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IEEE Conference on Decision and Control

作者： Junyao Hou Siyuan Liu Xiang Yin Majid Zamani Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Colorado Boulder CO USA Department of Computer Science LMU Munich Munich Germany

Opacity is an important information-flow security property which characterizes the plausible deniability of certain “secret behaviors” in dynamical systems. In this paper, we study the problem of synthesizing controllers enforcing a notion of opacity over discrete-time control systems with continuous state sets. In this paper, we develop an abstraction-based approach to tackle the controller synthesis problem. Specifically, we adopt a notion of approximate opacity which is suitable for continuous-space control systems. We propose a notion of approximate initial-state opacity preserving alternating simulation relation which characterizes the closeness between two systems in terms of opacity preservation. We show that, based on this new notion of system relation, one can synthesize an opacity-enforcing controller for the abstract system which is finite and then refine it back to enforce opacity over the original control system. Finally, we present a method for constructing opacity-preserving finite abstractions for discrete-time control systems under some stability properties. Our results are illustrated on a two-room temperature control problem.

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Blockchain-Based Efficient Public Integrity Auditing for Cloud Storage Against Malicious Auditors 16th

Blockchain-Based Efficient Public Integrity Auditing for Clo...

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16th International Conference on information security and Cryptology, Inscrypt 2020

作者： Li, Shanshan Xu, Chunxiang Zhang, Yuan Yang, Anjia Wen, Xinsheng Chen, Kefei School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Cyber Security Jinan University Guangzhou China Department of Mathematics Hangzhou Normal University Hangzhou China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783030718510

Public integrity auditing enables a user to delegate a third-party auditor (TPA) to periodically audit the integrity of the outsourced data. Whereas, the security of public auditing schemes relies on the trustworthiness of TPA: once TPA misbehaves, the data integrity auditing would be invalidated. In this paper, we propose a blockchain-based efficient public integrity auditing scheme to resist misbehaved TPA, where the user is required to check the behaviors of TPA in a much longer period compared with that of the data integrity auditing performed by TPA. To free users from heavy computation costs during the checking, our scheme uses two key techniques. We first design a smart contract to (1) enable TPA to record each auditing entry (which records the information about each auditing task) into the blockchain and (2) ensure the validity of each recorded entry. Then, we propose an auditing record chain built on the Ethereum blockchain to link all auditing entries corresponding to the same data in the chronological order. By doing so, the user only needs to check the last auditing entry generated by TPA to verify the trustworthiness of TPA (i.e., whether TPA has correctly performed the prescribed auditing tasks). Compared with existing schemes, where the user has to check multiple entries one by one, our scheme achieves the same security guarantee with constant and low costs in terms of communication and computation. © 2021, Springer Nature Switzerland AG.

关键词： Smart contract

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Device-to-Device Communications aided Integrated Sensing and Communication Networks: A Joint Design of Bandwidth and Power Allocations

Device-to-Device Communications aided Integrated Sensing and...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chenglong Dou Ning Huang Yuan Wu Liping Qian Tony Q.S. Quek State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China Zhuhai UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes.

关键词： Base stations Accuracy Bandwidth Benchmark testing Integrated sensing and communication Convex functions Device-to-device communication Resource management Data communication Optimization

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes

Seamless Decision-Making in the Big Data Era: A Modular Appr...

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International Conference on Electrical, Communication, and computer Engineering (ICECCE)

作者： Mohamed Zemmouri Fatma Zohra Laalam Okba Kazar Yassine Himeur Adel Oulefki Chahrazad Toumi Wathiq Mansoor Shadi Attala Artificial Intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla Algeria Department of Computer Science - LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security - College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah United Arab Emirates

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source.

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Towards Task Sampler Learning for Meta-Learning

arXiv

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arXiv 2023年

作者： Wang, Jingyao Qiang, Wenwen Su, Xingzhe Zheng, Changwen Sun, Fuchun Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models;(ii) over-constraining task diversity may incur the risk of under-fitting or overfitting during training;and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://***/WangJingyao07/AdaptiveSampler. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks security Noise measurement

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Mechanical Vibration Signal Compression Based on Speech Codecs for Intelligent Manufacturing

SSRN

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SSRN 2023年

作者： Tang, Lili Tian, Hui Chang, Chin-Chen Huang, Hui College of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen361021 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

How to effectively compress mechanical signals so that they can support remote and real-time health monitoring is a hot issue in the context of intelligent manufacturing. Therefore, this paper presents a novel mechanical vibration signal compression scheme based on speech codecs, which can realize signal compression in many scenarios such as tool wear detection and bearing fault diagnosis. In this work, signal compression is first transformed into a multi-objective optimization problem, and the feasibility of solving this problem from the perspective of speech coding is deeply analyzed. Further, this paper presents five compression algorithms with different compression ratios. The presented algorithms are evaluated with tool wear detection in stone processing and bearing fault diagnosis. The experimental results show that the proposed algorithms can achieve higher compression ratios (4.0~24.6) while ensuring low time overhead (average time not more than 0.0194ms) and high reconstruction quality (MSE index are all lower than 0.008). © 2023, The Authors. All rights reserved.

关键词： Fault detection

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Fuzzy Logic and Deep learning Techniques for Covid-19 Detection 23

Fuzzy Logic and Deep learning Techniques for Covid-19 Detect...

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23rd International Arab Conference on information technology, ACIT 2022

作者： Hassani, Belkis Akram, Khelili Mohamed Okba, Kazar Sihem, Slatnia Harous, Saad Athamena, Belkacem Houhamdi, Zina Smart Computer Science Laboratory Department of Computer Science Biskra Algeria University of Mohamed Khider Biskra Algeria College of Information Technology United Arab Emirate University Department of Information Systems and Security United Arab Emirates College of Computing and Informat Ics Department of Computer Science AL Ain United Arab Emirates University of Sharjah AL Ain United Arab Emirates College of Business Al Ain University Al Ain United Arab Emirates University of Al Ain Al Ain United Arab Emirates

ISBN: (纸本)9798350320244

With the development of ICT and its adoption in various domains, it gained remarkable intention in the healthcare sector which introduce the telemedicine term. The coronavirus pandemic has created several challenges for researchers to develop an accurate and fast detection system. In this paper, we present a new telemedicine application to predict Covid-19 using CNN and Fuzzy set techniques. The evaluation of the system indicates high performance with a 98% F1 score, 99% of recall, 98% for precision, and 97% of accuracy. © 2022 IEEE.

关键词： Telemedicine

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Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Singapore University of Technology and Design Singapore Singapore

Diffusion models have been extensively utilized in AI-generated content (AIGC) in recent years, thanks to the superior generation capabilities. Combining with semantic communications, diffusion models are used for tasks such as denoising, data reconstruction, and content generation. However, existing diffusion-based generative models do not consider the stringent bandwidth limitation, which limits its application in wireless communication. This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model. Our designed architecture utilizes the diffusion model, where the signal transmission process through the wireless channel acts as the forward process in diffusion. To reduce bandwidth requirements, we incorporate a downsampling module and a paired upsampling module based on a variational auto-encoder with reparameterization at the receiver to ensure that the recovered features conform to the Gaussian distribution. Furthermore, we derive the loss function for our proposed system and evaluate its performance through comprehensive experiments. Our experimental results demonstrate significant improvements in pixel-level metrics such as peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS). These enhancements are more profound regarding the compression rates and SNR compared to deep joint source-channel coding (DJSCC). © 2002-2012 IEEE.

关键词： Bandwidth

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