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arXiv 2020年

作者： Yue, Zongsheng Yong, Hongwei Zhao, Qian Zhang, Lei Meng, Deyu Wong, Kwan-Yee K. The School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China The Department of Computer Science The University of Hong Kong Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong The School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China The Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with its own merits and drawbacks. In this paper, we propose a novel blind image restoration method, aiming to integrate both the advantages of them. Specifically, we construct a general Bayesian generative model for the blind IR, which explicitly depicts the degradation process. In this proposed model, a pixel-wise non-i.i.d. Gaussian distribution is employed to fit the image noise. It is with more flexibility than the simple i.i.d. Gaussian or Laplacian distributions as adopted in most of conventional methods, so as to handle more complicated noise types contained in the image degradation. To solve the model, we design a variational inference algorithm where all the expected posteriori distributions are parameterized as deep neural networks to increase their model capability. Notably, such an inference algorithm induces a unified framework to jointly deal with the tasks of degradation estimation and image restoration. Further, the degradation information estimated in the former task is utilized to guide the latter IR process. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts. The source code is available at https://***/zsyOAOA/VIRNet. © 2020, CC BY-NC-SA.

关键词： Image reconstruction

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Study of CdZnTeSe Gamma-Ray Detector under Various Bias Voltages

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Materials Sciences and Applications 2020年第8期11卷 553-559页

作者： Mebougna L. Drabo Stephen U. Egarievwe Utpal N. Roy Benicia A. Harrison Carmella A. Goree Emmanuel K. Savage Ralph B. James Department of Mechanical & Civil Engineering and Construction Management Alabama A&M University Huntsville AL USA Nuclear Engineering and Radiological Science Center Alabama A&M University Huntsville AL USA Department of Electrical Engineering and Computer Science Alabama A&M University Huntsville AL USA Department of Nonproliferation and National Security Brookhaven National Laboratory Upton NY USA Science and Technology Directorate Savannah River National Laboratory Aiken SC USA Biological and Environmental Sciences Department Alabama A&M University Huntsville AL USA Department of Physics Chemistry and Mathematics Alabama A&M University Huntsville AL USA

Cadmium zinc telluride selenide (CdZnTeSe) is a new semiconductor material for gamma-ray detection and spectroscopy applications at room temperature. It has very high crystal quality compared to similar materials such as cadmium telluride and cadmium zinc telluride. The consistency of peak position in radiation detection devices is important to practical applications. In this paper, we have characterized a CdZnTeSe planar detector for bias voltages in the range of -20 V to -200 V and amplifier shaping time of 2, 3 and 6 μs. The peak position of the 59.6-keV gamma line of ²⁴¹Am becomes more stable as the absolute value of the applied voltage increases. The best energy resolution of 8.5% was obtained for the 59.6-keV gamma peak at -160 V bias voltage and 3-μs shaping time. The energy resolution was relatively stable in the -120 V to -200 V range for a 6-μs shaping time. Future work will be focused on the study of the peak position and energy resolution over time.

关键词： CdZnTeSe Energy Resolution Energy-Peak Stability Gamma-Ray Spectroscopy Nuclear Detectors

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Inconsistencies among spectral robustness metrics 1

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14th EAI International Conference on Heterogeneous Networking for Quality, Reliability, security and Robustness, Qshine 2018

作者： Wang, Xiangrong Feng, Ling Kooij, Robert E. Marzo, Jose L. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computing Science Department Institute of High Performance Computing A*STAR Singapore Singapore iTrust Centre for Research in Cyber Security Singapore University of Technology and Design Singapore Singapore Institute of Informatics and Applications University of Girona Girona Spain

ISBN: (数字)9783030144135

ISBN: (纸本)9783030144128

Network robustness plays a critical role in the proper functioning of modern society. It is common practice to use spectral metrics, to quantify the robustness of networks. In this paper we compare eight different spectral metrics that quantify network robustness. Four of the metrics are derived from the adjacency matrix, the others follow from the Laplacian spectrum. We found that the metrics can give inconsistent indications, when comparing the robustness of different synthetic networks. Then, we calculate and compare the spectral metrics for a number of real-world networks, where inconsistencies still occur, but to a lesser extent. Finally, we indicate how the concept of the R∗-value, a weighted sum of robustness metrics, can be used to resolve the found inconsistencies. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Circuit theory

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Erratum to:Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2024年第3期21卷 1618-1618页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad A.Alia Raed Abu Zitar College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq 25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio′n Basada en la Informacio′n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Erratum to:J Bionic Eng https://***/10.1007/*** this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.

关键词： statement section correctly

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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A customized educational booster for online students in cybersecurity education 11

A customized educational booster for online students in cybe...

引用

11th International Conference on computer Supported Education, CSEDU 2019

作者： Rahouti, Mohamed Xiong, Kaiqi Department of Electrical Engineering University of South Florida TampaFL33620 United States Cyber Florida University of South Florida TampaFL33620 United States Department of Mathematics and Statistics University of South Florida TampaFL33620 United States Intelligent Computer Networking and Security Lab University of South Florida TampaFL33620 United States

ISBN: (纸本)9789897583674

Real-world lab experiments have been an integral part of computer science and engineering curriculums. However, human and computing resources as well as financial support to courses may be limited at many universities ranging from small to large universities and from liberal arts colleges to top research universities as there is a dramatic increase of student enrollments in computer science and engineering for the past ten years. Nowadays, like many other universities nation wide, University of South Florida (USF) in collaboration with the Cyber Florida center offers online cyber security degrees as well as certificates. Throughout such programs, online students are expected to acquire knowledge via an interdisciplinary set of core courses prior to taking a deep dive into one of four following concentrations: cyber intelligence, digital forensics, information assurance and computer security fundamentals. However, when it comes to training student with real-world cyber security labs, both instructors and students face various challenges with regard to resources and virtual environment for exploring and running a broad range of security experiments and tests. In order to achieve the goal of our funded NSF project, in this paper we will discuss our teaching contributions to the development of a broad range of cyber security labs, facilitation of applied cryptography learning through experimental modules, and our customized virtual machine that fits the needs of various online computer networking and security courses. Specifically, we will first present our methodology for the design of our experimental modules and then present in details our pre-built Linux-based portable virtual machine. Those learning and experimental modules have been developed at different levels to meet the need of students with different academic and industrial backgrounds. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Virtual reality

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Cryptanalysis and improvement of a semi-quantum private comparison protocol based on Bell states

arXiv

引用

arXiv 2020年

作者： Xie, Li Li, Qin Yu, Fang Lou, Xiaoping Zhang, Cai School of Cyberspace Security Xiangtan University Xiangtan411105 China Faculty of Informatics Masaryk University Brno Czech Republic Department of Computer Science Jinan University Guangzhou510632 China College of Information Science and Engineering Hunan Normal University Changsha410081 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Semi-quantum private comparison (SQPC) allows two participants with limited quantum ability to securely compare the equality of their secrets with the help of a semi-dishonest third party (TP). Recently, Jiang proposed a SQPC protocol based on Bell states (Quantum Inf Process 19(6): 180, 2020) and claimed it is secure. In this paper, we present two types of attack on Jiang’s SQPC protocol. In the first type of attack, an outside eavesdropper will make participants accept a wrong result. In the second type of attack, a malicious participant will not only make the other participant accept a wrong result, but also learn the secret of the honest participant. Neither type of attack will be detected. In addition, we propose an improved SQPC protocol that can resist these two types of attack. © 2020, CC BY-NC-ND.

关键词： Quantum cryptography

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Privacy-preserving medical treatment system through nondeterministic finite automata

arXiv

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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Blockchain-based privacy preservation for 5G-enabled drone communications

arXiv

引用

arXiv 2020年

作者： Wu, Yulei Dai, Hong-Ning Wang, Hao Choo, Kim-Kwang Raymond College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom Faculty of Information Technology Macau University of Science and Technology United Kingdom Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

5G-enabled drones have potential applications in a variety of both military and civilian settings (e.g., monitoring and tracking of individuals in demonstrations and/or enforcing of social / physical distancing during pandemics such as COVID-19). Such applications generally involve the collection and dissemination of (massive) data from the drones to remote data centres for storage and analysis, for example via 5G networks. Consequently, there are security and privacy considerations underpinning 5G-enabled drone communications. We posit the potential of leveraging blockchain to facilitate privacy preservation, and therefore in this article we will review existing blockchain-based solutions after introducing the architecture for 5G-enabled drone communications and blockchain. We will also review existing legislation and data privacy regulations that need to be considered in the design of blockchain-based solutions, as well as identifying potential challenges and open issues which will hopefully inform future research agenda. Copyright © 2020, The Authors. All rights reserved.

关键词： Drones

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Exploratory Adversarial Attacks on Graph Neural Networks

Exploratory Adversarial Attacks on Graph Neural Networks

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IEEE International Conference on Data Mining (ICDM)

作者： Xixun Lin Chuan Zhou Hong Yang Jia Wu Haibo Wang Yanan Cao Bin Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence University of Technology Sydney Sydney Australia Department of Computing Macquarie University Sydney Australia Department of Computer Science and Technology Tsinghua University Beijing China Xiaomi AI Lab Beijing China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean network data. Recently, there emerge a number of works to investigate the robustness of GNNs by adding adversarial noises into the graph topology, where gradient-based attacks are widely studied due to their inherent efficiency and high effectiveness. However, the gradient-based attacks often lead to sub-optimal results due to the discrete structure of graph data. To this end, we design a novel exploratory adversarial attack (termed as EpoAtk) to boost the gradient-based perturbations on graphs. The exploratory strategy in EpoAtk includes three phases, generation, evaluation and recombination, with the goal of sidesteping the possible misinformation that the maximal gradient provides. In experiments, EpoAtk is evaluated on benchmark datasets for the task of semi-supervised node classification in different attack settings. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art attacks with the same attack budgets.

关键词： Network topology Perturbation methods Graph neural networks Robustness Topology Data mining Task analysis

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