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arXiv 2023年

作者： Xie, Guoyang Wang, Jinbao Liu, Jiaqi Lyu, Jiayi Liu, Yong Wang, Chengjie Zheng, Feng Jin, Yaochu The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science University of Surrey GuildfordGU2 7YX United Kingdom Tencent Youtu Lab Shenzhen518040 China The School of Engineering Science University of Chinese Academy of Sciences Beijing China The School of Engineering Westlake University Hangzhou310030 China The Department of Computer Science and Engineering University of Surrey GuildfordGU2 7YX United Kingdom

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently, many advanced algorithms have been reported, but their performance deviates considerably with various IM settings. We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications. In addition, it is difficult for researchers to analyze IAD algorithms without a uniform benchmark. To solve this problem, we propose a uniform IM benchmark, for the first time, to assess how well these algorithms perform, which includes various levels of supervision (unsupervised versus fully supervised), learning paradigms (few-shot, continual and noisy label), and efficiency (memory usage and inference speed). Then, we construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on seven major datasets with a uniform setting. Extensive experiments (17,017 total) on IM-IAD provide in-depth insights into IAD algorithm redesign or selection. Moreover, the proposed IM-IAD benchmark challenges existing algorithms and suggests future research directions. For reproducibility and accessibility, the source code is uploaded to the website: https://***/M-3LAB/open-iad © 2023, CC BY.

关键词： Anomaly detection

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FLeS: A Federated Learning-Enhanced Semantic Communication Framework for Mobile AIGC-Driven Human Digital Twins

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IEEE Network 2025年

作者： Okegbile, Samuel D. Gao, Haoran Talabi, Oluwasegun Cai, Jun Yi, Changyan Niyato, Dusit Shen, Xuemin University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing211106 China Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Innovative mobile artificial intelligence-generated content (AIGC) can support the evolution and updating processes of virtual twins (VTs) in human digital twin (HDT) systems. With a reliable and efficient automatic data generation process, the requirement for a timely physical-to-virtual synchronization in HDT can be satisfied. While such an AIGC-enabled HDT system can facilitate modelling high fidelity VTs, generating content that represents the true states in the physical environment and providing timely customized services, it may suffer from a poor understanding of contexts, a lack of creativity, and various security and privacy concerns. In this paper, we propose a novel framework, which integrates federated learning (FL) and semantic communication (SemCom) to enhance performance in the AIGC-enabled HDT system while improving accuracy and convergence properties. First, we present a holistic architectural framework for the proposed FL-enhanced SemCom (FLeS) solution for mobile AIGC-enabled HDT systems and discuss its design requirements and challenges. We later present key technologies necessary to realize the FLeS solution, followed by elaborating on important technical issues to suggest future directions. Experimental results demonstrate that FLeS not only facilitates reliable and personalized content generation but also shows better performance when compared to existing solutions. © 1986-2012 IEEE.

关键词： Federated learning

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Consideration of Alternative Coolants in AGR-like FHR Designs

Consideration of Alternative Coolants in AGR-like FHR Design...

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2022 International Conference on Physics of Reactors, PHYSOR 2022

作者： Margulis, Marat Cosgrove, Paul Shwageraus, Eugene Nuclear Futures Institute School of Computer Science and Electronic Engineering Bangor University BangorLL57 2DG United Kingdom Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

ISBN: (纸本)9780894487873

Replacing carbon-dioxide coolant in AGR-like reactor with molten salt significantly affects the reactor performance characteristics. Hence, a new core design is needed. In this work, a Multi-Objective Particle Swarm Optimisation is used to identify the most favourable configurations for a new system layout. The performed analysis examines each configuration with respect to its thermal-hydraulic performance to assess the power uprate potential which is limited by multiple temperature constraints (e.g. fuel centreline and cladding temperatures as well as the coolant freezing/boiling). The estimated maximum power was then used in the fuel burnup calculations, from which a discharge burnup and cycle average Coolant Temperature Coefficients (CTC) were obtained. As a result of the optimisation process, several families of possible solutions were identified, which form an optimal Pareto Front. The most attractive configurations in terms of achievable power density, however, were not necessarily on the Pareto front. Several coolant options are examined and compared in this paper. The newly identified design options have the potential to achieve power density that is higher than that of a typical AGR by up to a factor of five while maintaining negative CTC through the burnup cycle. © 2022 Proceedings of the International Conference on Physics of Reactors, PHYSOR 2022. All Rights Reserved.

关键词： AGR AGRESR FHR FLiBe MOPSO NaFZrF

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Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets

arXiv

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arXiv 2024年

作者： Zhang, Tianxiao Xu, Wenju Luo, Bo Wang, Guanghui Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States Amazon Palo AltoCA94301 United States Department of Computer Science Toronto Metropolitan University TorontoONM5B 2K3 Canada

The Vision Transformer (ViT) leverages the Transformer's encoder to capture global information by dividing images into patches and achieves superior performance across various computer vision tasks. However, the self-attention mechanism of ViT captures the global context from the outset, overlooking the inherent relationships between neighboring pixels in images or videos. Transformers mainly focus on global information while ignoring the finegrained local details. Consequently, ViT lacks inductive bias during image or video dataset training. In contrast, convolutional neural networks (CNNs), with their reliance on local filters, possess an inherent inductive bias, making them more efficient and quicker to converge than ViT with less data. In this paper, we present a lightweight Depth-Wise Convolution module as a shortcut in ViT models, bypassing entire Transformer blocks to ensure the models capture both local and global information with minimal overhead. Additionally, we introduce two architecture variants, allowing the Depth- Wise Convolution modules to be applied to multiple Transformer blocks for parameter savings, and incorporating independent parallel Depth-Wise Convolution modules with different kernels to enhance the acquisition of local information. The proposed approach significantly boosts the performance of ViT models on image classification, object detection, and instance segmentation by a large margin, especially on small datasets, as evaluated on CIFAR- 10, CIFAR-100, Tiny-ImageNet and ImageNet for image classification, and COCO for object detection and instance segmentation. The source code can be accessed at https://***/ZTX-100/Efficient_ViT_with_DW. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Ammara Nasim Divya Velayudhan Abdelfatah Hassan Ahmed Taimur Hassan Samet Akcay Muhammad Usman Akram Naoufel Werghi Department of Computer and Software Engineering National University of Sciences and Technology Pakistan Department of Electrical Engineering and Computer Science KUCARS and C2PS Khalifa University UAE Department of Electrical and Computer Engineering Abu Dhabi University Abu Dhabi UAE R&D Division Intel Corporation UK

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-the-art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time.

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Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

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Journal of Forestry Research 2024年第4期35卷 13-33页

作者： Dehui Christina Geng Amy Li Jieyu Zhang Howie W.Harshaw Christopher Gaston Wanli Wu Guangyu Wang National Park Research Centre Faculty of ForestryUniversity of British ColumbiaVancouver V6T 1Z4Canada Faculty of Applied Science University of British ColumbiaVancouver V6T 1Z3Canada Paul G.Allen School of Computer Science&Engineering University of WashingtonSeattleWA 98195USA Faculty of Kinesiology Sportand RecreationUniversity of AlbertaEdmonton T6G 2J9Canada Department of Wood Science Faculty of ForestryUniversity of British ColumbiaVancouver V6T 1Z4Canada

COVID-19 posed challenges for global tourism *** in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are *** was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)*** highlight that COVID-19 significantly changed park visitation *** tended to explore more remote areas *** GWR model also indicated distance to nearby trails was a significant influence on visitor *** results indicate that the pandemic influenced tourism temporal and spatial *** research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

关键词： Tourism management Social media big data National parks COVID-19 Geographical weighted regression

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An Effective Method for Managing Waste in Smart Cities Based on Unified CNN and LSTM Approach

An Effective Method for Managing Waste in Smart Cities Based...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： R. Rajagopal Mahesh Arjun Suryawanshi Shaik Akbar Chandra Sekhar Rao.B Harshal Patil Gourav Kalra Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Chemical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashtra India Department of AIML Geethanjali College of Engineering and Technology Hyderabad India Department of Environmental Science Block No-2 Vardhaman College of Engineering Autonomous Hyderabad India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India

The government of India launched programs like Smart Cities and the Clean Development Mission to build eco-friendly and technologically advanced cities. Solid waste management (SWM) is becoming more important to these types of programs. Because of unplanned urbanization and the fast increase of urban populations (as a result of migration), the dynamics of urban waste are changing, making it extremely difficult for local authorities in urban areas to design an efficient SWM policy. The development of a long-term SWM plan that is in line with the goals of government initiatives requires an in-depth knowledge of the waste qualities, quantities, and present management processes. Data preprocessing, feature extraction, and training the model ought to take precedence. Data from the characterization are linked to the entire magnitude of that influence for the relevant area and period in this step of preprocessing, which is where normalization is applied. It employs principal component analysis (PCA) for feature extraction. Feature selection is an essential first step in training unified CNN-LSTM models. Compared to the current front-runners, CNN and LSTM, the proposed technique performs far better. The accuracy was increased by 95.85% when the method was used.

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Rethinking Label-specific Features for Label Distribution Learning

arXiv

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arXiv 2025年

作者： Xu, Suping Dai, Chuyi Shang, Lin Shao, Changbin Yang, Xibei Pedrycz, Witold Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Department of Computer Science and Technology Nanjing University Nanjing210023 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China

Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting interactions among distinct clusters. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Clustering algorithms

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Exploring Help-Seeking Behavior, Performance, and Cognitive Load in Individual Tutoring: A Comparative Study between Human Tutors and Social Robots

Exploring Help-Seeking Behavior, Performance, and Cognitive ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Social robots have become increasingly prevalent in the context of one-on-one tutoring, serving as effective educational aids. In response to this trend, the present study was devised to conduct a comparative analysis between human tutors and robot tutors. Additionally, the study aims to investigate how varying previous knowledge in robots influence students’ tendencies for help-seeking. By examining the performance and physiological signals of participants, this research seeks to provide valuable insights into the effectiveness of social robots in educational contexts. 21 participants were divided into three groups, each seeking assistance from a human tutor (HT), seeking help from a robot without any prior knowledge of robots (RT1), and seeking help from a robot after gaining some understanding of its capabilities (RT2). Our results demonstrated that participants sought more help from robot than from human and participants in Group RT2 performed better than participants in Group RT1. However, participants experienced greater cognitive load when interacting with a robot tutor compared to interacting with a human tutor. Future work could focus on developing interventions to alleviate students’ cognitive load during interactions with robot tutors.

关键词： Social robots Robot vision systems Cognitive load Market research Cameras Physiology Behavioral sciences Robots Thermal stresses Stress

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