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International Conference on software Maintenance (ICSM)

作者： Yao Lu Xinjun Mao Minghui Zhou Yang Zhang Tao Wang Zude Li College of Computer Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China School of Electronics Engineering and Computer Science Peking University Beijing China School of Information Science and Engineering Central South University Changsha China

ISBN: (数字)9781728156194

ISBN: (纸本)9781728156200

Modern programming question & answer (Q&A) sites such as Stack Overflow (SO) employ gamified mechanisms to stimulate volunteers' contributions. To maximize the chances of winning gamification rewards such as reputation and badges, a portion of users race to post answers as quickly as possible (i.e., fast answers or FAs), which makes SO the fastest Q&A site; however, this behavior may affect the contribution quality as well. In this paper, we report on a large-scale, mixed-methods empirical study of the gamification-influenced FA phenomenon in SO. We first quantitatively investigate the popularity of the phenomenon and user behaviors regarding FAs. Then, we study the quality of FAs by using regression modeling and qualitatively analyzing 300 instances of FAs. Our main findings reveal that more than 70% and 90% of FAs are not edited by the answerers and other users, respectively, and that later incoming answers have lower chances of being voted on and accepted. Notably, we find that the answer length, code snippets length, and readability of FAs are significantly lower than those of non-fast answers. Although FAs have higher crowd assessment scores, they have no relationship with acceptance from the perspective of asker assessment, and a considerable portion of FAs solve the problem by interacting with the asker in the comments. These results help us better understand the effects of reward-based gamification on crowdsourced software engineering communitites and provide implications for designers of gamified systems.

关键词： software maintenance Analytical models Conferences Programming software engineering

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Consensus-Based Distributed Filtering with Fusion Step Analysis

arXiv

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arXiv 2021年

作者： Qian, Jiachen Duan, Peihu Duan, Zhisheng Chen, Guanrong Shi, Ling State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering. However, due to the limited communication resources in physical systems, the number of fusion steps cannot be infinite. To deal with this issue, the present paper analyzes the performance of CMDF with finite consensus fusion operations. First, by introducing a modified discrete-time algebraic Riccati equation and several novel techniques, the convergence of the estimation error covariance matrix of each sensor is guaranteed under a collective observability condition. In particular, the steady-state covariance matrix can be simplified as the solution to a discrete-time Lyapunov equation. Moreover, the performance degradation induced by reduced fusion frequency is obtained in closed form, which establishes an analytical relation between the performance of the CMDF with finite fusion steps and that of centralized filtering. Meanwhile, it provides a trade-off between the filtering performance and the communication cost. Furthermore, it is shown that the steady-state estimation error covariance matrix exponentially converges to the centralized optimal steady-state matrix with fusion operations tending to infinity during each sampling interval. Finally, the theoretical results are verified with illustrative numerical experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Information filtering

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Predicting extreme events from data using deep machine learning: When and where

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Physical Review Research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

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Computational Intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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Exploiting Crowd-based Learning Method in software engineering Course

Exploiting Crowd-based Learning Method in Software Engineeri...

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International Conference on computer Science & Education (ICCSE)

作者： Xinjun Mao Yao Lu Yi Yang Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

software engineering is one of main major course for computer science. The main objective of this course is to improve the engineering capabilities of students to develop complex software systems. However, current course practice methods are teacher-centered and thus highly depend on the limited talents, experiences and efforts of teachers. Such methods greatly restrict students' scopes to gain necessary skills and artifacts, and to draw on successful experiences to address various personalized engineering issues and therefore enhance abilities to tackle engineering complexity. This paper presents a crowd-based learning method and introduces our exploration to use the method to reform the software engineering course practice. The essence of our exploration is to exploit the wisdoms and artifacts of crowds over Internet to support students to accomplish their course projects. The former is the engineering knowledges shared in question and answering (Q&A) platforms such as StackOverflow, and the latter is the open source software (OSS) hosted in OSS sites such as GitHub. We use the method in two separate course practices that aim to improve students' abilities to develop original, high-quality and large-scale software systems. The results of three-year explorations are positive and impressive and show that the crowd-based learning method is effective.

关键词： Education software engineering Learning systems Crowdsourcing software systems

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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IEEE International Conference on Artificial Intelligence and computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence Distributed processing software engineering

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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Parallel Building: A complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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Many-body spectral statistics of relativistic quantum billiard systems

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Physical Review Research 2023年第1期5卷 013050-013050页

作者： Xianzhang Chen Zhen-Qi Chen Liang Huang Celso Grebogi Ying-Cheng Lai Lanzhou Center for Theoretical Physics and Key Laboratory for Magnetism and Magnetic Materials of MOE Lanzhou University Lanzhou Gansu 730000 China Université de Strasbourg CNRS Institut de Physique et Chimie des Matériaux de Strasbourg UMR 7504 F-67000 Strasbourg France Institute for Complex Systems and Mathematical Biology Kingdom College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

In the field of quantum chaos, spectral statistics is one of the most extensively investigated characteristics. Despite a large body of existing literature, the effects of many-body interactions on the spectral statistics of relativistic quantum systems remain poorly understood. Treating electron-electron interactions through the one-orbital mean-field Hubbard model, we address this fundamental issue using graphene billiards with the geometric shape of a circular sector as prototypical systems. Our approach is to consider the two characteristically different cases where the statistics are Poisson and Gaussian orthogonal ensemble (GOE) so the corresponding classical dynamics are typically integrable and chaotic, respectively, and to systematically investigate how the statistics change as the Hubbard interaction strength increases from zero. We find that, for energies near the Dirac point, the Hubbard interactions have a significant effect on the spectral statistics. Regardless of the type of spectral statistics to begin with, increasing the Hubbard interaction strength up to a critical value causes the statistics to approach GOE, rendering more applicable the random matrix theory. As the interaction strength increases beyond the critical value, the statistics evolve toward Poisson, due to the emergence of an energy gap rendering the quasiparticles massive. We also find that the energy levels above and below the Dirac point can exhibit different statistics, and the many-body interactions have little effect on the statistics for levels far from the Dirac point. These results reveal the intriguing interplay between many-body interactions and the spectral statistics, which we develop a physical picture to understand.

关键词： Quantum chaos Graphene Quantum billiards Quantum chaotic systems Quantum many-body systems Hubbard model

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Linear quadratic regulator design for multi-input systems with a distributed cooperative strategy

arXiv

引用

arXiv 2021年

作者： Duan, Peihu Duan, Zhisheng He, Lidong Shi, Ling Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

In this paper, a cooperative Linear Quadratic Regulator (LQR) problem is investigated for multi-input systems, where each input is generated by an agent in a network. The input matrices are different and locally possessed by the corresponding agents respectively, which can be regarded as different ways for agents to control the multi-input system. By embedding a fully distributed information fusion strategy, a novel cooperative LQR-based controller is proposed. Each agent only needs to communicate with its neighbors, rather than sharing information globally in a network. Moreover, only the joint controllability is required, which allows the multi-input system to be uncontrollable for every single agent or even all its neighbors. In particular, only one-time information exchange is necessary at every control step, which significantly reduces the communication consumption. It is proved that the boundedness (convergence) of the controller gains is guaranteed for time-varying (timeinvariant) systems. Furthermore, the control performance of the entire system is ensured. Generally, the proposed controller achieves a better trade-off between the control performance and the communication overhead, compared with the existing centralized/decentralized/consensus-based LQR controllers. Finally, the effectiveness of the theoretical results is illustrated by several comparative numerical examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Controllers

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