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Towards Integrated Spatial Crowdsourcing: Online Privacy-Preserving Selection

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Hengzhi Ma, Minghe Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China Simon Fraser University School of Computing Science Burnaby Canada

We study an intriguing and practical scenario of online Spatial Crowdsourcing (SC), in which workers have the flexibility to perform tasks using various methods, such as walking, driving, or utilizing Unmanned Aerial Vehicles (UAVs). This results in workers having heterogeneous, arbitrary, and non-stationary utilities over time. We refer to this scenario as integrated SC. Unfortunately, existing studies are limited in addressing integrated SC settings due to two aspects: (1) these studies are based on the assumption that workers' utilities are independently and identically distributed and follow a stationary distribution like Gaussian, which does not hold in integrated SC;(2) their approaches fail to provide personalized privacy preservation for different workers. Motivated by these limitations, we closely investigate the heterogeneous utility and personalized privacy requirement in integrated SC and propose an Online Personalized Privacy-preserving Selection framework (OPPS). In this framework, we present an online selection policy that balances the exploration-exploitation trade-off given heterogeneous utilities and develop a built-in privacy policy that ensures differential privacy guarantee. We then demonstrate that our framework effectively addresses the trade-off by deriving a sublinear, privacy-related upper bound on regret that scales as O(√T). Extensive numerical simulations based on real-world drone datasets are conducted to validate the effectiveness of our framework compared with state-of-the-art approaches. © 2004-2012 IEEE.

关键词： Differential privacy

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Prediction Instability in Machine Learning Ensembles

arXiv

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arXiv 2024年

作者： Kedziora, Jeremy Department of Computer Science and Software Engineering Milwaukee School of Engineering MilwaukeeWI United States

In machine learning ensembles predictions from multiple models are aggregated. Despite widespread use and strong performance of ensembles in applied problems little is known about the mathematical properties of aggregating models and associated consequences for safe, explainable use of such models. In this paper we prove a theorem that shows that any ensemble will exhibit at least one of the following forms of prediction instability. It will either ignore agreement among all underlying models, change its mind when none of the underlying models have done so, or be manipulable through inclusion or exclusion of options it would never actually predict. As a consequence, ensemble aggregation procedures will always need to balance the benefits of information use against the risk of these prediction instabilities. This analysis also sheds light on what specific forms of prediction instability to expect from particular ensemble algorithms;for example popular tree ensembles like random forest, or xgboost will violate basic, intuitive fairness properties. Finally, we show that this can be ameliorated by using consistent models in asymptotic conditions. © 2024, CC BY.

关键词： Prediction models

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Matching weak informative ontologies

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science China(Information sciences) 2021年第12期64卷 250-251页

作者： Peng WANG Baowen XU School of Computer Science and Engineering Southeast University State Key Laboratory for Novel Software Technology Nanjing University

Dear editor,Most existing ontology matching methods utilize literal information to discover alignments [1, 2]. However, some literal information in ontologies may be opaque and some ontologies may not have sufficient literal information. These ontologies are named weak informative ontologies(WIOs)and it is challenging for existing methods to match WIOs.

关键词： ontology matching weak informative ontology similarity propagation semantic subgraph ontology

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Light field super-resolution using complementary-view feature attention

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Computational Visual Media 2023年第4期9卷 843-858页

作者： Wei Zhang Wei Ke Da Yang Hao Sheng Zhang Xiong Faculty of Applied Sciences Macao Polytechnic UniversityMacao SAR 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191Chinaand Beihang Hangzhou Innovation Institute YuhangXixi Octagon CityYuhang DistrictHangzhou 310023China

Light field(LF)cameras record multiple perspectives by a sparse sampling of real scenes,and these perspectives provide complementary *** information is beneficial to LF super-resolution(LFSR).Compared with traditional single-image super-resolution,LF can exploit parallax structure and perspective correlation among different LF ***,the performance of existing methods are limited as they fail to deeply explore the complementary information across LF *** this paper,we propose a novel network,called the light field complementary-view feature attention network(LF-CFANet),to improve LFSR by dynamically learning the complementary information in LF ***,we design a residual complementary-view spatial and channel attention module(RCSCAM)to effectively interact with complementary information between complementary ***,RCSCAM captures the relationships between different channels,and it is able to generate informative features for reconstructing LF images while ignoring redundant ***,a maximum-difference information supplementary branch(MDISB)is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF *** branch also can guide the process of *** results on both synthetic and real-world datasets demonstrate the superiority of our *** proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.

关键词： light field(LF) super-resolution(SR) attention

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Building Highly Maintainable software for Energy Automation Systems using Abstraction Layering 50

Building Highly Maintainable Software for Energy Automation ...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liaqat, Arsalan Somerville, Max Kuo, Matthew M.Y. Spray, John Yang, Chen-Wei Sinha, Roopak Computer Science and Software Engineering Department Auckland University of Technology Auckland New Zealand Auckland New Zealand Lulea University of Technology Sweden School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9781665464543

In smaller components of industrial or energy automation systems, such as device controllers of Protection and Control (PAC) systems in smart grids, controller functionality is tightly coupled with the physical device or sensor capabilities. At this level, software is small and therefore easy to maintain and test. However, when multiple controllers are interconnected and higher-level functionality is added, software applications grow exponentially, and ensuring maintainability becomes proportionally challenging. In this paper, we extend the IEC 61499 reference architecture used to develop industrial automation software with the principles of Abstraction Layered Architecture (ALA) that has shown up to 400% improvements in industrial software maintainability. We show that even a light application of abstraction layering on the top, application-level of IEC 61499 applications makes them significantly more readable and slightly more maintainable. More concrete gains in maintainability are expected when abstraction layering is integrated into lower layers. © 2024 IEEE.

关键词： Office automation

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th International Conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques 3

Enhancing Medical Image-Based Diagnostics Through the Applic...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Byeon, Haewon Nair, Rajit Mahalakshmi, V. Khalaf, Mohammed I. Kaushik, Bharat Shabaz, Mohammad Inje University Department Of Ai And Software Gimhae50834 Korea Republic of School Of Computing Science & Engineering Vit Bhopal University Bhopal India College Of Engineering And Computer Science Jazan University Department Of Computer Science Jazan45142 Saudi Arabia Al Maarif University College Department Of Computer Science Al Anbar 31001 Iraq Masters University Of Northern Iowa IA United States Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques. © 2024 IEEE.

关键词： Diagnosis

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Dynamic Feature Extraction Using I-Vector for Video Fire Detection 3

Dynamic Feature Extraction Using I-Vector for Video Fire Det...

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Huang, Zhongming Wang, Yuxiang Hu, Haolan Liu, Xun Liu, Tongzhen Zhang, Zhanxu Tiangong University School of Electronic and Information Engineering Tianjin China Tiangong University School of Computer Science and Software Tianjin China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Fire detection technology has been researched and developed for decades. However, in videos and complex scenes, it still lacks fast recognition of fire's existence. The traditional model of fire recognition still needs a large number of samples and time-consuming machine learning progress. Meanwhile, the uncertain shape of fire leads to the reduction of accuracy using CNN. Based on the above problems, we establish a novel method based on I-Vector. We use an adapted I-Vector algorithm to extract the time sequence feature vector on the fire and its surroundings and train a G-PLDA classifier to recognize the dynamic occurrence of fire more quickly and accurately. This model requires fewer samples and a shorter learning time while obtaining an accuracy similar to the traditional fire recognition models, which provides a new effective solution for rapid analysis of whether there is a fire in the video scene. In addition, the algorithm has a sound universality and is easy to deploy in the application fields of video fire supervision, UAV fire inspection, and other related fields. © 2022 IEEE.

关键词： Feature extraction

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Design of AXI Bus-Based IP Core for Image Processing 12th

Design of AXI Bus-Based IP Core for Image Processing

引用

12th CCF Conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Palette-based Color Transfer between Images

arXiv

引用

arXiv 2024年

作者： Lv, Chenlei Zhang, Dan College of Computer Science and Software Engineering Shenzhen University China School of Computer Science Qinghai Normal University China

As an important subtopic of image enhancement, color transfer aims to enhance the color scheme of a source image according to a reference one while preserving the semantic context. To implement color transfer, the palette-based color mapping framework was proposed. It is a classical solution that does not depend on complex semantic analysis to generate a new color scheme. However, the framework usually requires manual settings, blackucing its practicality. The quality of traditional palette generation depends on the degree of color separation. In this paper, we propose a new palette-based color transfer method that can automatically generate a new color scheme. With a redesigned palette-based clustering method, pixels can be classified into different segments according to color distribution with better applicability. By combining deep learning-based image segmentation and a new color mapping strategy, color transfer can be implemented on foreground and background parts independently while maintaining semantic consistency. The experimental results indicate that our method exhibits significant advantages over peer methods in terms of natural realism, color consistency, generality, and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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