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Micromagnetics Simulation of Highly Efficient Oscillation of Magnetization in a Ferromagnetic Local Areas via Spin Waves Induced by Spin-Orbit Torque

Micromagnetics Simulation of Highly Efficient Oscillation of...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Koda, Tetsunori Muroga, Sho Endo, Yasushi Oshima College National Institute of Technology Department of Electronic Mechanical Engineering Suo-Oshima742-2193 Japan Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita010-8502 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai980-8579 Japan Tohoku University Center for Spintronics Research Network Sendai980-8577 Japan Sendai980-8577 Japan

ISBN: (纸本)9798350338362

We study numerical simulation of highly efficient oscillation of magnetization in a ferromagnetic local region using the spin-orbit torque. It is found that in the presence of multiple SOT regions, the precession motion can be excited at a lower current density through the interaction. This interaction is caused by spin waves, and as a result, phase control between the precession motions is possible through the gap between the areas. Our new finding indicates that the interaction between the wave source and the spin wave is very complex in devices that utilize the interference effects of spin waves. © 2023 IEEE.

关键词： Spin waves

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Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

Dynamic resource provisioning for workflow scheduling under ...

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作者： Xu, Xiaolong Cao, Hao Geng, Qingfan Liu, Xihua Dai, Fei Wang, Chuanjian School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China College of Information Science and Technology Shihezi University Xinjiang China

Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software-defined network (SDN)-based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm-III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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A Comprehensive Survey on Self-Interpretable Neural networks

arXiv

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arXiv 2025年

作者： Ji, Yang Sun, Ying Zhang, Yuting Wang, Zhigaoyuan Zhuang, Yuanxin Gong, Zheng Shen, Dazhong Qin, Chuan Zhu, Hengshu Xiong, Hui Guangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211100 China Computer Network Information Center Chinese Academy of Sciences Beijing100083 China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Post-hoc interpretability, which provides explanations for pre-trained models, is often at risk of robustness and fidelity. This has inspired a rising interest in self-interpretable neural networks, which inherently reveal the prediction rationale through model structures. Despite this progress, existing research remains fragmented, relying on intuitive designs tailored to specific tasks. To bridge these efforts and foster a unified framework, we first collect and review existing works on self-interpretable neural networks and provide a structured summary of their methodologies from five key perspectives: attribution-based, function-based, concept-based, prototype-based, and rule-based self-interpretation. We also present concrete, visualized examples of model explanations and discuss their applicability across diverse scenarios, including image, text, graph data, and deep reinforcement learning. Additionally, we summarize existing evaluation metrics for self-interpretation and identify open challenges in this field, offering insights for future research. To support ongoing developments, we present a publicly accessible resource to track advancements in this domain: https://***/yangji721/Awesome-Self-InterpretableNeural-network. Copyright © 2025, The Authors. All rights reserved.

关键词： Neural network models

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural networks

A New Method for Predefined-Time Synchronization Control of ...

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Memristive Computing and Applications (ICMCA), IEEE International Conference on

作者： Aidi Liu Wenyue Yu Lili Huang Jian Guo Tengfei Lei Hui Zhao School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory.

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Job-SDF: a multi-granularity dataset for job skill demand forecasting and benchmarking 24

Job-SDF: a multi-granularity dataset for job skill demand fo...

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

作者： Xi Chen Chuan Qin Chuyu Fang Chao Wang Chen Zhu Fuzhen Zhuang Hengshu Zhu Hui Xiong University of Science and Technology of China Computer Network Information Center Chinese Academy of Sciences Baidu Inc. Institute of Artificial Intelligence Beihang University and SKLSDE School of Computer Science Beihang University AI Thrust The Hong Kong University of Science and Technology (Guangzhou) and Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong SAR

ISBN: (纸本)9798331314385

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark.

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Multi-Modality 3d Object Detection in Autonomous Driving: A Review

SSRN

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SSRN 2023年

作者： Tang, Yingjuan He, Hongwen Wang, Yong Mao, Zan Wang, Haoyu School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Computer Network Information Center University of Chinese Academy of Sciences Beijing100190 China

Autonomous driving perception has made significant strides in recent years, but accurately sensing the environment using a single sensor remains a daunting task. This review offers a comprehensive overview of the current research on LiDAR and camera fusion for 3D object detection in multi-modality domains. The review first identifies the perception task, open public detection dataset, and data representation related to 3D object detection. It then presents an in-depth survey of coarse-grained and fine-grained fusion approaches, reporting their respective performances on the KITTI and nuScene datasets. The review identifies general trends in multi-modality 3D object detection and provides insights and promising research directions based on these observations. Additionally, the review summarizes the current challenges of fusion strategies for perception problems in autonomous driving. Based on a critical review of existing literature, this paper identifies and discusses key research directions in the field of fusion-based 3D object detection approach for perception problems in autonomous driving, which is instructive for future work. © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Multi-agent Reinforcement Learning Based Delay and Power Optimization for UAV-WMN Substation Inspection

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IEEE Transactions on network and Service Management 2025年

作者： Tang, Qingwei Sun, Wei Liu, Zhi Xiao, Yang Li, Qiyue Yuan, Xiaohui Zhang, Qian Hefei University of Technology School of Electrical and Automation Engineering Anhui Hefei230009 China Anhui Engineering Technology Research Center of Industrial Automation 193 Tunxi Rd Hefei230009 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan The University of Alabama Department of Computer Science TuscaloosaAL35487 United States University of North Texas Department of Science and Engineering DentonTX76207 United States

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms. © 2004-2012 IEEE.

关键词： Lyapunov functions

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Local anatomy for personalised privacy protection

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International Journal of Information and computer Security 2021年第2-3期15卷 254-271页

作者： Li, Boyu Liu, Yanheng Wang, Minghai Sun, Geng Li, Bin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China Big Data and Network Management Center Jilin University Changchun China College of Mathematics Jilin University Changchun China

Anonymisation technique has been extensively studied and widely applied for privacy-preserving data publishing. However, most existing methods ignore personal anonymity requirements. In these approaches, the microdata consist of three categories of attribute: explicit-identifier, quasi-identifier and sensitive attribute. In fact, the data sensitivity should be determined by individuals. An attribute is semi-sensitive if it contains both QI and sensitive values. In this paper, we propose a novel anonymisation approach, called local anatomy, to address personalised privacy protection. Local anatomy partitions the tuples who consider the value as sensitive into buckets inside each attribute. We conduct some experiments to illustrate that local anatomy can protect all the sensitive values and preserve great information utility. Additionally, we also present the concept of intelligent anonymisation system as our direction of future work. © 2021 Inderscience Enterprises Ltd.

关键词： Sensitive data

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BCPPAS : Blockchain-Based Cross-Domain Identity Authentication Scheme for IoT with Privacy Protection

BCPPAS : Blockchain-Based Cross-Domain Identity Authenticati...

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International Conference on computer Supported Cooperative Work in Design

作者： Qianqian Li Yubing Han Qi Liu Guijuan Wang Hao Yu Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In Internet of Things(IoT) systems, ensuring the secure exchange of information between devices from different domains is crucial. Current cross-domain authentication schemes based on a single blockchain struggle to meet the confidentiality requirements for data and information exchange in large-scale IoT systems. This paper proposes a blockchain-based identity authentication scheme (BCPPAS) for IoT, featuring dual-chain collaboration, and designs a novel certificateless aggregate signature algorithm to address complex certificate management and key escrow issues. The edge server is capable of aggregating different signatures to achieve batch authentication, markedly improving authentication efficiency and reducing computational and storage overhead. Also, BCPPAS is designed to avoid costly bilinear pairing operations, providing less computational overhead for IoT devices with limited resources. To protect the privacy of IoT devices, BCPPAS uses the pseudonym instead of the real identity. Finally, efficiency of BCPPAS are demonstrated through theoretical analysis and experiments.

关键词： Privacy Federated learning Aggregates Authentication Blockchains Computational efficiency Internet of Things Servers Protection Information exchange

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