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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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A Web Service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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Robust Continual Learning Model Driven by Diverse Samples 24

Robust Continual Learning Model Driven by Diverse Samples

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5th International Conference on computer Information and Big Data Applications, CIBDA 2024

作者： Nie, Xueyun Wei, Yifan Liu, Zhihong Chongqing Key Laboratory of Image Cognition School of Computer Science and Technology School of Artificial Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangxi University of Chinese Medicine Nanchang330004 China

ISBN: (纸本)9798400718106

The replay method is one of the effective methods to alleviate the catastrophic forgetting of deep learning models when the data is updated incrementally, but the existing methods have some shortcomings. In order to solve the problems of overfitting and bias of new and old categories, the feature distribution of new and old categories is confused in the feature space, and the balance between stability and plasticity of the model is destroyed, a robust continual learning model driven by diverse samples is proposed in this paper. This method selects a small number of samples through uncertainty measurement, retains enough old information for replay training, and designs a task attention module, which effectively alleviates the recency bias problem caused by sample imbalance in new and old tasks. The algorithm maintains the stability and plasticity balance of the model from multiple perspectives to mitigate catastrophic forgetting in the replay continual learning method. Through the evaluation of CIFAR100 data set, the accuracy of the proposed method is improved by 0.31% to 1.48% compared with the most advanced method. © 2024 ACM.

关键词： Plasticity

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Linear alignment: a closed-form solution for aligning human preferences without tuning and feedback 24

Linear alignment: a closed-form solution for aligning human ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Songyang Gao Qiming Ge Wei Shen Shihan Dou Junjie Ye Xiao Wang Rui Zheng Yicheng Zou Zhi Chen Hang Yan Qi Zhang Dahua Lin Shanghai Artificial Intelligence Laboratory School of Computer Science Fudan University ByteDance Inc

The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, traditional alignment algorithms, such as PPO, are hampered by complex annotation and training requirements. This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences. In this work, we introduce Linear Alignment, a novel algorithm that aligns language models with human preferences in one single inference step, eliminating the reliance on data annotation and model training. Linear alignment incorporates a new parameterization for policy optimization under divergence constraints, which enables the extraction of optimal policy in a closed-form manner and facilitates the direct estimation of the aligned response. Extensive experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment across diverse scenarios. Our code and dataset is published on https://***/Wizardcoast/Linear_***.

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Mal-lightDet: A light method to detect malicious encrypted traffic based on machine learning 23

Mal-lightDet: A light method to detect malicious encrypted t...

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Liu, Xu Liu, Zhang Zhao, Xiaojing Ren, Zhengwei School of Computer Science and Technology Changchun University of Science and Technology China Jilin Province Key Laboratory of Network and Information Security China School of Artificial Intelligence Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798400708190

Encryption not only protects the network security and privacy, but also encrypts attackers' malicious traffic to evade detection. Thus, how to detect malicious encrypted traffic is critical for network security. From the perspective of privacy, methods based on ciphertext seem to be more attractive than decryption-based methods. However, due to performance limitation or privacy violations, most existing methods are far from satisfactory. Machine learning models provide a solution to the malicious encrypted traffic detection, but they place great demand on data features. To balance the detection performance and feature effectiveness, this paper proposes a framework called Mal-lightDet for light detection of malicious encrypted traffic based on machine learning. Mal-lightDet includes a feature-group selection model that mines the relevance between features to represent the traffic flow, which is less considered in the existing methods, and a voting-based detection model to eliminate the bias of different machine learning models. The selected feature subset not only preserves the detection effectiveness but also reduces the computation brought by massive training data. The experimental results prove the efficiency and stability of Mal-lightDet to distinguish malicious flow data by comparing it with other state-of-art methods. Besides, Mal-lightDet performs faster, more accurately, and more stably than some deep learning methods. © 2023 ACM.

关键词： Network security

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