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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua science and Technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu school of Computer science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China school of information Engineering Huzhou UniversityHuzhou 313000China school of computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

Modifying Lyapunov exponent of chaotic map by self-cascading

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science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China school of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China school of Computer science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 40-53页

作者： Yiyi Yuan Jingbing Li Jing Liu Uzair Aslam Bhatti Zilong Liu Yen-wei Chen school of information and Communication Engineering Hainan UniversityHaikouChina Research Center for Healthcare Data science Zhejiang LabHangzhouChina graduate school of information science and Engineering Ritsumeikan University ShigaJapan

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical *** address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic ***,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling *** Daisy descriptor matrix for this point is then ***,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical *** scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image *** embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational *** results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

关键词： daisy descriptor DCT DWT encryption domain medical image zero-watermarking

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

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Computer Modeling in Engineering & sciences 2024年第7期140卷 1-40页

作者： Xianwei Jiang Yanqiong Zhang Juan Lei Yudong Zhang school of Mathematics and information science Nanjing Normal University of Special EducationNanjing210038China school of computing and Mathematical sciences University of LeicesterLeicesterLE17RHUK Department of information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 *** Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional *** from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung *** Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence *** addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification *** language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition ***,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among *** to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition ***,suitable datasets and evaluation criteria are also worth pursuing.

关键词： Chinese Sign Language Recognition deep neural networks artificial intelligence transfer learning hybrid network models

XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG school of Software Nanjing University of Information Science and Technology school of computing and Communications Lancaster University school of computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University school of Computer science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization school of Computer science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

Double DQN Method For Botnet Traffic Detection System

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Computers, Materials & Continua 2024年第4期79卷 509-530页

作者： Yutao Hu Yuntao Zhao Yongxin Feng Xiangyu Ma school of information science and Engineering Shenyang Ligong UniversityShenyang110159China graduate school Shenyang Ligong UniversityShenyang110159China

In the face of the increasingly severe Botnet problem on the Internet,how to effectively detect Botnet traffic in realtime has become a critical *** the existing deepQnetwork(DQN)algorithminDeep reinforcement learning can solve the problem of real-time updating,its prediction results are always higher than the actual *** Botnet traffic detection,although it performs well in the training set,the accuracy rate of predicting traffic is as high as%;however,in the test set,its accuracy has declined,and it is impossible to adjust its prediction strategy on time based on new data ***,in the new dataset,its accuracy has declined ***,this paper proposes a Botnet traffic detection system based on double-layer DQN(DDQN).Two Q-values are designed to adjust the model in policy and action,respectively,to achieve real-time model updates and improve the universality and robustness of the model under different data *** show that compared with the DQN model,when using DDQN,the Q-value is not too high,and the detectionmodel has improved the accuracy and precision of Botnet ***,when using Botnet data sets other than the test set,the accuracy and precision of theDDQNmodel are still higher than DQN.

关键词： DQN DDQN deep reinforcement learning botnet detection feature classification

Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 797-806页

作者： Edeh Vincent Mehdi Korki Mehdi Seyedmahmoudian Alex Stojcevski Saad Mekhilef school of science Computingand Engineering TechnologiesSwinburne University of TechnologyMelbourneAustralia

The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue, we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy, unlike other benchmark techniques.

关键词： Deep reinforcement learning graph convolutional network heterogeneous smart grid network

NeurDB: an AI-powered autonomous data system

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science China(information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO school of computing National University of Singapore College of Computer science and Technology Zhejiang University Department of Computer science and Engineering Shanghai Jiao Tong University school of information Renmin University of China school of Computer science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Robust video question answering via contrastive cross-modality representation learning

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science China(information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG school of information science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center school of Computer science and information Engineering Hefei University of Technology Institutes of Physical science and information Technology Anhui University school of Computer science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning