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A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

Computers, Materials & Continua 2022年第10期73卷 657-672页

作者： Muhammad Islam Kamran Muazzam A.Khan Suliman A.Alsuhibany Yazeed Yasin Ghadi Arshad Jameel Arif Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi15551UAE Institute for Energy and Environment University of StrathclydeGlasgowG11XQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)*** have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure *** of the classical random walk,this paper proposes the quantum walk to achieve high image *** random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave *** proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity *** experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally *** the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively.

关键词： Cryptography chaotic maps logistic map quantum walk security

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Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments

Generative Adversarial Networks for Robust Anomaly Detection...

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IEEE International Conference on Communications (ICC)

作者： Adel Abusitta Talal Halabi Ahmed Saleh Bataineh Mohammad Zulkernine Department of Computer and Software Engineering Poly technique Montreal Montreal QC Canada Department of Computer Science Université Laval Québec QC Canada School of Computing Queen's University Kingston ON Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The Internet of Things (IoT) enables us to collect and process vast amounts of data in real time. However, the security of IoT devices and networks is highly susceptible to cyber attacks that threaten data integrity and service availability. Furthermore, due to the diverse nature of data collected from numerous nodes in IoT systems and the disturbances occurring within them, detecting anomalous activities and compromised nodes is considerably more challenging than in conventional computer systems. Therefore, it is crucial to develop robust and dependable anomaly detection methods to identify and remove malicious and/or unwanted data, which ensures their exclusion from IoT-powered applications and data analytics. To achieve this, this paper proposes a Generative Adverserial Networks (GAN)-based anomaly detection for IoT systems. The proposed model enables the autoencoder - using the adversarial training of GAN - to learn a better representation of IoT data, making it robust against noisy and changing environments. Based on experiments with real-world IoT datasets, the proposed framework has shown to improve the accuracy of detecting malicious traffic in IoT and surpass state-of-the-art anomaly detection models.

关键词： Training Data analysis Data integrity Generative adversarial networks Real-time systems Data models Internet of Things

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Debiasing Graph Representation Learning Based on Information Bottleneck

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11092-11105页

作者： Ziyi Zhang Mingxuan Ouyang Wanyu Lin Hao Lan Lei Yang School of Software Engineering South China University of Technology Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong China Department of Computing Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong China Department of Computer Science and Technology Tsinghua University Beijing China

Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on the adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of graph representation learning based on fairness information bottleneck (GRAFair), a new framework based on a variational graph autoencoder (VGAE). The crux of GRAFair is the conditional fairness bottleneck (CFB), where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability.

关键词： Representation learning Adversarial machine learning Graph neural networks Optimization Electronic mail Training Predictive models Message passing Encoding Vectors

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming 42

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control

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Multi-Grade Tumor Detection Via Enhanced Lightweight Convnet

SSRN

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SSRN 2024年

作者： Ahmad, Ishaq Hamandawana, Prince Min Ullah, Fath U. Chung, Tae-Sun Ajou University Department of Artificial Intelligence Suwon16499 Korea Republic of Ajou University Department of Software Suwon16499 Korea Republic of University of Central Lancashire School of Engineering and Computing Preston United Kingdom

Brain tumor classification is an important problem in computer-aided diagnosis (CAD) for medical applications. To classify, describe, and detect findings in medical images, radiologists rely on visual entities from magnetic resonance imaging (MRI), computed tomography (CT), x-rays, and ultrasound. Deep learning is a subfield of machine learning that involves training Convolutional Neural Networks (CNNs) to extract visual entities from the images. This research builds upon the well-established high performance of EfficientNet in general medical diagnosis. We introduce an improved variant of EfficientNet, tailored specifically for multi-grade classification of four distinct tumor classes (glioma, meningioma, pituitary, and no tumors). This adaptation extends its capabilities from three-grade to four-grade classification, with the flexibility to expand to accommodate multiple classification heads. We adopt the concept of deep transfer learning and use a pre-trained EfficientNet7 to extract features from brain MRI images. We conduct experiments utilizing diverse datasets sourced from Kaggle. Since the EfficientNet model has proven to outperform prior state-of-the-art, we evaluated our proposed model with it. Results show that our proposed model outperforms EfficientNet in the area of multi-grade brain tumor classification. In addition, the paper addresses a practical aspect by evaluating the system with fewer training samples. Our findings imply that transfer learning is a useful technique when the availability of medical images is limited. © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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AMSC: Adaptive Multi-channel Graph Convolutional Network-Enhanced Web Services Classification 23

AMSC: Adaptive Multi-channel Graph Convolutional Network-Enh...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Qing, Yueying Cao, Buqing Peng, Mi Zhang, Lulu Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the development of Service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81%, 19.35%, 19.16%, 11.56%, and 7.75% respectively. © 2021 IEEE.

关键词： Correlation Adaptive systems Network topology Smart cities Quality of service Feature extraction Topology

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A Survey on IEEE 802.15.7 MAC Protocols for Visible Light Communication

A Survey on IEEE 802.15.7 MAC Protocols for Visible Light Co...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Abdulwadood Alawadhi Abdullah Almogahed Mohd. Hasbullah Omar InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok-Kedah Malaysia Department of Software Engineering Faculty of Engineering and Information Technolog Taiz University Taiz Yemen

Networks that use visible light communication (VLC) are suitable for meeting 5G and 6G criteria. The VLC is a new wireless high-speed communication technology. To meet the bandwidth requirements of bandwidth-hungry devices, VLC makes use of the illumination infrastructure. VLC is an excellence alternative to communication 6G technologies because of its immunology to electromagnetic interference, high data rate, high reuse factor and capacity to operate in an unlicensed band. Although the standard of IEEE 802.15.7 would provide detailed physical and medium access control layer standards, the emerging VLC technology confronts MAC issues. However, MAC-specific solutions in wireless networks need not resolve VLC network issues. Full-duplex capability, deafness, channel use, hidden nodes, and connectivity should all be considered in VLC MAC approaches.

关键词： IEEE 802.15 Standard 6G mobile communication Measurement Visualization Technological innovation Wireless networks Full-duplex system

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Preliminary Study on the Reproducibility of Fix Templates in Static Analysis Tool

Preliminary Study on the Reproducibility of Fix Templates in...

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Conference on software engineering Education & Training (CSEE&T)

作者： Sohyun Kim Youngkyoung Kim Eunseok Lee Department of Software Sungkyunkwan University Suwon Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea

Automated Program Repair (APR) automatically generates patches for identified defects. As a result, APR can encourage novice students to learn coding by providing appropriate patches. Since students have their coding conventions, we should be able to deal with many types of defects. Existing studies have used predefined RuleId-driven templates to automatically fix defects in Static Analysis Tools(SATs). However, the community periodically adds, deletes, or changes SAT’s RuleIds. This is difficult for existing RuleId-based templates to reflect those changes immediately. Existing studies only cover about 10 RuleIds, making it difficult to address all defects faced by all students. Therefore, it is necessary to establish appropriate criteria for classifying templates. The SAT has a predefined format for how Error Messages are written, and since Error Messages contain fixing actions, defects with similar Error Messages tend to have similar fixing actions. These characteristics of the Error Message are suitable for reproducible template classification criteria. Our preliminary study demonstrated that by classifying patterns based on Error Messages, we could effectively address various defects, including those in different programming languages, using a single template. This means that if a newly added RuleId corresponds to an Error Message format already in the predefined Error Message-based template, it can be modified without additional effort. We plan to construct reproducible templates for each Error Message and provide ongoing patching of defects to students.

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Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning

Regional Correlation Aided Mobile Traffic Prediction with Sp...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： JeongJun Park Lusungu J. Mwasinga Huigyu Yang Syed M. Raza Duc-Tai Le Moonseong Kim Min Young Chung Hyunseung Choo Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Department of Superintelligence Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea Department of IT Convergence Software Seoul Theological University Bucheon Korea

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.

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