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IEEE International Conference on Automated software engineering (ASE)

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

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Study on the Performance of Amorphous IGZO TFT Thin-Film Transistor with Ti/Au Stacking as the Source/Drain

Study on the Performance of Amorphous IGZO TFT Thin-Film Tra...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Qingru Lu Bing Li Rui Chen Xiaodong Huang School of Electron and Computer Southeast University Cheng Xian College Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Computer and Software Engineering Nanjing Vocational University of Industry Technology Nanjing China Key Laboratory of MEMS of the Ministry of Education Southeast University Nanjing China

Through magnetron sputtering, a Ti/Au stack was adopted as the source/drain of the transistor, and Amorphous indium-gallium-zinc-oxide (α-IGZO or IGZO) was utilized as the active layer (150μm×150μm) to fabricate a bottom-gate contact thin-film transistor (TFT) with a structure of Ti/Au/IGZO (150μm×150μm)/SiO2/P. The electrical characteristics of this device were studied. The experiment showed that by depositing Ti/Au electrodes as the source/drain, and generating a TiOx oxide layer at the interface between the IGZO layer and the Ti/Au electrodes after annealing, the threshold voltage of the device was 4.7V, and the sub-threshold swing was relatively small. The gate bias voltage had excellent regulation on the leakage current of the device, and the device exhibited good cut-off characteristics.

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Privacy-Preserving Quantum Two-Party Geometric Intersection

arXiv

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, James C.N. Yu, Wen-Bin Chi, Lian-Hua Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan Department of Computer Science and Information Technology La Trobe University VIC3086 Australia

Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph GA (GB ) as the set of numbered grids SA (SB ), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation OA (OB ) is firstly utilized to encode the private elements of SA (a0, a1,, aM1) (SB (b0,b1,bN1)) into the quantum states, and then the oracle operation Of is applied to obtain a new quantum state which includes the XOR results between each element of SA and SB . Finally, the quantum counting is introduced to get the amount (t ) of the states ai bj equaling to 0, and the intersection result can be obtained by judging t 0 or not. Compared with classical PGI protocols, our proposed protocol not only has higher security, but also holds lower communication complexity. © 2023, CC BY.

关键词： Computational geometry

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Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach

arXiv

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arXiv 2025年

作者： Wei, Shuyue Tong, Yongxin Zhou, Zimu He, Tianran Xu, Yi State Key Laboratory of Complex & Critical Software Environment Lab School of Computer Science and Engineering Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University China City University of Hong Kong Hong Kong

Federated learning (FL) has emerged as a prominent distributed learning paradigm to utilize datasets across multiple data providers. In FL, cross-silo data providers often hesitate to share their high-quality dataset unless their data value can be fairly assessed. Shapley value (SV) has been advocated as the standard metric for data valuation in FL due to its desirable properties. However, the computational overhead of SV is prohibitive in practice, as it inherently requires training and evaluating an FL model across an exponential number of dataset combinations. Furthermore, existing solutions fail to achieve high accuracy and efficiency, making practical use of SV still out of reach, because they ignore choosing suitable computation scheme for approximation framework and overlook the property of utility function in FL. We first propose a unified stratified-sampling framework for two widely-used schemes. Then, we analyze and choose the more promising scheme under the FL linear regression assumption. After that, we identify a phenomenon termed key combinations, where only limited dataset combinations have a high-impact on final data value. Building on these insights, we propose a practical approximation algorithm, IPSS, which strategically selects high-impact dataset combinations rather than evaluating all possible combinations, thus substantially reducing time cost with minor approximation error. Furthermore, we conduct extensive evaluations on the FL benchmark datasets to demonstrate that our proposed algorithm outperforms a series of representative baselines in terms of efficiency and effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

AutoLog: A Log Sequence Synthesis Framework for Anomaly Dete...

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IEEE International Conference on Automated software engineering (ASE)

作者： Yintong Huo Yichen Li Yuxin Su Pinjia He Zifan Xie Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK Shenzhen) China Huazhong University of Science and Technology Wuhan China

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AUTOLOG, the first automated log generation methodology for anomaly detection. AUTOLOG uses program analysis to generate runtime log sequences without actually running the system. AUTOLOG starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AUTOLOG propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AUTOLOG acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. AUTOLOG also provides hyper-parameters to adjust the data size, anomaly rate, and component indicator for simulating different real-world scenarios. We further demonstrate AUTOLOG's practicality by showing that AUTOLOG enables log-based anomaly detectors to achieve better performance (1.93%) compared to existing log datasets. We hope AUTOLOG can facilitate the benchmarking and adoption of automated log analysis techniques.

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Confidence-aware Contrastive Learning for Selective Classification

arXiv

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arXiv 2024年

作者： Wu, Yu-Chang Lyu, Shen-Huan Shang, Haopu Wang, Xiangyu Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model’s confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. © 2024, CC BY.

关键词： Classification (of information)

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A Method for Person Re-Identification and Trajectory Tracking in Cross-Device Edge Environments

A Method for Person Re-Identification and Trajectory Trackin...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jian An Yunxiang Zhao Feifei Wang Xin He Xiaolin Gui School of Computer Science and Technology Shaanxi Provincial Key Laboratory of Computer Networks Xi’an Jiaotong University Xi’an China Henan Jiaoyuan Engineering Technology Group Co. LTD Henan China School of Software Henan University Henan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Addressing expanded surveillance scopes, cross-device person re-identification emerges as a crucial issue. Traditional approaches to re-identification and tracking confront challenges in feature matching challenges, and fail to harness the potential of edge computing environments, which offer viable solutions to bandwidth and latency constraints. The paper introduces a novel, edge computing and shared cache-based method for person re-identification and tracking. In this framework, edge nodes exploit deep learning method to execute image recognition for person re-identification and trajectory tracking. Edge nodes are deployed within a tri-layered architecture, encompassing Hierarchical Edge Cloud (HEC), Mobile Edge Computing (MEC), and Central Cloud (CC). Emphasizing part feature significance for person re-identification, the approach employs a refined Part-based Convolutional Baseline model guided by the triplet loss function. Experimental outcomes validate the high re-identification precision and efficacious load balancing capability of this method in person re-identification and tracking operations. Achieving a harmonized trade-off between task accuracy and response time, the proposed framework offers a robust solution for person re-identification and tracking within edge computing contexts.

关键词： Accuracy Trajectory tracking Image edge detection Computational modeling computer architecture Trajectory Time factors

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Drowsy Driving Detection System Using Face Detection

Drowsy Driving Detection System Using Face Detection

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ismoiljon Mirabdullayev Mohamed Uvaze Ahamed Ayoobkhan A.M. Jasmine Hashana Liayakath Ali Khan Subair Ali Software Engineering New Uzbekistan University Tashkent Uzbekistan Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India School of Information Technology Digital Media and Mass Communication TMC Institute in Tashkent Tashkent Uzbekistan

Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone.

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Robust Imagined Speech Production Using AI-Generated Content Network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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Resource Allocation for Semantic Segmentation Tasks in Autonomous Driving: A Likelihood Active Inference Approach

Resource Allocation for Semantic Segmentation Tasks in Auton...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinxin Zhu F. Richard Yu Ying He Biao He College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The latest Segment Anything Model enables realtime scene annotation and understanding for autonomous driving systems, enhancing driving safety. However, effectively allocating resources for real-time performance and accuracy remains challenging in edge-cloud architectures. Traditional reinforcement learning struggles with poor generalization and the explorationexploitation dilemma, making it difficult to define clear reward functions. To address this, we propose a likelihood active inference approach to optimize resource allocation and improve system resource utilization. We use "intelligence" as a high-level indicator to quantify the efficiency of cognition in active inference, evaluating the difference between predicted and actual states during policy exploration. Experimental results show our algorithm outperforms mainstream deep reinforcement learning algorithms, improving sample efficiency and suitability for dynamically changing task workloads.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Signal processing Prediction algorithms Inference algorithms Real-time systems Resource management Speech processing Autonomous vehicles

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