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International Conference on Network and Information Systems for Computers (ICNISC)

作者： Yunqi Chen Jin Xu Measurement and Control Technology Research Institute China Coal Technology Engineering Group Chongqing Research Institute Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665453523

Coal is the most important basic energy supporting the development of the national economy, safe production and effective supervision and management are of great significance. Currently, the coal mine safety monitoring system can be monitored locally, but it cannot help the supervision department to realize the remote supervision and centralized supervision. Establishing a corresponding coal mine safety monitoring network system has become a urgent need of coal mine safety supervision. In the paper, we first study the service-oriented architecture(SOA), and describes the windows communication foundation(WCF) technology, the technology is a main way to achieve SOA architecture. Then combined with WCF technology, the function and logic structure of coal mine safety monitoring network system are designed. Finally, the network system of mine monitoring system based on SOA was realized in the background of a mine monitoring system networking project of a company.

关键词： Economics Companies Computer architecture Coal Safety Coal mining Service-oriented architecture

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Resource Allocation of IoT systems Integrated with Blockchain and Mobile Edge Computing

Resource Allocation of IoT systems Integrated with Blockchai...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Zihan Bai Jianxiong Wan Leixiao Li Chuyi Liu Mingda Duan Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China

With the development of the Internet of Things (IoT), IoT devices have been widely applied into several fields to collect and transmit data. However, it is crucial to ensure to the security of the collected data. On the other hand, computing resources of IoT devices have obvious constraint. Blockchain and Mobile Edge Computing (MEC) can significantly improved the security of the IoT system and the efficiency of the consensus process of IoT devices. However, they also bring a lot of energy consumption and computational overhead. Reasonable allocation of computational resources is an effective method to reduce the energy consumption and the computational overhead. The resource allocation problem of the IoT system supported by blockchain and MEC can be formulated as an Markov Decision Process (MDP). In the existing studies, Deep Q-Network (DQN)-based approaches are adopted to optimize the energy consumption and the computational overhead. However, as the dimensions of the actions become larger, the action space of DQN-based approaches will have scalability limitations. Therefore, we propose the Branching Dueling Q-Network Resource Allocation (BDQ-RA) algorithm to address the problem of scalability limitations. In this article, we consider the profit between the earnings of computational tasks and the weight cost as the reward. Simulation results show that our algorithm can reduce the action space to one ninth and improve the reward about 12% compared with DQN solutions.

关键词： Energy consumption Multi-access edge computing Scalability Simulation Reinforcement learning Blockchains Internet of Things

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Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Lon-Chen Hung Chun-Cheng Wei Hung-Jen Chen Chih-Fu Yang Wei-Min Cheng Department of Electronic Engineering Lunghwa University of Science and Technology School of Software and Big Data Changzhou College of Information Technology Department of Computer Information and Network Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781665454209

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.

关键词： Uncertainty Target tracking Trajectory tracking Simulation Neural networks Manipulators Stability analysis

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Preparation of Uniform SiO2 Insulating Layer on the Inner Wall of TSV by Thermal Oxidation

Preparation of Uniform SiO2 Insulating Layer on the Inner Wa...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Guo Fengjie Ran Jing Yang Wang Shuo Ma Kui Yang Fa Shun Guizhou Provincial Key Laboratory of Micro and nano Electronics and Software Technology College of Big Data and Information Engineering Guizhou University Guiyang City Guizhou Province China

ISBN: (纸本)9781665471343

SiO 2 insulating layer is an indispensable part of a TSV. In the current process, the SiO 2 insulating layer is commonly deposited on the inner wall of the TSV based on deep trench sputtering method. The thickness at different position (neck, middle, bottom) of the SiO 2 insulating layer, deposited by deep trench sputtering, is non-uniform. In this paper, the thickness uniformity of SiO 2 insulating layer prepared on the inner wall of TSV based on CVD&PVD process and thermal oxidation method is comparatively studied. The experimental results show that, based on the CVD&PVD process, the average thickness of the SiO 2 insulating layer at middle and bottom position of the TSV has changed by - 54.02% and - 58.30% compared with that at the top position, respectively. Based on the thermal oxidation method, the average thickness of the SiO 2 insulating layer at middle and bottom position of the TSV has changed by 1.17% and 0.26% compared with that at the top position, respectively. The thermal oxidation method can realize the SiO 2 insulating layer with uniform thickness on the inner wall of TSV.

关键词： Silicon compounds Semiconductor device manufacture Oxidation Neck Through-silicon vias Sputtering

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Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting

arXiv

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arXiv 2022年

作者： Wang, Fusen Liu, Kai Long, Fei Sang, Nong Xia, Xiaofeng Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Currently, for crowd counting, the fully supervised methods via density map estimation are the mainstream research directions. However, such methods need location-level annotation of persons in an image, which is time-consuming and laborious. Therefore, the weakly supervised method just relying upon the count-level annotation is urgently needed. Since CNN is not suitable for modeling the global context and the interactions between image patches, crowd counting with weakly supervised learning via CNN generally can not show good performance. The weakly supervised model via Transformer was sequentially proposed to model the global context and learn contrast features. However, the transformer directly partitions the crowd images into a series of tokens, which may not be a good choice due to each pedestrian being an independent individual, and the parameter number of the network is very large. Hence, we propose a Joint CNN and Transformer Network (JCTNet) via weakly supervised learning for crowd counting in this paper. JCTNet consists of three parts: CNN feature extraction module (CFM), Transformer feature extraction module (TFM), and counting regression module (CRM). In particular, the CFM extracts crowd semantic information features, then sends their patch partitions to TRM for modeling global context, and CRM is used to predict the number of people. Extensive experiments and visualizations demonstrate that JCTNet can effectively focus on the crowd regions and obtain superior weakly supervised counting performance on five mainstream datasets. The number of parameters of the model can be reduced by about 67%∼73% compared with the pure Transformer works. We also tried to explain the phenomenon that a model constrained only by count-level annotations can still focus on the crowd regions. We believe our work can promote further research in this field. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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Deep Reinforcement Learning for Dependency-aware Microservice Deployment in Edge Computing

Deep Reinforcement Learning for Dependency-aware Microservic...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Chenyang Wang Bosen Jia Hao Yu Xiuhua Li Xiaofei Wang Tarik Taleb College of Intelligence and Computing Tianjin University Tianjin China Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665435413

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm's priorities in terms of average system cost and system reward.

关键词： Costs Simulation Microservice architectures Quality of service Reinforcement learning Minimax techniques Markov processes

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features

Evolutionary Multiobjective Feature Selection Assisted by Un...

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Congress on Evolutionary Computation

作者： Xuan Duan Songbai Liu Junkai Ji Lingjie Li Qiuzhen Lin Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

关键词： Accuracy Evolutionary computation Multitasking Feature extraction Classification algorithms Task analysis Knowledge transfer

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FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning 39

FedAA: A Reinforcement Learning Perspective on Adaptive Aggr...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Jialuo Chen, Wei Zhang, Xiaojin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)157735897X

Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical heterogeneity and susceptibility to adversarial attacks, which can impact model robustness and fairness. Personalized FL attempts to provide some relief by customizing models for individual clients. However, it falls short in addressing server-side aggregation vulnerabilities. We introduce a novel method called FedAA, which optimizes client contributions via Adaptive Aggregation to enhance model robustness against malicious clients and ensure fairness across participants in non-identically distributed settings. To achieve this goal, we propose an approach involving a Deep Deterministic Policy Gradient-based algorithm for continuous control of aggregation weights, an innovative client selection method based on model parameter distances, and a reward mechanism guided by validation set performance. Empirically, extensive experiments demonstrate that, in terms of robustness, FedAA outperforms the state-of-the-art methods, while maintaining comparable levels of fairness, offering a promising solution to build resilient and fair federated systems. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Detecting and Corrupting Convolution-based Unlearnable Examples 39

Detecting and Corrupting Convolution-based Unlearnable Examp...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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