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2022 IEEE World Congress on Services, SERVICES 2022

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Macquarie University Department of Computing Sydney Australia Qufu Normal University School of Information Science and Engineering Rizhao China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services computing DOI 10.1109/TSC.2022.3142265]

ISBN: (数字)9781665481311

ISBN: (纸本)9781665481311

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services computing DOI 10.1109/TSC.2022.3142265]

关键词： COVID-19 Image edge detection Collaboration X-ray imaging

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Provably Secure and Efficient One-to-Many Authentication and Key Agreement Protocol for Resource-Asymmetric Smart Environments

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IEEE Internet of Things Journal 2025年

作者： Li, Long Hsu, Chingfang Cui, Jianqun Au, Man Ho Harn, Lein Li, Quanrun Central China Normal University Computer School Wuhan430079 China Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Department of Computing Hong Kong Polytechnic University 25809 Hong Kong University of Missouri-Kansas City Department of Computer Science Electrical Engineering MO64110 United States Zhejiang Sci-Tech University School of Cyber Science and Engineering Hangzhou310020 China

The smart environment is a crucial application of the Internet of Things(IoT). Due to its growing security and efficiency needs, recent years have seen the proposal of numerous authentication and key agreement (AKA) protocols. Unfortunately, most of existing AKA protocols only support one-to-one AKA and rely on the elliptic curve cryptosystem, resulting in huge overhead. In addition, these protocols fail to consider the resource-asymmetric characteristics of this scenario. That is, the resources on the gateway side are abundant, while the resources on user sides and device sides are limited. In order to achieve efficient and secure one-to-many AKA establishment in this scenario, where one-to-many means that users can realize key agreements with multiple smart devices at the same time. For the first time, this paper uses the one-to-many computing structure of the Chinese Remainder Theorem (CRT) to design an efficient one-to-many AKA establishment, which is perfectly adapted to resource-asymmetric allocation in smart environments. Compared with existing solutions, this solution has the following advantages. Firstly, our protocol is suitable for resource-asymmetric environments, where the gateway acts as an intermediate node and uses rich resources to integrate multiple AKA requests. Secondly, the solution supports users to negotiate session keys with multiple smart devices at the same time. Thirdly, we prove the protocol’s security under the real-or-Random (ROR) model. In addition, we perform formal security verification of the protocol using the Automated Validation of Internet Security Protocols and Applications(AVISPA) tool. Finally, the security and efficiency of this solution are superior to similar solutions. Specifically, our solution can meet 18 security and functionality requirements. Compared with the latest similar scheme, assuming that the number of smart devices is 10, our scheme reduces the computational cost by 75.75%. At the same time, in terms of comm

关键词： Authentication Protocol

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Use Coupled LSTM Networks to Solve Constrained Optimization Problems

Use Coupled LSTM Networks to Solve Constrained Optimization ...

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作者： Chen, Zheyu Leung, Kin K. Wang, Shiqiang Tassiulas, Leandros Chan, Kevin Towsley, Don Imperial College London Department of Computing LondonSW7 2AZ United Kingdom Ibm T.J. Watson Research Center Yorktown HeightsNY10598 United States Yale University Institute for Network Science Electrical Engineering Department New HavenCT06520 United States Devcom Army Research Laboratory Army Research Directorate AdelphiMD20783 United States University of Massachusetts Department of Computer Science AmherstMA01003 United States

Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained optimization problems. Two Coupled Long Short-Term Memory networks (CLSTMs) are proposed to find the optimal solution. The advantages of this framework include: (1) near-optimal solution for a given problem instance can be obtained in few iterations during the inference, (2) enhanced robustness as the CLSTMs can be trained using system parameters with distributions different from those used during inference to generate solutions. In this work, we analyze the relationship between minimizing the loss functions and solving the original constrained optimization problem for certain parameter settings. Extensive numerical experiments using datasets from Alibaba reveal that the solutions to a set of nonconvex optimization problems obtained by the CLSTMs reach within 90% or better of the corresponding optimum after 11 iterations, where the number of iterations and CPU time consumption are reduced by 81% and 33%, respectively, when compared with the gradient descent with momentum. © 2015 IEEE.

关键词： Gradient methods

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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Trajectory Design for 3D UAV Localization in UAV Based Networks

Trajectory Design for 3D UAV Localization in UAV Based Netwo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

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Efficient Error-Bounded Lossy Compression for CPU Architectures

Efficient Error-Bounded Lossy Compression for CPU Architectu...

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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems (MASCOTS)

作者： Griffin Dube Jiannan Tian Sheng Di Dingwen Tao Jon C. Calhoun Franck Cappello Department of Computer Science Northwestern University Evanston IL Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Lossy compression, helps to mitigate this issue by decreasing the size of data generated by these applications. SZ, a current state-of-the-art lossy compressor, is able to achieve high compression ratios, but its prediction/quantization methods contain RAW dependencies that prevent parallelizing this step of the compression. Recent work proposes a parallel dual prediction/quantization algorithm for GPUs which removes these dependencies. However, some HPC systems and applications do not use GPUs, and could still benefit from the fine-grained parallelism of this method. Using the dual-quantization technique, we implement and optimize a SIMD vectorized CPU version of SZ (vecSZ), and create a heuristic for selecting the optimal block size and vector length. We propose a novel block padding algorithm to decrease the number of unpredictable values along compression block borders and find it reduces the number of prediction outliers by up to 100%. We measure performance of our vecSZ against an CPU version of SZ using dual-quantization, pSZ, as well as SZ-1.4. Using real-world scientific datasets, we evaluate vecSZ on the Intel Skylake and AMD Rome architectures. vecSZ results in up to 32% improvement in rate-distortion and up to 15× speedup over SZ-1.4, achieving a prediction and quantization bandwidth in excess of 3.4 GB/s.

关键词： Analytical models Quantization (signal) Computational modeling Rate-distortion computer architecture Bandwidth Prediction algorithms

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A 3D Implementation of Convolutional Neural Network for Fast Inference

A 3D Implementation of Convolutional Neural Network for Fast...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Narasinga Rao Miniskar Pruek Vanna-Iampikul Aaron Young Sung Kyu Lim Frank Liu Jieun Yoo Corrinne Mills Nhan Tran Farah Fahim Jeffrey S Vetter Computer Science and Mathematic Division Oak Ridge National Laboratory Oak Ridge USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Physics University of Illinois Chicago Chicago USA Scientific Computing Division Fermi National Accelerator Laboratory Chicago USA

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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Enhancing real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore Singapore The Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Fortemedia Singapore138637 Singapore Institute for Infocomm Research A*STAR Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The University of Bremen 28359 Germany

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named ‘MuSED’, which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. © 2024, CC BY.

关键词： Extraction

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