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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Xiaoxue Li, Junhao Zhang, Jianjie Yan, Jijie Zhu, Enmin Chen, Kongyang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Pazhou Lab Guangzhou China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi China

ISBN: (纸本)9783031200953

Federated learning has become an emerging technology to protect data privacy in the distributed learning area, by keeping each client user’s data locally. However, recent work shows that client users’ data might still be stolen (or reconstructed) directly from gradient updates. After exploring the attack and defense techniques of these data reconstruction methods, we discover that the attacker cannot steal the victim’s data unless it has prior knowledge about the victim’s data size. Thus, the attacker can hardly reconstruct any useful information without these prior knowledge. In this paper, we provide a novel data reconstruction method to obtain a high-dimensional compressed data from the gradient updates, without these prior knowledge. Experiment results show that our reconstructed data can be used to attack the model, with high attack accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data privacy

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GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data

arXiv

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arXiv 2024年

作者： Zhao, Haochen Meng, Hui Yang, Deqian Xie, Xiaozheng Wu, Xiaoze Li, Qingfeng Niu, Jianwei School of Computer Science and Engineering Beihang University Beijing China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China Institute of Software Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Software Beihang University Beijing China

Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation. Existing state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled data. To address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs. A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same class. On this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated pseudo-labels. Furthermore, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-theart performance. The source code with our proposed model are available at https://***/kimjisoo12/GuidedNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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International Symposium on software Reliability engineering (ISSRE)

作者： Jun Liu Jiwei Yan Yuanyuan Xie Jun Yan Jian Zhang Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences (UCAS) Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers’ programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%.

关键词： Measurement Codes Large language models Static analysis Production Maintenance engineering Syntactics Programming software software reliability

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Robust Vietnam’s Motorcycle License Plate Detection and Recognition Using Deep Learning Model 10th

Robust Vietnam’s Motorcycle License Plate Detection and Re...

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10th International Conference on Future Data and Security engineering, FDSE 2023

作者： Le, Duc Hoa Mazumder, Debarshi Quach, Luyl-Da Banerjee, Shreya Nguyen, Vinh Dinh Bosch Global Software Technologies Company Limited Ho Chi Minh city Viet Nam School of Computing and Information Technology Eastern International University Thu Dau Mot Viet Nam Department of Software Engineering FPT University Can Tho City Can Tho Viet Nam Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819982950

Nowadays, driving fast is an emergency state in many countries in the world and Vietnam in particular, and the consequence is really cruel when an accident occurs. Therefore, it is very important to control the speed of vehicles. Motorcycle license plate recognition (MLPR) is one of many methods. If any motorcycle drives above the speed limit on the road that has a speed camera, a ticket will be sent to your living address. This research proposes a method that can automatically detect license plates (LP) and extract their data. The highest mAP achieved after three hundred epochs is 93% with Yolov8. There are three stages to extracting the LP’s data, the first is detecting the motorcycle, the second is detecting the LP in the motorcycle’s bounding box, and the third is LP recognition using Yolov8 also. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Digital storage

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Data Leakage with Label Reconstruction in Distributed Learning Environments 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Xiaoxue Zhou, Xiuhua Chen, Kongyang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Pazhou Lab Guangzhou China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Distributed learning is commonly applied for the high demands of computation resources while training models with large-scale data. However, existing solutions revealed that it may lead to information leakage of private data. Attackers are able to reconstruct data via minimizing the difference between shared gradients and fake gradients that are produced by themselves. This is so-called data leakage attack from gradients. We find that, in many cases, such attack can only obtain random noises or extreme fuzzy information, which are quite different from the raw data. To deal with this problem, we exploit the gradient updates as well as their labels during the parameter-server training, and discover significant information losses during the label coding. Thus, in our method, we reconstruct the labels of samples with a fine-grained method, and we recover the raw inputs with these processed labels. We test our method on different datasets (i.e. CIFAR100 dataset and solid color images) and obtained the reconstructed results which are obviously clearer than the ones of the state-of-the-arts. The results prove the effectiveness of our algorithm in fixing defects to some extent as well. Extensive experiments are also conducted to explore defense strategies against data leakage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data privacy

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Safeguarding Privacy in Real-Time Trajectory Releases for AI-Driven Consumer Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Cai, Sujin Tian, Wei Lv, Taizhi Wang, Xiangchen Li, Xin Jiangsu Maritime Institute School of Information and Engineering Nanjing211106 China Guizhou University of Commerce College of Computer and Information engineering Guiyang550014 China Southern University of Science and Technology College of Computer Science Shenzhen518055 China Hohai University College of Computer Science and Software Engineering Nanjing211106 China

Intelligent consumer applications optimize product recommendations by analyzing users' real-time trajectory information and mobility patterns to gain insights into their consumption preferences. However, privacy concerns often cause individuals to hesitate when sharing their locations. This paper introduces a real-time trajectory release scheme called Dynamic Trajectory Releasing based on Geo-indistinguishability (DTRG) that ensures client-side data protection. A significant contribution of this work is the introduction of a novel concept, w-event Geo-indistinguishability, which extends the measurement of privacy for real-time trajectories. Building upon the theoretical foundation of w-event Geo-indistinguishability, we design a dynamic privacy budget allocation strategy that doesn't require pre-setting trajectory length. This strategy recycles unused privacy budgets and progressively reallocating them in a backward cumulative manner, enhancing the utilization efficiency of the privacy budget. By incorporating users' travel information for prediction, DTRG adaptively perturbs real locations based on prediction results. Through the collaborative operation of the Prediction Mechanism (PM), Detection Mechanism (DM) and Noisy Mechanism (NM), this approach further optimizes the utility of the budget. Experimental results demonstrate that DTRG significantly improves data availability, achieving up to a 36% increase compared to some competing algorithms while maintaining the desired level of privacy. © 1975-2011 IEEE.

关键词： Differential privacy

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The Power of Age-based Reward in Fresh Information Acquisition 42

The Power of Age-based Reward in Fresh Information Acquisiti...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Zhiyuan Meng, Qingkai Zhang, Shan Luo, Hongbin Beihang University School of Computer Science and Engineering Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems China Beihang University Institute of Artificial Intelligence Beijing China State Key Laboratory of Software Development Environment China

ISBN: (纸本)9798350334142

Many Internet platforms collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer reward to incentivize users and compensate their costs incurred from information acquisition. In practice, the user cost (and its distribution) is hidden to the platform, thus it is challenging to determine the optimal reward. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with partial feedback. To overcome the challenge, we first design an age-based rewarding scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based rewarding scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived based on the cost distribution). Simulation results show that the age-based rewarding scheme protects the platform's OpEx from the influence of the user characteristics, and verify the asymptotic optimality of the EDAL policy. © 2023 IEEE.

关键词： Simulation platform

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One-Hot Coding Similarity of Vehicle Routing Particle Swarm Optimizer 8th

One-Hot Coding Similarity of Vehicle Routing Particle Swarm ...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： He, CHao Li, Zhi Su, Shoubao Xu, Liukai Wang, Chishe School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China Provincial Engineering Research Center of Digital Technology for Industrial Economic Jinling Institute of Technology Nanjing211169 China School of Computer Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783031067662

Aiming at the real-time problem of vehicle routing planning, an improved particle swarm optimization method, called AVSPSO, is proposed in this paper. In the method, the path similarity of vehicle routing is calculated by adopting based on the One-Hot coding vector and by improving the constraint processing through the combination of coding mode and fixed penalty, in which dynamically adjust the inertia weight through the evolution state is to balance the ability of local mining and global development of particles. Experimental results on two practical cases of vehicle routing optimization problems and comparisons with other methods have shown that the proposed method is feasible and stable with better performances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

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Design of Face Recognition Attendance 5

Design of Face Recognition Attendance

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5th IEEE International Conference on Image, Vision and Computing, ICIVC 2020

作者： Lee, Hai-Wu Gu, Wen-Tan Wang, Yuan-Yuan Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian City China

ISBN: (纸本)9781728166612

In recent years, face recognition technology has developed rapidly, and its application range has become more and more extensive. It is one of the most important application fields in computer vision technology. However, there are still many technical factors that restrict the application and promotion of face recognition technology. For example: shadows, occlusions, light and dark areas, dark light, highlights and other factors will make the face recognition rate drop sharply. Therefore, face recognition has extremely high research and application value. We use the Local Binary Patterns (LBP) algorithms with histogram equalization to obtain high-resolution images and improve the recognition rate in different scenarios, and try to apply face recognition to attendance. © 2020 IEEE.

关键词： Face recognition

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RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention

arXiv

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arXiv 2023年

作者： Qin, Ran Ma, Haoxiang Gao, Boyang Huang, Di State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China

Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse multi-modal clues. To address the two issues, this paper proposes a novel learning based approach to RGB-D grasp detection, namely Depth Guided Cross-modal Attention Network (DGCAN). To better leverage the geometry information recorded in the depth channel, a complete 6-dimensional rectangle representation is adopted with the grasp depth dedicatedly considered in addition to those defined in the common 5-dimensional one. The prediction of the extra grasp depth substantially strengthens feature learning, thereby leading to more accurate results. Moreover, to reduce the negative impact caused by the discrepancy of data quality in two modalities, a Local Cross-modal Attention (LCA) module is designed, where the depth features are refined according to cross-modal relations and concatenated to the RGB ones for more sufficient fusion. Extensive simulation and physical evaluations are conducted and the experimental results highlight the superiority of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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