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arXiv 2023年

作者： Chen, Fei Van Nguyen, Hoa Taggart, David A. Falkner, Katrina Rezatofighi, S. Hamid Ranasinghe, Damith C. School of Computer and Mathematical Sciences The University of Adelaide SA5005 Australia Department of Electrical and Computer Engineering Curtin University WA6102 Australia School of Animal and Veterinary Sciences The University of Adelaide SA5005 Australia Department of Data Science AI Monash University VIC3800 Australia

Radio tagging and tracking are fundamental to understanding the movements and habitats of wildlife in their natural environments. Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor-intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real-world conditions remains a challenge. We developed ConservationBots-small aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times-significant for energy-limited aerial systems while avoiding close encounters with potential, counter-productive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: i) planning to determine both trajectory and measurement actions guided by an information-theoretic objective, which allows the robot to strategically select near-instantaneous range-only measurements to achieve faster localization, and time-consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance;ii) a bearing detector more robust to noise and iii) a tracking algorithm formulation robust to missed and false detections experienced in real-world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high-resolution elevation data on diverse geographical terrains;field testing;studies with wombats (Lasiorhinus latifrons;nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method. © 2023, CC BY.

关键词： Radio transmission

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DEEP LEARNING FOR ACCELERATED AND ROBUST MRI RECONSTRUCTION: A REVIEW

arXiv

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arXiv 2024年

作者： Heckel, Reinhard Jacob, Mathews Chaudhari, Akshay Perlman, Or Shimron, Efrat Technical University of Munich Department of Computer Engineering Munich Germany University of Iowa Department of Electrical and Computer Engineering Iowa CityIA United States Stanford University Department of Radiology Department of Biomedical Data Science StanfordCA United States Tel Aviv University Department of Biomedical Engineering Tel Aviv Israel Tel Aviv University Sagol School of Neuroscience Tel Aviv Israel Technion - Israel Institute of Technology Department of Electrical and Computer Engineering Department of Biomedical Engineering Haifa Israel

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction. It focuses on DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. These include end-to-end neural networks, pre-trained networks, generative models, and self-supervised methods. The paper also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling subtle bias. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices. © 2024, CC BY.

关键词： Image reconstruction

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Information Security and Privacy in the Digital World: Some Selected Topics

arXiv

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arXiv 2024年

作者： Sen, Jaydip Mayer, Joceli Dasgupta, Subhasis Nandi, Subrata Krishnaswamy, Srinivasan Mitra, Pinaki Singh, Mahendra Pratap Kundeti, Naga Prasanthi Rao, Chandra Sekhara M.V.P. Chekuri, Sudha Sree Pallapothu, Seshu Babu Nanjundan, Preethi George, Jossy P. Allali, Abdelhadi E.I. Morino, Ilham Oussous, Salma A.I.T. Beloualid, Siham Tamtaoui, Ahmed Bajit, Abderrahim Department of Data Science Praxis Business School Kolkata India Department of Computer Science and Engineering IIT Guwahati Assam India Department of Electronics and Electrical Engineering IIT Guwahati Assam India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru India Department of CSE Lakireddy Balireddy College of Engineering Affiliated to JNTU Mylavaram Kakinada India Department of CSE RVR & JC college of Engineering Guntur India Department of Mathematics and statistics K.B.N. College Vijayawada India Department of Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil Department of Data Science Christ University Lavasa Pune India National School of Applied Sciences Kenitra Morocco SC Department Mohammed V University Laboratory of Advanced Systems National Institute of Posts and Telecommunications Rabat Morocco

In the era of generative artificial intelligence and the Internet of Things,while there is explosive growth in the volume of data and the associated needfor processing, analysis, and storage, several new challenges are faced inidentifying spurious and fake information and protecting the privacy ofsensitive data. This has led to an increasing demand for more robust andresilient schemes for authentication, integrity protection, encryption,non-repudiation, and privacy-preservation of data. The chapters in this bookpresent some of the state-of-the-art research works in the field ofcryptography and security in computing and communications. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Ggt-Snn: Graph Learning and Gaussian Prior Integrated Spiking Graph Neural Network for Event-Driven Tactile Object Recognition

SSRN

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SSRN 2024年

作者： Yu, Zukun Yang, Jing Li, Shaobo Cao, Yang Hu, JianJun Xu, Ji Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Ministry of Education Guizhou University Guiyang550025 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai201100 China School of Mechanical Engineering Guizhou University Guiyang550025 China Department of Computer Science and Engineering University of South Carolina Columbia29208 United States

Spiking neural networks possess asynchronous, discrete, and sparse characteristics that enable direct processing of event-based tactile sensor data and more efficient transmission of information. These networks have been widely applied in the tactile perception field. However, due to the sparsity of tactile data, these models are prone to generalization issues. Additionally, existing methods for tactile graph construction are constrained when spatially connecting high-dimensional tactile data, making it challenging for the model to fuse and process such information effectively. To address these challenges, we propose the GGT-SNN, which introduces a parameter estimation method with Gaussian priors to alleviate generalization problems caused by sparse tactile data. Furthermore, we design M-tree and Z-tree tactile graph construction methods to compensate for deficiencies of other approaches when handling high-dimensional tactile spatial connections. This approach enhances the model's ability to efficiently process high-dimensional event-driven tactile information. We introduce an approximate LIF neuron activation function to enable backpropagation within the model and comprehensively compare and evaluate its performance against different approximate functions. The experimental results demonstrate that our proposed method performs significantly better than SOTA methods, exhibiting a 10.83% improvement over TactileSGNet on the EvTouch-Containers dataset and a 2.92% improvement over TactileSGNet on the EvTouch-Objects dataset. © 2024, The Authors. All rights reserved.

关键词： Object recognition

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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Evaluation of China’s Digital Economy: A Case Study using Entropy and TOPSIS

引用

Procedia computer science 2024年 242卷 1256-1262页

作者： Junjie Liu Boya Wang Jiayu Xue Yi Qu Yong Shi Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Ningbo Zhejiang 315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth; Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy; Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces.

关键词： Digital economy Multi-source information Evaluation TOPSIS

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A Survey on Integrated Sensing, Communication, and Computation

arXiv

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arXiv 2024年

作者： Wen, Dingzhu Zhou, Yong Li, Xiaoyang Shi, Yuanming Huang, Kaibin Letaief, Khaled B. School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong 518172 China Departement of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services. Copyright © 20

关键词： Resource allocation

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BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation

arXiv

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arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Hu, Miao Wu, Di Liao, Pengshan Guizani, Mohsen Sheng, Michael The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China The Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia The Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that BGTplanner achieves an average improvement of 6.76% in training performance compared to state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Investigating the Big data Challenges of Deep Learning for data science

Investigating the Big Data Challenges of Deep Learning for D...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prateek Aggarwal Shweta Singh Krishnan Batri N.T. Velusudha Sunil D. Kale Gajendra Shrimal Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Deep-gaining knowledge is a subset of synthetic intelligence (AI) that employs superior technology, data mining, and system-gaining knowledge to use massive datasets to find insights and styles. It is an area of advanced computing technology that enables computer systems to get the right of entry to analyze and analyze from many statistics. Even as the ability of deep learning is enormous, where its impact remains uncovered, extensive statistics demanding situations lie at the crossroads of its software and effectiveness. Massive records datasets' high quality and shape are essential for successfully implementing deep studying fashions. There may be an increasing need for highly acceptable statistics, including correctly categorized information, for training deep learning fashions. Moreover, deep studying often requires big datasets to provide correct output. It, therefore, calls for records to be amassed from numerous assets, posing the need for more homogeneous, regular, and standardized information. Adequate statistics garage, processing, and analytic solutions must be followed to analyze massive facts to create reachable and usable datasets for successful deep mastering packages. It includes infrastructure capable of securely keeping a large amount of information, in addition to the cloud-based totally systems to efficiently method extensive quantities of statistics. There has been a shift in the direction of using “large records” technologies like Apache Hadoop, Apache Spark, and different associated libraries. Even as deep mastering holds massive capability for uncovering new insights and turning in gigantic value to fields which include healthcare, finance, enterprise control, and more incredible, the associated massive data demanding situations ought to be addressed for those programs to reach their full capability. Despite the present-day challenges, advances in extensive records solutions like Apache Hadoop and Apache Spark, coupled with advanced analytic

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