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Are You Still Working on This? An Empirical Study on Pull Request Abandonment

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第6期48卷 2173-2188页

作者： Li, Zhixing Yu, Yue Wang, Tao Yin, Gang Li, ShanShan Wang, Huaimin Natl Univ Def Technol Coll Comp Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China

The great success of numerous community-based open source software (OSS) is based on volunteers continuously submitting contributions, but ensuring sustainability is a persistent challenge in OSS communities. Although the motivations behind and barriers to OSS contributors' joining and retention have been extensively studied, the impacts of, reasons for and solutions to contribution abandonment at the individual level have not been well studied, especially for pull-based development. To bridge this gap, we present an empirical study on pull request abandonment based on a sizable dataset. We manually examine 321 abandoned pull requests on GitHub and then quantify the manual observations by surveying 710 OSS developers. We find that while the lack of integrators' responsiveness and the lack of contributors' time and interest remain the main reasons that deter contributors from participation, limitations during the processes of patch updating and consensus reaching can also cause abandonment. We also show the significant impacts of pull request abandonment on project management and maintenance. Moreover, we elucidate the strategies used by project integrators to cope with abandoned pull requests and highlight the need for a practical handover mechanism. We discuss the actionable suggestions and implications for OSS practitioners and tool builders, which can help to upgrade the infrastructure and optimize the mechanisms of OSS communities.

关键词： Tools Collaboration Sustainable development Open source software Manuals Maintenance engineering Computer bugs Pull request abandonment pull-based development open source software

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Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification 32

Meta-Tsallis-Entropy Minimization: A New Self-Training Appro...

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32nd International Joint Conference on Artificial Intelligence (IJCAI)

作者： Lu, Menglong Huang, Zhen Tian, Zhiliang Zhao, Yunxiang Fei, Xuanyu Li, Dongsheng Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Beijing Inst Biotechnol Beijing Peoples R China Suiren Informat Nanjing Peoples R China

ISBN: (纸本)9781956792034

Text classification is a fundamental task for natural language processing, and adapting text classification models across domains has broad applications. Self-training generates pseudo-examples from the model's predictions and iteratively train on the pseudo-examples, i.e., mininizes the loss on the source domain and the Gibbs entropy on the target domain. However, Gibbs entropy is sensitive to prediction errors, and thus, self-training tends to fail when the domain shift is large. In this paper, we propose Meta-Tsallis Entropy minimization (MTEM), which applies meta-learning algorithm to optimize the instance adaptive Tsallis entropy on the target domain. To reduce the computation cost of MTEM, we propose an approximation technique to approximate the Second-order derivation involved in the meta-learning. To efficiently generate pseudo labels, we propose an annealing sampling mechanism for exploring the model's prediction probability. Theoretically, we prove the convergence of the meta-learning algorithm in MTEM and analyze the effectiveness of MTEM in achieving domain adaptation. Experimentally, MTEM improves the adaptation performance of BERT with an average of 4 percent on the benchmark dataset.

关键词： Learning algorithms

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QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT REINFORCEMENT LEARNING VALUE FUNCTION FACTORIZATION 47

QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT ...

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47th IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Shen, Siqi Liu, Jun Qiu, Mengwei Liu, Weiquan Wang, Cheng Fu, Yongquan Wang, Qinglin Qiao, Peng Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781665405409

The Centralized Training with Decentralized Execution paradigm (CTDE), which trains policies centrally with additional information, is important for Multi-Agent Reinforcement Learning (MARL). For CTDE, value function factorization methods make use of state during training and factorize the value function into multiple local value functions for decentralized execution. These approaches do not fully consider the relational information among agents, resulting in sub-optimal models for complex tasks. To remedy this issue, we propose QRelation which is a graph neural network approach for value function factorization. It considers both the static relations (e.g., agent types) and dynamic relations (e.g., close-by). We show that QRelation can obtain better results than state-of-the-art methods on challenging StarCraft II benchmarks.

关键词： Training Conferences Reinforcement learning Benchmark testing Signal processing Graph neural networks Acoustics

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Three-Dimensional Tradeoffs for Consensus Algorithms: A Review

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第2期19卷 1216-1228页

作者： Li, Linhui Shi, Peichang Fu, Xiang Chen, Peng Zhong, Tao Kong, Jinzhu Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China Kylin Software Co Ltd Changsha 410073 Peoples R China

Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical constraints that limit the large-scale application of blockchain: scalability, security, and decentralization cannot be achieved altogether. Consensus algorithm is the core of blockchain, which determines the performance of blockchain system to a certain extent. The existing reviews or surveys mainly focus on processes of consensus algorithms, but fall short in covering the current trends and scenarios, thereby lacking intrinsic understanding of their design philosophy. In this paper, we propose a multi-dimensional tradeoff model and unearth various indicators of different dimensions to guide the construction of consensus algorithms. To summarize the existing efforts, we compare and analyze various classical consensus algorithms, and focus on the design principles of these algorithms under the multi-dimensional tradeoff model. According to different requirements, each algorithm has different tradeoffs. Furthermore, we provide different solutions for blockchain in different dimensions. Finally, we summarize the development trend of consensus design and the key technology prospects of blockchain. This is, to the best of our knowledge, the first survey that accomplishes such goals.

关键词： Blockchains Security Consensus algorithm Scalability Throughput Bitcoin Technological innovation Scalability decentralization security consensus

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Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs

Accelerate Graph Neural Network Training by Reusing Batch Da...

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40th IEEE International Performance, Computing, and Communications Conference (IPCCC)

作者： Ran, Zhejiang Lai, Zhiquan Zhang, Lizhi Li, Dongsheng Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781665443319

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes' data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79x over the state-of-the-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.

关键词： Graph Neural Network Data Transmission Reusing Batch Data GPU Resources

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IoUformer: Pseudo-IoU prediction with transformer for visual tracking

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NEURAL NETWORKS 2024年 170卷 548-563页

作者： Cai, Huayue Lan, Long Zhang, Jing Zhang, Xiang Zhan, Yibing Luo, Zhigang Natl Univ Def Technol Inst Quantum Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Univ Sydney Sch Comp Sci Sydney Australia JD Explore Acad Beijing Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

Siamese tracking has witnessed tremendous progress in tracking paradigm. However, its default box estimation pipeline still faces a crucial inconsistency issue, namely, the bounding box decided by its classification score is not always best overlapped with the ground truth, thus harming performance. To this end, we explore a novel simple tracking paradigm based on the intersection over union (IoU) value prediction. To first bypass this inconsistency issue, we propose a concise target state predictor termed IoUformer, which instead of default box estimation pipeline directly predicts the IoU values related to tracking performance metrics. In detail, it extends the long-range dependency modeling ability of transformer to jointly grasp target-aware interactions between target template and search region, and search sub-region interactions, thus neatly unifying global semantic interaction and target state prediction. Thanks to this joint strength, IoUformer can predict reliable IoU values near-linear with the ground truth, which paves a safe way for our new IoU-based siamese tracking paradigm. Since it is non-trivial to explore this paradigm with pleased efficacy and portability, we offer the respective network components and two alternative localization ways. Experimental results show that our IoUformer-based tracker achieves promising results with less training data. For its applicability, it still serves as a refinement module to consistently boost existing advanced trackers.

关键词： Visual tracking Transformer Siamese network IoU prediction

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Advancing RFID Technology for Virtual Boundary Detection

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3407-3422页

作者： Li, Xiaoyu Liu, Jia Lin, Zihao Liu, Xuan Wang, Yanyan Zhang, Shigeng Ye, Baoliu Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 210024 Peoples R China Cent South Univ Sch Comp Sci & Engn Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410017 Peoples R China

A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.

关键词： Image edge detection Robots Radiofrequency identification Calibration Mobile computing Radio frequency Robot sensing systems Location awareness Interference Wire Boundary detection Internet of Things RFID virtual boundary

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HMA: An Efficient Training Method for NLP Models 2021

HMA: An Efficient Training Method for NLP Models

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5th International Conference on Innovation in Artificial Intelligence (ICIAI)

作者： Yang, Yuetong Lai, Zhiquan Cai, Lei Li, Dongsheng Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9781450388634

Nowadays, deep learning has been widely used for solving natural language processing (NLP) problems. Embedding matrices are common-used in the NLP deep models for automatical feature learning. However, the sparsity of embedding matrices makes it challenging to efficiently train the NLP models in data parallelism. When training with synchronous optimization methods, the aggregation on sparse gradients brings high communication cost and low scalability for distributed training. In this paper, we combine Model Average (MA) and synchronous optimization methods together, and propose HMA, a hybrid training method for NLP deep models. Furthermore, we implement HMA method in Horovod+TensorFlow training framework and conduct experimental evaluation with representative NLP models. For NLP models with a large number of sparse parameters, HMA saves over 30% wall-clock time compared with the state-of-the-art distributed training framework, while maintaining the same final training loss.

关键词： distributed training deep learning model average local sgd

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Efficient Data Redistribution Algorithms From Irregular to Block Cyclic Data Distribution

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第12期33卷 3667-3677页

作者： Li, Shengguo Jiang, Hao Dong, Dezun Huang, Chun Liu, Jie Liao, Xia Chen, Xuguang Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Sci & Technol Parallel & Distributed Proc Lab Lab Software Engn Complex Syst Changsha 410073 Peoples R China

In this paper, we propose some efficient data redistribution algorithms for redistributing matrices from 1D or 2D irregular format to block cyclic data distribution (BCDD) format, which can be much faster than the BLACS routine PXGEMR2D. These algorithms can be used to combine direct methods with iterative methods. The proposed algorithms divide the communication into two phases: one for processes in the same column and the other for processes in the same row, and the whole data redistribution task is divided into several independent sub-communications. The communication time can be reduced a lot compared with BLACS. Performance results show that our algorithms can be 2x-5x faster than the BLACS routine PXGEMR2D when using 4096 processes and the experiments are performed on Tianhe-2A supercomputer.

关键词： BLACS data redistribution irregular data scalapack BCDD exascale computing

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GLOBAL-LOCALIZED AGENT GRAPH CONVOLUTION FOR MULTI-AGENT REINFORCEMENT LEARNING

GLOBAL-LOCALIZED AGENT GRAPH CONVOLUTION FOR MULTI-AGENT REI...

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IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Liu, Yuntao Dou, Yong Shen, Siqi Qiao, Peng Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781728176055

A lot of efforts have been devoted to solving the problem about complex relationship and localized cooperation among a large number of agents in large-scale multi-agent systems. However, global cooperation among all agents is also important while interactions between agents often happen locally. It is a challenging problem to enable agent to learn global and localized cooperate information simultaneously in multi-agent systems. In this paper, we model the global and localized cooperation among agents by global and localized agent graphs and propose a novel graph convolutional reinforcement learning mechanism based on these two graphs which allows each agent to communicate with neighbors and all agents to cooperate at the high level. Experiments on the largescale multi-agent scenarios in StarCraft II show that our proposed method gets better performance compared with state-of-the-art algorithms and allows agents learning to cooperate efficiently.

关键词： Machine Learning Reinforcement Learning Multi-agent System Graph Convolutional Network StarCraft II

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