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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Renmin Wang Tao Wang Huaimin Wang Key Lab. of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161075

Code comment is one of the most effective ways to help programmers to understand the source code. High-quality comment decisions can not only cover the core code snippets in the software system but also avoid generating redundant code comments. However, in actual development, there is no uniform comment specification, and most of the comment decisions depend on personal experience and domain knowledge. This paper has learned a common comment decision specification from a large number of code comment examples to assist programmers in making appropriate comment decisions during code development. This paper proposes a method to extract the code structure from the context code of the current code line, and use the machine learning algorithm to determine the possibility that the current code needs to add comments. The proposed method was evaluated on 8 well-known open-source Python projects in GitHub and the experimental results show the feasibility and effectiveness of the method in some code types.

关键词： decision making feature extraction knowledge acquisition learning (artificial intelligence) public domain software software engineering system documentation program and system documentation open source software Information Extraction Comment Software engineering Codes Feature extraction Computer personnel Structural properties Software systems Machine learning algorithms decision making

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Predicting Programming Behavior in OSS Communities: A Case Study of NLP-based Approach

Predicting Programming Behavior in OSS Communities: A Case S...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Manyan Huo Yue Yu Zhixing Li Junsheng Chang Key Lab. of Parallel and Distributed Computing Lab. College of Computer National University of Defence Technology ChangSha China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

Prediction of developers' programming behaviors is an effective way to improve their development efficiency and optimize the organization of project modules and files. However, little research exists investigating on this direction. In order to address this knowledge gap, we proposed a NLP-based approach to predict the programming behaviors in OSS (Open Source Software) communities. The proposed approach i) embeds the historical programming behavior data of a project into a multi-dimensional vector space to capture the potential laws in the data, ii) forms an eigenvector matrix reflecting the semantic relationship of the development behavior data, and iii) predicts the next programming behavior of a specific developer based on the eigenvector matrix. Our experiments on five OSS projects show that the prediction accuracy rate of the proposed prediction approach can reach up to about 50%, indicating that it can summarize the development behavior data law and effectively predict the programming behavior of developers. Our work can provide valuable assistance for developers' programming and projects' maintenance in practice.

关键词： Training Semantics Programming Predictive models Prediction algorithms Data models Context modeling

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Towards MLOps: A Case Study of ML Pipeline Platform

Towards MLOps: A Case Study of ML Pipeline Platform

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yue Zhou Yue Yu Bo Ding Key Lab. of Parallel and Distributed Computing Lab. College of Computer National University of Defence Technology ChangSha China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

The development and deployment of machine learning (ML) applications differ significantly from traditional applications in many ways, which have led to an increasing need for efficient and reliable production of ML applications and supported infrastructures. Though platforms such as TensorFlow Extended (TFX), ModelOps, and Kubeflow have provided end-to-end lifecycle management for ML applications by orchestrating its phases into multistep ML pipelines, their performance is still uncertain. To address this, we built a functional ML platform with DevOps capability from existing continuous integration (CI) or continuous delivery (CD) tools and Kubeflow, constructed and ran ML pipelines to train models with different layers and hyperparameters while time and computing resources consumed were recorded. On this basis, we analyzed the time and resource consumption of each step in the ML pipeline, explored the consumption concerning the ML platform and computational models, and proposed potential performance bottlenecks such as GPU utilization. Our work provides a valuable reference for ML pipeline platform construction in practice.

关键词： Training Computational modeling Pipelines Graphics processing units Tools Data models Task analysis

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Messages from the Program Co-chairs: NAS 2009

Proceedings - 2009 IEEE International Conference on Networki...

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Proceedings - 2009 IEEE International Conference on Networking, Architecture, and Storage, NAS 2009 2009年 xii页

作者： Zhu, Yifeng Fang, Liang Hu, Weiwu University of Maine United States National Lab of Parallel and Distributed Processing China Chinese Academy of Science China

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A Comparative Study of parallel Prefix Adders in FPGA Implementation of EAC

A Comparative Study of Parallel Prefix Adders in FPGA Implem...

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Euromicro Symposium on Digital System Design

作者： Feng Liu Fariborz Fereydouni Forouzandeh Otmane Ait Mohamed Gang Chen Xiaoyu Song Qingping Tan Parallel Distributed Processing National Laboratory Hunan China National Lab of Parallel Distributed processing Hunan China ECE department Concordia University Montreal Quebec Canada Lingcore Lab Portland OR USA ECE department Portland State University Portland OR USA

Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and interconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.

关键词： Field programmable gate arrays Adders Microprocessors distributed processing Logic circuits Delay Testing Digital systems Design methodology Design optimization

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An efficient local address generation for the block-cyclic distribution 3

An efficient local address generation for the block-cyclic d...

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3rd International Conference on Algorithms and Architectures for parallel processing, ICA3PP 1997

作者： Kwon, Oh-Young Kim, Tae-Geun Han, Tack-Don Yang, Sung-Bong Kim, Shin-Dug Distributed Computing Lab. Systems Engineering Research Institute 1 Ueun-dong Yusong-gu Taejon305-333 Korea Republic of Parallel Processing System Laboratory Dept. of Computer Science Yonsei University Seoul120-749 Korea Republic of

ISBN: (纸本)0780342291

In order to generate local addresses for an array section A(l:h:s) with block-cyclic distribution, an efficient compiling method is required. In this paper, two local address generation methods for the block-cyclic distribution are presented. One is a simple local address generation method that is modified from the virtual-block scheme. The other is a linear-time ΔM table construction method. The array elements of A(l:h:s) to be accessed at run-time build up a family of lines. By using the equation of the lines, a ΔM table can be generated in O(k) time. Experimental results show that a simple local address generation method has poor performance but a linear-time ΔM table generation method is faster than other algorithms in ΔM table generation time and access time for 10,000 array elements. © 1997 IEEE.

关键词： Virtual addresses

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NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing

arXiv

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arXiv 2024年

作者： Gao, Fei Hu, Ming Xie, Zhiyu Shi, Peichang Xie, Xiaofei Yi, Guodong Wang, Huaimin National Key Lab. of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computing and Information Systems Singapore Management University Singapore Xiangjiang Lab Changsha China

With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising to break through the resource constraints caused by heterogeneous edge devices in the traditional Federated Learning (FL) paradigm. Specifically, with the protection from TEE, data owners can achieve efficient model training with high-performance AI services in the cloud. By providing additional FL services, cloud service providers can achieve collab.rative learning among data owners. However, FLaaS still faces three challenges, i.e., i) low training performance caused by heterogeneous data among data owners, ii) high communication overhead among different clouds (i.e., data centers), and iii) lack of efficient resource scheduling strategies to balance training time and cost. To address these challenges, this paper presents a novel asynchronous FL approach named NebulaFL for collab.rative model training among multiple clouds. To address data heterogeneity issues, NebulaFL adopts a version control-based asynchronous FL training scheme in each data center to balance training time among data owners. To reduce communication overhead, NebulaFL adopts a decentralized model rotation mechanism to achieve effective knowledge sharing among data centers. To balance training time and cost, NebulaFL integrates a reward-guided strategy for data owners selection and resource scheduling. The experimental results demonstrate that, compared to the state-of-the-art FL methods, NebulaFL can achieve up to 5.71% accuracy improvement. In addition, NebulaFL can reduce up to 50% communication overhead and 61.94% costs under a target accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information Technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the parallel Programming Principles and Practice Class of Huazhong University of Science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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FINT: FIELD-AWARE INTERACTION NEURAL NETWORK FOR CTR PREDICTION

arXiv

引用

arXiv 2021年

作者： Zhao, Zhishan Yang, Sen Liu, Guohui Feng, Dawei Xu, Kele iQIYI Inc. China National Key Lab of Parallel and Distributed Processing Changsha China National University of Defense Technology Changsha China

As a critical component for online advertising and marketing, click-through rate (CTR) prediction has drawn lots of attention from both industry and academia. Recently, deep learning has become the mainstream methodological choice for CTR. Despite sustainable efforts have been made, existing approaches still pose several challenges. On the one hand, high-order interaction between the features is under-explored. On the other hand, high-order interactions may neglect the semantic information from the low-order fields. In this paper, we proposed a novel prediction method, named FINT, that employs the Field-aware INTeraction layer which explicitly captures high-order feature interactions while retaining the low-order field information. To empirically investigate the effectiveness and robustness of the FINT, we perform extensive experiments on the three realistic databases: KDD2012, Criteo and Avazu. The obtained results demonstrate that the FINT can significantly improve the performance compared to the existing methods, without increasing the amount of computation required. Moreover, the proposed method brought about 2.72% increase to the advertising revenue of iQIYI, a big online video app through A/B testing. To better promote the research in CTR field, we released our code as well as reference implementation at: https://***/zhishan01/FINT. © 2021, CC BY.

关键词： Deep learning

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An enhanced smart copying (ESC) method for the reconstruction of missing audio packets 1

An enhanced smart copying (ESC) method for the reconstructio...

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1st International Conference on Communications, Circuits and Systems, ICCCAS 2002

作者： Ng, Wei Keng Absar, J. George, S. Lau, C.T. Premkumar, B. Parallel and Distributed Processing Lab. School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore639798 Singapore ST Microelectronics Asia Pacific Pte. Ltd. Telectech Park Science Park 2 20 Science Park Road #01-28/30 Singapore117674 Singapore

ISBN: (纸本)0780375475

The paper describes an improved statistical approach for the reconstruction of missing audio packets. We demonstrate that the proposed enhanced smart copying method can successfully reconstruct lost packets with a much reduced computational time (approximately 21 times faster), while giving better quality as compared to the existing scheme. This efficiency is achieved by avoiding the direct inversion of a large matrix and requires no explicit solution for the coefficients of the underlying autoregressive model. © 2002 IEEE.

关键词： Signal reconstruction

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