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Privacy-Preserving Multi-Task Online Matching in Mobile Crowdsensing: A Snapshot-Based Approach

IEEE Internet of Things Journal 2025年

作者： Wei, Kaimin Zhao, Shiting Chen, Jinpeng Pei, Tingrui Sun, Dezhi Jinan University College of Information Science and Technology China Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Information Science China

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure Ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms. © 2014 IEEE.

关键词： Privacy by design

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The ABLoTS Approach for Bug Localization: is it replicable and generalizable?

The ABLoTS Approach for Bug Localization: is it replicable a...

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International Mining software Repositories, MSR

作者： Feifei Niu Christoph Mayr-Dorn Wesley K. G. Assunção LiGuo Huang Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.

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A Parallel and Pipelined Hardware Architecture for Fractional-Pixel Motion Estimation in AVS3

A Parallel and Pipelined Hardware Architecture for Fractiona...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Zeng, Yuning Zhu, Xizhong Xiang, Guoqing Hao, Zhijian Zhang, Peng Huang, Xiaofeng Yan, Wei Peking University School of Software and Microelectronics Beijing China Advanced Institute of Information Technology Peking University Beijing China Peking University Shenzhen Guraduate School School of Electronic and Computer Engineering Shenzhen China Fudan University State Key Laboratory of ASIC System Shanghai China Hangzhou Dianzi University School of Communication Engineering Hangzhou China

ISBN: (纸本)9781665441544

The latest generation of video coding standards has significantly improved video coding performance. The third generation of audio video coding standards (AVS3) is one of these latest standards. However, due to the more flexible block partition mechanism in AVS3, the computational overhead brought by fractional-pixel motion estimation (FME) increases significantly, making it more difficult to implement a real-time FME hardware. In this paper, a parallel and pipelined hardware architecture supporting block sizes from 4x4 to 64x64 is proposed for FME in AVS3, and it is specially designed for coding tree unit (CTU)-level pipelined architecture of AVS3 encoder. Three necessary modules are developed in this architecture. The first module concerns derivation of motion vector predictors (MVPs). The second module is used to derive fractional-pixel motion vectors (FMVs) of coding units (CUs) partitioned by Binary-tree (BT) and Quad-Tree (QT) mechanisms. And a motion vector substitution (MVS) module is proposed to substitute the FMVs of the remaining CUs with the FMVs derived in the second module. Experimental results show that the proposed methods suffer only 0.62% performance degradation in BD-Rate. Furthermore, the architecture is implemented on XILINX ALVEO U250 FPGA at 400 MHz and the presented FME hardware design can support the real-time encoding of 4K@30fps. © 2022 IEEE.

关键词： Pipelines

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iCTS: Iterative and Hierarchical Clock Tree Synthesis With Skew-Latency-Load Tree

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Li, Weiguo Huang, Zhipeng Yu, Bei Zhu, Wenxing Chen, Jian He, Zhixue Li, Xingquan South China University of Technology School of Software Engineering Guangzhou China Pengcheng Laboratory Shenzhen China Beijing Institute of Open Source Chip Beijing China Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Fuzhou University Center for Discrete Mathematics and Theoretical Computer Science Fuzhou China

The advancement of modern clock tree synthesis (CTS) encounters a bottleneck, primarily due to the difficulty in achieving multi-objective co-optimization among complex design processes. To concurrently optimize skew, latency, and load capacitance, we propose an iterative and hierarchical CTS framework, which is composed of clustering, topology generation and routing, buffering, and optimization. First, we introduce a capacitance-based metric to achieve adaptive balanced clustering and optimize the cluster results through simulated annealing. Second, to construct a clock tree with lower latency, load capacitance, and skew, we introduce the skew-latency-load tree (SLLT), which combines the advantages of bound skew tree and Steiner shallow-light tree, and we propose an effective SLLT construction algorithm. Third, to further optimize CTS result by buffering, we introduce the critical wirelength evaluation (CWE) to evaluate the capability of each buffer, and propose the insertion delay estimation (IDE) to reduce the evaluation bias during buffering, then design the iterative skew convergence algorithm (ISCA) to achieve complete convergence of skew. We validate our solution using 28nm process technology. Compared to our method, the commercial tool increases skew, latency, and clock capacitance by 39.5%, 13.0%, and 18.5%, respectively, while the OpenROAD by 101.6%, 50.7%, and 25.5%, respectively. © 1982-2012 IEEE.

关键词： Simulated annealing

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Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

arXiv

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arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A Lightweight VK-Net Based on Motion Profiles for Hazardous Driving Scenario Identification 23

A Lightweight VK-Net Based on Motion Profiles for Hazardous ...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Zhen Xu, Jingning Zheng, Jiang Yu Fan, Hongfei Yu, Rongjie Zong, Jiaqi Li, Xinyi School of Software Engineering Tongji University Shanghai China Indiana University-Purdue University Department of Computer Science Indianapolis United States Jiangsu China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education Shanghai China College of Transportation Engineering Tongji University Shanghai China

ISBN: (纸本)9781665494571

Hazardous driving scenarios are critical for the development and testing of highly autonomous vehicles. Recent studies have proposed new approaches for extracting hazardous scenarios from driving videos, which greatly improve the detection accuracy compared with traditional approaches that extract information from structured kinematic data. However, direct processing on driving videos is costly, and the videos need to be uploaded and processed in a centralized manner. In this study, we propose a novel approach for reducing the cost of video analysis while maintaining high accuracy in hazardous scenario identification. Firstly, the videos are significantly compressed into motion profile images that contain motion trajectories of objects. Secondly, a lightweight CNN with attention mechanism has been designed to extract risky trajectory features of the conflicting traffic objects. Furthermore, data augmentation has been applied to increase the generalizability of the model. The proposed approach achieves significantly lightweight network size, and meanwhile, experimental results have demonstrated that the approach achieves high accuracy with sensitivity 0.95, specificity 0.85 and AUC 0.95, which outperforms state-of-the-art hazardous scenario identification approaches. The efficient execution of identification implies that it is suitable to be distributed and deployed at each vehicle, and consequently, videos could be processed via edge computing, which would improve the diversity of hazardous scenarios and the efficiency of scenario collection for further research work. © 2021 IEEE.

关键词： Sensitivity Image coding Smart cities Kinematics Feature extraction Trajectory Data mining

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ computer Science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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Analytical Models for QoT Estimations in Super-C and Super-C + L Bands Optical Transmission systems

Analytical Models for QoT Estimations in Super-C and Super-C...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Yan, Baoluo Sun, Yijun Zhou, Ziwen Liu, Hong Wang, Hongya Huan, Chen Zhao, Zhiyong Feng, Lipeng Zhang, Anxu Feng, Zhenhua Shi, Hu WDM System Department Wireline Product R&D Institute ZTE Corp. Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology 518055 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optical and Electronic Information Wuhan National Laboratory for Optoelectronics Optics Valley Laboratory Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Beijing102209 China

We propose a novel physical model-based QoT estimation with achieved OSNR estimation error 1.5 dB in primary paths and OSNR prediction error 1.76 dB in unestablished lightpaths for both super-C bands and super-C+L ban... 详细信息

ISBN: (纸本)9781957171258

关键词： Light transmission

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Goat Feeding Behavior Modeling and Monitoring Utilizing Livestock Wearable Device and Machine Learning Algorithm

Goat Feeding Behavior Modeling and Monitoring Utilizing Live...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Melchizedek Alipio Gianne Marcus Obusan Julianne Abbe Ong Samuel Perez Anton Luis Garces Miroslav Bures Department of Computer Science System Testing Intelligent Laboratory FEE Czech Technical University in Prague Prague 2 Czech Republic Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Goat farming shows potential as one of the so-lutions for providing quality livestock products in developing countries. Goat health is essential in determining the quality of goods obtained from the goat. Smart livestock farming has been a reliable solution to achieve high-yield and high-quality goat products. However, current smart livestock monitoring systems do not consider the feeding pattern and behavior of the animal as a factor in its overall health. Hence, this study aims to develop a system that effectively monitors the feeding behavior of the goat using wearable devices, machine learning, and a visualization interface. The wearable device consists of an accelerometer, gyrometer, and temperature sensors to monitor the goat's feeding behavior and movement. The data collected by the wearable device are wirelessly transmitted to a base station to build a sensor-based dataset. This dataset is used to generate a k-means clustering model to classify the feeding behavior of the goats. Results show that the system was able to classify the feeding behavior of the goat with an accuracy of 98.02 % and a reliability of 100% in comparison with the actual goat behavior on the farm. Lastly, a graphical user interface is developed to provide monitoring and other relevant information to help the farmers in grazing the goats.

关键词： Temperature sensors Temperature measurement Temperature distribution Animals Agriculture Reliability Wearable devices

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Security Issues and Defensive Approaches in Deep Learning Frameworks

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Tsinghua Science and Technology 2021年第6期26卷 894-905页

作者： Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Department of Computer Science and Technology University of Science and Technology Beijing(USTB)Beijing 100083China Defense Electronics Institute China Industrial Control System Cyber Emergency Response TeamBeijing 100040China

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

关键词： adversarial examples deep learning frameworks defensive approaches security issues

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