Abidi, S, Piccardi, M, Tsang, WH & Williams, M-A 2019, 'Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.
Adak, C, Chaudhuri, BB & Blumenstein, M 2019, 'An Empirical Study on Writer Identification and Verification From Intra-Variable Individual Handwriting', IEEE ACCESS, vol. 7, pp. 24738-24758.
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Agarwal, A, Dowsley, R, McKinney, ND, Wu, D, Lin, C-T, De Cock, M & Nascimento, ACA 2019, 'Protecting Privacy of Users in Brain-Computer Interface Applications', IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 27, no. 8, pp. 1546-1555.
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Alnefaie, A, Fiaidhi, J & Mohammed, S 2019, 'Ontology-based food recommendation system for seniors: A peer to peer networking approach', International Journal of Advanced Science and Technology, vol. 123, pp. 41-46.
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© 2019 NADIA. This research is focused on the development of a state of art food recommendation system web platform for seniors with physical disability. The recommendation of food items is important for many reasons. Researchers have been studying these kinds of systems for many years, suggesting not only that can they be a means to help people find food they might want to eat, but also help them nourish themselves more healthily and avoids intolerances and food allergies. This paper created an ontology based Canada food allergies and intolerances databases 1 to guide the recommender according to the personalized information provided by the elder. The recommender incorporates other useful features such as the selections of different food items, their description and a peer to peer food delivery service. The delivery service is an added-value feature to connect seniors with the nearby couriers or online suppliers who are able to purchase the recommended items from stores and deliver it to the senior's home.
Alshehri, MD & Hussain, FK 2019, 'A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT)', Computing.
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© 2018, Springer-Verlag GmbH Austria, ein Teil von Springer Nature. Recently, the Internet of things (IoT) has received a lot of attention from both industry and academia. A reliable and secure IoT connection and communication is essential for the proper working of the IoT network as a whole. One of the ways to achieve robust security in an IoT network is to enable and build trusted communication among the things (nodes). In this area, the existing IoT literature faces many critical issues, such as the lack of intelligent cluster-based trust approaches for IoT networks and the detection of attacks on the IoT trust system from malicious nodes, such as bad service providers. The existing literature either does not address these issues or only addresses them partially. Our proposed solution can firstly detect on-off attacks using the proposed fuzzy-logic based approach, and it can detect contradictory behaviour attacks and other malicious nodes. Secondly, we develop a fuzzy logic-based approach to detect malicious nodes involved in bad service provisioning. Finally, to maintain the security of the IoT network, we develop a secure messaging system that enables secure communication between nodes. This messaging system uses hexadecimal values with a structure similar to serial communication. We carried out extensive experimentation under varying network sizes to validate the working of our proposed solution and also to test the efficiency of the proposed methods in relation to various types of malicious behavior. The experiment results demonstrate the effectiveness of our approach under various conditions.
Altaee, A, Braytee, A, Millar, GJ & Naji, O 2019, 'Energy efficiency of hollow fibre membrane module in the forward osmosis seawater desalination process', Journal of Membrane Science, vol. 587.
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© 2019 This study provided new insights regarding the energy efficiency of hollow fibre forward osmosis modules for seawater desalination; and as a consequence an approach was developed to improve the process performance. Previous analysis overlooked the relationship between the energy efficiency and operating modes of the hollow fibre forward osmosis membrane when the process was scaled-up. In this study, the module length and operating parameters were incorporated in the design of an energy-efficient forward osmosis system. The minimum specific power consumption for seawater desalination was calculated at the thermodynamic limits. Two FO operating modes: (1) draw solution in the lumen and (2) feed solution in the lumen, were evaluated in terms of the desalination energy requirements at a minimum draw solution flow rate. The results revealed that the operating mode of the forward osmosis membrane was important in terms of reducing the desalination energy. In addition, the length of the forward osmosis module was also a significant factor and surprisingly increasing the length of the forward osmosis module was not always advantageous in improving the performance. The study outcomes also showed that seawater desalination by the forward osmosis process was less energy efficient at low and high osmotic draw solution concentration and performed better at 1.2–1.4 M sodium chloride draw solution concentrations. The findings of this study provided a platform to the manufacturers and operators of hollow fibre forward osmosis membrane to improve the energy efficiency of the desalination process.
Altulyan, M, Yao, L, Kanhere, SS, Wang, X & Huang, C 2019, 'A unified framework for data integrity protection in people-centric smart cities', Multimedia Tools and Applications.
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With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, smart cities have the potential to improve various indicators of residents’ quality of life. However, threats to data integrity may affect the delivery of such benefits, especially in the IoT environment where most devices are inherently dynamic and have limited resources. Prior work has focused on ensuring integrity of data in a piecemeal manner and covering only some parts of the smart city ecosystem. In this paper, we address integrity of data from an end-to-end perspective, i.e., from the data source to the data consumer. We propose a holistic framework for ensuring integrity of data in smart cities that covers the entire data lifecycle. Our framework is founded on three fundamental concepts, namely, secret sharing, fog computing and blockchain. We provide a detailed description of various components of the framework and also utilize smart healthcare as use case.
Anaissi, A & Goyal, M 2019, 'Blending Association Rules for Knowledge Discovery in Big Data', Enabling Technologies and Architectures for Next-Generation Networking Capabilities, pp. 254-271.
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Aravanis, T, Peppas, P & Williams, MA 2019, 'An investigation of parametrized difference revision operators', Annals of Mathematics and Artificial Intelligence.
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© 2019, Springer Nature Switzerland AG. In this article, we provide the epistemic-entrenchment and partial-meet characterizations of a new, important class of concrete revision operators (all of which satisfy the AGM postulates for revision), called Parametrized Difference revision operators (PD operators, for short). PD operators are natural generalizations of Dalal’s revision operator, with a much greater range of applicability, hence, the epistemic-entrenchment and partial-meet characterizations of the latter are also provided, as a by-product. Lastly, we prove that PD operators satisfy the strong version of Parikh’s relevance-sensitive axiom for belief revision, showing that they are fully compatible with the notion of relevance.
Bennett, J, Froggett, L, Kenning, G, Manley, J & Muller, L 2019, 'Memory Loss and Scenic Experience: An Arts Based Investigation', Forum : Qualitative Social Research, vol. 20, no. 1.
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Beydoun, G, Hoffmann, A, Valencia-García, R, Shen, J & Gill, A 2019, 'Towards an assessment framework of reuse: a knowledge level analysis approach', Complex and Intelligent Systems.
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Bharill, N, Patel, OP, Tiwari, A, Mu, L, Li, DL, Mohanty, M, Kaiwartya, O & Prasad, M 2019, 'A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering', IEEE Access, vol. 7, pp. 50347-50361.
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© 2013 IEEE. Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.
Bharill, N, Tiwari, A, Malviya, A, Patel, OP, Gupta, A, Puthal, D, Saxena, A & Prasad, M 2019, 'Fuzzy knowledge based performance analysis on big data', Neurocomputing.
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© 2019 Elsevier B.V. Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM)algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM)called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM.
Bou Ghantous, G & Gill, A 2019, 'An Agile-DevOps Reference Architecture for Teaching Enterprise Agile', International Journal of Learning, Teaching and Educational Research.
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Bracci, M, Tarini, M, Pietroni, N, Livesu, M & Cignoni, P 2019, 'HexaLab. net: an online viewer for hexahedral meshes', Computer-Aided Design, vol. 1110, pp. 24-36.
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We introduce HexaLab: a WebGL application for real time visualization, exploration and assessment of hexahedral meshes. HexaLab can be used by simply opening www.hexalab.net. Our visualization tool targets both users and scholars. Practitioners who employ hexmeshes for Finite Element Analysis, can readily check mesh quality and assess its usability for simulation. Researchers involved in mesh generation may use HexaLab to perform a detailed analysis of the mesh structure, isolating weak points and testing new solutions to improve on the state of the art and generate high quality images. To this end, we support a wide variety of visualization and volume inspection tools. Our system offers also immediate access to a repository containing all the publicly available meshes produced with the most recent techniques for hexmesh generation. We believe HexaLab, providing a common tool for visualizing, assessing and distributing results, will push forward the recent strive for replicability in our scientific community
Buyya, R, Srirama, SN, Casale, G, Calheiros, R, Simmhan, Y, Varghese, B, Gelenbe, E, Javadi, B, Vaquero, LM, Netto, MAS, Toosi, AN, Rodriguez, MA, Llorente, IM, De Capitani Di Vimercati, S, Samarati, P, Milojicic, D, Varela, C, Bahsoon, R, Dias De Assuncao, M, Rana, O, Zhou, W, Jin, H, Gentzsch, W, Zomaya, AY & Shen, H 2019, 'A manifesto for future generation cloud computing: Research directions for the next decade', ACM Computing Surveys, vol. 51, no. 5.
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© 2018 Association for Computing Machinery. The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.
C. A. Tirado Cortes, H. Chen, D. Sturnieks, J. Garcia Marin, S. Lord & C. Lin 2019, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, pp. 1-1.
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Room-scale 3D position tracking allows users to explore the virtual environment by physically walking. However, since the eyesight of the user is blocked by a Head-Mounted Display (HMD), the user might lose her balance because of bumping into real-world obstacles or shifting the body weight onto virtual objects that are inexistent in the real-world. This paper investigates assistive fall recovery methods under the assumption that the onset of the fall is given. Our experiment simulated the forward loss-of-balance with a tether-release protocol. A magnetic lock attached to a counter-weight was released while the subject was in a static leaning posture and engaged in a secondary 3D object selection task. The experiment uses a two by two design that examines two assistive techniques, i.e. video-see-through and auditory warning, at two different timing, i.e. fall onset and 500ms prior to onset. The data from 17 subjects show that the video-see-through triggered 500ms before the onset of fall can effectively help users recover from falls. Surprisingly, the video-see-through at the fall onset has a significant negative impact on the fall recovery providing similar results to the baseline condition (no intervention).
CAO, X, Qiu, B & Xu, G 2019, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, vol. 22, no. 3, pp. 1015-1027.
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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 2019, '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, Chuang, CH, King, JK & Lin, CT 2019, 'Multi-channel EEG recordings during a sustained-attention driving task', Scientific data, vol. 6, no. 1, p. 19.
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We describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5-10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities.
Cao, Z, Ding, W, Wang, YK, Hussain, FK, Al-Jumaily, A & Lin, CT 2019, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing.
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© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Cao, Z, Lin, CT, Ding, W, Chen, MH, Li, CT & Su, TP 2019, 'Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG', IEEE Transactions on Biomedical Engineering.
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IEEE This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited 55 outpatients with TRD who were randomized into three approximately equal- sized groups (A: 0.5 mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton Depression Rating Scale (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p < 0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3 $\pm$ 9.5% accuracy, 82.1 $\pm$ 8.6% sensitivity and 91.9 $\pm$ 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry and cordance at baseline and early post-treatment changes. The prefrontal EEG patterns at baseline may account for recognizing ketamine effects in advance. Our randomized, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.
CHEN, J, SU, S & WANG, X 2019, 'Towards Privacy-Preserving Location Sharing over Mobile Online Social Networks', IEICE TRANSACTIONS on Information and Systems, vol. 102, pp. 133-146.
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Chen, K, Yao, L, Zhang, D, Wang, X, Chang, X & Nie, F 2019, 'A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition.', IEEE transactions on neural networks and learning systems.
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Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets.
Chen, S, Wang, Y, Lin, CT, Ding, W & Cao, Z 2019, 'Semi-supervised feature learning for improving writer identification', Information Sciences, vol. 482, pp. 156-170.
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© 2019 Elsevier Inc. Data augmentation is typically used by supervised feature learning approaches for offline writer identification, but such approaches require a mass of additional training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline is proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we propose a weighted label smoothing regularization (WLSR) method for data augmentation, which assigns a weighted uniform label distribution to the extra unlabeled data. The WLSR method regularizes the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach significantly improves the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline writer identification.
Cheng, C, Xiao, F & Cao, Z 2019, 'A New Distance for Intuitionistic Fuzzy Sets Based on Similarity Matrix', IEEE Access, vol. 7, pp. 70436-70446.
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© 2013 IEEE. Measuring the distance between two intuitionistic fuzzy sets (IFSs) is an open issue. Many types of distances for the IFSs have been proposed in previous studies. Some existing methods cannot satisfy the axioms of similarity or provide counterintuitive cases. Others ignore the relationship between three parameters characterizing the information carried by the IFS. To address these issues, a new distance is proposed by analyzing the similarity among the three parameters of the IFS. The comparison with some existing distances illustrates that the new distance has a higher sensitivity and can effectively measure the similarity between the IFSs. The results of the application of pattern recognition are also shown that the proposed method has better recognition ability.
Cheng, EJ, Chou, KP, Rajora, S, Jin, BH, Tanveer, M, Lin, CT, Young, KY, Lin, WC & Prasad, M 2019, 'Deep Sparse Representation Classifier for facial recognition and detection system', Pattern Recognition Letters, vol. 125, pp. 71-77.
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© 2019 Elsevier B.V. This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets.
Chubb, CT, Tomamichel, M & Korzekwa, K 2019, 'Moderate deviation analysis of majorization-based resource interconversion', PHYSICAL REVIEW A, vol. 99, no. 3.
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Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2019, '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.
Curiskis, SA, Drake, B, Osborn, TR & Kennedy, PJ 2019, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', Information Processing and Management.
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© 2019 Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.
Cutler, RL, Torres-Robles, A, Wiecek, E, Drake, B, Van der Linden, N, Benrimoj, SIC & Garcia-Cardenas, V 2019, 'Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system', PATIENT PREFERENCE AND ADHERENCE, vol. 13, pp. 853-862.
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Daniel, J, Naderpour, M & Lin, CT 2019, 'A Fuzzy Multilayer Assessment Method for EFQM', IEEE Transactions on Fuzzy Systems, vol. 27, no. 6, pp. 1252-1262.
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© 1993-2012 IEEE. Although the European Foundation for Quality Management (EFQM) is one of the best-known business excellence frameworks, its inherent self-assessment approaches have several limitations. A critical review of self-assessment models reveals that most models are ambiguous and limited to precise data. In addition, the impact of expert knowledge on scoring is overly subjective, and most methodologies assume the relationships between variables are linear. This paper presents a new fuzzy multilayer assessment method that relies on fuzzy inference systems to accommodate imprecise data and varying assessor experiences to overcome uncertainty and complexity in the EFQM model. The method was implemented, tested, and verified under real conditions at a regional electricity company. The case was assessed by internal company experts and external assessors from an EFQM business excellence organization and the model was implemented using MATLAB software. When comparing the classical model with the new model, assessors and experts favored outputs from the new model.
Dimuro, GP, Lucca, G, Bedregal, B, Mesiar, R, Sanz, JA, Lin, CT & Bustince, H 2019, 'Generalized CF1F2-integrals: From Choquet-like aggregation to ordered directionally monotone functions', Fuzzy Sets and Systems.
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© 2019 Elsevier B.V. This paper introduces the theoretical framework for a generalization of CF1F2-integrals, a family of Choquet-like integrals used successfully in the aggregation process of the fuzzy reasoning mechanisms of fuzzy rule based classification systems. The proposed generalization, called by gCF1F2-integrals, is based on the so-called pseudo pre-aggregation function pairs (F1,F2), which are pairs of fusion functions satisfying a minimal set of requirements in order to guarantee that the gCF1F2-integrals to be either an aggregation function or just an ordered directionally increasing function satisfying the appropriate boundary conditions. We propose a dimension reduction of the input space, in order to deal with repeated elements in the input, avoiding ambiguities in the definition of gCF1F2-integrals. We study several properties of gCF1F2-integrals, considering different constraints for the functions F1 and F2, and state under which conditions gCF1F2-integrals present or not averaging behaviors. Several examples of gCF1F2-integrals are presented, considering different pseudo pre-aggregation function pairs, defined on, e.g., t-norms, overlap functions, copulas that are neither t-norms nor overlap functions and other functions that are not even pre-aggregation functions.
Ding, W, Lin, C-T & Cao, Z 2019, '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 & Cao, Z 2019, 'Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces.', IEEE Transactions on Neural Networks and Learning Systems.
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The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.
Ding, W, Lin, C-T & Pedrycz, W 2019, '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.
Djebbara, Z, Fich, LB, Petrini, L & Gramann, K 2019, 'Sensory-motor brain dynamics reflect architectural affordances'.
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Anticipating meaningful actions in the environment is an essential function of the brain. Such predictive mechanisms originate from the motor system and allow for inferring actions from environmental affordances, the potential to act within a specific environment. Using architecture, we provide a unique perspective to the abiding debate in cognitive neuroscience and philosophy on whether cognition depends on movement or is decoupled from our physical structure. To investigate cognitive processes associated with architectural affordances, we used a Mobile Brain/Body Imaging approach recording brain activity synchronized to head-mounted virtual reality. Participants perceived and acted upon virtual transitions ranging from non-passable to easily passable. We demonstrate that early sensory brain activity, upon revealing the environment and before actual movement, differed as a function of affordances. Additionally, movement through transitions was preceded by a motor-related negative component also depended on affordances. Our results suggest that potential actions afforded by an environment influence perception.
Du, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2019, 'Personalized Video Recommendation Using Rich Contents from Videos', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.
Fahmidehgholami, M & Beydoun, G 2019, 'Big data analytics architecture design—An application in manufacturing systems', Computers and Industrial Engineering, vol. 128, pp. 948-963.
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© 2018 Elsevier Ltd Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected ...
Fang, XS, Sheng, QZ, Wang, X, Chu, D & Ngu, AHH 2019, 'SmartVote: a full-fledged graph-based model for multi-valued truth discovery', World Wide Web, vol. 22, no. 4, pp. 1855-1885.
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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.
Gay, VC, Garcia, JA & Leong, TW 2019, 'Using Asynchronous Exergames to Encourage an Active Ageing Lifestyle: Solitaire Fitness Study Protocol.', Studies in health technology and informatics, vol. 266, pp. 70-75.
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A healthy and active lifestyle can significantly improve the well-being and quality of life; however, some elderly people struggle to stay motivated and engaged with any form of exercise. The project Elaine (Elderly, AI and New Experiences) addresses this problem by seeking to improve the quality of life of the elderly through exergames. Currently, the project explores a novel approach in the field of health informatics called asynchronous exergaming. This approach, a new trend in games in the health domain, allows the elderly to workout at their own pace, and in their own time, with their physical activity linked asynchronously to a game. This paper presents the study protocol for Solitaire Fitness, a new asynchronous exergame developed by the team. The game aims at increasing the motivation of the elderly to engage in physical exercise whilst helping to maintain their cognitive abilities. It also describes the protocol for the trial. The result of this research has the potential to benefit elderly that need nudging to be motivated to exercise, health care providers treating people with sedentary lifestyles and researchers investigating ways to encourage the elderly to exercise.
Gill, AQ & Chew, E 2019, '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.
Gu, X, Yu, Y, Li, Y, Li, J, Askari, M & Samali, B 2019, 'Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control', Mechanical Systems and Signal Processing, vol. 119, pp. 380-398.
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In this paper, a “smart” base isolation strategy is proposed in this study utilising a semi-active magnetorheological elastomer (MRE) isolator whose stiffness can be controlled in real-time and reversible fashion. By modulating the applied current, the horizontal stiffness of the MRE isolator can be controlled and thus the control action can be generated for the isolated structure. To overcome the inherent nonlinearity and hysteresis of the MRE isolator, radial basis function neural network based fuzzy logic control (RBF-NFLC) was developed due to its inherent robustness and capability in coping with uncertainties. The NFLC was optimised by a non-dominated sorting genetic algorithm type II (NSGA-II) for better suited fuzzy control rules as well as most appropriate parameters for the membership functions. To evaluate the effectiveness of the proposed smart base isolation system, four scenarios are tested under various historical earthquake excitations, i.e. bare building with no isolation, passive isolated structure, MRE isolated structure with Bang-Bang control, MRE isolated structure with proposed NFLC. A three-storey shear building model was adopted as the testing bed. Through the testing results, limited performance of passive isolation system was revealed. In contrast, the adaptability of the proposed isolation strategy was demonstrated and it is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
Gupta, A, Agrawal, RK, Kirar, JS, Kaur, B, Ding, W, Lin, CT, Javier, AP & Prasad, M 2019, 'A hierarchical meta-model for multi-class mental task based brain-computer interfaces', Neurocomputing.
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© 2019 In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG)signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM)classifier is used for multi mental task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class mental task classification. Experimental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class mental task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.
Gupta, D, Pratama, M, Ma, Z, Li, J & Prasad, M 2019, 'Financial time series forecasting using twin support vector regression', PLoS ONE, vol. 14, no. 3.
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© 2019 Gupta et al. Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets.
Halasi, Z, Maróti, A, Pyber, L & Qiao, Y 2019, 'An improved diameter bound for finite simple groups of Lie type', Bulletin of the London Mathematical Society.
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© 2019 London Mathematical Society For a finite group (Formula presented.), let (Formula presented.) denote the maximum diameter of a connected Cayley graph of (Formula presented.). A well-known conjecture of Babai states that (Formula presented.) is bounded by (Formula presented.) in case (Formula presented.) is a non-abelian finite simple group. Let (Formula presented.) be a finite simple group of Lie type of Lie rank (Formula presented.) over the field (Formula presented.). Babai's conjecture has been verified in case (Formula presented.) is bounded, but it is wide open in case (Formula presented.) is unbounded. Recently, Biswas and Yang proved that (Formula presented.) is bounded by (Formula presented.). We show that in fact (Formula presented.) holds. Note that our bound is significantly smaller than the order of (Formula presented.) for (Formula presented.) large, even if (Formula presented.) is large. As an application, we show that more generally (Formula presented.) holds for any subgroup (Formula presented.) of (Formula presented.), where (Formula presented.) is a vector space of dimension (Formula presented.) defined over the field (Formula presented.).
Han, B, Tsang, IW, Chen, L, Zhou, JT & Yu, CP 2019, 'Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels.', IEEE transactions on neural networks and learning systems.
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Crowdsourcing has become the most appealing way to provide a plethora of labels at a low cost. Nevertheless, labels from amateur workers are often noisy, which inevitably degenerates the robustness of subsequent learning models. To improve the label quality for subsequent use, majority voting (MV) is widely leveraged to aggregate crowdsourced labels due to its simplicity and scalability. However, when crowdsourced labels are ``heavily'' noisy (e.g., 40% of noisy labels), MV may not work well because of the fact ``garbage (heavily noisy labels) in, garbage (full aggregated labels) out.'' This issue inspires us to think: if the ultimate target is to learn a robust model using noisy labels, why not provide partial aggregated labels and ensure that these labels are reliable enough for learning models? To solve this challenge by improving MV, we propose a coarse-to-fine label filtration model called double filter machine (DFM), which consists of a (majority) voting filter and a sparse filter serially. Specifically, the DFM refines crowdsourced labels from coarse filtering to fine filtering. In the stage of coarse filtering, the DFM aggregates crowdsourced labels by voting filter, which yields (quality-acceptable) full aggregated labels. In the stage of fine filtering, DFM further digs out a set of high-quality labels from full aggregated labels by sparse filter, since this filter can identify high-quality labels by the methodology of support selection. Based on the insight of compressed sensing, DFM recovers a ground-truth signal from heavily noisy data under a restricted isometry property. To sum up, the primary benefits of DFM are to keep the scalability by voting filter, while improve the robustness by sparse filter. We also derive theoretical guarantees for the convergence and recovery of DFM and reveal its complexity. We conduct comprehensive experiments on both the UCI simulated and the AMT crowdsourced datasets. Empirical results show that partial aggregated la...
Han, Y, Deng, Y, Cao, Z & Lin, CT 2019, 'An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents’ behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking.
Hao, P, Zhang, G, Martinez, L & Lu, J 2019, 'Regularizing Knowledge Transfer in Recommendation With Tag-Inferred Correlation', IEEE Transactions on Cybernetics.
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IEEE Traditional recommender systems suffer from the data sparsity problem. However, user knowledge acquired in one domain can be transferred and exploited in several other relevant domains. In this context, cross-domain recommender systems have been proposed to create a new and effective recommendation paradigm in which to exploit rich data from auxiliary domains to assist recommendations in a target domain. Before knowledge transfer takes place, building reliable and concrete domain correlation is the key ensuring that only relevant knowledge will be transferred. Social tags are used to explicitly link different domains, especially when neither users nor items overlap. However, existing models only exploit a subset of tags that are shared by heterogeneous domains. In this paper, we propose a complete tag-induced cross-domain recommendation (CTagCDR) model, which infers interdomain and intradomain correlations from tagging history and applies the learned structural constraints to regularize joint matrix factorization. Compared to similar models, CTagCDR is able to fully explore knowledge encoded in both shared and domain-specific tags. We demonstrate the performance of our proposed model on three public datasets and compare it with five state-of-the-art single and cross-domain recommendation approaches. The results show that CTagCDR works well in both rating prediction and item recommendation tasks, and can effectively improve recommendation performance.
Hesamian, MH, Jia, W, He, X & Kennedy, P 2019, 'Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.', Journal of Digital Imaging, vol. 32, no. 4, pp. 582-596.
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Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.
Hua, J, Huang, M, Huang, W & Zhao, C 2019, 'Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets', Symmetry, vol. 11, no. 1, pp. 30-30.
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Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.
Hua, J, Huang, ML & Huang, C 2019, 'Centrality Metrics’ Performance Comparisons on Stock Market Datasets', Symmetry, vol. 11, no. 7, pp. 1-15.
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The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.
Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2019, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters.
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Hussain, S, Al-Hitmi, M, Khaliq, S, Hussain, A & Saqib, MA 2019, 'Implementation and comparison of particle swarm optimization and genetic algorithm techniques in combined economic emission dispatch of an independent power plant', Energies, vol. 12, no. 11.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license This paper presents the optimization of fuel cost, emission of NOX, COX, and SOX gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2019, 'Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review', Journal of Cleaner Production, vol. 239.
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© 2019 Elsevier Ltd With rapid movement to combat climate change by reducing greenhouse gases, there is an increasing trend to use more electric vehicles (EVs) and renewable energy sources (RES). With more EVs integration into electricity grid, this raises many challenges for the distribution service operators (DSOs) to integrate such RES-based, distributed generation (DG) and EV-like distributed loads into distribution grids. Effective management of distribution network imbalance is one of the challenges. The distribution network reconfiguration (DNR) techniques are promising to address the issue of imbalance along with other techniques such as the optimal distributed generation placement and allocation (OPDGA) method. This paper presents a systematic and thorough review of DNR techniques for mitigating unbalance of distribution networks, based on papers published in peer-reviewed journals in the last three decades. It puts more focus on how the DNR techniques have been used to manage network imbalance due to distributed loads and DG units. To the best of our knowledge, this is the first attempt to review the research works in the field using DNR techniques to mitigate unbalanced distribution networks. Therefore, this paper will serve as a prime source of the guidance for mitigating network imbalance using the DNR techniques to the new researchers in this field.
Ivanyos, G & Qiao, Y 2019, 'Algorithms based on *-algebras, and their applications to isomorphism of polynomials with one secret, group isomorphism, and polynomial identity testing', SIAM Journal on Computing, vol. 48, no. 3, pp. 926-963.
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© 2019 Society for Industrial and Applied Mathematics. We consider two basic algorithmic problems concerning tuples of (skew-)symmetric matrices. The first problem asks us to decide, given two tuples of (skew-)symmetric matrices (B1,..., Bm) and (C1,..., Cm), whether there exists an invertible matrix A such that for every i ∈ {1,..., m}, AtBiA = Ci. We show that this problem can be solved in randomized polynomial time over finite fields of odd size, the reals, and the complex numbers. The second problem asks us to decide, given a tuple of square matrices (B1,..., Bm), whether there exist invertible matrices A and D, such that for every i ∈ {1,..., m}, ABiD is (skew-)symmetric. We show that this problem can be solved in deterministic polynomial time over fields of characteristic not 2. For both problems we exploit the structure of the underlying ∗-algebras (algebras with an involutive antiautomorphism) and utilize results and methods from the module isomorphism problem. Applications of our results range from multivariate cryptography to group isomorphism and to polynomial identity testing. Specifically, these results imply efficient algorithms for the following problems. (1) Test isomorphism of quadratic forms with one secret over a finite field of odd size. This problem belongs to a family of problems that serves as the security basis of certain authentication schemes proposed by Patarin [J. Patarin, in Advances in Cryptology, EUROCRYPT'96, Springer, Berlin, 1996, pp. 33-48]. (2) Test isomorphism of p-groups of class 2 and exponent p (p odd) with order p in time polynomial in the group order, when the commutator subgroup is of order pO(). (3) Deterministically reveal two families of singularity witnesses caused by the skew-symmetric structure. This represents a natural next step for the polynomial identity testing problem, in the direction set up by the recent resolution of the noncommutative rank problem [A. Garg et al., in Proceedings of the 57th Annual IEEE Sy...
Jay, B 2019, 'Intensional computation with higher-order functions', Theoretical Computer Science, vol. 768, pp. 76-90.
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© 2019 Elsevier B.V. Intensional computations are those that query the internal structure of their arguments. In a higher-order setting, such queries perform program analysis. This is beyond the expressive power of traditional term rewriting systems, such as lambda-calculus or combinatory logic, as they are extensional. In such settings it has been necessary to encode or quote the program before analysis. However, there are intensional calculi, specifically confluent term rewriting systems, that can analyse higher-order programs within the calculus proper, without quotation; there are even programs that produce the Goedel numbers of their program argument. This paper summarizes the current situation. Highlights include the following observations. We have known since 2011 that the simplest intensional calculus, SF-calculus, supports arbitrary queries of closed normal forms, including equality, pattern-matching, searching and self-interpretation. Recent work, verified using the Coq proof assistant, has shown that all recursive programs can be represented as closed normal forms in SF-calculus, and even in combinatory logic. Thus, we can here deduce that SF-calculus (but not combinatory logic) can define queries of programs. These results are compatible with direct support for lambda-abstraction. Although these results conflict with the traditional understanding of expressive power of combinatory logic and λ-calculus, as developed by Church and Kleene, our recent publication has shown that their approach is compromised by its reliance on encodings. To drive the point home, this paper uses a non-standard encoding to lambda-define a trivial solution of the Halting Problem.
Jiang, P, Li, R, Lu, H & Zhang, X 2019, 'Modeling of electricity demand forecast for power system', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.
Jiang, P, Wang, B, Li, H & Lu, H 2019, 'Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting', Energy, pp. 468-482.
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© 2019 Elsevier Ltd Wind-speed forecasting plays a crucial part in improving the operational efficiency of wind power generation. However, accurate forecasts are difficult owing to the uncertainty of the wind speed. Although numerous investigations of wind-speed forecasting have been performed, many of the previous studies used wind-speed data directly to make forecasts, which were rarely based on the structural characteristics of the data. Therefore, in this study, a hybrid linear-nonlinear modeling method based on the chaos theory was successfully employed to capture the linear and nonlinear factors hidden in chaotic time series. Before the forecast, the noise in the data was removed using a decomposition algorithm. Then, through the phase-space reconstruction, the one-dimensional time series were extended to the multi-dimensional space to determine the utilization form of the data. Finally, Holt's exponential smoothing based on the firefly optimization algorithm and support vector regression were combined to predict the wind speed. The experimental results show that the proposed model is not only better than the comparison models but also has great application potential in the wind power generation system.
Kenning, G 2019, 'Chronic health: if not us then who? A review of an exhibition exploring the assistive, speculative, provocative and challenging role of design for health — Embassy of Health, Dutch Design Week', Design for Health, pp. 1-18.
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Khan, HU, Aruya, JA & Gill, A 2019, 'Web 2.0 Technologies Adoption Barriers for External Contacts and Participation: A Case Study of Federal Establishment of Africa', International Journal of Business Information Systems.
Kieferova, M, Scherer, A & Berry, DW 2019, 'Simulating the dynamics of time-dependent Hamiltonians with a truncated Dyson series', PHYSICAL REVIEW A, vol. 99, no. 4.
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King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2019, '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.
Ko, LW, Lu, YC, Bustince, H, Chang, YC, Chang, Y, Ferandez, J, Wang, YK, Sanz, JA, Pereira Dimuro, G & Lin, CT 2019, 'Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 96-106.
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© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands.
Korzekwa, K, Chubb, CT & Tomamichel, M 2019, 'Avoiding Irreversibility: Engineering Resonant Conversions of Quantum Resources', PHYSICAL REVIEW LETTERS, vol. 122, no. 11.
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Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 2019, 'Automatic Design of Cable-Tensioned Glass Shells', COMPUTER GRAPHICS FORUM.
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Li, H, Wang, J, Li, R & Lu, H 2019, 'Novel analysis-forecast system based on multi-objective optimization for air quality index', JOURNAL OF CLEANER PRODUCTION, vol. 208, pp. 1365-1383.
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Li, M, Sun, Y, Su, S, Tian, Z, Wang, Y & Wang, X 2019, 'DPIF: A framework for distinguishing unintentional quality problems from potential shilling attacks', Computers, Materials and Continua, vol. 59, no. 1, pp. 331-344.
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Copyright © 2019 Tech Science Press. Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.
Li, Q, Cao, Z, Zhong, J & Li, Q 2019, 'Graph representation learning with encoding edges', Neurocomputing, vol. 361, pp. 29-39.
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© 2019 Elsevier B.V. Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real-world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.
Li, Q, Zhong, J, Cao, Z & Li, X 2019, 'Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy', Optimization Methods and Software.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.
Li, Q, Zhong, J, Li, Q, Wang, C & Cao, Z 2019, 'A community merger of optimization algorithm to extract overlapping communities in networks', IEEE Access, vol. 7, pp. 3994-4005.
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© 2018 IEEE. A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.
Li, YN, Zhang, K, Hu, DN & Huang, ML 2019, 'The influence of edge bundling on visual information search', Information Sciences, vol. 495, pp. 234-246.
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© 2019 Elsevier Inc. Millions of nodes and edges in a node-link diagram representing the network structure of big data may clutter the space, reducing the aesthetic quality and hindering the process of information search. Edge bundling offers an approach to solve this problem. However, to what extent that edge bundling can solve the problem remains unclear. Addressing the literature in visual complexity and Gestalt law of prägnanz, we empirically examine the influence of edge bundling on users’ information search performance, i.e., the accuracy, responding time and perceptual fluency under different levels of bundling strength. The paper reports our findings with detailed discussions.
Liang, H, Wang, H, Li, Q, Wang, J, Xu, G, Chen, J, Wei, JM & Yang, Z 2019, 'A general framework for learning prosodic-enhanced representation of rap lyrics', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks.
Liang, J, Zhang, Y, Zhong, JH & Yang, H 2019, 'A novel multi-segment feature fusion based fault classification approach for rotating machinery', Mechanical Systems and Signal Processing, vol. 122, pp. 19-41.
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© 2018 Elsevier Ltd Accurate and efficient rotating machinery fault diagnosis is crucial for industries to guarantee the productivity and reduce the maintenance cost. This paper systematically proposes a new fault diagnosis approach including signal processing techniques and pattern recognition method. In order to reveal more useful details in a fault residing signal, a novel automatic signal segmentation method named Grassmann manifold – angular central Gaussian distribution is proposed to divide a raw signal into several segments, resulting in a significant improvement of diagnosis accuracy. An improved empirical mode decomposition, wavelet transform – ensemble empirical mode decomposition, is also designed which could adequately solve the problems of mode mixing and end effects. Moreover, a morphological method usually used in image processing is investigated and adopted to change the shape of the intrinsic mode functions to further reveal the faulty impulses. In order to reduce the high dimension of the extracted features and improve the computational efficiency and accuracy, a deep belief network is designed to conduct information fusion, and compared with widely adopted kernel principal component analysis. For classification, a pairwise coupling strategy is proposed and combined with sparse Bayesian extreme learning machine. The experiments conducted using the proposed approach demonstrate the effectiveness of the proposed system.
Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2019, '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, C-Y, Singh, AK, King, J-T & Wang, YK 2019, '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, King, J-T, Chuang, C-H, Ding, W, Chuang, W-Y, Liao, L-D & Wang, Y-K 2019, 'Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements.', International journal of neural systems, p. 1950018.
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Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.
Liu, B, Chen, L, Zhu, X & Qiu, W 2019, 'Encrypted data indexing for the secure outsourcing of spectral clustering', Knowledge and Information Systems.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.
Liu, C, Nitschke, P, Williams, SP & Zowghi, D 2019, 'Data quality and the Internet of Things', Computing.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. The Internet of Things (IoT) is driving technological change and the development of new products and services that rely heavily on the quality of the data collected by IoT devices. There is a large body of research on data quality management and improvement in IoT, however, to date a systematic review of data quality measurement in IoT is not available. This paper presents a systematic literature review (SLR) about data quality in IoT from the emergence of the term IoT in 1999 to 2018. We reviewed and analyzed 45 empirical studies to identify research themes on data quality in IoT. Based on this analysis we have established the links between data quality dimensions, manifestations of data quality problems, and methods utilized to measure data quality. The findings of this SLR suggest new research areas for further investigation and identify implications for practitioners in defining and measuring data quality in IoT.
Liu, J, Ma, J, Xiang, Y, Zhou, W & Huang, X 2019, 'Authenticated Medical Documents Releasing with Privacy Protection and Release Control', IEEE Transactions on Dependable and Secure Computing.
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IEEE In the context of Information Societies, a tremendous amount of information is daily exchanged or released. Among various information-release cases, medical document release has gained significant attention for its potential in improving healthcare service quality and efficacy. However, integrity and origin authentication of released medical documents is the priority in subsequent applications. Moreover, sensitive nature of much of this information also gives rise to a serious privacy threat when medical documents are uncontrollably made available to untrusted third parties. Redactable signatures allow any party to delete pieces of an authenticated document while guaranteeing the origin and integrity authentication of the resulting (released) subdocument. Nevertheless, most of existing redactable signature schemes (RSSs) are vulnerable to dishonest redactors or illegal redaction detection. To address the above issues, we propose two distinct RSSs with flexible release control (RSSs-FRC). We also analyse the performance of our constructions in terms of security, efficiency and functionality. The analysis results show that the performance of our construction has significant advantages over others, from the aspects of security and efficiency.
Liu, XP, Zhang, GQ, Lu, J & Zhang, JQ 2019, 'Risk assessment using transfer learning for grassland fires', Agricultural and Forest Meteorology, vol. 269-270, pp. 102-111.
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© 2019 A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods.
Lu, J, Liu, A, Dong, F, Gu, F, Gama, J & Zhang, G 2019, 'Learning under Concept Drift: A Review', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.
LU, S, Oberst, S, Zhang, G & Luo, Z 2019, 'Bifurcation analysis of dynamic pricing processes with nonlinear external reference effects', Communications in Nonlinear Science and Numerical Simulation, vol. 79, pp. 104929-104929.
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Dynamic pricing has been widely implemented to hedge against
volatile demand. One challenging problem is the study of optimal price
choices under the influence of this volatility. Stochastic demand is a
prevalent assumption when it comes to model the volatility on pricing
decisions. However, the demand volatility might also be produced by
deterministic chaos, which has been rarely studied in this field of
research to-date. Disregarding deterministic dynamics may not only cause
revenue losses in practice but might also mislead regulators about the
underlying mechanisms used by market participants.
To improve pricing decisions and price regulations, we propose a
deterministic dynamic pricing process, of which the optimisation
objective is the revenue. Consumer expectations and discrete price
choices are considered to mimic a real pricing decision. Contradicted
expectations are quantitatively modelled on the consumer purchasing
decisions. Due to asymmetry in the perceptions of gains or losses, the
model becomes non-smooth. Period adding bifurcations, codimension-2
points and coexisting solutions can be observed.
Results highlight that an optimal pricing strategy should agree with the
dynamics of consumer expectations - which we show for the first time.
Based on that an optimal irregular pricing strategy is introduced: a
decision maker can make the first return iteration of each optimal price
choice non-periodic to follow non-periodic expectations when confronting
finite price choices. These results may justify implementing irregular
pricing strategies in the case of practical pricing decisions. Here, the
existence of coexisting solutions will assist to identify potential
market manipulations within a monopoly market. This not only contributes
to a fresh look on a volatile market but also spotlights the important
role of initial conditions to pricing decision and price regulations.
Luo, M, Yan, C, Zheng, Q, Chang, X, Chen, L & Nie, F 2019, 'Discrete Multi-Graph Clustering', IEEE Transactions on Image Processing, vol. 28, no. 9, pp. 4701-4712.
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© 1992-2012 IEEE. Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfortunately, directly optimizing the spectral clustering inevitably results in an NP-hard problem due to the discrete constraints on the clustering labels. Hence, conventional approaches intuitively include a relax-and-discretize strategy to approximate the original solution. However, there are no principles in this strategy that prevent the possibility of information loss between each stage of the process. This uncertainty is aggravated when a procedure of heterogeneous features fusion has to be included in multi-view spectral clustering. In this paper, we avoid an NP-hard optimization problem and develop a general framework for multi-view discrete graph clustering by directly learning a consensus partition across multiple views, instead of using the relax-and-discretize strategy. An effective re-weighting optimization algorithm is exploited to solve the proposed challenging problem. Further, we provide a theoretical analysis of the model's convergence properties and computational complexity for the proposed algorithm. Extensive experiments on several benchmark datasets verify the effectiveness and superiority of the proposed algorithm on clustering and image segmentation tasks.
Manzoor, M, Hussain, W, Sohaib, O, Hussain, FK & Alkhalaf, S 2019, 'Methodological investigation for enhancing the usability of university websites', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 2, pp. 531-549.
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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.
Mao, M, Lu, J, Han, J & Zhang, G 2019, 'Multiobjective e-commerce recommendations based on hypergraph ranking', INFORMATION SCIENCES, vol. 471, pp. 269-287.
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Melnikov, A, Chiang, YK, Quan, L, Oberst, S, Alú, A, Marburg, S & Powell, D 2019, 'Acoustic meta-atom with experimentally verified maximum Willis coupling', Nature Communications, vol. 10, pp. 3148-3148.
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Acoustic metamaterials are structures with exotic acoustic properties, with promising applications in acoustic beam steering, focusing, impedance matching, absorption and isolation. Recent work has shown that the efficiency of many acoustic metamaterials can be enhanced by controlling an additional parameter known as Willis coupling, which is analogous to bianisotropy in electromagnetic metamaterials. The magnitude of Willis coupling in a passive acoustic meta-atom has been shown theoretically to have an upper limit, however the feasibility of reaching this limit has not been experimentally investigated. Here we introduce a meta-atom with Willis coupling which closely approaches this theoretical limit, that is much simpler and less prone to thermo-viscous losses than previously reported structures. We perform two-dimensional experiments to measure the strong Willis coupling, supported by numerical calculations. Our meta-atom geometry is readily modeled analytically, enabling the strength of Willis coupling and its
peak frequency to be easily controlled. Together with its ease of fabrication, this will facilitate the design of future high efficiency acoustic devices.
Ming, Y, Ding, W, Pelusi, D, Wu, D, Wang, YK, Prasad, M & Lin, CT 2019, 'Subject adaptation network for EEG data analysis', Applied Soft Computing Journal, vol. 84.
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© 2019 Elsevier B.V. Biosignals tend to display manifest intra- and cross-subject variance, which generates numerous challenges for electroencephalograph (EEG) data analysis. For instance, in the context of classification, the discrepancy between EEG data can make the trained model generalising poorly for new test subjects. In this paper, a subject adaptation network (SAN) inspired by the generative adversarial network (GAN) to mitigate different variances is proposed for analysing EEG data. First the challenges faced by traditional approaches employed for EEG signal processing are emphasised. Then the problem is formulated from mathematical perspective to highlight the key points in resolving such discrepancies. Third, the motivation behind and design principle of the SAN are described in an intuitive manner to reflect its suitability for analysing EEG data. Then after depicting the overall architecture of the SAN, several experiments are used to justify the practicality and efficiency of using the proposed model from different perspectives. For instance, an EEG dataset captured during a stereotypical neurophysiological experiment called the VEP oddball task is utilised to demonstrate the performance improvement achieved by running the SAN.
Ming, Y, Lin, CT, Bartlett, SD & Zhang, WW 2019, 'Quantum topology identification with deep neural networks and quantum walks', npj Computational Materials, vol. 5, no. 1.
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© 2019, The Author(s). Topologically ordered materials may serve as a platform for new quantum technologies, such as fault-tolerant quantum computers. To fulfil this promise, efficient and general methods are needed to discover and classify new topological phases of matter. We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions. On a trial topological ordered model, our method’s accuracy of topological phase identification reaches 97.4%, and is shown to be robust to noise on the data. Furthermore, we demonstrate that our trained DNN is able to identify topological phases of a perturbed model, and predict the corresponding shift of topological phase transitions without learning any information about the perturbations in advance. These results demonstrate that our approach is generally applicable and may be used to identify a variety of quantum topological materials.
Musial, K, Brodka, P & De Meo, P 2019, 'Analysis and Applications of Complex Social Networks 2018', COMPLEXITY.
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Musial-Gabrys, K, Wahid-Ul-Ashraf, A & Budka, M 2019, 'Simulation and Augmentation of Social Networks for Building Deep Learning Models'.
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Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2019, 'Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.
Nawaz, F, Hussain, O, Hussain, FK, Janjua, NK, Saberi, M & Chang, E 2019, 'Proactive management of SLA violations by capturing relevant external events in a Cloud of Things environment', Future Generation Computer Systems, vol. 95, pp. 26-44.
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© 2018 Elsevier B.V. The cloud of things (CoT) is an emerging paradigm that has merged and combined cloud computing and the Internet of Things (IoT). Such a paradigm has enabled service providers to provide on-demand computing resources from devices spread across different locations for service users to be dynamically connected to them. While this benefits the CoT service providers and users in many ways, it also brings a key challenge of ensuring that the service is delivered according to the promised quality. Failure to ensure this will result in the service provider experiencing penalties of different types and the service user experiencing disruptions. The literature addresses this problem by proactively managing for SLA violations. However, given the geographically dispersed region of a formed CoT service, in this paper we argue that for proactive SLA violation identification, we need specialized techniques that also consider events that are outside the usual control of service providers and users, but will impact the CoT environment and the quality of service. We propose a framework that identifies such external events of interest and ascertains their impact on achieving the service according to the promised quality. We explain the working of our proposed framework in detail and demonstrate its superiority in proactively determining SLA violations as compared to existing approaches.
Oberst, S, Lenz, M, Lai, JCS & Evans, TA 2019, 'Termites manipulate moisture content of wood to maximise feeding', Biology Letters, vol. 15, no. 7.
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Animals use cues to find their food, in microhabitats within their physiological tolerances. Termites build and modify their microhabitat, to transform hostile environments into benign ones, which raises questions about the relative importance of cues. Termites are desiccation intolerant and foraging termites are attracted to water, so most research has considered moisture to be a cue. However, termites can also transport water to food, and so moisture may play other roles than previously considered. To examine the role of moisture, we compared Coptotermes acinaciformis termite foraging decisions in laboratory experiments when they were offered dry and moist wood, with and without load. Without load, termites preferred moist wood and ate it without any building, whereas they moistened dry wood after wrapping it in a layer of clay. For the ‘With load’ units, termites substituted some of the wood for load-bearing clay walls, and kept the wood drier than on the unloaded units. As drier wood has higher compressive strength and higher rigidity, it allows more of the wood to be consumed. These results suggest that moisture plays a more important role in termite ecology than previously thought. Termites manipulate the moisture content according to the situational context and use it for multiple purposes: increased moisture levels soften the fibre, which facilitates foraging, yet keeping the wood dry provides higher structural stability against buckling which is especially important when foraging on wood under load.
Ojeda, A, Klug, M, Kreutz-Delgado, K, Gramann, K & Mishra, J 2019, 'A Bayesian Framework for Unifying Data Cleaning, Source Separation and Imaging of Electroencephalographic Signals'.
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Abstract Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed by independent heuristics, limiting the use of EEG images on a variety of applications. Here, we propose a unifying parametric empirical Bayes framework in which these dissimilar problems can be solved using a single algorithm (PEB+). We use sparsity constraints to adaptively segregate brain sources into maximally independent components with known anatomical support, while minimally overlapping artifactual activity. Of theoretical relevance, we demonstrate the connections between Infomax ICA and our framework. On real data, we show that PEB+ outperforms Infomax for source separation on short time-scales and, unlike the popular ASR algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations, replicating the main findings of previous experimental literature.
Paryani, S & Ramadan-Jradi, R 2019, 'The Impact of Flipped Learning on Student Performance and Engagement: A Systematic Literature Review', International Journal for Learning and Teaching, vol. 5, no. 1.
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Patel, OP, Bharill, N, Tiwari, A & Prasad, M 2019, 'A Novel Quantum-inspired Fuzzy Based Neural Network for Data Classification', IEEE Transactions on Emerging Topics in Computing.
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IEEE The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter (m) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of m, which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.
Patel, OP, Tiwari, A, Chaudhary, R, Nuthalapati, SV, Bharill, N, Prasad, M, Hussain, FK & Hussain, OK 2019, '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.
Prokopenko, M, Harré, M, Lizier, J, Boschetti, F, Peppas, P & Kauffman, S 2019, 'Self-referential basis of undecidable dynamics: From the Liar paradox and the halting problem to the edge of chaos', Physics of Life Reviews.
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© 2019 In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical systems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these computational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of Gödel's proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality; (ii) the potential to access an infinite computational medium; and (iii) the ability to implement negation. The considered adaptations of Gödel's proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability.
Qiao, C, Lu, L, Yang, L & Kennedy, PJ 2019, 'Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology', APPLIED SCIENCES-BASEL, vol. 9, no. 10.
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Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2019, 'Adapting Stochastic Block Models to Power-Law Degree Distributions.', IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 626-637.
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Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Raza, M, Hussain, FK, Hussain, OK, Zhao, M & Rehman, ZU 2019, 'A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews', Future Generation Computer Systems, vol. 101, pp. 341-371.
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© 2019 Elsevier B.V. Software as a Service (SaaS) has emerged as the most widely used of all the current software delivery models. With the growth of edge computing, as SaaS services increasingly become distributed, selecting the best SaaS provider from those available is challenging but it is of critical importance. In the recent past, well-known cloud service providers such as Amazon Web Services and Microsoft have developed frameworks and service quality pillars for cloud applications. However, there are currently no mechanisms for product users to know if and to what extent a service satisfies the defined service pillar. Having such information would enable users to form trustworthy associations in edge computing. In this paper, we address this drawback by adopting a systematic approach of analysing customer reviews related to SaaS products and ascertain to which service quality pillar they refer. We use eleven traditional machine learning classification approaches and a weighted voting ensemble of these classifiers to achieve this task and test the performance of each of them. Since the dataset is unbalanced in terms of sample distribution per class, we use 10-fold cross-validation on the training dataset to determine the best parameters for each machine learning algorithm to achieve optimal performance. Friedman test and Nemenyi's post hoc test is applied to identify the significant difference among the classifiers performance during cross-validation. Based on the experimental results, a comparative analysis is conducted to identify the best performing machine learning classification model on the SaaS reviews. The results show that the performance of the logistic regression model has a higher performance among the individual classifiers and the weighted voting ensemble shows minimal improvement in overall performance.
Razzak, I, Imran, M & Xu, G 2019, 'Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks', IEEE Journal of Biomedical and Health Informatics.
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IEEE Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of Deep learning such as Convolutional Neural Networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here we describe a new model Two-Pathway-Group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the Two-Pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into Two-Pathway-Group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 datasets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.
Razzak, MI, Blumenstein, M & Xu, G 2019, 'Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.', IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 27, no. 6, pp. 1117-1127.
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Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications.
Razzak, MI, Imran, M & Xu, G 2019, 'Big data analytics for preventive medicine', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.
Saeed, Z, Abbasi, RA, Maqbool, O, Sadaf, A, Razzak, I, Daud, A, Aljohani, NR & Xu, G 2019, 'What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter', Journal of Grid Computing, vol. 17, no. 2, pp. 279-312.
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© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter.
Saeed, Z, Abbasi, RA, Razzak, I, Maqbool, O, Sadaf, A & Xu, G 2019, 'Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks', Expert Systems with Applications, vol. 136, pp. 115-132.
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© 2019 Elsevier Ltd With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.
Saeed, Z, Ayaz Abbasi, R, Razzak, MI & Xu, G 2019, 'Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]', IEEE Computational Intelligence Magazine, vol. 14, no. 3, pp. 29-38.
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© 2019 IEEE. Once an event is detected, WDHG approach suppresses the bursty keywords at subsequent time intervals. This characteristic enables other related information to be more visible and helps in capturing new and emerging events.
Salamai, A, Hussain, OK, Saberi, M, Chang, E & Hussain, FK 2019, 'Highlighting the Importance of Considering the Impacts of Both External and Internal Risk Factors on Operational Parameters to Improve Supply Chain Risk Management', IEEE Access, vol. 7, pp. 49297-49315.
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© 2013 IEEE. Operational risk management in supply chain activities is important for the successful achievement of the desired outcomes. Although it is an active area of research with an aim of improving a firm's success in its operations, a drawback of existing approaches is that they analyze it from only the perspective of events local to the supply chain. In this paper, we argue that it is also important for firms in a supply chain to consider external events as they will directly influence the internal ones and use various real-world examples of the risks in different processes of a supply chain as justification to prove our point. We then consider supply chain risk management not only as an operational research process, as do all the relevant survey papers, but a data science problem to gain deeper real-time insights for information risk management. Then, we suggest directions for future research that will assist supply chain risk managers to undertake better supply chain risk management processes.
Saqib, M, Khan, SD, Sharma, N & Blumenstein, M 2019, 'Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks', IEEE Access, vol. 7, pp. 35317-35329.
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© 2013 IEEE. Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd. Most of the existing approaches use regression on density maps for the crowd count from a single image. However, these methods cannot localize individual pedestrian and therefore cannot estimate the actual distribution of pedestrians in the environment. On the other hand, detection-based methods detect and localize pedestrians in the scene, but the performance of these methods degrades when applied in high-density situations. To overcome the limitations of pedestrian detectors, we proposed a motion-guided filter (MGF) that exploits spatial and temporal information between consecutive frames of the video to recover missed detections. Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos. We employ various state-of-the-art network architectures, namely, Visual Geometry Group (VGG16), Zeiler and Fergus (ZF), and VGGM in the framework of a region-based DCNN for detecting pedestrians. After pedestrian detection, the proposed motion guided filter is employed. We evaluate the performance of our approach on three publicly available datasets. The experimental results demonstrate the effectiveness of our approach, which significantly improves the performance of the state-of-the-art detectors.
Sarker, PC, Islam, MR, Guo, Y, Zhu, J & Lu, HY 2019, 'State-of-The-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
Stender, M, Oberst, S & Hoffmann, N 2019, 'Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction', Vibration, vol. 2, no. 1, pp. 25-46.
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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.
Stender, M, Oberst, S, Tiedemann, M & Hoffmann, N 2019, 'Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data', Nonlinear Dynamics, pp. 1-10.
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Complex machine dynamics, as caused by friction-induced vibrations and related to brake squeal, have gained significant attention in research and industry for decades. Today, remedies heavily rely on experimental testing due to the low prediction quality of numerical models. However, there is considerable lack of in-depth studies in characterizing self-excited oscillations encoded in scalar measurements. We complement previous works on phase-space reconstruction and recurrence plots analysis to a larger data base by applying a novel systematic approach using a large
data base. This framework considers appropriate delay embedding, time series partitioning into squealing and non-squealing parts and comparison to operational parameters of the brake system. By means of recurrence plot analysis, we illustrate that friction-excited vibrations are multi-scale in nature. Results confirm the existence of low-dimensional attractors in squealing regimes with increasing values of determinism and periodicity with rising vibration levels. It is shown that the squeal propensity can be directly linked to recurrence quantification measures. Using determinism and the clustering coefficient as metrics, we show for the first time that is possible to predict instabilities in regions of non-squealing conditions.
Sun, L, Dong, H, Hussain, OK, Hussain, FK & Liu, AX 2019, 'A framework of cloud service selection with criteria interactions', Future Generation Computer Systems, vol. 94, pp. 749-764.
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© 2018 Elsevier B.V. Existing cloud service selection techniques assume that service evaluation criteria are independent. In reality, there are different types of interactions between criteria. These interactions influence the performance of a service selection system in different ways. In addition, a lack of measurement indices to validate the performance of service selection methods has hindered the development of decision making techniques in the service selection area. This paper addresses these critical issues of modeling the interactions between cloud service selection criteria, and designing indices to validate service selection methods. In this paper, we propose a Cloud Service Selection with Criteria Interactions framework (CSSCI) that applies a fuzzy measure and Choquet integral to measure and aggregate non-linear relations between criteria. We employ a non-linear constraint optimization model to estimate the Shapley importance and criteria interaction indices. In addition, we design a priority-based CSSCI (PCSSCI) to solve service selection problems in the situation where there is a lack of historical information to determine criteria relations and weights. Furthermore, we discuss an approximate solution for CSSCI to reduce its computing complexity. Finally, we design three indices to validate the cloud service selection methods. The experimental results preliminarily prove the technical advantage of the proposed models in contrast to several existing models.
Sun, Y, Tian, Z, Wang, Y, Li, M, Su, S, Wang, X & Fan, D 2019, 'Lightweight Anonymous Geometric Routing for Internet of Things', IEEE ACCESS, vol. 7, pp. 29754-29762.
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Talaei-Khoei, A & Wilson, JM 2019, 'Using time-series analysis to predict disease counts with structural trend changes', INFORMATION PROCESSING & MANAGEMENT, vol. 56, no. 3, pp. 674-686.
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Talaei-Khoei, A, Wilson, JM & Kazemi, SF 2019, 'Period of measurement in time-series predictions of disease counts from 2007 to 2017 in northern Nevada: Analytics experiment', Journal of Medical Internet Research, vol. 21, no. 1.
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© Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi. Background: The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. Objective: This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. Methods: The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. Results: Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. Conclusions: The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.
Tien-Thong, ND, Chuang, C-H, Hsiao, S-J, Lin, C-T & Wang, Y-K 2019, 'Neural Comodulation of Independent Brain Processes Related to Multitasking', IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 27, no. 6, pp. 1160-1169.
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Torres Robles, A, Wiecek, E, Cutler, R, Drake, B, Benrimoj, SI, Fernandez-Llimos, F & Garcia Cardenas, M 2019, 'Using dispensing data to evaluate adherence implementation rates in community pharmacy', Frontiers in Pharmacology.
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Treadaway, C, Fennell, J, Taylor, A & Kenning, G 2019, 'Designing for playfulness through compassion: design for advanced dementia', Design for Health.
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van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2019, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning, vol. 52, no. 5.
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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.
Vincent Poor, H, Shi, Y, Tuan, HD, Savkin, AV & Duong, TQ 2019, 'Model predictive control for smart grids with multiple electric-vehicle charging stations', IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2127-2136.
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© 2017 IEEE Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEV arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. This paper develops a model predictive control-based approach to address joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, knowledge of PEVs' future demand, or unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla model S PEVs show the merit of this approach.
Vo, NNY, He, X, Liu, S & Xu, G 2019, 'Deep learning for decision making and the optimization of socially responsible investments and portfolio', Decision Support Systems, vol. 124.
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© 2019 Elsevier B.V. A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.
Wahid-Ul-Ashraf, A, Budka, M & Musial, K 2019, 'How to predict social relationships — Physics-inspired approach to link prediction', Physica A: Statistical Mechanics and its Applications, vol. 523, pp. 1110-1129.
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© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network research area. The formation of links in networks has been approached by scientists from different backgrounds, ranging from physics to computer science. To predict the formation of new links, we consider measures which originate from network science and use them in the place of mass and distance within the formalism of Newton's Gravitational Law. The attraction force calculated in this way is treated as a proxy for the likelihood of link formation. In particular, we use three different measures of vertex centrality as mass, and 13 dissimilarity measures including shortest path and inverse Katz score in place of distance, leading to over 50 combinations that we evaluate empirically. Combining these through gravitational law allows us to couple popularity with similarity, two important characteristics for link prediction in social networks. Performance of our predictors is evaluated using Area Under the Precision–Recall Curve (AUC)for seven different real-world network datasets. The experiments demonstrate that this approach tends to outperform the setting in which vertex similarity measures like Katz are used on their own. Our approach also gives us the opportunity to combine network's global and local properties for predicting future or missing links. Our study shows that the use of the physical law which combines node importance with measures quantifying how distant the nodes are, is a promising research direction in social link prediction.
Wakefield, J, Tyler, J, Dyson, LE & Frawley, JK 2019, '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.
Wang, G, Lu, J, Choi, KS & Zhang, G 2019, 'A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data', IEEE Transactions on Cybernetics.
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IEEE The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.
Wang, G, Zhang, G, Choi, KS & Lu, J 2019, 'Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer', IEEE Transactions on Systems Man and Cybernetics: Systems, vol. 49, no. 7, pp. 1527-1540.
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IEEE The additive kernel least squares support vector machine (AK-LS-SVM) has been well used in classification tasks due to its inherent advantages. For example, additive kernels work extremely well for some specific tasks, such as computer vision classification, medical research, and some specialized scenarios. Moreover, the analytical solution using AK-LS-SVM can formulate leave-one-out cross-validation error estimates in a closed form for parameter tuning, which drastically reduces the computational cost and guarantee the generalization performance especially on small and medium datasets. However, AK-LS-SVM still faces two main challenges: 1) improving the classification performance of AK-LS-SVM and 2) saving time when performing a grid search for model selection. Inspired by the stacked generalization principle and the transfer learning mechanism, a layer-by-layer combination of AK-LS-SVM classifiers embedded with transfer learning is proposed in this paper. This new classifier is called deep transfer additive kernel least square support vector machine (DTA-LS-SVM) which overcomes these two challenges. Also, considering that imbalanced datasets are involved in many real-world scenarios, especially for medical data analysis, the deep-transfer element is extended to compensate for this imbalance, thus leading to the development of another new classifier iDTA-LS-SVM. In the hierarchical structure of both DTA-LS-SVM and iDTA-LS-SVM, each layer has an AK-LS-SVM and the predictions from the previous layer act as an additional input feature for the current layer. Importantly, transfer learning is also embedded to guarantee generalization consistency between the adjacent layers. Moreover, both iDTA-LS-SVM and DTA-LS-SVM can ensure the minimal leave-one-out error by using the proposed fast leave-one-out cross validation strategy on the training set in each layer. We compared the proposed classifiers DTA-LS-SVM and iDTA-LS-SVM with the traditional LS-SVM and SVM using ...
Wang, J, Zhang, N & Lu, H 2019, 'A novel system based on neural networks with linear combination framework for wind speed forecasting', Energy Conversion and Management, vol. 181, pp. 425-442.
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© 2018 Elsevier Ltd The absence of accurate and stable prediction of wind speed remains a major obstacle to the rational planning, scheduling, and maintenance of wind power generation. Currently, an extensive body of methods that aim to enhance the accuracy of wind speed prediction have been proposed. However, the majority of previous studies have tended to emphasize the structural improvement of individual forecasting models without considering the validity of data preprocessing. This can result in poor forecasting accuracy due to their failure to fully capture the effective information of the wind speed data. A new approach is proposed in this paper that successfully combines a data preprocessing technique with a linear combination method. Further, a new neural network framework is employed to determine the required combination weights to ensure improved prediction performance, thereby overcoming the drawback of the low accuracy of individual prediction models. Six wind speed datasets from Penglai are regarded as expository cases to analyze the forecasting validity and stability of the developed model. It can be concluded from the experiments that the combined forecasting system outperforms the individual models and the traditional linear combination models with higher accuracy and stronger stability.
Wang, W, Zhang, G & Lu, J 2019, '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, Liu, Y, Lu, J, Xiong, F & Zhang, G 2019, 'TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators', FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, vol. 94, pp. 224-236.
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Wang, X, Qin, L, Lin, X, Zhang, Y & Chang, L 2019, 'Leveraging set relations in exact and dynamic set similarity join', VLDB Journal.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Set similarity join, which finds all the similar set pairs from two collections of sets, is a fundamental problem with a wide range of applications. Existing works study both exact set similarity join and approximate similarity join problems. In this paper, we focus on the exact set similarity join problem. The existing solutions for exact set similarity join follow a filtering-verification framework, which generates a list of candidate pairs through scanning indexes in the filtering phase and reports those similar pairs in the verification phase. Though much research has been conducted on this problem, set relations have not been well studied on improving the algorithm efficiency through computational cost sharing. Therefore, in this paper, we explore the set relations in different levels to reduce the overall computational cost. First, it has been shown that most of the computational time is spent on the filtering phase, which can be quadratic to the number of sets in the worst case for the existing solutions. Thus, we explore index-level set relations to reduce the filtering cost while keeping the same filtering power. We achieve this by grouping related sets into blocks in the index and skipping useless index probes in joins. Second, we explore answer-level set relations to further improve the algorithm based on the intuition that if two sets are similar, their answers may have a large overlap. We derive an algorithm which incrementally generates the answer of one set from an already computed answer of another similar set rather than compute the answer from scratch to reduce the computational cost. In addition, considering that in real applications, the data are usually updated dynamically, we extend our techniques and design efficient algorithms to incrementally update the join result when any element in the sets is updated. Finally, we conduct extensive performance studies using 21 real datasets...
Wang, Y, Sun, Y, Su, S, Tian, Z, Li, M, Qiu, J & Wang, X 2019, 'Location privacy in device-dependent location-based services: Challenges and solution', Computers, Materials and Continua, vol. 59, no. 3, pp. 983-993.
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Copyright c 2019 Tech Science Press With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS). In DLBS, the service provider (SP) will not only send the information according to the user’s location, more significant, he also provides a service device which will be carried by the user. DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London. In this paper, we, for the first time, blow the whistle of the new location privacy challenges caused by DLBS, since the service device is enabled to perform the localization without the permission of the user. To conquer these threats, we design a service architecture along with a credit system between DLBS provider and the user. The credit system tie together the DLBS device usability with the curious behaviour upon user’s location privacy, DLBS provider has to sacrifice their revenue in order to gain extra location information of their device. We make the simulation of our proposed scheme and the result convince its effectiveness.
Wen, D, Qin, L, Zhang, Y, Chang, L & Chen, L 2019, 'Enumerating k-vertex connected components in large graphs', Proceedings - International Conference on Data Engineering, vol. 2019-April, pp. 52-63.
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© 2019 IEEE. In social network analysis, structural cohesion (or vertex connectivity) is a fundamental metric in measuring the cohesion of social groups. Given an undirected graph, a k-vertex connected component (k-VCC) is a maximal connected subgraph whose structural cohesion is at least k. A k-VCC has many outstanding structural properties, such as high cohesiveness, high robustness, and subgraph overlapping. In this paper, given a graph G and an integer k, we study the problem of computing all k-VCCs in G. The general idea for this problem is to recursively partition the graph into overlapped subgraphs. We prove the upper bound of the number of partitions, which implies the polynomial running time algorithm for the k-VCC enumeration. However, the basic solution is costly in computing the vertex cut. To improve the algorithmic efficiency, we observe that the key is reducing the number of local connectivity testings. We propose two effective optimization strategies, namely neighbor sweep and group sweep, to significantly reduce the number of local connectivity testings. We conduct extensive performance studies using ten large real datasets to demonstrate the efficiency of our proposed algorithms. The experimental results demonstrate that our approach can achieve a speedup of up to two orders of magnitude compared to the state-of-the-art algorithm.
Wen, D, Qin, L, Zhang, Y, Chang, L & Lin, X 2019, 'Efficient structural graph clustering: an index-based approach', VLDB Journal, vol. 28, no. 3, pp. 377-399.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Graph clustering is a fundamental problem widely applied in many applications. The structural graph clustering (SCAN) method obtains not only clusters but also hubs and outliers. However, the clustering results heavily depend on two parameters, ϵ and μ, while the optimal parameter setting depends on different graph properties and various user requirements. In addition, all existing SCAN solutions need to scan at least the whole graph, even if only a small number of vertices belong to clusters. In this paper, we propose an index-based method for SCAN. Based on our index, we cluster the graph for any ϵ and μ in O(∑ C∈C| EC|) time, where C is the result set of all clusters and | EC| is the number of edges in a specific cluster C. In other words, the time spent on computing structural clustering depends only on the result size, not on the size of the original graph. Our index’s space complexity is O(m), where m is the number of edges in the graph. To handle dynamic graph updates, we propose algorithms and several optimization techniques for maintaining our index. We also design an index for I/O efficient query processing. We conduct extensive experiments to evaluate the performance of all our proposed algorithms on 10 real-world networks, with the largest one containing more than 1 billion edges. The experimental results demonstrate that our approaches significantly outperform existing solutions.
Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2019, 'I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 75-90.
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© 1989-2012 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, L, Liu, Q, Zhou, W, Mao, G, Huang, J & Huang, H 2019, 'A Semantic Web-Based Recommendation Framework of Educational Resources in E-Learning', Technology Knowledge and Learning.
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© 2018, Springer Nature B.V. A big challenge in educational resources construction is the intelligent and personalized resource recommendation for learners. This paper proposes a semantic recommendation framework of educational resources based on semantic web and pedagogics. In this framework, a domain ontology is constructed to describe the knowledge structure of the domain. All the resources and user portfolio are described with ontology technology and resource description framework to support semantic inference. Based on the semantic resource organization, we made a set of reasoning rules based on pedagogics. These rules are made from the synthesis of the type of the knowledge, the internal structure of knowledge and learner’s learning performance. A case study was implemented on the course “theory and practice of database”. In this case, learners are recommended different learning materials according to the different knowledge structure and different learning performance. Three typical learning modes are proposed to describe the personalized learning experience. This framework can be used as a guide for teachers and resource designers.
Wu, W, Li, B, Chen, L, Zhang, C & Yu, P 2019, '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, Y, Hou, J, Liu, J, Zhou, W & Yao, S 2019, 'Novel Multi-Keyword Search on Encrypted Data in the Cloud', IEEE Access, vol. 7, pp. 31984-31996.
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© 2013 IEEE. Searching on encrypted data has become a very important technique in cloud computing. Such searches enable the data owner to search on the encrypted data stored on the cloud without leaking any information. To obtain a better search experience, researchers have proposed many schemes which mainly focus on conjunctive and disjunctive keyword searches. However, conjunction of all the keywords may result in very few results, whereas a disjunction will return too many results. With the current schemes, customizing the relevancy of the keywords to obtain the desired results is difficult. To solve these problems, we propose a novel scheme that supports the search with the user-specified number of keywords contained in the search result. This number n can be used to customize the keyword relevancy. As a result, the data owner could obtain the desired search results containing any n keywords of a keyword set. The proposed scheme also supports the traditional disjunctive and conjunctive keyword searches when n equals 1 or the size of the keyword set, respectively. The keyword could be positive or negative. We first formally define its security and then prove that the proposed scheme is secure against the adaptively chosen keyword attack in the standard model and can defend against the offline keyword guessing attack to some extent. Furthermore, we present a theoretical performance comparison with other schemes as well as the experimental performance evaluations on our implemented scheme.
Xia, LY, Wang, QY, Cao, Z & Liang, Y 2019, 'Descriptor Selection Improvements for Quantitative Structure-Activity Relationships', International Journal of Neural Systems.
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© 2019 World Scientific Publishing Company. Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure-activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and P-values.
Xiao, G, Zheng, Z, Jiang, B & Sui, Y 2019, 'An Empirical Study of Regression Bug Chains in Linux', IEEE Transactions on Reliability.
Xiong, P, Zhang, L, Zhu, T, Li, G & Zhou, W 2019, 'Private collaborative filtering under untrusted recommender server', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Recommender systems play an increasingly vital role in modern E-commerce. However, exploiting users’ preferences with recommender algorithms leads to serious privacy risks, especially when recommender service providers are unreliable. To deal with the problem, this paper proposes a Client/Server framework to create a private recommender system (PrivateRS). The system assumes that the Server side is untrustworthy. On the Client side, each user firstly rates the items and randomizes the ratings with a differential privacy mechanism. The ratings are further substituted by private symbols which are autonomously defined by each user to hide the ordinal meaning of the ratings. Using those symbols, the Server applies a private collaborative filtering algorithm to predict the ratings of items for the user. During this process, new similarity metrics are provided to search the nearest neighbours for users or items without knowing the real meanings of those symbols. Experimental results demonstrate that even though the ordinal meaning of the rating is significantly obfuscated, the proposed algorithms can still generate accurate recommendations with acceptable loss.
Xuan, J, Lu, J & Zhang, G 2019, 'A Survey on Bayesian Nonparametric Learning', ACM COMPUTING SURVEYS, vol. 52, no. 1.
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Yan, Z, Liu, J & Liu, S 2019, 'DPWeVote: differentially private weighted voting protocol for cloud-based decision-making', Enterprise Information Systems, vol. 13, no. 2, pp. 236-256.
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© 2018, © 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 2019, '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, Zhang, Y, Zhang, W & Lin, X 2019, 'Cost optimization based on influence and user preference', Knowledge and Information Systems.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. The popularity of e-business and preference learning techniques have contributed a huge amount of product and user preference data. Analyzing the influence of an existing or new product among the users is critical to unlock the great scientific and social–economic value of these data. In this paper, we advocate the problem of influence-based cost optimization for the user preference and product data, which is fundamental in many real applications such as marketing and advertising. Generally, we aim to find a cost optimal position for a new product such that it can attract at least k or a particular percentage of users for the given user preference functions and competitors’ products. Although we show the solution space of our problem can be reduced to a finite number of possible positions (points) by utilizing the classical k-level computation techniques, the computation cost is still very expensive due to the nature of the high combinatorial complexity of the k-level problem. To alleviate this issue, we develop efficient pruning and query processing techniques to significantly improve the performance. In particular, our traverse-based 2-dimensional algorithm is very efficient with time complexity O(n) where n is the number of user preference functions. For general multi-dimensional spaces, we develop space partition-based algorithm to significantly improve the performance by utilizing cost-based, influence-based and local dominance-based pruning techniques. Then, we show that the performance of the partition-based algorithm can be further enhanced by utilizing sampling approach, where the problem can be reduced to the classical half-space intersection problem. Based on the problem of influence-based cost optimization, two naturally extended problems are proposed, which are Batch-Query and the most cost-effective influence query. We demonstrate the efficiency of our techniques with extensive experiment...
Yang, LX, Li, P, Zhang, Y, Yang, X, Xiang, Y & Zhou, W 2019, 'Effective repair strategy against advanced persistent threat: A differential game approach', IEEE Transactions on Information Forensics and Security.
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IEEE Advanced persistent threat (APT) is a new kind of cyber attack that poses a serious threat to modern society. When an APT campaign on an organization has been identified, the available repair resources must be reasonably allocated to the potentially insecure hosts to mitigate the potential loss of the organization. We refer to the feasible repair resource allocation strategies as repair strategies. This paper focuses on the APT repair problem, i.e., the problem of developing effective repair strategies for organizations. First, for an organization with timevarying communication relationship, we establish an evolution model of the organization’s expected state, in which the impact of lateral movement of APT is accommodated. On this basis, we model the APT repair problem as a differential Nash game problem (the APT repair game) in which the attacker attempts to maximize his potential benefit and the organization manages to minimize its potential loss. Second, we derive a system (the potential system) for calculating a potential Nash equilibrium of an APT repair game, and we examine the structure of the potential attack and repair strategies in a potential Nash equilibrium. Next, we solve some potential systems to get the corresponding potential Nash equilibria. Finally, by comparison with a large number of randomly generated attack and repair strategies, we conclude that the potential Nash equilibrium of each APT repair game is a Nash equilibrium of the game. Therefore, we recommend to organizations their respective potential repair strategies. Our findings help to better understand and effectively defend against APT.
Yang, M, Zhu, T, Liang, K, Zhou, W & Deng, RH 2019, 'A blockchain-based location privacy-preserving crowdsensing system', Future Generation Computer Systems, vol. 94, pp. 408-418.
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© 2018 Elsevier B.V. With the support of portable electronic devices and crowdsensing, a new class of mobile applications based on the Internet of Things (IoT) application is emerging. Crowdsensing enables workers with mobile devices to travel to specified locations and collect data, then send it back to the requester for rewards. However, the majority of the existing crowdsensing systems are based on centralized servers, which are prone to a high chance of attack, intrusion, and manipulation. Further, during the process of transmitting information to and from the service server, the worker's location is usually exposed. This raises the potential risk of a privacy infringement. In this paper, we first identify three ways locations can be disclosed in traditional crowdsensing systems. Then, we propose a novel solution, dubbed a blockchain privacy-preservation crowdsensing system, to address these privacy problems. The proposed system not only protects the privacy of worker locations but also increases the success rate of completing the assigned task. Specifically, the system entails a rewards-based task assignment process that, essentially, markets the given assignment and uses the anonymized characteristics of blockchain technology to hide the identity information of users. To prevent attacks through re-identification, we have introduced a private blockchain to distribute the worker's transaction records.
Yang, P, Ormerod, JT, Liu, W, Ma, C, Zomaya, AY & Yang, JYH 2019, 'AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.', IEEE Transactions on Cybernetics, vol. 49, no. 5, pp. 1932-1943.
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Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.
Yang, W, Wang, J, Lu, H, Niu, T & Du, P 2019, 'Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China', Journal of Cleaner Production, vol. 222, pp. 942-959.
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© 2019 Elsevier Ltd Wind energy, acknowledged as a promising form of renewable energy and the fastest-growing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind energy forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind energy forecasting and analysis system including a deterministic forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind energy time series and weaken the noises’ negative effects; second, multi-objective optimization is proposed to achieve the forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind energy deterministic forecasting and uncertainty ...
Yang, Z, Xiong, G, Cao, Z, Li, Y & Huang, L 2019, 'A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators', IEEE Access, vol. 7, pp. 77008-77026.
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© 2013 IEEE. Online reviews are crucial for evaluating product features and supporting consumers' purchase decisions. However, as a result of online buying behaviors, consumer habits, and discrete dynamic distribution characteristics of online reviews, and consumers typically randomly choose a limited number of reviews from discrete time frames among all reviews and give more weight to recent review information and less weight to earlier information to support their online purchase decisions; moreover, existing studies have ignored the discrete random dynamic characteristics and dynamic information preferences of consumers. To address this issue, this paper proposes a method based on sentiment orientation classification and discrete DIFWA (DDIFWA) operators for online purchase decisions considering dynamic information preferences. In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries, and matched pairs of features and sentiment phrases in the dictionaries. We subsequently employed three types of semantic orientation by defining semantic rules to extract the product features of each review. Of note, the semantic orientations were transformed from discrete time to semantic intuitionistic fuzzy numbers and semantic intuitionistic fuzzy information matrixes. Furthermore, we proposed two DDIFWA operators to aggregate the dynamic semantic intuitionistic fuzzy information. Specifically, we obtained the rankings of alternative products and their features to support consumers' purchase decisions using the intuitionistic fuzzy scoring function and the 'vertical projection distance' method. Finally, comparisons and experiments are provided to demonstrate the plausibility of our methods.
Yao, L, Wang, X, Sheng, QZ, Dustdar, S & Zhang, S 2019, 'Recommendations on the Internet of Things: Requirements, Challenges, and Directions', IEEE Internet Computing, vol. 23, no. 3, pp. 46-54.
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© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation.
Yao, S, Chen, J, He, K, Du, R, Zhu, T & Chen, X 2019, 'PBCert: Privacy-Preserving Blockchain-Based Certificate Status Validation Toward Mass Storage Management', IEEE Access, vol. 7, pp. 6117-6128.
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© 2013 IEEE. In the recent years, the vulnerabilities of conventional public key infrastructure are exposed by the real-world attacks, such as the certificate authority's single-point-of-failure or clients' private information leakage. Aimed at the first issue, one type of approach is that multiple entities are introduced to assist the certificate operations, including registration, update, and revocation. However, it is inefficient in computation. Another type is to make the certificate information publicly visible by bringing in the log servers. Nevertheless, the data synchronization among log servers may lead to network latency. Based on the second approach, the blockchain-based public key infrastructure schemes are proposed. Through these type of schemes, all the certificate operations are stored in the blockchain for public audit. However, the issue of revoked certificates' status storage is worth paying attention, especially in the setting with massive certificates. In addition, the target web server that a client wants to access is exposed in the process of certificate status validation. In this paper, we propose a privacy-preserving blockchain-based certificate status validation scheme called PBCert to solve these two issues. First, we separate the revoked certificates control and storage plane. Only the minimal control information (namely, certificate hashes and related operation block height) is stored in the blockchain and it uses external data stores for the detailed information about all revoked certificates. Second, we design an obscure response to the clients' certificate status query for the purpose of privacy preserving. Through the security analysis and experiment evaluation, our scheme is significant in practice.
Ying, H, Wu, J, Xu, G, Liu, Y, Liang, T, Zhang, X & Xiong, H 2019, '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 2019, 'Constrained nonnegative matrix factorization-based semi-supervised multi-label learning', International Journal of Machine Learning and Cybernetics.
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Zeng, X, Xu, G, Zheng, X, Xiang, Y & Zhou, W 2019, 'E-AUA: An Efficient Anonymous User Authentication Protocol for Mobile IoT', IEEE Internet of Things Journal.
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IEEE The emergence of the mobile Internet of Things (IoT) has made our lives smarter, relying on its various mobile IoT devices and services provided. However, with the explosively emerging mobile IoT services, malicious attackers can access them in an unauthorized way. In this paper, we designed an Efficient Anonymous User Authentication (E-AUA) protocol between the users and servers based on multi-server architectures, which contain multiple servers to address the problem of network congestion in mobile IoT. Furthermore, the E-AUA protocol was designed with a dual messages mechanism with strong anti-attack ability, lower communication and computation costs. Comparing with the state of the art protocols, our E-AUA protocol reduced both communication and computation costs. We also provided a security analysis to demonstrate that our E-AUA protocol is secure and meets a variety of security requirements in a motivated mobile IoT scenario.
Zhai, X, Zhou, W, Fei, G, Lu, C, Wen, S & Hu, G 2019, 'Edge-based stochastic network model reveals structural complexity of edges', Future Generation Computer Systems, vol. 100, pp. 1073-1087.
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© 2019 Elsevier B.V. Network Science defines complex systems as objects interacting in a network with nodes and edges. Stochastic network models that treat networks as a collection of nodes with fixed degree distributions and randomly-connected edges have provided significant theoretical support for network analyses. However, the structural characteristics of edges in complex networks remain largely unknown due to the lack of edge-based network models. Here, we propose a general edge-based stochastic network model with constrained edge-degree distributions and arbitrary node-degree distributions. The random edge configuration method is used to build the model with the explicit edge-connected probability, which can also be explained by Laplacian dynamics. The model reveals both basic and complex structural characteristics of edges in networks, including statistical structural characteristics, link community structure, and higher-order organization. The experimental results show the advantageous performance on both link community and motifs detection based on the edge-based stochastic network model, which demonstrate that the model is useful for conducting quantitative comparisons the complex structural characteristics of edges. The edge-based stochastic network model is fundamental model to help understand the complex structure of edges that is hard to quantify in the complex networks.
Zhan, K, Chang, X, Guan, J, Chen, L, Ma, Z & Yang, Y 2019, '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.
Zhang, F, Li, C, Zhang, Y, Qin, L & Zhang, W 2019, 'Finding Critical Users in Social Communities: The Collapsed Core and Truss Problems', IEEE Transactions on Knowledge and Data Engineering.
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IEEE In social networks, the leave of critical users may significantly break network engagement, i.e., lead a large number of other users to drop out. A popular model to measure social network engagement is k-core, the maximal subgraph in which every vertex has at least k neighbors. To identify critical users, we propose the collapsed k-core problem: given a graph G, a positive integer k and a budget b, we aim to find b vertices in G such that the deletion of the b vertices leads to the smallest k-core. We prove the problem is NP-hard and inapproximate. An efficient algorithm is proposed, which significantly reduces the number of candidate vertices. We also study the user leave towards the model of k-truss which further considers tie strength by conducting additional computation w.r.t. k-core. We prove the corresponding collapsed k-truss problem is also NP-hard and inapproximate. An efficient algorithm is proposed to solve the problem. The advantages and disadvantages of the two proposed models are experimentally compared. Comprehensive experiments on 9 real-life social networks demonstrate the effectiveness and efficiency of our proposed methods.
Zhang, K, Qu, Z, Dong, Y, Lu, H, Leng, W, Wang, J & Zhang, W 2019, 'Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting', Renewable Energy, vol. 130, pp. 814-830.
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© 2018 Elsevier Ltd As one of the most promising sustainable energy sources, wind energy is being paid more attention by the researchers. Because of the volatility and instability of wind speed series, wind power integration faces a severe challenge; thus, an accurate wind energy forecasting plays a key role in smart grid planning and management. However, many traditional forecasting models do not consider the necessity and importance of data preprocessing and neglect the limitation of using a single forecasting model, which leads to poor forecasting accuracy. To solve these problems, a novel combined model based on two linear and four nonlinear forecasting algorithms is proposed to adapt both the linear and nonlinear characteristics of the wind energy time series. In addition, a modified Artificial Fish Swarm Algorithm and Ant Colony Optimization (AFSA-ACO) algorithm is proposed and employed to determine the optimal weight coefficients of the combined models. To verify the forecasting performance of the developed combined model, several experiments were implemented by using 10-min interval wind speed data in Shandong, China. Then, one-step (10-min), three-step (30-min) and five-step (50-min) predictions were conducted. The experimental results indicate that the developed combined model is remarkably superior to all benchmark models for the high precision and stability of wind-speed predictions.
Zhang, L, Xiong, P, Ren, W & Zhu, T 2019, 'A differentially private method for crowdsourcing data submission', Concurrency Computation.
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© 2018 John Wiley & Sons, Ltd. In recent years, the ubiquity of mobile devices has made spatial crowdsourcing a successful business platform for conducting spatiotemporal projects. In spatial crowdsourcing, workers contribute to a project by performing a task at a specific location. However, these platforms present serious threats to people's location privacy because sensitive information may be leaked from submitted spatiotemporal data. As a result, people may be hesitant to join spatial crowdsourcing projects, which hampers further applications of this business model. In this paper, we propose a private spatial crowdsourcing data submission algorithm, called PS-Sub. This is a differentially private method that preserves people's location privacy and provides acceptable data utility. Rigorous privacy analyses theoretically demonstrate the privacy guarantees inherent in the proposed model. Experiments based on real-world datasets were conducted using practical evaluation metrics. The results show that our method is able to achieve location privacy preservation efficiently, at an acceptable cost for spatial crowdsourcing applications.
Zhang, Q, Lu, J, Wu, D & Zhang, G 2019, 'A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities.', IEEE transactions on neural networks and learning systems, vol. 30, no. 7, pp. 1998-2012.
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The aim of recommender systems is to automatically identify user preferences within collected data, then use those preferences to make recommendations that help with decisions. However, recommender systems suffer from data sparsity problem, which is particularly prevalent in newly launched systems that have not yet had enough time to amass sufficient data. As a solution, cross-domain recommender systems transfer knowledge from a source domain with relatively rich data to assist recommendations in the target domain. These systems usually assume that the entities either fully overlap or do not overlap at all. In practice, it is more common for the entities in the two domains to partially overlap. Moreover, overlapping entities may have different expressions in each domain. Neglecting these two issues reduces prediction accuracy of cross-domain recommender systems in the target domain. To fully exploit partially overlapping entities and improve the accuracy of predictions, this paper presents a cross-domain recommender system based on kernel-induced knowledge transfer, called KerKT. Domain adaptation is used to adjust the feature spaces of overlapping entities, while diffusion kernel completion is used to correlate the non-overlapping entities between the two domains. With this approach, knowledge is effectively transferred through the overlapping entities, thus alleviating data sparsity issues. Experiments conducted on four data sets, each with three sparsity ratios, show that KerKT has 1.13%-20% better prediction accuracy compared with six benchmarks. In addition, the results indicate that transferring knowledge from the source domain to the target domain is both possible and beneficial with even small overlaps.
Zhang, Y, Huang, Y, Porter, AL, Zhang, G & Lu, J 2019, 'Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study', Technological Forecasting and Social Change, vol. 146, pp. 795-807.
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© 2018 As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.
Zhang, Y, Lu, X & Li, J 2019, 'Single-sample face recognition under varying lighting conditions based on logarithmic total variation', Signal, Image and Video Processing.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. The logarithmic total variation (LTV) algorithm is a classical algorithm that is proposed to address the illumination interference in face recognition. Some state-of-the-art techniques based on LTV assume that the illumination component mainly lies in the low-frequency features among face images. However, these techniques adopt unsuitable methods to process low-frequency features, resulting in final unsatisfactory recognition rates. In this paper, we propose an improved illumination normalization method based on the LTV method, called the RETINA&TH-LTV algorithm. In this algorithm, the retina model is utilized to eliminate most of the illumination component in low-frequency features. Then, an advanced contrast-limited adaptive histogram equalization technique is proposed to remove the residual lighting component. At the same time, through realizing threshold-value filtering on high-frequency features, the enhancement of facial features is achieved. Finally, the processed frequency features are combined to form a robust holistic feature image, which is then utilized for recognition. Insufficient training images in face recognition are also taken into consideration in this research. Comparative experiments for single-sample face recognition are conducted on YALE B, CMU PIE and our self-built driver databases. The nearest neighbor classifier and extended sparse representation classifier are employed as classification methods. The results indicate that the RETINA&TH-LTV algorithm has promising performance, especially in serious illumination and insufficient training sample conditions.
Zhang, Y, Ren, W, Zhu, T & Faith, E 2019, 'MoSa: A modeling and sentiment analysis system for mobile application big data', Symmetry, vol. 11, no. 1.
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© 2019 by the authors. The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments' sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.
Zhang, Y, Ren, W, Zhu, T & Ren, Y 2019, 'SaaS: A situational awareness and analysis system for massive android malware detection', Future Generation Computer Systems, vol. 95, pp. 548-559.
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© 2018 Elsevier B.V. A large amount of mobile applications (Apps) are uploaded, distributed and updated in various Android markets, e.g., Google Play and Huawei AppGallery every day. One of the ongoing challenges is to detect malicious Apps (also known as malware) among those massive newcomers accurately and efficiently in the daily security management of Android App markets. Customers rely on those detection results in the selection of Apps upon downloading, and undetected malware may result in great damages. In this paper, we propose a cloud-based malware detection system called SaaS by leveraging and marrying multiple approaches from diverse domains such as natural language processing (n-gram), image processing (GLCM), cryptography (fuzzy hash), machine learning (random forest) and complex networks. We firstly extract n-gram features and GLCM features from an App's smali code and DEX file, respectively. We next feed those features into training data set, to create a machine learning detect model. The model is further enhanced by fuzzy hash to detect whether inspected App is repackaged or not. Extensive experiments (involving 1495 samples) demonstrates that the detecting accuracy is more than 98.5%, and support a large-scale detecting and monitoring. Besides, our proposed system can be deployed as a service in clouds and customers can access cloud services on demand.
Zhang, Y, Wang, J & Lu, H 2019, 'Research and application of a novel combined model based on multiobjective optimization for multistep-ahead electric load forecasting', Energies, vol. 12, no. 10.
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© 2019 by the authors. Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model's effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.
Zhang, Z, Oberst, S & Lai, J 2019, 'A non-linear friction work formulation for the analysis of self-excited vibrations', Journal of Sound and Vibration, vol. 443, pp. 328-340.
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Even though much research has been devoted to understand friction-induced vibrations, its root cause is not yet fully understood. Reliable prediction of friction-induced unstable vibrations such as in brake squeal or hip squeak remains a challenge because of nonlinearities involved and because the complex eigenvalue analysis (CEA) widely used in industry is linear. The energy fed back into the system by friction has been shown to be useful for analysis of measurements and numerical simulations. In numerical simulations, the linearised method of feed-in energy, calculated purely based on friction work has provided some insights into the physical mechanism for instabilities. However, the dynamics due to friction-induced instabilities is highly nonlinear and damping may offset some or all of the excess friction energy provided to the system. By using a nonlinear 2-DOF dry friction oscillator, a nonlinear friction work formulation is proposed to demonstrate that in combination with viscous damping the energy budget provides an improved analysis capability over linearised friction work. The results highlight the potential of nonlinear friction work as a reliable tool to study friction-induced instabilities to gain deeper physical insights into squeal triggering mechanisms and to better understand the over- and under-predictive character inherent to linear methods.
Zheng, R, Jiang, J, Hao, X, Ren, W, Xiong, F & Zhu, T 2019, 'CaACBIM: A context-aware access control model for BIM', Information (Switzerland), vol. 10, no. 2.
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© 2019 by the authors. A building information model (BIM) is of upmost importance with a full life-time cycle in architecture engineering and construction industry. Smart construction relies on BIM to manipulate information flow, data flow, and management flow. Currently, BIM has been explored mainly for information construction and utilization, but there exist few works concerning information security, e.g., audits of critical models and exposure of sensitive models. Moreover, few BIM systems have been proposed to make use of new computing paradigms, such as mobile cloud computing, blockchain and Internet of Things. In this paper, we propose a Context-aware Access Control (CaAC) model for BIM systems on mobile cloud architectures. BIM data can be confidentially accessed according to contexts in a fine-grained manner. We describe functions of CaAC formally by illustrating location-aware access control and time-aware access control. CaAC model can outperform role-based access control for preventing BIM data leakage by distinguishing contexts. In addition, grouping algorithms are also presented for flexibility, in which basic model (user grouping based on user role permissions) and advanced model (user grouping based on user requests) are differentiated. Compared with the traditional role-based access control model, security and feasibility of CaAC are remarkably improved by distinguishing an identical role with multiple contexts. The average efficiency is improved by 2n/(2n p q), and time complexity is O(n).
Zhu, T, Xiong, P, Li, G, Zhou, W & Yu, PS 2019, 'Differentially private model publishing in cyber physical systems', Future Generation Computer Systems.
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© 2018 Elsevier B.V. With the development of Cyber Physical Systems, privacy issues become an important topics in the past few years. It is worthwhile to apply differential privacy, one of the most influential privacy definitions, in cyber physical system. However, as the essential idea of differential privacy is to release query results rather than entire datasets, a large volume of noise has to be introduced. To provide high quality services we need to decrease the correlation between large sets of queries, while to predict on newly entered queries. This paper transfers the data publishing problem in cyber physical systems to a machine learning problem, in which a prediction model will be shared with clients. The predict model is used to answer current submitted queries and predict results for newly entered queries from the public.
Zijlema, AF, Van den hoven, E & Eggen, B 2019, 'A qualitative exploration of memory cuing by personal items in the home', Memory Studies, vol. 12, no. 4, pp. 377-397.
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We are surrounded by personal items that can trigger memories, such as photos, souvenirs, and heirlooms. Also during holidays, we collect items to remind us of the events, but not all bring back memories to the same extent. Therefore, we explored peoples’ responses to personal items related to a holiday, using the home tour interviewing method. Sixty-three accounts of cuing responses from nine home tours were analysed using thematic analysis. This resulted in four types of cuing responses: a) ‘no-memory’ responses, b) ‘know’ responses, c) ‘memory evoked think or feel’ responses, and d) ‘remember’ responses. For each of these cuing response categories, we looked into the types of items and their characteristics. Further, we found that some items can evoke multiple memories. The majority of the memories’ content refer to events close to the moment of acquiring the item.
Zuo, H, Lu, J, Zhang, G & Liu, F 2019, 'Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 291-303.
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© 2018 IEEE. Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.
Zuo, H, Lu, J, Zhang, G & Pedrycz, W 2019, 'Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 348-361.
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© 2018 IEEE. Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.
Abu Ul Fazal, M, Karim, S, Ferguson, S & Johnston, A 2019, 'Vinfomize: A framework for multiple voice-based information communication', ACM International Conference Proceeding Series, pp. 143-147.
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© 2019 Association for Computing Machinery. In this paper, we discuss investigations conducted with 10 visually challenged users (VCUs) and 8 sighted users (SUs) that aimed to determine user's experience, interest and expectations from concurrent information communication systems. In the first study, we concurrently played two voice-based streams in continuous form in both the ears, and in the second study, we concurrently communicated one stream continuously in one ear and three news headlines as interval-based short interruptions in another ear. We first reported the participants' experience qualitatively and then based on the feedback received from the users, we proposed a framework that may help in developing systems to communicate multiple voice-based information to the users. It is expected that the application of this new framework to information systems that provide multiple concurrent communication will provide a better user experience for users subject to their contextual and perceptual needs and limitations.
Ahadi, A & Mathieson, L 2019, 'A Comparison of Three Popular Source code Similarity Tools for Detecting Student Plagiarism', ACE 2019 Proceedings of the 21st Australasian Computing Education Conference, Australasian Computing Education Conference, ACM, Australia, pp. 112-117.
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© 2019 Association for Computing Machinery. This paper investigates automated code plagiarism detection in the context of an undergraduate level data structures and algorithms module. We compare three software tools which aim to detect plagiarism in the students' programming source code. We evaluate the performance of these tools on an individual basis and the degree of agreement between them. Based on this evaluation we show that the degree of agreement between these tools is relatively low. We also report the challenges faced during utilization of these methods and suggest possible future improvements for tools of this kind. The discrepancies in the results obtained by these detection techniques were used to devise guidelines for effectively detecting code plagiarism.
Ahadi, A, Lister, R & Mathieson, L 2019, 'ArAl: An Online Tool for Source Code Snapshot Metadata Analysis', Proceedings of the Twenty-First Australasian Computing Education Conference, Australasian Computing Education Conference, ACM, Australia, pp. 118-125.
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Aldini, S, Akella, A, Singh, A, Wang, Y-K, Carmichael, M, Liu, D & Lin, C-T 2019, 'Effect of Mechanical Resistance on Cognitive Conflict in Physical Human-Robot Collaboration', International Conference on Robotics and Automation, Canada.
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Anwar, M & Gill, A 2019, 'A Review of the Seven Modelling Approaches for Digital Ecosystem Architecture', 21st IEEE Conference on Business Informatics, Moscow, Russia.
Bai, L, Yao, L, Kanhere, SS, Wang, X & Sheng, QZ 2019, 'STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization.
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Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.
Bai, L, Yao, L, Kanhere, SS, Wang, X & Yang, Z 2018, 'Automatic Device Classification from Network Traffic Streams of Internet of Things', 2018 IEEE 43rd Conference on Local Computer Networks (LCN 2018), IEEE 43rd Conference on Local Computer Networks, USA, pp. 597-605.
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© 2018 IEEE. With the widespread adoption of Internet of Things (IoT), billions of everyday objects are being connected to the Internet. Effective management of these devices to support reliable, secure and high quality applications becomes challenging due to the scale. As one of the key cornerstones of IoT device management, automatic cross-device classification aims to identify the semantic type of a device by analyzing its network traffic. It has the potential to underpin a broad range of novel features such as enhanced security (by imposing the appropriate rules for constraining the communications of certain types of devices) or context-awareness (by the utilization and interoperability of IoT devices and their high-level semantics) of IoT applications. We propose an automatic IoT device classification method to identify new and unseen devices. The method uses the rich information carried by the traffic flows of IoT networks to characterize the attributes of various devices. We first specify a set of discriminating features from raw network traffic flows, and then propose a LSTM-CNN cascade model to automatically identify the semantic type of a device. Our experimental results using a real-world IoT dataset demonstrate that our proposed method is capable of delivering satisfactory performance. We also present interesting insights and discuss the potential extensions and applications.
Bai, L, Yao, L, Kanhere, SS, Yang, Z, Chu, J & Wang, X 2019, 'Passenger demand forecasting with multi-task convolutional recurrent neural networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 29-42.
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© Springer Nature Switzerland AG 2019. Accurate prediction of passenger demands for taxis is vital for reducing the waiting time of passengers and drivers in large cities as we move towards smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence of external factors (e.g., weather, holidays) in a simplistic manner by directly mapping external features to demands through fully-connected layers and thus result in substantial bias as the influence of external factors is not unified. To tackle these problems, we propose an end-to-end multi-task deep learning model for passenger demand prediction. First, we select similar regions for each target region based on their Point-of-Interest (PoI) information or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale real-world dataset collected from a city in China with a population of 1.5 million. The results demonstrate that our model significantly outperforms the state-of-the-art and a set of baseline methods.
Bell, J & Leong, T 2019, 'Collaborative Futures: Co-Designing Research Methods for Younger People Living with Dementia', ACM Transactions on Software Engineering and Methodology, Computer Human Interaction, Association for Computing Machinery, Glasgow, Scotland.
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Designing new technologies to support the lived experience of dementia is of increasing interest within HCI. While there is guidance on qualitative research methods to use in areas such as dementia, there is a need for more appropriate ways to research in the younger demographic. In Younger Onset Dementia (YOD), the circumstances and experiences are markedly different from dementia in the later stage of life – requiring a different approach. This paper presents insights into the methods and approaches used in a fieldwork with five people living with YOD; where they engaged as co-researchers in a co-directed inquiry into their lived experiences. Through this, we make a number of methodological contributions to HCI and Participatory Action Research (PAR) for research in the YOD setting. This includes productive approaches that are sensitive, respectful and empowering to the participants. It also extends current approaches to using probes in HCI and dementia research.
Bhagvathiperumal, S & Goyal, M 2018, 'Dynamic Provisioning of Cloud Resources Based on Workload Prediction', Computing and Network Sustainability, Second International Research Symposium on Computing and Network Sustainability, SpringerLink, Goa, India, pp. 41-49.
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Bian, J & Huang, ML 2019, 'Semantic Topic Discovery for Lecture Video', Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, Intelligent Systems Conference (IntelliSys), 2019, Springer, London, UK, pp. 457-466.
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With more and more lecture videos are available on the In-
ternet, on-line learning and e-learning are getting increasing concerns
because of many advantages such as high degree of interactivity. The se-
mantic content discovery for lecture video is a key problem. In this paper,
we propose a Multi-modal LDA model, which discovers the semantic top-
ics of lecture videos by considering audio and visual information. Specif-
ically, the speaking content and the information of presentation slides
are extracted from the lecture videos. With the proposed inference and
learning algorithm, the semantic topics of the video can be discovered.
The experimental results show that the proposed method can e ectively
discover the meaningful semantic characters of the lecture videos.
Bluff, A & Johnston, A 2019, 'Devising Interactive Theatre: Trajectories of Production with Complex Bespoke Technologies', Proceedings of the 2019 on Designing Interactive Systems Conference - DIS '19, Designing Interactive Systems 2019, ACM Press, San Diego, CA, USA, pp. 279-289.
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Borah, P, Gupta, D & Prasad, M 2019, 'Improved 2-norm Based Fuzzy Least Squares Twin Support Vector Machine', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, pp. 412-419.
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© 2018 IEEE. In order to reduce the higher training cost of support vector machine (SVM) and its sensitivity towards noise and outliers, two fuzzy based approaches are proposed in this paper. The proposed approaches are based on least squares twin support vector machine (LSTWSVM) and fuzzy support vector machine (FSVM). The effects of noise and outliers are reduced by assigning lower membership values to the data points which are away from the class centers. Further, 2-norm of the slack vectors of the LSTWSVM formulation is taken after multiplying to their respective diagonal matrices of the membership values to effectively utilize the fuzzy membership principle and to make the optimization problem strongly convex. Moreover, the proposed approaches solve linear equations instead of quadratic programming problems which help in training faster. The effectiveness of the proposed approaches are established by comparing the classification accuracies and training time with support vector machine, fuzzy support vector machine, twin support vector machine and least squares twin support vector machine.
Bown, O, Ferguson, S, Bray, L, Fraietta, A & Loke, L 2019, 'Facilitating Creative Exploratory Search with Multiple Networked Audio Devices Using HappyBrackets', http://www.nime.org/proceedings/2019/, New Interfaces for Musical Expression, Porto Alegre, Brazil, pp. 286-291.
Brankaert, R, Kenning, G, Welsh, D, Foley, S, Hodge, J & Unbehaun, D 2019, 'Intersections in HCI, design and dementia: Inclusivity in participatory approaches', DIS 2019 Companion - Companion Publication of the 2019 ACM Designing Interactive Systems Conference, pp. 357-360.
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© 2019 Copyright is held by the owner/author(s). Participatory approaches are used to design interactive systems, services and products to improve their impact and usability. However, these approaches are not always suitable for people with cognitive limitations such as dementia. This workshop will focus on participatory approaches for working with people living with dementia, challenge assumptions and provide concrete examples to inform design and technology development. Participants will review current design and technology offerings and work towards the development of a shared research agenda for future work. The workshop will explore how to negotiate the need for inclusion, personalization, and scalability to accommodate the growing needs in dementia. It will focus on setting an inclusive agenda for developments in Design and HCI in the 'here and now' to build sustainable approaches for the future.
Butler, A, Xu, G & Musial-Gabrys, K 2018, 'Research Performance Reporting is Fallacious', International Conference on Behavioral, Economic, and Socio-Cultural Computing, IEEE, Taiwan.
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Cheng, EJ, Young, KY & Lin, CT 2018, 'Image-based EEG signal processing for driving fatigue prediction', 2018 International Automatic Control Conference, CACS 2018, International Automatic Control Conference, IEEE, Taoyuan, Taiwan.
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© 2018 IEEE. This study proposes a EEG-based prediction system that transform the measured EEG record into an image-liked data for estimating the drowsiness level of drivers. Drowsy driving is one of the main factors to the occurrence of traffic accident. Since drivers themselves may not always immediately recognize that they are in the drowsy state, the risk of traffic accident increases while the driver is in the low vigilance state. In order to address this problem, the estimation of drowsy driving state via brain-computer interfaces (BCI) becomes a major concern in the driving safety field. This study transforms the measured EEG record into a image-liked feature maps, and then passes these feature maps to a Convolutional Neural Network (CNN) to learn the discriminative representations. The proposed drowsiness prediction system is evaluated by leave-one-subject-out cross-validation. The results indicate that our approach provides impressive and robust prediction performance on the EEG dataset without artifact removal process.
Dasgupta, A, Gill, A & Hussain, F 2019, 'A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems', Proceedings of the 8th International Conference on Data Science, Technology and Applications: DATA, 8th International Conference on Data Science, Technology and Applications, SCITEPRESS – Science and Technology Publications, Prague, Czech Republic, pp. 209-216.
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Dasgupta, A, Gill, A & Hussain, F 2019, 'A Review of General Data Protection Regulation for Supply Chain Ecosystem', https://link.springer.com/chapter/10.1007/978-3-030-22263-5_44, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, Cham, Sydney, Australia.
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Demediuk, S, Tamassia, M, Li, X & Raffe, WL 2019, 'Challenging Al: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment', PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2019), Australasian Computer Science Week Multiconference (ACSW), ASSOC COMPUTING MACHINERY, Sydney, AUSTRALIA.
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Dong, M, Yao, L, Wang, X, Benatallah, B & Huang, C 2019, 'Similarity-aware deep attentive model for clickbait detection', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 56-69.
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© Springer Nature Switzerland AG 2019. Clickbait is a type of web content advertisements designed to entice readers into clicking accompanying links. Usually, such links will lead to articles that are either misleading or non-informative, making the detection of clickbait essential for our daily lives. Automated clickbait detection is a relatively new research topic. Most recent work handles the clickbait detection problem with deep learning approaches to extract features from the meta-data of content. However, little attention has been paid to the relationship between the misleading titles and the target content, which we found to be an important clue for enhancing clickbait detection. In this work, we propose a deep similarity-aware attentive model to capture and represent such similarities with better expressiveness. In particular, we present the ways of either using similarity only or integrating it with other available quality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods.
Gardenfors, P, Williams, M-A, Johnston, B, Billingsley, R, Vitale, J, Peppas, P & Clark, J 2018, 'Event boards as tools for holistic AI', International Workshop on Artificial Intelligence and Cognition 2018, Palermo, Italy.
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Gehrke, L, Akman, S, Lopes, P, Chen, A, Singh, AK, Chen, H-T, Lin, CT & Gramann, K 2019, 'Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials', CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI Conference on Human Factors in Computing Systems (CHI), ASSOC COMPUTING MACHINERY, Glasgow, SCOTLAND.
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Gong, S, Oberst, S & Wang, X 2019, 'Dynamic analysis of vibrating flip-flow screens equipped with support and shear rubber springs', Recent Advances in Structural Dynamics, Lyon, France.
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Vibrating flip-flow screens provide an effective means of screening highly viscous or fine materials, and the dynamic characteristics of the main and the floating screen frames are largely responsible for a flip-flow screen’s screen performance and its processing capacity. An accurate dynamic model of the rubber shear springs used within the frame of the screen is critical for its dynamic analysis – to understand deficiencies and improve its performance. In this paper, the Sjöberg model is used to predict the frequency-and amplitude-dependent behaviour of the rubber shear springs. A friction model represents the amplitude dependency of the rubber shear springs. The fractional derivative model is used to describe its frequency dependency with its elasticity being represented by a linear spring. This model is further validated by cyclic tests of the rubber shear springs. Furthermore, dynamic response of the VFFS have been analysed using the Sjöberg model and the Kelvin-Voigt model, respectively. Experimental results indicate that dynamic response of VFFS can be better predict using the Sjöberg model than Kelvin-Voigt model in time region as well as in the frequency domain.
Gong, S, Oberst, S & Xinwen, W 2019, 'A non-linear model of rubber shear springs validated by experiments', Nonlinear Dynamics, Rome, Italy.
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Vibrating flip-flow screens provide an effective solution for screening highly viscous or fine materials. However,
yet, only linear theory has been applied to their design. Yet, to understand deficiencies and to improve performance an
accurate model especially of the rubber shear springs equipped in screen frames is critical for its dynamics to predict e.g.
frequency- and amplitude-dependent behaviour. In this paper, the amplitude dependency of the rubber shear spring is
represented by employing a friction model in which parameters are fitted to an affine function rather constant values used for
the classic Berg’s model; the fractional derivative model is used to describe its frequency dependency and compared to
conventional dashpot and Maxwell models with its elasticity being represented by a nonlinear spring. The experimentally
validated results indicate that the proposed model with a nonlinear spring, friction and fractional derivative model is able to
more accurately describe the dynamic characteristics of a rubber shear spring compared with other models.
Gudi, SLKC, Ojha, S, Sidra, Johnston, B & Williams, MA 2017, 'A proactive robot tutor based on emotional intelligence', Advances in Intelligent Systems and Computing, International Conference on Robot Intelligence Technology and Applications, Springer, Korea, pp. 113-120.
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© Springer International Publishing AG, part of Springer Nature 2019. In recent years, social robots are playing a vital role in various aspects of acting as a companion, assisting in regular tasks, health, interaction, teaching, etc. Coming to the case of robot tutor, the actions of the robot are limited. It may not fully understand the emotions of the student. It may continue to give lecture even though the user is bored or left away from the robot. This situation makes a user feel that robot cannot supersede a human being because it is not in a position to understand emotions. To overcome this issue, in this paper, we present an Emotional Classification System (ECS) where the robot adapts to the mood of the user and behaves accordingly by becoming proactive. It works based on the emotion tracked by the robot using its emotional intelligence. A robot as a sign language tutor scenario is considered to assist speech and hearing impairment people for validating our model. Real-time implementations and analysis are further discussed by considering Pepper robot as a platform.
Gupta, U, Gupta, D & Prasad, M 2019, 'Kernel Target Alignment based Fuzzy Least Square Twin Bounded Support Vector Machine', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, pp. 228-235.
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© 2018 IEEE. A kernel-target alignment based fuzzy least square twin bounded support vector machine (KTAFLSTBSVM) is proposed to reduce the effects of outliers and noise. The proposed model is an effective and efficient fuzzy based least square twin bounded support vector machine for binary classification where the membership values are assigned based on kernel-target alignment approach. The proposed KTA-FLSTBSVM solves the two systems of linear equations, which is computationally very fast with significant comparable performance. To development the robust model, this approach minimizes the structural risk which is the gist of statistical learning theory. This powerful KTA-FLSTBSVM approach is tested on artificial data sets as well as benchmark real-world datasets that provide significantly better result in terms of generalization performance and computational time.
Halkon, B, Rauter, A, Oberst, S & Marburg, S 2019, 'Research and development of an air-puff excitation system for lightweight structures', 8th International Operational Modal Analysis Conference 2019, Copenhagen.
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Lightweight, thin-walled structures appear in numerous engineering and natural structures. Due to their sensitivity, vibration excitation by, now traditional, contacting techniques, such as modally-tuned impact hammers or electrodynamic shakers, to investigate their dynamics is challenging since it typically adds substantial mass and/or stiffness at the excitation location. The research presented in this article, therefore, is intended to yield a system for the non-contact excitation of thin-walled structures through small, controlled blasts of air. An air-puff system, consisting of two fast-acting solenoid controlled valves, a small air outlet nozzle and bespoke control software with a programmable valve control sequence, is researched and developed. The excitation impulse characteristics are investigated
experimentally and described in detail for varying input control parameters. Ultimately, suitability of the system for the excitation of thin-walled structures is explored, for both a 3D-printed micro-satellite panel and a natural bee honeycomb, with promising results when compared to that of an impact hammer.
Haque, MN, Mathieson, L & Moscato, P 2019, 'A Memetic Algorithm Approach to Network Alignment', The Genetic and Evolutionary Computation Conference, Prague, Czech Republic.
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Hesamian, MH, Jia, W, He, X & Kennedy, PJ 2019, 'Atrous Convolution for Binary Semantic Segmentation of Lung Nodule', ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1015-1019.
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© 2019 IEEE. Accurately estimating the size of tumours and reproducing their boundaries from lung CT images provides crucial information for early diagnosis, staging and evaluating patients response to cancer therapy. This paper presents an advanced solution to segment lung nodules from CT images by employing a deep residual network structure with Atrous convolution. The Atrous convolution increases the field of view of the filters and helps to improve classification accuracy. Moreover, in order to address the significant class imbalance issue between the nodule pixels and background non-nodule pixels, a weighted loss function is proposed. We evaluate our proposed solution on the widely adopted benchmark dataset LIDC. A promising result of an average DCS of 81.24% is achieved, outperforming the state of the arts. This demonstrates the effectiveness and importance of applying the Atrous convolution and weighted loss for such problems.
Huang, ML, YUE, Z, Liang, J, Nguyen, QV & Luo, Z 2019, 'Stroke Data Analysis through a HVN Visual Data Mining Platform', Proc. of 23rd International Conference on Information Visualization, 23rd International Conference on Information Visualization, IEEE CSP, Adelaide, Australia.
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Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets.
This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients.
The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements - curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes.
The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.
Islam, MR, Helen Lu, H, Hossain, MJ & Li, L 2019, 'Improving Power Quality of Distributed PV-EV Distribution Grid by Mitigating Unbalance', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, Australia.
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Islam, MR, Lu, H, Fang, G, Li, L & Hossain, MJ 2019, 'Optimal Dispatch of Electrical Vehicle and PV Power to Improve the Power Quality of an Unbalanced Distribution Grid', 2019 Proceedings of International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE, Shenzhen, China.
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Islam, MR, Lu, H, Hossain, MJ & Li, L 2019, 'Compensating Neutral Current, Voltage Unbalance and Improving Voltage of an Unbalanced Distribution Grid Connected with EV and Renewable Energy Sources', Harbin, China.
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Islam, MR, Lu, HH, Hossain, MJ & Li, L 2019, 'A Comparison of Performance of GA, PSO and Differential Evolution Algorithms for Dynamic Phase Reconfiguration Technology of a Smart Grid', 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 858-865.
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© 2019 IEEE. Increasing penetration of Distributed Generations (Photovoltaic solar energy (PV), Wind energy, and Battery Energy Storage) and PEVs (Plug-in Electric Vehicles) into smart grid induce network imbalance which reduces power quality. The uncertainty of demand-generation requires balancing for mitigating network imbalance. Several researchers have used various optimization methods for mitigating unbalance. Moreover, a few researchers have done comparative studies of optimization methods for mitigating unbalance till now. This paper proposes a method to mitigate unbalance and reduce the total power loss by optimizing load distribution among phases. This paper compares the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on the application of phase balancing. Finally, the efficacy of these algorithms are evaluated for the proposed unbalance mitigation technique, and it is found that the proposed technique using DE algorithm can reduce a significant amount of unbalance at all the buses of the distribution grid with less computational effort.
Islam, MR, Lu, HY, Hossain, MJ & Li, L 2019, 'A Comparison of Performance of GA, PSO and Differential Evolution Algorithms for Dynamic Phase Reconfiguration Technology of a Smart Grid', Congress on evolutionary computation, IEEE, Wellington, New Zealand, pp. 835-842.
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Jay, B 2019, 'A simpler lambda calculus', PEPM 2019 - Proceedings of the 2019 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation, ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation, ACM, Portugal, pp. 1-9.
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© 2019 Association for Computing Machinery. Closure calculus is simpler than pure lambda-calculus as it does not mention free variables or index manipulation, variable renaming, implicit substitution, or any other meta-theory. Further, all programs, even recursive ones, can be expressed as normal forms. Third, there are reduction-preserving translations to calculi built from combinations of operators, in the style of combinatory logic. These improvements are achieved without sacrificing three fundamental properties of lambda-calculus, being a confluent rewriting system, supporting the Turing computable numerical functions, and supporting simple typing.
Li, Q, Zhong, J, Li, Q, Cao, Z & Wang, C 2019, 'Enhancing network embedding with implicit clustering', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 452-467.
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© Springer Nature Switzerland AG 2019. Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real-world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.
Li, W, Zhang, Y, Qiao, M, Chang, L, Qin, L & Lin, X 2019, 'Scaling distance labeling on small-world networks', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1060-1077.
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© 2019 Association for Computing Machinery. Distance labeling approaches are widely adopted to speed up the online performance of shortest distance queries. The construction of the distance labeling, however, can be exhaustive especially on big graphs. For a major category of large graphs, small-world networks, the state-of-the-art approach is Pruned Landmark Labeling (PLL). PLL prunes distance labels based on a node order and directly constructs the pruned labels by performing breadth-first searches in the node order. The pruning technique, as well as the index construction, has a strong sequential nature which hinders PLL from being parallelized. It becomes an urgent issue on massive small-world networks whose index can hardly be constructed by a single thread within a reasonable time. This paper scales distance labeling on small-world networks by proposing a Parallel Shortest-distance Labeling (PSL) scheme and further reducing the index size by exploiting graph and label properties. PSL insightfully converts the PLL's node-order dependency to a shortest-distance dependence, which leads to a propagation-based parallel labeling in D rounds where D denotes the diameter of the graph. Extensive experimental results verify our efficiency on billion-scale graphs and near-linear speedup in a multi-core environment.
LU, S, Oberst, S, Zhang, G & Luo, Z 2019, 'Period adding bifurcations in dynamic pricing processes', IEEE CIFEr 2019: 2019 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, Shenzhen.
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Price information enables consumers to anticipate
a price and to make purchasing decisions based on their price
expectations, which are critical for agents with pricing decisions
or price regulations. A company with pricing decisions can
aim to optimise the short-term or the long-term revenue, each
of which leads to different pricing strategies thereby different
price expectations. The choices between the two optimisation
objectives consider the maximal revenue and the robustness of
a chosen pricing strategy against market volatility. However
the robustness is rarely identified in a volatile market. Here,
we investigate the robustness of optimal pricing strategies with
the short-term or long-term optimisation objectives through the
analysis of nonlinear dynamics of price expectations. Bifurcation
diagrams and period diagrams are introduced to compare their
change in dynamics. Our results highlight that period adding
bifurcations occur during the dynamic pricing processes studied.
These bifurcations would challenge the robustness of an optimal
pricing strategy. The consideration of the long-term revenue
allows a company to charge a higher price, which in turn
increases the revenue. However, the consideration of the shortterm
revenue can avoid period adding bifurcations, contributing
to a robust pricing strategy. This allows a company to harvest
a good revenue through a robust pricing strategy in a volatile
market and to satisfy regulations of a control in price volatility.
Madhisetty, S & Williams, MA 2019, 'Managing privacy through key performance indicators when photos and videos are shared via social media', Advances in Intelligent Systems and Computing, pp. 1103-1117.
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© Springer Nature Switzerland AG 2019. There are many definitions of privacy. What is considered sensitive varies from individual to individual. When a document is shared it may reveal certain information, the exchange of information is grounded with a specific context. This contextual grounding may not be afforded when photos and videos are shared, because they may contain rich semantic and syntactic information coded as tacit knowledge. Identifying sensitive information in a photo or a video is a major problem; therefore, rather than making assumptions about what is sensitive in a photo or a video, this research asked a group of study participants why they share content and what their concerns are (if any)? This enabled inferences to be made about categories of sensitivity in accordance with the participants’ responses. Interview data was gathered and Grounded Theory was applied. The following themes emerged from the data: a major theme, in which no privacy concerns were developed, three sub-themes in which varying levels of privacy concerns were developed and key performance indicators which manage levels of privacy were determined. This paper focuses on the main themes’ key performance indicators and how they can manage privacy when photos and videos are shared over social media.
Madhisetty, S & Williams, MA 2019, 'The role of trust and control in managing privacy when photos and videos are stored or shared', Advances in Intelligent Systems and Computing, pp. 127-140.
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© Springer Nature Switzerland AG 2019. A photo or a video could contain sensitive information coded as tacit information, which makes it difficult gauge, the loss of privacy, if such photo or a video were shared. Social media applications like Facebook, Twitter, WhatsApp and many more such applications are becoming popular. The instant sharing of information via photos and videos is making the management of issues which rise out of loss of privacy more difficult. Many users of social media trust that their content will not be misused other than purposes that were originally intended. This paper discusses not only about how much of that trust is real and how much of it was forced, but demonstrates the reasoning behind forced trust. These interferences were made after data collection via interviews and data analysis using Grounded Theory.
Madhisetty, S, Williams, MA, Massy-Greene, J, Franco, L & El Khoury, M 2019, 'How to manage privacy in photos after publication', ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems, pp. 162-168.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Photos and videos once published may stay available for people to view it unless they are deleted by the publisher of the photograph. If the content is downloaded and uploaded by others then they lose all the privacy settings once afforded by the publisher of the photograph or video via social media settings. This means that they could be modified or in some cases misused by others. Photos also contain tacit information, which cannot be completely interpreted at the time of their publication. Sensitive information may be revealed to others as the information is coded as tacit information. Tacit information allows different interpretations and creates difficulty in understanding loss of privacy. Free flow and availability of tacit information embedded in a photograph could have serious privacy problems. Our solution discussed in this paper illuminates the difficulty of managing privacy due the tacit information embedded in a photo. It also provides an offline solution for the photograph such that it cannot be modified or altered and gets automatically deleted over a period of time. By extending the Exif data of a photograph by incorporating an in-built feature of automatic deletion, and the access to an image by scrambling the image via adding a hash value. Only a customized application can unscramble the image therefore making it available. This intends to provide a novel offline solution to manage the availability of the image post publication.
Mahdavi, F, Hayati, H, Kennedy, P & Eager, D 2019, 'Ageing and resulting injuries – effects on racing greyhounds', European Society of Biomechanics, Vienna, Austria.
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Mahdavi, F, Hayati, H, Kennedy, P & Eager, D 2019, 'Effects of the number of starts on greyhound racing dynamics', International Society of Biomechanics Conference, Calgary, Canada.
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Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 2018, 'A fine-grained ontology-based sentiment aggregation approach', Advances in Intelligent Systems and Computing, International Conference on Complex, Intelligent, and Software Intensive Systems, Springer, Japan, pp. 252-262.
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© 2019, Springer International Publishing AG, part of Springer Nature. Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way.
Musial-Gabrys, K, Wang, Y, Jin, D & Dang, J 2019, 'Community Detection in Social Networks Considering Topic Correlations', Proceedings of the AAAI Conference on Artificial Intelligence, Thirty Third AAAI Conference on Artificial Intelligence, Hawaii, USA.
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Naji, M, Al-Ani, A, Braytee, A, Anaissi, A & Kennedy, P 2019, 'Queue Formation Augmented with Particle Swarm Optimisation to Improve Waiting Time in Airport Security Screening', Advances in Intelligent Systems and Computing, pp. 923-935.
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© 2019, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of both passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement. It may also lead to delays for passengers and airlines. This paper proposes a novel queue formation method based on a queueing theory model augmented with a particle swarm optimisation method known as QQT-PSO to improve the average waiting time in airport security areas. Extensive experiments were conducted using real-world datasets collected from Sydney airport. Compared to the existing system, our method significantly reduces the average waiting time and operating cost by 11.89% compared to the one-queue formation.
Ng, CK, Jiang, F, Zhang, LY & Zhou, W 2019, 'Static malware clustering using enhanced deep embedding method', Concurrency Computation.
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© 2019 John Wiley & Sons, Ltd. Malware refers to any software, programs, or files that are intentionally utilised to compromise the system and cause unexpected losses to end-users such as economical losses or privacy breaches. The rapid growth of malware makes it impossible to keep up with its progress merely via human interventions or manual analysis. One of the challenges for the human-oriented approaches is they will cause backlog and inability to keep up with the development traces of the malware. Hence, an efficient method is needed urgently to analyse effectively and identify accurately the malware in their domain. Malware clustering has been extensively studied in the machine learning area with regards to distance functions, grouping algorithm and cluster validation. A large number of research studies have been done via behavioral analysis for clustering to achieve high performance of malware detections. However, there is a trade-off for better detection performance between behaviorial approaches and high computational forces. Up to date, little work focuses on the deep learning representations for malware clustering. Therefore, in this paper, we propose an enhanced deep embedded clustering method to facilitate an effective and efficient malware clustering process. The new method takes advantage of linear dimensionality reduction and a customised deep neural network to learn malware representations in an orthogonal space and performs cluster assignments. Our experimental results demonstrate that the proposed clustering model outperforms the traditional K-means method with regards to the enhanced features using various auto-encoder, pre-trained weight and principle component analysis (PCA).
Ning, G, Liu, P, Fan, X & Zhang, C 2018, 'A top-down approach to articulated human pose estimation and tracking', Computer Vision – ECCV 2018 Workshops, European Conference on Computer Vision 2018, Springer, Germany, pp. 227-234.
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© 2019, Springer Nature Switzerland AG. Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging. Existing methods can be categorized into two groups: top-down and bottom-up approaches. In this paper, following the top-down approach, we aim to build a strong baseline system with three modules: human candidate detector, single-person pose estimator and human pose tracker. Firstly, we choose a generic object detector among state-of-the-art methods to detect human candidates. Then, cascaded pyramid network is used to estimate the corresponding human pose. Finally, we use a flow-based pose tracker to render keypoint-association across frames, i.e., assigning each human candidate a unique and temporally-consistent id, for the multi-target pose tracking purpose. We conduct extensive ablative experiments to validate various choices of models and configurations. We take part in two ECCV’18 PoseTrack challenges (https://posetrack.net/workshops/eccv2018/posetrack_eccv_2018_results.html ): pose estimation and pose tracking.
Ojha, S, Gudi, SLKC, Vitale, J, Williams, MA & Johnston, B 2019, 'I remember what you did: A behavioural guide-robot', Advances in Intelligent Systems and Computing, pp. 273-282.
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© Springer International Publishing AG, part of Springer Nature 2019. Robots are coming closer to human society following the birth of emerging field called Social Robotics. Social Robotics is a branch of robotics that specifically pertains to the design and development of robots that can be employed in human society for the welfare of mankind. The applications of social robots may range from household domains such as elderly and child care to educational domains like personal psychological training and tutoring. It is crucial to note that if such robots are intended to work closely with young children, it is extremely important to make sure that these robots teach not only the facts but also important social aspects like knowing what is right and what is wrong. It is because we do not want to produce a generation of kids that knows only the facts but not morality. In this paper, we present a mechanism used in our computational model (i.e EEGS) for social robots, in which emotions and behavioural response of the robot depends on how one has previously treated a robot. For example, if one has previously treated a robot in a good manner, it will respond accordingly while if one has previously mistreated the robot, it will make the person realise the issue. A robot with such a quality can be very useful in teaching good manners to the future generation of kids.
Paryani, S & Ramadan-Jradi, R 2018, 'The Impact of Flipped Learning on Student Performance and Engagement: A Systematic Literature Review', International Journal on Learning and Teaching, International Conference on Learning and Teaching, Singapore.
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Pelchen, T & Lister, R 2019, 'On the Frequency of Words Used in Answers to Explain in Plain English Questions by Novice Programmers', Proceedings of the Twenty-First Australasian Computing Education Conference, ACM, pp. 11-20.
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Prasad, M, Rajora, S, Gupta, D, Daraghmi, YA, Daraghmi, E, Yadav, P, Tiwari, P & Saxena, A 2019, 'Fusion based En-FEC Transfer Learning Approach for Automobile Parts Recognition System', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, pp. 2193-2199.
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© 2018 IEEE. The artificially supervised classification of real world entities have gained a phenomenal significance in recent year of computational advancements. An intelligent classification model focuses on rendering accurate outcomes vide the implicated paradigms with respect to the subjected data employed to train the classifier. This paper proposes a novel deep learning approach to classify the various parts of any operational engine such as crank shafts, rock-arms, distributer, air duct, assecorybelt etc. Deployed in automobiles. The proposed architecture distinctively utilizes convolution neural networks for this typical classification problem and altogether constructs a robust transfer learning paradigm to render the correct class label against the validation and test images as the conclusive result of the classification. The proposed methodology poses in such a way that it can qualitatively classify and henceforth give the corresponding class label of the machinery/engine part under consideration. This computationally intelligent architecture requires the user to feed the image of the engine part to the model in order to achieve the requisite responses of classification. The main contribution of the proposed method is the development of a robust algorithm that can exhibit pronounced results without training the entire ConvNet architecture from scratch, thereby enabling the proposed paradigm to be deployable in application instances wherein limited labeled training data is available.
Prior, J, Laudari, S & Leaney, J 2019, 'What is the Effect of a Software Studio Experience on a Student's Employability?', ACE 2019 Proceedings of the 21st Australasian Computing Education Conference, Australasian Computing Education Conference, ACM, Australia, pp. 28-36.
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© 2019 Association for Computing Machinery. Our software studio demonstrably increases students' employability, according to the empirical findings of this study, and an evaluation of these findings based on the CareerEDGE Employability Development Profile. We provide a studio environment in which students work in mixed teams on real software projects for clients, under the guidance of industry and academic mentors. This study used open-ended interviews and ethnographic observations in the studio sessions to understand employability success. Skills found important for employability include: Collaboration and communication, project management, supporting each other to resolve technical issues, seeking help from industry mentors and academics, social aspects of work (working with clients and mentors), reflection skills and technical skills. These skills were compared with the CareerEDGE Employability Development Profile and found to give good coverage of employability skills. Contributions made by this study to computing education are: • A deep empirical understanding of students' perspectives and what they value about their employability as a result of participating in the software studio • An evaluation of our findings against the CareerEDGE employability framework, in a technical learning environment • Findings from an investigation that is complementary to students' perspectives collected in accordance with the CareerEDGE approach, where the data is collected via a questionnaire with 5-point Likert scale responses; our interviews were open-ended and accompanied by ethnographic observations.
Qararyah, F, Daraghmi, YA, Daraghmi, E, Rajora, S, Lin, CT & Prasad, M 2018, 'A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, IEEE Symposium Series on Computational Intelligence, IEEE, India, pp. 83-87.
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© 2018 IEEE. Computation intelligence plays a major role in developing intelligent vehicles, which contains a Traffic Sign Recognition (TSR) system for increasing vehicle safety. Traffic sign recognition systems consist of an initial phase called Traffic Sign Detection (TSD), where images and colors are segmented and fed to the recognition phase. The most challenging process in TSR systems in terms of time consumption is the detection phase. The previous studies proposed different models for traffic sign detection, however, the computation time of these models still requires improvement for enabling real time systems. Therefore, this paper focuses on the computational time and proposes a novel time efficient color segmentation model based on logistic regression. This paper uses RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of the proposed model, since no color conversion is needed. The trained segmentation classifier is tested on 1000 traffic sign images taken in different lighting conditions. The experimental results show that the proposed model segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation methods.
Qu, Y, Yu, S, Zhang, J, Binh, HTT, Gao, L & Zhou, W 2019, 'GAN-DP: Generative Adversarial Net Driven Differentially Privacy-Preserving Big Data Publishing', IEEE International Conference on Communications.
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© 2019 IEEE. Increasing massive volume of data are generated every single second in this big data era. With big data from multiple sources, adversaries continuously mine private information for potential benefits. Motivated by this, we propose a generative adversarial net (GAN) driven noise generation method under the framework of differential privacy. We add one more perceptron, which is a specifically devised differential privacy identifier. After the generator produces the noise, the discriminator and the proposed identifier game with each other to derive the Nash Equilibrium. Extensive experimental results demonstrate the proposed model meets differential privacy constraints and upgrade data utility simultaneously.
Rajora, S, Kumar Vishwakarma, D, Singh, K & Prasad, M 2019, 'CSgI: A Deep Learning based approach for Marijuana Leaves Strain Classification', 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, pp. 209-214.
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© 2018 IEEE. This paper proposes a novel approach that classifies the images of various marijuana/cannabis leaves into their respective classes of strains and types. The proposed architecture works on a two-fold technique which when implemented in the requisite sequence delivers phenomenal results to the classification problem statement. The first fold, being the segmentation or foreground extraction in the images, focuses on extracting the RDI (Region of Interest) using a robust segmentation algorithm which can suitable separate the foreground from the image; and the second fold, being the Deep Learning aspect focuses on the result classification task. This literature gives a quantitative analysis of implementing this classification problem vide a transfer learning paradigm (for application instances with less training data in hand) training the entire CNN archetype from scratch (for application instances with sufficient training data in hand). Thus, altogether the proposed methodology distinctively deploys ConvNets for the posed classification problem having dual aspects of approaches implementation wiz: a) Transfer Learning b) Training the entire CNN from scratch. The novelty of the proposed work can be counted upon as the construction of a robust algorithm very first of its kind in this respective application domain which is potent enough to render the correct class label of the strain/type of marijuana or cannabis leaf image when fed to the system for classification.
Rajora, S, Li, DL, Jha, C, Bharill, N, Patel, OP, Joshi, S, Puthal, D & Prasad, M 2018, 'A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, IEEE Symposium Series on Computational Intelligence, IEEE, Bangalore, India, India, pp. 1958-1963.
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© 2018 IEEE. This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.
Saqib, M, Daud Khan, S, Sharma, N, Scully-Power, P, Butcher, P, Colefax, A & Blumenstein, M 2019, 'Real-Time Drone Surveillance and Population Estimation of Marine Animals from Aerial Imagery', International Conference Image and Vision Computing New Zealand.
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© 2018 IEEE. Video analysis is being rapidly adopted by marine biologists to asses the population and migration of marine animals. Manual analysis of videos by human observers is labor intensive and prone to error. The automatic analysis of videos using state-of-the-art deep learning object detectors provides a cost-effective way for the study of marine animals population and their ecosystem. However, there are many challenges associated with video analysis such as background clutter, illumination, occlusions, and deformation. Due to the high-density of objects in the images and sever occlusion, current state-of-the-art object often results in multiple detections. Therefore, customized Non-Maxima-Suppression is proposed after the detections to suppress false positives which significantly improves the counting and mean average precision of the detections. An end-to-end deep learning framework of Faster-RCNN [1] was adopted for detections with base architectures of VGG16 [2], VGGM [3] and ZF [4].
Shah, S & Goyal, M 2019, 'Anomaly Detection in Social Media Using Recurrent Neural Network', Computational Science – ICCS 2019 19th International Conference Faro, Portugal, June 12–14, 2019 Proceedings, Part IV, 19th International Conference on Computational Science, Springer, Faro, Portugal, pp. 74-83.
Skillicorn, D, Alsadhan, N, Billingsley, R & Williams, MA 2019, 'Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 29-41.
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© 2019, Springer Nature Switzerland AG. Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect. We suggest, from empirical results, that this is because emotions are the wrong granularity; and that emotions contain subemotions that are much more clearly separated from one another, and so are both easier to detect and to exploit.
Taghikhah, F, Raffe, WL, Mitri, G, Du Toit, S, Voinov, A & Garcia, JA 2019, 'Last Island: Exploring Transitions to Sustainable Futures through Play', PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2019), Australasian Computer Science Week Multiconference (ACSW), ASSOC COMPUTING MACHINERY, Sydney, AUSTRALIA.
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Tiwary, M, Sharma, S, Mishra, P, El-Sayed, H, Prasad, M & Puthal, D 2018, 'Building Scalable Mobile Edge Computing by Enhancing Quality of Services', 2018 International Conference on Innovations in Information Technology (IIT), International Conference on Innovations in Information Technology, IEEE, Al Ain, United Arab Emirates, pp. 141-146.
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Vishwa, A & Hussain, FK 2018, 'A Blockchain based approach for multimedia privacy protection and provenance', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, IEEE Symposium Series on Computational Intelligence, IEEE, Bangalore, India, India, pp. 1941-1945.
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© 2018 IEEE. There has been a vast increase in incidents related to multimedia copyright and security breaches in the past few years, compromising users' privacy. One such breach involved the seventh season of the TV series 'Game of Thrones', where episodes were illegally downloaded before the official release date etc. Such security breaches raise questions about the approaches and models that currently apply to data privacy and security, where the user saves and distributes his data personally or depends on a third party or stakeholder to manage the distribution rights of sensitive data. When it comes to multimedia, many companies or multimedia owners rely on third parties, distributors and sales persons to monitor their publicity, maintain their popularity and sell their multimedia content. Blockchain technology, which was originally devised for the digital currency (cryptocurrency), has distinct features such as distributed networking, data privacy, trust less computing etc. This technology attracts great interest from the research community due to its innovative properties which can be applied to many business applications, one being access control over data. In this paper, we present a decentralized data management framework that ensures user data privacy and control. We propose a protocol that uses blockchain technology to take control of the user's data. This protocol enables the user to have full control over his multimedia files and he doesn't need to trust a third party. The framework allows the user to not only store data but also to query and share data as well as auditing. Finally, we discuss possible future extensions of blockchain technology as a medium to ensure privacy, data control, auditing and trust management in different areas.
Wu, D, Chen, J, Sharma, N, Pan, S, Long, G & Blumenstein, M 2019, 'Adversarial Action Data Augmentation for Similar Gesture Action Recognition', The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.
Wu, D, Liu, J, Sui, Y, Chen, S & Xue, J 2019, 'Precise Static Happens-Before Analysis for Detecting UAF Order Violations in Android', Proceedings of 12th International Conference on Software Testing, Verification and Validation.
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Wu, D, Sharma, N & Blumenstein, M 2018, 'An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition', 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018), International Conference on Image and Vision Computing New Zealand, IEEE, New Zealand, pp. 73-78.
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© 2018 IEEE. Human action recognition from the RGB video is widely applied on varies real applications. Many works have been done by researchers in computer vision and machine learning area to address the challenges and complexity involved in video-based human action recognition. Deep learning approaches including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been introduced in the human action recognition research area. However, due to the drawbacks of the CNNs, recognizing actions with similar gestures and describing complex actions is still very challenging. Hence, an end-to-end hierarchical classification architecture has been proposed in this paper to resolve the confusion between similar gesture. The proposed approach firstly classifies the whole dataset and generates the accuracy for each class in stage 1. Based on the confusion matrix obtained from stage-1, the approach combines the most confused similar gesture pairs into one class, and classify them along with all other class, in the stage-2. In stage 3, similar gesture pairs will be classified by binary classifiers, which will increase the performance of each class and the overall accuracy. We apply and evaluate the developed models to recognize the similar human actions on the both KTH and UCF101 dataset. The result shows that the proposed approach can boost the classification performance on both the datasets. The proposed architecture is robust and any classification technique can be used in stage 1 and stage 2.
Wu, D, Sharma, N & Blumenstein, M 2019, 'Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos', 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018.
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© 2018 IEEE. Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.
Xu, X, Sui, Y, Yan, H & Xue, J 2019, 'VFix: Value-Flow-Guided Precise Program Repair for Null Pointer Dereferences', Proceedings of 41th International Conference on Software Engineering.
Yang, B, Wen, D, Qin, L, Zhang, Y, Chang, L & Li, R-H 2019, 'Index-based Optimal Algorithm for Computing K-Cores in Large Uncertain Graphs', 2019 IEEE 35nd International Conference on Data Engineering (ICDE), 2019 IEEE 35nd International Conference on Data Engineering (ICDE), IEEE, Macau SAR, China, pp. 64-75.
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Yang, Y, Zhang, W, Zhang, Y, Lin, X & Wang, L 2019, 'Selectivity estimation on set containment search', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 330-349.
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© Springer Nature Switzerland AG 2019. In this paper, we study the problem of selectivity estimation on set containment search. Given a query record Q and a record dataset S, we aim to accurately and efficiently estimate the selectivity of set containment search of query Q over S. The problem has many important applications in commercial fields and scientific studies. To the best of our knowledge, this is the first work to study this important problem. We first extend existing distinct value estimating techniques to solve this problem and develop an inverted list and G-KMV sketch based approach IL-GKMV. We analyse that the performance of IL-GKMV degrades with the increase of vocabulary size. Motivated by limitations of existing techniques and the inherent challenges of the problem, we resort to developing effective and efficient sampling approaches and propose an ordered trie structure based sampling approach named OT-Sampling. OT-Sampling partitions records based on element frequency and occurrence patterns and is significantly more accurate compared with simple random sampling method and IL-GKMV. To further enhance performance, a divide-and-conquer based sampling approach, DC-Sampling, is presented with an inclusion/exclusion prefix to explore the pruning opportunities. We theoretically analyse the proposed techniques regarding various accuracy estimators. Our comprehensive experiments on 6 real datasets verify the effectiveness and efficiency of our proposed techniques.
Yang, Y, Zhang, Y, Zhang, W & Huang, Z 2019, 'Gb-KMV: An augmented KMV sketch for approximate containment similarity search', Proceedings - International Conference on Data Engineering, pp. 458-469.
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© 2019 IEEE. In this paper, we study the problem of approximate containment similarity search. Given two records Q and X, the containment similarity between Q and X with respect to Q is |Q intersect X|/ |Q|. Given a query record Q and a set of records S, the containment similarity search finds a set of records from S whose containment similarity regarding Q are not less than the given threshold. This problem has many important applications in commercial and scientific fields such as record matching and domain search. Existing solution relies on the asymmetric LSH method by transforming the containment similarity to well-studied Jaccard similarity. In this paper, we use a different framework by transforming the containment similarity to set intersection. We propose a novel augmented KMV sketch technique, namely GB-KMV, which is data-dependent and can achieve a good trade-off between the sketch size and the accuracy. We provide a set of theoretical analysis to underpin the proposed augmented KMV sketch technique, and show that it outperforms the state-of-the-art technique LSH-E in terms of estimation accuracy under practical assumption. Our comprehensive experiments on real-life datasets verify that GB-KMV is superior to LSH-E in terms of the space-accuracy trade-off, time-accuracy trade-off, and the sketch construction time. For instance, with similar estimation accuracy (F-1 score), GB-KMV is over 100 times faster than LSH-E on some real-life dataset.
Zhang, C, Zhang, Y, Zhang, W, Qin, L & Yang, J 2019, 'Efficient maximal spatial clique enumeration', Proceedings - International Conference on Data Engineering, pp. 878-889.
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© 2019 IEEE. Maximal clique enumeration is a fundamental problem in graph database. In this paper, we investigate this problem in the context of spatial database. Given a set P of spatial objects in a 2-dimensional space (e.g., geo-locations of users or point of interests) and a distance threshold r, we can come up with a spatial neighbourhood graph Pr by connecting every pair of objects (vertices) in P within distance r. Given a clique S of Pr, namely a spatial clique, it is immediate that any pairwise distance among objects in S is bounded by r. As the maximal pairwise distance has been widely used to capture the spatial cohesiveness of a group of objects, the maximal spatial clique enumeration technique can identify groups of spatially close objects in a variety of location-based-service (LBS) applications. In addition, we show that the maximal spatial clique enumeration can also be used to identify maximal clique pattern instances in the co-location pattern mining applications. Given the existing techniques for maximal clique enumeration, which can be immediately applied on the spatial neighbourhood graph Pr, two questions naturally arise for the enumeration of maximal spatial cliques: (1) the maximal clique enumeration on general graph is NP hard, can we have a polynomial time solution on the spatial neighbourhood graph? and (2) can we exploit the geometric property of the spatial clique to speed up the computation? In this paper, we give a negative answer to the first question by an example where the number of maximal spatial cliques is exponential to the number of the objects. While the answer to the second question is rather positive: we indeed develop two pruning techniques based on geometric properties of the maximal spatial clique to significantly enhance the computing efficiency. Extensive experiments on real-life geolocation data demonstrate the superior performance of proposed methods compared with two baseline algorithms.
Zhang, W, Zhang, F, Zhang, Y & Qin, L 2019, 'Cohesive subgraphs with hierarchical decomposition on big graphs', Database Systems for Advanced Applications DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA Chiang Mai, Thailand, April 22–25, 2019 Proceedings, International Conference on Database Systems for Advanced Applications, Springer, Thailand, pp. 587-589.
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Zhou, W, Zomaya, A, Fox, G, Martino, BD, Yang, L, Dong, M, Ranjan, R, Cafaro, M & Wang, W 2019, 'Message from the ISPA 2018 Chairs', Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, pp. XXII-XXIII.
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Zurn, X, Broekhuijsen, M, van Gennip, D, Bakker, S, Zijlema, A & van den Hoven, E 2019, 'Stimulating Photo Curation on Smartphones', PROCEEDINGS OF THE 2019 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL (CHIIR'19), 4th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), ASSOC COMPUTING MACHINERY, Univ Strathclyde, Glasgow, SCOTLAND, pp. 255-259.
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