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6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Safae, Belkhyr Jaouad, Boumhidi Nikolov Nikola, S. University Sidi Mohamed Ben LISAC Laboratory Computer Science Departement Faculty of Science Dhar EI Mahraz Abdellah Fez Morocco University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9798350351200

Deep Reinforcement Learning (DRL) has emerged as a pivotal technology in modern transportation systems, offering considerable potential to enhance driving safety and optimize performance. This study underscores the significance of DRL in autonomous driving (AD) and introduces the Advantage Actor-Critic (A2C) approach for lane change decision-making. A2C, representing policy-based methods, is compared with the Prioritized Replay Deep Q-Network (PRDQN), a value-based approach previously used in similar scenarios. Both approaches were evaluated using the CARLA (Car Learning to Act) simulation environment, focusing on static obstacle scenarios. The comparative analysis aims to assess the performance differences between A2C and PRDQN, highlighting the effectiveness of A2C in improving decision-making accuracy, safety, and adaptability. The results demonstrate the superior performance of A2C in improving safety and decision-making within autonomous driving systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations 24

DualFed: Enjoying both Generalization and Personalization in...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Guogang Liu, Xuefeng Niu, Jianwei Tang, Shaojie Wu, Xinghao Zhang, Jiayuan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States

ISBN: (纸本)9798400706868

In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a trade-off between these two objectives. This raises an interesting question: Is it feasible to develop a model capable of achieving both objectives simultaneously? Our paper presents an affirmative answer, and the key lies in the observation that deep models inherently exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. A straightforward approach stemming from this observation is to select multiple representations from these layers and combine them to concurrently achieve generalization and personalization. However, the number of candidate representations is commonly huge, which makes this method infeasible due to high computational costs. To address this problem, we propose DualFed, a new method that can directly yield dual representations correspond to generalization and personalization respectively, thereby simplifying the optimization task. Specifically, DualFed inserts a personalized projection network between the encoder and classifier. The pre-projection representations are able to capture generalized information shareable across clients, and the post-projection representations are effective to capture task-specific information on local clients. This design minimizes the mutual interference between generalization and personalization, thereby achieving a win-win situation. Extensive experiments show that DualFed can outperform other FL methods. Code is available at https://***/GuogangZhu/DualFed. © 2024 ACM.

关键词： Federated learning

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ROBUSTIFYING AND BOOSTING TRAINING-FREE NEURAL ARCHITECTURE SEARCH 12

ROBUSTIFYING AND BOOSTING TRAINING-FREE NEURAL ARCHITECTURE ...

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12th International Conference on Learning Representations, ICLR 2024

作者： He, Zhenfeng Shu, Yao Dai, Zhongxiang Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore China Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States

Neural architecture search (NAS) has become a key component of AutoML and a standard tool to automate the design of deep neural networks. Recently, training-free NAS as an emerging paradigm has successfully reduced the search costs of standard training-based NAS by estimating the true architecture performance with only training-free metrics. Nevertheless, the estimation ability of these metrics typically varies across different tasks, making it challenging to achieve robust and consistently good search performance on diverse tasks with only a single training-free metric. Meanwhile, the estimation gap between training-free metrics and the true architecture performances limits training-free NAS to achieve superior performance. To address these challenges, we propose the robustifying and boosting training-free NAS (RoBoT) algorithm which (a) employs the optimized combination of existing training-free metrics explored from Bayesian optimization to develop a robust and consistently better-performing metric on diverse tasks, and (b) applies greedy search, i.e., the exploitation, on the newly developed metric to bridge the aforementioned gap and consequently to boost the search performance of standard training-free NAS further. Remarkably, the expected performance of our RoBoT can be theoretically guaranteed, which improves over the existing training-free NAS under mild conditions with additional interesting insights. Our extensive experiments on various NAS benchmark tasks yield substantial empirical evidence to support our theoretical results. Our code has been made publicly available at https://***/hzf1174/RoBoT. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep neural networks

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Latest Innovations in Intelligent Network-on-Chip Architectures: A Systematic Review 17

Latest Innovations in Intelligent Network-on-Chip Architectu...

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17th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2024

作者： Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331506421

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures. © 2024 IEEE.

关键词： Federated learning

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Holistic Correction with Object Prototype for Video Object Segmentation 39

Holistic Correction with Object Prototype for Video Object S...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qiao, Shengye Xia, Changqun Liang, Yanjie Lan, Gongjin Li, Jia State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing China Pengcheng Laboratory Shenzhen China Department of Computer Science and Technology Southern University of Science and Technology Shenzhen China

ISBN: (纸本)157735897X

Recently, memory-based methods have achieved progress in semi-supervised video object segmentation. However, these methods still suffer from unstructured challenges, such as object transformations, occlusions and disappearance-reappearance. To this end, we propose a Holistic Correction Network (HCNet) to adaptively acquire concise object prototypes for holistic correction at semantic, spatial and temporal aspects. Specifically, an Adaptive Prototype Update module is firstly designed to construct multi-level core object representations by associating object variations in consecutive frames with segmentation quality assessment. Based on the updated object prototypes, Semantic, Spatial and Temporal Correction modules are respectively designed to enhance the object semantics in the entire frame, eliminate the incorrect semantic enhancement outside the object regions and calibrate the estimated object regions with temporal changes of objects. Through the holistic correction mechanism with effective object prototypes, our proposed HCNet can robustly and efficiently deal with diverse complex scenarios. Extensive and comprehensive experiments conducted on seven datasets demonstrate that our proposed HCNet can significantly improve the segmentation performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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A Robust Security Detection Strategy for Next Generation IoT Networks

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computers, Materials & Continua 2025年第1期82卷 443-466页

作者： Hafida Assmi Azidine Guezzaz Said Benkirane Mourade Azrour Said Jabbour Nisreen Innab Abdulatif Alabdulatif Technology Higher School Essaouira Cadi Ayyad UniversityEssaouira44000Morocco IMIA Laboratory MSIA TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia50050Morocco CRIL-CNRS Artois UniversityLens62300France Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia

Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating *** connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented *** ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and *** these approaches,Intrusion Detection systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of *** study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification *** combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of *** analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT *** results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack *** the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of *** work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.

关键词： IoT security intrusion detection RF KNN SVM EL NSL-KDD Edge-IIoT

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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CPL-SLAM: Centralized Collaborative Multirobot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年第12期12卷 21866-21875页

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Carleton University School of Information Technology Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Traditional visual-inertial Simultaneous Localization and Mapping (SLAM) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues, such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intrarobot and interrobot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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