multi-output architecture: Application to offshore wind speed', Energy Conversion and Management, vol. 156, pp. 597-617.
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© 2017 Elsevier Ltd Wind energy is attracting more attention with the growing demand for energy. However, the efficient development and utilization of wind energy are restricted due to the intermittency and randomness of wind speed. Although abundant investigations concerning wind speed forecasting have been conducted by numerous researchers, most of the studies merely attach importance to point forecasts, which cannot quantitatively characterize the uncertainties as developing intervals. In this study, a novel interval prediction architecture has been designed, aiming at constructing effective prediction intervals for a wind speed series, composed of a preprocessing module, a feature selection module, an optimization module, a forecast module and an evaluation module. The feature selection module, in cooperation with the preprocessing module, is developed to determine the optimal model input. Furthermore, the forecast module optimized by the optimization module is considered a predictor for giving prediction intervals. The experimental results shed light on the architecture that not only outperforms the benchmark models considered, but also has great potential for application to wind power systems.
Oberst, S & Tuttle, S 2018, 'Nonlinear dynamics of thin-walled elastic structures for applications in space', Mechanical Systems and Signal Processing, vol. 110, pp. 469-484.
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Driven by the need for multi-functionality and increasing demands for low mass and compact-stowing, unfolding, self-deploying or –morphing smart mechanical structures have become popular space engineering
designs for flexible appendages. Extensive research has been conducted on the use of tape springs as hinge deployment mechanisms for space booms, solar sails, or optical membranes or directly for used as antennas.
However, the vibrational behaviour of tape springs and its related dynamics have rarely been addressed in detail, even though missions are underway with similarly flexible appendages installed.
By conducting quasi-static bending tests on a tape spring antenna, we evidence hysteresis behaviours in both the opposite- and equal sense bending directions. Apart from the well-known snap-through buckling, the
structure exhibits torsional buckling in the equal sense bending direction before collapsing. Micro-vibrational excitation triggers nonlinear jump phenomena and the period-doubling route to chaos. Using a computational tape spring model and simplified environmental loads similar to those encountered in near-Earth orbits, coupling between the first bending and torsional modes generates a dynamic instability which is predicted by a complex eigenvalue analysis step. The current study highlights that high perturbation sensitivity and system-inherent nonlinearities can lead to stability issues.
In the course of designing a spacecraft with thin-walled appendages, system-level trade-offs are routinely performed. Since it is unclear how severely the vibrations of flexible appendages might affect their proper
functioning or the control of the spacecraft, it is of paramount importance to validate experimentally thin-walled structures thoroughly for their dynamic and stability behaviours.
Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, N & Morlock, MM 2018, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction in cavity preparations', International Journal of Mechanical Sciences, vol. 144, pp. 739-745.
Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, NP & Morlock, M 2018, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction in cavity preparations', MATEC Web of Conferences, vol. 148, pp. 14007-14007.
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Owing to an ageing population, the impact of unhealthy lifestyle, or simply congenital or gender specific issues (dysplasia), degenerative bone and joint disease (osteoarthritis) at the hip pose an increasing problem in many countries. Osteoarthritis is painful and causes mobility restrictions; amelioration is often only achieved by replacing the complete hip joint in a total hip arthroplasty (THA). Despite significant orthopaedic progress related to THA, the success of the surgical process relies heavily on the judgement, experience, skills and techniques used of the surgeon. One common way of implanting the stem into the femur is press fitting uncemented stem designs into a prepared cavity. By using a range of compaction broaches, which are impacted into the femur, the cavity for the implant is formed. However, the surgeon decides whether to change the size of the broach, how hard and fast it is impacted or when to stop the excavation process, merely based on acoustic, haptic or visual cues which are subjective. It is known that non-ideal cavity preparations increase the risk of peri-prosthetic fractures especially in elderly people.
This study reports on a simulated hip replacement surgery on a cadaver and the analysis of impaction forces and the microphone signals during compaction. The recorded transient signals of impaction forces and acoustic pressures (≈ 80 μs - 2 ms) are statistically analysed for their trend, which shows increasing heteroscedasticity in the force-pressure relationship between broach sizes.
TIKHONOV regularisation, as inverse deconvolution technique, is applied to calculate the acoustic transfer functions from the acoustic responses and their mechanical impacts. The extracted spectra highlight that sys- tem characteristics altered during the cavity preparation process: in the high-frequency range the number of resonances increased with impacts and broach size. By applying nonlinear time series analysis the system dy- namics increase in c...
Oberst, S, Niven, RK, Lester, DR, Ord, A, Hobbs, B & Hoffmann, N 2018, 'Detection of unstable periodic orbits in mineralising geological systems', Chaos, vol. 28, no. 8, pp. 085711-085721.
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Worldwide, mineral exploration is suffering from rising capital costs, due to the depletion of readily recoverable reserves and the need to discover and assess more inaccessible or geologically complex deposits. For gold exploration, this problem is particular acute. We propose an innovative approach to mineral exploration and orebody characterisation, based on the analysis of geological core data as a spatial dynamical system, using the mathematical tools of dynamical systems analysis. This approach is highly relevant for orogenic gold deposits, which - in contrast to systems formed at chemical equilibrium - exhibit many features of nonlinear dynamical systems, including episodic fluctuations on various length and time scales. Feedback relationships between thermo-chemical and deformation processes produce recurrent fluid temperatures and pressures, and the deposition of vein-filling minerals such as pyrite and gold. We therefore relax the typical assumption of chemical equilibrium and analyse the underlying processes as aseismic, non-adiabatic and inherent to a hydrothermal, nonlinear dynamical open-flow chemical reactor. These processes are approximated using the Gray-Scott model (GSM) of reaction-diffusion as complex toy system, which captures some of the features of the underlying mineralisation processes, including the spatio-temporal Turing patterns of unsteady chemical reactions. By use of this analysis, we demonstrate the capability of recurrence plots, recurrence power spectra and recurrence time probabilities to detect underlying unstable periodic orbits, as one sign of deterministic dynamics, and their robustness for the analysis of data contaminated by noise. Recurrence plot based quantification is then applied to three mineral concentrations in core data from the Sunrise Dam gold deposit in the Yilgarn region of Western Australia. Using a moving window, we reveal the episodic recurring low-dimensional dynamic structures and the period doubling rout...
Oberst, S, Tuttle, S, Griffith, D, Lambert, A & Boyce, R 2018, 'Experimental validation of tape springs to be used as thin-walled space structures', Journal of Sound and Vibration, vol. 419, pp. 558-570.
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With the advent of standardised launch geometries and off-the-shelf payloads, space programs utilising nano-satellite platforms are growing worldwide. Thin-walled, flexible and self-deployable structures are commonly used for antennae, instrument booms or solar panels owing to their lightweight, ideal packaging characteristics and near zero energy consumption. However their behaviour in space, in particular in Low Earth Orbits with continually changing environmental conditions, raises many questions. Accurate numerical models, which are often not available due to the difficulty of experimental testing under 1g-conditions, are needed to answer these questions.
In this study, we present on-earth experimental validations, as a starting point to study the response of a tape spring as a representative of thin-walled flexible structures under static and vibrational loading. Material parameters of tape springs in a singly (straight, open cylinder) and a doubly curved design, are compared to each other by combining finite element calculations, with experimental laser vibrometry within a single and multi-stage model updating approach. While the determination of the Young's modulus is unproblematic, the damping is found to be inversely proportional to deployment length. With updated material properties the buckling instability margin is calculated using different slenderness ratios. Results indicate a high sensitivity of thin-walled structures to miniscule perturbations, which makes proper experimental testing a key requirement for stability prediction on thin-elastic space structures. The doubly curved tape spring provides closer agreement with experimental results than a straight tape spring design.
Ojha, S, Williams, MA & Johnston, B 2018, 'The Essence of Ethical Reasoning in Robot-Emotion Processing', International Journal of Social Robotics, vol. 10, no. 2, pp. 211-223.
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© 2017, Springer Science+Business Media B.V., part of Springer Nature. As social robots become more and more intelligent and autonomous in operation, it is extremely important to ensure that such robots act in socially acceptable manner. More specifically, if such an autonomous robot is capable of generating and expressing emotions of its own, it should also have an ability to reason if it is ethical to exhibit a particular emotional state in response to a surrounding event. Most existing computational models of emotion for social robots have focused on achieving a certain level of believability of the emotions expressed. We argue that believability of a robot’s emotions, although crucially necessary, is not a sufficient quality to elicit socially acceptable emotions. Thus, we stress on the need of higher level of cognition in emotion processing mechanism which empowers social robots with an ability to decide if it is socially appropriate to express a particular emotion in a given context or it is better to inhibit such an experience. In this paper, we present the detailed mathematical explanation of the ethical reasoning mechanism in our computational model, EEGS, that helps a social robot to reach to the most socially acceptable emotional state when more than one emotions are elicited by an event. Experimental results show that ethical reasoning in EEGS helps in the generation of believable as well as socially acceptable emotions.
Orth, D, Thurgood, C & van den Hoven, E 2018, 'Designing Objects with Meaningful Associations', International Journal of Design, vol. 12, no. 2, pp. 91-104.
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Objects often become cherished for their ties to beliefs, experiences, memories, people, places or values that are significant to their owner. These ties can reflect the ways in which we as humans use objects to characterise, communicate and develop our sense of self. This paper outlines our approach to applying product attachment theory to design practices. We created six artefacts that were inspired by interviews conducted with three individuals who discussed details of their life stories. We then evaluated the associations that came to mind for our participants when interacting with these newly designed artefacts to determine whether these links brought meaning to them. Our findings highlight the potential of design to bring emotional value to products by embodying significant aspects of a person’s self-identity. To do so, designers must consider both the importance and authenticity of the associations formed between an object and an individual.
Orth, D, Thurgood, C & van den Hoven, E 2018, 'Designing Objects with Meaningful Associations', INTERNATIONAL JOURNAL OF DESIGN, vol. 12, no. 2, pp. 91-104.
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Patel, OP, Tiwari, A, Chaudhary, R, Nuthalapati, SV, Bharill, N, Prasad, M, Hussain, FK & Hussain, OK 2018, 'Enhanced quantum-based neural network learning and its application to signature verification', Soft Computing, pp. 1-14.
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© 2017 Springer-Verlag GmbH Germany, part of Springer Nature In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm.
Pfister, C, Rol, MA, Mantri, A, Tomamichel, M & Wehner, S 2018, 'Capacity estimation and verification of quantum channels with arbitrarily correlated errors.', Nature communications, vol. 9, no. 1, pp. 27-27.
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The central figure of merit for quantum memories and quantum communication devices is their capacity to store and transmit quantum information. Here, we present a protocol that estimates a lower bound on a channel's quantum capacity, even when there are arbitrarily correlated errors. One application of these protocols is to test the performance of quantum repeaters for transmitting quantum information. Our protocol is easy to implement and comes in two versions. The first estimates the one-shot quantum capacity by preparing and measuring in two different bases, where all involved qubits are used as test qubits. The second verifies on-the-fly that a channel's one-shot quantum capacity exceeds a minimal tolerated value while storing or communicating data. We discuss the performance using simple examples, such as the dephasing channel for which our method is asymptotically optimal. Finally, we apply our method to a superconducting qubit in experiment.
Pham, P. & Huang, M. 2018, 'Quantitative Approach on Parallel Coordinates andScatter Plots for Multidimensional-Data Visual Analytics', Journal of computers, vol. 13, no. 4, pp. 407-416.
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Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases.
Ploderer, B & Leong, TW 2018, 'Manual engagement and automation in amateur photography', Media International Australia, vol. 166, no. 1, pp. 44-56.
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© 2017, © The Author(s) 2017. Automation has been central to the development of modern photography and, in the age of digital and smartphone photography, now largely defines everyday experience of the photographic process. In this article, we question the acceptance of automation as the default position for photography, arguing that discussions of automation need to move beyond binary concerns of whether to automate or not and, instead, to consider what is being automated and the degree of automation couched within the particularities of people’s practices. We base this upon findings from ethnographic fieldwork with people engaging manually with film-based photography. While automation liberates people from having to interact with various processes of photography, participants in our study reported a greater sense of control, richer experiences and opportunities for experimentation when they were able to engage manually with photographic processes.
Qumer Gill, A, Loumish, A, Riyat, I & Han, S 2018, 'DevOps for information management systems', VINE Journal of Information and Knowledge Management Systems, vol. 48, no. 1, pp. 122-139.
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© 2018, Emerald Publishing Limited. Purpose: Development and operations (DevOps) is complex in nature. Organizations are unsure how to effectively establish a DevOps capability for the continuous delivery of information management systems. This paper aims to compile and analyze DevOps by applying the well-known systematic literature review (SLR) approach. This review is intended to provide a knowledge base to support the informed, effective and less risky adoption of DevOps for information management systems. Design/methodology/approach: In this qualitative research study, the SLR method was applied to identify 3,790 papers, of which, 32 relevant papers were selected and reviewed. Findings: The results are organized using the well-known ISO/IEC 24744 metamodel elements: people (roles), process, technology and artifacts. In total 11 major roles, 6 processes, 23 technologies, 5 artifacts and 7 challenges (including 6 corresponding solutions) were found. DevOps engineer is becoming a newly identified role. Continuous delivery pipeline and continuous improvement are the most highlighted major DevOps processes. Build system technology is becoming the key focus of DevOps. Finally, major challenges are around people and culture and the misunderstanding of DevOps. Potential research areas are: DevOps analytics, artifacts and tool–chain integration. Research limitations/implications: The research findings will serve as a resource for both practitioners and researchers who have interest in the research and adoption of DevOps for information management systems. Originality/value: This paper provides a comprehensive systematic review of the body of knowledge to support the ongoing research and adoption of emerging trends of DevOps for information management systems.
Redshaw, S 2018, 'Combustion, hydraulic, and other forms of masculinity: An essay exploring dominant values and representations of the driver in driverless technology', Transfers, vol. 8, no. 1, pp. 86-103.
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© Transfers. This article presents two representations of masculinity based on media images found in television and online promotion related to motor vehicles. The dominant image in much advertising is the bursting, thrusting power of what I refer to as "combustion" masculinity, identified as active engagement and connected with significant road trauma. The less visible, fluid power found in professional driving that I refer to as "hydraulic" masculinity draws on precision and awareness of the surroundings rather than aggressive force. Social analysis of electric and driverless vehicle promotion and media discussion indicate that moving to electric and fully automated driving requires overcoming the essential contradiction of combustion power, as it is associated with cars and freedom. Alternative modes and images of being mobile must be highlighted in order to challenge the combustion image. Fundamentally, activity should be ascribed to all mobile persons, and policy and mobility systems should be designed to maximize mobility for all.
Redshaw, S, Ingham, V, McCutcheon, M, Hicks, J & Burmeister, O 2018, 'Assessing the impact of vulnerability on perceptions of social cohesion in the context of community resilience to disaster in the Blue Mountains.', Australian Journal of Rural Health, vol. 26, no. 1, pp. 14-19.
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To assess the impact of network communications, community participation and elements of vulnerability on the perception of social cohesiveness in the Blue Mountains local government area (Blue Mountains LGA).A questionnaire was administered to residents of the Blue Mountains LGA. Econometric analysis of the resulting data was undertaken.Blue Mountains LGA, Australia.One thousand one hundred and three residents of the Blue Mountains LGA responded to the questionnaire.The responses enabled the construction of variables measuring individual perceptions of community cohesiveness, their network communications and community participation. Demographic data and data on the vulnerabilities of individuals were also collected.The data were used in an econometric model which identified that network communications and community participation impacted positively on perceptions of social cohesiveness while vulnerability factors had a negative impact.Remedial action to build community cohesiveness and network communications can be expected to have a positive impact on social cohesiveness. In developing strategies to build community cohesiveness and network communication, particular care needs to be taken to ensure the inclusion of those members of society who are regarded as the most vulnerable.
Redshaw, SJ & Ingham, V 2018, '‘Neighbourhood is if they come out and talk to you’: neighbourly connections and bonding social capital as weak or strong ties', Journal of Sociology.
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Neighbourly relations have been theorised as ‘friendly distance’ in contrast to connections which are theorised as strong or intensive ties. The article explores the neighbourly relationships between residents of a peri-urban regional area outside Sydney in Australia. Strong interview themes emerged regarding the ways in which residents who were well connected within their locality talked about their neighbours, and this was in direct contrast to those living with a chronic condition – these people expressed a lack of connection with their neighbours. The major theme, ‘not in each other’s pockets’ reflects the negotiated nature of neighbour interactions, while the theme ‘neighbourhood is if they come out and talk to you’ speaks of isolation. The interactions of neighbours may in many cases constitute bonding capital as important weak or casual ties. These may not be available to the chronically ill or socially isolated or adequate without linking and bridging capital.
Rezvani, M, Liang, W, Liu, C & Yu, JX 2018, 'Efficient Detection of Overlapping Communities Using Asymmetric Triangle Cuts', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 11, pp. 2093-2105.
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© 1989-2012 IEEE. Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising.
Saberi, M, Theobald, M, Hussain, OK, Chang, E & Hussain, FK 2018, 'Interactive feature selection for efficient customer recognition in contact centers: Dealing with common names', Expert Systems with Applications, vol. 113, pp. 356-376.
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© 2018 Elsevier Ltd We propose an interactive decision-making framework to assist a Customer Service Representative (CSR) in the efficient and effective recognition of customer records in a database with many ambiguous entries. Our proposed framework consists of three integrated modules. The first module focuses on the detection and resolution of duplicate records to improve effectiveness and efficiency in customer recognition. The second module determines the level of ambiguity in recognizing an individual customer when there are multiple records with the same name. The third module recommends the series of feature-related questions that the CSR should ask the customer to enable rapid recognition, based on that level of ambiguity. In the first module, the F-Swoosh approach for duplicate detection is used, and in the second module a dynamic programming-based technique is used to determine the level of ambiguity within the customer database for a given name. In the third module, Levenshtein edit distance is used for feature selection in combination with weights based on the Inverse Document Frequency (IDF) of terms. The algorithm that requires the minimum number of questions to be put to the customer to achieve recognition is the algorithm that is chosen. We evaluate the proposed framework on a synthetic dataset and demonstrate how it assists the CSR to rapidly recognize the correct customer.
Singh, AK 2018, 'Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering', Journal of Healthcare Engineering, vol. 2018.
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Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
Singh, AK, Chen, HT, Cheng, YF, King, JT, Ko, LW, Gramann, K & Lin, CT 2018, 'Visual Appearance Modulates Prediction Error in Virtual Reality', IEEE Access, vol. 6, pp. 24617-24624.
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© 2013 IEEE. Different rendering styles induce different levels of agency and user behaviors in virtual reality environments. We applied an electroencephalogram-based approach to investigate how the rendering style of the users' hands affects behavioral and cognitive responses. To this end, we introduced prediction errors due to cognitive conflicts during a 3-D object selection task by manipulating the selection distance of the target object. The results showed that, for participants with high behavioral inhibition scores, the amplitude of the negative event-related potential at approximately 50-250 ms correlated with the realism of the virtual hands. Concurring with the uncanny valley theory, these findings suggest that the more realistic the representation of the user's hand is, the more sensitive the user becomes toward subtle errors, such as tracking inaccuracies.
Song, J, Luo, X, Gao, J, Zhou, C, Wei, H & Yu, JX 2018, 'UniWalk: Unidirectional Random Walk Based Scalable SimRank Computation over Large Graph', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 992-1006.
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© 1989-2012 IEEE. SimRank is an important measure of vertex-pair similarity according to the structure of graphs. Although progress has been achieved, existing methods still face challenges to handle large graphs. Besides huge index construction and maintenance cost, existing methods may require considerable search space and time overheads in the online SimRank query. In this paper, we design a Monte Carlo based method, UniWalk, to enable the fast top- $k$ SimRank computation over large undirected graphs. UniWalk directly locates the top- $k$ similar vertices for any single source vertex $u$ via $R$ sampling paths originating from $u$ , which avoids selecting candidate vertex set $\mathcal{C}$ and the following $O(|\mathcal{C}|R)$ bidirectional sampling paths. We also devise a path enumeration strategy to improve the SimRank precision by using path probabilities instead of path frequencies when sampling, a space-efficient method to reduce intermediate results, and a path-sharing strategy to lower the redundant path sampling cost for multiple source vertices. Furthermore, we extend UniWalk to existing distributed graph processing frameworks to improve its scalability. We conduct extensive experiments to illustrate that UniWalk has high scalability, and outperforms the state-of-the-art methods by orders of magnitude.
Song, J, Wang, J & Lu, H 2018, 'A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting', Applied Energy, vol. 215, pp. 643-658.
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© 2018 Elsevier Ltd Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.
Stender, M, Tiedemann, M, Hoffmann, N & Oberst, S 2018, 'Impact of an irregular friction formulation on dynamics of a minimal model for brake squeal', Mechanical Systems and Signal Processing, vol. 107, pp. 439-451.
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Friction-induced vibrations are of major concern in the design of reliable, efficient and comfortable technical systems. Well-known examples for systems susceptible to self-excitation can be found in fluid structure interaction, disk brake squeal, rotor dynamics, hip implants noise and many more. While damping elements and amplitude reduction are well-understood in linear systems, nonlinear systems and especially self-excited dynamics still constitute a challenge for damping element design. Additionally, complex dynamical systems exhibit deterministic chaotic cores which add severe sensitivity to initial conditions to the system response. Especially the complex friction interface dynamics remain a challenging task for measurements and modeling. Today, mostly simple and regular friction models are investigated in the field of self-excited brake system vibrations. This work aims at investigating the effect of high-frequency irregular interface dynamics on the nonlinear dynamical response of a self-excited structure. Special focus is put on the characterization of the system response time series.
A low-dimensional minimal model is studied which features self-excitation, gyroscopic effects and friction-induced damping. Additionally, the employed friction formulation exhibits temperature as inner variable and superposed chaotic fluctuations governed by a Lorenz attractor. The time scale of the irregular fluctuations is chosen one order smaller than the overall system dynamics. The influence of those fluctuations on the structural response is studied in various ways, i.e. in time domain and by means of recurrence analysis. The separate time scales are studied in detail and regimes of dynamic interactions are identified. The results of the irregular friction formulation indicate dynamic interactions on multiple time scales, which trigger larger vibration amplitudes as compared to regular friction formulations conventionally studied in the field of friction-induced vibr...
Sui, Y & Xue, J 2018, 'Value-Flow-Based Demand-Driven Pointer Analysis for C and C++', IEEE Transactions on Software Engineering.
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IEEE We present SUPA, a value-flow-based demand-driven flow- and context-sensitive pointer analysis with strong updates for C and C++ programs. SUPA enables computing points-to information via value-flow refinement, in environments with small time and memory budgets. We formulate SUPA by solving a graph-reachability problem on an inter-procedural value-flow graph representing a program's def-use chains, which are pre-computed efficiently but over-approximately. To answer a client query (a request for a variable's points-to set), SUPA reasons about the flow of values along the pre-computed def-use chains sparsely (rather than across all program points), by performing only the work necessary for the query (rather than analyzing the whole program). In particular, strong updates are performed to filter out spurious def-use chains through value-flow refinement as long as the total budget is not exhausted.
Sui, Y, Fan, X, Zhou, H & Xue, J 2018, 'Loop-Oriented pointer analysis for automatic SIMD vectorization', ACM Transactions on Embedded Computing Systems, vol. 17, no. 2.
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© 2018 ACM. Compiler-based vectorization represents a promising solution to automatically generate code that makes efficient use of modern CPUs with SIMD extensions. Two main auto-vectorization techniques, superword-level parallelism vectorization (SLP) and loop-level vectorization (LLV), require precise dependence analysis on arrays and structs to vectorize isomorphic scalar instructions (in the case of SLP) and reduce dynamic dependence checks at runtime (in the case of LLV). The alias analyses used in modern vectorizing compilers are either intra-procedural (without tracking inter-procedural data-flows) or inter-procedural (by using field-sensitive models, which are too imprecise in handling arrays and structs). This article proposes an inter-procedural Loop-oriented Pointer Analysis for C, called Lpa, for analyzing arrays and structs to support aggressive SLP and LLV optimizations effectively. Unlike field-insensitive solutions that pre-allocate objects for each memory allocation site, our approach uses a lazy memory model to generate access-based location sets based on how structs and arrays are accessed. Lpa can precisely analyze arrays and nested aggregate structures to enable SIMD optimizations for large programs. By separating the location set generation as an independent concern from the rest of the pointer analysis, Lpa is designed so that existing points-to resolution algorithms (e.g., flow-insensitive and flow-sensitive pointer analysis) can be reused easily. We have implemented Lpa fully in the LLVM compiler infrastructure (version 3.8.0). We evaluate Lpa by considering SLP and LLV, the two classic vectorization techniques, on a set of 20 C and Fortran CPU2000/2006 benchmarks. For SLP, Lpa outperforms LLVM’s BasicAA and ScevAA by discovering 139 and 273 more vectorizable basic blocks, respectively, resulting in the best speedup of 2.95% for 173.applu. For LLV, LLVM introduces totally 551 and 652 static bound checks under BasicAA and ScevAA, respecti...
Sui, Y, Yan, H, Zheng, Z, Zhang, Y & Xue, J 2018, 'Parallel construction of interprocedural memory SSA form', Journal of Systems and Software, vol. 146, pp. 186-195.
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© 2018 Elsevier Inc. Interprocedural memory SSA form, which provides a sparse data-flow representation for indirect memory operations, paves the way for many advanced program analyses. Any performance improvement for memory SSA construction benefits for a wide range of clients (e.g., bug detection and compiler optimisations). However, its construction is much more expensive than that for scalar-based SSA form. The memory objects distinguished at a pointer dereference significantly increases the number of variables that need to be put on SSA form, resulting in considerable analysis overhead when analyzing large programs (e.g., millions of lines of code). This paper presents PARSSA, a fully parameterised approach for parallel construction of interprocedural memory SSA form by utilising multi-core computing resources. PARSSA partitions whole-program memory objects into uniquely identified memory regions. The indirect memory accesses in a function are fully parameterised using partitioned memory regions, so that the memory SSA construction of a parameterised function is readily parallelised. We implemented PARSSA in LLVM using Intel Threading Building Block (TBB) for creating parallel tasks. We evaluated PARSSA using 15 large applications. PARSSA achieves up to 6.9 × speedup against the sequential version on an 8-core machine.
Talaei-Khoei, A & Daniel, J 2018, 'How younger elderly realize usefulness of cognitive training video games to maintain their independent living', International Journal of Information Management, vol. 42, pp. 1-12.
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© 2018 The objective of this paper is to understand the perception that younger elderly persons have towards the usefulness of playing Xbox Kinect video games as an assistive technology that is designed to maintain their cognitive abilities. Available literature highlights two kinds of assistive technologies; the first being Supportive Technologies that provide aid for already-declined functional abilities (such as hearing aids), and the second being Empowering Technologies that maintain functional abilities which have not yet declined (such as Xbox Kinect cognitive games). The difference in the nature between supportive and empowering technologies plays an important role in perceiving their benefits. For instance, while hearing aids as a supportive technology are perceived as useful through the improvement of hearing abilities, cognitive training games as an empowering technology have a long-term usefulness for cognitive abilities. This study conducts twenty-one qualitative interviews (range 65–87 years; mean = 71; SD = 3.81) and introduces perceived transfer effect. This effect allows the elderly to perceive the usefulness of playing cognitive training video games, which are designed to cultivate the cognitive abilities. In addition, this study found that the elderly value their independent living, and through cognitive video games, the elderly may remain capable of living independently.
Tamassia, M, Zambetta, F, Raffe, WL, Mueller, FF & Li, X 2018, 'Learning Options From Demonstrations: A Pac-Man Case Study', IEEE TRANSACTIONS ON GAMES, vol. 10, no. 1, pp. 91-96.
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Tomamichel, M & Hayashi, M 2018, 'Operational interpretation of Rényi information measures via composite hypothesis testing against product and markov distributions', IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 1064-1082.
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© 1963-2012 IEEE. We revisit the problem of asymmetric binary hypothesis testing against a composite alternative hypothesis. We introduce a general framework to treat such problems when the alternative hypothesis adheres to certain axioms. In this case, we find the threshold rate, the optimal error and strong converse exponents (at large deviations from the threshold), and the second order asymptotics (at small deviations from the threshold). We apply our results to find the operational interpretations of various Rényi information measures. In case the alternative hypothesis is comprised of bipartite product distributions, we find that the optimal error and strong converse exponents are determined by the variations of Rényi mutual information. In case the alternative hypothesis consists of tripartite distributions satisfying the Markov property, we find that the optimal exponents are determined by the variations of Rényi conditional mutual information. In either case, the relevant notion of Rényi mutual information depends on the precise choice of the alternative hypothesis. As such, this paper also strengthens the view that different definitions of Rényi mutual information, conditional entropy, and conditional mutual information are adequate depending on the context in which the measures are used.
van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2018, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning.
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In order for organisations to remain competitive in times of ambiguity and uncertainty, there is a need to detect and anticipate unknown unknowns, also called ‘black swans’. When these are ignored they may lead to competitive struggles. In this paper, we build on this view and suggest that big data analytics can provide necessary insights to help change strategy making. Research suggests that ambidextrous organisations should focus on developing and maintaining their dynamic capabilities. Following on from this, we take a dynamic capabilities perspective and propose a theoretical framework to explain the intricacies of big data analytics. This framework explains the ability of organisations to detect, anticipate and respond strategically in ambiguous and uncertain business environments. For a meta- synthesis of 101 cases of big data analytics, we employ a multi-method approach that incorporates Natural Language Processing, semantic analysis and case analysis, allowing extraction and analysis of structured information from unstructured data. Overall, we find evidence of big data analytics helping to detect, anticipate and respond to industry disruption. We offer six propositions about the relationships between the levels of data analytics capabilities and strategic dynamic capabilities. We find that descriptive data analytics improves the capability of an organisation to understand the business context (sensing) and that predictive data analytics aids in the realisation of business opportunities (seizing). This study contributes to an understanding of big data analytics as a dynamic organisational capability that supports strategic decision-making in times of ambiguity and uncertainty. We conclude by suggesting areas for further investigation, particularly in regard to the strategic application of prescriptive data analytics.
Vichitvanichphong, S, Talaei-Khoei, A, Kerr, D & Ghapanchi, AH 2018, 'Assistive technologies for aged care: Comparative literature survey on the effectiveness of theories for supportive and empowering technologies', Information Technology and People, vol. 31, no. 2, pp. 405-427.
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© 2018, Emerald Publishing Limited. Purpose: One may categorize assistive technologies for aged care into two types, namely, supportive (helping the elderly with their everyday activities) and empowering (obtaining physical or educational training to help seniors to maintain their capabilities). This paper looks at the impact of this perspective in the adoption of technologies that are used to aid already declined functions in comparison with technologies that are used to empower an elderly person’s capabilities. The purpose of this paper is to extract the factors that influence adoption of assistive technologies among seniors and the theories used in this context. Design/methodology/approach: A systematic literature review was conducted on relevant peer reviewed papers. A preliminary exploratory search was conducted to identify keywords and online databases for the search. A total of 104 papers published since 2000 were analyzed after title, abstract and full text filtering. Findings: The study summarizes and categorizes the factors impacting the adoption of assistive technologies among seniors. The paper analyses the theoretical support utilized by literature and maps the empirical evidence for supportive and empowering technologies. Research limitations/implications: This research like any other literature review is limited to the search keys. However, the keys have brought to this work from a preliminary search. Practical implications: The work informs nursing professionals on the process to adopt technologies among seniors. The paper also helps technology developers to design technological products that are easier to adopt for older adults. Originality/value: It was found that existing adoption theories are able to handle supportive technology adoption mainly because of the direct link between usage and improved functionalities. However due to the indirect effect of empowering technologies, elderlies lag in benefit realization for empowering technologies. Th...
Wakefield, J, Tyler, J, Dyson, LE & Frawley, JK 2018, 'Implications of student-generated screencasts on final examination performance', Accounting and Finance.
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© 2017 AFAANZ. While educational technologies can play a vital role in students' active participation in introductory accounting subjects, learning outcome implications are less clear. We believe this is the first accounting education study examining the implications of student-generated screencast assignments. We find benefits in developing the graduate attributes of communication, creativity and multimedia skills, consistent with calls by the profession. Additionally, we find improvement in final examination performance related to the assignment topic, notably in lower performing students. The screencast assignment was optional, and the findings suggest a tailored approach to assignment design related to students' developmental needs is appropriate.
Wan, Y, Chen, L, Xu, G, Zhao, Z, Tang, J & Wu, J 2018, 'SCSMiner: mining social coding sites for software developer recommendation with relevance propagation', World Wide Web.
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Wan, Y, Xu, G, Chen, L, Zhao, Z & Wu, J 2018, 'Exploiting cross-source knowledge for warming up community question answering services', Neurocomputing, vol. 320, pp. 25-34.
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© 2018 Elsevier B.V. Community Question Answering (CQA) services such as Yahoo! Answers, Quora and StackOverflow are collaborative platforms where users can share and exchange their knowledge explicitly by asking and answering questions. One essential task in CQA is learning topical expertise of users, which may benefit many applications such as question routing and best answers identification. One limitation of existing related works is that they only consider the warm-start users who have posted many questions or answers, while ignoring cold-start users who have few posts. In this paper, we aim to exploit knowledge from cross sources such as GitHub and StackOverflow to build up the richer views of expertise for better CQA. Inspired by the idea of Bayesian co-training, we propose a topical expertise model from the perspective of multi-view learning. Specifically, we incorporate the consistency existing among multiple views into a unified probabilistic graphic model. Comprehensive experiments on two real-world datasets demonstrate the performance of our proposed model with the comparison of some state-of-the-art ones.
Wang, D, Deng, S & Xu, G 2018, 'Sequence-based context-aware music recommendation', Information Retrieval Journal, vol. 21, no. 2-3, pp. 230-252.
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© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data.
Wang, D, Deng, S, Zhang, X & Xu, G 2018, 'Learning to embed music and metadata for context-aware music recommendation', World Wide Web, pp. 1-25.
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© 2017 Springer Science+Business Media, LLC, part of Springer Nature Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.
Wang, J, Du, P, Lu, H, Yang, W & Niu, T 2018, 'An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting', Applied Soft Computing Journal, vol. 72, pp. 321-337.
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© 2018 Elsevier B.V. Accurate and stable annual electricity consumption forecasting play vital role in modern social and economic development through providing effective planning and guaranteeing a reliable supply of sustainable electricity. However, establishing a robust method to improve prediction accuracy and stability simultaneously of electricity consumption forecasting has been proven to be a highly challenging task. Most previous researches only pay more attention to enhance prediction accuracy, which usually ignore the significant of forecasting stability, despite its importance to the effectiveness of forecasting models. Considering the characteristics of annual power consumption data as well as one criterion i.e. accuracy or stability is insufficient, in this study a novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability. Case studies of annual power consumption datasets from several regions in China are utilized as illustrative examples to estimate the effectiveness and efficiency of the proposed hybrid forecasting model. Finally, experimental results indicated that the proposed forecasting model is superior to the comparison models.
Wang, J, Li, H & Lu, H 2018, 'Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China', Applied Soft Computing Journal, vol. 71, pp. 783-799.
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© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution.
Wang, J, Niu, T, Lu, H, Guo, Z, Yang, W & Du, P 2018, 'An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms', Applied Energy, vol. 211, pp. 492-512.
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© 2017 Elsevier Ltd The uncertainty analysis and modeling of wind speed, which has an essential influence on wind power systems, is consistently considered a challenging task. However, most investigations thus far were focused mainly on point forecasts, which in reality cannot facilitate quantitative characterization of the endogenous uncertainty involved. An analysis-forecast system that includes an analysis module and a forecast module and can provide appropriate scenarios for the dispatching and scheduling of a power system is devised in this study; this system superior to those presented in previous studies. In order to qualitatively and quantitatively investigate the uncertainty of wind speed, recurrence analysis techniques are effectively developed for application in the analysis module. Furthermore, in order to quantify the uncertainty accurately, a novel architecture aimed at uncertainty mining is devised for the forecast module, where a non-parametric model optimized by an improved multi-objective water cycle algorithm is considered a predictor for producing intervals for each mode component after feature selection. The results of extensive in-depth experiments show that the devised system is not only superior to the considered benchmark models, but also has good potential practical applications in wind power systems.
Wang, W, Zhang, G & Lu, J 2018, 'Hierarchy Visualization for Group Recommender Systems', IEEE Transactions on Systems Man and Cybernetics: Systems, pp. 1-12.
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IEEE Most recommender systems (RSs), especially group RSs, focus on methods and accuracy but lack explanations, hence users find them difficult to trust. We present a hierarchy visualization method for group recommender (HVGR) systems to provide visual presentation and intuitive explanation. We first use a hierarchy graph to organize all the entities using nodes (e.g., neighbor nodes and recommendation nodes) and illustrate the overall recommender process using edges. Second, a pie chart is attached to every entity node in which each slice represents a single member, which makes it easy to track the influence of each member on a specific entity. HVGR can be extended to adapt different pseudouser modeling methods by resizing group member nodes and pseudouser nodes. It can also be easily extended to individual RSs through the use of a single member group. An implementation has been developed and feasibility is tested using a real data set.
Wang, X, Huang, C, Yao, L, Benatallah, B & Dong, M 2018, 'A Survey on Expert Recommendation in Community Question Answering', Journal of Computer Science and Technology, vol. 33, no. 4, pp. 625-653.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.
Wang, X, Xu, X, Sheng, QZ, Wang, Z & Yao, L 2018, 'Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection', IEEE Transactions on Services Computing, pp. 1-1.
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Wang, Y, Feng, C, Chen, L, Yin, H, Guo, C & Chu, Y 2018, 'User identity linkage across social networks via linked heterogeneous network embedding', World Wide Web, pp. 1-22.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.
Wang, Y-K, Jung, T-P & Lin, C-T 2018, 'Theta and Alpha Oscillations in Attentional Interaction during Distracted Driving.', Frontiers in Behavioral Neuroscience, vol. 12, p. 3.
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Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely.
Wei, CS, Lin, YP, Wang, YT, Lin, CT & Jung, TP 2018, 'A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection', NeuroImage, vol. 174, pp. 407-419.
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© 2018 Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min–1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications.
Wei, C-S, Wang, Y-T, Lin, C-T & Jung, T-P 2018, 'Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces.', IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 26, no. 2, pp. 400-406.
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Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects ( ). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.
Wei, H, Yu, JX & Lu, C 2018, 'String similarity search: A hash-based approach', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 1, pp. 170-184.
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© 2017 IEEE. String similarity search is a fundamental query that has been widely used for DNA sequencing, error-tolerant query autocompletion, and data cleaning needed in database, data warehouse, and data mining. In this paper, we study string similarity search based on edit distance that is supported by many database management systems such as Oracle and PostgreSQL. Given the edit distance, ed(s; t), between two strings, s and t, the string similarity search is to find every string t in a string database D which is similar to a query string s such that ed(s; t) ≤ τ for a given threshold τ. In the literature, most existing work takes a filter-and-verify approach, where the filter step is introduced to reduce the high verification cost of two strings by utilizing an index built offline for D. The two up-to-date approaches are prefix filtering and local filtering. In this paper, we study string similarity search where strings can be either short or long. Our approach can support long strings, which are not well supported by the existing approaches due to the size of the index built and the time to build such index. We propose two new hash-based labeling techniques, named OX label and XX label, for string similarity search. We assign a hash-label, Hs, to a string s, and prune the dissimilar strings by comparing two hash-labels, Hs and Ht, for two strings s and t in the filter step. The key idea is to take the dissimilar bit-patterns between two hash-labels. We discuss our hash-based approaches, address their pruning power, and give the algorithms. Our hash-based approaches achieve high efficiency, and keep its index size and index construction time one order of magnitude smaller than the existing approaches in our experiment at the same time.
Wei, H, Yu, JX, Lu, C & Jin, R 2018, 'Reachability querying: an independent permutation labeling approach', VLDB Journal, vol. 27, no. 1.
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© 2017, Springer-Verlag Berlin Heidelberg. Reachability query is a fundamental graph operation which answers whether a vertex can reach another vertex over a large directed graph G with n vertices and m edges and has been extensively studied. In the literature, all the approaches compute a label for every vertex in a graph G by index construction offline. The query time for answering reachability queries online is affected by the quality of the labels computed in index construction. The three main costs are the index construction time, the index size, and the query time. Some of the up-to-date approaches can answer reachability queries efficiently, but spend nonlinear time to construct an index. Some of the up-to-date approaches construct an index in linear time and space, but may need to depth-first search G at run-time in O(n+ m). In this paper, we discuss a new randomized labeling approach, named IP label, to answer reachability queries with probability guarantee, and the randomness is by independent permutation. Two additional labels are also proposed to further enhance the query processing. In addition, to deal with dynamic graphs, we discuss the label maintenance over dynamic graphs and give efficient algorithms for the labels proposed. We conduct extensive experimental studies to compare with the up-to-date approaches using 19 large real datasets used in the existing work and synthetic datasets. We confirm the efficiency and scalability of our approach in static graphs testing, and our maintenance algorithms are about one order of magnitude faster than the existing ones in dynamic graphs testing.
Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2018, 'I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are too big to reside in memory. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. We propose a semi-external algorithm and an optimized algorithm for I/O efficient core decomposition. To handle dynamic graph updates, we firstly show that our algorithm can be naturally extended to handle edge deletion. Then we propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost. In addition, based on our core decomposition algorithms, we further propose an I/O efficient semi-external algorithm for degeneracy ordering, which is an important graph problem that is highly related to core decomposition. We also consider how to maintain the degeneracy order. We conduct extensive experiments on 12 real large graphs. Our optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption. They are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978:5 million nodes and 42:6 billion edges using less than 4:2 GB memory. We also show that our proposed algorithms for degeneracy order computation and maintenance can handle big graphs efficiently with small memory overhead.
Wu, D, King, JT, Chuang, CH, Lin, CT & Jung, TP 2018, 'Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 771-781.
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© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%.
Wu, J & Hei, TK 2018, 'Focus small to find big - the microbeam story', INTERNATIONAL JOURNAL OF RADIATION BIOLOGY, vol. 94, no. 8, pp. 782-788.
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Wu, J, Pan, S, Zhou, C, Li, G, He, W & Zhang, C 2018, 'Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications', COMPLEXITY.
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Wu, J, Pan, S, Zhu, X, Zhang, C & Wu, X 2018, 'Multi-instance learning with discriminative bag mapping', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 6, pp. 1065-1080.
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© 1989-2012 IEEE. Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning because it allows a bag of instances to share one label. Bag mapping transforms a bag into a single instance in a new space via instance selection and has drawn significant attention recently. To date, most existing work is based on the original space, using all instances inside each bag for bag mapping, and the selected instances are not directly tied to an MIL objective. As a result, it is difficult to guarantee the distinguishing capacity of the selected instances in the new bag mapping space. In this paper, we propose a discriminative mapping approach for multi-instance learning (MILDM) that aims to identify the best instances to directly distinguish bags in the new mapping space. Accordingly, each instance bag can be mapped using the selected instances to a new feature space, and hence any generic learning algorithm, such as an instance-based learning algorithm, can be used to derive learning models for multi-instance classification. Experiments and comparisons on eight different types of real-world learning tasks (including 14 data sets) demonstrate that MILDM outperforms the state-of-The-Art bag mapping multi-instance learning approaches. Results also confirm that MILDM achieves balanced performance between runtime efficiency and classification effectiveness.
Wu, J, Pan, S, Zhu, X, Zhang, C & Yu, PS 2018, 'Multiple Structure-View Learning for Graph Classification', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.
Wu, W, Li, B, Chen, L, Zhang, C & Yu, P 2018, 'Improved Consistent Weighted Sampling Revisited', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch weighted sets and has drawn increasing interest from the community. Due to its constant-time complexity independent of the values of the weights, Improved CWS (ICWS) is considered as the state-of-the-art CWS algorithm. In this paper, we revisit ICWS and analyze its underlying mechanism to show that there actually exists dependence between the two components of the hash-code produced by ICWS, which violates the condition of independence. To remedy the problem, we propose an Improved ICWS (I2CWS) algorithm which not only shares the same theoretical computational complexity as ICWS but also abides by the required conditions of the CWS scheme. The experimental results on a number of synthetic data sets and real-world text data sets demonstrate that our I2CWS algorithm can estimate the Jaccard similarity more accurately, and also competes with or outperforms the compared methods, including ICWS, in classification and top-K retrieval, after relieving the underlying dependence.
Wu, W, Li, B, Chen, L, Zhu, X & Zhang, C 2018, 'K-Ary Tree Hashing for Fast Graph Classification', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 936-949.
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IEEE Existing graph classification usually relies on an exhaustive enumeration of substructure patterns, where the number of substructures expands exponentially w.r.t. with the size of the graph set. Recently, the Weisfeiler-Lehman (WL) graph kernel has achieved the best performance in terms of both accuracy and efficiency among state-of-the-art methods. However, it is still time-consuming, especially for large-scale graph classification tasks. In this paper, we present a < formula > < tex > $k$ < /tex > < /formula > -Ary Tree based Hashing (KATH) algorithm, which is able to obtain competitive accuracy with a very fast runtime. The main idea of KATH is to construct a traversal table to quickly approximate the subtree patterns in WL using < formula > < tex > $k$ < /tex > < /formula > -ary trees. Based on the traversal table, KATH employs a recursive indexing process that performs only < formula > < tex > $r$ < /tex > < /formula > times of matrix indexing to generate all < formula > < tex > $(r-1)$ < /tex > < /formula > -depth < formula > < tex > $k$ < /tex > < /formula > -ary trees, where the leaf node labels of a tree can uniquely specify the pattern. After that, the MinHash scheme is used to fingerprint the acquired subtree patterns for a graph. Our experimental results on both real world and synthetic datasets show that KATH runs significantly faster than state-of-the-art methods while achieving competitive or better accuracy.
Wu, Z, Li, G, Liu, Q, Xu, G & Chen, E 2018, 'Covering the Sensitive Subjects to Protect Personal Privacy in Personalized Recommendation', IEEE Transactions on Services Computing, pp. 493-508.
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Wu, Z, Xu, G, Lu, C, Chen, E, Jiang, F & Li, G 2018, 'An effective approach for the protection of privacy text data in the CloudDB', World Wide Web, pp. 1-24.
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© 2017 Springer Science+Business Media, LLC Due to the advantages of pay-on-demand, expand-on-demand and high availability, cloud databases (CloudDB) have been widely used in information systems. However, since a CloudDB is distributed on an untrusted cloud side, it is an important problem how to effectively protect massive private information in the CloudDB. Although traditional security strategies (such as identity authentication and access control) can prevent illegal users from accessing unauthorized data, they cannot prevent internal users at the cloud side from accessing and exposing personal privacy information. In this paper, we propose a client-based approach to protect personal privacy in a CloudDB. In the approach, privacy data before being stored into the cloud side, would be encrypted using a traditional encryption algorithm, so as to ensure the security of privacy data. To execute various kinds of query operations over the encrypted data efficiently, the encrypted data would be also augmented with additional feature index, so that as much of each query operation as possible can be processed on the cloud side without the need to decrypt the data. To this end, we explore how the feature index of privacy data is constructed, and how a query operation over privacy data is transformed into a new query operation over the index data so that it can be executed on the cloud side correctly. The effectiveness of the approach is demonstrated by theoretical analysis and experimental evaluation. The results show that the approach has good performance in terms of security, usability and efficiency, thus effective to protect personal privacy in the CloudDB.
Xiang, L, Zhao, G, Li, Q, Hao, W & Li, F 2018, 'TUMK-ELM: A fast unsupervised heterogeneous data learning approach', IEEE Access, vol. 6, pp. 35305-35315.
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© 2013 IEEE. Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information from heterogeneous data. However, most of them are with huge time consumption, which obstructs their further application in the big data analytics scenarios, where an enormous amount of heterogeneous data are provided but real-time learning are strongly demanded. In this paper, we address this problem by proposing a fast unsupervised heterogeneous data learning algorithm, namely two-stage unsupervised multiple kernel extreme learning machine (TUMK-ELM). TUMK-ELM alternatively extracts information from multiple sources and learns the heterogeneous data representation with closed-form solutions, which enables its extremely fast speed. As justified by theoretical evidence, TUMK-ELM has low computational complexity at each stage, and the iteration of its two stages can be converged within finite steps. As experimentally demonstrated on 13 real-life data sets, TUMK-ELM gains a large efficiency improvement compared with three state-of-the-art unsupervised heterogeneous data learning methods (up to 140 000 times) while it achieves a comparable performance in terms of effectiveness.
Xiong, F, Wang, X, Pan, S, Yang, H, Wang, H & Zhang, C 2018, 'Social Recommendation With Evolutionary Opinion Dynamics', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE When users in online social networks make a decision, they are often affected by their neighbors. Social recommendation models utilize social information to reveal the impact of neighbors on user preferences, and this impact is often described by the linear superposition of neighbor preferences or by global trust propagation. Further exploration needs to be undertaken to determine whether the influence pattern of other users from online interaction behaviors is adequately described. In this paper, we introduce evolutionary opinion dynamics from the field of statistical physics into recommender systems, characterizing the impact of other users. We propose an opinion dynamic model by evolutionary game theory. To describe online user interactions, we define the strategies during an interaction between two users, and present the payoff for each strategy in terms of errors of estimated ratings. Therefore, user behaviors are associated with their preferences and ratings. In addition, we measure user influence according to their topological roles in the social network. We incorporate evolutionary opinion dynamics and user influence into the recommendation framework for the prediction of unknown ratings. Experiment results on two real-world datasets demonstrate that our method outperforms state-of the-art models in terms of accuracy, and it also performs well for cold-start users. Our method reduces the divergence of user preferences, in accordance with online opinion interactions. Furthermore, our method has approximate computational complexity with matrix factorization, and results in less computation than state-of-the-art models. Our method is quite general, and indicates that studies in social physics, statistics, and other research fields may be involved in recommendation to improve the performance.
Xu, D, Tsang, IW & Zhang, Y 2018, 'Online Product Quantization', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 11, pp. 2185-2198.
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© 1989-2012 IEEE. Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behavior of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.
Xu, G 2018, 'Unsupervised Keyword Extraction from Microblog Posts via Hashtags', Journal of Web Engineering, vol. 17 (1&2).
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Xu, X, Motta, G, Tu, Z, Xu, H, Wang, Z & Wang, X 2018, 'A new paradigm of software service engineering in big data and big service era', Computing, vol. 100, no. 4, pp. 353-368.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. In the big data era, servitization becomes one of the important development trends of the IT world. More and more software resources are developed and existed in the format as services on the Internet. These services from multi-domains and multi-networks are converged as a huge complicated service network or ecosystem, which can be called as Big Service. How to reuse the abundant open service resources to rapidly develop the new applications or comprehensive service solutions to meet massive individualized customer requirements is a key issue in the big data and big service ecosystem. Based on analyzing the ecosystem of big service, this paper presents a new paradigm of software service engineering, Requirement-Engineering Two-Phase of Service Engineering Paradigm (RE2SEP), which includes service oriented requirement engineering, domain oriented service engineering, and the development approach of software services. By means of the RE2SEP approach, the adaptive service solutions can be efficiently designed and implemented to match the requirement propositions of massive individualized customers in Big Service ecosystem. A case study of the RE2SEP applications, which is a project on citizens mobility service in smart city environment, is also given in this paper. The RE2SEP paradigm will change the way of traditional life-cycle oriented software engineering, and lead a new approach of software service engineering.
Yan, Z, Liu, J & Liu, S 2018, 'DPWeVote: differentially private weighted voting protocol for cloud-based decision-making', Enterprise Information Systems, pp. 1-21.
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© 2018 Informa UK Limited, trading as Taylor & Francis Group With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express ‘YES/NO’ opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semi-honest environment.
Yan, Z, Wu, Q, Ren, M, Liu, J, Liu, S & Qiu, S 2018, 'Locally private Jaccard similarity estimation', Concurrency Computation.
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© 2018 John Wiley & Sons, Ltd. Jaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP-JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.
Yang, J, Sun, X, Lai, YK, Zheng, L & Cheng, MM 2018, 'Recognition from Web Data: A Progressive Filtering Approach', IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5303-5315.
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© 1992-2012 IEEE. Leveraging the abundant number of web data is a promising strategy in addressing the problem of data lacking when training convolutional neural networks (CNNs). However, the web images often contain incorrect tags, which may compromise the learned CNN model. To address this problem, this paper focuses on image classification and proposes to iterate between filtering out noisy web labels and fine-tuning the CNN model using the crawled web images. Overall, the proposed method benefits from the growing modeling capability of the learned model to correct labels for web images and learning from such new data to produce a more effective model. Our contribution is two-fold. First, we propose an iterative method that progressively improves the discriminative ability of CNNs and the accuracy of web image selection. This method is beneficial toward selecting high-quality web training images and expanding the training set as the model gets ameliorated. Second, since web images are usually complex and may not be accurately described by a single tag, we propose to assign a web image multiple labels to reduce the impact of hard label assignment. This labeling strategy mines more training samples to improve the CNN model. In the experiments, we crawl 0.5 million web images covering all categories of four public image classification data sets. Compared with the baseline which has no web images for training, we show that the proposed method brings notable improvement. We also report the competitive recognition accuracy compared with the state of the art.
Yang, J, Zhang, W, Yang, S, Zhang, Y, Lin, X & Yuan, L 2018, 'Efficient set containment join', VLDB Journal, vol. 27, no. 4, pp. 471-495.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. In this paper, we study the problem of set containment join. Given two collections R and S of records, the set containment join R⋈⊆S retrieves all record pairs { (r, s) } ∈ R× S such that r⊆ s. This problem has been extensively studied in the literature and has many important applications in commercial and scientific fields. Recent research focuses on the in-memory set containment join algorithms, and several techniques have been developed following intersection-oriented or union-oriented computing paradigms. Nevertheless, we observe that two computing paradigms have their limits due to the nature of the intersection and union operators. Particularly, intersection-oriented method relies on the intersection of the relevant inverted lists built on the elements of S. A nice property of the intersection-oriented method is that the join computation is verification free. However, the number of records explored during the join process may be large because there are multiple replicas for each record in S. On the other hand, the union-oriented method generates a signature for each record in R and the candidate pairs are obtained by the union of the inverted lists of the relevant signatures. The candidate size of the union-oriented method is usually small because each record contributes only one replica in the index. Unfortunately, union-oriented method needs to verify the candidate pairs, which may be cost expensive especially when the join result size is large. As a matter of fact, the state-of-the-art union-oriented solution is not competitive compared to the intersection-oriented ones. In this paper, we propose a new union-oriented method, namely TT-Join, which not only enhances the advantage of the previous union-oriented methods but also integrates the goodness of intersection-oriented methods by imposing a variant of prefix tree structure. We conduct extensive experiments on 20 real-life datasets and syn...
Yao, L, Sheng, QZ, Benatallah, B, Dustdar, S, Wang, X, Shemshadi, A & Kanhere, SS 2018, 'WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes', Computing, vol. 100, no. 4, pp. 369-385.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Over the past few years, activity recognition techniques have attracted unprecedented attentions. Along with the recent prevalence of pervasive e-Health in various applications such as smart homes, automatic activity recognition is being implemented increasingly for rehabilitation systems, chronic disease management, and monitoring the elderly for their personal well-being. In this paper, we present WITS, an end-to-end web-based in-home monitoring system for convenient and efficient care delivery. The system unifies the data- and knowledge-driven techniques to enable a real-time multi-level activity monitoring in a personalized smart home. The core components consist of a novel shared-structure dictionary learning approach combined with rule-based reasoning for continuous daily activity tracking and abnormal activities detection. WITS also exploits an Internet of Things middleware for the scalable and seamless management and learning of the information produced by ambient sensors. We further develop a user-friendly interface, which runs on both iOS and Andriod, as well as in Chrome, for the efficient customization of WITS monitoring services without programming efforts. This paper presents the architectural design of WITS, the core algorithms, along with our solutions to the technical challenges in the system implementation.
Yao, L, Sheng, QZ, Li, X, Gu, T, Tan, M, Wang, X, Wang, S & Ruan, W 2018, 'Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength', IEEE Transactions on Mobile Computing, vol. 17, no. 2, pp. 293-306.
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© 2017 IEEE. Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.
Yao, L, Sheng, QZ, Wang, X, Wang, S, Li, X & Wang, S 2018, 'Collaborative text categorization via exploiting sparse coefficients', World Wide Web, vol. 21, no. 2, pp. 373-394.
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© 2017, Springer Science+Business Media New York. Text categorization is widely characterized as a multi-label classification problem. Robust modeling of the semantic similarity between a query text and training texts is essential to construct an effective and accurate classifier. In this paper, we systematically investigate the Web page/text classification problem via integrating sparse representation with random measurements. In particular, we first adopt a very sparse data-independent random measurement matrix to map the original high dimensional text feature space to a lower dimensional space without loss of key information. We then propose a generic sparse representation method to obtain the sparse solution by decoding the semantic correlations between the query text and entire training samples. Based on the above method, we also design and examine a series of rules by taking advantage of the sparse coefficients to propagate multiple labels for the given query texts. We have conducted extensive experiments using real-world datasets to examine our proposed approach, and the results show the effectiveness of the proposed approach.
Yao, L, Sheng, QZ, Wang, X, Zhang, WE & Qin, Y 2018, 'Collaborative location recommendation by integrating multi-dimensional contextual information', ACM Transactions on Internet Technology, vol. 18, no. 3.
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© 2018 ACM. Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, tomodel themulti-dimensional contextual information. Tensor Factorization naturally extendsMatrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users' check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.
Yao, L, Wang, X, Sheng, QZ, Benatallah, B & Huang, C 2018, 'Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations', IEEE Transactions on Services Computing.
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IEEE Mashup is a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and few work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for composition task
Yasin, A, Liu, L, Li, T, Wang, J & Zowghi, D 2018, 'Design and preliminary evaluation of a cyber Security Requirements Education Game (SREG)', Information and Software Technology, vol. 95, pp. 179-200.
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© 2017 Elsevier B.V. Context: Security, in digitally connected organizational environments of today, involves many different perspectives, including social, physical, and technical factors. In order to understand the interactions among these correlated aspects and elicit potential threats geared towards a given organization, different security requirements analysis approaches are proposed in the literature. However, the body of knowledge is yet to unleash its full potential due to the complex nature of security problems, and inadequate ways to improve security awareness of key players in the organization. Objective: Objective(s) of the research study is to improve the security awareness of players utilizing serious games via: (i) Know-how of security concepts and security protection; (ii) guided process of identifying valuable assets and vulnerabilities in a given organizational setting; (iii) guided process of defining successful security attacks to the organization. Method: Important methods used to address the above objectives include: (i) a comprehensive review of the literature to better understand security and game design elements; (ii) designing a serious game using cyber security knowledge and game-based techniques combined with security requirements engineering concepts; (iii) using empirical evaluation (observation and survey) to verify the effectiveness of the proposed game design. Result: The solution proposed is a serious game for security requirements education, which: (i) can be an effective and fun way of learning security related concepts; (ii) mimics a real life problem setting in a presentable and understandable way; (iii) motivates players to learn more about security related concepts in future. Conclusion: From this study, we conclude that the proposed Security Requirement Education Game (SREG) has positive results and is helpful for players of the game to get an understanding of security attacks and vulnerabilities.
Ying, H, Wu, J, Xu, G, Liu, Y, Liang, T, Zhang, X & Xiong, H 2018, 'Time-aware metric embedding with asymmetric projection for successive POI recommendation', World Wide Web, pp. 1-16.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.
Yu, D, Fu, B, Xu, G & Qin, A 2018, 'Constrained nonnegative matrix factorization-based semi-supervised multi-label learning', International Journal of Machine Learning and Cybernetics.
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Yu, Q, Che, X, Ma, S, Pan, S, Yang, Y, Xing, W & Wang, X 2018, 'A Hybrid User Experience Evaluation Method for Mobile Games', IEEE Access, vol. 6, pp. 49067-49079.
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© 2013 IEEE. With the development of the mobile phone industry, mobile applications market becomes thriving. However, the immature information technology and unfriendly interface bring negative user experience (UX) to the mobile users and thus affect the service life of mobile applications, especially for the most concerned entertaining applications, mobile games. As a result, the evaluating UX and finding crucial factors of UX become a challenge. Over the last decades, numerous researches have tried to deal with this issue, but none of them has clearly identified the relations among positive-negative UX, sufficient human characteristics and specific events of applications. This paper proposes a subjective-objective evaluation method. Subjective UX of mobile games is sufficiently obtained and objective UX is verified through the electrocardiogram signals and heart rate variability. In order to reveal distinct relations among UX factors, sufficient user characteristics and categories of game events, an improved RIPPER algorithm is proposed by this paper to obtain the relations. Experiments are performed with 300 testers who played mobile parkour games for at least five minutes. The accuracy and efficiency of the proposed method have been verified through real experiments and objective measures. In addition, this paper provides an effective sampling method and a data analysis algorithm to obtain crucial UX factors for mobile applications.
Zhan, K, Chang, X, Guan, J, Chen, L, Ma, Z & Yang, Y 2018, 'Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features.', IEEE transactions on cybernetics.
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Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.
Zheng, L, Yang, Y & Tian, Q 2018, 'SIFT Meets CNN: A Decade Survey of Instance Retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1224-1244.
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In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.
Zheng, L, Yang, Y & Zheng, Z 2018, 'A Discriminatively Learned CNN Embedding for Person Re-identification', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 14, no. 1, pp. 1-20.
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In this article, we revisit two popular convolutional neural networks in person re-identification (re-ID): verification and identification models. The two models have their respective advantages and limitations due to different loss functions. Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations. Our method can be easily applied on different pretrained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show that our architecture can also be applied to image retrieval. The code is available at https://github.com/layumi/2016_person_re-ID.
Zhou, J, Yu, JX, Li, N, Wei, H, Chen, Z & Tang, X 2018, 'Accelerating reachability query processing based on DAG reduction', VLDB Journal, vol. 27, no. 2, pp. 271-296.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Answering reachability queries is one of the fundamental graph operations. The existing approaches build indexes and answer reachability queries on a directed acyclic graph (DAG) G, which is constructed by coalescing each strongly connected component of the given directed graph G into a node of G. Considering that G can still be large to be processed efficiently, there are studies to further reduce G to a smaller graph. However, these approaches suffer from either inefficiency in answering reachability queries, or cannot scale to large graphs. In this paper, we study DAG reduction to accelerate reachability query processing, which reduces the size of G by computing transitive reduction (TR) followed by computing equivalence reduction (ER). For TR, we propose a bottom-up algorithm, namely buTR, which removes from G all redundant edges to get the unique smallest DAGGtsatisfying that Gthas the same transitive closure as that of G. For ER, we propose a divide-and-conquer algorithm, namely linear-ER. Given the result Gtof TR, linear-ER gets a smaller DAGGεin linear time based on equivalence relationship between nodes in G. Our DAG reduction approaches (TR and ER) significantly improve the cost of time and space and can be scaled to large graphs. Based on the result of DAG reduction, we further propose a graph decomposition-based algorithm to efficiently answer reachability queries. We confirm the efficiency of our approaches by extensive experimental studies for TR, ER, and reachability query processing using 20 real datasets. The complete source code is available for download at https://pan.baidu.com/s/1skHBXXN.
Zuo, H, Zhang, G, Pedrycz, W, Behbood, V & Lu, J 2018, 'Granular Fuzzy Regression Domain Adaptation in Takagi-Sugeno Fuzzy Models', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 847-858.
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© 1993-2012 IEEE. In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data in many real-world applications is insufficient, so establishing a prediction model is impossible. Transfer learning has recently emerged as a solution to this problem. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing transfer learning methods solve classification tasks; only a few are devoted to regression problems. In addition, the current methods ignore the inherent phenomenon of information granularity in transfer learning. In this study, granular computing techniques are applied to transfer learning. Three granular fuzzy regression domain adaptation methods to determine the estimated values for a regression target are proposed to address three challenging cases in domain adaptation. The proposed granular fuzzy regression domain adaptation methods change the input and/or output space of the source domain's model using space transformation, so that the fuzzy rules are more compatible with the target data. Experiments on synthetic and real-world datasets validate the effectiveness of the proposed methods.
Abou Maroun, E, Daniel, J, Zowghi, D & Talaei-Khoei, A 2018, 'Blockchain in Supply Chain Management: Australian Manufacturer Case Study', Seventh Australasian Symposium on Service Research and Innovation, Sydney, Australia.
Ahadi, A, Lal, S, Lister, R & Hellas, A 2018, 'Learning Programming, Syntax Errors and Institution-specific Factors', ACM International Conference Proceeding Series, pp. 90-96.
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Learning programming is a road that is paved with mistakes. Initially, novices are bound to write code with syntactic mistakes, but after a while semantic mistakes take a larger role in the novice programmers’ lives. Researchers who wish to understand that road are increasingly using data recorded from students’ programming processes. Such data can be used to draw inferences on the typical errors, and on how students approach fixing them. At the same time, if the lens that is used to analyze such data is used only from one angle, the view is likely to be narrow. In this work, we replicate a previous multi-institutional study by Brown et al. [5]. That study used a large scale programming process data repository to analyze mistakes that novices make while learning programming. In our single institution replication of that study, we use data collected from approximately 800 students. We investigate the frequency, time required to fix, and the development of mistakes through the semester. We contrast our findings from our single institution with the multi-institutional study, and show that whilst the data collection tools and the research methodology are the same, the results can differ solely due to how the course is conducted.
Ahadi, A, Lister, R & Mathieson, L 2018, 'Syntax error based quantification of the learning progress of the novice programmer', Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, pp. 10-14.
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© 2018 Association for Computing Machinery. Recent data-driven research has produced metrics for quantifying a novice programmer’s error profile, such as Jadud’s error quotient. However, these metrics tend to be context dependent and contain free parameters. This paper reviews the caveats of such metrics and proposes a more general approach to developing a metric. The online implementation of the proposed metric is publicly available at http://online-analysis-demo.herokuapp.com/.
Alshehri, MD & Hussain, F 2017, 'A Centralized Trust Management Mechanism for the Internet of Things (CTM-IoT)', Advances on Broad-Band Wireless Computing, Communication and Applications, The 12th International Conference on Broad-Band Wireless Computing, Communication and Applications, Springer, Barcelona, Spain, pp. 533-543.
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The Internet of Things (IoT) is an extended network that allows all devices to be connected to one another over the Internet. This new network faces numerous challenges, but mainly security issues. One such issue is how the IoT’s nodes can trust each other when they are connected over the Internet. There is a lack of studies that address the issue of trust management in IoT, or that provide a fully trustworthy framework. This paper proposes and delivers a centralized trust management mechanism for IoT by adding trust modules as a feature of the central trust manager, the Super Node (SN). To deliver a comprehensive approach, the SN includes other modules which are integrated with the whole IoT Trust Management framework to provide trustworthy communication between all nodes.
Angelini, L, Mugellini, E, Abou Khaled, O, Couture, N, van den Hoven, EA & Bakker, S 2018, 'Internet of Tangibles: Exploring the Interaction-Attention Continuum', the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, Stockholm, Sweden, pp. 740-743.
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Aravanis, TI, Peppas, P & Williams, MA 2018, 'Iterated belief revision and dalal⇔s operator', ACM International Conference Proceeding Series.
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© 2018 Association for Computing Machinery. One of the main shortcomings of the early AGM paradigm is its lack of any guidelines for iterated revision; it formalizes only one-step rational belief revision. Darwiche and Pearl, subsequently, addressed this problem by introducing four additional postulates (the DP postulates), supplementing the AGM ones. Despite the popularity of the DP approach, there are still controversies surrounding the DP postulates. In this article, we prove a conflict between each one of the latter, and one of the most popular and intuitive “off the shelf” revision functions, that is Dalal’s revision operator.
Bano, M & Zowghi, D 2017, 'Crowd Vigilante Detecting Sabotage in Crowdsourcing', Requirements Engineering for Internet of Things, 4th Asia Pacific Requirements Engineering Symposium 2017, Springer, Melaka, Malaysia, pp. 114-120.
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Crowdsourcing is a complex and sociotechnical problem solving approach for collaboration of geographically distributed volunteer crowd to contribute to the achievement of a common task. One of the major issues faced by crowdsourced projects is the trustworthiness of the crowd. This paper presents a vision to develop a framework with supporting methods and tools for early detection of the malicious acts of sabotage in crowdsourced projects by utilizing and scaling digital forensic techniques. The idea is to utilize the crowd to build the digital evidence of sabotage with systematic collection and analysis of data from the same crowdsourced project where the threat is situated. The proposed framework aims to improve the security of the crowdsourced projects and their outcomes by building confidence about the trustworthiness of the workers.
Bano, M & Zowghi, D 2018, 'Crowd vigilante: Detecting sabotage in crowdsourcing', Communications in Computer and Information Science, pp. 114-120.
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© Springer Nature Singapore Pte Ltd. 2018. Crowdsourcing is a complex and sociotechnical problem solving approach for collaboration of geographically distributed volunteer crowd to contribute to the achievement of a common task. One of the major issues faced by crowdsourced projects is the trustworthiness of the crowd. This paper presents a vision to develop a framework with supporting methods and tools for early detection of the malicious acts of sabotage in crowdsourced projects by utilizing and scaling digital forensic techniques. The idea is to utilize the crowd to build the digital evidence of sabotage with systematic collection and analysis of data from the same crowdsourced project where the threat is situated. The proposed framework aims to improve the security of the crowdsourced projects and their outcomes by building confidence about the trustworthiness of the workers.
Bano, M, Zowghi, D & da Rimini, F 2018, 'Power and Politics of User Involvement in Software Development', ACM, 22nd International Conference on Evaluation and Assessment in Software Engineering 2018, ACM, Christchurch Newzealand, pp. 157-162.
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[CONTEXT] Involving users in software development is a complex and multi-faceted concept. Empirical research that studies power and politics of user involvement in software development is scarce. [OBJECTIVE] In this paper, we present the results from a case study of a software development project, where organizational politics was explored in context of user involvement in software development. [METHOD] We collected data through 30 interviews with 20 participants, attending workshops, observing project meetings, and analysing projects documents. The qualitative data was rigorously and iteratively analyzed. [RESULTS] The results indicate that the politics was a significant factor used to exert power and influence in decision-making processes. Communication channels were exploited for political purposes. These contributed to the users' dissatisfaction with their involvement thus impacting on the project outcome. [CONCLUSION] Having multiple teams of stakeholders with different levels of power in decision-making, the politics is inevitable and inescapable. Without careful attention, the political aspect of user involvement in software development can contribute to unsuccessful project.
Bano, M, Zowghi, D, Ferrari, A, Spoletini, P & Donati, B 2018, 'Learning from Mistakes: An Empirical Study of Elicitation Interviews performed by Novices', 2018 IEEE 26th International Requirements Engineering Conference (RE), International Requirements Engineering Conference, IEEEE, Banff, Alberta, Canada, pp. 182-193.
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[Context] Interviews are the most widely used elici-tation technique in requirements engineering. However, conduct-ing effective requirements elicitation interviews is challenging, due to the combination of technical and soft skills that requirements analysts often acquire after a long period of professional prac-tice. Empirical evidence about training the novices on conducting effective requirements elicitation interviews is scarce. [Objectives] We present a list of most common mistakes that novices make in requirements elicitation interviews. The objective is to assist the educators in teaching interviewing skills to student analysts. [Re-search Method] We conducted an empirical study involving role-playing and authentic assessment with 110 students, teamed up in 28 groups, to conduct interviews with a customer. One researcher made observation notes during the interview while two research-ers reviewed the recordings. We qualitatively analyzed the data to identify the themes and classify the mistakes. [Results and conclu-sion] We identified 34 unique mistakes classified into 7 high level themes. We also give examples of the mistakes made by the novices in each theme, to assist the educationists and trainers. Our re-search design is a novel combination of well-known pedagogical approaches described in sufficient details to make it repeatable for future requirements engineering education and training research.
Barbar, M, Sui, Y, Zhang, H, Chen, S & Xue, J 2018, 'Live path CFI against control flow hijacking attacks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 768-779.
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© Springer International Publishing AG, part of Springer Nature 2018. Through memory vulnerabilities, control flow hijacking allows an attacker to force a running program to execute other than what the programmer has intended. Control Flow Integrity (CFI) aims to prevent the adversarial effects of these attacks. CFI attempts to enforce the programmer’s intent by ensuring that a program only runs according to a control flow graph (CFG) of the program. The enforced CFG can be built statically or dynamically, and Per-Input Control Flow Integrity (PICFI) represents a recent advance in dynamic CFI techniques. PICFI begins execution with the empty CFG of a program and lazily adds edges to the CFG during execution according to concrete inputs. However, this CFG grows monotonically, i.e., edges are never removed when corresponding control flow transfers become illegal. This paper presents LPCFI, Live Path Control Flow Integrity, to more precisely enforce forward edge CFI using a dynamically computed CFG by both adding and removing edges for all indirect control flow transfers from indirect callsites, thereby raising the bar against control flow hijacking attacks.
Barbar, M, Sui, Y, Zhang, H, Chen, S & Xue, J 2018, 'POSTER: Live path control flow integrity', Proceedings - International Conference on Software Engineering, pp. 195-196.
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© 2018 Authors. Per-Input Control Flow Integrity (PICFI) represents a recent advance in dynamic CFI techniques. PICFI starts with the empty CFG of a program and lazily adds edges to the CFG during execution according to concrete inputs. However, this CFG grows monotonically, i.e., invalid edges are never removed when corresponding control flow transfers (via indirect calls) become illegal (i.e., will never be executed again). This paper presents LPCFI, Live Path Control Flow Integrity, to more precisely enforce forward edge CFI using a dynamically computed CFG by both adding and removing edges for all indirect control flow transfers from function pointer calls, thereby raising the bar against control flow hijacking attacks.
Biddle, R, Liu, S, Tilocca, P & Xu, G 2018, 'Automated Underwriting in Life Insurance: Predictions and Optimisation', Australasian Database Conference.
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Bou Ghantous, G & Gill, AQ 2018, 'DevOps Reference Architecture for Multi-Cloud IOT Applications', 20th IEEE International Conference on Business Informatics, Vienna, Austria..
Brownlow, J, Chu, C, Fu, B, Xu, G, Culbert, B & Meng, Q 2018, 'Cost-sensitive churn prediction in fund management services', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 776-788.
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© Springer International Publishing AG, part of Springer Nature 2018. Churn prediction is vital to companies as to identify potential churners and prevent losses in advance. Although it has been addressed as a classification task and a variety of models have been employed in practice, fund management services have presented several special challenges. One is that financial data is extremely imbalanced since only a tiny proportion of customers leave every year. Another is a unique cost-sensitive learning problem, i.e., costs of wrong predictions for churners should be related to their account balances, while costs of wrong predictions for non-churners should be the same. To address these issues, this paper proposes a new churn prediction model based on ensemble learning. In our model, multiple classifiers are built using sampled datasets to tackle the imbalanced data issue while exploiting data fully. Moreover, a novel sampling strategy is proposed to deal with the unique cost-sensitive issue. This model has been deployed in one of the leading fund management institutions in Australia, and its effectiveness has been fully validated in real applications.
Brownlow, J, Chu, C, Fu, B, Xu, G, Culbert, B & Meng, Q 2018, 'Cost-sensitive Churn Prediction in Fund Management Services - Industrial Track', the 23rd International Conference on Database Systems for Advanced Applications, Australia.
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Brownlow, J, Chu, C, Xu, G, Culbert, B, Fu, B & Meng, Q 2018, 'A Multiple Source based Transfer Learning Framework for Marketing Campaigns', the 2018 International Joint Conference on Neural Networks, Rio De Janeiro, Brasil.
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Buchan, J, Bano, M & Zowghi, D 2018, 'Semi-automated Extraction of New Requirements from Online Reviews for Software Product Evolution', Australian Software Engineering Conference, IEEE, Adelaide, Australia.
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In order to improve and increase their utility, software products must evolve continually and incrementally to meet the new requirements of current and future users. Online reviews from users of the software provide a rich and readily available resource for discovering candidate new features for future software releases. However, it is challenging to manually analyze a large volume of potentially unstructured and noisy data to extract useful information to support software release planning decisions. This paper investigates machine learning techniques to automatically identify text that represents users’ ideas for new features from their online reviews. A binary classification approach to categorize extracted text as either a feature or non-feature was evaluated experimentally. Three machine learning algorithms were evaluated in the experiments: Naïve Bayes (with multinomial and Bernoulli variants), Support Vector Machines (with linear and multinomial variants) and Logistic Regression. Variations on the configurations of k-fold cross validation, the use of n-grams and review sentiment were also experimentally evaluated. Based on binary classification of over a thousand separate reviews of two products, Trello and Jira, linear Support Vector Machines with review sentiment as an input, using n-gram (1,4) together with k-fold 10 cross validation gave the best performance. The results have confirmed the feasibility and accuracy of semi-automated extraction of candidate requirements from a large volume of unstructured and noisy online user reviews. The next steps planned are to experiment with machine supported grouping, prioritizing and visualizing the extracted features to best support release planners’ work, as well as extending the sources of candidate requirements.
Chantamit-O-Pas, P & Goyal, M 2018, 'A Case-Based Reasoning Framework for Prediction of Stroke', Advances in Intelligent Systems and Computing, pp. 219-227.
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© Springer Nature Singapore Pte Ltd. 2018. Case-based reasoning (CBR) has been a popular method in health care sector from the last two decades. It is used for analysis, prediction, diagnosis and recommending treatment for patients. This research purposes a conceptual CBR framework for stroke disease prediction that uses previous case-based knowledge. The outcomes of this approach not only assist in stroke disease decision-making, but also will be very useful for prevention and early treatment of patients.
Chantamit-o-Pas, P & Goyal, M 2018, 'Long Short-Term Memory Recurrent Neural Network for Stroke Prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 312-323.
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© 2018, Springer International Publishing AG, part of Springer Nature. Electronic Healthcare Records (EHRs) describe the details about a patient’s physical and mental health, diagnosis, lab results, treatments or patient care plan and so forth. Currently, the International Classification of Diseases, 10thRevision or ICD-10 code is used for representing each patient record. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Various data mining techniques are used for the analysis of data deriving from these patient records. Recurrent Neural Network (RNN) is a powerful and widely used technique in machine learning and bioinformatics. This research aims at the investigation of RNN with Long Short-Term Memory (LSTM) hidden units. The empirical research is intended to evaluate the ability of LSTMs to recognize patterns in multi-label classification of cerebrovascular symptoms or stroke. First, we integrated ICD-10 code into health record, as well as other potential risk factors within EHRs into the pattern and model for prediction. Next, we modelled the effectiveness of LSTMs for prediction of stroke based on healthcare records. The results show several strong baselines that include accuracy, recall, and F1 measure score.
Chen, J, Lin, Z, Liu, X, Deng, Z & Wang, X 2018, 'Reputation-based framework for internet of things', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 592-597.
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© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Internet of Things (IoT) is going to create a world where physical objects are integrated into traditional networks in order to provide intelligent services for human-beings. Trust plays an important role in communications and interactions of objects in IoT. Two vital tasks of trust management are trust model design and reputation evaluation. However, current literature cannot be simply and directly applied to the IoT due to smart node hardware constraints, very limited computing and energy resources. Therefore a general and flexible model is needed to meet the special requirements for IoT. In this paper, we firstly design LTrust, a layered trust model for IoT. Then, a Reputation Evaluation Scheme for the Node (RES-N) has been presented. The proposed trust model and reputation evaluation scheme provide a general framework for the study of trust management for IoT. The efficiency of RES-N is validated by the simulation results.
Culbert, B, Fu, B, Brownlow, J, Chu, C, Meng, Q & Xu, G 2018, 'Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach', Australasian Database Conference.
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Demediuk, S, Murrin, A, Bulger, D, Hitchens, M, Drachen, A, Raffe, WL & Tamassia, M 2018, 'Player retention in league of legends: A study using survival analysis', ACM International Conference Proceeding Series.
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© 2018 ACM. Multi-player online esports games are designed for extended durations of play, requiring substantial experience to master. Furthermore, esports game revenues are increasingly driven by in-game purchases. For esports companies, the trends in players leaving their games therefore not only provide information about potential problems in the user experience, but also impacts revenue. Being able to predict when players are about to leave the game-churn prediction-is therefore an important solution for companies in the rapidly growing esports sector, as this allows them to take action to remedy churn problems. The objective of the work presented here is to understand the impact of specific behavioral characteristics on the likelihood of a player continuing to play the esports title League of Legends. Here, a solution to the problem is presented based on the application of survival analysis, using Mixed Effects Cox Regression, to predict player churn. Survival Analysis forms a useful approach for the churn prediction problem as it provides rates as well as an assessment of the characteristics of players who are at risk of leaving the game. Hazard rates are also presented for the leading indicators, with results showing that duration between matches played is a strong indicator of potential churn.
Demediuk, S, Tamassia, M, Raffe, WL, Zambetta, F, Mueller, FF & Li, X 2018, 'Measuring player skill using dynamic difficulty adjustment', ACM International Conference Proceeding Series.
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© 2018 ACM. Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a player's skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition.
Dong, M, Yao, L, Wang, X, Benatallah, B, Sheng, QZ & Huang, H 2018, 'DUAL: A Deep Unified Attention Model with Latent Relation Representations for Fake News Detection', Web Information Systems Engineering – WISE 2018, Springer International Publishing, pp. 199-209.
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The prevalence of online social media has enabled news to spread wider and faster than traditional publication channels. The easiness of creating and spreading the news, however, has also facilitated the massive generation and dissemination of fake news. It, therefore, becomes especially important to detect fake news so as to minimize its adverse impact such as misleading people. Despite active efforts to address this issue, most existing works focus on mining news’ content or context information from individuals but neglect the use of clues from multiple resources. In this paper, we consider clues from both news’ content and side information and propose a hybrid attention model to leverage these clues. In particular, we use an attention-based bi-directional Gated Recurrent Units (GRU) to extract features from news content and a deep model to extract hidden representations of the side information. We combine the two hidden vectors resulted from the above extractions into an attention matrix and learn an attention distribution over the vectors. Finally, the distribution is used to facilitate better fake news detection. Our experimental results on two real-world benchmark datasets show our approach outperforms multiple baselines in the accuracy of detecting fake news.
Gao, Q, Ma, S, Shao, S, Sui, Y, Zhao, G, Ma, L, Ma, X, Duan, F, Deng, X, Zhang, S & Chen, X 2018, 'CoBOT: Static C/C++ bug detection in the presence of incomplete code', Proceedings - International Conference on Software Engineering, pp. 385-388.
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© 2018 Authors. To obtain precise and sound results, most of existing static analyzers require whole program analysis with complete source code. However, in reality, the source code of an application always interacts with many third-party libraries, which are often not easily accessible to static analyzers. Worse still, more than 30% of legacy projects [1] cannot be compiled easily due to complicated configuration environments (e.g., third-party libraries, compiler options and macros), making ideal "whole-program analysis" unavailable in practice. This paper presents CoBOT [2], a static analysis tool that can detect bugs in the presence of incomplete code. It analyzes function APIs unavailable in application code by either using function summarization or automatically downloading and analyzing the corresponding library code as inferred from the application code and its configuration files. The experiments show that CoBOT is not only easy to use, but also effective in detecting bugs in real-world programs with incomplete code. Our demonstration video is at: https://youtu.be/bhjJp3e7LPM.
Garcia, JA, Raffe, WL & Navarro, KF 2018, 'Assessing user engagement with a fall prevention game as an unsupervised exercise program for older people', ACM International Conference Proceeding Series.
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© 2018 ACM. Falling is, unfortunately, a leading cause of injury and death in the global elderly population. However, it has previously been shown that increased physical and cognitive activity can decrease the occurrence of falls in the elderly. This paper investigates the potential for a long-term, unsupervised fall prevention training tool in the form of the StepKinnection game, which was designed to exercise both reflex times and movement speed while also providing entertainment. Specifically, this game was used in a three month user study consisting of 10 participants over the age of 65. Adherence to the training program, enjoyment of the game, and ease of use of the game were investigated using a custom usability questionnaire, four established usability scales, heuristic evaluation of gameplay data, and semi-structured interviews. Results show that participants generally had positive attitudes towards the game, they felt that they would engage with this training program more than there current exercises, and that the game was easy to use without guidance or supervision beyond the initial set up support and instructions provided at the start of the experiment period.
Ghafouri, M, Wang, X, Yuan, L, Zhang, Y & Lin, X 2018, 'Maintaining boolean Top-K spatial temporal results in publish-subscribe systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 147-160.
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© Springer International Publishing AG, part of Springer Nature 2018. Nowadays many devices and applications in social networks and location-based services are producing, storing and using description, location and occurrence time of objects. Given a massive number of boolean top-k spatial-temporal queries and the spatial-textual message streams, in this paper we study the problem of continuously updating top-k messages with the highest ranks, each of which contains all the requested keywords when rank of a message is calculated by its location and freshness. Decreasing the ranks of existing top-k results over time and producing new incoming messages, cause continuously computing and maintaining the best results. To the best of our knowledge, there is no prior work that can exactly solve this problem. We propose two indexing and matching methods, then conduct an experimental evaluation to show the impact of parameters and analyse the models.
Gheisari, S, Catchpoole, DR, Charlton, A & Kennedy, PJ 2018, 'Patched completed local binary pattern is an effective method for neuroblastoma histological image classification', Communications in Computer and Information Science, pp. 57-71.
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© Springer Nature Singapore Pte Ltd. 2018. Neuroblastoma is the most common extra cranial solid tumour in children. The histology of neuroblastoma has high intra-class variation, which misleads existing computer-aided histological image classification methods that use global features. To tackle this problem, we propose a new Patched Completed Local Binary Pattern (PCLBP) method combining Sign Binary Pattern (SBP) and Magnitude Binary Pattern (MBP) within local patches to build feature vectors which are classified by k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The advantage of our method is extracting local features which are more robust to intra-class variation compared to global ones. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our experiments show the proposed method improves the weighted average F-measure by 1.89% and 0.81% with k-NN and SVM classifiers, respectively.
Gong, S, Wang, X & Oberst, S 2018, 'Non-linear analysis of vibrating flip-flow screens', MATEC Web of Conferences, 3rd International Conference on Design and Manufacturing Engineering, EDP Sciences, Monash University, Melbourne, Australia.
Gui, M, Zhang, Z, Yang, Z, Gu, Y & Xu, G 2018, 'An Effective Joint Framework for Document Summarization', Companion of the The Web Conference 2018 on The Web Conference 2018.
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Han, S, Zou, L & Yu, JX 2018, 'Speeding up set intersections in graph algorithms using SIMD instructions', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1587-1602.
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© 2018 Association for Computing Machinery. In this paper, we focus on accelerating a widely employed computing pattern-set intersection, to boost a group of graph algorithms. Graph's adjacency-lists can be naturally considered as node sets, thus set intersection is a primitive operation in many graph algorithms. We propose QFilter, a set intersection algorithm using SIMD instructions. QFilter adopts a merge-based framework and compares two blocks of elements iteratively by SIMD instructions. The key insight for our improvement is that we quickly filter out most of unnecessary comparisons in one byte-checking step. We also present a binary representation called BSR that encodes sets in a compact layout. By combining QFilter and BSR, we achieve dataparallelism in two levels-inter-chunk and intra-chunk parallelism. Moreover, we find that node ordering impacts the performance of intersection by affecting the compactness of BSR.We formulate the graph reordering problem as an optimization of the compactness of BSR, and prove its strong NP-completeness. Thus we propose an approximate algorithm that can find a better ordering to enhance the intra-chunk parallelism. We conduct extensive experiments to confirm that our approach can improve the performance of set intersection in graph algorithms significantly.
Herron, D, Moncur, W, Marija, C, Grubisic, D, Vistica, O & van den Hoven, E 2018, 'Digital Possessions in the Museum of Broken Relationships', CHI EA '18 Extended Abstracts, Conference on Human Factors in Computing Systems, Montreal QC, Canada.
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Herse, S, Vitale, J, Ebrahimian, D, Tonkin, M, Ojha, S, Sidra, S, Johnston, B, Phillips, S, Gudi, SLKC, Clark, J, Judge, W & Williams, MA 2018, 'Bon Appetit! Robot Persuasion for Food Recommendation', ACM/IEEE International Conference on Human-Robot Interaction, pp. 125-126.
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© 2018 Authors. The integration of social robots within service industries requires social robots to be persuasive. We conducted a vignette experiment to investigate the persuasiveness of a human, robot, and an information kiosk when offering consumers a restaurant recommendation. We found that embodiment type significantly affects the persuasiveness of the agent, but only when using a specific recommendation sentence. These preliminary results suggest that human-like features of an agent may serve to boost persuasion in recommendation systems. However, the extent of the effect is determined by the nature of the given recommendation.
Inibhunu, C & McGregor, C 2018, 'Fusing dimension reduction and classification for mining interesting frequent patterns in patients data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 1-15.
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© Springer International Publishing AG, part of Springer Nature 2018. Vast amounts of data are collected about elderly patients diagnosed with chronic conditions and receiving care in telehealth services. The potential to discover hidden patterns in the collected data can be crucial in making effective decisions on dissemination of services and lead to improved quality of care for patients. In this research, we investigate a knowledge discovery method that applies a fusion of dimension reduction and classification algorithms to discover interesting patterns in patient data. The research premise is that discovery of such patterns could help explain unique features about patients who are likely or unlikely to have an adverse event. This is a unique and innovative technique that utilizes the best of probability, rules, random trees and association algorithms for; (a) feature selection, (b) predictive modelling and (c) frequent pattern mining. The proposed method has been applied in a case study context to discover interesting patterns and features in patients participating in telehealth services. The results of the models developed shows that identification of best feature set can lead to accurate predictors of adverse events as well as effective in generation of frequent patterns and discovery of interesting features in varying patient cohort.
Kenning, G 2018, 'Evaluating Art and Design projects for Dementia'.
Lai, JCS, Oberst, S & Evans, TA 2018, 'Termites use vibrations to eavesdrop on predatory ants', Internoise 2018, Chicago, Illinois, USA..
Lazar, A, Toombs, AL, Morrissey, K, Kenning, G, Boger, J & Brankaert, R 2018, 'CHI 2018 Engage with CHI', CHI 2018 Engage with CHI, Montreal, Canada.
Li, M, Zhang, Y, Sun, Y, Wang, W, Tsang, IW & Lin, X 2018, 'An efficient exact nearest neighbor search by compounded embedding', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 37-54.
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© Springer International Publishing AG, part of Springer Nature 2018. Nearest neighbor search (NNS) in high dimensional space is a fundamental and essential operation in applications from many domains, such as machine learning, databases, multimedia and computer vision. In this paper, we first propose a novel and effective distance lower bound computation technique for Euclidean distance by using the combination of linear and non-linear embedding methods. As such, each point in a high dimensional space can be embedded into a low dimensional space such that the distance between two embedded points lower bounds their distance in the original space. Following the filter-and-verify paradigm, we develop an efficient exact NNS algorithm by pruning candidates using the new lower bounding technique and hence reducing the cost of expensive distance computation in high dimensional space. Our comprehensive experiments on 10 real-life and diverse datasets, including image, video, audio and text data, demonstrate that our new algorithm can significantly outperform the state-of-the-art exact NNS techniques.
Li, RH, Qin, L, Ye, F, Yu, JX, Xiaokui, X, Xiao, N & Zheng, Z 2018, 'Skyline community search in multi-valued networks', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 457-472.
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© 2018 Association for Computing Machinery. Given a scientific collaboration network, how can we find a group of collaborators with high research indicator (e.g., hindex) and diverse research interests? Given a social network, how can we identify the communities that have high influence (e.g., PageRank) and also have similar interests to a specified user? In such settings, the network can be modeled as a multi-valued network where each node has d (d = 1) numerical attributes (i.e., h-index, diversity, PageRank, similarity score, etc.). In the multi-valued network, we want to find communities that are not dominated by the other communities in terms of d numerical attributes. Most existing community search algorithms either completely ignore the numerical attributes or only consider one numerical attribute of the nodes. To capture d numerical attributes, we propose a novel community model, called skyline community, based on the concepts of k-core and skyline. A skyline community is a maximal connected k-core that cannot be dominated by the other connected k-cores in the d-dimensional attribute space. We develop an elegant space-partition algorithm to efficiently compute the skyline communities. Two striking advantages of our algorithm are that (1) its time complexity relies mainly on the size of the answer s (i.e., the number of skyline communities), thus it is very efficient if s is small; and (2) it can progressively output the skyline communities, which is very useful for applications that only require part of the skyline communities. Extensive experiments on both synthetic and real-world networks demonstrate the efficiency, scalability, and effectiveness of the proposed algorithm.
Liu, C, Talaei-Khoei, A & Zowghi, D 2018, 'Theoretical Support for Enhancing Data Quality: Application in Electronic Medical Records', 2018 Americas Conference on Information Systems, Americas Conference on Information Systems, New Orleans.
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This paper aims at reviewing the existing theoretical support to enhance data quality and utilizing the findings of the review in the context of electronic medical records (EMRs). For this to happen, we first conducted a survey of publications that have a focus on an empirical investigation of factors influencing data quality in the conceptual models. By using a well-established taxonomy development method from the discipline of information systems, we then proposed 3 dimensions for studying factors influencing data quality and constructing the conceptual model for enhancing data quality: breadth, depth, and interaction, within 9 characteristics under different dimensions. Last, we compared related studies using the proposed dimensions and utilized the findings of the review in enhancing EMRs quality to disclose the limitations and possibilities of new areas for further study.
Liu, C, Zowghi, D, Talaei-Khoei, A & Daniel, J 2018, 'Achieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development', Proceedings of the 51st Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences 2018, -, Hilton Waikoloa Village, Hawaii, USA, pp. 2824-2833.
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This paper aims at proposing a conceptual model of achieving data completeness in electronic medical records (EMR). For this to happen, firstly, we draw on the model of factors influencing data quality management to construct our conceptual model. Secondly, we develop hypotheses of relationships between influencing factors for data completeness and mediators for achieving data completeness in EMR based on the literature. Our conceptual model extends the prior model for factors influencing data quality management by adding a new factor and exploring the relationships between the influencing factors within the context of data completeness in EMR. The proposed conceptual model and the presented hypotheses once empirically validated will be the basis for the development of tools and techniques for achieving data completeness in EMR.
Liu, W, Chang, X, Chen, L & Yang, Y 2018, 'Semi-supervised Joint Learning of Representation and Relation for Person Re-identification', Association for the Advancement of Artificial Intelligence, Louisiana, USA.
LU, S, Oberst, S, Zhang, G & Luo, Z 2018, 'Comparing complex dynamics using machine learning-reconstructed attracting sets', Colloquium on Irregular Engineering Oscillations and Signal Processing, TUHH, Hamburg, Germany.
LU, S, Oberst, S, Zhang, G & Luo, Z 2018, 'Order patterns recurrence plots and new quantifications to unveil nonlinear dynamics from stochastic systems', International Conference on Time Series and Forecasting 2018, Granada, Spain.
Mehar, AM, Gill, AQ & Matawie, K 2018, 'Analytical Model for Residential Predicting Energy Consumption', 20th IEEE International Conference on Business Informatics, The BAPAR workshop, Vienna, Austria..
Melnikov, A, Quann, L, Alú, A, Oberst, S, Marburg, S & Powell, D 2018, 'Theory for Willis coupling prediction of acoustic meta-atoms', Symposium on Acoustic Metamaterials, Xatavia, Spain.
Nassir, SA & Leong, TW 2018, 'Conducting Qualitative Fieldwork with Ageing Saudis: A Visual Diary', DIS'18 Proceedings of the 2018 Designing Interactive Systems Conference, Designing Interactive Systems (DIS'18), ACM, Hong Kong.
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This pictorial offers a visual diary of our qualitative fieldwork to understand ageing people's experiences in Saudi Arabia. It provides insights gained through conducting qualitative fieldwork with ageing Saudis. We present a range of cultural considerations that shaped the design of the fieldwork and highlight opportunities, challenges, and issues that we faced when conducting interviews and deploying research probes. In particular, we highlight the power and effectiveness of using probes to elicit participants' values, views and desires when working within the sociocultural norms of Saudi Arabia.
Nejad, MZ, Lu, J & Behbood, V 2017, 'Applying dynamic Bayesian tree in property sales price estimation', Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, NanJing, JiangSu, China, pp. 1-6.
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© 2017 IEEE. Accurate prediction of Residential Property Sale Price is very important and significant in the operation of the real estate market. Property sellers and buyers/Investors wish to know a fair value for their properties in particular at the time of the sales transaction. The main reason to build an Automated Valuation Model is to be accurate enough to replace human. To select a most suitable model for the property sale price prediction, this paper examined seven Tree-based machine learning models including Dynamic Bayesian Tree (online learning method), Random Forest, Stochastic Gradient Boosting, CART, Bagged CART, Tree Bagged Ensembles and Boosted Tree (batch learning methods) by comparing their RMSE and MAE performances. The performance of these models are tested on 1967 records of unit sales from 19 suburbs of Sydney, Australia. The main purpose of this study is to compare the performance of batch models with the online model. The results demonstrated that Dynamic Bayesian Tree as an online model stands in the middle of batch models based on the root mean square error (RMSE) and mean absolute error (MAE). It shows using online model for estimating the property sale price is reasonable for real world application.
Nguyen, Q, Lau, CW, Qu, Z, Simoff, SJ, Huang, M & Catchpoole, DR 2018, 'A Mobile Tool for Interactive Visualisation of Genomics Data', Proceedings of ITME 2018, 2018 9th International Conference on Information Technology in Medicine and Education, IEEE Computer Society CPS, Hangzhou, Zhejiang, China, pp. 688-697.
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Advancement in genomic research and technology
has significantly improved our understandings of biology,
health, and medicine. Genomics data are very complex and
contain genotype and phenotype information. Health
researchers have long known that many diseases such as cancer
are hereditary. Gaining insight and understanding of such data
would enable a better understanding of the correlation between
genes and diseases, which could facilitate personalised
treatment for the patients. Visualisations have been increasingly
used to break the complexity of genomics data to guide better
decisions. Unfortunately, research works on interactive
visualisations of the genomics data on mobile devices and
immersive platforms are still limited. This paper presents a new
interactive visualisation and navigation of genomics data on the
mobile platform. The visualisation provides an overview of the
entire patient cohort in a 3D similarity-space environment as
well as 2D detail views of genes of interests. We introduce a new
algorithm that enables effective touch-based interaction and
exploration of large number of items on small mobile screens.
We illustrate the effectiveness of our platform through a
childhood cancer dataset, B-cell Acute Lymphoblastic
Leukaemia (ALL) as well as a pilot qualitative study with the
domain experts.
Ning, X, Yao, L, Wang, X, Benatallah, B, Zhang, S & Zhang, X 2018, 'Data-Augmented Regression with Generative Convolutional Network', Web Information Systems Engineering – WISE 2018, Springer International Publishing, pp. 301-311.
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Generative adversarial networks (GAN)-based approaches have been extensively investigated whereas GAN-inspired regression (i.e., numeric prediction) has rarely been studied in image and video processing domains. The lack of sufficient labeled data in many real-world cases poses great challenges to regression methods, which generally require sufficient labeled samples for their training. In this regard, we propose a unified framework that combines a robust autoencoder and a generative convolutional neural network (GCNN)-based regression model to address the regression problem. Our model is able to generate high-quality artificial samples via augmenting the size of a small number of training samples for better training effects. Extensive experiments are conducted on two real-world datasets and the results show that our proposed model consistently outperforms a set of advanced techniques under various evaluation metrics.
Nobakht, M, Sui, Y, Seneviratne, A & Hu, W 2018, 'Permission Analysis of Health and Fitness Apps in IoT Programming Frameworks', Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, pp. 533-538.
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© 2018 IEEE. Popular IoT programming frameworks, such as Google Fit, enable third-party developers to build apps to store and retrieve user data from a variety of data sources (e.g., wearables). The problem of overprivilege stands out due to the diversity and complexity of IoT apps, and developers rushing to release apps to meet the high demand in the IoT market. Any incorrect API usage of the IoT frameworks by third-party developers can lead to security risks, especially in health and fitness apps. Protecting sensitive user information is critically important to prevent financial and psychological harms. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies overprivilege issues in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fitenabled apps to determine whether the requested permission scopes are actually necessary. In this way, PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We used PGFIT to perform overprivilege analysis on a set of 20 Google Fit-enabled apps and with manual inspection, we found that 6 (30%) of them are overprivileged.
Oberst, S 2018, 'Nonlinear Dynamics: Towards a paradigm change via evidence-based complex dynamics modelling', Noise and Vibrations Emerging Methods, Ibiza, Spain.
Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, N & Morlock, M 2018, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction for cavity preparation in hip implants', MATEC Web of Conferences, EDP Sciences.
Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, N & Morlock, MM 2017, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impactionin cavity preparations', MATEC Web of Conferences, International Congress on Engineering Vibration, EDP Sciences, Sofia, Bulgaria, pp. 1-6.
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Owing to an ageing population, the impact of unhealthy lifestyle, or simply congenital or gender
specific issues (dysplasia), degenerative bone and joint disease (osteoarthritis) at the hip pose an increasing
problem in many countries. Osteoarthritis is painful and causes mobility restrictions; amelioration is often only
achieved by replacing the complete hip joint in a total hip arthroplasty (THA). Despite significant orthopaedic
progress related to THA, the success of the surgical process relies heavily on the judgement, experience, skills
and techniques used of the surgeon. One common way of implanting the stem into the femur is press fitting
uncemented stem designs into a prepared cavity. By using a range of compaction broaches, which are impacted
into the femur, the cavity for the implant is formed. However, the surgeon decides whether to change the size of
the broach, how hard and fast it is impacted or when to stop the excavation process, merely based on acoustic,
haptic or visual cues which are subjective. It is known that non-ideal cavity preparations increase the risk of
peri-prosthetic fractures especially in elderly people.
This study reports on a simulated hip replacement surgery on a cadaver and the analysis of impaction forces
and the microphone signals during compaction. The recorded transient signals of impaction forces and acoustic
pressures (≈ 80 µs - 2 ms) are statistically analysed for their trend, which shows increasing heteroscedasticity
in the force-pressure relationship between broach sizes.
TIKHONOV regularisation, as inverse deconvolution technique, is applied to calculate the acoustic transfer
functions from the acoustic responses and their mechanical impacts. The extracted spectra highlight that system
characteristics altered during the cavity preparation process: in the high-frequency range the number of
resonances increased with impacts and broach size. By applying nonlinear time series analysis the system dynamics
increase in compl...
Oberst, S, Lai, JCS & Evans, TA 2018, 'Excitation signal extraction of ant walking pattern under the influence of noise using a biomechanical bipedal mathematical model', 2nd International Symposium on Biotremology, San Michele all’Adige, Italy.
Oberst, S, Lim, S, Romão, AC, Lai, JCS, Stender, M, Hoffmann, NP & Evans, TA 2018, 'A coupled mono-bipedal biomechanical surrogate model to mimic ants walking and running gait analysed using recurrence plot quantification analysis', Colloquium on Irregular Oscillations and Signal Processing,, Hamburg, Germany.
Ouyang, D, Qin, L, Chang, L, Lin, X, Zhang, Y & Zhu, Q 2018, 'When Hierarchy meets 2-hop-labeling: Effiicient shortest distance ?eries on road networks', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 709-724.
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© 2018 Association for Computing Machinery. Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hopbased solutions. However, the hierarchy-based solutions require a large search space for long-distance queries while the hop-based solutions result in a high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an e?cient query processing algorithm with performance guarantees by visiting part of the labels for the source and destination based on the vertex hierarchy. We also propose an algorithm to construct the H2H-Index based on distance preserved graphs. The algorithm is further optimized by computing the labels based on the partially computed labels of other vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.
Pang, G, Cao, L, Chen, L, Liu, H & Lian, D 2018, 'Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data.', Association for the Advancement of Artificial Intelligence.
Pang, G, Chen, L, Cao, L & Liu, H 2018, 'Learning representations of ultrahigh-dimensional data for random distance-based outlier detection', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2041-2050.
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© 2018 Association for Computing Machinery. Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers). This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO. Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement.
Peppas, P & Williams, M 2018, 'Parametrised Difference Revision', Principles of Knowledge Representation and Reasoning.
Raffe, WL & Garcia, JA 2018, 'Combining skeletal tracking and virtual reality for game-based fall prevention training for the elderly', 2018 IEEE 6th International Conference on Serious Games and Applications for Health, SeGAH 2018, pp. 1-7.
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© 2018 IEEE. This paper provides a preliminary appraisal of combining commercial skeletal tracking and virtual reality technologies for the purposes of innovative gameplay interfaces in fall prevention exergames for the elderly. This work uses the previously published StepKinnection game, which used skeletal tracking with a flat screen monitor, as a primary point of comparison for the proposed combination of these interaction modalities. Here, a Microsoft Kinect is used to track the player's skeleton and represent it as an avatar in the virtual environment while the HTC Vive is used for head tracking and virtual reality visualization. Multiple avatar positioning modes are trialled and discussed via a small self-reflective study (with the authors as participants) to examine their ability to allow accurate stepping motions, maintain physical comfort, and encourage self-identification or empathy with the avatar. While this is just an initial study, it highlights promising opportunities for designing engaging step training games with this integrated interface but also highlights its limitations, especially in the context of an unsupervised exercise program of older people in independent living situations.
Razzak, I, Saris, RA & Xu, G 2018, 'Robust 2D Joint Sparse Principle Component Analysis with F-norm Minimization for Sparse Modelling: 2D-RJSPCA', the 2018 International Joint Conference on Neural Networks, Rio De Janeiro.
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Saeed, Z, Razzak, I, Abbasi, R & Xu, G 2018, 'Text Stream to Temporal Network - A Dynamic Heartbeat Graph to Detect Emerging Events on Twitter', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data, Australia.
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Saqib, M, Daud Khan, S, Sharma, N & Blumenstein, M 2018, 'Extracting descriptive motion information from crowd scenes', International Conference Image and Vision Computing New Zealand, pp. 1-6.
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© 2017 IEEE. An important contribution that automated analysis tools can generate for management of pedestrians and crowd safety is the detection of conflicting large pedestrian flows: this kind of movement pattern, in fact, may lead to dangerous situations and potential threats to pedestrian's safety. For this reason, detecting dominant motion patterns and summarizing motion information from the scene are inevitable for crowd management. In this paper, we develop a framework that extracts motion information from the scene by generating point trajectories using particle advection approach. The trajectories obtained are then clustered by using unsupervised hierarchical clustering algorithm, where the similarity is measured by the Longest Common Sub-sequence (LCS) metric. The achieved motions patterns in the scene are summarized and represented by using color-coded arrows, where speeds of the different flows are encoded with colors, the width of an arrow represents the density (number of people belonging to a particular motion pattern) while the arrowhead represents the direction. This novel representation of crowded scene provides a clutter free visualization which helps the crowd managers in understanding the scene. Experimental results show that our method outperforms state-of-the-art methods.
Schraefel, MC, Andres, J & Van Den Hoven, E 2018, 'The body as starting point: Exploring inside and around body boundaries for body-centric computing design', Conference on Human Factors in Computing Systems - Proceedings.
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© 2018 Copyright is held by the owner/author(s). More HCI designs and devices are embracing what is being dubbed “body centric computing,” where designs both deliberately engage the body as the locus of interest, whether to move the body into play or relaxation, or to track and monitor its performance, or to use it as a surface for interaction. Most HCI researchers are engaging in these designs, however, with little direct knowledge of how the body itself works either as a set of complex internal systems or as sets of internal and external systems that interact dynamically. The science of how our body interacts with the microbiome around us also increasingly demonstrates that our presumed boundaries between what is inside and outside us may be misleading if not considered harmful. Developing both (1) introductory knowledge and (2) design practice of how these in-bodied and circum-bodied systems work with our understanding of the em-bodied self, and how this gnosis/praxis may lead to innovative new body-centric computing designs is the topic of this workshop.
Shakeri Hossein Abad, Z, Rahman, M, Cheema, A, Gervasi, V, Zowghi, D & Barker, K 2018, 'Dynamic Visual Analytics for Elicitation Meetings with ELICA', IEEE, International Requirements Engineering Conference, Banff, Canada.
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Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requirements provided by stakeholders. We present ELICA, a generic interactive visual analytics tool to assist analysts during requirements elicitation process. ELICA uses a novel information extraction algorithm based on a combination of Weighted Finite State Transducers (WFSTs)(generative model) and SVMs (discriminative model). ELICA presents the extracted relevant information in an interactive GUI (including zooming, panning, and pinching) that allows analysts to explore which parts of the ongoing conversation (or specification document) match with the extracted information. In this demonstration, we show that ELICA is usable and effective in practice, and is able to extract the related information in real-time. We also demonstrate how carefully designed features in ELICA facilitate the interactive and dynamic process of information extraction.
Shakeri, Z, Gervasi, V, Zowghi, D & Barker, K 2018, 'ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information', 5th International Workshop on Artificial Intelligence for Requirements Engineering, IEEE, Banff, Canada.
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Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analysts and stakeholders. In this paper, we propose a requirements ELICitation Aid tool (ELICA) to help analysts better understand the target application domain by dynamic extraction and labeling of requirements-relevant knowledge. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural language processing tasks. In addition to the information conveyed through text, ELICA captures and processes non-linguistic information about the intention of speakers such as their confidence level, analytical tone, and emotions. The extracted information is made available to the analysts as a set of labeled snippets with highlighted relevant terms which can also be exported as an artifact of the Requirements Engineering (RE) process. The application and usefulness of ELICA are demonstrated through a case study. This study shows how pre-existing relevant information about the application domain and the information captured during an elicitation meeting, such as the conversation and stakeholders' intentions, can be captured and used to support analysts achieving their tasks.
Shakeri, Z, Noaeen, M, Zowghi, D, Far, B & Barker, K 2018, 'Two Sides of the Same Coin: Software Developers' Perceptions of Task Switching and Task Interruption', ACM, Evaluation and Assessment in Software Engineering, ACM, Christchurch, New Zealand, pp. 175-180.
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In the constantly evolving world of software development, switching back and forth between tasks has become the norm. While task switching often allows developers to perform tasks effectively and may increase creativity via the flexible pathway, there are also consequences to frequent task-switching. For high-momentum tasks like software development, "flow", the highly productive state of concentration, is paramount. Each switch distracts the developers' flow, requiring them to switch mental state and an additional immersion period to get back into the flow. However, the wasted time due to time fragmentation caused by task switching is largely invisible and unnoticed by developers and managers. We conducted a survey with 141 software developers to investigate their perceptions of differences between task switching and task interruption and to explore whether they perceive task switchings as disruptive as interruptions. We found that practitioners perceive considerable similarities between the disruptiveness of task switching (either planned or unplanned) and random interruptions. The high level of cognitive cost and low performance are the main consequences of task switching articulated by our respondents. Our findings broaden the understanding of flow change among software practitioners in terms of the characteristics and categories of disruptive switches as well as the consequences of interruptions caused by daily meetings.
Shang, Z, Yu, JX & Elmore, AJ 2018, 'RushMon: Real-time isolation anomalies monitoring', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 647-662.
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© 2018 Association for Computing Machinery. Motivated by the applicability of HogWild!-style algorithms, people turn their focus on system architectures that provide ultra-high throughput random-access with very limited or no isolation guarantees, and build inconsistent-tolerant applications (i.e., large scale optimization algorithms) on top of them. Although some optimization algorithms have theoretical convergence guarantees, sometimes these systems fail to compute the correct results when the presumptions of convergence cannot hold. Moreover, there is no practical way to tell whether a given result is accurate (without cross validation) or to tune the isolation strength on-the-fly. To resolve these problems, these systems need an indicator to report the number of lbad eventž caused by lout-of-orderž executions. In this paper, we tackle this problem. Based on transaction processing theory, we find the number of cycles in the dependency graph, and demonstrate it is a good indicator. With this observation, we propose the first real-time isolation anomalies monitor. Our monitor is at least 1000x faster than naive implementations and reports accurate isolation anomalies levels with less than 1% extra overhead. Monitoring anomalies in a real-time manner efficiently protects the systems from excessive isolation anomalies which could lead to incorrect results. We verify the performance and effectiveness of our monitor via extensive experimental studies.
Shi, H, He, W & Xu, G 2018, 'Workshop Proposal on Knowledge Discovery from Digital Libraries', Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 429-430.
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© 2018 Authors. The workshop is with the ACM/IEEE Joint Conference on Digital Libraries in 2018 (JCDL 2018) which will be held in Fort Worth, Texas, USA on June 3 - 7, 2018. The Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, and social issues.
Sohaib, O, Lu, H & Hussain, W 2017, 'Internet of Things (IoT) in E-commerce: For People with Disabilities', 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Cambodia.
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Internet of Things (IoT) is an interconnection between the physical object and digital world. As a result, many e-commerce companies seize the advantages of the IoT to grow their business. However, the world's largest minority are people with disabilities. IoT can lower barriers for the disabled people by offering assistance in accessing information. Increasing Internet accessibility can help to make that happen for both social and economic benefit. This paper presents the proposed integrated framework of the IoT and cloud computing for people with disabilities such as sensory (hearing and vision), motor (limited use of hands) and cognitive (language and learning disabilities) impairments in the context of business-to-consumer e-commerce context. We conclude that IoT-enabled services offer great potential for success of disabled people in the context of online shopping.
Song, Y, Zhang, G, Lu, H & Lu, J 2018, 'A Self-adaptive Fuzzy Network for Prediction in Non-stationary Environments', 2018 IEEE International Conference on Fuzzy Systems, 2018 IEEE International Conference on Fuzzy Systems, Brazil, pp. 1-8.
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Spoletini, P, Ferrari, A, Bano, M, Zowghi, D & Gnesi, S 2018, 'Interview Review: an Empirical Study on Detecting Ambiguities in Requirements Elicitation Interviews', Requirements Engineering: Foundation for Software Quality, International Working Conference on Requirements Engineering: Foundation for Software Quality, Springer, Ultrecht, Netherlands, pp. 101-118.
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[Context and Motivation] Ambiguities identified during requirements elicitation interviews can be used by the requirements analyst as triggers for additional questions and, consequently, for disclosing further – possibly tacit – knowledge. Therefore, every unidentified ambiguity may be a missed opportunity to collect additional information. [Question/problem] Ambiguities are not always easy to recognize, especially during highly interactive activities such as requirements elicitation interviews. Moreover, since different persons can perceive ambiguous situations differently, the unique perspective of the analyst in the interview might not be enough to identify all ambiguities. [Principal idea/results] To maximize the number of ambiguities recognized in interviews, this paper proposes a protocol to conduct reviews of requirements elicitation interviews. In the proposed protocol, the interviews are audio recorded and the recordings are inspected by both the analyst who performed the interview and another reviewer. The idea is to use the identified cases of ambiguity to create questions for the follow-up interviews. Our empirical evaluation of this protocol involves 42 students from Kennesaw State University and University of Technology Sydney. The study shows that, during the review, the analyst and the other reviewer identify 68% of the total number of ambiguities discovered, while 32% were identified during the interviews. Furthermore, the ambiguities identified by analysts and other reviewers during the review significantly differ from each other. [Contribution] Our results indicate that interview reviews allow the identification of a considerable number of undetected ambiguities, and can potentially be highly beneficial to discover unexpressed information in future interviews.
Stender, M, Oberst, S & Hoffmann, NP 2018, 'Reconstruction of differential equations from time-series data for feature engineering and model identification', Colloquium on Irregular Oscillations and Signal Processing, Hamburg, Germany.
Talaei-Khoei, A, Motiwalla, LF & Kazemi, SF 2018, 'A predictive method to determine incomplete electronic medical records', SIGMIS-CPR 2018 - Proceedings of the 2018 ACM SIGMIS Conference on Computers and People Research, pp. 99-106.
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© 2018 Association for Computing Machinery. This paper is utilizing predictive models to determine missing electronic medical records (EMR) at general practice offices. Prior research has addressed the missing values problem in the EMRs used for secondary analysis. However, health care providers are overlooking the missing records problem that stores the patients’ medical visits information in EMRs. Our study provides a technique to predict the number of EMR entries for each practice based on their past data records. If the number of EMR entries is less than predicted, it warns the occurrence of missing records with the 95% confidence interval. The study uses seven years of EMRs from 14 general practice offices to train the predictive model. The model predicts EMR data entries and accordingly identified missing EMRs for the following year. We compared the actual visits illustrated by de-identified billing data to the predictive model. The study found auto-correlation method improves the performance of identifying missing records by detecting the period of prediction. In addition, artificial neural networks and support vector machines perform better than other predictive methods depending on whether the analysis aims at detecting missing EMRs or when identifying complete EMRs with no missing records. Results suggest that clinicians and medical professionals should be mindful of the potential missing records of EMRs prior any secondary analysis.
Tonkin, M, Vitale, J, Herse, S, Williams, MA, Judge, W & Wang, X 2018, 'Design Methodology for the UX of HRI: A Field Study of a Commercial Social Robot at an Airport', ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415.
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© 2018 ACM. Research in robotics and human-robot interaction is becoming more and more mature. Additionally, more affordable social robots are being released commercially. Thus, industry is currently demanding ideas for viable commercial applications to situate social robots in public spaces and enhance customers experience. However, present literature in human-robot interaction does not provide a clear set of guidelines and a methodology to (i) identify commercial applications for robotic platforms able to position the users needs at the centre of the discussion and (ii) ensure the creation of a positive user experience. With this paper we propose to fill this gap by providing a methodology for the design of robotic applications including these desired features, suitable for integration by researchers, industry, business and government organisations. As we will show in this paper, we successfully employed this methodology for an exploratory field study involving the trial implementation of a commercially available, social humanoid robot at an airport.
Vitale, J, Tonkin, M, Herse, S, Ojha, S, Clark, J, Williams, M, Wang, X & Judge, W 2018, 'Be More Transparent and Users Will Like You: A Robot Privacy and User Experience Design Experiment', Proceedings of 2018 ACM/IEEE International Conference on Human- Robot Interaction, ACM/IEEE International Conference on Human-Robot Interaction 2018, ACM, Chicago, pp. 379-387.
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Vo, NNY, Liu, S, Xu, G & He, X 2018, 'Multimodal Mixture Density Boosting Network for Personality Mining', Advances in Knowledge Discovery and Data Mining, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Melbourne, Australia, pp. 644-655.
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Vo, NNY, Xu, G, Liu, S, Brownlow, EJ, Culbert, B & Chu, C 2018, 'Client Churn Prediction with Call Log Analysis', Database Systems for Advanced Applications, 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018), Springer, Gold Coast, Australia, pp. 752-763.
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Wan, Y, Zhao, Z, Yang, M, Xu, G, Ying, H, Wu, J & Yu, PS 2018, 'Improving Automatic Source Code Summarization via Deep Reinforcement Learning', The 33rd IEEE/ACM International Conference on Automated Software Engineering, Corum, Montpellier, France.
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Wang, H, Chen, J, Wang, X, Liu, X & Na, Z 2018, 'Privacy protection for location sharing services in social networks', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 97-102.
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© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Recently, there is an increase interest in location sharing services in social networks. Behind the convenience brought by location sharing, there comes an indispensable security risk of privacy. Though many efforts have been made to protect user’s privacy for location sharing, they are not suitable for social network. Most importantly, little research so far can support user relationship privacy and identity privacy. Thus, we propose a new privacy protection protocol for location sharing in social networks. Different from previous work, the proposed protocol can provide perfect privacy for location sharing services. Simulation results validate the feasibility and efficiency of the proposed protocol.
Wang, S, Zhang, Y, Lin, X & Cheema, MA 2018, 'Maximize spatial influence of facility bundle considering reverse k nearest neighbors', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 684-700.
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© Springer International Publishing AG, part of Springer Nature 2018. Consider a two dimensional Euclidean space, let F be a set of points representing facilities and U be a set of points representing users. Spatial influence of a facility is the number of users who have this facility as one of their k nearest facilities. This is because that users normally prefer to go to their nearby facilities and are naturally influenced the most by their nearest facilities. Given a facility bundle of size t, spatial influence of the bundle is the number of distinct users influenced by any one of them. Existing works on facility selection problem find out top-t facilities with the highest spatial influence. However, the literature lacks study on this problem when the t facilities have the highest spatial influence as a bundle. We are the first to study the problem of Maximizing Bundled Reverse k Nearest Neighbors (MB-RkNN), where the spatial influence of a facility bundle of size t is maximized. We prove its NP-hardness, and propose a branch-and-bound best first search algorithm that greedily select the currently best facility until we get t facilities. We introduce the concept of kNN region such that a group of users have their kNN facilities all belong to the same kNN region. This sharing property of kNN region allows us to avoid redundant calculation with dynamic programming technique. We conduct experiments on real data sets and show that our algorithm is orders of magnitudes better than our baseline algorithm both in terms of CPU time and IO cost.
Wang, X, Deng, K, Li, J, Yu, JX, Jensen, CS & Yang, X 2018, 'Targeted influence minimization in social networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 689-700.
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© Springer International Publishing AG, part of Springer Nature 2018. An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence minimization in relation to a particular group of social network users, called targeted influence minimization. Thus, the objective is to protect a set of users, called target nodes, from malicious information originating from another set of users, called active nodes. This study also addresses two fundamental, but largely ignored, issues in different influence minimization problems: (i) the impact of a budget on the solution; (ii) robust sampling. To this end, two scenarios are investigated, namely unconstrained and constrained budget. Given an unconstrained budget, we provide an optimal solution; Given a constrained budget, we show the problem is NP-hard and develop a greedy algorithm with an (1 - 1 / e) -approximation. More importantly, in order to solve the influence minimization problem in large, real-world social networks, we propose a robust sampling-based solution with a desirable theoretic bound. Extensive experiments using real social network datasets offer insight into the effectiveness and efficiency of the proposed solutions.
Wu, W, Li, B, Chen, L & Zhang, C 2018, 'Efficient attributed network embedding via recursive randomized hashing', IJCAI International Joint Conference on Artificial Intelligence, pp. 2861-2867.
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© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Attributed network embedding aims to learn a low-dimensional representation for each node of a network, considering both attributes and structure information of the node. However, the learning based methods usually involve substantial cost in time, which makes them impractical without the help of a powerful workhorse. In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity. NetHash employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i.e., the predefined highest-order neighboring nodes) to top (i.e., the root node), and particularly, to preserve as much information closer to the root node as possible. Our extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy.
Yin, J, Zhou, Z, Liu, S, Wu, Z & Xu, G 2018, 'Social Spammer Detection: A Multi-Relational Embedding Approach', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data, Australia.
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Ying, H, Zhuang, F, Zhang, F, Liu, Y, Xu, G, Xie, X, Xiong, H & Wu, J 2018, 'Sequential Recommender System based on Hierarchical Attention Networks', the 27th International Joint Conference on Artificial Intelligence.
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Yue, L, Wen, D, Cui, L, Qin, L & Zheng, Y 2018, 'K-connected cores computation in large dual networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 169-186.
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© Springer International Publishing AG, part of Springer Nature 2018. Computing k-cores is a fundamental and important graph problem, which can be applied in many areas, such as community detection, network visualization, and network topology analysis. Due to the complex relationship between different entities, dual graph widely exists in the applications. A dual graph contains a physical graph and a conceptual graph, both of which have the same vertex set. Given that there exist no previous studies on the k-core in dual graphs, we formulate a k-connected core (k-CCO) model in dual graphs. A k-CCO is a k-core in the conceptual graph, and also connected in the physical graph. Given a dual graph and an integer k, we propose a polynomial time algorithm for computing all k-CCOs. We also propose three algorithms for computing all maximum-connected cores (MCCO), which are the existing k-CCOs such that a (k +1)-CCO does not exist. We conduct extensive experiments on six real-world datasets and several synthetic datasets. The experimental results demonstrate the effectiveness and efficiency of our proposed algorithms.
Zhang, F, Yuan, L, Zhang, Y, Qin, L, Lin, X & Zhou, A 2018, 'Discovering strong communities with user engagement and tie strength', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 425-441.
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© Springer International Publishing AG, part of Springer Nature 2018. In this paper, we propose and study a novel cohesive subgraph model, named (k,s)-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (k,s)-core model with k-core and k-truss theoretically and experimentally. We propose efficient algorithms to compute the (k,s)-core and decompose the graph by a particular sub-model k-fami. Extensive experiments show (1) our (k,s)-core and k-fami are effective cohesive subgraph models and (2) the (k,s)-core computation and k-fami decomposition are efficient on various real-life social networks.
Zhang, Z, Oberst, S & Lai, JCS 2018, 'Instability analysis of brake squeal with uncertain contact conditions', International Congress on Sound and Vibration, Hiroshima, Japan.
Zhou, A, Zhang, F, Yuan, L, Zhang, Y & Lin, X 2018, 'Finding maximal stable cores in social networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 224-235.
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© Springer International Publishing AG, part of Springer Nature 2018. Maximal Stable Cores are a cohesive subgraph on a social network which use both engagement and similarity to identify stable groups of users. The problem is, when given a query user and a similarity threshold, to find all Maximal Stable Cores relative to the user. We propose a baseline algorithm and as the problem is NP-Hard, an improved heuristic algorithm which utilises linear time k-core decomposition. Experiments how that when the two algorithms differ, the improved algorithm significantly outperforms the baseline.
Zhou, Z, Liu, S, Xu, G, Xie, X, Yin, J, Li, Y & Zhang, W 2018, 'Knowledge-based Recommendation with Hierarchical Collaborative Embedding', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Australia.
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Zowghi, D, Bano, M, Ferrari, A, Spoletini, P & Gnesi, S 2018, 'The 24th International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018)', Lecture Notes in Computer Science, International Working Conference on Requirements Engineering: Foundation for Software Quality, Springer Verlag, Utrecht, The Netherlands, pp. 101-118.
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[Context and Motivation] Ambiguities identified during requirements elicitation interviews can be used by the requirements analyst as triggers for additional questions and, consequently, for disclosing further – possibly tacit – knowledge. Therefore, every unidentified ambiguity may be a missed opportunity to collect additional information. [Question/problem] Ambiguities are not always easy to recognize, especially during highly interactive activities such as requirements elicitation interviews. Moreover, since different persons can perceive ambiguous situations differently, the unique perspective of the analyst in the interview might not be enough to identify all ambiguities. [Principal idea/results] To maximize the number of ambiguities recognized in interviews, this paper proposes a protocol to conduct reviews of requirements elicitation interviews. In the proposed protocol, the interviews are audio recorded and the recordings are inspected by both the analyst who performed the interview and another reviewer. The idea is to use the identified cases of ambiguity to create questions for the follow-up interviews. Our empirical evaluation of this protocol involves 42 students from Kennesaw State University and University of Technology Sydney. The study shows that, during the review, the analyst and the other reviewer identify 68% of the total number of ambiguities discovered, while 32% were identified during the interviews. Furthermore, the ambiguities identified by analysts and other reviewers during the review significantly differ from each other. [Contribution] Our results indicate that interview reviews allow the identification of a considerable number of undetected ambiguities, and can potentially be highly beneficial to discover unexpressed information in future interviews.