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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Improving Data Utility Through Game Theory in Personalized Differential Privacy

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Journal of computer science & Technology 2019年第2期34卷 272-286页

作者： Lei Cui Youyang Qu Mohammad Reza Nosouhi Shui Yu Jian-Wei Niu Gang Xie College of Information and Computer Taiyuan University of Technology Taiyuan 030024 China School of Information Technology Deakin University Melbourne VIC 3125 Australia School of Software University of Technology Sydney Sydney NSW 2007 Australia School of Computer Science and Engineering Beihang University Beijing 100191 China Shanxi Key Laboratory of Advanced Control and Intelligent Information System Taiyuan University of Science and Technology Taiyuan 030024 China

Due to dramatically increasing information published in social networks,privacy issues have given rise to public *** the presence of differential privacy provides privacy protection with theoretical foundations,the trade-off between privacy and data utility still demands further ***,most existing studies do not consider the quantitative impact of the adversary when measuring data *** this paper,we firstly propose a personalized differential privacy method based on social ***,we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each *** formalize all the payoff functions in the differential privacy sense,which is followed by the establishment of a static Bayesian *** trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning *** proposed method achieves fast convergence by reducing the cardinality from n to *** addition,the in-place trade-off can maximize the user's data utility if the action sets of the user and the adversary are public while the strategy sets are *** extensive experiments on the real-world dataset prove the proposed model is effective and feasible.

关键词： personalized privacy protection game theory trade-off reinforcement learning

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Evolving the very act of reading with socio-collaborative dimensions - Ar-annotations over physical books

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2020年第2期82卷 3-14页

作者： Stănică, Iulia-Cristina Moldoveanu, Alin Alabbasi, Hesham Adnan Moldoveanu, Florica Grădinaru, Alexandru Dascălu, Maria Morar, Anca Asavei, Victor Rădoi, Mireille Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania Department of Engineering in Foreign Language University POLITEHNICA of Bucharest Romania Computer Science Department College of Education Mustansiriyah University Iraq Central University Library of Bucharest "Carol I" Romania

Revitalizing reading and modernizing books have become important issues today, when technology is everywhere around us. New means are needed to make reading and physical books attractive to all people, regardless of age, hobbies or occupation. Our current paper presents an innovative project called Lib2Life which creates a mobile application that can improve books by adding annotations and social capabilities to them. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Annotation Digital reading Digitization

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COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

arXiv

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arXiv 2020年

作者： Hemdan, Ezz El-Din Shouman, Marwa A. Karar, Mohamed Esmail Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Computer Engineering College of Computing and Information Technology Shaqra University Saudi Arabia

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework;namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. The worst classification performance is obtained for the InceptionV3 model with f1-scores of 0.67 for normal cases and 0.00 for COVID-19 cases. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work. Copyright © 2020, The Authors. All ri

关键词： Coronavirus

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Bitstream protocol classification mechanism based on feature extraction

Bitstream protocol classification mechanism based on feature...

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Wang, Wei Bai, Binbin Wang, Yichuan Hei, Xinhong Zhang, Li Science and Technology on Communication Information Security Control Laboratory No.36 Research Institution of CETC China College of Computer Science and Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9781728126296

In recent years, with the continuous expansion of network communication scale, the types of niche protocols and various proprietary communication protocols are increasing day by day, but botnet and various network attacks have also emerged in endlessly. However, most of the commonly used network protocol recognition software uses a single method, and can only identify specific network data packets or data streams, the degree of automation is low, and the recognition accuracy is not high. In view of the above situation, this paper takes the bit flow protocol data frame as the research object, takes the multi-protocol recognition as the goal, and carries on the protocol feature recognition by comparing the data flow characteristics and the evaluation index. The features of the protocol to be detected are compared with the existing protocol features of the feature base, and the similarity between each protocol is judged. The extracted composite features are digitalized to generate 0-1 matrix, and clustering analysis is completed. The frame vector quantification algorithm of message data is designed and implemented to improve the clustering efficiency. The experimental results show that, compared with the traditional algorithm, the protocol eigenvector algorithm improves the accuracy by 15%, which can make the unknown protocol format be generated faster. © 2019 IEEE.

关键词： Network security

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Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization 3rd

Towards the Future Data Market: Reward Optimization in Mobil...

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3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020

作者： Xiong, Zehui Zhao, Jun Kang, Jiawen Niyato, Dusit Deng, Ruilong Xie, Shengli Alibaba-NTU Joint Research Institute NTU Singapore Singapore School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute NTU Singapore Singapore College of Control Science and Engineering School of Cyber Science and Technology Zhejiang University Hangzhou China Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China

ISBN: (纸本)9783030639402

Mobile data subsidization launched by network operators is a promising business model to provide some economic insights on the evolving direction of the 4G/5G and beyond mobile data market. The scheme allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. The users are motivated to access and consume more content without being concerned about overage charges, yielding higher revenue to the data subsidization ecosystem. For each content provider, how to provide appropriate data subsidization (reward) competing with others to earn more revenue and gain higher profit naturally becomes the key concern in such a ecosystem. In this paper, we adopt a hierarchical game approach to model the reward optimization process for the content providers. We formulate an Equilibrium Programs with Equilibrium Constraints (EPEC) problem to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexities of the EPEC problem, we propose to utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Game theory

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White blood cell segmentation based on dual path network and channel attention

White blood cell segmentation based on dual path network and...

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International Conference on Information science and control engineering (ICISCE)

作者： Yan Lu Xinrong Cao Zuoyong Li Tao Wang Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China Fuzhou Digital Healthcare Industry Technology Innovation Center Fuzhou China

White blood cell (WBC, leukocyte) in blood smear image plays a crucial role in the diagnosis of many blood diseases. WBC segmentation is an important preprocessing step in blood disease diagnosis. However, due to some factors such as different dying technologies and different WBC appearances, WBC segmentation is still challenging. To improve accuracy of WBC segmentation, we propose an end-to-end WBC segmentation network. Specifically, we first introduce Dual Path Network (DPN) as a context-aware feature encoder to extract multi-scale image features. Then, we use a channel attention module to capture the interdependencies between channel maps, and further enhance feature representation capability of the proposed network. Finally, we reconstruct WBC feature maps by a feature decoder, and use skip connections to reduce image information loss. Experimental results on three datasets show that the proposed method obtains higher segmentation accuracy than the other methods.

关键词： White blood cells Image segmentation Information science control engineering Fuses Feature extraction Decoding

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Reconfigurable all-dielectric metalens for diffraction-limited imaging 12

Reconfigurable all-dielectric metalens for diffraction-limit...

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Active Photonic Platforms XII 2020

作者： Shalaginov, Mikhail Y. An, Sensong Zhang, Yifei Yang, Fan Su, Peter Liberman, Vladimir Chou, Jeffrey B. Roberts, Christopher M. Kang, Myungkoo Rios, Carlos Du, Qingyang Fowler, Clayton Agarwal, Anuradha Richardson, Kathleen A. Rivero-Baleine, Clara Zhang, Hualiang Hu, Juejun Gu, Tian Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA United States College of Optics and Photonics Department of Materials Science and Engineering University of Central Florida OrlandoFL United States Missiles and Fire Control Lockheed Martin Corporation OrlandoFL United States

ISBN: (纸本)9781510637283

Optical metasurfaces, planar sub-wavelength nano-antenna arrays with the singular ability to sculpt wave front in almost arbitrary manners, are poised to become a powerful tool enabling compact and high-performance optics with novel functionalities. A particularly intriguing research direction within this field is active metasurfaces, whose optical response can be dynamically tuned post-fabrication, thus allowing a plurality of applications unattainable with traditional bulk optics. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency especially for non-mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning covering the full 2 range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling binary switching of metasurfaces between arbitrary phase profiles. We implement the approach to realize a high-performance varifocal metalens. The metalens is constructed using Ge2Sb2Se4Te1 GSST), an O-PCM with a large refractive index contrast and unique broadband low-loss characteristics in both amorphous and crystalline states. The reconfigurable metalens features focusing efficiencies above 20 at both states for linearly polarized light and a record large switching contrast ratio (CR) close to 30 dB. We further validate aberration-free and multi-depth imaging using the metalens, which represents the first experimental demonstration of a non-mechanical active metalens with diffraction-limited performance © 2020 SPIE. All rights reserved.

关键词： Phase change materials

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An Adaptive Data Rate-Based Task Offloading Scheme in Vehicular Networks

An Adaptive Data Rate-Based Task Offloading Scheme in Vehicu...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaofan Chen Wendi Nie Yaoxin Duan Victor C.S. Lee Kai Liu Huamin Li School of Automation Chongqing University Chongqing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong China College of Computer Science Chongqing University Chongqing China

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation-intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Comprehensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.

关键词： Energy consumption Adaptive systems Multi-access edge computing Intelligent vehicles Sensors Time factors Servers

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JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution

arXiv

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arXiv 2021年

作者： Gao, Guangwei Tang, Lei Wu, Fei Lu, Huimin Yang, Jian The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The College of Automation Nanjing University of Posts and Telecommunications Nanjing China The Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu804-8550 Japan The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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