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4th International Conference on computer, Big Data and artificial intelligence, ICCBD+AI 2023

作者： Zhang, Zhenjie Shen, Zhanfeng Xu, Xiaobin He, Hong Artificial Intelligence and Advanced Manufacturing China-Austria Belt and Road Joint Laboratory China Hangzhou China Hangzhou Dianzi University School of Computer Science Hangzhou China

ISBN: (纸本)9798350373233

Due to the uncertainty of the cloud environment, the response time of service inherently exhibits fluctuations and randomness, making it challenging to ensure the accuracy of latency fault detection of service composition. Therefore, this paper proposes a latency fault detection approach for service composition. Firstly, a global response time monitoring framework is introduced by analyzing the execution process of service composition. Secondly, according to the acquired monitoring data, an inference algorithm based on evidential reasoning(ER) rule for response time anomaly alarm is designed. We evaluate the approach with an actual service composition scenario about elevator traction wire rope design. Experimental results show the accuracy of the proposed approach can achieve 91.57%, significantly outperforming traditional approaches. © 2023 IEEE.

关键词： Fault detection

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Energy-aware Integrated Scheduling for Container Terminals with Conflict-free AGVs

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Journal of Systems science and Systems Engineering 2023年第4期32卷 413-443页

作者： Zhaolin Zhong Yiyun Guo Jihui Zhang Shengxiang Yang Institute of Complexity Science School of AutomationQingdao UniversityQingdao266071China Shandong Key Laboratory of Industrial Control Technology Qingdao266071China Qingdao Port International Company Ltd.Qingdao266011China Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityLeicesterLE19BHUK

For automated container terminals,the effective integrated scheduling of different kinds of equipment such as quay cranes(QCs),automated guided vehicles(AGVs),and yard cranes(YCs)is of great significance in reducing energy consumption and achieving sustainable *** at the joint scheduling of AGVs and YCs with consideration of conflict-free path planning for AGVs as well as capacity constraints on AGV-mate which is also called buffer bracket in blocks,a mixed integer programming model is established to minimize the energy consumption of AGVs and YCs for the given loading/unloading task.A solution method based on a novel bi-level genetic algorithm(BGA),in which the outer and the inner layer search the optimal dispatching strategy for QCs and YCs,respectively,is *** validity of the model and the algorithm is verified by simulation experiments,which take the Port of Qingdao as an example and the performance under different conflicting resolution strategies is *** results show that,for the given task,the proposed solution to conflict-free path and the schedule provided by the algorithm can complete the task with minimum energy consumption without loss of AGVs utilization,and the number of AGV-mates should be adjusted according to the task rather than keeping *** results indicate that our proposed approach could efficiently find solutions within 6%optimality *** consumption is dropped by an average of 15%.

关键词： Automated container terminal conflict-free routing energy saving improved genetic algorithm

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A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization 37

A Single-Loop Accelerated Extra-Gradient Difference Algorith...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Yuanyuan Shang, Fanhua An, Weixin Liu, Junhao Liu, Hongying Lin, Zhouchen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China School of Computer Science and Technology College of Intelligence and Computing Tianjin University China Medical College Tianjin University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China

ISBN: (纸本)9781713899921

In this paper, we propose a novel extra-gradient difference acceleration algorithm for solving constrained nonconvex-nonconcave (NC-NC) minimax problems. In particular, we design a new extra-gradient difference step to obtain an important quasi-cocoercivity property, which plays a key role to significantly improve the convergence rate in the constrained NC-NC setting without additional structural assumption. Then momentum acceleration is also introduced into our dual accelerating update step. Moreover, we prove that, to find an Ε-stationary point of the function f, the proposed algorithm attains the complexity O(Ε-2) in the constrained NC-NC setting, while the best-known complexity bound is Oe(Ε-4), where Oe(·) hides logarithmic factors compared to O(·). As the special cases of the constrained NC-NC setting, the proposed algorithm can also obtain the same complexity O(Ε-2) for both the nonconvex-concave (NC-C) and convex-nonconcave (C-NC) cases, while the best-known complexity bounds are Oe(Ε-2.5) for the NC-C case and Oe(Ε-4) for the C-NC case. For fair comparison with existing algorithms, we also analyze the complexity bound to find Ε-stationary point of the primal function φ for the constrained NC-C problem, which shows that our algorithm can improve the complexity bound from Oe(Ε-3) to O(Ε-2). To the best of our knowledge, this is the first time that the proposed algorithm improves the best-known complexity bounds from Oe(Ε-4) and Oe(Ε-3) to O(Ε-2) in both the NC-NC and NC-C settings. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Constrained optimization

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Transformed Graph Attention for Credit Rating 18

Transformed Graph Attention for Credit Rating

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Liu, Charles Z. Xiang, Sheng Cheng, Dawei Liu, Junyi Zhang, Ying Qin, Lu University of Technology Aaii Sydney Australia Tongji University Department of Computer Science & Technology Shanghai China Ministry of Education Key Laboratory of Artificial Intelligence Shanghai China

ISBN: (纸本)9798350312201

In banking finance and lending, credit rating is an important tool to measure creditor and debt reliability and prevent systemic risk among banks. Intrinsic features are key factors in risk control decisions. Decision models built with these intrinsic features are often used to assess risk classification and asset safety, but there lacks research on credit prediction for bank rating outlooks with financial correlation, which reflects the structured pattern of systemic risk and contagion. Although correlations among financial entities are valuable for rating outlook, it is hard to integrate such interactions into feature-based solutions. Aiming at the issues above, we propose a hyper-feature mapping operator based on an attention-based knowledge representation to build a knowledge system for the credit rating prediction of the interbank to represent the knowledge of credit rating. Combined with the interrelated behavior graph of each entity, a hyper context graph attention with intrinsic properties (assets, capabilities, buffers) are represented as knowledge for rating prediction to extract the context representation, helping understand the interaction of risk between intrinsic properties and entity-to-entity. Through the experimental comparison and evaluation based on 10 years of actual interbank data, the results show that the proposed model is effective with higher performance compared with the popular rating prediction methods currently used in the industry. © 2023 IEEE.

关键词： Data mining

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Unsupervised Cell Localization and Segmentation via Synchronized Spectral Clustering 22

Unsupervised Cell Localization and Segmentation via Synchron...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Han, Yikai Jiang, Jimao Pei, Yuru School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of General Artificial Intelligence Peking University Beijing100871 China

ISBN: (纸本)9798331520526

In this paper, we propose a unified model, deep synchronized spectral clustering (DeepSSC), for unsupervised cellular structure detection and segmentation. Unlike previous deep spectral clustering methods, which handle segmentation of single or small sets of salient objects through iterative mask-cuts and additional synchronization in postprocessing to correct cross-image clustering inconsistencies, DeepSSC constructs a joint spectral embedding and handles synchronized cluster assignments by capturing both inter-and intra-image relevance to identify relevant cellular structures. Additionally, we fine-tune the pre-trained model using self-supervised learning to address domain shifts caused by varying appearances and textures in microscopic images. DeepSSC is designed to simultaneously detect and segment large numbers of fine-grained, repetitively occurring cells. Experimental results demonstrate that DeepSSC achieves robust, superior performance compared to state-of-the-art methods. © 2025 IEEE.

关键词： Supervised learning

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Modeling Speech Emotion Recognition via ImageBind representations

Modeling Speech Emotion Recognition via ImageBind representa...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Chakhtouna, Adil Sekkate, Sara Adib, Abdellah Team Data Science & Artificial Intelligence Laboratory of Mathematics Computer Science and Applications (LMCSA) Faculty of Sciences and Technologies Hassan II University Mohammedia Morocco Higher National School of Arts and Crafts Hassan II University Casablanca Morocco

Speech Emotion Recognition (SER) refers to the ability of Machine Learning (ML) and Deep Learning (DL) techniques to accurately predict people's emotional states from speech signals. Significant progress has been achieved in the SER domain involving the incorporation of DL models to introduce novel features extraction processes. This paper introduces the use of deep representations learned from the multi-modal Large Language Model (LLM) called ImageBind. These representations were subsequently provided as input to the Nu-Support Vector Machine (Nu-SVM) with RBF kernel for the classification task. The experiments were executed using the IEMOCAP database within the context of a Speaker-Dependent (SD) scenario. The method achieved a noteworthy overall accuracy rate of 80.58% for the four emotions of IEMOCAP, representing a substantial improvement over well-established methods in the existing body of literature. Thus, affirming that the proposed methodology, founded upon ImageBind representations, introduces a novel perspective to the field of SER. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Support vector machines

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CA-DTS:A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network

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Journal of computer science & Technology 2023年第5期38卷 1113-1131页

作者：胡世红罗渠元李光辉施巍松叶保留 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Houhai UniversityNanjing 210098China School of Computer and Information Houhai UniversityNanjing 210098China School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuci 214122China Department of Computer and Information Sciences University of DelawareNewarkDE 19716U.S.A National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China

Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing *** continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this ***,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized *** this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov *** that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of *** reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple *** evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed *** with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.

关键词： edge computing deep reinforcement learning task scheduling vehicular edge computing

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Effective High-order Graph Representation Learning for Credit Card Fraud Detection

arXiv

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arXiv 2025年

作者： Zou, Yao Cheng, Dawei Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

Credit card fraud imposes significant costs on both cardholders and issuing banks. Fraudsters often disguise their crimes, such as using legitimate transactions through several benign users to bypass anti-fraud detection. Existing graph neural network (GNN) models struggle with learning features of camouflaged, indirect multi-hop transactions due to their inherent over-smoothing issues in deep multi-layer aggregation, presenting a major challenge in detecting disguised relationships. Therefore, in this paper, we propose a novel High-order Graph Representation Learning model (HOGRL) to avoid incorporating excessive noise during the multi-layer aggregation process. In particular, HOGRL learns different orders of pure representations directly from high-order transaction graphs. We realize this goal by effectively constructing high-order transaction graphs first and then learning the pure representations of each order so that the model could identify fraudsters’ multi-hop indirect transactions via multi-layer pure feature learning. In addition, we introduce a mixture-of-expert attention mechanism to automatically determine the importance of different orders for jointly optimizing fraud detection performance. We conduct extensive experiments in both the open source and real-world datasets, the result demonstrates the significant improvements of our proposed HOGRL compared with state-ofthe-art fraud detection baselines. HOGRL’s superior performance also proves its effectiveness in addressing high-order fraud camouflage *** Codes 68T07, 91B06 © 2025, CC BY-NC-SA.

关键词： Graph neural networks

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Compact Model Build Upon Dynamic Weighting artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging

Optimal Control of Connected Automated Vehicles with Event-T...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Sabouni, Ehsan Ahmad, H.M. Sabbir Xiao, Wei Cassandras, Christos G. Li, Wenchao Boston University Division of Systems Engineering Department of Electrical & Computer Engineering BostonMA United States Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA United States

ISBN: (纸本)9798350335446

We address the problem of safely coordinating a network of Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network. Such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved online over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may limit the controller's application to real CAVs. We tackle this limitation by adopting an event-triggered control approach for CAVs such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed developed to emulate merging roadways using mobile robots as CAVs. We present results to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety. © 2023 IEEE.

关键词： Merging

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