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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Junjie Wu, Qian School of Information Wuhan Software Engineering Vocational College Wuhan Open University Hubei Wuhan China School of Computer Science Wuhan University Hubei Wuhan China Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts Wuhan China

ISBN: (纸本)9798400712883

With the rapid development of artificial intelligence technology, its application in the field of education and teaching is becoming increasingly widespread, bringing revolutionary changes to traditional education models. To cultivate high-quality software vocational skills talents, a digital management platform based on AI+capability model is constructed to explore the innovative application of artificial intelligence in education and teaching, realizing intelligent auxiliary learning functions such as personalized resource recommendation, student growth monitoring, and achievement label portrait. At the same time, a series of intelligent teaching tools such as"AI+Code Generation, AI+Inspection Assistant, AI+Process Map" will be developed to explore and promote the vertical application of big models in talent cultivation. Through innovative models such as"introducing enterprises into education, cultivating students through real-life situations, AI+empowerment, teaching according to students’ aptitude, trinity, innovative training, strengthening processes, and diverse evaluations", it has been fully applied to the talent training scene of software technology field engineers, and its teaching effectiveness is significant. © 2024 Copyright held by the owner/author(s).

关键词： Students

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OptEx: expediting first-order optimization with approximately parallelized iterations 24

OptEx: expediting first-order optimization with approximatel...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yao Shu Jiongfeng Fang Ying Tiffany He Fei Richard Yu Guangdong Lab of AI and Digital Economy (SZ) China College of Computer Science and Software Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China and School of Information Technology Carleton University Canada

ISBN: (纸本)9798331314385

First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence. In response, we introduce first-order optimization expedited with approximately parallelized iterations (OptEx), the first general framework that enhances the optimization efficiency of FOO by leveraging parallel computing to directly mitigate its requirement of many sequential iterations for convergence. To achieve this, OptEx utilizes a kernelized gradient estimation that is based on the history of evaluated gradients to predict the gradients required by the next few sequential iterations in FOO, which helps to break the inherent iterative dependency and hence enables the approximate paral-lelization of iterations in FOO. We further establish theoretical guarantees for the estimation error of our kernelized gradient estimation and the iteration complexity of SGD-based OptEx, confirming that the estimation error diminishes to zero as the history of gradients accumulates and that our SGD-based OptEx enjoys an effective acceleration rate of θ(√N) over standard SGD given parallelism of N, in terms of the sequential iterations required for convergence. Finally, we provide extensive empirical studies, including synthetic functions, reinforcement learning tasks, and neural network training on various datasets, to underscore the substantial efficiency improvements achieved by OptEx in practice. Our implementation is available at https://***/youyve/OptEx.

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Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests 24

Automatic Adjustment of HPA Parameters and Attack Prevention...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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Research on Topological Deformation of 3D Human Image Based on Laplace Optimization 4

Research on Topological Deformation of 3D Human Image Based ...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Zhou, Dongdong Chen, Xiaobing Zhang, Chuangchuang Guo, Shuxin Ashim, Khadka Lin, Jianchu Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China Gandaki College of Engineering and Science Pokhara University Pokhara Nepal

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Image deformation technology is an important technology in computer graphics image processing, which can satisfy people's requirements for visual and sensory effects, and its research results have been widely used in film and television animation, game development, virtual reality and other fields. Mesh-based deformation technology has been a research emphasis in image deformation technology, through the virtual model for the mesh division, the use of mesh nodes control, to achieve the deformation of the model. However, the more complex the model is, the greater number of mesh control nodes, which greatly increases the complexity of mesh deformation. In this paper, the deformation of 3D virtual character model is studied based on the method of image topological deformation. In order to solve the problem of large number of network nodes in 3D model, the Laplace deformation method is further combined to reduce the number of control nodes in the network. The experimental results show that the deformation can be effectively completed using a small number of control nodes by using the laplace coordinate system for adjacent topological mesh node control. © 2022 IEEE.

关键词： Virtual reality

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HSI-DETR: A DETR-based Transfer Learning from RGB to Hyperspectral Images for Object Detection of Live and Dead Cells: To achieve better results, convert models with the fewest changes from RGB to HSI. 22

HSI-DETR: A DETR-based Transfer Learning from RGB to Hypersp...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Songxin Ye Nanying Li Jiaqi Xue Yaqian Long Sen Jia College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450397056

Traditional cell viability judgment methods are invasive and damaging to cells. Moreover, even under a microscope, it is difficult to distinguish live cells from dead cells by the naked eye alone. With the development of optical imaging technology, hyperspectral imaging is more and more widely used in various fields. Hyperspectral imaging is a non-contact optical technique that provides both spectral and spatial information in a single measurement. It becomes a fast, non-invasive option to differentiate between live and dead cells. In recent years, the rapid development of deep learning has provided a better way to distinguish the difference between living and dead cells through a large amount of data. However, it is often necessary to acquire large amounts of labeled data at an expensive cost to train models. This is more difficult to achieve on medical hyperspectral images. Therefore, in this paper, a new model called HSI-DETR is proposed to solve the above problem on the target detection task of live and dead cells, which is based on the detection transformer (DETR) model. The HSI-DETR model suitable for hyperspectral images (HSI) is proposed with minimal modification. Then, some parameters of DETR trained on RGB images are transferred to HSI-DETR trained on hyperspectral images. Compared to the general method, this method can train a better model with a small number of labeled samples. And compared to the DETR-R50, the AP50 of HSI-DETR-R50 has increased by 5.15%.

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Chang-Long Xu, Xin-Xin Chen, Chun-Hua Hong, Jun He, Zhenan Yu, Dengxiu Kwong, Sam Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Computer Science and Technology Ocean University of China Qingdao266100 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9781665410205

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations. © 2024 IEEE.

关键词： Charging stations

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Mind-Body TaoRelax: Relieving Stress Through Immersive Virtual Reality Relaxation Training in a Taoist Atmosphere

Mind-Body TaoRelax: Relieving Stress Through Immersive Virtu...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Hao Yan Ding Ding Zhuying Li College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Virtual reality technology is providing a new method for psychologi-cal relaxation training, but most systems focus primarily on medi-tation as a singular relaxation method. We propose a novel virtual reality relaxation system called TaoRelax. Building upon traditional Taoist culture, the system integrates both meditation and progressive muscle relaxation training. It combines electromyography (EMG) as a means of feedback and assessment during training, providing users with a dual experience of relaxation for both body and mind. Our preliminary results indicate that after training in our system, participants experienced a significant reduction in psychological stress and a notable improvement in meditation abilities.

关键词： Training Three-dimensional displays Conferences Psychology Virtual reality Human factors User interfaces

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On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Zhao, Ze Yu Che, Yue Ling Luo, Sheng Luo, Gege Wu, Kaishun Leung, Victor C.M. College of Computer Science and Software Engineering Shenzhen University China

This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN type updating rule, each WN can dynamically and repeatedly update its WN type as an E-node for non-linear energy harvesting over time slots or an I-node for transmitting data over sub-slots. To maximize the total transmission data size of all the WNs over T slots, each of the UAVs individually determines its trajectory and binary wireless energy transmission (WET) decisions over times slots and its binary wireless data collection (WDC) decisions over sub-slots, under the constraints of each UAV’s limited on-board energy and each WN’s node type updating rule. However, due to the UAVs’ tightly-coupled trajectories with their WET and WDC decisions, as well as each WN’s time-varying battery energy, this problem is difficult to solve optimally. We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV’s continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV’s binary WDC decisions over sub-slots under the given UAV trajectory from tier-1. Both of the SAC policy and the DQN policy are executed distributively at each UAV. Finally, extensive simulation results are provided to validate the outweighed performance of the proposed MAHDRL approach over various state-of-the-art benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Enhanced Fire Detection for Industrial and Environmental Safety using IoT-Integrated Vision Sensors and Residual Clutch Attention Network and Depthwise Distinguishable Convolutional Neural Networks 3

Enhanced Fire Detection for Industrial and Environmental Saf...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Christy Sujatha, D. Singh, Priyanshu Vallikannu, C. Varman, Ravi Skanda, M.G. Gavaskar, T. Department of Software Engineering Periyar Maniammai Institute of Science & Technology Tamil Nadu Thanjavur India Department of Computer Science and Engineering R.V college of engineering Karnataka Bangalore India Department of Humanities and Sciences Rajalakshmi Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Karpagam academy of Higher Education Tamil Nadu Coimbatore India Department of Industrial and Production Engineering SJCE JSS Science and Technology University Karnataka Mysore India Department of Mechanical Engineering St. Joseph's College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensitivity, and low coverage when compared with our requirement in large industrial scenes and vast natural terrains vulnerable to fire occurrence. These systems are highly unsuitable for dynamic and high-risk situations as they require a higher accuracy and a faster reaction time for the alarms to be efficient as is now the case due to the aforementioned reasons. In an attempt to remedy these limitations, this research introduces a fire detection system using IoT and vision sensors that combines a RCAN with DDCNN. This architecture is tuned using the Crayfish Optimization Algorithm (COA), which seeks to improve model parameter to improve response time and accuracy. By incorporating feedbacks from sensors placed at its different parts and reading from the environment, the RCAN-DDCNN model can quickly identify the fire patterns and respond by activating the sprinkler systems as well as setting off alarms. The proposed system presents a superior option for fire detecting because it is scalable and adaptive and it provides high degree of precision, making it an optimal development in fire detection technology for industry and environment. The introduced approach attains higher accuracy as 99%. © 2025 IEEE.

关键词： Sprinkler systems (fire fighting)

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