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Mining the Contributions Along the Lifecycles of Open-Source Projects 17th

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Mining the Contributions Along the Lifecycles of Open-Source...

16th National Conference on software and Applications, NASAC 2017 and 17th National Conference on software and Applications, NASAC 2018

作者： Zhou, Hang Xu, Lei Li, Yanhui State Key Laboratory for Novel Software Technology Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9789811503092

Recently the impact of developers’ behavior on the evolution of open-source software (OSS) has become a hot topic. When does the developer commit his/her code? Is there any regularity of the time distribution of commit along the lifecycles of open-source project? Will the change of the core member in a development team has an impact on software evolution process? We are quite interested in these above questions so we conducted an empirical study in this paper. We collect more than 50,000 commits from 6 open-source software in Github and design a formula to measure the contributor’s contribution value. We then take four major experiments to analyze some issues about inert intervals and the impact of the change of main contributors on software evolution. To make the result visible, we also design an automatic mining tool which can automatically mine the metadata from specified repository and make it graphically presented. Through the experiments we gained some interesting findings such as there is no inevitable statistical connection between a contributor’s inert interval and his contribution value, and main contributors’ change has a huge impact on the software evolution. We believe that these findings will have deeper research significance in the future. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Open source software

Revisiting smoothed online learning

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arXiv 2021年

作者： Zhang, Lijun Jiang, Wei Lu, Shiyin Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science The University of Iowa Iowa CityIA52242 United States

In this paper, we revisit the problem of smoothed online learning, in which the online learner suffers both a hitting cost and a switching cost, and target two performance metrics: competitive ratio and dynamic regret with switching cost. To bound the competitive ratio, we assume the hitting cost is known to the learner in each round, and investigate the simple idea of balancing the two costs by an optimization problem. Surprisingly, we find that minimizing the hitting cost alone is max(1, α2 )- competitive for α-polyhedral functions and 1 + λ4 -competitive for λ-quadratic growth functions, both of which improve state-of-the-art results significantly. Moreover, when the hitting cost is both convex and λ-quadratic growth, we reduce the competitive ratio to 1 + √2λ by minimizing the weighted sum of the hitting cost and the switching cost. To bound the dynamic regret with switching cost, we follow the standard setting of online convex optimization, in which the hitting cost is convex but hidden from the learner before making predictions. We modify Ader, an existing algorithm designed for dynamic regret, slightly to take into account the switching cost when measuring the performance. The proposed algorithm, named as Smoothed Ader, attains an optimal O(pT (1 + PT )) bound for dynamic regret with switching cost, where PT is the path-length of the comparator sequence. Furthermore, if the hitting cost is accessible in the beginning of each round, we obtain a similar guarantee without the bounded gradient condition, and establish an Ω(pT (1 + PT )) lower bound to confirm the optimality. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

Non-stationary Online Learning with Memory and Non-stochastic Control

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arXiv 2021年

作者： Zhao, Peng Yan, Yu-Hu Wang, Yu-Xiang Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science University of California Santa BarbaraCA93106 United States

We study the problem of Online Convex Optimization (OCO) with memory, which allows loss functions to depend on past decisions and thus captures temporal effects of learning problems. In this paper, we introduce dynamic policy regret as the performance measure to design algorithms robust to non-stationary environments, which competes algorithms' decisions with a sequence of changing comparators. We propose a novel algorithm for OCO with memory that provably enjoys an optimal dynamic policy regret in terms of time horizon, non-stationarity measure, and memory length. The key technical challenge is how to control the switching cost, the cumulative movements of player's decisions, which is neatly addressed by a novel switching-cost-aware online ensemble approach equipped with a new meta-base decomposition of dynamic policy regret and a careful design of meta-learner and base-learner that explicitly regularizes the switching cost. The results are further applied to tackle non-stationarity in online non-stochastic control (Agarwal et al., 2019), i.e., controlling a linear dynamical system with adversarial disturbance and convex cost functions. We derive a novel gradient-based controller with dynamic policy regret guarantees, which is the first controller provably competitive to a sequence of changing policies for online non-stochastic control. Copyright © 2021, The Authors. All rights reserved.

关键词： Controllers

Online convex optimization with continuous switching constraint

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arXiv 2021年

作者： Wang, Guanghui Wan, Yuanyu Yang, Tianbao Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science University of Iowa Iowa CityIA52242 United States

In many sequential decision making applications, the change of decision would bring an additional cost, such as the wear-and-tear cost associated with changing server status. To control the switching cost, we introduce the problem of online convex optimization with continuous switching constraint, where the goal is to achieve a small regret given a budget on the overall switching cost. We first investigate the hardness of the problem, and provide a lower bound of order Ω(√T) when the switching cost budget S = Ω(√T), and Ω(min{TS, T}) when S = O(√T), where T is the time horizon. The essential idea is to carefully design an adaptive adversary, who can adjust the loss function according to the cumulative switching cost of the player incurred so far based on the orthogonal technique. We then develop a simple gradient-based algorithm which enjoys the minimax optimal regret bound. Finally, we show that, for strongly convex functions, the regret bound can be improved to O(log T) for S = Ω(log T), and O(min{T/exp(S) + S, T}) for S = O(log T). Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Data Anonymous Authentication for BIoMT with Proxy Group Signature

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Journal of Cyber Security 2021年第4期3卷 207-216页

作者： Chaoyang Li Yalan Wang Gang Xu Xiubo Chen Xiangjun Xin Jian Li College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450001China Department of Computer Science University of SurreyGuildfordSurreyGU27XHUK School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Along with the increase of wearable medical device,the privacy leakage problem in the process of transmission between these edge medical *** blockchain-enabled Internet of Medical Things(BIoMT)has been developed to reform traditional centralized medical system in recent *** paper first introduces a data anonymous authentication model to protect user privacy and medical data in ***,a proxy group signature(PGS)scheme has been proposed based on lattice *** scheme can well satisfy the anonymous authentication demand for the proposed model,and provide anti-quantum attack security for BIoMT in the future general quantum computer ***,the security analysis shows this PGS scheme is secure against the dynamical-almost-full anonymous and *** efficiency comparison shows the proposed model and PGS scheme is more efficient and practical.

关键词： Blockchain-enabled Internet of Medical Things anonymous authentication proxy group signature medical data

LibFewShot: A Comprehensive Library for Few-shot Learning

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arXiv 2021年

作者： Li, Wenbin Wang, Ziyi Yang, Xuesong Dong, Chuanqi Tian, Pinzhuo Qin, Tiexin Huo, Jing Shi, Yinghuan Wang, Lei Gao, Yang Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University 210023 China The School of Computer Engineering and Science Shanghai University China The Department of Electrical Engineering City University of Hong Kong Hong Kong The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science University of Rochester United States

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or "tricks", such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, with respect to the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results confirm that such a mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to enter the area of few-shot learning but also elucidate the effects of nontrivial tricks to facilitate intrinsic research on few-shot learning. The source code is available from https://***/RL-VIG/LibFewShot. Copyright © 2021, The Authors. All rights reserved.

关键词： Image classification

Detecting Floating-point errors via atomic conditions

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Detecting Floating-point errors via atomic conditions

Proceedings of the ACM on Programming Languages

作者： Zou, Daming Zeng, Muhan Xiong, Yingfei Fu, Zhoulai Zhang, Lu Su, Zhendong Key Laboratory of High Confidence Software Technologies Peking University MoE China Department of Computer Science and Technology Peking University Beijing100871 China Department of Computer Science IT University of Copenhagen Rued Langgaards Vej 7 Copenhagen2300 Denmark Department of Computer Science ETH Zurich Universitatstrasse 6 Zurich8092 Switzerland

This paper tackles the important, difficult problem of detecting program inputs that trigger large floating-point errors in numerical code. It introduces a novel, principled dynamic analysis that leverages the mathematically rigorously analyzed condition numbers for atomic numerical operations, which we call atomic conditions, to effectively guide the search for large floating-point errors. Compared with existing approaches, our work based on atomic conditions has several distinctive benefits: (1) it does not rely on high-precision implementations to act as approximate oracles, which are difficult to obtain in general and computationally costly; and (2) atomic conditions provide accurate, modular search guidance. These benefits in combination lead to a highly effective approach that detects more significant errors in real-world code (e.g., widely-used numerical library functions) and achieves several orders of speedups over the state-of-the-art, thus making error analysis significantly more practical. We expect the methodology and principles behind our approach to benefit other floating-point program analysis tasks such as debugging, repair and synthesis. To facilitate the reproduction of our work, we have made our implementation, evaluation data and results publicly available on GitHub at https://***/FP-Analysis/atomic-condition. © 2020 Copyright held by the owner/author(s).

关键词： Digital arithmetic Atoms Cell proliferation Codes (symbols) Dynamic analysis Error analysis Number theory Program debugging Testing Atomic condition Condition numbers Effective approaches Floating point errors Floating point programs Numerical library Numerical operations state of the art

BASGD: Buffered asynchronous SGD for Byzantine learning

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arXiv 2020年

作者： Yang, Yi-Rui Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Distributed learning has become a hot research topic due to its wide application in cluster-based large-scale learning, federated learning, edge computing and so on. Most traditional distributed learning methods typically assume no failure or attack. However, many unexpected cases, such as communication failure and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with failure or attack, has recently attracted much attention. Most existing BL methods are synchronous, which are impractical in some applications due to heterogeneous or offline workers. In these cases, asynchronous BL (ABL) is usually preferred. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for ABL. To the best of our knowledge, BASGD is the first ABL method that can resist malicious attack without storing any instances on server. Compared with those methods which need to store instances on server, BASGD has a wider scope of application. BASGD is proved to be convergent, and be able to resist failure or attack. Empirical results show that BASGD significantly outperforms vanilla asynchronous stochastic gradient descent (ASGD) and other ABL baselines when there exists failure or attack on workers. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods