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Journal articles

Chen, J., Mao, G., Li, C., Zafar, A. & Zomaya, A. 2017, 'Throughput of Infrastructure-Based Cooperative Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems.
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In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.

Chen, Y., Ding, M., Li, J., Lin, Z., Mao, G. & Hanzo, L. 2017, 'Probabilistic Small-Cell Caching: Performance Analysis and Optimization', IEEE Transactions on Vehicular Technology, vol. 66, no. 5, pp. 4341-4354.
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© 1967-2012 IEEE.Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents, for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macro-cell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) the SBSs are always active, which is referred to as the always-on architecture, 2) the SBSs are activated on demand by mobile users (MUs), referred to as the dynamic on-off architecture. We focus our attention on the probability that MUs can successfully download contents from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities by maximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on-off architecture, they also relate to the MU-To-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement compared to the benchmark schemes.

Chomsiri, T., He, X.S., Nanda, P. & Tan, Z. 2017, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Cooper, C., Franklin, D., Ros, M., Safaei, F. & Abolhasan, M. 2017, 'A Comparative Survey of VANET Clustering Techniques', IEEE Communications Surveys Tutorials, vol. 19, no. 1, pp. 657-681.
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A vehicular ad hoc network (VANET) is a mobile ad hoc network (MANET) in which network nodes are vehicles – most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organised nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, inforomation dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming – the lack of realistic vehicular channel modelling – is identified. The importance of a rigorous and standardised performance evaluation regime utilising realistic vehicular channel models is demonstrated.

Dai, M., Cheng, S. & He, X.S. 2017, 'Hybrid generative–discriminative hash tracking with spatio-temporal contextual cues', Neural Computing and Applications.
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Visual object tracking is of a great application value in video monitoring systems. Recent work on video tracking has taken into account spatial relationship between the targeted object and its background. In this paper, the spatial relationship is combined with the temporal relationship between features on different video frames so that a real-time tracker is designed based on a hash algorithm with spatio-temporal cues. Different from most of the existing work on video tracking, which is regarded as a mechanism for image matching or image classification alone, we propose a hierarchical framework and conduct both matching and classification tasks to generate a coarse-to-fine tracking system. We develop a generative model under a modified particle filter with hash fingerprints for the coarse matching by the maximum a posteriori and a discriminative model for the fine classification by maximizing a confidence map based on a context model. The confidence map reveals the spatio-temporal dynamics of the target. Because hash fingerprint is merely a binary vector and the modified particle filter uses only a small number of particles, our tracker has a low computation cost. By conducting experiments on eight challenging video sequences from a public benchmark, we demonstrate that our tracker outperforms eight state-of-the-art trackers in terms of both accuracy and speed.

Ding, T., Ding, M., Mao, G., Lin, Z., Lopez-Perez, D. & Zomaya, A.Y. 2017, 'Uplink Performance Analysis of Dense Cellular Networks with LoS and NLoS Transmissions', IEEE Transactions on Wireless Communications, vol. 16, no. 4, pp. 2601-2613.
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© 2002-2012 IEEE.In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 10-1∼ 10-3 BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 10-2∼ 10-3 BSs/km2 , while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 10-1∼ 10-2,BSs/km-2.

Guo, D., Xu, J., Zhang, J., Xu, M., Cui, Y. & He, X. 2017, 'User relationship strength modeling for friend recommendation on Instagram', Neurocomputing, vol. 239, pp. 9-18.
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© 2017 Elsevier B.V.Social strength modeling in the social media community has attracted increasing research interest. Different from Flickr, which has been explored by many researchers, Instagram is more popular for mobile users and is conducive to likes and comments but seldom investigated. On Instagram, a user can post photos/videos, follow other users, comment and like other users' posts. These actions generate diverse forms of data that result in multiple user relationship views. In this paper, we propose a new framework to discover the underlying social relationship strength. User relationship learning under multiple views and the relationship strength modeling are coupled into one process framework. In addition, given the learned relationship strength, a coarse-to-fine method is proposed for friend recommendation. Experiments on friend recommendations for Instagram are presented to show the effectiveness and efficiency of the proposed framework. As exhibited by our experimental results, it can obtain better performance over other related methods. Although our method has been proposed for Instagram, it can be easily extended to any other social media communities.

Jan, M., Nanda, P., Usman, M. & He, X. 2017, 'PAWN: A Payload-based mutual Authentication scheme for Wireless Sensor Networks', Concurrency and Computation: Practice and Experience.
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Jan, M.A., Nanda, He, X.S. & Liu, R.P. 2017, 'A Sybil Attack Detection Scheme for a Forest Wildfire Monitoring Application', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications.
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Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in human-inaccessible terrains to monitor and collect time-critical and delay-sensitive events. There have been several studies on the use of WSN in different applications. All such studies have mainly focused on Quality of Service (QoS) parameters such as delay, loss, jitter, etc. of the sensed data. Security provisioning is also an important and challenging task lacking in all previous studies. In this paper, we propose a Sybil attack detection scheme for a cluster-based hierarchical network mainly deployed to monitor forest wildfire. We propose a two-tier detection scheme. Initially, Sybil nodes and their forged identities are detected by high-energy nodes. However, if one or more identities of a Sybil node sneak through the detection process, they are ultimately detected by the two base stations. After Sybil attack detection, an optimal percentage of cluster heads are elected and each one is informed using nomination packets. Each nomination packet contains the identity of an elected cluster head and an end user’s specific query for data collection within a cluster. These queries are user-centric, on-demand and adaptive to an end user requirement. The undetected identities of Sybil nodes reside in one or more clusters. Their goal is to transmit high false-negative alerts to an end user for diverting attention to those geographical regions which are less vulnerable to a wildfire. Our proposed approach has better network lifetime due to efficient sleep–awake scheduling, higher detection rate and low false-negative rate.

Lam, S., Sandrasegaran & Ghosal 2017, 'Performance Analysis of Frequency Reuse for PPPNetworks in Composite Rayleigh–Lognormal FadingChannel', Wireless Personal Communications.
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Pourashraf, P., Safaei, F. & Franklin, D.R. 2017, 'A Study of User Perception of the Quality of Video Content Rendered Inside a 3D Virtual Environment', IEEE Journal of Selected Topics in Signal Processing.
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This paper reports on the result of a user study to assess the impact of resolution and frame rate of video on the Quality of Experience of the users, when the video is rendered inside a 3D virtual space, and consequently viewed from arbitrary perspectives. A mathematical model for video rate is presented that expresses the total rate as the product of separate functions of spatial and temporal resolutions. Results from the user study are combined with the model to predict the rate parameters which will result in perceptually acceptable quality using the 3D features of the virtual environment. The results show that by exploiting the insensitivity of users to controlled quality degradation, the downstream network load for the client can be significantly reduced with little or no perceptual impact on the clients.

Qu, X., Yi, W., Wang, T., Wang, S., Xiao, L. & Liu, Z. 2017, 'Mixed-Integer Linear Programming Models for Teaching Assistant Assignment and Extensions', Scientific Programming, vol. 2017.
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© 2017 Xiaobo Qu et al.In this paper, we develop mixed-integer linear programming models for assigning the most appropriate teaching assistants to the tutorials in a department. The objective is to maximize the number of tutorials that are taught by the most suitable teaching assistants, accounting for the fact that different teaching assistants have different capabilities and each teaching assistant's teaching load cannot exceed a maximum value. Moreover, with optimization models, the teaching load allocation, a time-consuming process, does not need to be carried out in a manual manner. We have further presented a number of extensions that capture more practical considerations. Extensive numerical experiments show that the optimization models can be solved by an off-the-shelf solver and used by departments in universities.

Usman, M., Jan, M.A. & He, X.S. 2017, 'Cryptography-Based Secure Data Storage and Sharing Using HEVC and Public Clouds', Information Sciences, vol. 387, pp. 90-102.
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Mobile devices are widely used for uploading/downloading media files such as audio, video and images to/from the remote servers. These devices have limited resources and are required to offload resource-consuming media processing tasks to the clouds for further processing. Migration of these tasks means that the media services provided by the clouds need to be authentic and trusted by the mobile users. The existing schemes for secure exchange of media files between the mobile devices and the clouds have limitations in terms of memory support, processing load, battery power, and data size. These schemes lack the support for large-sized video files and are not suitable for resource-constrained mobile devices. This paper proposes a secure, lightweight, robust and efficient scheme for data exchange between the mobile users and the media clouds. The proposed scheme considers High Efficiency Video Coding (HEVC) Intra-encoded video streams in unsliced mode as a source for data hiding. Our proposed scheme aims to support real-time processing with power-saving constraint in mind. Advanced Encryption Standard (AES) is used as a base encryption technique by our proposed scheme. The simulation results clearly show that the proposed scheme outperforms AES-256 by decreasing the processing time up to 4.76% and increasing the data size up to 0.72% approximately. The proposed scheme can readily be applied to real-time cloud media streaming.

Wang, S., Qu, X., Wang, T. & Yi, W. 2017, 'Optimal Container Routing in Liner Shipping Networks Considering Repacking 20 ft Containers into 40 ft Containers', Journal of Advanced Transportation, vol. 2017, pp. 1-9.
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Zhang, T., Jia, W., He, X.S. & Yang, J. 2017, 'Discriminative Dictionary Learning with Motion Weber Local Descriptor for Violence Detection', IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 3, pp. 696-709.
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Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of motion Weber local descriptor (WLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the motion IWLD. Second, we propose a modified sparse-representation-based classification model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. The experimental results on three benchmark data sets have demonstrated the superior performance of the proposed approach over the state of the arts.

Zhang, T., Jia, W., Yang, B., Yang, J., He, X. & Zheng, Z. 2017, 'MoWLD: a robust motion image descriptor for violence detection', Multimedia Tools and Applications, vol. 76, no. 1, pp. 1419-1438.
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© 2015 Springer Science+Business Media New York Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts.

Zhou, M., Qu, X. & Jin, S. 2017, 'On the Impact of Cooperative Autonomous Vehicles in Improving Freeway Merging: A Modified Intelligent Driver Model-Based Approach', IEEE Transactions on Intelligent Transportation Systems, pp. 1-7.
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Conferences

Agrawal, S. & Williams, M.A. 2017, 'Robot authority and human obedience: A study of human behaviour using a robot security guard', ACM/IEEE International Conference on Human-Robot Interaction, pp. 57-58.
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© 2017 Authors.There has been much debate, sci-fi movie scenes, and several scientific studies exploring the concept of robot authority. Some of the key research questions include: when should humans follow/question robot instructions; how can a robot increase its ability to convince humans to follow their instructions or to change their behaviour. In this paper, we describe a recent experiment designed to explore the notions of robot authority and human obedience. We set up a robot in a publicly accessible building to act as a security guard that issued instructions to specific humans. We identified and analysed the factors that affected a human's decisions to follow the robot's instruction. The four key factors were: perceived aggression, responsiveness, anthropomorphism, level of safety and intelligence in the robot's behaviour. We implemented various social cues to exhibit and convey authority and aggressiveness in the robot's behaviour. The results suggest that the degree of aggression that different people perceived in the robot's behaviour did not have a significant impact in their decision to follow the robot's instruction. Although, the people who disobeyed the robot, perceived the robot's behaviour to be more unsafe and less human-like than the people who followed the robot's instructions and also found the robot to be more responsive.

Dang, T.D., Hoang, D. & Nanda, P. 2017, 'A novel hash-based file clustering scheme for efficient distributing, storing, and retrieving of large scale health records', Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016, pp. 1485-1491.
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© 2016 IEEE.Cloud computing has been adopted as an efficient computing infrastructure model for provisioning resources and providing services to users. Several distributed resource models such as Hadoop and parallel databases have been deployed in healthcare-related services to manage electronic health records (EHR). However, these models are inefficient for managing a large number of small files and hence they are not widely deployed in Healthcare Information Systems. This paper proposed a novel Hash-Based File Clustering Scheme (HBFC) to distribute, store and retrieve EHR efficiently in cloud environments. The HBFC possesses two distinctive features: it utilizes hashing to distribute files into clusters in a control way and it utilizes P2P structures for data management. HBFC scheme is demonstrated to be effective in handling big health data that comprises of a large number of small files in various formats. It allows users to retrieve and access data records efficiently. The initial implementation results demonstrate that the proposed scheme outperforms original P2P system in term of data lookup latency.

Ding, M., Lopez Perez, D., Mao, G. & Lin, Z. 2017, 'Study on the idle mode capability with LoS and NLoS transmissions', 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings.
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© 2016 IEEE.In this paper, we study the impact of the base station (BS) idle mode capability (IMC) on the network performance in dense small cell networks (SCNs). Different from existing works, we consider a sophisticated path loss model incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions. Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance for SCNs with IMCs at the BSs. The upper bound, the lower bound and the approximate expression of the activated BS density are also derived. The performance impact of the IMC is shown to be significant. As the BS density surpasses the UE density, thus creating a surplus of BSs, the coverage probability will continuously increase toward one. For the practical regime of the BS density, the results derived from our analysis are distinctively different from existing results, and thus shed new light on the deployment and the operation of future dense SCNs.

Khruahong, S., Kong, X., Sandrasegaran, K. & Liu, L. 2017, 'Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services', Proceedings of ", The 18th IEEE International Symposium on High Assurance Systems Engineering, The 18th IEEE International Symposium on High Assurance Systems Engineering, Singapore.
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Ojha, S. & Williams, M.-.A. 2017, 'Emotional Appraisal : A Computational Perspective', Fifth Annual Conference on Advances in Cognitive Systems, Troy, USA.
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Research on computational modelling of emotions has received significant attention in the last few decades. As such, several computational models of emotions have been proposed which have provided an unprecedented insight into the implications of the emotion theories emerging from cognitive psychology studies. Yet the existing computational models of emotion have distinct limitations namely:(i) low replicability - difficult to implement the given computational model by reading the description of the model, (ii) domain dependence - model only applicable in one or more predefined scenarios or domains, (iii) low scalability and integrability - difficult to use the system in larger or different domains and difficult to integrate the model in wide range of other intelligent systems. In this paper, we propose a completely domain-independent mathematical representation for computational modelling of emotion that provides better replicability and integrability. The implementation of our model is inspired by appraisal theory - an emotion theory which assumes that emotions result from the cognitive evaluation of a situation.