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arXiv 2022年

作者： Zhang, Shun Zhang, Tao Chen, Zhili Xiong, N. School of Computer Science and Technology Anhui University Hefei230601 China Software Engineering Institute East China Normal University Shanghai200062 China The National Engineering Research Center for ELearning Central China Normal University Wuhan430079 China

The rapid development of mobile Internet and sharing economy brings the prosperity of Spatial Crowdsourcing (SC). SC applications assign various tasks according to reported location information of task's requesters and outsourced workers (such as DiDi, MeiTuan and Uber). However, SC-servers are often untrustworthy and the exposure of users' locations raises privacy concerns. In this paper, we design a framework called Geo-MOEA (Multi-Objective Evolutionary Algorithm with Geo-obfuscation) to protect location privacy of workers involved on SC platform in mobile networks environment. We propose an adaptive regionalized obfuscation approach with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This enables each worker to report a pseudo-location that is adaptively generated with a personalized inference error threshold. Moreover, as a popular computational intelligence method, MOEA is introduced to optimize the trade-off between SC service availability and privacy protection while ensuring theoretically the most general condition on protection location sets for larger search space. Finally, the experimental results on two public datasets show that our Geo-MOEA approach achieves up to 20% reduction in service quality loss while guaranteeing differential and geo-distortion location privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Crowdsourcing

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TAM: A Computing in Memory based on Tandem Array within STT-MRAM for Energy-Efficient Analog MAC Operation

TAM: A Computing in Memory based on Tandem Array within STT-...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jinkai Wang Zhengkun Gu Hongyu Wang Zuolei Hao Bojun Zhang Weisheng Zhao Yue Zhang MIIT Key Laboratory of Spintronics Fert Beijing Institute School of Integrated Circuit Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Nanoelectronics Science and Technology Center Hefei Innovation Research Institute Beihang University Hefei China

Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher bit density, for which the spin transfer torque magnetic RAM (STT-MRAM) with high bit density and performance arises as an up-and-coming candidate solution. In this work, we propose an analog CIM scheme based on tandem array within STT-MRAM (TAM) to further improve energy efficiency while achieving high bit density. First, the resistance summation based analog MAC operation minimizes the effect of low tunnel magnetoresistance (TMR) by the serial magnetic tunnel junctions (MTJs) structure in the proposed tandem array with smaller area overhead. Moreover, a read scheme of resistive-to-binary is designed to achieve the MAC results accurately and reliably. Besides, the data-dependent error caused by MTJs in series has been eliminated with a proposed dynamic selection circuit. Simulation results of a 2Kb TAM architecture show 113.2 TOPS/W and 63.7 TOPS/W for 4-bit and 8-bit input/weight precision, respectively, and reduction by 39.3% for bit-cell area compared with existing array of MTJs in series.

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Extreme Hourly Rainfall in the Middle and Lower Reaches of the Yangtze River Basin - Spatiotemporal Transition and Causality Analysis

SSRN

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SSRN 2023年

作者： Gu, Haoting He, Guangxin Li, Zhongliang Zhang, Ping Cheng, Yuexuan Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing210044 China Guangzhou Institute of Tropical Marine Meteorology China Meteorological Administration Guangzhou510080 China Key Laboratory of Meteorology and Ecological Environment of Hebei Province Hebei Provincial Institute of Meteorological Sciences Shijiazhuang050021 China College of Computer and Software Nanjing Vocational University of Industry Technology Nanjing210046 China School of Information Management & Engineering Shanghai University of Finance and Economics Shanghai Shanghai200433 China

This study employs ground observation data and hourly ERA5 data from 1980 to 2020, utilizing methods such as trend analysis and M-K tests, to explore the spatiotemporal evolution characteristics and difference analysis of daily-scale and hourly-scale extreme precipitation in the Middle and Lower Yangtze River Basin(MLYRB). The research finds that:(1) The daily precipitation amount and intensity are on an upward trend, while the frequency and the proportional area of occurrence of heavy rain are rapidly declining, indicating a concentration trend in daily-scale extreme precipitation. (2) The intensity of extreme hourly precipitation increases from southwest to northeast, potentially related to local topography, with more vigorous precipitation intensity in the plains than in the mountainous areas. (3) The characteristics of extreme rainstorms at different scales are heterogeneous, primarily exhibiting less frequent occurrences, dispersed occurrences, and increased intensity. The harm is most severe in summer least in winter, while frequent spring precipitation elevates the risk of spring floods. (4) The frequency and intensity of extreme hourly rainstorms show a reverse trend between 19:00~21:00, and the probability of occurrence of severe hourly rainstorms is higher in the early night. (5) The peak value trend of extreme hourly rainstorm amounts is weakly declining, and its frequency of occurrence is rapidly decreasing, but all three-time intervals, 1:00-7:00, 8:00-19:00, and 20:00-24:00, show an upward trend. (6) The basin is dominated by post-type precipitation, indicating a very high probability that the maximum value of extreme hourly precipitation occurs in the latter half of the process. This study offers new insights into understanding the spatiotemporal characteristics and possible causes of extreme precipitation in the MLYRB, providing beneficial enlightenment for related risk assessment and response strategies. © 2023, The Authors. All rights reserved.

关键词： Topography

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI C...

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Annual Conference on Privacy, Security and Trust, PST

作者： Yihao Yang Xiaolei Dong Jiachen Shen Zhenfu Cao Yunbo Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Zongyang Hou Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan Print-Rite Technology Development Co. Ltd. Of Zhuhai China

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

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Action-Manipulation Attack and Defense to X-Armed Bandits

Action-Manipulation Attack and Defense to X-Armed Bandits

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhi Luo Youqi Li Lixing Chen Zichuan Xu Pan Zhou School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science School of Cyberspace Science and Technology Beijing Institute of Technology Institute of Cyber Science and Technology Shanghai Jiao Tong University School of Software Dalian University of Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology

As a continuous variant of Multi-armed bandits (MAB), $\mathcal{X}$-armed bandits have enriched many applications of online machine learning like personalized recommendation system. However, the attack and defense to the $\mathcal{X}$-armed bandits remain largely unexplored, though the MAB has proved to be vulnerable. In this paper, we aim to bridge this gap and investigate the robustness analysis for the $\mathcal{X}$-armed bandits. Specifically, we consider action-manipulation attack, which is practical but harder than the existing reward-manipulation attack. We propose an attack algorithm based on a lower bound tree (LBT), which can continuously hijack the learner’s action by perturbing $\mathcal{X}$-armed bandits’ high confidence tree (HCT) construction. As a result, the nodes including the arm targeted by the attacker is selected frequently with a sublinear attack cost. To defend against the LBT attack, we propose a robust version of the HCT algorithm, called RoHCT. We theoretically analyze that the regret of RoHCT is related to the upper bound of the total cost Q and still sublinear to total number of rounds T. We carry out experiments to evaluate the effectiveness of LBT and RoHCT.

关键词： Threat modeling Bridges Privacy Costs Upper bound Machine learning Big Data

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Data Poisoning Attack to X-armed Bandits

Data Poisoning Attack to X-armed Bandits

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhi Luo Youqi Li Lixing Chen Zichuan Xu Pan Zhou School of Cyber Science and Engineering Huazhong University of Science and Technology School of Cyberspace Science and Technology and School of Computer Science Beijing Institute of Technology Institute of Cyber Science and Technology Shanghai Jiao Tong University School of Software Dalian University of Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology

X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine learning tasks, specially in big data-driven personalized recommendation. However, bandit algorithms are vulnerable to adversarial attacks. Existing works mainly focus on attacking multi-armed bandits in discrete setting; nevertheless, the attacks against X-armed bandits in continuous setting have not been well explored. In this paper, we aim to bridge this gap and investigate the robustness problem for the X-armed bandits. Specifically, we consider data poisoning attack and propose an attack algorithm named Confidence Poisoning Attack algorithm, which could hijack the clean tree-based X-armed bandits algorithm, i.e., high confidence tree (HCT) and make it choose the nodes including the arm targeted by the attacker very frequently with a sub-linear attack cost, i.e., O(T α )(0

关键词： Threat modeling Privacy Costs Machine learning algorithms Stochastic processes Machine learning Robustness

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Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

arXiv

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arXiv 2023年

作者： Chen, Longfei Liu, Qianyu Zhang, Chenyang Huang, Yangkun Peng, Zhenhui Zeng, Haipeng Sun, Zhida Ma, Xiaojuan Li, Quan The School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging China The Department of Computer Science University of Illinois Urbana-Champaign United States Tandon School of Engineering New York University United States The School of Artificial Intelligence Sun Yat-Sen University China The School of Intelligent Systems Engineering Sun Yat-Sen University China The College of Computer Science and Software Engineering Shenzhen University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations. © 2023, CC BY.

关键词： Music

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UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation

arXiv

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arXiv 2024年

作者： Liu, Wentao Liang, Bowen Xu, Weijin Tian, Tong Lu, Qingsheng Pan, Xipeng Li, Haoyuan Tian, Siyu Yang, Huihua Su, Ruisheng School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Vascular Surgery Shanghai Changhai Hospital Naval Medical University Shanghai China Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Aeronautics and Astronautics Dalian University of Technology Dalian China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Ultrasonic Department The Fourth Hospital of Hebei Medical University Hebei Tumor Hospital Shijiazhuang China Department of Radiology & Nuclear Medicine Erasmus MC University Medical Center Rotterdam Netherlands

The rigid registration of aortic Digital Subtraction Angiography (DSA) and Computed Tomography Angiography (CTA) can provide 3D anatomical details of the vasculature for the interventional surgical treatment of conditions such as aortic dissection and aortic aneurysms, holding significant value for clinical research. However, the current methods for 2D/3D image registration are dependent on manual annotations or synthetic data, as well as the extraction of landmarks, which is not suitable for cross-modal registration of aortic DSA/CTA. In this paper, we propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning. Leveraging the imaging principles and characteristics of DSA and CTA, we have constructed a cross-dimensional registration environment based on spatial transformations. Specifically, we propose an overlap degree calculation reward function that measures the intensity difference between the foreground and background, aimed at assessing the accuracy of registration between segmentation maps and DSA images. This method is highly flexible, allowing for the loading of pre-trained models to perform registration directly or to seek the optimal spatial transformation parameters through online learning. We manually annotated 61 pairs of aortic DSA/CTA for algorithm evaluation. The results indicate that the proposed UDCR achieved a Mean Absolute Error (MAE) of 2.85 mm in translation and 4.35° in rotation, showing significant potential for clinical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Carousel Storage and Picking Scheduling Issues: A Review

Carousel Storage and Picking Scheduling Issues: A Review

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shujin Qin Xinyi Yang Jiacun Wang Shixin Liu Xiwang Guo Liang Qi Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal University Shangqiu China Department of Computer Science and Software Engineering Monmouth University West Long Branch NJ USA College of Information Science and Engineering Northeast University Shenyang China College of Information and Control Engineering Liaoning Petrochemical University Fushun China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions.

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