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arXiv 2023年

作者： Liu, Xutong Zhou, Yufei Tang, Yutian Qian, Junyan Zhou, Yuming The State Key Laboratory for Novel Software Technology Nanjing University China The School of Computer Science and Engineering School of Software Guanxi Normal University China The School of Computing Science University of Glasgow United Kingdom

Online Just-In-Time software Defect Prediction (O-JIT-SDP) uses an online model to predict whether a new software change will introduce a bug or not. However, existing studies neglect the interaction of software Quality Assurance (SQA) staff with the model, which may miss the opportunity to improve the prediction accuracy through the feedback from SQA staff. To tackle this problem, we propose Human-In-The-Loop (HITL) O-JIT-SDP that integrates feedback from SQA staff to enhance the prediction process. Furthermore, we introduce a performance evaluation framework that utilizes a k-fold distributed bootstrap method along with the Wilcoxon signed-rank test. This framework facilitates thorough pairwise comparisons of alternative classification algorithms using a prequential evaluation approach. Our proposal enables continuous statistical testing throughout the prequential process, empowering developers to make real-time decisions based on robust statistical evidence. Through experimentation across 10 GitHub projects, we demonstrate that our evaluation framework enhances the credibility of model evaluation, and the incorporation of HITL feedback elevates the prediction performance of online JIT-SDP models. These advancements hold the potential to significantly enhance the value of O-JIT-SDP for industrial applications. © 2023, CC BY-NC-ND.

关键词： Forecasting

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NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields

arXiv

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arXiv 2023年

作者： Liu, Xiaoliang Shen, Furao Han, Feng Zhao, Jian Nie, Changhai State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns. Existing research primarily focuses on transferability to different FR models, overlooking the direct transferability to victim’s face images, which is a practical threat in real-world scenarios. In this study, we propose a novel adversarial attack method that considers both the transferability to the FR model and the victim’s face image, called NeRFTAP. Leveraging NeRF-based 3D-GAN, we generate new view face images for the source and target subjects to enhance transferability of adversarial patches. We introduce a style consistency loss to ensure the visual similarity between the adversarial UV map and the target UV map under a 0-1 mask, enhancing the effectiveness and naturalness of the generated adversarial face images. Extensive experiments and evaluations on various FR models demonstrate the superiority of our approach over existing attack techniques. Our work provides valuable insights for enhancing the robustness of FR systems in practical adversarial settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Investigating the Effectiveness of Data Augmentation from Similarity and Diversity: An Empirical Study

SSRN

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SSRN 2023年

作者： Yang, Suorong Guo, Suhan Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Data augmentation has emerged as a widely adopted technique for improving the generalization capabilities of deep neural networks. However, evaluating the effectiveness of data augmentation methods solely based on model training is computationally demanding and lacks interpretability. Moreover, the absence of quantitative standards hinders our understanding of the underlying mechanisms of data augmentation approaches and the development of novel techniques. To this end, we propose interpretable quantitative measures that decompose the effectiveness of data augmentation methods into two key dimensions: similarity and diversity. The proposed similarity measure describes the overall similarity between the original and augmented datasets, while the diversity measure quantifies the divergence in inherent complexity between the original and augmented datasets in terms of categories. Importantly, our proposed measures are model training-agnostic, ensuring efficiency in their calculation. Through experiments on several benchmark datasets, including MNIST, CIFAR-10, CIFAR-100, and ImageNet, we demonstrate the efficacy of our measures in evaluating the effectiveness of various data augmentation methods. Furthermore, although the proposed measures are straightforward, they have the potential to guide the design and parameter tuning of data augmentation techniques and enable the validation of data augmentation methods' efficacy before embarking on large-scale model training. © 2023, The Authors. All rights reserved.

关键词： Image classification

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COUNTING RANDOM k-SAT NEAR THE SATISFIABILITY THRESHOLD

arXiv

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arXiv 2024年

作者： Chen, Zongchen Lonkar, Aditya Wang, Chunyang Yang, Kuan Yin, Yitong School of Computer Science Georgia Institute of Technology AtlantaGA United States State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University 163 Xianlin Avenue Jiangsu Province Nanjing210023 China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China

We present efficient counting and sampling algorithms for random k-SAT when the clause density satisfies α ≤ 2k poly(k) . In particular, the exponential term 2k matches the satisfiability threshold Θ(2k) for the existence of a solution and the (conjectured) algorithmic threshold 2k (ln k)/k for efficiently finding a solution. Previously, the best-known counting and sampling algorithms required far more restricted densities α 2k/3 [HWY23]. Notably, our result goes beyond the lower bound d 2k/2 for worst-case k-SAT with bounded-degree d [BGG+19], showing that for counting and sampling, the average-case random k-SAT model is computationally much easier than the worst-case model. At the heart of our approach is a new refined analysis of the recent novel coupling procedure by [WY24], utilizing the structural properties of random constraint satisfaction problems (CSPs). Crucially, our analysis avoids reliance on the 2-tree structure used in prior works, which cannot extend beyond the worst-case threshold 2k/2. Instead, we employ a witness tree similar to that used in the analysis of the Moser-Tardos algorithm [MT10] for the Lovász Local lemma, which may be of independent interest. Our new analysis provides a universal framework for efficient counting and sampling for random atomic CSPs, including, for example, random hypergraph colorings. At the same time, it immediately implies as corollaries several structural and probabilistic properties of random CSPs that have been widely studied but rarely justified, including replica symmetry and non-reconstruction. Copyright © 2024, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Advmask: A Sparse Adversarial Attack-Based Data Augmentation Method for Image Classification

SSRN

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SSRN 2023年

作者： Yang, Suorong Li, Jinqiao Zhang, Tianyue Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Data augmentation has been an essential technique for improving the generalization ability of deep neural networks in image classification ***, intensive changes in appearance and different degrees of occlusion in images are the key factors that severely affect the generalization ability of image classification models. Therefore, in order to enhance the generalization performance and robustness of deep models, data augmentation approaches by providing models with more diverse training data in various scenarios are widely applied. Although many existing data augmentation methods simulate occlusion in the augmented images to enhance the generalization of models, these methods randomly delete some areas in images without considering the semantic information of images. In this work, we propose a novel data augmentation method named AdvMask for image classification based on sparse adversarial attack techniques. AdvMask first identifies the key points that have the greatest influence on the classification results via a proposed end-to-end sparse adversarial attack module. During the data augmentation process, AdvMask efficiently generates diverse augmented data with structured occlusions based on the key points. By doing so, AdvMask can force deep models to seek other relevant content while the most discriminative content is hidden. Extensive experimental results on various benchmark datasets and deep models demonstrate that our proposed method can effectively improve the generalization performance of deep models and significantly outperforms previous data augmentation methods. © 2023, The Authors. All rights reserved.

关键词： Image classification

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PathLAD+: Towards effective exact methods for subgraph isomorphism problem

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Artificial Intelligence 2024年 337卷

作者： Wang, Yiyuan Jin, Chenghou Cai, Shaowei School of Computer Science and Information Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Fudan University Shanghai China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China

The subgraph isomorphism problem (SIP) is a challenging problem with wide practical applications. In the last decade, despite being a theoretical hard problem, researchers designed various algorithms for solving SIP. In this work, we propose five main strategies and develop an improved exact algorithm for SIP. First, we design a probing search procedure to try whether the search procedure can successfully obtain a solution at first sight. Second, we design a novel matching ordering strategy as a value-ordering heuristic, which uses some useful information obtained from the probing search procedure to preferentially select some promising target vertices. Third, we discuss the characteristics of different propagation methods in the context of SIP and present an adaptive propagation method to make a good balance between these methods. Moreover, to further improve the performance of solving large graphs, we propose an enhanced implementation of the edge constraint method and a domain limitation strategy, which aims to accelerate the search process. Experimental results on a broad range of classic and graph-database benchmarks show that our proposed algorithm performs better than several state-of-the-art algorithms for the SIP. © 2024 Elsevier B.V.

关键词： Graph algorithms

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

arXiv

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arXiv 2023年

作者： Tang, Ze Ge, Jidong Liu, Shangqing Zhu, Tingwei Xu, Tongtong Huang, Liguo Luo, Bin National Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore Huawei Software Engineering Application Technology China Department of Computer Science Southern Methodist University United States

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose kNM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that kNM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that kNM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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ML-based Privacy Leakage Behavior Detection in Android Apps at Scale 19

ML-based Privacy Leakage Behavior Detection in Android Apps ...

引用

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Bu, ZhiLiang Zhao, Chunlei Gong, Liangyi Wang, Yan Yang, Yi Wang, Xi Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin China Tianjin University of Technology School of Computer Science and Engineering Tianjin China Computer Network Information Center Chinese Academy of Sciences Beijing China Southeast University School of Cyber Science and Engineering Nanjing China

ISBN: (纸本)9798350346558

In recent years, Android application privacy leaking issues frequently occur, with the results that privacy leakage detection becomes a critical role in app market security review, and numerous mobile apps have been removed from the app stores as a consequence. At the same time, manual review is still considered as a main method for market security audit, but it would cause low efficiency and high false positive when faced with thousands of auditing tasks. To overcome the above problems, this paper implements a large volume, automated detection of privacy leaks in Android applications via machine learning algorithms. In order to achieve a comprehensive and in-depth analysis of application behavior features, firstly, 2346 benign software from app market and 1912 malware identified by a national regulatory authority as leaking privacy in the past two years were selected and studied using a hybrid static-dynamic analysis. Then by developing novel sensitivity criteria, we opt privacy-sensitive key permissions and API calls that are highly relevant to privacy leakage behavior. Moreover, in order to improve the practicability of our system, this thesis systematically optimize the application exerciser on the Android emulator, and achieve average per-app analysis time with less than 2 minutes. Finally, the results of the comparison experiments reveal that the stacking ensemble learning algorithm displayed the best classification performance and achieved 97.81% in accuracy of privacy leakage behavior detection. © 2022 IEEE.

关键词： Application programs

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MRM: Site Selection via Mutil-round Meanshift for Global Fairness

MRM: Site Selection via Mutil-round Meanshift for Global Fai...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yu-xin Ge Hong-tao Luo Chen-xuan Fang Zhi-wei Zhu Chong-jun Wang Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University Nanjing University Nanjing China

Elevator maintenance plays an important role in the safety and economic aspects of daily life, and the location of elevator maintenance sites is crucial in this scenario. A reasonable location can help reduce economic expenses and ensure the healthy operation of the system of maintenance sites. However, due to the lack of candidate location sets and unevenly distributed data layers, existing coverage siting methods have difficulties and challenges in solving this problem, and ignore the fairness of load among different sites. To address above problems, We propose a heuristic coverage siting method called MRM, which is based on multi-round MeanShift clustering with coverage radius variation. Experiments on a real elevator dataset with noise show that MRM not only achieves 99.21 % coverage with fewer sites but also achieves fairly good global fairness.

关键词： Economics Distributed databases Maintenance engineering Elevators Safety Artificial intelligence

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