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FENSE: A feature-based ensemble modeling approach to cross-project just-in-time defect prediction

EMPIRICAL software ENGINEERING 2022年第7期27卷 162-162页

作者： Zhang, Tanghaoran Yu, Yue Mao, Xinjun Lu, Yao Li, Zhixing Wang, Huaimin Natl Univ Def Technol Key Lab Software Engn Complex Syst Changsha Hunan Peoples R China Natl Univ Def Technol Key Lab Parallel & Distributed Comp Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

Context: Just-in-time defect prediction (JITDP) leverages modern machine learning models to predict the defect-proneness of commits. Such models require adequate training data, which is unavailable in projects with short histories. To address this problem, cross-project methods reuse the data or models in other projects to make predictions, grounded on the assumption that they share similar defect-related features. However, these features are overlooked, which leads to unsatisfying model performance. Objective: This study aims to investigate the relationship between cross-project JITDP performances and project features, thereby improving the performance of cross-project models. Method: We propose a F eature-based ENSE mble modeling approach (FENSE) to cross-project JITDP. For a target project, FENSE pairs it to each source project and obtains 20 features. Leveraging them, it can predict the transferability of each off-the-shelf JITDP model. Then FENSE identifies the most transferable ones and combines them to make cross-project predictions. To achieve this, we conduct a large-scale empirical study of 113,906 project pairs in GitHub and investigate the impact of project features. Results: The results show that: (1) cross-project transferability is highly related to features including programming language and the defect ratio of the source project;(2) our feature-based model selection scheme can improve the cross-project JITDP performance by 10%;(3) FENSE outperforms other models on five evaluation measures without extra time and space costs. Conclusions: Our study suggests that project features can help identify powerful cross-project JITDP models and improve the performance of ensemble approaches.

关键词： Just-in-time defect prediction Cross project Ensemble learning Model selection software project feature

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The Inherent Mechanism and a Case Study of the Constructional Evolution of the JointCloud Ecosystem

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IEEE INTERNET OF THINGS JOURNAL 2020年第3期7卷 1561-1571页

作者： Shi, Peichang Liu, Hui Yang, Shangzhi Zhang, Yiqi Zhong, Yaoqi 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

There have been dramatic increases in industrial investment and growing research interests in Internet of Things over the past few years. Driven by the application demand from Internet of everything in the future, the collaboration between traditional cloud and edge cloud is already on the way to provide better service to diverse users. However, due to the high marginal operation and maintenance costs, there remains a need for collaboration between traditional clouds. JointCloud computing is a new computing paradigm that supports collaboration within heterogeneous clouds, which is a complex problem that is difficult to manage using the traditional methodology of "top-down, decomposition, and reduction" in the benign construction of the JointCloud ecosystem. In this article, we first describe the inherent mechanism behind the constructional evolution of the JointCloud ecosystem, which details the inner workings of its elements and the general principals of how these various elements influence each other. We then propose a series of forward-looking, feasible operational methods to support the constructional evolution of the JointCloud ecosystem that incorporate efficient development methods, data-driven operations, AI-assisted decision making and so on. Finally, a case study of a cross-cloud migration deployment is used to illustrate the scientific basis of the systems inherent mechanism, as well as the efficacy of the supporting methods.

关键词： Cloud computing Ecosystems Internet of Things Collaboration Complex systems software systems BlockChain cloud computing constructional evolution edge cloud Internet of Things (IoT) JointCloud ecosystem

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Evaluation of Single-Node Performance of parallel Algorithms for Multigroup Monte Carlo Particle Transport Methods

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Ma, Donghui Yang, Bo Zhang, Qingyang Liu, Jie Li, Tiejun Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China

Monte Carlo (MC) methods have been widely used to solve the particle transport equation due to their high accuracy and capability of processing complex geometries. History-based and event-based algorithms that are applicable to different architectures are two methods for parallelizing the MC code. There is a large work on evaluating and optimizing parallel algorithms with continuous-energy schemes. In this work, we evaluate the single-node performance of history-based and event-based algorithms for multigroup MC methods on both CPUs and GPUs with Quicksilver, a multigroup MC transport code that has already implemented the history-based algorithms. We first implement and optimize the event-based algorithm based on Quicksilver and then perform the evaluation work extensively on the Coral2 benchmark. Numerical results indicate that contrary to continuous-energy schemes, the history-based approach with multigroup schemes outperforms the event-based algorithm on both architectures in all cases. We summarize that the performance loss of the event-based algorithm is mainly due to: 1) extra operations to reorganize particles, 2) batched atomic operations, and 3) poor particle data locality. Despite the poor performance, the event-based algorithm achieves higher memory bandwidth utilization. We further discuss the impact of memory access patterns and calculation of cross sections (xs) on the performance of the GPU. Built on the analytics, and shed light on the algorithm choice and optimizations for paralleling the MC transport code on different architectures.

关键词： parallel computing performance evaluation history-based event-based particle transport

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Large-Scale Heterogeneous Computing for 3D Deterministic Particle Transport on Tianhe-2A Supercomputer

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Li, Biao Liu, Jie Zhu, Xiaoxiong Ding, Shengjie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China

Scalable parallel algorithm for particle transport is one of the main application fields in high-performance computing. Discrete ordinate method (S-n) is one of the most popular deterministic numerical methods for solving particle transport equations. In this paper, we introduce a new method of large-scale heterogeneous computing of one energy group time-independent deterministic discrete ordinates neutron transport in 3D Cartesian geometry (Sweep3D) on Tianhe-2A supercomputer. In heterogeneous programming, we use customized Basic Communication Library (BCL) and Accelerated Computing Library (ACL) to control and communicate between CPU and the Matrix2000 accelerator. We use OpenMP instructions to exploit the parallelism of threads based on Matrix 2000. The test results show that the optimization of applying OpenMP on particle transport algorithm modified by our method can get 11.3 times acceleration at most. On Tianhe-2A supercomputer, the parallel efficiency of 1.01 million cores compared with 170 thousand cores is 52%.

关键词： heterogeneous parallel algorithm HPC openmp particle transport SN method

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Document-level relation extraction with three channels

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KNOWLEDGE-BASED SYSTEMS 2024年 284卷

作者： Zhang, Zhanjun Zhao, Shan Zhang, Haoyu Wan, Qian Liu, Jie Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China Def Innovat Inst Beijing Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Natl Univ Def Technol 109 Deya Rd Changsha Peoples R China

Relation extraction aims to identify the relation between two entities. Early works on relation extraction are performed within a sentence. Numerous entity pairs with relations are distributed across sentences, so the task of document-level relation extraction is proposed. The current works perform well on document-level relation extraction through graph neural network, transformer, etc. However, these works extract relation features from one perspective, resulting in limited extraction performance of the model. To break through the limitation, this paper proposes the DRETC model consisting of three channels. Channel one first constructs the mention matrix and then extracts the relation features between entities. Channel two performs sequence inference from mention to entity before generating the relation matrix. Moreover, the bridge method is proposed to introduce information in channel one that is beneficial to relation extraction. Channel three directly generates entity representations through text sequences to make up for the absence of text information related to the entity. The three channels extract relation features from three different perspectives, and the combination leads to better extraction performance. Experimental results show that the proposed DRETC model outperforms previous strong baseline models with one channel on the public dataset DocRED. Further analysis demonstrates that the combination of multiple channels has a better relation extraction performance than a single channel.

关键词： Document-level relation extraction Three channels Relation features Relation matrix

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LELNER: A Lightweight and Effective Low-resource Named Entity Recognition model

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KNOWLEDGE-BASED SYSTEMS 2022年 251卷

作者： Zhang, Zhanjun Zhang, Haoyu Wan, Qian Liu, Jie Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China Def Innovat Inst Beijing Peoples R China

Named entity recognition aims to find the target entity from the input sentence and determine the category it belongs to. Low-resource means that the training data used by the model is scarce. In order to improve the performance of the model with a small quantity of labeled data, previous works propose the concept of the trigger and introduce trigger information. Despite their excellent performance, these methods still have shortcomings in trigger representation generation, information fusion and model training. To remedy these deficiencies, this paper proposes the LELNER model including information interaction module and information fusion network. The information interaction module realizes the interaction between trigger and sentence, which leads to trigger representation containing more entity information. The information fusion network perfectly merges the trigger representation into the sentence sequence. As a result, the network has a better fusion effect than previous nonlinear fusion methods. In terms of model training, this paper designs a one-step training method to replace the two-step training method used in previous models, which provides convenience for the entire training process. Experimental results show that the proposed LELNER model achieves state-of-the-art results on three public datasets BC5CDR, CONLL and SemEval-lap.(1) (C)2022 Elsevier B.V. All rights reserved.

关键词： Named entity recognition Information interaction Information fusion One-step training

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Three-Dimensional MT Conductive Anisotropic and Magnetic Modeling Using A - φ Potentials Employing a Mixed Nodal and Edge-Based Element Method

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APPLIED SCIENCES-BASEL 2024年第19期14卷 9019页

作者： Zhou, Zongyi Yi, Mingkuan Zhou, Junjun Cheng, Lianzheng Song, Tao Gong, Chunye Yang, Bo Xiao, Tiaojie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Henan Polytech Univ Dept Phys & Elect Informat Jiaozuo 454000 Peoples R China Kunming Univ Sch Math Kunming 650500 Peoples R China Guiyang Univ Coll Math & Informat Sci Guiyang 550005 Peoples R China Natl Supercomp Ctr Tianjin Tianjin 300450 Peoples R China

Magnetotelluric (MT) sounding is a geophysical technique widely utilized in mineral resource surveys, where conductivity and magnetic permeability serve as essential physical parameters for forward modeling and inversion. However, the effects of conductive anisotropy and non-zero magnetic susceptibility are usually ignored. In this study, we present a three-dimensional (3D) MT modeling algorithm using Coulomb-gauged electromagnetic potentials, incorporating a mixed nodal and edge-based finite element method capable of simulating MT responses for conductive anisotropic and magnetic anomalies. Subsequently, the algorithm's accuracy was validated in two steps: first, it was compared with analytical solutions for a 1D magnetic model;then, a comparison was made with previously published numerical results for a 3D generalized conductive anisotropic model. The results of two tests show that the maximum relative error is below 0.5% for both models. Furthermore, representative models were computed to comprehensively analyze the responses of MT. The findings illustrate the relationship between anisotropic parameters and electric fields and emphasize the significance of considering the impact of magnetic susceptibility in magnetite-rich regions.

关键词： magnetotelluric anisotropy three-dimensional magnetic forward modeling finite element method Coulomb-gauged potentials

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hongzhou Wu Yifan Lyu Xingyu Shen Xuechen Zhao Mengzhu Wang Xiang Zhang Zhigang Luo Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Computer National University of Defense Technology Changsha China Institute for Quantum Information and the State Key Lab of High Performance Computing National University of Defense Technology Changsha China

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

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You Ought to Look Around: Precise, Large Span Action Detection

You Ought to Look Around: Precise, Large Span Action Detecti...

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International Conference on Pattern Recognition

作者： Ge Pan Han Zhang Fan Yu Yonghong Song Yuanlin Zhang Han Yuan School of Software Engineering Xi'an Jiaotong University Xi'an China Distributed and parallel software lab Huawei Technologies Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

For the action localization task, pre-defined action anchors are the cornerstone of mainstream techniques. State-of-the-art models mostly rely on a dense segmenting scheme, where anchors are sampled uniformly over the temporal domain with a predefined set of scales. However, it is not sufficient because action duration varies greatly. Therefore, it is necessary for the anchors or proposals to have a variable receptive field. In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features. In this part, we employ a stronger backbone I3D with SPN (Segment Pyramid Network) instead of C3D to obtain multi-scale features; 2) a simple but useful feature fusion module named LFE (Local Feature Extraction). Compared with the fully connected layer and global average pooling, our LFE model is more advantageous for network to fit and fuse features. 3) a new feature segment aligning method called TPGC (Two Pathway Graph Convolution), which allows one proposal to leverage semantic features of adjacent proposals to update its content and make sure the proposals have a variable receptive field. YOLA add only a small overhead to the baseline network, and is easy to train in an end-to-end manner, running at a speed of 1097 fps. YOLA achieves a mAP of 58.3%, outperforming all existing models including both RGB-based and two stream on THUMOS'14, and achieves competitive results on ActivityNet 1.3.

关键词： Location awareness Fuses Convolution Semantics Feature extraction Pattern recognition Proposals

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A self-attention based neural architecture for Chinese medical named entity recognition

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第4期17卷 3498-3511页

作者： Wan, Qian Liu, Jie Wei, Luona Ji, Bin Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

The combination of medical field and big data has led to an explosive growth in the volume of electronic medical records (EMRs), in which the information contained has guiding significance for diagnosis. And how to extract these information from EMRs has become a hot research topic. In this paper, we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records (CEMRs). Firstly, a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs. Then we use the encoder from Transformer (ET) as our model's encoder to alleviate the long context dependency problem, and the CRF is utilized as the decoder. At last, we compare the BiLSTM-CRF and ET-CRF model with word2vec and ELMo embeddings to CEMRs respectively to validate the effectiveness of ELMo-ET-CRF model. With the same training data and test data, the ELMo-ET-CRF outperforms all the other mentioned model architectures in this paper with 85.59% F1 -score, which indicates the effectiveness of the proposed model architecture, and the performance is also competitive on the CCKS2019 leaderboard.

关键词： self-attention ELMo named entity recognition Chinese electronic medical records natural language processing

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