Abdulkareem, SA, Augustijn, E-W, Filatova, T, Musial, K & Mustafa, YT 2020, 'Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.', PLoS One, vol. 15, no. 1, pp. e0226483-e0226483.
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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.
Abu ul Fazal, M, Ferguson, S & Johnston, A 2020, 'Investigating efficient speech-based information communication: a comparison between the high-rate and the concurrent playback designs', MULTIMEDIA SYSTEMS, vol. 26, no. 5, pp. 621-630.
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Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 2020, 'Intra-Variable Handwriting Inspection Reinforced With Idiosyncrasy Analysis', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 3567-3579.
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Al-Hadhrami, Y & Hussain, FK 2020, 'Real time dataset generation framework for intrusion detection systems in IoT', Future Generation Computer Systems, vol. 108, pp. 414-423.
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© 2020 The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.
Altaee, A, Khlaifat, N, Zhou, J, Huang, Y & Braytee, A 2020, 'Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia', Energies, vol. 13.
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The performance of a wind turbine is profoundly affected by wind conditions. Small wind turbines usually achieve the demand for electricity in rural areas. The shape of the blade greatly influences the performance of the wind turbine. The present study aims to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. ANSYS Fluent was used to investigate the aerodynamic performance of the 20 KW HAWT. The effects of four Reynolds Averaged Navier Stokes (RANS) turbulence models on predicting the flow over the wind turbine under separation condition were examined. Transition SST model had the best agreement with NREL CER data, which was used to investigate the mechanical output at different rotational speeds and variable pitch angles. Then the aerodynamic shape of the rotor of the wind turbine was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. HARP_Opt was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pitch angle. Pitch angle remained at 0ᵒ while the rising wind speed improved rotor speed to 148.4482 rpm at rated speed. This optimization improved the AEP rate by 9.068% when compared to the original NREL design.
Alzoubi, YI & Gill, AQ 2020, 'An Empirical Investigation of Geographically Distributed Agile Development: The Agile Enterprise Architecture is a Communication Enabler', IEEE ACCESS, vol. 8, pp. 80269-80289.
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Anshar, M & Williams, M-A 2020, 'Simplified Pain Matrix Method for Artificial Pain Activation Embedded into Robot Framework', INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS.
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Awan, I, Younas, M & Hussain, F 2020, 'Emerging challenges and frontiers in cloud computing', Concurrency Computation, vol. 32, no. 1.
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Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2020, 'Efficiency measurement of cloud service providers using network data envelopment analysis', IEEE Transactions on Cloud Computing.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
Bai, L, Yao, L, Wang, X, Kanhere, SS, Guo, B & Yu, Z 2020, 'Adversarial Multi-view Networks for Activity Recognition', Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4, no. 2.
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© 2020 ACM. Human activity recognition (HAR) plays an irreplaceable role in various applications and has been a prosperous research topic for years. Recent studies show significant progress in feature extraction (i.e., data representation) using deep learning techniques. However, they face significant challenges in capturing multi-modal spatial-temporal patterns from the sensory data, and they commonly overlook the variants between subjects. We propose a Discriminative Adversarial MUlti-view Network (DAMUN) to address the above issues in sensor-based HAR. We first design a multi-view feature extractor to obtain representations of sensory data streams from temporal, spatial, and spatio-temporal views using convolutional networks. Then, we fuse the multi-view representations into a robust joint representation through a trainable Hadamard fusion module, and finally employ a Siamese adversarial network architecture to decrease the variants between the representations of different subjects. We have conducted extensive experiments under an iterative left-one-subject-out setting on three real-world datasets and demonstrated both the effectiveness and robustness of our approach.
Beydoun, G, Hoffmann, A, Valencia-García, R, Shen, J & Gill, A 2020, 'Towards an assessment framework of reuse: a knowledge level analysis approach', Complex and Intelligent Systems.
Bharill, N, Tiwari, A, Malviya, A, Patel, OP, Gupta, A, Puthal, D, Saxena, A & Prasad, M 2020, 'Fuzzy knowledge based performance analysis on big data', NEUROCOMPUTING, vol. 389, pp. 218-228.
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Bommes, D, Pietroni, N & Hu, R 2020, 'Foreword to the Special Section on Shape Modeling International 2020', COMPUTERS & GRAPHICS-UK, vol. 90, pp. A4-A6.
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Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting Spreading Processes in Multilayer Networks: A Systematic Review', IEEE ACCESS, vol. 8, pp. 10316-10341.
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Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting Spreading Processes in Multilayer Networks: A Systematic Review', IEEE Access, vol. 8, pp. 10316-10341.
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C. A. Tirado Cortes, H. Chen, D. Sturnieks, J. Garcia Marin, S. Lord & C. Lin 2020, '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, Z, Ding, W, Wang, YK, Hussain, FK, Al-Jumaily, A & Lin, CT 2020, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing, vol. 389, pp. 198-206.
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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, C-T, Lai, K-L, Ko, L-W, King, J-T, Liao, K-K, Fuh, J-L & Wang, S-J 2020, 'Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 1, pp. 14-27.
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Chen, H, Fang, Y, Zhang, Y, Zhang, W & Wang, L 2020, 'ESPM: Efficient Spatial Pattern Matching', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, pp. 1227-1233.
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© 1989-2012 IEEE. With recent advances in information technologies such as global position system and mobile internet, a huge volume of spatio-textual objects have been generated from location-based services, which enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture the user's intention. It has been demonstrated to be fundamental and useful for many real applications. Despite its usefulness, the SPM problem is computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. We experimentally evaluate the performance of ESPM on real large datasets. Our results show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost.
Chen, K, Yao, L, Zhang, D, Wang, X, Chang, X & Nie, F 2020, '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.
Cheng, C, Cao, Z & Xiao, F 2020, 'A generalized belief interval-valued soft set with applications in decision making', SOFT COMPUTING, vol. 24, no. 13, pp. 9339-9350.
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Cheng, EJ, Prasad, M, Yang, J, Khanna, P, Chen, BH, Tao, X, Young, KY & Lin, CT 2020, 'A fast fused part-based model with new deep feature for pedestrian detection and security monitoring', Measurement: Journal of the International Measurement Confederation, vol. 151.
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© 2019 Elsevier Ltd In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection.
Chiang, YK, Oberst, S, Melnikov, A, Quan, L, Marburg, S, Alù, A & Powell, D 2020, 'Reconfigurable acoustic metagrating for high efficiency anomalous reflections', Physcal Review Applied, vol. 13, pp. 064067-064067.
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Recent study revealed that the scattering behaviors of bianisotropic scatterers can be controlled by an additional degree of freedom, represented as Willis coupling, which can be endowed with asymmetric wave scattering to form an acoustic metagrating for wavefront manipulation. Here, we introduce a flexible acoustic metagrating, formed by periodic arrays of properly design Willis scatterers, for anomalous reflection with nearly unitary efficiency and significantly less necessity of fine discretization. Numerical
approaches to predict the wave steering efficiency of the proposed acoustic metagratings with infinite and finite length are developed, which are utilized to demonstrate the strength and flexible features of the metagratings. Results reveal that the proposed acoustic metagrating can reroute incident wave into desired direction at a large angle with nearly unitary efficiency in reflection. The numerical predictions also show that the proposed designs offer a high efficient tunable platform in controlling the steering angles and operating frequencies. To practically realize the ability of extreme angle steering and tunable characteristics of the metagratings, designed structures are fabricated and examined experimentally. The acoustic wave is successfully rerouted to the targeted reflection angles by the finite metagrating. The flexibility regarding different steering angles and operating frequencies of the proposed metagratings are also demonstrated experimentally.
Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2020, '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 2020, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', Information Processing and Management, vol. 57, no. 2.
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© 2019 Elsevier Ltd 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.
Dimuro, GP, Lucca, G, Bedregal, B, Mesiar, R, Sanz, JA, Lin, CT & Bustince, H 2020, 'Generalized CF1F2-integrals: From Choquet-like aggregation to ordered directionally monotone functions', Fuzzy Sets and Systems, vol. 378, pp. 44-67.
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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 & Pedrycz, W 2020, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce.', IEEE Transactions on Cybernetics.
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Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.
Ding, W, Lin, CT, Liew, AWC, Triguero, I & Luo, W 2020, 'Current trends of granular data mining for biomedical data analysis', Information Sciences, vol. 510, pp. 341-343.
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Ding, W, Pedrycz, W, Triguero, I, Cao, Z & Lin, CT 2020, 'Multigranulation Super-Trust Model for Attribute Reduction', IEEE Transactions on Fuzzy Systems.
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IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.
Ding, W, Yen, GG, Cai, X & Cao, Z 2020, 'Foreword: Evolutionary data mining for big data', Swarm and Evolutionary Computation, vol. 57.
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Do, TTN, Wang, YK & Lin, CT 2020, 'Increase in brain effective connectivity in multitasking but not in a high-fatigue state', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.
Dong, M, Yao, L, Wang, X, Benatallah, B, Huang, C & Ning, X 2020, 'Opinion fraud detection via neural autoencoder decision forest', PATTERN RECOGNITION LETTERS, vol. 132, pp. 21-29.
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Downie, AS, Hancock, M, Shaheed, CA, McLachlan, AJ, Kocaballi, AB, Williams, CM, Michaleff, ZA & Maher, CG 2020, 'An Electronic Clinical Decision Support System for the Management of Low Back Pain in Community Pharmacy: Development and Mixed Methods Feasibility Study', JMIR MEDICAL INFORMATICS, vol. 8, no. 5.
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Du, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2020, '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.
Fachrunnisa, O & Hussain, FK 2020, 'A methodology for creating sustainable communities based on dynamic factors in virtual environments', International Journal of Electronic Business, vol. 15, no. 2, pp. 133-159.
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Copyright © 2020 Inderscience Enterprises Ltd. A virtual community is one of communities that exist in an internet economy; however, little research has been conducted on how to make it sustainable. We propose a methodology for creating sustainable virtual communities which depends on the community’s respond to the dynamic factors in its environment such as number of members, shared contents and interaction rules. The methodology proposes the use of iterative negotiation and a panel of expert agents to assess the quality of service (QoS) delivered. This QoS assessment is based on an interaction agreement between the community members and expert agent as the administrator’s representative. The administrators use this QoS assessment to determine whether an individual’s membership will be renewed or terminated after a certain period of time. We present a metric to measure the sustainability index and demonstrate the validity of the methodology by engineering a prototype setup and running simulations under various operational conditions.
Fan, H, Zhu, L, Yang, Y & Wu, F 2020, 'Recurrent Attention Network with Reinforced Generator for Visual Dialog', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3, pp. 1-16.
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Fang, L, Li, Y, Yun, X, Wen, Z, Ji, S, Meng, W, Cao, Z & Tanveer, M 2020, 'THP: A Novel Authentication Scheme to Prevent Multiple Attacks in SDN-Based IoT Network', IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5745-5759.
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© 2014 IEEE. SDN has provided significant convenience for network providers and operators in cloud computing. Such a great advantage is extending to the Internet of Things network. However, it also increases the risk if the security of an SDN network is compromised. For example, if the network operator's permission is illegally obtained by a hacker, he/she can control the entry of the SDN network. Therefore, an effective authentication scheme is needed to fit various application scenarios with high-security requirements. In this article, we design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time. We examined THP in the perspectives of both security and usability, with a total number of 694 participants in 63 days. Our evaluation shows that THP can provide better performance than the existing schemes in terms of security and usability.
Fang, X, Sheng, QZ, Wang, X, Zhang, W, NGU, AHH & Yang, J 2020, 'From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth', ACM Transactions on Intelligent Systems and Technology.
Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'A survey of community search over big graphs', The VLDB Journal, vol. 29, no. 1, pp. 353-392.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.
Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'Correction: A survey of community search over big graphs (The VLDB Journal, (2019), 10.1007/s00778-019-00556-x)', VLDB Journal.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In the original article, the Table 1 was published with incorrect figures. The correct Table 1 is given below:(Table presented).
Fang, Z, Lu, J, Liu, F, Xuan, J & Zhang, G 2020, 'Open Set Domain Adaptation: Theoretical Bound and Algorithm.', IEEE transactions on neural networks and learning systems, vol. PP.
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The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain--the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this article, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely, open set difference, which reflects the risk of the target classifier on unknown classes. Furthermore, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with open difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark data sets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.
Ferrari, A, Spoletini, P, Bano, M & Zowghi, D 2020, 'SaPeerandReverseSaPeer: teaching requirements elicitation interviews with role-playing and role reversal', REQUIREMENTS ENGINEERING.
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Fraietta, A, Bown, O, Ferguson, S, Gillespie, S & Bray, L 2020, 'Rapid composition for networked devices: Happybrackets', Computer Music Journal, vol. 43, no. 2-3, pp. 89-108.
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© 2020 Massachusetts Institute of Technology. This article introduces an open-source Java-based programming environment for creative coding ofagglomerative systems using Internet-of-Things (IoT) technologies. Our software originally focused on digital signalprocessing of audio—including synthesis, sampling, granular sample playback, and a suite of basic effects—butcomposers now use it to interface with sensors and peripherals through general-purpose input/output and externalnetworked systems. This article examines and addresses the strategies required to integrate novel embedded musicalinterfaces and creative coding paradigms through an IoT infrastructure. These include: The use of advanced toolingfeatures of a professional integrated development environment as a composition or performance interface rather thanjust as a compiler; techniques to create media works using features such as autodetection of sensors; seamless andserverless communication among devices on the network; and uploading, updating, and running of new compositionsto the device without interruption.Furthermore, we examined the difficulties many novice programmers experience when learning to write code,and we developed strategies to address these difficulties without restricting the potential available in the codingenvironment. We also examined and developed methods to monitor and debug devices over the network, allowingartists and programmers to set and retrieve current variable values to or from these devices during the performanceand composition stages. Finally, we describe three types of art work that demonstrate how the software, calledHappyBrackets, is being used in live-coding and dance performances, in interactive sound installations, and as anadvanced composition and performance tool for multimedia works.
Gong, S, Oberst, S & Wang, X 2020, 'An experimentally validated rubber shear spring model for vibrating flip-flow screens', Mechanical Systems and Signal Processing, vol. 139.
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© 2020 Elsevier Ltd Vibrating flip-flow screens (VFFS) provide an effective solution for screening highly moist and fine-grained minerals, and the dynamic response of the main and the floating screen frames largely accounts for a VFFS's screening performance and its processing capacity. An accurate dynamic model of the rubber shear springs inserted between the frames of the VFFS is critical for its dynamic analysis but has rarely been studied in detail. In this paper, a variance-based global sensitivity analysis is applied to actually illustrate that the rubber shear spring is the most important component for the dynamics of VFFS. Then a nonlinear rubber shear spring model is proposed to predict its amplitude and frequency dependency, which is described by a friction model and a fractional derivative viscoelastic model, respectively, and the elasticity is predicted by a nonlinear spring. The reasonability of the proposed model is verified by experimental cyclic tests of the rubber shear spring. Comparisons between the newly proposed model and other classic models, including the Generalized Maxwell model, adopted for the dynamic analysis of the VFFS are carried out, and experimental tests of an industrial VFFS's dynamic response show that dynamics of the VFFS can be better described using the proposed model than the existing models. Furthermore, the method of the global sensitivity analysis is also applied to the newly VFFS dynamic model to calculate the sensitivities of model outputs caused by the input parameters. The results reveal that the dynamic response of an operating VFFS is most sensitive to changes in the stiffness of the rubber shear spring, followed by the mass of the floating screen frames.
Gupta, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2020, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13.
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Gupta, A, Agrawal, RK, Kirar, JS, Kaur, B, Ding, W, Lin, CT, Andreu-Perez, J & Prasad, M 2020, 'A hierarchical meta-model for multi-class mental task based brain-computer interfaces', Neurocomputing, vol. 389, pp. 207-217.
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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.
Han, Y, Deng, Y, Cao, Z & Lin, CT 2020, '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, Z, Cook, K, Canning, J, Chen, HT & Martelli, C 2020, '3-D printed smart orthotic insoles: Monitoring a person's gait step by step', IEEE Sensors Letters, vol. 4, no. 1.
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© 2017 IEEE. This article reports a 3-D printing intelligent insole gait monitoring system based on an embedded fiber Bragg grating (FBG). The smart insole combines 3-D printing technology and FBG sensors providing high sensitivity and end-point low cost. Results using pressure points measured by four FBGs are sufficient to differentiate foot loads and gait types.
Hesam-Shariati, N, Newton-John, T, Singh, AK, Tirado Cortes, CA, Do, T-TN, Craig, A, Middleton, JW, Jensen, MP, Trost, Z, Lin, C-T & Gustin, SM 2020, 'Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines.', JMIR Res Protoc, vol. 9, no. 9, p. e20979.
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BACKGROUND: Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. OBJECTIVE: The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury. METHODS: We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symp...
Hua, J, Wang, G, Huang, ML, Hua, S & Yang, S 2020, 'A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis', International Journal of Environmental Research and Public Health, vol. 17, no. 11, pp. 1-16.
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Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome
Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant
virus data research. In this work, we conduct a proposed approach that is non-medical/clinical,
generate graphs from five features of the SARS outbreak data in five countries and regions, and
offer insights from a visual analysis perspective. The results show that prevention measures such as
quarantine are the most common control policies used, and areas with strict measures did have
fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data
conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a
useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis
perfective.
Hua, W, Sui, Y, Wan, Y, Liu, G & Xu, G 2020, 'FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks', IEEE Transactions on Reliability, pp. 1-15.
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Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2020, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters, vol. 130, pp. 46-53.
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Huang, ML, Lu, L, Ren, X, Cui, C & Luo, Y 2020, 'Tensor mutual information and its application', Concurrency and Computation: Practice and Experience, vol. 2020, pp. 1-10.
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Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual‐information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low‐dimensional subspace.
Huang, Y, Zhu, F, Porter, AL, Zhang, Y, Zhu, D & Guo, Y 2020, 'Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective', IEEE Transactions on Engineering Management.
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IEEE Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.
Hussain, T, Muhammad, K, Ullah, A, Cao, Z, Baik, SW & de Albuquerque, VHC 2020, 'Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 16, no. 1, pp. 77-86.
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Islam, MR, Lu, H, Hossain, J, Islam, MR & Li, L 2020, 'Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation', IEEE SYSTEMS JOURNAL, vol. 14, no. 3, pp. 3676-3686.
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Islam, MR, Lu, H, Islam, MR, Hossain, MJ & Li, L 2020, 'An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected with Distributed Energy Sources and Electric Vehicles', IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4552-4562.
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© 1972-2012 IEEE. The growing penetration of distributed energy sources (DES), such as photovoltaic (PV) solar power, battery energy systems and electric vehicles (EVs) into low voltage distribution networks is creating serious challenges for distribution network operators. Uncertain nature of these DES and EV charging is a key factor to cause unbalance, which degrade network performance in terms of energy loss, voltage unbalance, and voltage profile of the distribution network, etc. Some methods were proposed to mitigate such negative impact of these uncertain DES and EV charging from both centralized and decentralized approaches by controlling charging or discharging power of EVs. However, these methods involve all active EVs to participate in coordination and this causes significant inconvenience to EV owners along with requirements of complex communication infrastructure and huge data processing overhead. This article proposes an Internet of Things -based centralized control strategy to coordinate EV and DES distribution by using the differential evolution (DE) optimization algorithm. The obtained results show that the proposed control strategy can improve network performance (voltage imbalance, neutral current, energy loss, and node voltage) significantly. In addition, the control strategy is less demanding on communication infrastructure and convenient for EV owners as well as having a lighter data processing overhead.
Jiang, P, Li, R, Lu, H & Zhang, X 2020, '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.
Jin, D, Zhang, B, Song, Y, He, D, Feng, Z, Chen, S, Li, W & Musial, K 2020, 'ModMRF: A modularity-based Markov Random Field method for community detection', Neurocomputing, vol. 405, pp. 218-228.
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© 2020 Elsevier B.V. Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability. To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.
Khan, HU, Aruya, JA & Gill, A 2020, '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.
King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2020, '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.
Kocaballi, AB, Ijaz, K, Laranjo, L, Quiroz, JC, Rezazadegan, D, Tong, HL, Willcock, S, Berkovsky, S & Coiera, E 2020, 'Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners', Journal of the American Medical Informatics Association.
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Abstract
Objective
The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners.
Materials and Methods
Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed.
Results
Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care.
...
Kocaballi, AB, Quiroz, JC, Rezazadegan, D, Berkovsky, S, Magrabi, F, Coiera, E & Laranjo, L 2020, 'Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures', JOURNAL OF MEDICAL INTERNET RESEARCH, vol. 22, no. 2.
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Kong, L, Qu, W, Yu, J, Zuo, H, Chen, G, Xiong, F, Pan, S, Lin, S & Qiu, M 2020, 'Distributed Feature Selection for Big Data Using Fuzzy Rough Sets', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 5, pp. 846-857.
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Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 2020, 'Automatic Design of Cable-Tensioned Glass Shells', Computer Graphics Forum.
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© 2019 The Authors Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. We propose an optimization algorithm for the design of post-tensioned architectural shell structures, composed of triangular glass panels, in which glass has a load-bearing function. Due to its brittle nature, glass can fail when it is subject to tensile forces. Hence, we enrich the structure with a cable net, which is specifically designed to post-tension the shell, relieving the underlying glass structure from tension. We automatically derive an optimized cable layout, together with the appropriate pre-load of each cable. The method is driven by a physically based static analysis of the shell subject to its service load. We assess our approach by applying non-linear finite element analysis to several real-scale application scenarios. Such a method of cable tensioning produces glass shells that are optimized from the material usage viewpoint since they exploit the high compression strength of glass. As a result, they are lightweight and robust. Both aesthetic and static qualities are improved with respect to grid shell competitors.
Li, DL, Prasad, M, Liu, CL & Lin, CT 2020, 'Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning', IEEE Transactions on Intelligent Transportation Systems.
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IEEE Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.
Li, F, Cao, Z, Xu, P, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D & Zhang, Y 2020, 'Reconfiguration of Brain Network between Rest-ing-state and P300 task', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of our study help us to under-stand the updates in functional connectivity from resting-state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.
Li, J, Pan, Y, Sui, Y & Tsang, IW 2020, 'Secure Metric Learning via Differential Pairwise Privacy', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 3640-3652.
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Li, Q, Cao, Z, Ding, W & Li, Q 2020, 'A multi-objective adaptive evolutionary algorithm to extract communities in networks', SWARM AND EVOLUTIONARY COMPUTATION, vol. 52.
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Li, Q, Cao, Z, Tanveer, M, Pandey, HM & Wang, C 2020, 'A Semantic Collaboration Method Based on Uniform Knowledge Graph', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 5, pp. 4473-4484.
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Li, Q, Zhong, J, Cao, Z & Li, X 2020, '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, X, Cao, Y, Li, Q, Shang, Y, Li, Y, Liu, Y & Xu, G 2020, 'RLINK: Deep reinforcement learning for user identity linkage', World Wide Web.
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© 2020, The Author(s). User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.
Lin, A, Lu, J, Xuan, J, Zhu, F & Zhang, G 2020, 'A Causal Dirichlet Mixture Model for Causal Inference from Observational Data', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 3.
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© 2020 ACM. Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today's data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects - average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.
Lin, C-T, King, J-T, Chuang, C-H, Ding, W, Chuang, W-Y, Liao, L-D & Wang, Y-K 2020, 'Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements.', International Journal of Neural Systems, vol. 30, no. 1.
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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.
Lin, C-T, Yu, Y-H, King, J-T, Liu, C-H & Liao, L-D 2020, 'Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements', IEEE SENSORS JOURNAL, vol. 20, no. 7, pp. 3831-3837.
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Liu, A, Lu, J & Zhang, G 2020, 'Concept Drift Detection via Equal Intensity k-Means Space Partitioning.', IEEE transactions on cybernetics.
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The data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such a distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on the grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind spots. There is a need to improve the drift detection accuracy for the histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.
Liu, A, Lu, J & Zhang, G 2020, 'Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation.', IEEE transactions on neural networks and learning systems, vol. PP.
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Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance-weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.
Liu, F, Zhang, G & Lu, J 2020, 'Heterogeneous Domain Adaptation: An Unsupervised Approach.', IEEE transactions on neural networks and learning systems.
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Domain adaptation leverages the knowledge in one domain--the source domain--to improve learning efficiency in another domain--the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed but only in situations where the target domain contains at least a few labeled instances. In contrast, heterogeneous domain adaptation with an unlabeled target domain has not been well-studied. To contribute to the research in this emerging field, this article presents: 1) an unsupervised knowledge transfer theorem that guarantees the correctness of transferring knowledge and 2) a principal angle-based metric to measure the distance between two pairs of domains: one pair comprises the original source and target domains and the other pair comprises two homogeneous representations of two domains. The theorem and the metric have been implemented in an innovative transfer model, called a Grassmann-linear monotonic maps-geodesic flow kernel (GLG), which is specifically designed for heterogeneous unsupervised domain adaptation (HeUDA). The linear monotonic maps (LMMs) meet the conditions of the theorem and are used to construct homogeneous representations of the heterogeneous domains. The metric shows the extent to which the homogeneous representations have preserved the information in the original source and target domains. By minimizing the proposed metric, the GLG model learns the homogeneous representations of heterogeneous domains and transfers knowledge through these learned representations via a geodesic flow kernel (GFK). To evaluate the model, five public data sets were reorganized into ten HeUDA tasks across three applications: cancer detection, the credit assessment, and text classification. The experiments demonstrate that the proposed model delivers superior performance over the existing baselines.
Liu, J, Hou, J, Huang, X, Xiang, Y & Zhu, T 2020, 'Secure and efficient sharing of authenticated energy usage data with privacy preservation', Computers and Security, vol. 92.
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© 2020 Elsevier Ltd As a technological innovation, smart grid improves electricity services in terms of substantiality, economics, efficiency, and reliability. This owes to the bi-directional communication property which not only enables the fine-grained energy usage data to be available for different entities but also facilitates the automated grid management. However, sharing such energy usage data with other parties could potentially cause the leakage of customers’ sensitive information. In addition, the data are vulnerable to be tampered with by any internal or external attacker such that the value of data could be destroyed. Therefore, it is crucial to preserve customers’ privacy and provide authenticity and verifiability while sharing energy usage data with other parties. In this paper, we propose a bilinear-map accumulator based redactable signature scheme (RSS-BMA) which allows customers to safeguard their privacy while guaranteeing the verifiability of shared data. Furthermore, the batch-data-block verification property of our design enhances not only efficiency but also security by prohibiting additional redaction to a batch of data blocks. We analyze the efficiency and security of our proposed scheme extensively, and the results indicate our construction is more practical than others.
Liu, Q, Huang, H, Xuan, J, Zhang, G, Gao, Y & Lu, J 2020, 'A Fuzzy Word Similarity Measure for Selecting Top-k Similar Words in Query Expansion', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Liu, W, Chang, X, Chen, L, Phung, D, Zhang, X, Yang, Y & Hauptmann, AG 2020, 'Pair-based uncertainty and diversity promoting early active learning for person re-identification', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 2.
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© 2020 Association for Computing Machinery. The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
Liu, X, Song, W, Musial, K, Zhao, X, Zuo, W & Yang, B 2020, 'Semi-supervised stochastic blockmodel for structure analysis of signed networks', Knowledge-Based Systems, vol. 195.
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© 2020 Elsevier B.V. Finding hidden structural patterns is a critical problem for all types of networks, including signed networks. Among all of the methods for structural analysis of complex network, stochastic blockmodel (SBM) is an important research tool because it is flexible and can generate networks with many different types of structures. However, most existing SBM learning methods for signed networks are unsupervised, leading to poor performance in terms of finding hidden structural patterns, especially when handling noisy and sparse networks. Learning SBM in a semi-supervised way is a promising avenue for overcoming the above difficulty. In this type of model, a small number of labelled nodes and a large number of unlabelled nodes, coupled with their network structures, are simultaneously used to train SBM. We propose a novel semi-supervised signed stochastic blockmodel and its learning algorithm based on variational Bayesian inference, with the goal of discovering both assortative (the nodes connect more densely in same clusters than that in different clusters) and disassortative (the nodes link more sparsely in same clusters than that in different clusters) structures from signed networks. The proposed model is validated through a number of experiments wherein it compared with the state-of-the-art methods using both synthetic and real-world data. The carefully designed tests, allowing to account for different scenarios, show our method outperforms other approaches existing in this space. It is especially relevant in the case of noisy and sparse networks as they constitute the majority of the real-world networks.
Liu, Z, Cao, J, Tan, Y, Xiao, Q & Prasad, M 2020, 'Planning Above the API Clouds Before Flying Above the Clouds: A Real-Time Personalized Air Travel Planning Approach', International Journal of Parallel Programming, vol. 48, no. 1, pp. 137-156.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The rapid growth of the airline industry has resulted in the availability of a large number of flights, however this can also create a paralyzing problem. Flight information on all airlines across the world can be obtained via the Internet. Today, passengers trend to be interested in user personalized service. How to effectively find a passenger’s most preferred air travel plan, which might include multiple transfers from millions of possible choices with certain constraints, such as time and price, is a critical challenge. This paper presents an efficient air travel planning approach, which can find a number of air travel plans by invoking the APIs offered by airline companies. At the same time, these plans also best match the customer’s preference based on an analysis of historical orders. An algorithm to extract user preference features is introduced and heuristic rules to speed up the K path search process under constraints are presented. The experiment results show that the proposed model finds optimal air travel plans efficiently on a real-world dataset.
Liu, Z, Xiao, F, Lin, C-T, Kang, BH & Cao, Z 2020, 'A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-12.
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Livesu, M, Pietroni, N, Puppo, E, Sheffer, A & Cignoni, P 2020, 'LoopyCuts: Practical Feature-Preserving Block Decomposition for Strongly Hex-Dominant Meshing', ACM Transactions on Graphics, vol. 39, no. 4.
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© 2020 ACM. We present a new fully automatic block-decomposition algorithm for feature-preserving, strongly hex-dominant meshing, that yields results with a drastically larger percentage of hex elements than prior art. Our method is guided by a surface field that conforms to both surface curvature and feature lines, and exploits an ordered set of cutting loops that evenly cover the input surface, defining an arrangement of loops suitable for hex-element generation. We decompose the solid into coarse blocks by iteratively cutting it with surfaces bounded by these loops. The vast majority of the obtained blocks can be turned into hexahedral cells via simple midpoint subdivision. Our method produces pure hexahedral meshes in approximately 80% of the cases, and hex-dominant meshes with less than 2% non-hexahedral cells in the remaining cases. We demonstrate the robustness of our method on 70+ models, including CAD objects with features of various complexity, organic and synthetic shapes, and provide extensive comparisons to prior art, demonstrating its superiority.
Lu, L, Ren, X, Cui, C, Luo, Y & Huang, M 2020, 'Tensor mutual information and its applications', Concurrency Computation.
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© 2020 John Wiley & Sons, Ltd. Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual-information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low-dimensional subspace.
Majbouri Yazdi, K, Majbouri Yazdi, A, Khodayi, S, Hou, J, Zhou, W, Saedy, S & Rostami, M 2020, 'Prediction optimization of diffusion paths in social networks using integration of ant colony and densest subgraph algorithms', Journal of High Speed Networks, vol. 26, no. 2, pp. 141-153.
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© 2020 - IOS Press and the authors. All rights reserved. One of the most important challenges of social networks is to predict information diffusion paths. Studying and modeling the propagation routes is important in optimizing social network-based platforms. In this paper, a new method is proposed to increase the prediction accuracy of diffusion paths using the integration of the ant colony and densest subgraph algorithms. The proposed method consists of 3 steps; clustering nodes, creating propagation paths based on ant colony algorithm and predicting information diffusion on the created paths. The densest subgraph algorithm creates a subset of maximum independent nodes as clusters from the input graph. It also determines the centers of clusters. When clusters are identified, the final information diffusion paths are predicted using the ant colony algorithm in the network. After the implementation of the proposed method, 4 real social network datasets were used to evaluate the performance. The evaluation results of all methods showed a better outcome for our method.
Mansoor, A & Bano, M 2020, 'Decolonial pedagogy and English literary studies: problematics in a Pakistani context', Compare: a journal of comparative and international education.
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© 2019, © 2019 British Association for International and Comparative Education. We propose a decolonial pedagogy in the teaching of English Literature at the undergraduate level in postcolonial Pakistan. We argue that the English literary texts that are taught in conjunction with different supplementary materials retain a West-centric tilt that requires disbanding. Therefore, we administered carefully designed worksheets. These encouraged marginal thinking across the differential socio-historical canvases that outline the prescribed texts, online lectures, supplementary reading materials and the students’ situatedness. The students used these resources as tools for practising a decolonial hermeneutics. By directing these tools towards their own lived situation, they intervened within the Western means of knowledge production and undid their alterity and historical silencing. By analysing the students’ assignments as sites of decolonial articulation, we propose that decolonial pedagogy can be used to generate an intercultural intellectual solidarity that allows the formerly colonised to rethink themselves in a manner that no longer talks back to the Empire.
Melnikov, A, Maeder, M, Friedrich, N, Pozhanka, Y, Oberst, S, Wollmann, A, Scheffler, M, Powell, D & Marburg, S 2020, 'Acoustic metamaterial capsule for reduction of stage machinery noise', Journal of the Acoustical Society of America, vol. 147, pp. 1491-1491.
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Noise mitigation of stage machinery can be quite demanding and requires innovative solutions. In this work we propose an acoustic metamaterial capsule to reduce the noise emission of several stage machinery drive trains, while still allowing the ventilation required for cooling. The metamaterial capsule consists of c-shape meta atoms, which have a simple structure that facilitates manufacturing. We design, simulate, manufacture, and experimentally validate two different metamaterial capsules, which utilize an ultra-sparse and air-permeable reflective meta-grating. Both designs demonstrate transmission loss peaks that effectively suppress gear mesh noise or other narrow band noise sources. The ventilation by natural convection was numerically verified, and was shown to give adequate cooling, whereas a conventional sound capsule would lead to overheating. The noise spectra of three common stage machinery drive trains are numerically modelled, enabling us to design meta-gratings and determine their noise suppression performance. The results fulfill the stringent stage machinery noise limits, highlighting the benefit of using metamaterial capsule of simple c-shape structure.
Miner, AS, Laranjo, L & Kocaballi, AB 2020, 'Chatbots in the fight against the COVID-19 pandemic', npj Digital Medicine, vol. 3, no. 1.
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Ming, Y, Wu, D, Wang, YK, Shi, Y & Lin, CT 2020, 'EEG-Based Drowsiness Estimation for Driving Safety Using Deep Q-Learning', IEEE Transactions on Emerging Topics in Computational Intelligence.
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IEEE Fatigue is the most vital factor of road fatalities, and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to study the correlation between drowsiness and driving performance. This study is carried out by analyzing an electroencephalogram (EEG) dataset captured during a simulated endurance driving test. Driving safety research using EEG data represents an important brain-computer interface (BCI) paradigm from an application perspective. To formulate the drowsiness estimation problem as an optimization of a Q-learning task, we adapt the terminologies in the driving test to fit the reinforcement learning framework. Based on that, a deep Q-network (DQN) is tailored by referring to the latest DQN technologies. The designed network merits the characteristics of the EEG data and can generate actions to indirectly estimate drowsiness. The results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which confirms the feasibility and practicability of this new computation paradigm. By comparison, it also reveals that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can potentially be generalized to other BCI cases.
Naseem, U, Razzak, I, Musial, K & Imran, M 2020, 'Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis', Future Generation Computer Systems, vol. 113, pp. 58-69.
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© 2020 Elsevier B.V. Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present DICET, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that DICET considerably outperforms the state of the art in sentiment classification.
Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2020, 'Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications, vol. 32, no. 3, pp. 839-854.
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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.
Nguyen, QV, Miller, N, Arness, D, Huang, W, Huang, ML & Simoff, S 2020, 'Evaluation on interactive visualization data with scatterplots', Visual Informatics.
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Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2020, 'Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting', EXPERT SYSTEMS WITH APPLICATIONS, vol. 148.
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Nobakht, M, Sui, Y, Seneviratne, A & Hu, W 2020, 'PGFIT: Static permission analysis of health and fitness apps in IoT programming frameworks', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 152.
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Nosouhi, MR, Yu, S, Zhou, W, Grobler, M & Keshtiar, H 2020, 'Blockchain for secure location verification', JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, vol. 136, pp. 40-51.
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Oberst, S, Lai, JCS, Martin, R, Halkon, B, Saadatfar, M & Evans, TA 2020, 'Revisiting stigmergy in light of multi-functional, biogenic, termite structures as communication channel', Computational and Structural Biotechnology Journal, vol. 18, pp. 2522-2534.
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Termite mounds are fascinating because of their intriguing composition of nu- merous geometric shapes and materials. However, little is known about these structures, or of their functionalities. Most research has been on the basic com- position of mounds compared with surrounding soils. There has been some targeted research on the thermoregulation and ventilation of the mounds of a few species of fungi-growing termites, which has generated considerable inter- est from human architecture. Otherwise, research on termite mounds has been scattered, with little work on their explicit properties.
This review is focused on how termites design and build functional structures as nest, nursery and food storage; for thermoregulation and climatisation; as defence, shelter and refuge; as a foraging tool or building material; and for colony communication, either as in indirect communication (stigmergy) or as an information channel essential for direct communication through vibrations (biotremology).
Our analysis shows that systematic research is required to study the prop- erties of these structures such as porosity and material composition. High res- olution computer tomography in combination with nonlinear dynamics and methods from computational intelligence may provide breakthroughs in un- veiling the secrets of termite behaviour and their mounds. In particular, the ex- amination of dynamic and wave propagation properties of termite-built struc- tures in combination with a detailed signal analysis of termite activities is re- quired to better understand the interplay between termites and their nest as superorganism. How termite structures serve as defence in the form of disguis- ing acoustic and vibration signals from detection by predators, and what role local and global vibration synchronisation plays for building are open ques- tions that need to be addressed to provide insights into how termites utilise materials to thrive in a world of predators and competitors.
Pan, Y, Tsang, IW, Singh, AK, Lin, C-T & Sugiyama, M 2020, 'Stochastic Multichannel Ranking with Brain Dynamics Preferences', NEURAL COMPUTATION, vol. 32, no. 8, pp. 1499-1530.
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Phat, H, Phan, KT, Liu, B & Ross, R 2020, 'Throughput Analysis of Buffer-Aided Decode-and-Forward Wireless Relaying with RF Energy Harvesting', SENSORS, vol. 20, no. 4.
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Prakash, S, Joshi, S, Bhatia, T, Sharma, S, Samadhiya, D, Shah, RR, Kaiwartya, O & Prasad, M 2020, 'Characteristic of enterprise collaboration system and its implementation issues in business management', International Journal of Business Intelligence and Data Mining, vol. 16, no. 1, pp. 49-65.
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© 2020 Inderscience Enterprises Ltd. Collaboration is an extremely useful area for the most of the enterprise systems particularly within Web 2.0 and Enterprise 2.0. The collaboration provides help in enterprise collaboration system (ECS) to achieve the desired goal by unifying completed tasks of employees or people working on a similar or the same task. Thus, the collaboration systems have witnessed significant attention. The ECS provides consistent and off-the-shelf support to processes and managements within organisations. Management techniques of the ECS may be useful to a community which manages ECS systems for collaboration. In this context, this paper focuses on enterprise collaboration system and answers critical questions related to ECS including: 1) what does collaboration really means for an enterprise system; 2) how can the collaboration help to improve internal processes and management of the system; 3) how it is helpful to improve interactions with customers and partners?
Qiao, Y, Sun, X & Yu, N 2020, 'Local Equivalence of Multipartite Entanglement', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 38, no. 3, pp. 568-574.
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Qu, X, Yu, Y, Zhou, M, Lin, C-T & Wang, X 2020, 'Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach', APPLIED ENERGY, vol. 257.
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Quiroz, JC, Laranjo, L, Kocaballi, AB, Briatore, A, Berkovsky, S, Rezazadegan, D & Coiera, E 2020, 'Identifying relevant information in medical conversations to summarize a clinician-patient encounter', Health Informatics Journal.
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© The Author(s) 2020. To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions—such as digital scribes—must focus on identifying the 20% relevant information for automatically generating consultation summaries.
Razzak, I, Saris, RA, Blumenstein, M & Xu, G 2020, 'Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection.', Neural networks : the official journal of the International Neural Network Society, vol. 121, pp. 441-451.
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Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods.
Razzak, I, Zafar, K, Imran, M & Xu, G 2020, 'Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data', Future Generation Computer Systems, vol. 112, pp. 715-723.
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© 2020 Elsevier B.V. Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921, 0.7501, 0.6711, 0.6692, 0.4789, 0.6462, 0.6812, 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson's, Hepatitis, Breast Cancer, Blood Transfusion, Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets.
Razzak, MI, Imran, M & Xu, G 2020, 'Big data analytics for preventive medicine', Neural Computing and Applications, vol. 32, no. 9, pp. 4417-4451.
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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.
Ren, W, Hu, J, Zhu, T, Ren, Y & Choo, KKR 2020, 'A flexible method to defend against computationally resourceful miners in blockchain proof of work', Information Sciences, vol. 507, pp. 161-171.
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© 2019 Elsevier Inc. Blockchain is well known as a decentralized and distributed public digital ledger, and is currently used by most cryptocurrencies to record transactions. One of the fundamental differences between blockchain and traditional distributed systems is that blockchain's decentralization relies on consensus protocols, such as proof of work (PoW). However, computation systems, such as application specific integrated circuit (ASIC) machines, have recently emerged that are specifically designed for PoW computation and may compromise a decentralized system within a short amount of time. These computationally resourceful miners challenge the very nature of blockchain, with potentially serious consequences. Therefore, in this paper, we propose a general and flexible PoW method that enforces memory usage. Specifically, the proposed method blocks computationally resourceful miners and retains the previous design logic without requiring one to replace the original hash function. We also propose the notion of a memory intensive function (MIF) with a memory usage parameter k (kMIF). Our scheme comprises three algorithms that construct a kMIF Hash by invoking any available hash function which is not kMIF to protect against ASICs, and then thwarts the pre-computation of hash results over a nonce. We then design experiments to evaluate memory changes in these three algorithms, and the findings demonstrate that enforcing memory usage in a blockchain can be an effective defense against computationally resourceful miners.
Sarker, PC, Guo, Y, Lu, HY & Zhu, JG 2020, 'A generalized inverse Preisach dynamic hysteresis model of Fe-based amorphous magnetic materials', Journal of Magnetism and Magnetic Materials, vol. 514.
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© 2020 Elsevier B.V. Fe-based amorphous magnetic materials are attracting more and more attentions in the application of low and medium frequency transformers due to their favorable properties of low core loss and high saturation magnetic flux density. Accurate modelling of their static and dynamic characteristics is required for analysis and design optimization of low and medium frequency transformers. In particular, for numerical analysis using the vectorial magnetic potential, an inverse magnetic hysteresis model is needed to predict the magnetic field strength from the magnetic flux density. When the excitation varies with time, the magnetic hysteresis model must be able to predict the dynamic hysteresis characteristics. This paper presents a generalized inverse Preisach dynamic hysteresis model for dynamic characterization of Fe-based magnetic materials. This model incorporates the reversible magnetization and magnetization dependent hysteresis, as well as all core loss components, including the hysteresis, eddy current, and excess losses. The proposed model can predict accurately the magnetic field strength from the magnetic flux density and hence accurate core losses. The predicted results are verified by experimental measurements.
Shaham, S, Ding, M, Liu, B, Dang, S, Lin, Z & Li, J 2020, 'Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model', IEEE Transactions on Mobile Computing, pp. 1-1.
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Shaham, S, Ding, M, Liu, B, Dang, S, Lin, Z & Li, J 2020, 'Privacy Preserving Location Data Publishing: A Machine Learning Approach', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Shi, G, Ma, Z, Feng, J, Zhu, F, Bai, X & Gui, B 2020, 'The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms', PLoS ONE, vol. 15, no. 5.
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© 2020 Shi et al. As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver's willingness to receive, and receiver's capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.
Shi, Y, Hoang, DT, Duong, TQ, Poor, HV & Savkin, A 2020, 'PMU Placement Optimization for Efficient State Estimation in Smart Grid', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 38, no. 1, pp. 71-83.
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Shi, Y, Lin, CT, Chang, YC, Ding, W, Shi, Y & Yao, X 2020, 'Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment', IEEE Transactions on Emerging Topics in Computational Intelligence.
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IEEE Uncertainty and distributed nature inherently exist in big data environment. Distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm.
Shi, Y, Tuan, HD & Apkarian, P 2020, 'Parameterized bilinear matrix inequality techniques for Script capital H infinity gain-scheduling proportional integral derivative control design', INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, vol. 30, no. 10, pp. 3886-3905.
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Shu, Y, Li, Q, Liu, S & Xu, G 2020, 'Learning with privileged information for photo aesthetic assessment', NEUROCOMPUTING, vol. 404, pp. 304-316.
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Si, Y, Li, F, Duan, K, Tao, Q, Li, C, Cao, Z, Zhang, Y, Biswal, B, Li, P, Yao, D & Xu, P 2020, 'Predicting individual decision-making responses based on single-trial EEG.', NeuroImage, vol. 206, pp. 116333-116333.
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Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). We proposed an electroencephalogram (EEG)-based computational intelligence framework to predict individual responses. Specifically, the discriminative spatial network pattern (DSNP), a supervised learning approach, was applied to single-trial EEG data to extract the DSNP feature from the single-trial brain network. A linear discriminate analysis (LDA) trained on the DSNP features was then used to predict the individual response trial-by-trial. To verify the performance of the proposed DSNP, we recruited two independent subject groups, and recorded the EEGs using two types of EEG systems. The performances of the trial-by-trial predictors achieved an accuracy of 0.88 ± 0.09 for the first dataset, and 0.90 ± 0.10 for the second dataset. These trial-by-trial prediction performances suggested that individual responses could be predicted trial-by-trial by using the specific pattern of single-trial EEG networks, and our proposed method has the potential to establish the biologically inspired artificial intelligence decision system.
Singh, AK, Chen, H-T, Gramann, K & Lin, C-T 2020, 'Intraindividual Completion Time Modulates the Prediction Error Negativity in a Virtual 3-D Object Selection Task', IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, vol. 12, no. 2, pp. 354-360.
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Singh, AK, Wang, Y-K, King, J-T & Lin, C-T 2020, 'Extended Interaction with a BCI Video Game Changes Resting-State Brain Activity', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1.
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Song, Y, Lu, J, Lu, H & Zhang, G 2020, 'Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 3, pp. 544-557.
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Taccola, G, Gad, P, Culaclii, S, Ichiyama, RM, Liu, W & Edgerton, VR 2020, 'Using EMG to deliver lumbar dynamic electrical stimulation to facilitate cortico-spinal excitability.', Brain Stimulation, vol. 13, no. 1, pp. 20-34.
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BACKGROUND:Potentiation of synaptic activity in spinal networks is reflected in the magnitude of modulation of motor responses evoked by spinal and cortical input. After spinal cord injury, motor evoked responses can be facilitated by pairing cortical and peripheral nerve stimuli. OBJECTIVE:To facilitate synaptic potentiation of cortico-spinal input with epidural electrical stimulation, we designed a novel neuromodulation method called dynamic stimulation (DS), using patterns derived from hind limb EMG signal during stepping. METHODS:DS was applied dorsally to the lumbar enlargement through a high-density epidural array composed of independent platinum-based micro-electrodes. RESULTS:In fully anesthetized intact adult rats, at the interface array/spinal cord, the temporal and spatial features of DS neuromodulation affected the entire lumbosacral network, particularly the most rostral and caudal segments covered by the array. DS induced a transient (at least 1 min) increase in spinal cord excitability and, compared to tonic stimulation, generated a more robust potentiation of the motor output evoked by single pulses applied to the spinal cord. When sub-threshold pulses were selectively applied to a cortical motor area, EMG responses from the contralateral leg were facilitated by the delivery of DS to the lumbosacral cord. Finally, based on motor-evoked responses, DS was linked to a greater amplitude of motor output shortly after a calibrated spinal cord contusion. CONCLUSION:Compared to traditional tonic waveforms, DS amplifies both spinal and cortico-spinal input aimed at spinal networks, thus significantly increasing the potential and accelerating the rate of functional recovery after a severe spinal lesion.
Tanveer, M, Khanna, P, Prasad, M & Lin, CT 2020, 'Introduction to the special issue on computational intelligence for biomedical data and imaging', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 16, no. 1s.
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Tanveer, M, Richhariya, B, Khan, RU, Rashid, AH, Khanna, P, Prasad, M & Lin, CT 2020, 'Machine learning techniques for the diagnosis of alzheimer's disease: A review', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 16, no. 1s.
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© 2020 ACM. Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions.
Verhoeven, D, Musial, K, Palmer, S, Taylor, S, Abidi, S, Zemaityte, V & Simpson, L 2020, 'Controlling for openness in the male-dominated collaborative networks of the global film industry.', PLoS One, vol. 15, no. 6, pp. e0234460-e0234460.
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Studies of gender inequality in film industries have noted the persistence of male domination in creative roles (usually defined as director, producer, writer) and the slow pace of reform. Typical policy remedies are premised on aggregate counts of women as a proportion of overall industry participation. Network science offers an alternative way of identifying and proposing change mechanisms, as it puts emphasis on relationships instead of individuals. Preliminary work on applying network analysis to understand inequality in the film industry has been undertaken. However, in this study we offer a comprehensive approach that enables us to not only understand what inequality in the film industry looks like through the lens of network science but also how we can attempt to address this issue. We offer a data-driven simulation framework that investigates various what-if scenarios when it comes to network evolution. We then assess each of these scenarios with respect to its potential to address gender inequality in the film industry. As suggested by previous studies, inequality is exacerbated when industry networks are most closed. We review evidence from three different national film industries on network relationships in creative teams and identify a high proportion of men who only work with other men. In response to this observation, we test several mechanisms through which industry structures may generate higher levels of openness. Our results reveal that the most critical factor for improving network openness is not simply the statistical improvement of the number of women in a network, nor the removal of men who do not work with women. The most likely behavioural changes to a network will involve the production of connections between women and powerful men.
Waheed, W, Deng, G & Liu, B 2020, 'Discrete Laplacian Operator and Its Applications in Signal Processing', IEEE ACCESS, vol. 8, pp. 89692-89707.
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Wang, B, Li, T, Yan, Z, Zhang, G & Lu, J 2020, 'DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting', NEUROCOMPUTING, vol. 397, pp. 11-19.
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Wang, D, Zhang, X, Yu, D, Xu, G & Deng, S 2020, 'CAME: Content- and Context-Aware Music Embedding for Recommendation', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Wang, G, Lu, J, Choi, KS & Zhang, G 2020, 'A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data', IEEE Transactions on Cybernetics, vol. 50, no. 2, pp. 739-752.
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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, Lam, KM & Lu, J 2020, 'Output based transfer learning with least squares support vector machine and its application in bladder cancer prognosis', Neurocomputing, vol. 387, pp. 279-292.
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© 2019 Elsevier B.V. Two dilemmas frequently occur in many real-world clinical prognoses. First, the on-hand data cannot be put entirely into the existing prediction model, since the features from new data do not perfectly match those of the model. As a result, some unique features collected from the patients in the current domain of interest might be wasted. Second, the on-hand data is not sufficient enough to learn a new prediction model. To overcome these challenges, we propose an output-based transfer learning approach with least squares support vector machine (LS-SVM) to make the maximum use of the small dataset and guarantee an enhanced generalization capability. The proposed approach can learn a current domain of interest with limited samples effectively by leveraging the knowledge from the predicted outputs of the existing model in the source domain. Also, the extent of output knowledge transfer from the source domain to the current one can be automatically and rapidly determined using a proposed fast leave-one-out cross validation strategy. The proposed approach is applied to a real-world clinical dataset to predict 5-year overall and cancer-specific mortality of bladder cancer patients after radical cystectomy. The experimental results indicate that the proposed approach achieves better classification performances than the other comparative methods and has the potential to be implemented into the real-world context to deal with small data problems in cancer prediction and prognosis.
Wang, J, Li, H, Lu, H, Yang, H & Wang, C 2020, 'Integrating offline logistics and online system to recycle e-bicycle battery in China', Journal of Cleaner Production, vol. 247.
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© 2019 Elsevier Ltd E-bicycles are powered by batteries including lithium-ion, lead–acid, and others. The reuse of waste batteries shows promise for grid-scale storage. The New National Standard for e-bicycles is to be introduced in China, that might result in the country becoming the largest source of battery waste in the world. If the waste batteries are not recycled appropriately, it will cause significant heavy metal pollution, which will in turn, pose a serious threat to the ecological environment and human health. This paper discusses the current status of recycling of e-bicycle batteries in China and reviews the current recycling approaches. We developed a waste e-bicycle battery recycling system based on “Internet+” to solve the dilemma of recycling end-of-life batteries; this system has three subsystems: offline reverse logistics recovery system, online network recycling system, and traceability management system. In particular, the participation of consumers and government, reward-penalty mechanism, “Internet +” development, and other strategies are considered to improve recycling systems throughout life cycle of the products. The proposed recycling system can increase the waste battery recycling rate by 2.59% under the reward-penalty mechanism, and reduce carbon dioxide emissions by 58%, which is conducive to promoting sustainable development.
Wang, J, Niu, T, Lu, HY, Yang, W & Du, P 2020, 'A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 337-349.
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Wang, M, Zhu, T, Zhang, T, Zhang, J, Yu, S & Zhou, W 2020, 'Security and privacy in 6G networks: New areas and new challenges', Digital Communications and Networks.
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© 2020 Chongqing University of Posts and Telecommunications As more and more 5G networks are deployed, the limitations of 5G networks are not only discovered, but also promoting exploratory research on 6G networks as a next-generation solution. These investigations include the fundamental security and privacy problems associated with 6G technologies. Therefore, in order to consolidate and solidify this foundational research as a basis for future investigations, we have prepared a survey on the status quo of 6G security and privacy. The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking. We then discuss four key aspects of 6G networks – real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms – and some promising emerging technologies in each area, along with the relevant security and privacy issues. The survey concludes with a report on the potential use of 6G. Some of the references used in this paper along and further details of several points raised can be found at: security-privacyin5g-6g.github.io.
Wang, Q, Zhou, Y, Ding, W, Zhang, Z, Muhammad, K & Cao, Z 2020, 'Random forest with self-paced bootstrap learning in lung cancer prognosis', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 16, no. 1s.
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© 2020 ACM. Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this study, we present a random forest with self-paced learning bootstrap for improvement of lung cancer classification and prognosis based on gene expression data. To be specific, we propose an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigate the sampling strategy by gradually embedding from high- to low-quality samples by self-paced learning. The experimental results based on five public lung cancer datasets show that our proposed method could select significant genes exactly, which improves classification performance compared to that of existing approaches. We believe that our proposed method has the potential to assist doctors in gene selections and lung cancer prognosis.
Wang, W, Zhang, Y, Sui, Y, Wan, Y, Zhao, Z, Wu, J, Yu, P & Xu, G 2020, 'Reinforcement-Learning-Guided Source Code Summarization via Hierarchical Attention', IEEE Transactions on Software Engineering, pp. 1-1.
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Wang, Y, Zhang, C, Wang, S, Yu, PS, Bai, L, Cui, L & Xu, G 2020, 'Generative temporal link prediction via self-tokenized sequence modeling', World Wide Web, vol. 23, no. 4, pp. 2471-2488.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks. GLSM captures the temporal link formation patterns from the observed links with a sequence modeling framework and has the ability to generate the emerging links by inferring from the probability distribution on the potential future links. To avoid overfitting caused by treating each link as a unique token, we propose a self-tokenization mechanism to transform each raw link in the network to an abstract aggregation token automatically. The self-tokenization is seamlessly integrated into the sequence modeling framework, which allows the proposed GLSM model to have the generalization capability to discover link formation patterns beyond raw link sequences. We compare GLSM with the existing state-of-art methods on five real-world datasets. The experimental results demonstrate that GLSM obtains future positive links effectively in a generative fashion while achieving the best performance (2-10% improvements on AUC) among other alternatives.
Wu, L, Falque, R, Perez-Puchalt, V, Liu, L, Pietroni, N & Vidal-Calleja, T 2020, 'Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1532-1539.
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Wu, L, Liu, Q, Zhou, W, Mao, G, Huang, J & Huang, H 2020, '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, Z, Li, G, Shen, S, Lian, X, Chen, E & Xu, G 2020, 'Constructing dummy query sequences to protect location privacy and query privacy in location-based services', World Wide Web.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.
Wu, Z, Wang, R, Li, Q, Lian, X, Xu, G, Chen, E & Liu, X 2020, 'A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services', IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5244-5254.
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© 1967-2012 IEEE. Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users' location privacy, due to its more and more untrusted server-side. In this article, we propose a location privacy-preserving system for LBS by constructing 'cover-up ranges' to protect the query ranges associated with a location query sequence. Firstly, we present a client-based system framework for location privacy protection in LBS, which requires no compromise to the accuracy and usability of LBS. Secondly, based on the framework, we introduce a location privacy model to formulate the constraints that ideal cover-up ranges should satisfy, so as to improve the efficiency of location services and the security of location privacy. Finally, we describe an implementation algorithm to well meet the location privacy model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our system, which can improve the security of users' location privacy on the untrusted server-side, without compromising the accuracy and usability of LBS.
Xia, Y, Zhu, T, Ding, X, Jin, H & Zou, D 2020, 'Heterogeneous differential privacy for vertically partitioned databases', Concurrency and Computation: Practice and Experience.
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© 2019 John Wiley & Sons, Ltd. Existing privacy-preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information-based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.
Xiao, G, Zheng, Z, Jiang, B & Sui, Y 2020, 'An Empirical Study of Regression Bug Chains in Linux', IEEE Transactions on Reliability.
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Xiao, R, Ren, W, Zhu, T & Choo, KKR 2020, 'A Mixing Scheme Using a Decentralized Signature Protocol for Privacy Protection in Bitcoin Blockchain', IEEE Transactions on Dependable and Secure Computing.
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IEEE Bitcoin transactions are not truly anonymous as an attacker can attempt to reveal a user's private information by tracing related transactions. Existing approaches to protect privacy (e.g. mixcoin, shuffle, and blinded token) suffer from a number of limitations. For example, some approaches assume the existence of a trusted third party, rely on exchanges among various currencies, or broadcast sensitive details before mixing. Therefore, there is a real risk of privacy breach or losing tokens. Thus in this paper, we design a mixing scheme with one decentralized signature protocol, which does not rely on a third party or require a transaction fee. Specifically, our scheme uses a negotiation process to guarantee transaction details, which is monitored by the participants. Furthermore, the scheme includes a signature protocol based on the ElGamal signature protocol and secret sharing. The proposed scheme is then proven secure.
Xiao, Y, Pei, Q, Yao, L & Wang, X 2020, 'RecRisk: An enhanced recommendation model with multi-facet risk control', Expert Systems with Applications, vol. 158.
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© 2020 Elsevier Ltd Recommender systems (RSs) play a crucial role in helping users quickly find their desired services and promoting sales of service providers in e-commerce. However, service providers intentionally upload cherry-picked service information to mislead RSs for greater profit. Such misleading recommendations pose the risk of degrading user experience and gradually undermine users’ confidence in the whole service market over the long term. Worse still, most current expert recommendation methods are more susceptible to such risk because they are heavily dependent on this incomplete service information and assume it is trusted. Therefore, how to satisfy users’ service requirements with risks minimized at the same time motivates our work. In this paper, we first discern two risky facets that pose significant challenges to expert recommendation: Sense Drop and Blue Joy. Then we propose a unified framework called RecRisk, which integrates trust, heat equation, and modern portfolio theory to address the above challenges. The main contributions of RecRisk are twofold: (1) To select the services which satisfy the users’ preferences, we design a trust-aware heat equation model (TAHE) that combines heat flow theory with trust elements. (2) We develop a flexible model based on modern portfolio theory to weigh users’ satisfaction and services’ risk facets, and finally recommend a ranked service list to users. Our experimental results demonstrate that RecRisk simultaneously achieves higher recommendation precision and decreases risks when compared to state-of-the-art approaches.
Xiao, Y, Pei, Q, Yao, L, Yu, S, Bai, L & Wang, X 2020, 'An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 156.
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Xiao, Y, Yao, L, Pei, Q, Wang, X, Yang, J & Sheng, QZ 2020, 'MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation', IEEE Intelligent Systems.
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IEEE Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated in social network services. However, most existing research either predicts users' consumption preferences or recommends friends to users without dealing with them simultaneously. We propose a holistic approach to predict users' preferences on friends and items jointly and thereby make better recommendations. To this end, we design a graph neural network that incorporates a mutualistic mechanism to model the mutual reinforcement relationship between users' consumption behaviors and social behaviors. Our experiments on the two-real world datasets demonstrate the effectiveness of our approach in both social recommendation and link prediction.
Xiong, P, Zhang, L, Zhu, T, Li, G & Zhou, W 2020, '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.
Xu, K, Wang, F, Wang, H, Wang, Y & Zhang, Y 2020, 'Mitigating the Impact of Data Sampling on Social Media Analysis and Mining', IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 546-555.
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© 2014 IEEE. The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.
Xuan, J, Luo, X, Lu, J & Zhang, G 2020, 'Web event evolution trend prediction based on its computational social context', WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, vol. 23, no. 3, pp. 1861-1886.
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Yang, LX, Li, P, Yang, X, Xiang, Y, Jiang, F & Zhou, W 2020, 'Effective Quarantine and Recovery Scheme Against Advanced Persistent Threat', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE Advanced persistent threat (APT) for cyber espionage poses a great threat to modern organizations. In order to mitigate the impact of APT on an organization, all the compromised systems in the organization must be quarantined and recovered in a timely and effective way. This article focuses on the problem of customizing a dynamic quarantine and recovery (QAR) scheme for an organization so that the APT impact is minimized. Based on a novel node-level epidemic model characterizing the effect of the QAR scheme on the expected state of the underlying network, we estimate the expected impact of APT under a QAR scheme. On this basis, we model the original problem as an optimal control problem. By use of optimal control theory, we derive the optimality system for the optimal control problem and thereby introduce the concept of normal potential optimal (NPO) control. Next, through comparative experiments, we find that the NPO control outperforms a set of heuristic controls. Hence, the QAR scheme associated with the NPO control is satisfactory in terms of the effectiveness of defending against APT. Finally, we examine the effect of some factors on the expected APT impact under the NPO control. This article would be helpful to the defense against APT for cyber espionage.
Yang, Z, Garg, H, Li, J, Srivastava, G & Cao, Z 2020, 'Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm', NEURAL COMPUTING & APPLICATIONS.
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Yu, H, Lu, J & Zhang, G 2020, 'An Online Robust Support Vector Regression for Data Streams', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Yu, H, Lu, J & Zhang, G 2020, 'Continuous Support Vector Regression for Nonstationary Streaming Data', IEEE Transactions on Cybernetics, pp. 1-14.
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Yu, H, Lu, J & Zhang, G 2020, 'Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Zambetta, F, Raffe, W, Tamassia, M, Mueller, FF, Li, X, Quinten, N, Patibanda, R, Dang, D & Satterley, J 2020, 'Reducing Perceived Waiting Time in Theme Park Queues via an Augmented Reality Game', ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION, vol. 27, no. 1.
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Zhai, X, Zhou, W, Fei, G, Lu, C & Hu, G 2020, 'Network sparse representation: Decomposition, dimensionality-reduction and reconstruction', Information Sciences, vol. 521, pp. 307-325.
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© 2020 Network science is a field in which real-world systems are abstracted into complex networks for scientific analysis. Dimensionality reduction for large-scale complex networks to reduce the complexity of problems has become a research focus. In this study, we found that real-world networks are composed of a finite number of atoms through self-replication and superposition. Thus, they can be decomposed into a dictionary and sparse coding. The sparse representation we propose simplifies redundant complex structures and reveals the basis and its representation methods for complex networks. Difficult problems can be solved through this representation, including network similarity metrics, recognition, and reconstruction. Experimental results show that the atoms and sparse coding describe the basic structure and connection pattern of complex networks.
Zhang, F, Li, C, Zhang, Y, Qin, L & Zhang, W 2020, 'Finding Critical Users in Social Communities: The Collapsed Core and Truss Problems', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, pp. 78-91.
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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, L, Shi, Y, Chang, Y-C & Lin, C-T 2020, 'Hierarchical fuzzy neural networks with privacy preservation on heterogeneous big data', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Zhang, T, Ye, D, Zhu, T, Liao, T & Zhou, W 2020, 'Evolution of cooperation in malicious social networks with differential privacy mechanisms', NEURAL COMPUTING & APPLICATIONS.
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Zhang, T, Zhu, T, Xiong, P, Huo, H, Tari, Z & Zhou, W 2020, 'Correlated Differential Privacy: Feature Selection in Machine Learning', IEEE Transactions on Industrial Informatics, pp. 1-1.
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Zhang, W, Xiong, J, Gui, L, Liu, B, Qiu, M & Shi, Z 2020, 'Distributed Caching Mechanism for Popular Services Distribution in Converged Overlay Networks', IEEE Transactions on Broadcasting.
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IEEE With the proliferation of portable devices, the exponential growth of the global mobile traffic brings great challenges to the traditional communication networks and the traditional wireless communication technologies. In this context, converged networks and cache-based data offloading have drawn more and more attention based on the strong correlation of services. This paper proposes a novel popular services pushing and caching scheme by using converged overlay networks. The most popular services are pushed by terrestrial broadcasting networks. And they are cached in n router-nodes with limited cache sizes. Each router-node only interconnects with its neighbor nodes. Users are served through the router's WiFi link. If the services requested are cached in the routers, the user can be immediately responded; otherwise, the requests can be responded through the link from cellular stations to the router. In the proposed scheme, the cache size of the router, the maximum number of requests each router can serve, and the whole-time delay are limited. Three node-selecting and dynamic programming algorithms are adopted to maximize the equivalent throughput. Analytical and numerical results demonstrate that the proposed scheme is very effective.
Zhang, Y, Sui, Y, Pan, S, Zheng, Z, Ning, B, Tsang, I & Zhou, W 2020, 'Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning', IEEE Transactions on Information Forensics and Security.
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IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples.
Zhao, X, Guo, J, Nie, F, Chen, L, Li, Z & Zhang, H 2020, 'Joint Principal Component and Discriminant Analysis for Dimensionality Reduction', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 433-444.
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© 2012 IEEE. Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix St via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. However, 'most variance' is very often the most important, but not always in PCA. Thus, this two-step strategy may not be able to obtain the most discriminant information for classification tasks. Different from traditional approaches which conduct PCA and LDA in sequence, we propose a novel method referred to as joint principal component and discriminant analysis (JPCDA) for dimensionality reduction. Using this method, we are able to not only avoid the small sample size problem but also extract discriminant information for classification tasks. An iterative optimization algorithm is proposed to solve the method. To validate the efficacy of the proposed method, we perform extensive experiments on several benchmark data sets in comparison with some state-of-the-art dimensionality reduction methods. A large number of experimental results illustrate that the proposed method has quite promising classification performance.
Zhu, F, Lu, J, Lin, A & Zhang, G 2020, 'A Pareto-smoothing method for causal inference using generalized Pareto distribution', Neurocomputing, vol. 378, pp. 142-152.
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© 2019 Elsevier B.V. Causal inference aims to estimate the treatment effect of an intervention on the target outcome variable and has received great attention across fields ranging from economics and statistics to machine learning. Observational causal inference is challenging because the pre-treatment variables may influence both the treatment and the outcome, resulting in confounding bias. The classic inverse propensity weighting (IPW) estimator is theoretically able to eliminate the confounding bias. However, in observational studies, the propensity scores used in the IPW estimator must be estimated from finite observational data and may be subject to extreme values, leading to the problem of highly variable importance weights, which consequently makes the estimated causal effect unstable or even misleading. In this paper, by reframing the IPW estimator in the importance sampling framework, we propose a Pareto-smoothing method to tackle this problem. The generalized Pareto distribution (GPD) from extreme value theory is used to fit the upper tail of the estimated importance weights and to replace them using the order statistics of the fitted GPD. To validate the performance of the new method, we conducted extensive experiments on simulated and semi-simulated datasets. Compared with two existing methods for importance weight stabilization, i.e., weight truncation and self-normalization, the proposed method generally achieves better performance in settings with a small sample size and high-dimensional covariates. Its application on a real-world heath dataset indicates its utility in estimating causal effects for program evaluation.
Zhu, T, Xiong, P, Li, G, Zhou, W & Yu, PS 2020, 'Differentially private model publishing in cyber physical systems', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications.
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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.
Adak, C, Chaudhuri, BB, Lin, CT & Blumenstein, M 2019, 'Detecting named entities in unstructured bengali manuscript images', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, International Conference on Document Analysis and Recognition, IEEE, Sydney, Australia, pp. 196-201.
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© 2019 IEEE. In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate text/character recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural network-based architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long Short-Term Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publicly-available offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.
Al-Hadhrami, Y & Hussain, FK 2020, 'A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT', Advances in Intelligent Systems and Computing, pp. 417-429.
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© 2020, Springer Nature Switzerland AG. Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.
Al-Hadhrami, Y, Al-Hadhrami, N & Hussain, FK 2020, 'Data exportation framework for IoT simulation based devices', Advances in Intelligent Systems and Computing, pp. 212-222.
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© Springer Nature Switzerland AG 2020. Internet of things (IoT) is part of everyday life nowadays. Millions of devices are connected to the internet to collect and share data. Although IoT devices are evolving quickly in the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators are also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. A need for an IoT simulation tool is necessary for development purposes and testing before deployments. One of the widely used tools among IoT researchers is the open-source tool Cooja simulator. Cooja has limitations—one is the lack of a way to export collected data as a data set for further processing. Therefore, this study introduces an extension tool to present and export the data into different forms.
Almansor, EH & Hussain, FK 2020, 'Modeling the Chatbot Quality of Services (CQoS) Using Word Embedding to Intelligently Detect Inappropriate Responses', Advances in Intelligent Systems and Computing, pp. 60-70.
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© 2020, Springer Nature Switzerland AG. The rapid growth of intelligent chatbots as conversational agents with the assistance of artificial intelligence has recently attracted much research attention. The major role of a chatbot is to generate appropriate responses to the user, however sometimes the chatbot fails to understand the user’s meaning. Therefore, detecting inappropriate responses from a chatbot is a critical issue. Several studies based on annotated datasets have investigated the problem of inappropriate responses from a chatbots perspective without considering the user’s perspective. Understanding the context of the conversation is an important point in determining whether a response is appropriate or inappropriate. Sentiment analysis is a natural language processing task that supports mining in user behavior. Therefore, we propose an intelligent framework that combines automated sentiment scoring and a word embedding model to detect the quality of chatbot responses considering the end-user’s point of view. We find our model achieves higher quality results than logistic regression.
Almansor, EH & Hussain, FK 2020, 'Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, pp. 534-543.
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© 2020, Springer Nature Switzerland AG. Human-computer interaction (HCI) is an area of interest which plays a major role in understanding the interaction between humans and machines. Dialogue systems or conversational systems including chatbots, voice control interfaces and personal assistants are examples of HCI application that have been developed to interact with users using natural language. Chatbots can help customers find useful information for their needs. Thus, numerous organizations are using chatbots to automate their customer service. Thus, the needs for using artificial intelligence has been increasing due to the needs of automated services. However, devolving smart bots that can respond at the human level is challenging. In this paper, we survey the state-of-art chatbot approaches from based on the ability to generate appropriate responses perspective. After summarizing the review from this aspect, we identify the research issues and challenges in chatbots. The findings of this research will highlight directions for future work.
Almansor, EH, Al-Ani, A & Hussain, FK 2020, 'Transferring Informal Text in Arabic as Low Resource Languages: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, pp. 176-187.
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© 2020, Springer Nature Switzerland AG. Rapid growth in internet technology lead to increase the usage of social media platforms which make communication between users easier. Through the communication users used their daily languages which considered as non-standard language. The non-slandered text contains lots of noise, such as abbreviations, slang which used more in English languages and dialect words which are widely used in Arabic language. These texts face challenging using any natural language processing tools. Therefore, these texts need to be treated and transferred to be similar to their standard form. According to that the normalization and translation approach have been used to transfer the informal text. However, using these approach need large label or parallel datasets. While high resource languages such as English have enough parallel datasets, low resource languages such as Arabic is lack of enough parallel dataset. Therefore, in this paper we focus on the Arabic and Arabic dialects as a low resource language in the era of transferring non-stander text using normalization and translation approach.
Anwar, M, Gill, A & Beydoun, G 2019, 'Using Adaptive Enterprise Architecture Framework for Defining the Adaptable Identity Ecosystem Architecture', https://aisel.aisnet.org/acis2019/, Australasian Conference on Information Systems, AIS, Perth, pp. 1-11.
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Digital identity management is often used to handle fraud detection and hence reduce identity thefts. However, using digital identity management presents additional challenges in terms of privacy of the identity owner meanwhile managing the security of the verification. In this paper, drawing on adaptive enterprise architecture (EA) with an ecosystem approach to digital identity, we describe an identity ecosystem (IdE) architecture to handle identity management (IdM) while safeguarding security and privacy. This study is a part of the larger action design research project with our industry partner DZ. We have used Adaptive EA as a baseline to define a privacy aware adaptive IdE to make ID operations more efficient and improve the delivery of services in the public and private sector. The value of the anticipated architecture is in its generic yet comprehensive structure, component orientation and layered approach which aim to enable the contemporary IdM
Armstrong, T & Leong, TW 2019, 'SNS and the lived experiences of queer youth', ACM International Conference Proceeding Series, Australian Conference on Human-Computer-Interaction, ACM, Fremantle, Australia, pp. 376-380.
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© 2019 Association for Computing Machinery. Technology design has not adequately included a queer perspective, even though digital technologies such as social networking sites (SNS) have been shown to play vital roles in the lives and well-being of queer people. SNS provide queer people with a means to explore their identities, learn about queerness and connect to others with similar experiences. However, SNS use can also have detrimental effects, exposing queer people to harm and victimisation. To date, there is not much effort in HCI to understand the experiences of queer people with SNS. As a result, we lack understanding of how SNS and other social technologies could be designed in ways that are supportive of queer people's well-being. The findings from this exploratory study reveal how particular digital technologies can have complex effects in shaping queer people's experiences and their well-being.
Biddle, R, Joshi, A, Liu, S, Paris, C & Xu, G 2020, 'Leveraging Sentiment Distributions to Distinguish Figurative from Literal Health Reports on Twitter', The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, pp. 1217-1227.
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© 2020 ACM. Harnessing data from social media to monitor health events is a promising avenue for public health surveillance. A key step is the detection of reports of a disease (referred to as ĝ€?health mention classification') amongst tweets that mention disease words. Prior work shows that figurative usage of disease words may prove to be challenging for health mention classification. Since the experience of a disease is associated with a negative sentiment, we present a method that utilises sentiment information to improve health mention classification. Specifically, our classifier for health mention classification combines pre-trained contextual word representations with sentiment distributions of words in the tweet. For our experiments, we extend a benchmark dataset of tweets for health mention classification, adding over 14k manually annotated tweets across diseases. We also additionally annotate each tweet with a label that indicates if the disease words are used in a figurative sense. Our classifier outperforms current SOTA approaches in detecting both health-related and figurative tweets that mention disease words. We also show that tweets containing disease words are mentioned figuratively more often than in a health-related context, proving to be challenging for classifiers targeting health-related tweets.
Bown, O, Fraietta, A, Ferguson, S & Loke, L 2020, 'Creative coding and interaction design for media multiplicities: Challenges, paradigms and frameworks', TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction, pp. 877-880.
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© 2020 Association for Computing Machinery. Media multiplicities are media artworks that employ multiple networked digital devices to create holistic aesthetic effects. Examples include the networked light artworks of Squidsoup, the Spaxels drone-mounted light performances, DrawBots, Siftables and many others. In multiplicitous media artworks, each individual device is a programmable node connected to other nodes via a network connection, and may combine any number of sensors and actuators. A number of development technologies support artists and designers to configure and create media multiplicities, but this domain offers new challenges for creative practitioners. This workshop aims to bring together experts in creative coding and interaction design to discuss and conceptualise frameworks for the practice of media multiplicities. Open challenges include: speed of setup; ease of hardware configuration; speed of code deployment; ability to model and simulate works in VR; network connectivity and stability; and understanding network, computation and power constraints.
Buchan, J, Zowghi, D & Bano, M 2020, 'Applying Distributed Cognition Theory to Agile Requirements Engineering', Requirements Engineering: Foundation for Software Quality, International Working Conference on Requirements Engineering: Foundation for Software Quality, Springer, Pisa, Italy, pp. 186-202.
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© 2020, Springer Nature Switzerland AG. [Context & Motivation] Agile Requirements Engineering (ARE) is a collaborative, team-based process based on frequent elicitation, elaboration, estimation and prioritization of the user requirements, typically represented as user stories. While it is claimed that this Agile approach and the associated RE activities are effective, there is sparse empirical evidence and limited theoretical foundation to explain this efficacy. [Question/problem] We aim to understand and explain aspects of the ARE process by focusing on a cognitive perspective. We appropriate ideas and techniques from Distributed Cognition (DC) theory to analyze the cognitive roles of people, artefacts and the physical work environment in a successful collaborative ARE activity, namely requirement prioritization. [Principal idea/results] This paper presents a field study of two early requirements related meetings in an Agile product development project. Observation data, field notes and transcripts were collected and qualitatively analyzed. We have used DiCoT, a framework for systematically applying DC as a methodological contribution, to analyze the ARE process and explain its efficacy from a cognitive perspective. The analysis identified three main areas of cognitive effort in the ARE process as well as the significant information flows and artefacts. Analysis of these have identified that the use of physical user story cards, specific facilitator skills, and development of shared understanding of the user stories, were all key to the effectiveness of the ARE activity observed. [Contribution] The deeper understanding of cognition involved in ARE provides an empirically evidenced explanation, based on DC theory, of why this way of collaboratively prioritizing requirements was effective. Our result provides a basis for designing other ARE activities.
Buruk, OO, Özcan, O, Baykal, GE, Göksun, T, Acar, S, Akduman, G, Baytaş, MA, Beşevli, C, Best, J, Coşkun, A, Genç, HU, Kocaballi, AB, Laato, S, Mota, C, Papangelis, K, Raftopoulos, M, Ramchurn, R, Sádaba, J, Thibault, M, Wolff, A & Yildiz, M 2020, 'Children in 2077: Designing Children's Technologies in the Age of Transhumanism', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM.
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Cao, Y, Chen, X, Yao, L, Wang, X & Zhang, WE 2020, 'Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems', Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, ACM.
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Chen, SK & Chen, CS 2019, 'Motion Primitive Recognition on Human Guided Robotic Arm using Machine Learning', International Conference on Control, Automation and Systems, International Conference on Control, Automation and Systems, IEEE, Jeju, Korea (South), pp. 955-960.
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© 2019 Institute of Control, Robotics and Systems - ICROS. This paper proposed a novel intuitive teaching technologies by reconstructing the recorded motion information during human guided robotic arm. A learning algorithm is proposed in this paper to recognize the motion primitives according to therbligs definition. The hybrid sensing interface is used to record and modified the positional trajectory, force/torque, and gripper information. Furthermore, an extended Kalman filter is used to pre-process the data and estimate the velocity and acceleration profile as motion features. The motion features, output data via the hybrid sensing interface, is finally used to recognize the target therblig by proposed cascade support vector machine. The experimental results show that the proposed method can recognize the motion features into therbligs correctly and efficiently. The recognition results can be further used to reconstruct an assembly operation.
Chen, X, Huang, C, Yao, L, Wang, X, Liu, W & Zhang, W 2020, 'Knowledge-guided deep reinforcement learning for interactive recommendation', 33rd International Joint Conference on Neural Networks, IEEE, Glasgow, UK.
Chu, D, Zhang, F, Lin, X, Zhang, W, Zhang, Y, Xia, Y & Zhang, C 2020, 'Finding the best k in core decomposition: A time and space optimal solution', Proceedings - International Conference on Data Engineering, pp. 685-696.
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© 2020 IEEE. The mode of k-core and its hierarchical decomposition have been applied in many areas, such as sociology, the world wide web, and biology. Algorithms on related studies often need an input value of parameter k, while there is no existing solution other than manual selection. In this paper, given a graph and a scoring metric, we aim to efficiently find the best value of k such that the score of the k-core (or k-core set) is the highest. The problem is challenging because there are various community scoring metrics and the computation is costly on large datasets. With the well-designed vertex ordering techniques, we propose time and space optimal algorithms to compute the best k, which are applicable to most community metrics. The proposed algorithms can compute the score of every k-core (set) and can benefit the solutions to other k-core related problems. Extensive experiments are conducted on 10 real-world networks with size up to billion-scale, which validates both the efficiency of our algorithms and the effectiveness of the resulting k-cores.
Darwish, A, Halkon, B, Oberst, S, Fitch, R & Rothberg, S 2020, 'CORRECTION OF LASER DOPPLER VIBROMETER MEASUREMENTS AFFECTED BY SENSOR HEAD VIBRATION USING TIME DOMAIN TECHNIQUES', Athens.
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, Sydney, Australia.
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The data-intensive digital supply chain management (SCM) ecosystems seem to be impacted by the recent changes in the regulations and advancement in technologies such as Artificial Intelligence, Big Data, Analytics, Networking, IoT including proliferation of less expensive hardware devices. There is limited guidance available on how to govern the logistics sector, particularly from a regulatory compliance perspective. Through this paper, we investigate the impact of General Data Protection Regulation (GDPR) on digitized SCM. The key questions are: What are the GPDR specific legal obligations? What is the best approach to manage data access, quality, privacy, security and ownership effectively in SCM? This research paper aims to assist researchers and practitioners to understand the impact of GDPR on SCM, provide the 4I (Identify, Insulate, Inspect, Improve) Framework and its applicability to streamline the GDPR compliance activities.
Dawson, NJ, Rizoiu, M-A, Johnston, B & Williams, M-A 1970, 'Adaptively selecting occupations to detect skill shortages from online job ads', IEEE International Conference on Big Data (IEEE Big Data 2019), IEEE International Conference on Big Data, Los Angeles, CA, USA, pp. 1-7.
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Labour demand and skill shortages have historically been difficult to assess given the high costs of conducting representative surveys and the inherent delays of these indicators. This is particularly consequential for fast developing skills and occupations, such as those relating to Data Science and Analytics (DSA). This paper develops a data-driven solution to detecting skill shortages from online job advertisements (ads) data. We first propose a method to generate sets of highly similar skills based on a set of seed skills from job ads. This provides researchers with a novel method to adaptively select occupations based on granular skills data. Next, we apply this adaptive skills similarity technique to a dataset of over 6.7 million Australian job ads in order to identify occupations with the highest proportions of DSA skills. This uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019. Finally, we propose five variables for detecting skill shortages from online job ads: (1) posting frequency; (2) salary levels; (3) education requirements; (4) experience demands; and (5) job ad posting predictability. This contributes further evidence to the goal of detecting skills shortages in real-time. In conducting this analysis, we also find strong evidence of skills shortages in Australia for highly technical DSA skills and occupations. These results provide insights to Data Science researchers, educators, and policy-makers from other advanced economies about the types of skills that should be cultivated to meet growing DSA labour demands in the future.
Derix, EC & Leong, TW 2019, 'Towards a probe design framework', ACM International Conference Proceeding Series, Australian Conference on Human-Computer-Interaction, ACM, Fremantle, Australia, pp. 117-127.
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© 2019 Association for Computing Machinery. Since their introduction, probes have been widely used in HCI. Despite this, there have not been much reflections and discussions about the design thinking behind their creation and use. There is also a lack of actionable guidance on designing and using probes. This lack may have contributed to some concerns that the method has been misinterpreted and misunderstood. We reviewed HCI literature surrounding probes and found one of the few papers that offers a nascent framework for probe design and use. We used it to guide the design of a collection of probes and reflected on the framework's usefulness. We extend this framework by offering a more useful way of visualizing and working with probe design properties. We also provide further clarity and advice on how others may think and approach the design and use of probes more effectively, especially those turning to probes for the first time.
Dong, Y, Fauth, A, Huang, M, Chen, Y & Liang, J 2020, 'PansyTree: Merging Multiple Hierarchies', IEEE Pacific Visualization Symposium, pp. 131-135.
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© 2020 IEEE. Hierarchical structures are very common in the real world for recording all kinds of relational data generated in our daily life and business procedures. A very popular visualization method for displaying such data structures is called "Tree". So far, there are a variety of Tree visualization methods that have been proposed and most of them can only visualize one hierarchical dataset at a time. Hence, it raises the difficulty of comparison between two or more hierarchical datasets.In this paper, we proposed Pansy Tree which used a tree metaphor to visualize merged hierarchies. We design a unique icon named pansy to represent each merged node in the structure. Each Pansy is encoded by three colors mapping data items from three different datasets in the same hierarchical position (or tree node). The petals and sepal on Pansy are designed for showing each attribute's values and hierarchical information. We also redefine the links in force layout encoded by width and animation to better convey hierarchical information. We further apply Pansy Tree into CNCEE datasets and demonstrate two use cases to verify its effectiveness.The main contribution of this work is to merge three datasets into one tree that makes it much easier to explore and compare the structures, data items and data attributes with visual tools.
Dutta, LK, Xiong, J, Gui, L, Liu, B & Shi, Z 2019, 'On Hit Rate Improving and Energy Consumption Minimizing in Cache-Based Convergent Overlay Network on High-speed Train', IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, IEEE, Jeju, Korea (South).
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© 2019 IEEE. Content caching and energy consumption to protract user device lifetime while bolstering the hit rate when mobility is beyond 300Kmph is challenging. In this paper, we consider a cache-based convergent overlay network comprising cellular base stations and terrestrial broadcast networks as effective means to deliver the services in high-speed train(HST). Most popular contents with high Zipf rank are pushed and cached using broadcast network in user devices and relay system on HST to offload the network traffic and bring user experience upfront leveraging energy. Presence of cache in relay system (RS), users can get the services without delay. However, if user's request for contents are served by cellular network from cloud, they face throughput bottleneck with delay and increased transmission time. This eventually leads increase in user device energy consumption. A popular contents caching scheme with constraints of location, limited storage size and limited power of user device is modeled as closed form expression to maximize users local hit rate with minimal power consumption. we propose an algorithm to cache the contents in user device to minimize total energy consumption of user devices. Moreover, user's content activity behavior is retained to minimize user device's energy by dynamic cache space algorithm. Simulation results justify that the proposed scheme can effectively improve cache hit rate and reduce the power consumption of user devices.
Gong, S, Oberst, S & Xinwen, W 2019, 'A non-linear model of rubber shear springs validated by experiments', Nonlinear Dynamics of Structures, Systems and Devices, International Nonlinear Dynamics Conference, Springer, 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.
Halkon, B, Cheong, I, Visser, G, Walker, P & Oberst, S 2020, 'An experimental assessment of torsional and package vibration in an industrial engine-compressor system', Vibrations in Rotating Machinery 12, Liverpool.
Hua, J, Huang, ML, Zhao, C, Hua, S & Shih, C 2020, 'An Initial Visual Analysis of the Relationship between COVID-19 and Local Community Features', Proc. of 24th International Conference Information Visualisation (IV), 2020 24th International Conference Information Visualisation (IV), IEEE CS, Melbourne in Australia, pp. 663-667.
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Virus outbreaks are threats to humanity, and
coronaviruses are the latest of many epidemics in the last few
decades. In this work, we conduct a non-medical/clinical
approach, generating graphs from five features concluded from
the COVID-19 outbreak data and local community data in NSW
(New South Wales), Australia, and offering insights from a
visual analysis perspective. The results show that household
income, population density and ethnicity affect the infection in
different areas. Features such as human behaviours need to be
imported for further COVID-19 research in the data science
sector. This work is an initial step into this area and allows more
insights to be brought into future COVID-19 research through
a visual analysis perfective.
Hua, J, Huang, ML, Zhao, C, Hua, S & Shih, C 2020, 'An Initial Visual Analysis of the Relationship between COVID-19 and Local Community Features', 24th International Conference Information Visualisation (IV), IEEE CS, Melbourne, Australia, pp. 678-682.
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Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades. In this work, we conduct a non-medical/clinical approach, generating graphs from five features concluded from the COVID-19 outbreak data and local community data in NSW (New South Wales), Australia, and offering insights from a visual analysis perspective. The results show that household income, population density and ethnicity affect the infection in different areas. Features such as human behaviours need to be imported for further COVID-19 research in the data science sector. This work is an initial step into this area and allows more insights to be brought into future COVID-19 research through a visual analysis perfective.
Huang, ML, Zhao, R, Hua, J, Nguyen, QV, Huang, W & Wang, J 2020, 'Designing infographics/visual icons of social network by referencing to the design concept of ancient Oracle Bone characters', Proc. of 24th International Conference Information Visualisation (IV), 2020 24th International Conference Information Visualisation (IV), IEEE CS, Melbourne in Australia,, pp. 639-644.
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This paper introduces the use of pictogram design
concept in ancient China for the development of a set of today's
"graphic icons" or "infographics" in modern social network
visualization systems. These graphic icons should be close to
sensory symbols that derive their expressive power from their
ability to use the perceptual processing power of the brain
without learning. Therefore, with the use of such a set of
"sensory symbols" we aim to achieve the identification of
corresponding physical objects (or their attributes) to be
performed close to the pre-attentive time.
Huang, W, Goodwin, MJ, Huang, ML & Huang, X 2020, 'DGViewer: A Hybrid Approach towards Visualisation of Dynamic Networks', Proc. of 24th International Conference Information Visualisation (IV), 2020 24th International Conference Information Visualisation (IV), IEEE CS, Melbourne, Victoria, Australia, pp. 42-46.
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Dynamic networks are networks in which nodes
and edges come and go at different times. The dynamic nature
of this type of networks has imposed a challenge in network
visualisation. Many techniques have been proposed to visualise
dynamic networks in an effort of not only capturing the dynamic
features of networks over the time but also laying out the
network in a way that is fast and makes the visualisation
aesthetically pleasing. However, empirical research has shown
that the current techniques have limitations in one way or
another. In the paper, we present an interactive visualisation
called DGViewer which gives users control and flexibility
allowing them to decide what, when and how to view
visualisations to improve the analysis and comprehension of
dynamic networks. A preliminary usability study indicates that
users are positive with this user-centred visualisation for better
user experience and task performance
Ikram, MA, Sharma, N, Raza, M & Hussain, FK 2020, 'Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP', Advances in Intelligent Systems and Computing, pp. 1000-1010.
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© 2020, Springer Nature Switzerland AG. Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer’s preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer’s non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.
Inibhunu, C & Mcgregor, C 2020, 'Application of TPRMine method for identification of temporal changes on patients with COPD: A case study in telehealth', Proceedings - IEEE Symposium on Computer-Based Medical Systems, pp. 380-383.
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© 2020 IEEE. Monitoring the vital status of an aging population especially those with chronic diseases can potentially reduce the multiple emergency room visits and hospitalizations if patients and care providers are provided with information that might help them make informed decisions on appropriate cause of actions. This process can be enabled by utilizing temporal abstraction and deriving temporal patterns in order to understand the underlying temporal relationships on vital status in data collected from patients participating in telehealth programs in combination with other data sets in order to get a complete patient flow. Such discovery can highlight when an elderly patient is at risk of an adverse event and this is information that can be utilized for provision of appropriate care for the patient. This paper demonstrates application of a method for deriving temporal patterns from patient's physiological data thereby quantifying the many states a patient can transition to before, during and after an adverse event. With this approach, it is possible to quantify patients with vital scores based on their physiology.
Inibhunu, C & Mcgregor, C 2020, 'Identification of temporal changes on patients at risk of lons with TPRMine: A case study in NICU', Proceedings - IEEE Symposium on Computer-Based Medical Systems, pp. 33-36.
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© 2020 IEEE. A neonatal intensive care unit (NICU) provides specialized care for preterm or ill term infants. The onset of many conditions they can develop are not obvious to physicians until they are significantly impacted and this could result in death. An example of such a problem is neonatal infection which is a common cause of death for premature infants. It remains a challenging task for clinicians to accurately diagnose the presence of bacteria on patients with frequent presence of multiple comorbidities. There is potential for early detection of neonatal infections by timely analysis of patient physiological data and this can lead to improved health outcome of critically ill patients. This paper demonstrates application of a method for Temporal Pattern Recognition and Mining (TPRMine) in order to (a) understand if continuous analysis of temporal changes in patient physiological data streams can lead to discovery of pathophysiological patterns from patients at risk of neonatal sepsis and, (b) utilize the resulting analysis for formulating and testing hypothesis facilitating statistical quantification of patients.
Khan, S & Hussain, FK 2020, 'A soa based sla negotiation and formulation architecture for personalized service delivery in sdn', Advances in Intelligent Systems and Computing, pp. 108-119.
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© Springer Nature Switzerland AG 2020. Supporting end-to-end personalized Quality of Services (QoS) delivery in existing network architecture is an ongoing issue. Software Defined Networking (SDN) model has emerged in response to the limitations of traditional network. Integrating Software Defined Network (SDN) architecture with Service Oriented Architecture (SOA) brings new concept for future service oriented delivery in SDN services. Researchers from both academic and industry are working to resolve the QoS limitations of service delivery, however; most of the proposed solutions are application oriented and unable to provide a reliable personalized QoS delivery in future service oriented SDN. This research propose a reliable Service Level Agreement (SLA) oriented Service Negotiation framework that would be able to provide reputation based personalized service delivery and assist in QoS management in SDN for informed decision making. Moreover, potential benefits of the proposed framework are also discussed in this paper in social, scientific and business aspects.
Khan, S & Hussain, FK 2020, 'Evaluation of SLA Negotiation for Personalized SDN Service Delivery', Advances in Intelligent Systems and Computing, pp. 579-590.
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© 2020, Springer Nature Switzerland AG. Ensuring the quality of services (QoS) is crucial in a service-oriented business model. A service level agreement (SLA) is an important agreement between a consumer and a provider and is a key element in ensuring QoS. Service negotiation occurs in an initial stage of the SLA where service requirements are agreed upon to avoid conflict situations. Guaranteeing QoS is one of the key challenges in software defined networking (SDN). Several intelligent solutions have been proposed, however most of them are application focused and are unable to provide personalized and reliable QoS delivery in SDN. This paper presents a reputation data-driven SLA negotiation framework that provides personalized and reliable service delivery in SDN and assists in QoS management for informed decision making. In addition, a fuzzy inference system (FIS) is used to implement the framework and the results are discussed in this paper.
Kitto, K, Sarathy, N, Gromov, A, Liu, M, Musial, K & Shum, SB 2020, 'Towards Skills-based Curriculum Analytics: Can we automate the recognition of prior learning?', LAK20: THE TENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, 10th International Conference on Learning Analytics and Knowledge (LAK), ASSOC COMPUTING MACHINERY, ELECTR NETWORK, pp. 171-180.
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Kocaballi, AB, Coiera, E & Berkovsky, S 2020, 'Revisiting Habitability in Conversational Systems', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM.
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Kocaballi, AB, Quiroz, JC, Laranjo, L, Rezazadegan, D, Kocielnik, R, Clark, L, Liao, QV, Park, SY, Moore, RJ & Miner, A 2020, 'Conversational Agents for Health and Wellbeing', Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20: CHI Conference on Human Factors in Computing Systems, ACM.
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Lawrence, C, Leong, TW, Brereton, M, Taylor, JL, Bidwell, N & Wadley, G 2019, 'Indigenous HCI workshop at ozCHI 2019, perth', ACM International Conference Proceeding Series, Australian Conference on Human-Computer-Interaction, ACM, Fremantle, Australia, pp. 17-19.
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© 2019 Association for Computing Machinery. The Workshop on Indigenous HCI aims to bring together researchers and practitioners (Indigenous and otherwise) who work with Indigenous people and communities on technology projects and human-computer interaction (HCI) research The workshop is oriented to establishing connections and supporting discussion; to enabling participants to network and to share ideas and experiences in a diverse environment. As such, we invite academic and industry participants as well as community representatives. We also recognize the importance of all aspects of the technology ecosystem including teaching, research, design, development and implementation. We invite those who are interested to do technology work with Indigenous communities but are unsure of how to begin. Our long-Term aims are to build on earlier work to establish Indigenous HCI as an ongoing theme in Australian HCI. and at OzCHI. and to shape a set of guidelines for doing this work.
Leong, TW, Lawrence, C & Wadley, G 2019, 'Designing for diversity in Aboriginal Australia: Insights from a national technology project', ACM International Conference Proceeding Series, Australian Conference on Human-Computer-Interaction, ACM, Fremantle, Australia, pp. 418-422.
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© 2019 Association for Computing Machinery. Aboriginal Australians have been colonized for over 230 years. As a result, many have been disconnected from their communities and identity. This paper reports on a national-scale HCI project that aims to design technology that allows Aboriginal Australians to reconnect with their communities and to reaffirm their Aboriginal identity. Our project faces significant challenges, some due to the effects of colonization and some due to the great (and underrecognized) diversity of Aboriginal Australia. In this paper, we report the design phase of our project, and discuss some of these challenges we faced. Through this, we offer insights for HCI designers and researchers undertaking similar work.
Li, G, Zhu, L, Liu, P & Yang, Y 2019, 'Entangled transformer for image captioning', Proceedings of the IEEE International Conference on Computer Vision, IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Korea (South), pp. 8927-8936.
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© 2019 IEEE. In image captioning, the typical attention mechanisms are arduous to identify the equivalent visual signals especially when predicting highly abstract words. This phenomenon is known as the semantic gap between vision and language. This problem can be overcome by providing semantic attributes that are homologous to language. Thanks to the inherent recurrent nature and gated operating mechanism, Recurrent Neural Network (RNN) and its variants are the dominating architectures in image captioning. However, when designing elaborate attention mechanisms to integrate visual inputs and semantic attributes, RNN-like variants become unflexible due to their complexities. In this paper, we investigate a Transformer-based sequence modeling framework, built only with attention layers and feedforward layers. To bridge the semantic gap, we introduce EnTangled Attention (ETA) that enables the Transformer to exploit semantic and visual information simultaneously. Furthermore, Gated Bilateral Controller (GBC) is proposed to guide the interactions between the multimodal information. We name our model as ETA-Transformer. Remarkably, ETA-Transformer achieves state-of-the-art performance on the MSCOCO image captioning dataset. The ablation studies validate the improvements of our proposed modules.
Li, Q, Cao, Z, Tanveer, M, Pandey, HM & Wang, C 2020, 'An Effective Reliability Evaluation Method for Power Communication Network Based on Community Structure', IEEE Transactions on Industry Applications, pp. 4489-4500.
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© 1972-2012 IEEE. The reliability evaluation of the power communication network is beneficial for the improvement of the stable operation of the power system and the robustness of the power grid. However, the existing reliability evaluation models of the power communication network cannot meet the current situation of timeliness performance, due to rapidly increasing scale and complexity of information across varying services. In this study, we used the complex network theory to analyze the structure of the power communication network. Then we constructed the evaluation index of node (link) reliability of the power communication network based on community reliability. Compared with the traditional reliability indexes, our index not only considers the influence of the environment of the node (link) on the single structure of the power communication network, but also possesses the reliability evaluation rate of the node (link), which have the opportunities for improving the performance of the reliability evaluation of the wide-area power communication network. To verify the rationality of the index, we developed random, low reliability, and high-betweenness deliberate attacks to attack the designated node (link), and compared the network efficiency before and after the attack. Based on the simulation results, it can verify the rationality and superiority of our proposed evaluation index.
Lin, A, Lu, J, Xuan, J, Zhu, F & Zhang, G 2019, 'One-stage deep instrumental variable method for causal inference from observational data', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Beijing, China, pp. 419-428.
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© 2019 IEEE. Causal inference from observational data aims to estimate causal effects when controlled experimentation is not feasible, but it faces challenges when unobserved confounders exist. The instrumental variable method resolves this problem by introducing a variable that is correlated with the treatment and affects the outcome only through the treatment. However, existing instrumental variable methods require two stages to separately estimate the conditional treatment distribution and the outcome generating function, which is not sufficiently effective. This paper presents a one-stage approach to jointly estimate the treatment distribution and the outcome generating function through a cleverly designed deep neural network structure. This study is the first to merge the two stages to leverage the outcome to the treatment distribution estimation. Further, the new deep neural network architecture is designed with two strategies (i.e., shared and separate) of learning a confounder representation account for different observational data. Such network architecture can unveil complex relationships between confounders, treatments, and outcomes. Experimental results show that our proposed method outperforms the state-of-the-art methods. It has a wide range of applications, from medical treatment design to policy making, population regulation and beyond.
Linghu, Q, Zhang, F, Lin, X, Zhang, W & Zhang, Y 2020, 'Global Reinforcement of Social Networks: The Anchored Coreness Problem', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 2211-2226.
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© 2020 Association for Computing Machinery. The stability of a social network has been widely studied as an important indicator for both the network holders and the participants. Existing works on reinforcing networks focus on a local view, e.g., the anchored k-core problem aims to enlarge the size of the k-core with a fixed input k. Nevertheless, it is more promising to reinforce a social network in a global manner: considering the engagement of every user (vertex) in the network. Since the coreness of a user has been validated as the "best practice" for capturing user engagement, we propose and study the anchored coreness problem in this paper: anchoring a small number of vertices to maximize the coreness gain (the total increment of coreness) of all the vertices in the network. We prove the problem is NP-hard and show it is more challenging than the existing local-view problems. An efficient heuristic algorithm is proposed with novel techniques on pruning search space and reusing the intermediate results. Extensive experiments on real-life data demonstrate that our model is effective for reinforcing social networks and our algorithm is efficient.
Liu, B, Xiong, J, Wu, Y, Ding, M & Wu, CM 2019, 'Protecting Multimedia Privacy from Both Humans and AI', IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, IEEE, Jeju, Korea (South).
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© 2019 IEEE. With the development of artificial intelligence (AI), multimedia privacy issues have become more challenging than ever. AI-assisted malicious entities can steal private information from multimedia data more easily than humans. Traditional multimedia privacy protection only considers the situation when humans are the adversaries, therefore they are ineffective against AI-assisted attackers. In this paper, we develop a new framework and new algorithms that can protect image privacy from both humans and AI. We combine the idea of adversarial image perturbation which is effective against AI and the obfuscation technique for human adversaries. Experiments show that our proposed methods work well for all types of attackers.
Liu, B, Zhu, T, Wang, K, Zhou, H, Ding, M & Zhou, W 2019, 'Protecting Privacy-Sensitive Locations in Trajectories with Correlated Positions', 2019 IEEE Global Communications Conference (GLOBECOM), 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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The location privacy issue has become a critical research topic recently. The existing solutions do not solve one typical problem in practice: people may only want to protect certain privacy-sensitive locations among a group of temporal and spatial correlated points in a trajectory. As an effort towards this issue, we analyze the impact of space-time relationship on location privacy preservation. In addition, we propose new privacy definitions to better evaluate the privacy level and prove that the target location's privacy can be enhanced by randomizing its time and space related points. Moreover, under the constraint of the total noise power, the problem of obfuscating a location in a temporal and spatial correlated trajectory is formulated as finding the best noise allocation vector which can achieve the highest privacy level. This problem is solved by our proposed location privacy preserving method which applies differential privacy scheme on a series of points with noise budget allocation. Lastly, the performance of the proposed scheme is evaluated by simulations.
Liu, Z, Lina, Y, Wang, X, Bai, L & An, J 2020, 'Are you a risk taker? Adversarial learning of asymmetric cross-domain alignment for risk tolerance prediction', 33rd International Joint Conference on Neural Networks, IEEE, Glasgow, UK.
Madhuri, M, Gill, AQ & Khan, HU 2020, 'IoT-enabled smart child safety digital system architecture', Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020, pp. 166-169.
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© 2020 IEEE. Safety of a child in a large public event is a major concern for event organizers and parents. This paper addresses this important concern and proposes an architecture model of the IoT-enable smart child safety tracking digital system. This IoT-enabled digital system architecture integrates the Cloud, Mobile and GPS technology to precisely locate the geographical location of a child on an event map. The proposed architecture model describes the people, information, process, and technology architecture elements, and their relationships for the complex IoT-enable smart child safety tracking digital system. The proposed architecture model can be used as a reference or guide to assist in the safe architecture driven development of the various child tracking digital systems for different public events.
Majbouri Yazdi, K, Majbouri Yazdi, A, Khodayi, S, Hou, J, Zhou, W, Saedy, S & Rostami, M 2019, 'Improving Recommender Systems Accuracy in Social Networks Using Popularity', 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), IEEE, Gold Coast, Australia, pp. 304-310.
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With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches.
Marynowsky, W, Ferguson, S, Fraietta, A & Bown, O 2020, ''The ghosts of roller disco', a choreographed, interactive performance for robotic roller skates', TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction, pp. 631-637.
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© 2020 Association for Computing Machinery. The project investigates how interactions with complex (biologically inspired swarming) behaviors of multiple robots are understood by human participants within a performative and dramaturgical system. Nonanthropomorphic robots in the form of roller skates are used in innovative ways by creating social formations from their movements, for example a leader and followers in a conga line. Synchronized audio signals and speech-like sonic structures are used in innovative ways by influencing and engaging the participant's interactions with the robots. Localization data of the robots in space is mapped to control the surround sound and lighting within the space. This is used to enhance audience immersion and engagement within the interactive performance work.
McGregor, C, Inibhunu, C, Glass, J, Doyle, I, Gates, A, Madill, J & Pugh, JE 2020, 'Health Analytics as a Service with Artemis Cloud: Service Availability*', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, IEEE.
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Mols, I, Van Den Hoven, E & Eggen, B 2020, 'Everyday life reflection: Exploring media interaction with balance, cogito & dott', TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction, pp. 67-79.
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© 2020 Association for Computing Machinery. Reflection is of increasing interest in HCI as it has many potential benefits in design, education and everyday life. In this paper, we explore media-supported reflection through the design and deployment of three concepts. In contrast to prevalent reflective approaches that are based on system-collected data, we explore how user-created media can support personal reflection. Three interactive prototypes were developed, focusing on different modalities: Balance uses audio, Cogito uses text, and Dott uses visual media. We evaluate these concepts in an inthe-wild study that is both explorative and comparative. We found that the open-ended systems primarily supported reflection during the creation of media and that the use depended on opportunity and triggers. We conclude the paper with a discussion of our findings regarding the method and the implications of our findings for the broader area of design for reflection.
Naderpour, M, Rizeei, HM & Ramezani, F 2020, 'Wildfire prediction: Handling uncertainties using integrated bayesian networks and fuzzy logic', IEEE International Conference on Fuzzy Systems.
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© 2020 IEEE. Wildfire is one of the most frequent natural hazards across the globe, one which has cast a malevolent shroud over many communities in recent years, causing significant risk to human lives, infrastructure, and property. Wildfires are hydrogeological events which are bound to escalate, especially due to climate change. They are different from other natural hazards as they are mainly triggered by human interventions rather than natural triggers. The wildfire risk management is a complex process with many uncertainties in the assessment, fire behavior and spread modelling, and decision making. To predict wildfires, sophisticated temporal geospatial methods are required. This paper develops a wildfire probability prediction method considering the capabilities of Bayesian networks and fuzzy logic that can handle uncertainties and update probabilities in response to the availability of new data. The model takes into account the data from a geographic information system (GIS) for a specific area at micro level to estimate the wildfire probability and is able to update the probability due to any planned or unplanned changes in the area. Therefore, the proposed method can feed to future macro and micro risk-based decision-making situations in wildfire prone areas. The method is evaluated through a sensitivity analysis and its performance is investigated through a case study in New South Wales (NSW), Australia.
Nahar, K & Gill, AQ 2020, 'A Review Towards the Development of Ontology Based Identity and Access Management Metamodel', Advances in Intelligent Systems and Computing, pp. 223-232.
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© 2020, Springer Nature Switzerland AG. Building an identity and access management (IAM) system that satisfies business needs and can evolve over time with the ever-changing business environment, is always a challenging endeavour. This calls for the need of an ontology based adaptive IAM metamodel, which can adapt to and instantiated for different situations. To achieve this objective, the first step is to identify the relevant elements and their relationships for developing a detailed IAM ontology. Thus, this paper mainly focuses on the review of the available key models as a starting point for the identification of relevant elements and relationships for the development of the adaptable and yet generic metamodel for our industry research partner. This paper uses the graph modelling approach to present the identified elements and their relationship as an ontology, which can be used for developing the metamodel.
Naji, M, Braytee, A, Al-Ani, A, Anaissi, A, Goyal, M & Kennedy, PJ 2020, 'Design of airport security screening using queueing theory augmented with particle swarm optimisation', Service Oriented Computing and Applications, pp. 119-133.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Designing an efficient and reliable airport security screening system is a critical and challenging task. It is an essential element of airline and passenger safety which aims to provide the expected level of confidence and to ensure the safety of passengers and the aviation industry. In recent years, security at airports has gone through noticeable improvements with the utilisation of advanced technology and highly trained security officers. However, for many airports, it is important to find the best compromise between the capacity of the security area, the number of passengers and the number of screening machines and officers to maintain a high level of security and to ensure that the cost and waiting times for passengers and airlines are at acceptable levels. This paper proposes a novel method based on queueing theory augmented with particle swarm optimisation (QT-PSO) to predict passenger waiting times in a security screening context. This model consists of multiple servers operating in parallel and takes into consideration the complete scenario such as normal, slow and express lanes. Such an approach has the potential to be a reliable model that is able to assimilate variations in the number of passengers, security officers and security machines on the service time. To evaluate our proposed method, we collected real-world security screening data from an Australian airport from December to March for the two consecutive years of 2016 and 2017. The results show that our proposed QT-PSO method is superior to predict the average waiting time of passengers compared to the state of the art.
Naji, M, Braytee, A, Anaissi, A, Sianaki, OA & Al-Ani, A 2020, 'Optimizing the Waiting Time for Airport Security Screening Using Multiple Queues and Servers', Advances in Intelligent Systems and Computing, pp. 496-507.
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© 2020, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of 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 and can cause delays for passengers and airlines. In optimising a security process it is essential to strike a balance between time delays, security and reduced operation cost. This paper uses queueing theory as a method to study the impact of queue formation and the size of the security area on the average waiting time for the case of multi-lane parallel servers. An experiment is conducted to validate the proposed approach.
Naseem, U & Musial, K 2019, 'DICE: Deep intelligent contextual embedding for twitter sentiment analysis', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, International Conference on Document Analysis and Recognition, IEEE, Sydney, Australia, pp. 953-958.
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© 2019 IEEE. The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.
Naseem, U & Musial-Gabrys, K 2019, 'Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis', International Conference on Document Analysis and Recognition, IEEE, Sydney, Australia.
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The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.
Nguyen, QV, Huang, ML & Simoff, S 2020, 'Enhancing Scatter-plots with Start-plots for Visualising Multi-dimensional Data', Proc. of 24th International Conference Information Visualisation (IV), 2020 24th International Conference Information Visualisation (IV), IEEE CS, Melbourne, Victoria, Australia, pp. 59-65.
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Scatter-plot visualisation techniques are useful for
analysing the correlations of variables on the axes as well as
revealing patterns or abnormality in multidimensional data sets.
However, Scatter-plot techniques have a drawback that they are
not effective in showing a high number of dimensions where each
plot in two-dimensional space can show a pair-wise of two
variables on the X- and Y-axis. Star-plots are suitable for
showing small data set with a low number of dimensions thank to
the compactness in the visualisation. This paper proposes a
hybrid technique that integrates Start-plots with Scatter-plots in
the visualisation to enable the greater capability of scatter-plots
in showing more information on each individual Star-plot. Our
visualisation provides both overall views of mapping variables on
multiple Scatter-plots whilst we also utilise Star-plots for showing
the selected attributes on individual items for better comparison
among and within variables. We also demonstrate the
effectiveness of this hybrid method through case studies on
various data sets.
Nguyen, TD, Maszczyk, T, Musial, K, Zöller, MA & Gabrys, B 2020, 'AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 352-365.
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© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
Orth, D, Thurgood, C & Van Den Hoven, E 2020, 'Embodying meaningful digital media: A strategy to design for product attachment in the digital age', TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction, pp. 81-94.
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© 2020 Association for Computing Machinery. Technological products have become central to the ways in which many people communicate with others, conduct business and spend their leisure time. Despite their prevalence and significance in people's lives, these devices are often perceived to be highly replaceable. From a sustainability perspective, there is value in creating technological products with meaning directly associated with their materiality to reduce the rate of product consumption. We set out to explore the potential for design to promote the formation of product attachment by creating technological devices with meaningful materiality, closely integrating the physical form with the significance of its digital contents. We used the life stories and ongoing input of our intended user as inspiration for the creation of Melo, a bespoke music player. The evaluation and critical reflection of our design process and resulting artefact are used to propose a design strategy for promoting product attachment within the growing sector of technological devices.
Ouyang, D, Wen, D, Qin, L, Chang, L, Zhang, Y & Lin, X 2020, 'Progressive Top-K Nearest Neighbors Search in Large Road Networks', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1781-1795.
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© 2020 Association for Computing Machinery. Computing top-k nearest neighbors (kNN) is a fundamental problem in road networks. Existing solutions either need a complicated parameter configuration in index construction or incur high costs when scanning an unbounded number of vertices in query processing. In this paper, we propose a novel parameter-free index-based solution for the kNN query based on the concept of tree decomposition in large road networks. Based on our index structure, we propose an efficient and progressive algorithm that returns each result in a bounded delay. We also optimize the index structure, which improves the efficiency of both index construction and index maintenance in large road networks. We conduct extensive experiments to show the efficiency of our proposed algorithms and the effectiveness of our optimization techniques in real-world road networks from ten regions.
Pelchen, T, Mathieson, L & Lister, R 2020, 'On the evidence for a learning hierarchy in data structures exams', ACE 2020 - Proceedings of the 22nd Australasian Computing Education Conference, Held in conjunction with Australasian Computer Science Week, pp. 122-131.
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© 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7686-0/20/02...$15.00 Several previous research studies have found a relationship between the ability of novices to trace and explain code, and the ability to write code. Harrington and Cheng refer to that relationship as the Learning Hierarchy. However, almost all of those studies examined students at the end of their first semester of learning to program (i.e. CS1). This paper is only the third paper to describe a study of explain in plain English questions on students at the end of an introductory data structures course. The preceding two papers reached contradictory conclusions. Corney et al. presented results consistent with the Learning Hierarchy identified in the CS1 studies. However, Harrington and Cheng presented results for data structures students suggesting that the hierarchy reversed by the time students had progressed to the level of learning about data structures; that is, tracing and explaining were skills that followed writing. In our study of data structures students, we present results that are consistent with the Learning Hierarchy derived from the CS1 students. We believe that the reversal identified by Harrington and Cheng can occur, but only as a consequence of a mismatch in the relative difficulty of tracing, explaining and writing questions.
Pillai, AG, Ahmadpour, N, Yoo, S, Kocaballi, AB, Pedell, S, Sermuga Pandian, VP & Suleri, S 2020, 'Communicate, critique and co-create (CCC) future technologies through design fictions in VR environment', DIS 2020 Companion - Companion Publication of the 2020 ACM Designing Interactive Systems Conference, pp. 413-416.
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© 2020 Owner/Author. Design fiction enables HCI and design researchers to co-create, explore and speculate the future. It is growing in popularity given the growing complexities of emerging HCI systems and innovations. Diegetic props (like sound, videos, images) are sometimes used in design fiction to blur the lines between imagination and reality. These props enable the designers to be empathetic, feel present in the fiction as they investigate the complexity of technologies explored within the fiction, critique these technologies and think about their consequences. With a higher level of immersion and sense of embodiment, Virtual Reality (VR) can be a powerful tool for mediating and creating design fiction. However, there are few examples of VR as platform for design fiction. This workshop aims to investigate new opportunities for communicating, critiquing and co-creating design fiction narratives in immersive VR environments.
Prestigiacomo, R, Hadgraft, R, Hunter, J, Lockyer, L, Knight, S, van den Hoven, E & Martinez-Maldonado, R 2020, 'Learning-centred Translucence: An Approach to Understand How Teachers Talk About Classroom Data', LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, International Learning Analytics & Knowledge Conference, ACM, Frankfurt, Germany, pp. 100-105.
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Teachers are increasingly being encouraged to embrace evidence- based practices to improve their teaching. Learning analytics (LA) offer great promise in supporting these practices by providing evidence for teachers and learners to make informed decisions and transform the educational experience. However, LA limitations and their uptake by educators are also coming under critical scrutiny. This is in part due to the lack of involvement of teachers and learners in the design of LA tools to understand existing educational practices. In this paper, we propose a human-centred approach to generate understanding ofteachers’ data needs through the lens of three key principles of translucence: visibility, awareness and accountability. We illustrate our approach through a participatory design sprint to identify how teachers talk about classroom data. We describe teachers’ perspectives on the evidence they need for making better-informed decisions and discuss the implications of our approach for the design of human- centred LA in the next years.
Quan, R, Dong, X, Wu, Y, Zhu, L & Yang, Y 2019, 'Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification', IEEE International Conference on Computer Vision, ICCV, IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Korea (South).
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Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are usually built upon ResNet or VGG backbones, which were originally designed for classification. Because reID is different from classification, the architecture should be modified accordingly. We propose to automatically search for a CNN architecture that is specifically suitable for the reID task. There are three aspects to be tackled. First, body structural information plays an important role in reID but it is not encoded in backbones. Second, Neural Architecture Search (NAS) automates the process of architecture design without human effort, but no existing NAS methods incorporate the structure information of input images. Third, reID is essentially a retrieval task but current NAS algorithms are merely designed for classification. To solve these problems, we propose a retrieval-based search algorithm over a specifically designed reID search space, named Auto-ReID. Our Auto-ReID enables the automated approach to find an efficient and effective CNN architecture for reID. Extensive experiments demonstrate that the searched architecture achieves state-of-the-art performance while reducing 50% parameters and 53% FLOPs compared to others.
Rahman, MA, Singh, P, Muniyandi, RC, Mery, D & Prasad, M 2019, 'Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method', Image and Video Technology, Pacific-Rim Symposium on Image and Video Technology, Springer, Sydney, NSW, Australia, pp. 325-336.
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© 2019, Springer Nature Switzerland AG. Prostate cancer is the second most common cancer occurring in men worldwide, about 1 in 41 men will die because of prostate cancer. Death rates of prostate cancer increases with age. Even though, it being a serious condition only about 1 man in 9 will be diagnosed with prostate cancer during his lifetime. Accurate and early diagnosis can help clinician to treat the cancer better and save lives. This paper proposes two phases feature selection method to enhance prostate cancer early diagnosis based on artificial neural network. In the first phase, Best First Search method is used to extract the relevant features from original dataset. In the second phase, Taguchi method is used to select the most important feature from the already extracted features from Best First Search method. A public available prostate cancer benchmark dataset is used for experiment, which contains two classes of data normal and abnormal. The proposed method outperforms other existing methods on prostate cancer benchmark dataset with classification accuracy of 98.6%. The proposed approach can help clinicians to reach at more accurate and early diagnosis of different stages of prostate cancer and so that they make most suitable treatment decision to save lives of patients and prevent death due to prostate cancer.
Shen, S, Zhu, T, Ye, D, Yang, M, Liao, T & Zhou, W 2020, 'Simultaneously Advising via Differential Privacy in Cloud Servers Environment', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 550-563.
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© 2020, Springer Nature Switzerland AG. Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers’ capabilities and make optimal decisions on selecting the best available servers for users’ tasks. We consider the process that users learn servers’ capabilities as a multi-agent Reinforcement learning process. The learning speed and efficiency in Reinforcement learning can be improved by transferring the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by a requirement during experience transfer, which all learning agents in a Reinforcement learning environment must have the completely same available choices, also called actions. To address the above limit, this paper proposes a novel differential privacy agent advising approach in Reinforcement learning. Our proposed approach can significantly improve the conventional advising frameworks’ application when agents’ choices are not the completely same. The approach can also speed up the Reinforcement learning by the increase of possibility of experience transfer among agents with different available choices.
Singh, AK, Aldini, S, Leong, D, Wang, YK, Carmichael, MG, Liu, D & Lin, CT 2020, 'Prediction Error Negativity in Physical Human-Robot Collaboration', 8th International Winter Conference on Brain-Computer Interface, BCI 2020.
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© 2020 IEEE. Cognitive conflict is a fundamental phenomenon of human cognition, particularly during interaction with the real world. Understanding and detecting cognitive conflict can help to improve interactions in a variety of applications, such as in human-robot collaboration (HRC), which involves continuously guiding the semi-autonomous robot to perform a task in given settings. There have been several works to detect cognitive conflict in HRC but without physical control settings. In this work, we have conducted the first study to explore cognitive conflict using prediction error negativity (PEN) in physical human-robot collaboration (pHRC). Our results show that there was a statistically significant (p =. 047) higher PEN for conflict condition compared to normal conditions, as well as a statistically significant difference between different levels of PEN (p =. 020). These results indicate that cognitive conflict can be detected in pHRC settings and, consequently, provide a window of opportunities to improve the interaction in pHRC.
Soomro, AM, Paryani, S, Rehman, J, Echeverria, R, Prasad, M & Biloria, N 2019, 'Influencing Human Behaviour to Optimise Energy in Commercial Buildings', https://www.acis2019.org/papers-by-session/, Australian Conference on Information Systems, AIS, Perth, Australia, pp. 901-907.
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This paper discusses the impact of user energy choices on building energy demand, and how energy choices could be influenced to minimise building energy consumption using information systems.
Accordingly, a socio-technical framework is designed and presented, which draws upon the use of energy interventions. A novel Social-Economic-Environmental (SEE) model is presented within the sociotechnical framework which is aimed at nudging inhabitants enabling them to conserve energy in the university buildings, thereby making the world a sustainable place to live. The framework takes into account the Agent-based Modelling (ABM) approach to model user energy choices and their willingness to conserve energy in buildings. This research intends to test the socio-technical framework in the next stage of this study. Finally, this paper highlights gaps and the significance of understanding how user behaviour and their energy consumption can be influenced to optimise energy in university buildings, thereby reducing global greenhouse emissions.
Tan, Z, Xiong, J, Liu, B & Gui, L 2019, 'A Novel Random Access Mechanism based on Real-time Access Intensity Detection', IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, IEEE, Jeju, Korea (South).
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© 2019 IEEE. The Random Access (RA) procedure in 4G LTE serves for the uplink synchronization between UE and eNB and the allocation of the channel resource for data transmission. The existing 4G LTE RA Channel (RACH) lacks the adjustment to the real-time RA traffic and suffers from preamble collision and system congestion caused by the RA request flooding, which will not meet the requirement of ubiquitous and massive connection in 5G. To better improve the system throughput and the RA success probability of RACH in the dense device network, we propose a novel congestion-aware RA mechanism via a two-phase process in which concurrent devices carry out a Real-time Access Intensity Detection (RAID) prior to RA preamble message. In this paper, we develop an analytical model based on stochastic geometry and derive the RA success probability of our proposed model for typical device in single RA slot. The analytical results demonstrate the improvement of our proposed RA mechanism in terms of RA success probability under heavy RA traffic. Furthermore, a large amount of simulations under homogeneous and cluster Poisson Point Process (PPP) is carried out to analyze the performance of the proposed RA mechanism in terms of the system throughput, the RA success probability, the number of retransmission and the RA delay.
Thiyagarajan, K, Kodagoda, S, Ulapane, N & Prasad, M 2020, 'A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System', 2020 15th IEEE Conference on Industrial Electronics and Applications, Kristiansand, Norway.
Tonkin, M, Vitale, J, Herse, S, Raza, SA, Madhisetty, S, Kang, L, Vu, TD, Johnston, B & Williams, MA 2019, 'Privacy First: Designing Responsible and Inclusive Social Robot Applications for in the Wild Studies', 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE International Conference on Robot and Human Interactive Communication, IEEE, New Delhi, India.
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Deploying social robots applications in public spaces for conducting in the wild studies is a significant challenge but critical to the advancement of social robotics. Real world environments are complex, dynamic, and uncertain. Human-Robot interactions can be unstructured and unanticipated. In addition, when the robot is intended to be a shared public resource, management issues such as user access and user privacy arise, leading to design choices that can impact on users' trust and the adoption of the designed system. In this paper we propose a user registration and login system for a social robot and report on people's preferences when registering their personal details with the robot to access services. This study is the first iteration of a larger body of work investigating potential use cases for the Pepper social robot at a government managed centre for startups and innovation. We prototyped and deployed a system for user registration with the robot, which gives users control over registering and accessing services with either face recognition technology or a QR code. The QR code played a critical role in increasing the number of users adopting the technology. We discuss the need to develop social robot applications that responsibly adhere to privacy principles, are inclusive, and cater for a broad spectrum of people.
Ubaid, A, Dong, F & Hussain, FK 2020, 'Framework for Feature Selection in Health Assessment Systems', Advances in Intelligent Systems and Computing, pp. 313-324.
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© 2020, Springer Nature Switzerland AG. Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.
Ubaid, A, Hussain, FK & Charles, J 2020, 'Machine Learning-Based Regression Models for Price Prediction in the Australian Container Shipping Industry: Case Study of Asia-Oceania Trade Lane', Advances in Intelligent Systems and Computing, pp. 52-59.
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© 2020, Springer Nature Switzerland AG. The objective of this paper is to train a data-driven price prediction model for container pricing based on demand and supply for the Australian container shipping industry. The sourcing of demand, supply and pricing data has been done from Australian ports, Sea-Intelligence maritime analysis and the Shanghai Freight Index (SCFI) respectively. Data-driven prediction have been realized by applying three different regression models that include support vector regression (SVR), random forest regression (RFR) and gradient booster regression (GBR) over the gathered datasets after initial feature engineering. A comparison of research outcomes shows that GBR outperforms all the other models by offering a test accuracy of 84%.
Van Den Hoven, E, Shaer, O, Loke, L, Van Dijk, J & Kun, A 2020, 'TEI 2020 Chairs? Welcome', TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction, pp. III-IV.
Wadley, G, Krause, A, Liang, J, Wang, Z & Leong, TW 2019, 'Use of music streaming platforms for emotion regulation by international students', ACM International Conference Proceeding Series, Australian Conference on Human-Computer-Interaction, ACM, Fremantle, Australia, pp. 337-341.
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© 2019 Association for Computing Machinery. Listening to music has always been an emotion-laden experience. Early research involving analog platforms showed that people use recorded music as a resource to manage their emotions, enhancing desired affective states and attenuating unwanted states. More recently, technological advances such as streaming services have made an almost-unlimited selection of music ubiquitously available. This paper examines whether this intensified access to recorded music has afforded new ways of shaping emotion. We studied the practices of international university students, a cohort who face significant stresses and make significant use of digital technology. We found that students actively and routinely use music streaming services to manage their emotional responses to the challenges of studying abroad.
Wan, Y, Shu, J, Sui, Y, Xu, G, Zhao, Z, Wu, J & Yu, P 2019, 'Multi-modal attention network learning for semantic source code retrieval', Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019, IEEE/ACM International Conference on Automated Software Engineering, IEEE, San Diego, CA, USA, pp. 13-25.
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© 2019 IEEE. Code retrieval techniques and tools have been playing a key role in facilitating software developers to retrieve existing code fragments from available open-source repositories given a user query (e.g., a short natural language text describing the functionality for retrieving a particular code snippet). Despite the existing efforts in improving the effectiveness of code retrieval, there are still two main issues hindering them from being used to accurately retrieve satisfiable code fragments from large-scale repositories when answering complicated queries. First, the existing approaches only consider shallow features of source code such as method names and code tokens, but ignoring structured features such as abstract syntax trees (ASTs) and control-flow graphs (CFGs) of source code, which contains rich and well-defined semantics of source code. Second, although the deep learning-based approach performs well on the representation of source code, it lacks the explainability, making it hard to interpret the retrieval results and almost impossible to understand which features of source code contribute more to the final results. To tackle the two aforementioned issues, this paper proposes MMAN, a novel Multi-Modal Attention Network for semantic source code retrieval. A comprehensive multi-modal representation is developed for representing unstructured and structured features of source code, with one LSTM for the sequential tokens of code, a Tree-LSTM for the AST of code and a GGNN (Gated Graph Neural Network) for the CFG of code. Furthermore, a multi-modal attention fusion layer is applied to assign weights to different parts of each modality of source code and then integrate them into a single hybrid representation. Comprehensive experiments and analysis on a large-scale real-world dataset show that our proposed model can accurately retrieve code snippets and outperforms the state-of-the-art methods.
Wang, X 2020, 'Prototype Similarity Learning for Activity Recognition', Lecture Notes in Computer Science, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, online, pp. 649-649.
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Human Activity Recognition (HAR) plays an irreplaceable role in various applications such as security, gaming, and assisted living. Recent studies introduce deep learning to mitigate the manual feature extraction (i.e., data representation) efforts and achieve high accuracy. However, there are still challenges in learning accurate representations for sensory data due to the weakness of representation modules and the subject variances. We propose a scheme called Distance-based HAR from Ensembled spatial-temporal Representations (DHARER) to address above challenges. The idea behind DHARER is straightforward—the same activities should have similar representations. We first learn representations of the input sensory segments and latent prototype representations of each class, using a Convolution Neural Network (CNN)-based dual-stream representation module; then the learned representations are projected to activity types by measuring their similarity to the learned prototypes. We have conducted extensive experiments under a strict subject-independent setting on three large-scale datasets to evaluate the proposed scheme, and our experimental results demonstrate superior performance of DHARER to several state-of-the-art methods.
Wang, X 2020, 'Spectrum-guided adversarial disparity learning', 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, San Diego, CA, United States.
Wang, X, Li, Q, Zhang, W, Xu, G, Liu, S & Zhu, W 2020, 'Joint Relational Dependency Learning for Sequential Recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 168-180.
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© Springer Nature Switzerland AG 2020. Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or union-level interactions. Existing sequential recommendations mainly focus on user’s individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring short-term pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation.
Wang, X, Wu, Y, Zhu, L & Yang, Y 2020, 'Symbiotic Attention with Privileged Information for Egocentric Action Recognition', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI.
Wang, Z, Li, Q, Li, G & Xu, G 2019, 'Polynomial representation for persistence diagram', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, CA, USA, pp. 6116-6125.
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© 2019 IEEE. Persistence diagram (PD) has been considered as a compact descriptor for topological data analysis (TDA). Unfortunately, PD cannot be directly used in machine learning methods since it is a multiset of points. Recent efforts have been devoted to transforming PDs into vectors to accommodate machine learning methods. However, they share one common shortcoming: the mapping of PDs to a feature representation depends on a pre-defined polynomial. To address this limitation, this paper proposes an algebraic representation for PDs, i.e., polynomial representation. In this work, we discover a set of general polynomials that vanish on vectorized PDs and extract the task-adapted feature representation from these polynomials. We also prove two attractive properties of the proposed polynomial representation, i.e., stability and linear separability. Experiments also show that our method compares favorably with state-of-the-art TDA methods.
Wen, D, Huang, Y, Zhang, Y, Qin, L, Zhang, W & Lin, X 2020, 'Efficiently answering span-reachability queries in large temporal graphs', Proceedings - International Conference on Data Engineering, pp. 1153-1164.
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© 2020 IEEE. Reachability is a fundamental problem in graph analysis. In applications such as social networks and collaboration networks, edges are always associated with timestamps. Most existing works on reachability queries in temporal graphs assume that two vertices are related if they are connected by a path with non-decreasing timestamps (time-respecting) of edges. This assumption fails to capture the relationship between entities involved in the same group or activity with no time-respecting path connecting them. In this paper, we define a new reachability model, called span-reachability, designed to relax the time order dependency and identify the relationship between entities in a given time period. We adopt the idea of two-hop cover and propose an index-based method to answer span-reachability queries. Several optimizations are also given to improve the efficiency of index construction and query processing. We conduct extensive experiments on 17 real-world datasets to show the efficiency of our proposed solution.
Wu, J, Qiang, W, Zhu, T, Jin, H, Xu, P & Shen, S 2020, 'Differential Privacy Preservation for Smart Meter Systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 669-685.
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© 2020, Springer Nature Switzerland AG. With the rapid development of IoT and smart homes, smart meters have received extensive attention. The third-party applications, such as smart home controlling, dynamic demand-response, power monitoring, etc., can provide services to users based on consumption data of household electricity collected from smart meters. With the emergence of non-intrusive load monitoring, privacy issues from the data of smart meters become more and more severe. Differential privacy is a recognized concept that has become an important standard of privacy preservation for data with personal information. However, the existing privacy protection methods for the data of smart meters that are based on differential privacy sacrifices the actual energy consumption to protect the privacy of users, thus affecting the charging of power suppliers. To solve this problem, we propose a group-based noise adding method, so as to ensure the correct electricity billing. The experiments with two real-world data sets demonstrate that our approach can not only provide a strict privacy guarantee but also improve performance significantly.
Wu, J, Xie, R, Song, L & Liu, B 2019, 'Deep feature guided image retargeting', 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019, IEEE Visual Communications and Image Processing, IEEE, Sydney, Australia.
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© 2019 IEEE. Image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional content-Aware retargeting methods rely on low-level features to predict pixel-wise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pre-Trained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current state-of-Art image retargeting methods.
Wu, Y, Zhu, L, Yan, Y & Yang, Y 2019, 'Dual Attention Matching for Audio-Visual Event Localization', IEEE International Conference on Computer Vision, ICCV, IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Korea (South).
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In this paper, we investigate the audio-visual event localization problem. This task is to localize a visible and audible event in a video. Previous methods first divide a video into short segments, and then fuse visual and acoustic features at the segment level. The duration of these segments is usually short, making the visual and acoustic feature of each segment possibly not well aligned. Direct concatenation of the two features at the segment level can be vulnerable to a minor temporal misalignment of the two signals. We propose a Dual Attention Matching (DAM) module to cover a longer video duration for better high-level event information modeling, while the local temporal information is attained by the global cross-check mechanism. Our premise is that one should watch the whole video to understand the high-level event, while shorter segments should be checked in detail for localization. Specifically, the global feature of one modality queries the local feature in the other modality in a bi-directional way. With temporal co-occurrence encoded between auditory and visual signals, DAM can be readily applied in various audio-visual event localization tasks, e.g., cross-modality localization, supervised event localization. Experiments on the AVE dataset show our method outperforms the state-of-the-art by a large margin.
Xue, H, Liu, B, Din, M, Song, L & Zhu, T 2020, 'Hiding Private Information in Images From AI', ICC 2020 - 2020 IEEE International Conference on Communications (ICC), ICC 2020 - 2020 IEEE International Conference on Communications (ICC), IEEE.
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Yang, B, Wen, D, Qin, L, Zhang, Y, Wang, X & Lin, X 2020, 'Fully Dynamic Depth-First Search in Directed Graphs', Proceedings of the VLDB Endowment, 46th International Conference on Very Large Data Bases, VLDB Endowment, Tokyo, Japan, pp. 142-154.
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Depth-first search (DFS) is a fundamental and important algorithm in graph analysis. It is the basis of many graph algorithms such as computing strongly connected components, testing planarity, and detecting biconnected components. The result of a DFS is normally shown as a DFS-Tree. Given the frequent updates in many real-world graphs (e.g., social networks and communication networks), we study the problem of DFS-Tree maintenance in dynamic directed graphs. \other{In the literature, most works focus on the DFS-Tree maintenance problem in undirected graphs and directed acyclic graphs.} However, their methods cannot easily be applied in the case of general directed graphs. Motivated by this, we propose a framework and corresponding algorithms for both edge insertion and deletion in general directed graphs. We further give several optimizations to speed up the algorithms. We conduct extensive experiments on 12 real-world datasets to show the efficiency of our proposed algorithms.
Yin, J, Liu, S, Li, Q & Xu, G 2019, 'Prediction and Analysis of Rumour's Impact on Social Media', BESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings, International Conference on Behavioral, Economic and Socio-Cultural Computing, IEEE, Beijing, China.
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© 2019 IEEE. Rumour, as an important form of social communication, has been run through the whole evolutionary history of mankind. People maliciously disseminate rumours in order to increase awareness, slander others or cause panic, etc. To eliminate this issue, many researchers resort to detecting rumours on social media. However, rumour detection is not sufficient to eliminate the negative impact, which also requires official institutions to provide the refutations. In practice, the number of rumours on social media is too large, there is no need to refute some rumours with little or no concern. Therefore, we need to evaluate the impact of the rumours in advance. In this paper, we devise a rumour influence prediction model RISM (Rumour Impact on Social Media) based on a popular rumour intensity formula to predict the impact of a newborn rumour. Extensive numerical experiments are carried out on the real rumour data that are collected from Toutiao.com, which demonstrate the effectiveness of our proposed RISM model.
Zhai, X, Zhou, W, Fei, G, Hu, H, Qu, Y & Hu, G 2020, 'Null Model and Community Structure in Heterogeneous Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 151-163.
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© 2020, Springer Nature Switzerland AG. Finding different types of communities has become a research hot spot in network science. Plenty of the real-world systems containing different types of objects and relationships can be perfectly described as the heterogeneous networks. However, most of the current research on community detection is applied for the homogeneous networks, while there is no effective function to quantify the quality of the community structure in heterogeneous networks. In this paper, we first propose the null model with the same heterogeneous node degree distribution of the original heterogeneous networks. The probability of there being an edge between two nodes is given to build the modularity function of the heterogeneous networks. Based on our modularity function, a fast algorithm of community detection is proposed for the large scale heterogeneous networks. We use the algorithm to detect the communities in the real-world twitter event networks. The experimental results show that our method perform better than other exciting algorithms and demonstrate that the modularity function of the heterogeneous networks is an effective parameter that can be used to quantify the quality of the community structure in heterogeneous networks.
Zhang, L, Wang, X, Yao, L & Zheng, F 2020, 'Zero-shot object detection with textual descriptions using convolutional neural networks', 33rd International Joint Conference on Neural Networks, IEEE, Glasgow, UK.
Zhang, L, Wang, X, Yao, L, Wu, L & Zheng, F 2020, 'Zero-Shot Object Detection via Learning an Embedding from Semantic Space to Visual Space', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization.
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Zero-shot object detection (ZSD) has received considerable attention from the community of computer vision in recent years. It aims to simultaneously locate and categorize previously unseen objects during inference. One crucial problem of ZSD is how to accurately predict the label of each object proposal, i.e. categorizing object proposals, when conducting ZSD for unseen categories.
Previous ZSD models generally relied on learning an embedding from visual space to semantic space or learning a joint embedding between semantic description and visual representation. As the features in the learned semantic space or the joint projected space tend to suffer from the hubness problem, namely the feature vectors are likely embedded to an area of incorrect labels, and thus it will lead to lower detection precision. In this paper, instead, we propose to learn a deep embedding from the semantic space to the visual space, which enables to well alleviate the hubness problem, because, compared with semantic space or joint embedding space, the distribution in visual space has smaller variance. After learning a deep embedding model, we perform $k$ nearest neighbor search in the visual space of unseen categories to determine the category of each semantic description. Extensive experiments on two public datasets show that our approach significantly outperforms the existing methods.
Zhang, X, Yao, L, Wang, X, Zhang, W, Zhang, S & Liu, Y 2019, 'Know your mind: Adaptive cognitive activity recognition with reinforced CNN', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Beijing, China, pp. 896-905.
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© 2019 IEEE. Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of brain-computer Interface applications.
Zhao, M, Shu, Y, Liu, S & Xu, G 2019, 'Electricity Price Forecast using Meteorology data: A study in Australian Energy Market', BESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings, International Conference on Behavioral, Economic and Socio-Cultural Computing, IEEE, Beijing, China.
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© 2019 IEEE. Electricity price as a fundamental cost for each family which is an essential segment in the electricity market. The adjustment of electricity price can present the change in electricity supply and demand relationship. For the electricity supply companies, an appropriate defined electricity price can eventually determine the level of profit. On the other hand, an accurate prediction can help to seize opportunities in the electricity market. In this paper, we aim to predict the electricity price with more confident accuracy by leveraging data mining techniques. Our experiment on 12 months of electricity prices as well as climate data in the New South Wales has achieved a promising prediction result.
Zheng, C, Cai, Y, Xu, J, Leung, HF & Xu, G 2020, 'A boundary-aware neural model for nested named entity recognition', EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, pp. 357-366.
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© 2019 Association for Computational Linguistics In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on nested NER datasets and the experimental results demonstrate that our model outperforms other state-of-the-art methods.
Zhu, F, Wang, X, Zhu, L, Dong, X & Yang, Y 2020, 'UTS CAI submission at TRECVID 2018 ad-hoc video search task', 2018 TREC Video Retrieval Evaluation, TRECVID 2018.
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Copyright © TRECVID 2018.All rights reserved. This work describes our approach used for the fully automatic Ad-hoc Video Search (AVS) task [7] for TRECVID[1] 2018. Our model is divided into two parts, visual model and language model. Our motivation is mapping video embedding and language embedding into same semantic space. We observe that by constructing triplets in the feature space we can take better advantage of large batches and hard examples. Our models are trained on MSR-VTT [12] and TGIF [5] dataset with different visual and language architectures. The final ensemble model achieves 6.7% mAP.
Zhu, F, Zhu, L & Yang, Y 2019, 'Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation', IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, CA, USA, pp. 11388-11397.
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There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while synthetic data is much easier to construct by render-ing. Though it is promising to utilize the synthetic environments to facilitate navigation training in the real world, real environment are heterogeneous from synthetic environment in two aspects. First, the visual representation of the two environments have significant variances. Second, the houseplans of these two environments are quite different. There-fore two types of information,i.e. visual representation and policy behavior, need to be adapted in the reinforce mentmodel. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We pro-pose to jointly adapt visual representation and policy behavior to leverage the mutual impacts of environment and policy. Specifically, our method employs an adversarial feature adaptation model for visual representation transfer anda policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations.
Zhu, L, Sevilla-Lara, L, Tran, D, Feiszli, M, Yang, Y & Wang, H 2020, 'FASTER Recurrent Networks for Video Classification', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI.
Zou, C, Sui, Y, Yan, H & Xue, J 2019, 'TCD: Statically Detecting Type Confusion Errors in C++ Programs', Proceedings - International Symposium on Software Reliability Engineering, ISSRE, International Symposium on Software Reliability Engineering, IEEE, Berlin, Germany, pp. 292-302.
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© 2019 IEEE. For performance reasons, C++, albeit unsafe, is often the programming language of choice for developing software infrastructures. A serious type of security vulnerability in C++ programs is type confusion, which may lead to program crashes and control flow hijack attacks. While existing mitigation solutions almost exclusively rely on dynamic analysis techniques, which suffer from low code coverage and high overhead, static analysis has rarely been investigated. This paper presents TCD, a static type confusion detector built on top of a precise demand-driven field-, context-and flow-sensitive pointer analysis. Unlike existing pointer analyses, TCD is type-aware as it not only preserves the type information in the pointed-to objects but also handles complex language features of C++ such as multiple inheritance and placement new, making it therefore possible to reason about type casting in C++ programs. We have implemented TCD in LLVM and evaluated it using seven C++ applications (totaling 526,385 lines of C++ code) from Qt, a widely-adopted C++ toolkit for creating GUIs and cross-platform software. TCD has found five type confusion bugs, including one reported previously in prior work and four new ones, in under 7.3 hours, with a low false positive rate of 28.2%.