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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Ruoyu Fan Meiyu Liang Mengran Yin Junping Du Beijing Key Laboratory of Intelligent Communication Software Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset.

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Toward Coordination Control of Multiple Fish-Like Robots:Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1964-1976页

作者： Tianhao Zhang Jiuhong Xiao Liang Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LaboratoryCollege of EngineeringPeking UniversityBeijing 100871China National Engineering Research Center of Software Engineering Peking UniversityBeijing 100871 Institute of Ocean Research Peking UniversityBeijing100871 Peng Cheng Laboratory Shenzhen 518055China

Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists,for which a fundamental but challenging topic is to robustly track the postures of the individuals in real *** requires detecting multiple robots,estimating multi-joint postures,and tracking identities,as well as processing fast in real *** the best of our knowledge,this challenge has not been tackled in the previous *** this paper,to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time,we propose a novel deep neural network-based method,named *** TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation,while the IOL part with long short-term memory considers the motion constraints among joints for precise pose *** satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy,speed,and robustness with most state-of-the-art ***,based on the precise pose estimation and tracking realized by our TAB-IOL,several formation control experiments are conducted for the group of fish-like *** results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working *** believe our proposed method will facilitate the growth and development of related fields.

关键词： Deep neural networks formation control multiple fish-like robots pose estimation pose tracking

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Causality-Guided Context-Aware Multimodal Public Speaking Anxiety Detection for Out-of-Distribution Generalization

Causality-Guided Context-Aware Multimodal Public Speaking An...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tingting Zhang Jiachen Tan Zihua Xiong Bin Wu Chunping Zheng Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China School of Humanities Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Public Speaking Anxiety Detection (PSAD) is a complex and challenging task that involves detecting anxiety through diverse multimodal cues. While deep neural networks have achieved remarkable success in this task, their performance tends to degrade significantly under distribution shifts, especially in Out-Of-Distribution (OOD) scenarios. Given the substantial impact of latent contexts in PSAD, it is crucial to explore how contexts influence OOD generalization. Causality plays a fundamental role in uncovering the relationships between multimodal data and labels. Understanding these causal relationships helps tackle the OOD generalization challenge via causal invariance under context-driven shifts. In this work, we propose a novel Context-Aware Multimodal Adversarial Learning (CAMAL) framework to model and debias complex multimodal contexts in PSAD. A dual adversarial learning strategy jointly optimizes the causal independence between multimodal causal features and contexts. Experimental results on two benchmark datasets demonstrate the superiority of our approach over state-of-the-art baselines under distribution shifts.

关键词： Training Anxiety disorders Cause effect analysis Artificial neural networks Signal processing Benchmark testing Speech Adversarial machine learning Speech processing Context modeling

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Self-Supervised Reinforcement Learning for Out-of-Distribution Recovery via Auxiliary Reward

Self-Supervised Reinforcement Learning for Out-of-Distributi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yufeng Xie Yinan Wang Han Wang Qingshan Li Xi’an Key Laboratory of Intelligent Software Engineering Xidian University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

Recently, the real-world applications of reinforcement learning (RL) have seen the problem of taking actions in an out-of-distribution (OOD) state. However, most existing research is limited to take actions to narrow the visited training distribution and OOD, and does not consider the efficiency to choose such actions. In this paper, we propose a novel approach, called Self-Supervised Reinforcement Learning for OOD recovery via Auxiliary Reward (SRL-AR), to address this issue. By leveraging cumulative reward, we force the representations to discriminate state-action pairs with different returns as auxiliary task. Then, the auxiliary reward calculated from the auxiliary loss is used to generate a new policy that can effectively handle OOD situations. Moreover, we show that our method outperforms prior works in terms of asymptotic performance and sample efficiency on MuJoCo tasks.

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Reconfigurable intelligent Surface-Aided Covert Communications: A Multi-User Scenario

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IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Xiaoyan Zhao, Pengze Xiao, Han Zheng, Tong-Xing Wang, Wenjie Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering United Kingdom

This paper explores a multi-user covert communication scenario with the assistance of a reconfigurable intelligent surface (RIS). Except a covert user monitored by the warden, there are multiple public users existing in the service area. For the purpose of evaluating the efficacy of the covert communication system, a covert constraint is derived measuring the minimum detection error probability of the warden through the Kullback-Leibler (KL) divergence. Then an optimization problem is formulated to maximize the convert rate, while ensuring the constraints of covertness, transmit power and quality of service (QoS) for public users. A two-step iterative algorithm jointly designing the active and passive beamforming, is put forth to efficiently resolve this non-convex optimization problem. Additionally, the numerical results clearly demonstrate that the proposed RIS-assisted scheme can significantly enhance the covert communication performance on the premise of guaranteeing the public communication performance of the system. © 1967-2012 IEEE.

关键词： Convex optimization

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SFSM: A Serverless Function Scheduling Method for FaaS Applications over Edge Computing

SFSM: A Serverless Function Scheduling Method for FaaS Appli...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Tian Cheng Chen Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University China College of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Serverless edge computing is emerging as an enabler to provision scalable and flexible Function-as-a-Service (FaaS) applications with lightweight function instances at network edge. In serverless edge computing, the function instances with inter-dependencies are scheduled to proximate edge nodes in a distributed manner. However, the heterogeneity and unpredictability of edge networks bring significant challenge in realizing optimal scheduling decision to guarantee execution performance of applications without any prior. In view of this challenge, a Serverless Function Scheduling Method, named SFSM, is proposed in this paper for FaaS applications over edge computing. First, a long-term optimization problem is formulated to reduce completion time and decoupled into time-slot sub-problems via Lyapunov optimization. To avoid the cross-edge redundant data transmission overhead of inter-functions, a two-level graph optimization is designed with vertical and horizontal data merging. Then, SFSM incorporates an online multi-armed bandit-based scheduling algorithm that only requires the context of requests without complete information of edge networks. Finally, extensive experimental results based on real-world datasets demonstrate the effectiveness and superiority of SFSM.

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Coupling vibration characteristics of stator-winding system in synchronous generators

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Dianji yu Kongzhi Xuebao/Electric Machines and Control 2024年第11期28卷 12-24页

作者： Xu, Mingxing He, Yuling Zhang, Wen Dai, Derui Fu, Zixiang Wan, Shuting Hebei Engineering Research Center for Advanced Manufacturing and Intelligent Operation and Maintenance of Electric Power Machinery North China Electric Power University Baoding071000 China Baoding Key Laboratory of Intelligent Measurement and Control and Industrial Interconnection Hebei Software Institute Baoding071000 China

Regarding the issues of synchronous generator stator-winding system coupling vibration, the research focuses on the first and second forced coupling vibration characteristics of the generator stator-winding system. Firstly, the mechanical analytical model of the generator core and winding system was established, and the stator core magnetic pull force analytical expression and stator winding electromagnetic force analytical expression were derived to analyze the excitation characteristics of the two kinds of loads and their mutual coupling characteristics, as well as the vibration response characteristics of the stator core and stator winding. Then, a three-dimensional global model of the generator was established in finite element simulation software and the mechanical-electrical-magnetic multi-field coupling simulation was carried out, and the load excitation data and mechanical response data of the stator core and the winding were obtained. Finally, the first forced vibration, second forced vibration and first and second forced coupled vibration characteristics of the windings subjected to in the YC - 3 type first and second forced vibration coupling experimental set were verified. It is shown that the stator-winding system excitation and its first and second forced vibrations during generator operation are both coupled and additive. The coupled excitation / coupled vibration of the end winding mainly consists of DC component and 2nd frequency component, and the amplitude of DC component and 2nd frequency component is larger than that of the single excitation only. © 2024 Editorial Department of Electric Machines and Control. All rights reserved.

关键词： Synchronous generators

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Optimal Strategies for the Game with Two Faster 3D Pursuers and One Slower 2D Evader 41

Optimal Strategies for the Game with Two Faster 3D Pursuers ...

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第41届中国控制会议

作者： Runqi Zhang Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LabCollege of EngineeringPeking University National Engineering Research Center of Software Engineering Peking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A pursuit-evasion differential game with two faster pursuers in 3 D space and one slower evader in a 2 D plane is *** pursuers,which have different speeds,attempt to cooperate to capture the evader while the evader struggles to avoid or delay the *** differential games and the concept of isochron,we present a complete solution to this game and develop the optimal strategies for both the pursuers and the *** order to guarantee the optimality of the proposed strategies,the value function of the game is given and is further proved to be the solution of the corresponding Hamilton-Jacobi-Isaacs ***,simulations are carried out with comparisons between the proposed strategies and other typical strategies,the results of which show the optimality of the proposed strategies.

关键词： Pursuit-evasion differential games multi-agent system optimal strategy

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DNAT: Multi-scale Transformer with Dilated Neighborhood Attention for Image Classification 15

DNAT: Multi-scale Transformer with Dilated Neighborhood Atte...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Jiang, Chenfeng Zhou, Quan Mo, Zhiyi Wang, Jing Fan, Yawen Wu, Xiaofu Zhang, Suofei Kang, Bin Nanjing University of Posts & Telecommunications National Engineering Research Center of Communications and Networking China Wuzhou University Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou China Nanjing University of Posts & Telecommunications Department of Internet of Things China

ISBN: (纸本)9798350324662

Recently, Transformer-based models have achieved remarkable progress for image classification. In order to reduce the computational costs of multi-head self-attention (MHSA) that is widely adopted in recently-proposed visual Transformers, some window-based Transformers employ local self-attention via shrinking number of input tokens. Nevertheless, these approaches to some extent are short to model long-range dependencies. To address this problem, this paper presents an effective and powerful Transformer backbone, called Dilated Neighborhood Attention Transformer (DNAT) that has a mutli-path architecture, for image classification. DNAT is able to explore long-range interactions through dilated neighborhood attention (DNA). When dilation rate is small, DNAT degenerates to local self-attention that is computationally efficient to capture local features. As the dilation rate increases, more and more long-range token interactions are explored step-by-step. Finally, by integrating weighted features derived from various dilation rates, DNAT seamlessly harvests local-to-global context, yet with acceptable growth of model size and computational budgets. The extensive experiments on ImageNet-1K and CIFAR100 datasets demonstrate the effectiveness of DNAT for image classification. © 2023 IEEE.

关键词： Dilated Neighborhood Attention Image Classification Self-attention visual Transformer

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QianXun: A Novel Enterprise File Search Framework 25

QianXun: A Novel Enterprise File Search Framework

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Si, Chun Qi, Peng Xue, Hongjia Sun, Yan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing100876 China University of Nottingham Computer Science Faculty of Science and Engineering United Kingdom

ISBN: (纸本)9781665405270

With the deepening of the digital transformation, enterprises have accumulated plenty of electronic files. For users, how to quickly find their desired files from the massive resources is a big challenge, which is also a critical concern for enterprises. Although a series of enterprise search engines have emerged in the market, there is a lack of search systems for enterprise files. In this paper, we design a novel enterprise file search framework, named QianXun, which supports searching in both Chinese and English. By introducing technologies such as automatic text summarization, online file previewing, and Knowledge Graph-enhanced search, our proposed framework improves the efficiency of file search as well as the user experience, which is of great significance to enterprises. © 2022 IEEE.

关键词： Knowledge graph

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