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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

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Detecting Manipulated Smartphone Data on Android and iOS Devices 1

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17th International Conference on Information security, ISSA 2018

作者： Pieterse, Heloise Olivier, Martin van Heerden, Renier Defence Peace Safety and Security Council for Scientific and Industrial Research Pretoria South Africa Department of Computer Science University of Pretoria Pretoria South Africa National Integrated Cyber Infrastructure System Council for Scientific and Industrial Research Pretoria South Africa School of Information and Communication Technology Nelson Mandela University Port Elizabeth South Africa

ISBN: (数字)9783030114077

ISBN: (纸本)9783030114060

Ever improving technology allows smartphones to become an integral part of people’s lives. The reliance on and ubiquitous use of smartphones render these devices rich sources of data. This data becomes increasingly important when smartphones are linked to criminal or corporate investigations. To erase data and mislead digital forensic investigations, end-users can manipulate the data and change recorded events. This paper investigates the effects of manipulating smartphone data on both the Google Android and Apple iOS platforms. The deployed steps leads to the formulation of a generic process for smartphone data manipulation. To assist digital forensic professionals with the detection of such manipulated smartphone data, this paper introduces an evaluation framework for smartphone data. The framework uses key traces left behind as a result of the manipulation of smartphone data to construct techniques to detect the changed data. The outcome of this research study successfully demonstrates the manipulation of smartphone data and presents preliminary evidence that the suggested framework can assist with the detection of manipulated smartphone data. © 2019, Springer Nature Switzerland AG.

关键词： Smartphones

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Mobile Data offloading in the Vehicular Networks: A Truthful Incentive Mechanism

Mobile Data offloading in the Vehicular Networks: A Truthful...

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IEEE International Conference on High Performance Computing and Communications

作者： Enfang Zhong Peng Li Tao Zhang Weiyi Huang Qin Liu College of Computer Science and Technology Wuhan University of Science and Technology China Department of Computer Science New York Institute of Technology USA School of Cyber Science and Engineering Wuhan University USA

Mobile data offloading is a cost-effective and promising approach to alleviate the burden of the cellular network. In this paper, we focus on designing a truthful incentive mechanism that the auction cloud platform motivates mobile users with deadline constraints and capacity constraints to offload data. First, we formulate the mobile data offloading problem as a reverse auction. Then, we reduce this social welfare maximization reverse auction for data offloading as an integer linear programming (ILP) problem. To solve this problem, we propose a data offloading auction greedy algorithm to choose the most cost-effective bids. We decompose the data offload auction greedy algorithm into the winner determination problem (WDP) and the threshold payment scheme. Moreover, we propose a delay winner winning algorithm based on the data offloading auction greedy algorithm. Extensive simulation experiments demonstrate that the data offloading auction mechanism dramatically reduces the burden of the cellular network while meeting the offloading tasks deadline constraints and mobile user capacity constraints.

关键词： Task analysis Cellular networks Wireless fidelity Greedy algorithms Delays Data models Conferences

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Graph stochastic neural networks for semi-supervised learning 20

Graph stochastic neural networks for semi-supervised learnin...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Haibo Wang Chuan Zhou Xin Chen Jia Wu Shirui Pan Jilong Wang Department of Computer Science and Technology Tsinghua University Beijing China and Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Faculty of Science and Engineering Macquarie University Sydney Australia Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9781713829546

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure. To improve the rigidness and inflexibility of deterministic classification functions, this paper proposes a novel framework named Graph Stochastic Neural Networks (GSNN), which aims to model the uncertainty of the classification function by simultaneously learning a family of functions, i.e., a stochastic function. Specifically, we introduce a learnable graph neural network coupled with a high-dimensional latent variable to model the distribution of the classification function, and further adopt the amortised variational inference to approximate the intractable joint posterior for missing labels and the latent variable. By maximizing the lower-bound of the likelihood for observed node labels, the instantiated models can be trained in an end-to-end manner effectively. Extensive experiments on three real-world datasets show that GSNN achieves substantial performance gain in different scenarios compared with state-of-the-art baselines.

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RETRACTED ARTICLE: Cloud enabled robust authenticated key agreement scheme for telecare medical information system

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Connection Science 2021年第4期33卷 I页

作者： Liu, Tian Liu, Xuchong Li, Xiong Amin, Ruhul Liang, Wei Meng-Yen, Hsieh School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan People's Republic of China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Changsha People's Republic of China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China Department of Computer Science and Engineering Dr. SPM International Institute of Information Technology Naya Raipur India College of Computer Science and Electronic Engineering Hunan University Changsha People's Republic of China Deptment of Computer Science and Information Engineering Providence University Taichung Taiwan

We, the authors, Editors and Publisher of the journal Connection Science, have retracted the following article:Tian Liu, Xuchong Liu, Xiong Li, Ruhul Amin, Wei Liang & Meng-Yen Hsieh (2021) Cloud enabled robust authenticated key agreement scheme for telecare medical information system, Connection Science, DOI: 10.1080/09540091.2021.1901072Since publication, the authors identified some technical issues that they had overlooked. In general, biometrics are data that owns fuzzy properties but the authors in this paper have neglected this technical point and the biometric information was authenticated using a hash function. Since the output of a hash function is sensitive to the input it cannot be applied to verify the validity of fuzzy data such as biometrics. Since this scheme is based on a hash function for biometric verification, it must not be used in real applications. The authors alerted the issue to the Editor and Publisher and all have agreed to retract the article to ensure the integrity of the scholarly *** have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on *** retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

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Achieving Guaranteed Performance for Protection Traffic in Smart Grid Wide-Area Networks

Achieving Guaranteed Performance for Protection Traffic in S...

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IEEE International Conference on Smart Energy Grid engineering (SEGE)

作者： Charles M. Adrah Aditya K. Kamath Steinar Bjørnstad Mohit P. Tahiliani Department of Information Security and Communication Technology NTNU Trondheim Norway Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

Recent years, tele-protection applications in utility grids have been deployed using Ethernet. However, Ethernet without Time Sensitive Network (TSN) mechanisms is nondeterministic. Hence, challenges of the queuing delays occurring on multi-hop paths result in Packet Delay Variations (PDV) and may even result in packet losses due to buffer overflows. There have been recommendations to use Priority Scheduling (PS) to lower the latency of tele-protection messages. However, for PS, maximum PDV occurs on higher priority packets when contending with lower priority packets, needing to wait until a lower priority packet with maximum length have exited a switch. In this paper, we explore through a performance simulation study the suitability of applying FUSION in smart grid tele-protection applications. FUSION is a packet switched principle applying Ethernet, offering circuit-switched quality of service with deterministic latency, zero packet loss and ultra-low PDV for high priority packets. We demonstrate FUSION performance in tele-protection for power system networks, and compare it with Strict Priority Queuing (SPQ), which is recommended for realtime industrial applications. Our results show that by applying FUSION, we are able to guarantee a fixed delay, zero PDV and packet loss through the network. Furthermore, we show that through proper network dimensioning, lower priority traffic can additionally be added with delays within acceptable limits.

关键词： Delays Ethernet Job shop scheduling Quality of service Packet loss Logic gates

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Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video

arXiv

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arXiv 2020年

作者： Wang, Jianyi Deng, Xin Xu, Mai Chen, Congyong Song, Yuhang School of Electronic and Information Engineering Beihang University China School of Cyber Science and Technology Beihang University China College of Software Beihang University Beijing China Hangzhou Innovation Institute Beihang University Zhejiang China Department of Computer Science University of Oxford United Kingdom

The past few years have witnessed fast development in video quality enhancement via deep learning. Existing methods mainly focus on enhancing the objective quality of compressed video while ignoring its perceptual quality. In this paper, we focus on enhancing the perceptual quality of compressed video. Our main observation is that enhancing the perceptual quality mostly relies on recovering high-frequency sub-bands in wavelet domain. Accordingly, we propose a novel generative adversarial network (GAN) based on multi-level wavelet packet transform (WPT) to enhance the perceptual quality of compressed video, which is called multi-level wavelet-based GAN (MW-GAN). In MW-GAN, we first apply motion compensation with a pyramid architecture to obtain temporal information. Then, we propose a wavelet reconstruction network with wavelet-dense residual blocks (WDRB) to recover the high-frequency details. In addition, the adversarial loss of MW-GAN is added via WPT to further encourage high-frequency details recovery for video frames. Experimental results demonstrate the superiority of our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Data sharing and privacy for patient IoT devices using blockchain 7th

Data sharing and privacy for patient IoT devices using block...

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7th International Conference on Smart City and Informatization, iSCI 2019

作者： Srivastava, Gautam Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Mathematics and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Center for Interneural Computing China Medical University Taichung40402 Taiwan College of Computing and Software Engineering Kennesaw State University KennesawGA30144 United States Cyber Science Lab School of Computer Science University of Guelph GuelphONN1G 2W1 Canada Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249 United States

ISBN: (纸本)9789811513008

Once a fitness fad, wearable and other related Internet of Things (IoT) devices are fast becoming common place in many different smart city applications such as healthcare. However, IoT devices, particularly inexpensive devices, often trade security and privacy for usability. One solution to protect privacy in the healthcare domain which has begun to be explored is blockchain-based technology. However, there are a number of limitations underpinning the use of blockchain, which limits its adoption particularly in applications that require low energy and computational footprints. In this paper, we present a transactional protocol for remote patient monitoring using directed acyclic graphs. We use a newer blockchain protocol called GHOSTDAG in both a public blockchain and a private blockchain. Our novel proposed solution aims to resolve known security issues for healthcare, without affecting scalability (a feature of classic blockchain architecture). © Springer Nature Singapore Pte Ltd. 2019.

关键词： Internet of things

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Source camera attribution of Multi-format devices

arXiv

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arXiv 2019年

作者： Taspinar, Samet Mohanty, Manoranjan Memon, Nasir Cyber Security New York University Abu Dhabi United Arab Emirates Department of Computer Science University of Auckland New Zealand Department of Computer Science and Engineering New York University New York United States

Photo Response Non-Uniformity (PRNU) based source camera attribution is an effective method to determine the origin camera of visual media (an image or a video). However, given that modern devices, especially smartphones, capture images, and videos at different resolutions using the same sensor array, PRNU attribution can become ineffective as the camera fingerprint and query visual media can be misaligned. We examine different resizing techniques such as binning, line-skipping, cropping and scaling that cameras use to downsize the raw sensor image to different media. Taking such techniques into account, this paper studies the problem of source camera attribution. We define the notion of Ratio of Alignment, which is a measure of shared sensor elements among spatially corresponding pixels within two media objects resized with different techniques. We then compute the Ratio of Alignment between the different combinations of three common resizing methods under simplified conditions and experimentally validate our analysis. Based on the insights drawn from the different techniques used by cameras and the RoA analysis, the paper proposes an algorithm for matching the source of a video with an image and vice versa. We also present an efficient search method resulting in significantly improved performance in matching as well as computation time. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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Efficient Online Resource Allocation in Heterogeneous Clusters with Machine Variability

Efficient Online Resource Allocation in Heterogeneous Cluste...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Huanle Xu Yang Liu Wing Cheong Lau Jun Guo Alex Liu College of Computer Science and Network Security Dongguan University of Technology Department of Information Engineering The Chinese University of Hong Kong

Approximation jobs that allow partial execution of their many tasks to achieve valuable results have played an important role in today's large-scale data analytics [1], [2]. This fact can be utilized to maximize the system utility of a big data computing cluster by choosing proper tasks in scheduling for each approximation job. A fundamental challenge herein, however, is that the machine service capacity may fluctuate substantially during a job's lifetime, which makes it difficult to assign valuable tasks to well-performed machines. In addition, the cluster scheduler needs to make online scheduling decisions without knowing future job arrivals according to machine availabilities. In this paper, we tackle this online resource allocation problem for approximation jobs in parallel computing clusters. In particular, we model a cluster with heterogeneous machines as a multi-armed bandit where each machine is treated as an arm. By making estimations on machine service rates while balancing the exploration-exploitation trade-off, we design an efficient online resource allocation algorithm from a bandit perspective. The proposed algorithm extends existing online convex optimization techniques and yields a sublinear regret bound. Moreover, we also examine the performance of the proposed algorithm via extensive trace-driven simulations and demonstrate that it outperforms the baselines substantially.

关键词： Task analysis Clustering algorithms Resource management Computational modeling Approximation algorithms Optimization Training

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