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Computer Science, Computer engineering, & Applied computing (CSCE), Congress in

作者： Ralf Luis de Moura Virginia N. L. Franqueira Gustavo Pessin Operational Technology Architecture Vale S.A. Vitória Brazil School of Computing University of Kent Canterbury UK Department of Automation Engineering Instituto de Tecnologia Vale Ouro Preto Brazil

Industrial control systems (ICS) operate on serial-based networks which lack proper security safeguards by design. They are also becoming more integrated to corporate networks, creating new vulnerabilities which expose ICS networks to in-creasing levels of risk with potentially significant impact. Despite those risks, only a few mechanisms have been suggested and are available in practice as cybersecurity safeguards for the ICS network layer, maybe because they might not be commercially viable. Intrusion detection systems (IDS) are typically deployed in the corporate networks to protect against attacks since they are based on TCP/IP. However, IDS are not used in serial-based ICS networks yet. This study examines and compares modern Artificial Intelligence (AI) techniques applied in IDS that are potentially useful for serial-based ICS networks. The results showed that current AI-based IDS methods are viable in such networks. A mix of AI techniques would be the best way forward to detect known attacks via rules and novel attacks, not previously mapped, via supervised and unsupervised techniques. Despite these strategies' limited use in serial-based networks, their adoption could significantly strengthen cybersecurity of ICS networks.

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Hwamei: A Learning-Based Synchronization Scheme for Hierarchical Federated Learning

Hwamei: A Learning-Based Synchronization Scheme for Hierarch...

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International Conference on Distributed computing Systems

作者： Tianyu Qi Yufeng Zhan Peng Li Jingcai Guo Yuanqing Xia School of Automation Beijing Institute of Technology Beijing China School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China

Federated learning (FL) enables collaborative model training among distributed devices without data sharing, but existing FL suffers from poor scalability because of global model synchronization. To address this issue, hierarchical federated learning (HFL) has been recently proposed to let edge servers aggregate models of devices in proximity, while synchronizing via the cloud periodically. However, a critical open challenge about how to design a good synchronization scheme (when devices and edges should be synchronized) is still unsolved. Devices are heterogeneous in computing and communication capability, and their data could be non-IID. No existing work can well synchronize various roles (e.g., devices and edge) in HFL to guarantee high learning efficiency and accuracy. In this paper, we propose a learning-based synchronization scheme for HFL systems. By collecting data such as edge models, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. We build an HFL testbed and conduct experiments using real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experimental results have confirmed the effectiveness of Hwamei.

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Real-time Semantic Segmentation of Solar Photovoltaic Arrays for Autonomous UAV Flights

Real-time Semantic Segmentation of Solar Photovoltaic Arrays...

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第43届中国控制会议

作者： Nanyan Li Hao Chen Zhigang Sun Jing Gao Dewei Yi Cunjia Liu Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University State Grid Jiangsu Integrated Energy Service Co. Ltd. Department of Computing Science University of Aberdeen Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper considers the real-time semantic segmentation of solar PhotoVoltaic(PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles(UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union(IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and96.5% IoU, with a 27.59 fps(using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights.

关键词： CNN Machine Vision Solar Inspection UAV Navigation Real-time and Embedded systems

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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A New Federated Learning Framework Against Gradient Inversion Attacks

arXiv

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arXiv 2024年

作者： Guo, Pengxin Zeng, Shuang Chen, Wenhao Zhang, Xiaodan Ren, Weihong Zhou, Yuyin Qu, Liangqiong School of Computing and Data Science The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Beijing University of Technology China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering UC Santa Cruz United States

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively "breaks the direct connection" between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Code is available at https://***/Pengxin-Guo/HyperFL. © 2024, CC BY.

关键词： Federated learning

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Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study

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IEEE Network 2025年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Wen, Yonggang Kim, Dong In Nanyang Technological University College of Computing and Data Science Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

Generative AI (GenAI), exemplified by Large Language Models (LLMs), such as OpenAI’s ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the infrastructure support for GenAI operation, but also provisions GenAI services to users. Hence, this article explores the interplay between GenAI and DCNs, analyzing their symbiotic relationship and mutual advances. We begin by reviewing the current challenges of DCNs and GenAI-based solutions, such as data augmentation, process automation, and domain transfer. We then discuss the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on GenAI-empowered DCN digital twins. Specifically, we employ an LLM equipped with retrieval augmented generation to formulate optimization problems for DCNs (e.g., resource allocation and routing) and adopt diffusion-deep reinforcement learning to solve optimization. The experimental results on a representative DCN optimization problem, i.e., knowledge placement, demonstrate the validity and efficiency of our proposals. We anticipate that this article can promote further research to enhance the virtuous interaction between GenAI and DCNs. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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IR Design for Application-Specific Natural Language: 、A Case Study on Traffic Data

arXiv

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arXiv 2023年

作者： Hu, Wei Wang, Xuhong Wang, Ding Yao, Shengyue Mao, Zuqiu Li, Li Wang, Fei-Yue Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China 51 WORLD Shanghai China The Department of Automation Tsinghua University Beijing China The Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data. © 2023, CC BY.

关键词： Application programs

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Analysis of current harmonics in data center power system

Analysis of current harmonics in data center power system

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Proceedings of the International Convention MIPRO

作者： K. Raič Raguž M. Miletić V. Zelenič ić D. Sumina I. Erceg Ž . Nastasić Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing Zagreb Croatia Databox d.o.o. Zagreb Croatia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

This paper presents power quality analysis at different points in a data center. Electrical distribution network scheme and data center description are also given. The method of measurements performed is briefly described and the results of real measurements of current and voltage Total Harmonic Distortion (THD) are presented and compared with IEEE 519-2014 recommended practice and requirements for harmonic control in electric power system. All measured values comply with the standard but measurements at downstream levels closer to the nonlinear loads have larger THD.

关键词： Total harmonic distortion Voltage measurement Power measurement Current measurement Power quality Harmonic analysis Distortion measurement

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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Uncovering neural scaling laws in molecular representation learning 23

Uncovering neural scaling laws in molecular representation l...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Department of Computer Science University of California Los Angeles The Paul G. Allen School of Computer Science and Engineering University of Washington Department of Computer Science Cornell University Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.

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