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arXiv 2020年

作者： Hu, Fenghe Deng, Yansha Saad, Walid Bennis, Mehdi Hamid Aghvami, A. Department of Informatics King's College London LondonWC2R 2LS United Kingdom Centre for Wireless Communications University of Oulu Finland Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

Cellular-connected wireless connectivity provides new opportunities for virtual reality (VR) to offer seamless user experience from anywhere at anytime. To realize this vision, the quality-of-service (QoS) for wireless VR needs to be carefully defined to reflect human perception requirements. In this paper, we first identify the primary drivers of VR systems, in terms of applications and use cases. We then map the human perception requirements to corresponding QoS requirements for four phases of VR technology development. To shed light on how to provide short/long-range mobility for VR services, we further list four main use cases for cellular-connected wireless VR and identify their unique research challenges along with their corresponding enabling technologies and solutions in 5G systems and beyond. Last but not least, we present a case study to demonstrate the effectiveness of our proposed solution and the unique QoS performance requirements of VR transmission compared with that of traditional video service in cellular networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Virtual reality

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Perception matters: Exploring imperceptible and transferable anti-forensics for GAN-generated fake face imagery detection

arXiv

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arXiv 2020年

作者： Wang, Yongwei Ding, Xin Ding, Li Ward, Rabab Jane Wang, Z. Department of Electrical and Computer Engineering University of British Columbia VancouverV6T1Z4 Canada Department of Statistics University of British Columbia VancouverV6T1Z4 Canada School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710048 China

Recently, generative adversarial networks (GANs) can generate photo-realistic fake facial images which are perceptually indistinguishable from real face photos, promoting research on fake face detection. Though fake face forensics can achieve high detection accuracy, their anti-forensic counterparts are less investigated. Here we explore more imperceptible and transferable anti-forensics for fake face imagery detection based on adversarial attacks. Since facial and background regions are often smooth, even small perturbation could cause noticeable perceptual impairment in fake face images. Therefore it makes existing adversarial attacks ineffective as an anti-forensic method. Our perturbation analysis reveals the intuitive reason of the perceptual degradation issue when directly applying existing attacks. We then propose a novel adversarial attack method, better suitable for image anti-forensics, in the transformed color domain by considering visual perception. Simple yet effective, the proposed method can fool both deep learning and non-deep learning based forensic detectors, achieving higher attack success rate and significantly improved visual quality. Specially, when adversaries consider imperceptibility as a constraint, the proposed anti-forensic method can improve the average attack success rate by around 30% on fake face images over two baseline attacks. More imperceptible and more transferable, the proposed method raises new security concerns to fake face imagery detection. We have released our code for public use, and hopefully the proposed method can be further explored in related forensic applications as an anti-forensic benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Incentivize to Build: A Crowdsourcing Framework for Federated Learning

Incentivize to Build: A Crowdsourcing Framework for Federate...

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IEEE Global communications Conference

作者： Shashi Raj Pandey Nguyen H. Tran Mehdi Bennis Yan Kyaw Tun Zhu Han Choong Seon Hong Department of Computer Science and Engineering Kyung Hee University Rep. of Korea School of Computer Science The University of Sydney NSW Australia Centre for Wireless Communications University of Oulu Oulu Finland Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to the central aggregator for improving the global model. However, a key challenge is to maintain communication efficiency (i.e., the number of communications per iteration) when participating clients implement uncoordinated computation strategy during aggregation of model parameters. We formulate a utility maximization problem to tackle this difficulty, and propose a novel crowdsourcing framework, involving a number of participating clients with local training data to leverage FL. We show the incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Further, we illustrate the efficacy of our proposed framework with simulation results. Results show that the proposed mechanism outperforms the heuristic approach with up to 22% gain in the offered reward to attain a level of target accuracy.

关键词： Servers Computational modeling Crowdsourcing Data models Games Training Training data

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Online distributed job dispatching with outdated and partially-observable information

arXiv

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arXiv 2020年

作者： Hong, Yuncong Lv, Bojie Wang, Rui Tan, Haisheng Han, Zhenhua Zhou, Hao Lau, Francis C.M. LINKE Lab University of Science and Technology of China Hefei China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Computer Science The University of Hong Kong Hong Kong Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

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Preprocessing Monitoring Information on the SDN Data-Plane using P4

Preprocessing Monitoring Information on the SDN Data-Plane u...

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IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

作者： Rhaban Hark Divyashri Bhat Michael Zink Ralf Steinmetz Amr Rizk Multimedia Communications Engineering Lab Technische Universität Darmstadt Germany Department of Electrical and Computer Engineering University of Massachusetts Amherst USA

ISBN: (数字)9781728145457

ISBN: (纸本)9781728145464

Network management applications such as routing, load-balancing, or traffic forecasting, require up-to-date state information about the underlying data-plane. However, it is well known that data-plane measurements contain redundant information. In this work, we propose an approach that estimates how informative data-plane measurements are for control-plane applications that operate on such information. Using programmable data-planes, we present a novel approach on how the decision on forwarding data-plane measurements can be taken at network switches, and how this aids in filtering irrelevant monitoring information to save the controller's computational and networking resources.

关键词： Bandwidth Control systems Monitoring Mathematical model Training Linear regression Estimation

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Spatial modulation or spatial multiplexing for mmWave communications? 9th

Spatial modulation or spatial multiplexing for mmWave commun...

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9th International EAI Conference on Broadband communications, Networks, and Systems, Broadnets 2018

作者： Elkawafi, Salma Younis, Abdelhamid Mesleh, Raed Abouda, Abdulla Elbarsha, Ahmed Elmusrati, Mohammed Electrical and Electronics Engineering Department Faculty of Engineering University of Benghazi P.O. Box 7051 Benghazi Libya Electrical and Communications Engineering Department School of Electrical Engineering and Information Technology German Jordanian University P.O. Box 35247 Amman11180 Jordan Almadar Research and Development Office Almadar Aljadid Company Tripoli Libya The Department of Computer Science University of Vaasa Vaasa Finland

ISBN: (纸本)9783030051945

In this paper, two large scale (LS)–multiple–input multiple–output (MIMO) systems and their performance over 3D statistical outdoor millimeter wave (mmWave) channel model are considered and thoroughly analyzed. Namely, spatial multiplexing (SMX) and spatial modulation (SM) systems are considered. The performance of both systems in terms of average bit error ratio (ABER) and channel capacity are derived and studied. Obtained results divulge that SM can achieve higher theoretical capacity than SMX system. Further, SMX system is shown to offer better ABER and mutual information performance as compared to SM system for the same system configuration. Yet, SM demonstrate significant energy efficiency (EE) enhancement for large scale number of transmit antennas. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Millimeter waves

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PVrosion: ACCELERATED CORROSION BLIND SPOTS INHERENT TO PHOTOVOLTAIC SYSTEMS' OPERATION AND UNDETECTED DC FAULTS - TROJAN HORSE FOR DETERIORATING THE INTEGRITY OF CRITICAL INFRASTRUCTURES AND BUILDINGS

PVrosion: ACCELERATED CORROSION BLIND SPOTS INHERENT TO PHOT...

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The 12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020)

作者： C. Melios A. Demetriou A. Christofe K. Neocleous T. Polydorou D. Hadjimitsis E. Christoforou S. Kallis M. Nicolaou L. Loizou G. Kyriacou Y. Papademetriou V. P. Androvitsaneas I. F. Gonos C. A. Charalambous Power Systems Modelling (PSM) Laboratory Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Civil Engineering and Geomatics Faculty of Engineering and Technology Cyprus University of Technology Lemesos Cyprus Nortest Cyprus LTD Nicosia Cyprus Department of Electrical and Mechanical Services (EMS) Ministry of Transport Communications & Works Nicosia Cyprus Hellenic Copper Mines Ltd Nicosia Cyprus Union of Licensed Electricians Cyprus (SEHK) Nicosia Cyprus Cypenergia Nicosia Cyprus High Voltage Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

PVrosion: Accelerated Corrosion Blind Spots Inherent to Photovoltaic Systems' Operation and Undetected DC Faults - Trojan horse for Deteriorating the Integrity of Critical Infrastructures and Buildings, is a 2019 project funded by the Research and Innovation Foundation of Cyprus. The project elaborates on a new issue that progressively receives attention by the photovoltaic (PV) installation industry and critical infrastructure's operators. It specifically introduces “DC stray current corrosion blind spots” that are inherent to PV systems' earthing and associated DC ground fault detection mechanisms. Blind spots arise as the existing protection thresholds for DC leakage currents have been based on other issues, such as fire or personnel safety. Using soil and concrete as conductive mediums, DC leakage currents can stray out of the area of PV installations and interfere with nearby third-party critical infrastructure. This article presents an overview of the project activities, attainments and intended outcomes.

关键词： leakage currents fault diagnosis corrosion photovoltaic power systems earthing power generation protection electrical installation power generation faults life testing

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Content-aware robust semantic transmission of images over wireless channels with GANs

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Digital communications and Networks 2024年

作者： Xuyang Chen Daquan Feng Qi He Yao Sun Gaojie Chen Xiang-Gen Xia College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu 611731 China James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UK 5GIC & 6GIC Institute for Communication Systems University of Surrey Guildford GU2 7XH UK Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA

Semantic Communication (SemCom) can significantly reduce the transmitted data volume and keep robustness. Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly, rather than achieving precise bit-by-bit reconstruction. Existing image SemCom systems directly perform semantic encoding and decoding on the entire image, which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise. To this end, we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks (GANs). Specifically, the accurate semantics of the image are extracted by the semantic encoder, and divided into two parts for different downstream tasks: Regions of Interest (ROI) and Regions of Non-Interest (RONI). By reducing the quantization accuracy of RONI, the amount of transmitted data volume is reduced significantly. During the transmission process of semantics, a Signal-to-Noise Ratio (SNR) is randomly initialized, enabling the model to learn the average noise distribution. The experimental results demonstrate that by reducing the quantization level of RONI, transmitted data volume is reduced up to 60.53% compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks. Moreover, our model exhibits increased robustness with variable SNRs.

关键词： Semantic communication GANs Image transmission ROI

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Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

Analysis of Negative Capacitance MOSFETs Characteristic with...

引用

International Symposium on VLSI Technology, Systems and Applications

作者： Yu-Chun Lee Sekhar Reddy Kola Chieh-Yang Chen Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering EECS International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable I on /I off ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS.

关键词： Capacitance Iron Semiconductor device modeling MOSFET Integrated circuit modeling Mathematical model Ferroelectric materials

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Reinforcement learning-based black-box evasion attacks to link prediction in dynamic graphs

arXiv

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arXiv 2020年

作者： Fan, Houxiang Wang, Binghui Zhou, Pan Li, Ang Pang, Meng Xu, Zichuan Fu, Cai Li, Hai Chen, Yiran School of Electronic Information and Communications Huazhong University of Science Technology China Department of Electrical and Computer Engineering Duke University United States School of Cyber Science and Engineering Huazhong University of Science Technology China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Software Dalian University of Technology China

Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc. Various LPDG methods based on graph embedding and graph neural networks have been recently proposed and achieved state-of-the-art performance. In this paper, we study the vulnerability of LPDG methods and propose the first practical black-box evasion attack. Specifically, given a trained LPDG model, our attack aims to perturb the graph structure, without knowing to model parameters, model architecture, etc., such that the LPDG model makes as many wrong predicted links as possible. We design our attack based on a stochastic policy-based RL algorithm. Moreover, we evaluate our attack on three real-world graph datasets from different application domains. Experimental results show that our attack is both effective and efficient. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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