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EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

Science China(Information Sciences) 2024年第8期67卷 130-146页

作者： Yingjian LIU Jiang LI Xiaoping WANG Zhigang ZENG School of artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Key Laboratory of Brain-inspired intelligent systems Huazhong University of Science and Technology Key Laboratory of Image Processing and intelligent Control (Huazhong University of Science and Technology) Ministry of Education Institute of artificial Intelligence Huazhong University of Science and Technology

Emotion recognition in conversation(ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention(IMMHA), dialogue-based gated recurrent unit(DiaGRU), and skip-chain conditional random field(SkipCRF).Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while Dia GRU is utilized to extract speaker-and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark *** ablation studies confirm that our modules can effectively model emotional inertia and contagion.

关键词： emotion recognition in conversation emotional inertia and contagion multi-head attention gated recurrent unit conditional random field

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Automatic velocity picking based on optimal key points tracking algorithm

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Petroleum Science 2024年第2期21卷 903-917页

作者： Yong-Hao Wang Wen-Kai Lu Song-Bai Jin Yang Li Yu-Xuan Li Xiao-Feng Gu The Institute for artificial Intelligence(THUAI) Tsinghua UniversityBeijing100084China State Key Laboratory of intelligent Technology and systems Tsinghua UniversityBeijing100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing100084China The department of Automation Tsinghua UniversityBeijing100084China

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

关键词： Velocity picking Multi-object tracking Density clustering Kalman filter

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Spontaneous Recovery in Directed Dynamical Networks

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Engineering 2024年第6期37卷 208-214页

作者： Xueming Liu Xian Yan H.Eugene Stanley School of artificial Intelligence and Automation&The MOE Engineering Research Center of Autonomous intelligent Unmanned systems&the Key Laboratory of Image Processing Huazhong University of Science and TechnologyWuhan 430074China Center for Polymer Studies Department of PhysicsBoston UniversityBostonMA 02215USA

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a *** studies on the spontaneous recovery of dynamical networks have been limited to undirected ***,most real-world networks are *** fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical *** find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network *** to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis *** addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery *** findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

关键词： Network resilience Directed dynamical networks Spontaneous recovery

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems Science & Complexity 2024年

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Engineering Laboratory of human-Robot Integration System and intelligent Equipment School of Electrical Engineering and Automation Anhui University Key Laboratory of intelligent Control and Decision of Complex systems School of Automation Beijing Institute of Technology department of Control Science and Engineering Tongji University Shanghai Research Institute for intelligent Autonomous systems Tongji University

This paper develops distributed algorithms for solving Sylvester equations. The authors transform solving Sylvester equations into a distributed optimization problem, unifying all eight standard distributed matrix structures. Then the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence rate. These results are obtained by carefully choosing the step-size of the algorithm, which requires particular information of data and Laplacian matrices. To avoid these centralized quantities, the authors further develop a distributed scaling technique by using local information only. As a result, the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable intervals. Finally, the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of intelligent and Integrated systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal artificial Intelligence systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China department of Electrical Engineering-systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal artificial Intelligence systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex systems Chinese Academy of SciencesBeijing 100190 School of artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Investigating Optical Transport Network Performance:A Recurrence Plot Approach

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China Communications 2024年第5期21卷 166-176页

作者： Sun Xiaochuan Cao Difei Wei Biao Li Zhigang Li Yingqi College of artificial Intelligence North China University of Science and TechnologyTangshan 063210China Hebei Key Laboratory of Industrial intelligent Perception Tangshan 063210China

In future optical transport networks,lightpath performance analysis is of great practical significance for fully automated *** general,the quality of transmission(QoT)of lightpaths,measured by optical quality factor or optical signal-to-noise ratio,has a complex time-varying process,along with the interactions of the other lightpath state parameters(LSPs),such as transmit power,chromatic dispersion,polarization mode *** studies are mostly focused on lightpath QoT estimation,but ignoring lightpath-level data *** this case,our article proposes a novel lightpath performance analysis method considering recurrence plot(RP)and cross recurrence plot(CRP).Firstly,we give a detailed interpretation on the recurrence patterns of LSPs via a qualitative 2-D RP representation and its quantitative *** can potentially enable the accurate and fast lightpath failure detection with a low computational *** the other hand,CRP is devoted to modeling the relationships between lightpath QoT and LSPs,and the correlation degree is determined by a geometric mean of multiple indexes of cross recurrence quantification *** the view of application,such CRP analysis can provide the effective knowledge sharing to guarantee more credible QoT *** experiments on a real-world optical network dataset have clearly demonstrated the effectiveness of our proposal.

关键词： CRP analysis lightpath QoT optical transport networks RP analysis

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Distributed algorithm for solving variational inequalities over time-varying unbalanced digraphs

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Control Theory and Technology 2024年第3期22卷 431-441页

作者： Yichen Zhang Yutao Tang Zhipeng Tu Yiguang Hong School of artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China Network Technology Laboratory Huawei Technologies Co.Ltd.Beijing 100095China department of Control Science and Engineering Tongji UniversityShanghai 200092China Shanghai Research Institute for intelligent Autonomous systems Tongji UniversityShanghai 200092China

In this paper,we study a distributed model to cooperatively compute variational inequalities over time-varying directed ***,each agent has access to a part of the full mapping and holds a local view of the global set *** virtue of an auxiliary vector to compensate the graph imbalance,we propose a consensus-based distributed projection algorithm relying on local computation and communication at each *** show the convergence of this algorithm over uniformly jointly strongly connected unbalanced digraphs with nonidentical local *** also provide a numerical example to illustrate the effectiveness of our algorithm.

关键词： Variational inequality Distributed computation Multi-agent system Weight-unbalanced graph

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Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

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Computer systems Science & Engineering 2024年第2期48卷 413-429页

作者： Seoyun Kim Hyerim Yu Jeewoo Yoon Eunil Park department of Applied artificial Intelligence Sungkyunkwan UniversitySeoul03063Korea department of human-artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea AI Team Raon DataSeoul03073Korea

Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s ***,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a ***,a significant problem arises where not every region receives the same level of air quality *** disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available *** address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was ***,continuous location features in the form of latitude and longitude values were incorporated into our *** utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were *** experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly ***,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was ***,the implications and contributions of our research were *** implementing our approach,the cost associated with relying solely on existing observatories can be substantially *** reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.

关键词： Fine dust PM_(10) air quality prediction machine learning LSTM

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Collective Movement Simulation:Methods and Applications

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Machine Intelligence Research 2024年第3期21卷 452-480页

作者： Hua Wang Xing-Yu Guo Hao Tao Ming-Liang Xu School of Computer and artificial Intelligence Zhengzhou UniversityZhengzhou450001China Engineering Research Center of intelligent Swarm systems Ministry of EducationZhengzhou450001China National Supercomputing Center in Zhengzhou Zhengzhou450001China China Ship Development and Design Center Wuhan430060China

Collective movement simulations are challenging and important in many areas,including life science,mathematics,physics,information science and public *** this survey,we provide a comprehensive review of the state-of-the-art techniques for collective movement *** start with a discussion on certain concepts to help beginners understand it more ***,we analyze the intelligence among different collective objects and the emphasis in different ***,we classify existing collective movement simulation methods into four categories according to their effects,namely versatility,accuracy,dynamic adaptability,and assessment feedback ***,we introduce five applications of layout optimization,emergency control,dispatching,unmanned systems,and other derivative ***,we summarize possible future research directions.

关键词： Collective movement simulation multiple objects multiple discipline simulation effect collective intelligence

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DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer Science 2024年第6期18卷 186912-186912页

作者： WEI Pijing WANG Qianqian GAO Zhen CAO Ruifen ZHENG Chunhou Information Materials and intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Key Lab of intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Lab of intelligent Computing and Signal Processing of Ministry of Education School of artificial Intelligence Anhui University

MicroRNAs (miRNAs) are closely related to numerous complex human diseases, therefore, exploring miRNA-disease associations (MDAs) can help people gain a better understanding of complex disease mechanism. An increasing number of computational methods have been developed to predict MDAs. However, the sparsity of the MDAs may hinder the performance of many methods. In addition, many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor nodes. In this study, we propose a deep matrix factorization model with variational autoencoder (DMFVAE) to predict potential MDAs. DMFVAE first decomposes the original association matrix and the enhanced association matrix, in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method, to obtain sparse vectors and dense vectors, respectively. Then, the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors, and meanwhile, node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense vectors. Finally, sample features are acquired by combining the latent vectors and network structure embedding vectors, and the final prediction is implemented by convolutional neural network with channel attention. To evaluate the performance of DMFVAE, we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs well. Furthermore, case studies on lung neoplasms, colon neoplasms, and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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