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International Conference on Computer Supported Cooperative Work in Design

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The goal of Emotion Recognition in Conversations (ERC) is to accurately identify the emotions expressed in each utterance within a dialogue. Despite advancements made by current ERC methods, particularly those using RNN-based and GCN-based models to capture emotional dynamics and model speaker relationships, there remain two primary limitations: first, an insufficient integration of multiple feature representations and commonsense knowledge, which hampers the model's ability for deep emotional understanding; and second, the reliance on a single cross-entropy loss for classification optimization, which restricts the accuracy and robustness of emotion recognition. To address these issues, we propose a method for ERC using Multilayer Feature Fusion and Joint Loss Optimization (MFFJL). This approach combines contextual information, speaker dependency, and commonsense knowledge features by extracting feature vectors through RoBERTa and COMET, utilizing bidirectional LSTM and attention mechanisms to capture conversational context, and applying a cross-fusion module to deeply integrate various features, thus enhancing comprehension of complex emotional expressions. Additionally, the feature classification module incorporates joint cross-entropy and KL divergence optimization, further improving classification accuracy and consistency. Experimental results demonstrate the effectiveness of our method, as evidenced by superior performance on the IEMOCAP and MELD datasets. Our code is available at https://***/r/MFFJL-D9D8.

关键词： Emotion recognition Accuracy Federated learning Computational modeling Feature extraction Nonhomogeneous media Vectors Robustness Optimization Commonsense reasoning

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Unique in the Metro system: The Likelihood to Re-Identify a Metro User with Limited Trajectory Points

SSRN

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SSRN 2023年

作者： Yang, Hongtai Ping, An Wei, Hongmin Zhai, Guocong School of Transportation and Logistics Institute of System Science and Engineering National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities Shenzhen518060 China School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Department of Civil & Environmental Engineering Old Dominion University 129C Kaufman Hall NorfolkVA23529 United States

Though the collection of metro smart card data could help improve the operations of the metro system, the release of such data might lead to privacy issues. Few studies have quantified the probability to re-identify a user from the smart card data using very limited trajectory points. Thus, this study investigates this topic by analyzing eight-day metro smart card data of Chengdu, China. Results reveal that, on the macro level, three random trajectory points with a temporal resolution of one minute and one hour are enough to identify over 90% and 67% of the users. Even when the resolution is reduced to one day, 20% of the users could be still be identified by three points. On the individual level, three carefully selected points with a temporal resolution of one minute, one hour, and one day could lead to a re-identification risk no less than 0.5 for 99%, 89%, and 52% of the users. The effects of number of points, number of users, and other temporal resolutions are also thoroughly evaluated. These findings emphasize the great privacy issues involved in the release of metro smart card data and remind metro operators to take proactive measures to enhance privacy protection. © 2023, The Authors. All rights reserved.

关键词： Smart cards

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Optimal margin distribution machine for multi-instance learning 29

Optimal margin distribution machine for multi-instance learn...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Teng Jin, Hai 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 China

ISBN: (纸本)9780999241165

Multi-instance learning (MIL) is a celebrated learning framework where each example is represented as a bag of instances. An example is negative if it has no positive instances, and vice versa if at least one positive instance is contained. During the past decades, various MIL algorithms have been proposed, among which the large margin based methods is a very popular class. Recently, the studies on margin theory disclose that the margin distribution is of more importance to generalization ability than the minimal margin. Inspired by this observation, we propose the multi-instance optimal margin distribution machine, which can identify the key instances via explicitly optimizing the margin distribution. We also extend a stochastic accelerated mirror prox method to solve the formulated minimax problem. Extensive experiments show the superiority of the proposed method. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Optimization

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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arXiv

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arXiv 2024年

作者： Wang, Yuhe Wang, Yating National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China

Fluid flow through bimodal porous media, characterized by a distinct separation in pore size distribution, is critical in various scientific and engineering applications, including groundwater management, oil and gas production, and carbon sequestration. This note delves into the physical similarity of fluid flow within such media, bridging the gap between microscale phenomena and macroscale observations. We present a representative mathematical model that conceptualizes bimodal porous media as a double-continuum system, distinguishing between macroporous and microporous regions. The model captures the complex interactions between these regions, particularly focusing on the challenges of modeling fluid flow when there is significant disparity in pore sizes. By employing a heuristic approach grounded in pore-scale tomography, we derive governing equations that describe fluid flow and analyze the solution characteristics. The results reveal unique features of the fluid flow in bimodal systems, such as the occurrence of boundary discontinuities and the delayed transient response, which are not observed in conventional porous media. This work provides ground for further studies in bimodal porous media, offering insights that could enhance predictive modeling and optimization in various applications concerning porous media with similar bimodal pore size distributions. © 2024, CC BY.

关键词： Transient analysis

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FaceBench: A Multi-View Multi-Level Facial Attribute VQA dataset for Benchmarking Face Perception MLLMs

arXiv

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arXiv 2025年

作者： Wang, Xiaoqin Ma, Xusen Hou, Xianxu Ding, Meidan Li, Yudong Chen, Junliang Chen, Wenting Peng, Xiaoyang Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China AIAC Xi’an Jiaotong-Liverpool University China Tsinghua University China The Hong Kong Polytechnic University Hong Kong City University of Hong Kong Hong Kong Sun Yat-sen University China

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://***/CVI-SZU/FaceBench. Copyright © 2025, The Authors. All rights reserved.

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Fil-Cad: A Fault Detection Method for Iiot Based on Federated Incremental Learning with Class Accuracy Distillation

SSRN

引用

SSRN 2024年

作者： Liu, Yanhua Fang, Wenyu Huang, Wei Wang, Xiaofeng Zhao, Baokang Liu, Ximeng College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Fault detection is a research topic attracting much attention in IIoT. However, the continuous accumulation and growth of IIoT data leads to the catastrophic forgetting problem in the fault detection model and data heterogeneity, which creates a bottleneck for the performance and generalization of federated learning in fault detection. To improve the fault detection performance of federated learning models in data incremental scenarios, we propose a federatedincremental-learning-based fault detection method for IIoT, namely FIL-CAD. Firstly, comparing the knowledge distillation, the accuracies of different data classes in the global and local models are chosen as the model lightweight learning features, and the loss function is weighted. Secondly, we introduce the concept of data learning depth. It computes different model weights in the aggregation based on their degrees of data sample learning and model training participation and obtains the global model through weighted aggregation. Additionally, FIL-CAD supports the combination of differential privacy to protect data privacy. The proposed method is compared with four benchmark methods on four datasets, which include CWRU and MFPT bearing datasets. Then, the experimental results show that FIL-CAD performs optimally in accuracy and F1-Score, and the average accuracy is improved by 1.32%-18.52%, demonstrating the superiority of FIL-CAD. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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

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

引用

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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