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When crowdsensing meets federated learning: Privacy-preserving mobile crowdsensing system

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arXiv 2021年

作者： Zhao, Bowen Liu, Ximeng Chen, Weineng the School of Computing and Information Systems Singapore Management University Singapore Singapore the College of Mathematics and Computer Science Fuzhou University Fujian China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China the School of Computer Science and Engineering South China University of Technology Guangzhou China

Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than that of data processing. To tackle faced problems of MCS, in this paper, we integrate federated learning (FL) into MCS and propose a privacy-preserving MCS system, called CROWDFL. Specifically, in order to protect privacy, participants locally process sensing data via federated learning and only upload encrypted training models. Particularly, a privacy-preserving federated averaging algorithm is proposed to average encrypted training models. To reduce computation and communication overhead of restraining dropped participants, discard and retransmission strategies are designed. Besides, a privacy-preserving posted pricing incentive mechanism is designed, which tries to break the dilemma of privacy protection and data evaluation. Theoretical analysis and experimental evaluation on a practical MCS application demonstrate the proposed CROWDFL can effectively protect participants privacy and is feasible and efficient. © 2021, CC BY.

关键词： Data acquisition

Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

Generative AI for Space-Air-Ground Integrated Networks

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arXiv 2023年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI’s ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN. © 2023, CC BY.

关键词： Generative adversarial networks

Machine Learning Classification-Regression Schemes for Desert Locust Presence Prediction in Western Africa

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SSRN

SSRN 2022年

作者： Cornejo-Bueno, Laura María Pérez-Aracil, Jorge Casanova-Mateo, Carlos Sanz-Justo, Julia Salcedo-Sanz, Sancho Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid 28805 Spain Department of Information Systems Universidad Politécnica de Madrid Ctra. Valencia Km. 7 Madrid28031 Spain LATUV Remote Sensing Laboratory Universidad de Valladolid Valladolid Spain School of Mathematics Physics and Computing University of Southern Queensland SpringfieldQLD4300 Australia

For decades, humans have been confronted with numerous pest species, being the desert locust one of the most damaging and with the greatest socio-economic impact. Trying to predict the occurrence of such pests is often complicated by the small number of records and observations in databases. This paper proposes a methodology based on a combination of classification and regression techniques to address not only the problem of locusts sightings prediction, but also the number of locusts individuals that may be expected. For this purpose we apply different Machine Learning (ML) and related techniques, such as Linear Regression, Support Vector Machines, Decision Trees, Random Forests and Neural Networks. The considered ML algorithms will be evaluated in 3 different scenarios in Western Africa, mainly Mauritania, and for the elaboration of the forecasting process, a number of meteorological variables, obtained from the ERA5 Reanalysis data will be used as input variables for the classification-regression machines. The results obtained show a good performance in terms of classification (appearance or not of desert locust), and acceptable regression results in terms of predicting the number of locusts, a harder problem due to the small number of samples available. © 2022, The Authors. All rights reserved.

关键词： Forecasting

When Deep Learning Meets information Retrieval-based Bug Localization: A Survey

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arXiv 2025年

作者： Niu, Feifei Li, Chuanyi Liu, Kui Xia, Xin Lo, David State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing China Software Engineering Application Technology Lab Huawei Hangzhou China College of Computer Science and Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area. © 2025, CC BY.

关键词： Online searching

User grouping and reflective beamforming for IRS-aided URLLC

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arXiv 2021年

作者： Xie, Hailiang Xu, Jie Liu, Ya-Feng Liu, Liang Ng, Derrick Wing Kwan School of Information Engineering Guangdong University of Technology Guangzhou510006 China Shenzhen518172 China Shenzhen518172 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 Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales Australia

This paper studies an intelligent reflecting surface (IRS)-aided downlink ultra-reliable and low-latency communication (URLLC) system, in which an IRS is dedicatedly deployed to assist a base station (BS) to send individual short-packet messages to multiple users. To enhance the URLLC performance, the users are divided into different groups and the messages for users in each group are encoded into a single codeword. By considering the time division multiple access (TDMA) protocol among different groups, our objective is to minimize the total latency for all users subject to their individual reliability requirements, via jointly optimizing the user grouping and block-length allocation at the BS together with the reflective beamforming at the IRS. We solve the latency minimization problem via the alternating optimization, in which the blocklengths and the reflective beamforming are optimized by using the techniques of successive convex approximation (SCA) and semi-definite relaxation (SDR), while the user grouping is updated by K-means and greedy-based methods. Numerical results show that the proposed designs can significantly reduce the communication latency, as compared to various benchmark schemes, which unveil the importance of user grouping and reflective beamforming optimization for exploiting the joint encoding gain and enhancing the worst-case user performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Beamforming

Investigating what promotes and deters Scottish cybercrime reporting

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Journal of Economic Criminology

Journal of Economic Criminology 2024年 6卷

作者： Juraj Sikra Karen V. Renaud Daniel R. Thomas Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Department of Information Systems Rhodes University Grahamstown South Africa School of Computing University of South Africa Pretoria South Africa Division of Cybersecurity Abertay University Dundee United Kingdom Computer Laboratory University of Cambridge Cambridge United Kingdom

Cybercrime is under-reported in Scotland, with the reasons for this being poorly understood. To investigate underreporting, we commenced with a search of the related research and then carried out a review of actual cases. Next, to uncover Scottish-specific factors, we qualitatively interviewed 10 Scottish cybercrime victims. It emerged that victims blamed themselves for falling prey to cybercrime and were reluctant to report the incident. This is arguably a direct consequence of the UK government’s cybersecurity responsibilization strategy. Informed by our findings, we articulated a national strategy for promoting cybercrime reporting using the MINDSPACE behavioral influence model. Subsequently, we verified this model with a survey of 380 Scottish respondents, a representative sample of the general population in terms of age and gender. We report on and discuss our findings. Finally, we recommend two interventions to inform a national strategy for improving cybercrime reporting in Scotland.

关键词： Cybercrime Victims Reporting Responsibilization Scotland MINDSPACE

Segment Anything Model is a Good Teacher for Local Feature Learning

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arXiv 2023年

作者： Wu, Jingqian Xu, Rongtao Wood-Doughty, Zach Wang, Changwei Xu, Shibiao Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China Northwestern University EvanstonIL60201 United States The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in any scene and any downstream task. Data-driven local feature learning methods need to rely on pixel-level correspondence for training. However, a vast number of existing approaches ignored the semantic information on which humans rely to describe image pixels. In addition, it is not feasible to enhance generic scene keypoints detection and description simply by using traditional common semantic segmentation models because they can only recognize a limited number of coarse-grained object classes. In this paper, we propose SAMFeat to introduce SAM (segment anything model), a foundation model trained on 11 million images, as a teacher to guide local feature learning. SAMFeat learns additional semantic information brought by SAM and thus is inspired by higher performance even with limited training samples. To do so, first, we construct an auxiliary task of Attention-weighted Semantic Relation Distillation (ASRD), which adaptively distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination. Second, we develop a technique called Weakly Supervised Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic groupings derived from SAM as weakly supervised signals, to optimize the metric space of local descriptors. Third, we design an Edge Attention Guidance (EAG) to further improve the accuracy of local feature detection and description by prompting the network to pay more attention to the edge region guided by SAM. SAMFeat’s performance on various tasks such as image matching on HPatches, and long-term visual localization on Aachen Day-Night showcases its superiority over previous local features. The release code is available at https://***/vignywang/SAMFeat

关键词： Computer vision

Comparative study of decoding the surface code using simulated annealing under depolarizing noise

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arXiv 2023年

作者： Takeuchi, Yusaku Takada, Yugo Sakashita, Tatsuya Fujisaki, Jun Oshima, Hirotaka Sato, Shintaro Fujii, Keisuke Division of Advanced Electronics and Optical Science Department of Systems Innovation Graduate School of Engineering Science Osaka University Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University Osaka560-0043 Japan Quantum Laboratory Fujitsu Research Fujitsu Limited 4-1-1 Kawasaki Kanagawa211-8588 Japan Fujitsu Quantum Computing Joint Research Division Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka Osaka565-8531 Japan Center for Quantum Information and Quantum Biology Osaka University 560-0043 Japan Saitama Wako351-0198 Japan

We explored decoding methods for the surface code under depolarizing noise by mapping the problem into the Ising model optimization. We consider two kinds of mapping with and without a soft constraint and also various optimization solvers, including simulated annealing implemented on a CPU,"Fujitsu Digital Annealer" (DA), a hardware architecture specialized for the Ising problems, and CPLEX, an exact integer programming solver. We find that the proposed Ising-based decoding approaches provide higher accuracy compared to the minimum-weight perfect matching (MWPM) algorithm for depolarizing noise and comparable to minimum distance decoding using CPLEX. While decoding time is longer than MWPM when we compare it with a single core CPU, our method is amenable to parallelization and easy to implement on dedicated hardware, suggesting potential future speedups. Regarding the mapping methods to the Ising model with and without a soft constraint, the SA decoder yielded higher accuracy without a soft constraint. In contrast, the DA decoder shows less difference between the two mapping methods, which indicates that DA can find a better solution with smaller number of iterations even under the soft constraint. Our results are important for devising efficient and fast decoders feasible with quantum computer control devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Simulated annealing