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Research Square 2022年

作者： Zhang, Ruihua Zhou, Jincheng Hai, Tao Zhang, Shixue Osamor, Jude Iwendi, Marvellous GodsPraise Shah, Mohammad School of Computer and Information Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Qiannan Normal University for Nationalities Guizhou Duyun China UTM Skudai Johor Johor Bahru81310 Malaysia Faculty of Education Language and Psychology Postgraduate Department Segi University Malaysia Department of Cyber Security and Networks Glasgow Caledonian University Cowcaddens Road GlasgowG4 0BA United Kingdom University of Nigeria Nsukka Nigeria University of Westminster London United Kingdom

Like other countries, China has suffered severe consequences as a result of the COVID-19 outbreak and pandemic. The lockdown, physical distancing, social isolation has disrupted the day-to-day activities of its citizens. The higher education sector has had to adopt state-of-the-art technologies and use online platforms to hold classes in order to engage students and ensure the continuity of education for an effective learning process. The second wave and Delta variant of the virus compelled numerous Higher Educational Institutes (HEIs) to start offline and remote work for both academic and non-academic staff;a stressful ordeal for them. Teaching has always been a stressful career path, and the stress faced by teachers has adverse consequences on the learning and performance of students. In this paper, we aim to identify and study the stress HEIs staff perceive and its impact on their physical stress and wellbeing. The resilient coping mechanism was evaluated as a moderator in the relationship between the staff and faculty’s perceived and physical stress. Using a standard survey questionnaire, 500 responses were collected. The findings revealed that the staff with the highest perceived stress also showed high symptoms of physical stress. It also indicated that resilience copers experienced reduced or very inconsequential symptoms of physical stress regardless of high perceived stress, thus validating the important moderating function of resilience in the relationship between the physical and perceived stress of the employees. © 2022, CC BY.

关键词： COVID-19

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Infer-AVAE: An Attribute inference model based on adversarial variational autoencoder

arXiv

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arXiv 2020年

作者： Zhou, Yadong Ding, Zhihao Liu, Xiaoming Shen, Chao Tong, Lingling Guan, Xiaohong MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China National Computer network Emergency Response technical Team Center for Intelligent and Networked Systems and TNLIST Lab Tsinghua University

User attributes, such as gender and education, face severe incompleteness in social networks. In order to make this kind of valuable data usable for downstream tasks like user profiling and personalized recommendation, attribute inference aims to infer users' missing attribute labels based on observed data. Recently, variational autoencoder (VAE), an end-to-end deep generative model, has shown promising performance by handling the problem in a semi-supervised way. However, VAEs can easily suffer from over-fitting and over-smoothing when applied to attribute inference. To be specific, VAE implemented with multi-layer perceptron (MLP) can only reconstruct input data but fail in inferring missing parts. While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing. In this paper, we propose an attribute Inference model based on Adversarial VAE (Infer-AVAE) to cope with these issues. Specifically, to overcome over-smoothing, Infer-AVAE unifies MLP and GNNs in encoder to learn positive and negative latent representations respectively. Meanwhile, an adversarial network is trained to distinguish the two representations and GNNs are trained to aggregate less noise for more robust representations through adversarial training. Finally, to relieve over-fitting, mutual information constraint is introduced as a regularizer for decoder, so that it can make better use of auxiliary information in representations and generate outputs not limited by observations. We evaluate our model on 4 real-world social network datasets, experimental results demonstrate that our model averagely outperforms baselines by 7.0% in accuracy. © 2020, CC BY-NC-SA.

关键词： Learning systems

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Graph representation learning via graphical mutual information maximization

arXiv

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arXiv 2020年

作者： Peng, Zhen Huang, Wenbing Luo, Minnan Zheng, Qinghua Rong, Yu Xu, Tingyang Huang, Junzhou Ministry of Education Key Lab for Intelligent Networks and Network Security School of Computer Science and Technology Xi'an Jiaotong University China State Key Lab of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tencent AI Lab China

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and extract the abundant information from graph-structured data into embedding space in an unsupervised manner. To this end, we propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. GMI generalizes the idea of conventional mutual information computations from vector space to the graph domain where measuring mutual information from two aspects of node features and topological structure is indispensable. GMI exhibits several benefits: First, it is invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing graph representation learning algorithms;Besides, it can be efficiently estimated and maximized by current mutual information estimation methods such as MINE;Finally, our theoretical analysis confirms its correctness and rationality. With the aid of GMI, we develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder. Considerable experiments on transductive as well as inductive node classification and link prediction demonstrate that our method outperforms state-of-the-art unsupervised counterparts, and even sometimes exceeds the performance of supervised ones. Copyright © 2020, The Authors. All rights reserved.

关键词： Vector spaces

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Multi-task learning for object keypoints detection and classification

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Pattern Recognition Letters 2020年 130卷 182-188页

作者： Jie Xu Lin Zhao Shanshan Zhang Chen Gong Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China State Key Laboratory of Integrated Services Networks Xidian Univeristy Xi’an 710071 China

Object keypoints detection and classification are both central research topics in computer vision . Due to their wide range potential applications in the real world, substantial efforts have been taken to advance their performance. However, these two related tasks are mainly treated separately in previous works. We argue that keypoints detection and classification can be complementary tasks and beneficial to each other. Knowing the category of a object is able to reduce the searching space of keypoints detection models and facilitate more precise localization . On the other hand, having the knowledge of object keypoints can make classification models pay more attention on areas that are more associated with the object, which will inevitably promote classification accuracy . Embracing this observation, we propose to model keypoints detection and classification in a multi-task learning framework. Specifically, a multi-task deep network is designed and trained to conduct both tasks, where we devise the model structure delicately to carry out sufficient training of both tasks. Extensive experiments are set up on the AIFASHION DATASET and Human3.6M DATASET to validate our proposal, we show that our algorithm outperforms separate models trained individually on each task.

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衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

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Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Implementation analysis of rectangle cipher and its variant 3

Implementation analysis of rectangle cipher and its variant

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3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Philip, Merly Annie Vaithiyanathan, V. Jain, Kurunandan Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9781538624401

Lightweight ciphers are symmetric cryptographic systems with lower area, lower power and lower cost than standard systems like AES and DES. They were designed to provide security in resource constrained devices like smart cards, IoT devices, RFID tags and wireless sensor networks. After a literature survey on the existing ciphers, Rectangle cipher was chosen for implementation analysis and a variant of it is introduced. The Rectangle cipher is a lightweight cipher which is much similar to AES and incorporates bit slice technique by Zhang, et al in 2015. In this paper we discuss the implementation analysis of Rectangle cipher and an increased block size variant of it. Both the ciphers were implemented on low cost Xilinx Spartan 3E XC3S1200EFG. The synthesis reports were compared with each other and it was found that the variant has slightly degraded performance than the existing Rectangle cipher, but it is well suited for cipher block chaining. © 2018 IEEE.

关键词： Smart cards

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Long-Distance Continuous-Variable Quantum Key Distribution over 202.81 km of Fiber

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Physical Review Letters 2020年第1期125卷 010502-010502页

作者： Yichen Zhang Ziyang Chen Stefano Pirandola Xiangyu Wang Chao Zhou Binjie Chu Yijia Zhao Bingjie Xu Song Yu Hong Guo State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics and Center for Quantum Information Technology Peking University Beijing 100871 China Department of Computer Science University of York York YO10 5GH United Kingdom Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu 610041 China

Quantum key distribution provides secure keys resistant to code-breaking quantum computers. The continuous-variable version of quantum key distribution offers the advantages of higher secret key rates in metropolitan areas, as well as the use of standard telecom components that can operate at room temperature. However, the transmission distance of these systems (compared with discrete-variable systems) are currently limited and considered unsuitable for long-distance distribution. Herein, we report the experimental results of long distance continuous-variable quantum key distribution over 202.81 km of ultralow-loss optical fiber by suitably controlling the excess noise and employing highly efficient reconciliation procedures. This record-breaking implementation of the continuous-variable quantum key distribution doubles the previous distance record and shows the road for long-distance and large-scale secure quantum key distribution using room-temperature standard telecom components.

关键词： Optical quantum information processing Quantum cryptography Quantum information processing with continuous variables Quantum optics

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Smart City Cybersecurity Projects Financing Model in Case of Description of Investors’ Resources With Fuzzy Sets

Smart City Cybersecurity Projects Financing Model in Case of...

引用

IEEE International Conference on Advanced Trends in Information Theory (ATIT)

作者： Valeriy Lakhno Yurii Matus Volodymyr Malyukov Alyona Desyatko Tetyana Hnatchenko Department of Computer systems and networks National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Engineering and Cyber Security National University of Trade and Economics Kyiv Ukraine

ISBN: (数字)9781728161440

ISBN: (纸本)9781728161457

The paper describes the model of choosing mutual investment strategies for Smart City cybersecurity projects with incomplete information on the financial resources of the second investor. The case when the financial resources of the parties belong to fuzzy sets is considered. The model is designed for the developing decision support system in the tasks of choosing rational options for investing in Smart City cybersecurity projects. The solution was obtained through the use of tools of the theory of multi-step games with several terminal surfaces. Distinctive feature of the model is the assumption that the first investor does not have complete information about both the financial strategies of the second investor and of his financial resources state. Verifying the model was performed using computational experiment in MatLab.

关键词： Smart cities computer security Investment Computational modeling Mathematical model Fuzzy sets Games

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Triple layer data hiding mechanism using cryptography and steganography 3

Triple layer data hiding mechanism using cryptography and st...

引用

3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Menon, Nidhi Vaithiyanathan, V. Amrita Center for Cyber Security Systems and Networks Amrita University Amritapuri Kerala India Amrita University Department of Computer Science and Engineering Amritapuri Kerala India

ISBN: (纸本)9781538624401

In many areas security systems are well known as the advances are expanding step by step. Using encryption and steganography data security can be accomplished. Cryptography is the process of secure correspondence within the presence of third parties. Steganography is method of sending hidden messages with the end goal that apart from the sender and receiver, nobody even speculates the presence of the message. In the present tech age, using cryptography and steganography separately may not provide the intended protection from the intruders. In this paper, we propose an encoding technique that uses the combination of cryptography and steganography. Two levels of data encryption is done and then the encrypted data is hidden inside the image. The image in which the ciphertext is embedded is used for further purposes. © 2018 IEEE.

关键词： Cryptography

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Finite-size analysis of continuous variable source-independent quantum random number generation

arXiv

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arXiv 2020年

作者： Zhang, Junyu Zhang, Yichen Zheng, Ziyong Chen, Ziyang Xu, Bingjie Yu, Song State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China

We study the impact of finite-size effect on continuous variable source-independent quantum random number generation. The central-limit theorem and maximum likelihood estimation theorem are used to derive the formula which could output the statistical fluctuations and determine upper bound of parameters of practical quantum random number generation. With these results, we can see the check data length and confidence probability has intense relevance to the final randomness, which can be adjusted according to the demand in implementation. Besides, other key parameters, such as sampling range size and sampling resolution, have also been considered in detail. It is found that the distribution of quantified output related with sampling range size has significant effects on the loss of final randomness due to finite-size effect. The overall results indicate that the finite-size effect should be taken into consideration for implementing the continuous variable source-independent quantum random number generation in practical. Copyright © 2020, The Authors. All rights reserved.

关键词： Random number generation

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