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International Conference on Emerging Technologies, ICET

作者： J aviria Naveed Irfan Ahmed Jabar Mahmood Khadija Batool Department of Computer Sciences University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Formal Methods (FM) uses mathematical notations to precisely and accurately define specifications, which helps detect errors and inconsistencies in software development. For mathematical notations, a proper mathematical framework is needed to describe and specify the behavior and structure of software systems. A few specification languages provide that framework; one is Z, which significantly enhances the quality of system specification, design, and verification. This paper introduces a formal specification for a virtual reminder system using Z schema, which signifies the importance of formal specifications that can also be used for voice assistant specifications. A virtual Reminder system (VRS) is an assistant based on speech recognition and speaker identification; a personal voice-controlled aid for the user to remind about event-based tasks that do not need some specific time to be completed but are event-based like reaching a location or any keyword/phrase spoken by the user related to some specific topic. The specified keyword/phrase can be anything, such as a person's name, place name, or phrase. Using FM reduced development time, verification efforts. and maintenance costs.

关键词： Frequency modulation Costs Event detection Personal voice assistants Software systems Specification languages Software reliability Maintenance Formal specifications Software development management

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Generating Highly Structured Inputs: A Survey 6

Generating Highly Structured Inputs: A Survey

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Li, Yabin Qin, Yinfan Tian, Jianwen Gu, Taotao Nie, Yuanping Kuang, Xiaohui School Of Computer Science Beijing University Of Posts And Telecommunications Beijing China Institute Of System Engineering Academy Of Military Sciences Beijing China National Key Laboratory Of Science And Technology On Information System Security Beijing China

ISBN: (纸本)9781665418157

Fuzzing is currently one of the most popular vulnerability mining technology. The test case generation technique plays a key role in fuzzing, and the quality of the generated test cases directly affects the effect of fuzzing. Some programs process highly structured inputs which need to satisfy clear syntactic and semantic constraints, but traditional fuzzers (such as AFL) are difficult to generate such inputs. In this paper, we focus on the techniques of generating highly structured inputs. We first systematically introduce the mainstream techniques of generating highly structured inputs, then discuss the key issues facing the current technique and main technique trends, finally summarize the relevant open-source tools to promote future research. © 2021 IEEE.

关键词： Semantics

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A Multimodal Medical Image Fusion Method Incorporating an Adaptive Attention Mechanism

A Multimodal Medical Image Fusion Method Incorporating an Ad...

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International Conference on computer Supported Cooperative Work in Design

作者： Yingxian Zhang Yushui Geng Hu Liang Hai Zhong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China Department of Radiology The Second Hospital of Shandong University Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Multimodal medical image fusion technology provides more comprehensive and accurate image support for clinical diagnosis and treatment by integrating complementary information from different imaging modalities. Aiming at the problem that existing methods are still insufficient in detail feature extraction and inter-modal information fusion, this paper proposes a multimodal medical image fusion method combined with an adaptive attention mechanism. First, we design the Grouped Receptive Field Attentional Convolution (GRFAConv) to solve the problem of insufficient detail feature extraction capability. With the multi-head receptive field adaptive weighting strategy of grouped convolution, the range and weight of the receptive field of the convolution kernel can be adaptively adjusted according to the different demands of local and global features of the image to improve the effect of detail retention. Second, for the problem of information fusion between different modalities, we introduce an improved CBAM attention module in the feature fusion process, which adaptively selects and enhances the features in the key regions through the channel attention and spatial attention mechanisms, which greatly improves the clarity of the fused image details and the accuracy of the information expression in the key regions. Furthermore, experimental results on several medical image datasets show that the algorithm proposed in this paper can generate relatively high-quality fused images. It not only enriches the detailed features of the image, but also achieves significant advantages in several evaluation metrics.

关键词： Measurement Attention mechanisms Accuracy Convolution Federated learning Feature extraction Clinical diagnosis Kernel Medical diagnostic imaging Image fusion

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DapperFL: domain adaptive federated learning with model fusion pruning for edge devices 24

DapperFL: domain adaptive federated learning with model fusi...

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Yongzhe Jia Xuyun Zhang Hongsheng Hu Kim-Kwang Raymond Choo Lianyong Qi Xiaolong Xu Amin Beheshti Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas at San Antonio USA College of Computer Science and Technology China University of Petroleum (East China) China School of Computer and Software Nanjing University of Information Science and Technology China

ISBN: (纸本)9798331314385

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL.

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Federated Online/Offline Remote Data Inspection for Distributed Edge Computing

arXiv

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arXiv 2023年

作者： Ali, Mohammad Liu, Ximeng The Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran The Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or unintentional events. Given the limited resources of edge servers and prohibitive storage costs incurred by app vendors, designing an efficient approach to inspect and maintain the data over tremendous edge servers is a critical issue. To tackle the problem, we design a novel data inspection approach, named O2DI, that provides the following services: i) using O2DI, app vendors can inspect the data cached in edge servers without having the original data, which reduces the incurred I/O and storage overhead significantly;ii) computational operations conducted by both edge servers and app vendors are highly efficient because of a novel online/offline technique;iii) many data files cached in different edge servers can be verified quickly and at once by using a novel batch verification method;iv) corrupted data in edge servers can be localized and repaired efficiently. We analyze the security and performance of O2DI. We see that it is secure in the random oracle model, much faster, and more cost-effective compared to state-of-the-art approaches. © 2023, CC BY.

关键词： Inspection

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A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective

arXiv

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arXiv 2023年

作者： Seyedi, Zahra Rahmati, Farhad Ali, Mohammad Liu, Ximeng Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme’s security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods. © 2023, CC0.

关键词： Game theory

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Reduced-Complexity CRB Optimization for Dual-Function Radar-Communication systems using Hybrid Linear-Nonlinear Precoding

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IEEE Transactions on Signal Processing 2025年 73卷 2123-2138页

作者： Wen, Cai Chen, Yating Huang, Yan Davidson, Timothy N. Northwest University School of Information Science and Technology Xi’an China Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing China McMaster University Department of Electrical and Computer Engineering Hamilton Canada

This paper proposes a waveform design technique for dual-function radar-communication (DFRC) systems that employ hybrid linear-nonlinear precoding (HLNP). The HLNP signal is a superposition of linear precoding of the communication symbols that enables conventional coherent decoding and a nonlinearly precoded auxiliary signal that introduces additional degrees of design freedom that can be used to improve system performance. Our design goal is to obtain accurate direction of arrival (DOA) estimation and satisfactory waveform ambiguity properties, and hence we optimize a weighted sum of a Cramer-Rao bound (CRB) on DOA estimation and a waveform similarity metric. To simultaneously enable effective communication at the chosen data rates, we set lower bounds on the communication SINRs, and to facilitate implementation, we constrain the total transmission power, the per-antenna power, and the peak-to-average power ratio (PAPR) on each antenna. We deploy successive convex approximation to solve the resultant nonconvex design problem, while leveraging feasible point pursuit to provide a feasible initial point. To reduce the computational cost, we introduce sub-block and null space variants of our design technique. Simulation results verify the effectiveness of the proposed algorithm and its variants, and validate their performance advantages over regular nonlinear precoding schemes. © 1991-2012 IEEE.

关键词： Signaling

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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BTC-Shadow: an analysis and visualization system for exposing implicit behaviors in Bitcoin transaction graphs

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Frontiers of computer science 2023年第6期17卷 167-169页

作者： Ding BAO Wei REN Yuexin XIANG Weimao LIU Tianqing ZHU Yi REN Kim-Kwang Raymond CHOO School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan 430074China NSFOCUS Technologies Group Co. Ltd.Beijing 100089China School of Computing Sciences University of East AngliaNorwich NR47TJUK Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

1 Introduction A Bitcoin ledger comprises a sizable number of transaction records,which can be utilized to make it easier to track and analyze the traits and patterns of cryptocurrency-related *** facilitate the visual analysis of Bitcoin,numerous tools with various aims have been *** example,MiningVis[1]and SuPoolVisor[2]are the analytics systems for Bitcoin mining pools,as well as[3-5]focus on the Bitcoin transaction graphs ***,due to our previous research requirements for Bitcoin transaction graphs,none of the available tools can provide exploring the features of connection related to the address and observe significant visual ***,using these tools is challenging to navigate to abnormal node clusters effortlessly from large node groups and then analyze the local interlink characteristics between transaction nodes with interactive analytics.

关键词： analytic implicit utilized

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