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arXiv 2021年

作者： Wang, Ying Wang, Yibo Wang, Sinan Liu, Yepang Xu, Chang Cheung, Shing-Chi Yu, Hai Zhu, Zhiliang The Software College Northeasthern University China The Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China The State Key Lab for Novel Software Technology The Department of Computer Science and Technology Nanjing University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Android introduces a new permission model that allows apps to request permissions at runtime rather than at the installation time since 6.0 (Marshmallow, API level 23). While this runtime permission model provides users with greater flexibility in controlling an app’s access to sensitive data and system features, it brings new challenges to app development. First, as users may grant or revoke permissions at any time while they are using an app, developers need to ensure that the app properly checks and requests required permissions before invoking any permission-protected APIs. Second, Android’s permission mechanism keeps evolving and getting customized by device manufacturers. Developers are expected to comprehensively test their apps on different Android versions and device models to make sure permissions are properly requested in all situations. Unfortunately, these requirements are often impractical for developers. In practice, many Android apps suffer from various runtime permission issues (ARP issues). While existing studies have explored ARP issues, the understanding of such issues is still preliminary. To better characterize ARP issues, we performed an empirical study using 135 Stack Overflow posts that discuss ARP issues and 199 real ARP issues archived in popular open-source Android projects on GitHub. Via analyzing the data, we observed 11 types of ARP issues that commonly occur in Android apps. For each type of issues, we systematically studied: (1) how they can be manifested, (2) how pervasive and serious they are in real-world apps, and (3) how they can be fixed. We also analyzed the evolution trend of different types of issues from 2015 to 2020 to understand their impact on the Android ecosystem. Furthermore, we conducted a field survey and in-depth interviews among practitioners, to gain insights from industrial practices and learn practitioners’ requirements of tools that can help combat ARP issues. Finally, to understand the strengths and weaknesse

关键词： Android (operating system)

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Genetic evidence for facial variation being a composite phenotype of cranial variation and facial soft tissue thickness

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Journal of Genetics and Genomics 2022年第10期49卷 934-942页

作者： Wei Qian Manfei Zhang Kaiwen Wan Yunxia Xie Siyuan Du Jiarui Li Xiongzheng Mu Jiange Qiu Xiangyang Xue Xiahai Zhuang Yingzhi Wu Fan Liu Sijia Wang State Key Laboratory of Genetic Engineering Human Phenome InstituteZhangjiang Fudan International Innovation CenterFudan UniversityShanghai 201203China School of Computer Science Fudan UniversityShanghai 200438China CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China School of Data Science Fudan UniversityShanghai 200433China Academy of Medical Science Zhengzhou UniversityZhengzhouHenan 450052China Department of Plastic Surgery Huashan HospitalFudan UniversityShanghai 200040China CAS Key Laboratory of Genomic and Precision Medicine Beijing Institute of GenomicsUniversity of Chinese Academy of SciencesBeijing 100101China School of Future Technology University of Chinese Academy of SciencesBeijing 101408China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunmingYunnan 650223China

Facial and cranial variation represent a multidimensional set of highly correlated and heritable *** is known about the genetic basis explaining this *** develop a software package ALo SFL for simultaneous localization of facial and cranial landmarks from head computed tomography(CT)images,apply it in the analysis of head CT images of 777 Han Chinese women,and obtain a set of phenotypes representing variation in face,skull and facial soft tissue thickness(FSTT).Association analysis of 301 single nucleotide polymorphisms(SNPs)from 191 distinct genomic loci previously associated with facial variation reveals an unexpected larger number of loci showing significant associations(P<1e-3)with cranial phenotypes than expected under the null(O/E=3.39),suggesting facial and cranial phenotypes share a substantial proportion of genetic *** FSTT to a SNP-only model shows a large impact in explaining facial variance.A gene ontology analysis reveals that bone morphogenesis and osteoblast differentiation likely underlie our cranial-significant ***,this study simultaneously investigates the genetic effects on both facial and cranial variation of the same sample,supporting that facial variation is a composite phenotype of cranial variation and FSTT.

关键词： Facial variation Cranial variation Association analysis Single nucleotide polymorphism Facial soft tissue thickness Computed tomography

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Approximate equivalence checking of noisy quantum circuits

arXiv

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arXiv 2021年

作者： Hong, Xin Ying, Mingsheng Feng, Yuan Zhou, Xiangzhen Li, Sanjiang Centre for Quantum Software and Information University of Technology Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science and Technology Tsinghua University China State Key Lab of Millimeter Waves Southeast University China

We study the fundamental design automation problem of equivalence checking in the NISQ (Noisy Intermediate-Scale Quantum) computing realm where quantum noise is present inevitably. The notion of approximate equivalence of (possibly noisy) quantum circuits is defined based on the Jamiolkowski fidelity which measures the average distance between output states of two super-operators when the input is chosen at random. By employing tensor network contraction, we present two algorithms, aiming at different situations where the number of noises varies, for computing the fidelity between an ideal quantum circuit and its noisy implementation. The effectiveness of our algorithms is demonstrated by experimenting on benchmarks of real NISQ circuits. When compared with the state-of-the-art implementation incorporated in Qiskit, experimental results show that the proposed algorithms outperform in both efficiency and scalability. Copyright © 2021, The Authors. All rights reserved.

关键词： computer aided design

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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GUARD: Multigranularity-based Unsupervised Anomaly Detection Algorithm for Multivariate Time Series 8

GUARD: Multigranularity-based Unsupervised Anomaly Detection...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Meng, Fan Yang, Qunli He, Zhengda Yang, Shangdong Tang, Weidong Public Credit Information Center Jiangsu Strategy and Development Research Center Nanjing China Nanjing University State Key Lab for Novel Software Technology Nanjing China China Pharmaceutical University School of Science Nanjing China The School of Computer Science Nanjing University of Posts and Teleconnnunications Nanjing China China Telecom Corporation Limited Nanjing Branch Customer Operations Department Nanjing China

ISBN: (纸本)9781665477352

Unsupervised anomaly detection on multivariate time series (MTS) is a valuable problem in practice because it significantly reduces labeling costs. Even though most classical methods begin to consider inter-series dependencies, the hierarchical relationships between and within-series data are often ignored. To overcome these limitations, in this paper, we propose a multiGranUlArity based unsupeRvised anomaly Detection (GUARD) algorithm that captures the hierarchical relationships in three granularities and merges these dependencies in the following anomaly detection task. Specifically, the proposed algorithm first captures the internal dependencies within the subsequence and learns a hidden representation based on a trained autoencoder. Then, a hierarchical mechanism is designed to capture the local and global dependencies between the given subsequence and the whole set and learn their hidden representation. The experimental results show that the proposed algorithm can effectively and stably exploit the potential relationship of MTS and detect anomalies more accurately than the basic approaches. © 2022 IEEE.

关键词： Anomaly detection

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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors Data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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QUSL: Quantum Unsupervised Image Similarity Learning with Enhanced Performance

arXiv

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arXiv 2024年

作者： Yu, Lian-Hui Li, Xiao-Yu Chen, Geng Zhu, Qin-Sheng Li, Hui Yang, Guo-Wu School of Physics University of Electronic Science and Technology of China Si Chuan Chengdu610054 China School of Information and Software Engineering University of Electronic Science and Technology of China Si Chuan Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Si Chuan Chengdu610054 China JILA Department of Physics University of Colorado BoulderCO80309-0440 United States Advanced Cryptography and System Security Key Laboratory of Sichuan Province Si Chuan Chengdu610103 China

Leveraging quantum properties to enhance complex learning tasks has been proven feasible, with excellent recent achievements in the field of unsupervised learning. However, current quantum schemes neglect adaptive adjustments for unsupervised task scenarios. This work proposes a novel quantum unsupervised similarity learning method — QUSL. Firstly, QUSL uses similarity triplets for unsupervised learning, generating positive samples by perturbing anchor images, achieving a learning process independent of classical algorithms. Subsequently, combining the feature interweaving of triplets, QUSL employs metaheuristic algorithms to systematically explore high- performance mapping processes, obtaining quantum circuit architectures more suitable for unsupervised image similarity tasks. Ultimately, QUSL realizes feature learning with lower quantum resource costs. Comprehensive numerical simulations and experiments on quantum computers demonstrate that QUSL outperforms state-of-the-art quantum methods. QUSL achieves over 50% reduction in critical quantum resource utilization. QUSL improves similarity detection correlation by up to 19.5% across multiple datasets, exhibiting robustness in NISQ environments. While using fewer quantum resources, QUSL shows potential for large-scale unsupervised tasks. © 2024, CC BY-NC-SA.

关键词： Quantum computers

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QUEST-A: Untrained Filtering with Trained Focusing led to Enhanced Quantum Architectures

arXiv

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arXiv 2024年

Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning, with SOTA methods primarily categorized into training-free and gradient-guided approaches. However, treating QAS solely as either a discrete pruning process or a continuous optimization problem fails to balance accuracy and efficiency. This work decomposes QAS into two alternately solved subproblems: optimal circuit structure retrieval and parameter optimization. Based on this insight, we propose Quantum Untrained-Explored Synergistic Trained Architecture (QUEST-A), which implements rapid architecture pruning through inherent circuit properties and develops focused optimization with parameter reuse strategies. QUEST-A unifies discrete structure search and continuous parameter optimization within an evolutionary framework that integrates rapid pruning and fine-grained optimization. Experiments demonstrate QUEST-A’s superiority over existing methods: enhancing model expressivity in signal representation, maintaining high performance across varying complexities in image classification, and achieving order-of-magnitude precision improvements in variational quantum eigensolver tasks. These results validate QUEST-A’s effectiveness and provide transferable methodologies for QAS. © 2024, CC BY-NC-SA.

关键词： Image enhancement

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Four-Valued Monitorability of ω -Regular Languages 22nd

Four-Valued Monitorability of ω -Regular Languages

引用

22nd International Conference on Formal Engineering Methods, ICFEM 2020

作者： Chen, Zhe Chen, Yunyun Hierons, Robert M. Wu, Yifan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shanghai Key Laboratory of Trustworthy Computing Shanghai China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9783030634056

The use of runtime verification has led to interest in deciding whether a property is monitorable: whether it is always possible for the satisfaction or violation of the property to be determined after a finite future continuation during system execution. However, classical two-valued monitorability suffers from two inherent limitations, which eventually increase runtime overhead. First, no information is available regarding whether only one verdict (satisfaction or violation) can be detected. Second, it does not tell us whether verdicts can be detected starting from the current monitor state during system execution. This paper proposes a new notion of four-valued monitorability for ω -languages and applies it at the state-level. Four-valued monitorability is more informative than two-valued monitorability as a property can be evaluated as a four-valued result, denoting that only satisfaction, only violation, or both are active for a monitorable property. We can also compute state-level weak monitorability, i.e., whether satisfaction or violation can be detected starting from a given state in a monitor, which enables state-level optimizations of monitoring algorithms. Based on a new six-valued semantics, we propose procedures for computing four-valued monitorability of ω -regular languages, both at the language-level and at the state-level. Experimental results show that our tool implementation Monic can correctly, and quickly, report both two-valued and four-valued monitorability. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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