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arXiv 2021年

作者： Bi, Tingting Liang, Peng Tang, Antony Xia, Xin School of Computer Science Wuhan University Wuhan430072 China Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Faculty of Information Technology Monash University MelbourneVIC3166 Australia Software and Services Research Group Vrije Universiteit Amsterdam Amsterdam1101 Netherlands

Context: Architecture Tactics (ATs) are architectural building blocks that provide general architectural solutions for addressing Quality Attributes (QAs) issues. Mining and analyzing QA-AT knowledge can help the software architecture community better understand architecture design. However, manually capturing and mining this knowledge is labor-intensive and difficult. Objective: Using Stack Overflow (SO) as our source, our main goals are to effectively mine such knowledge;and to have some sense of how developers use ATs with respect to QA concerns from related discussions. Methods: We applied a semi-automatic dictionary-based mining approach to extract the QA-AT posts in SO. With the mined QA-AT posts, we identified the relationships between ATs and QAs. Results: Our approach allow us to mine QA-AT knowledge effectively with an F-measure of 0.865 and Performance of 82.2%. Using this mining approach, we are able to discover architectural synonyms of QAs and ATs used by designers, from which we discover how developers apply ATs to address quality requirements. Conclusions: We make two contributions in this work: First, we demonstrated a semi-automatic approach to mine ATs and QAs from SO posts;Second, we identified little-known design relationships between QAs and ATs and grouped architectural design considerations to aid architects make architecture tactics design decisions. © 2021, CC BY.

关键词： Data mining

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A human vision system-based approach to improve computing efficiency in information systems

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International Journal of information Systems and Change Management 2019年第1期11卷 44-55页

作者： Li, Jianxun Chen, Jim Q. Li, Weiqian Department of Management Science and Engineering School of Economics and Management Xi'an University of Technology Xi'an China Department of Information Systems Herberger Business School St. Cloud State University St. CloudMN United States Department of Software Engineering School of Computer Science Xi'an Polytechnic University Xi'an China

While a lot of research has been done in system design, reducing computational complexity is still a core problem in information systems. In order to break through the bottleneck of computing efficiency, this paper draws on the powerful processing capability of the human vision system (HVS) to propose a pixel domain computing framework. It converts the original problem into pixel operations, and pixels are taken as the basic unit to change the construction process of data analysis. The framework significantly decreases computational complexity, simplifies programming logic, and enhances the automaticity of information system. Our results demonstrate that the efficiency of the containment test is improved by 30 times, the logic complexity of the intersection area measurement is reduced by a factor of five, and the automaticity of root finding is significantly heightened. © 2019 Inderscience Enterprises Ltd.

关键词： information systems

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Single-path Bit Sharing for Automatic Loss-aware Model Compression

arXiv

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arXiv 2021年

作者： Liu, Jing Zhuang, Bohan Chen, Peng Shen, Chunhua Cai, Jianfei Tan, Mingkui The School of Software Engineering South China University of Technology China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China The School of Computer Science the University of Adelaide Australia The Faculty of Information Technology Monash University Australia The College of Computer Science and Technology Zhejiang University Hangzhou China

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance. To address this, performing pruning and quantization jointly is essential. Nevertheless, how to make a trade-off between pruning and quantization is non-trivial. Moreover, existing compression methods often rely on some pre-defined compression configurations (i.e., pruning rates or bitwidths). Some attempts have been made to search for optimal configurations, which however may take unbearable optimization cost. To address these issues, we devise a simple yet effective method named Single-path Bit Sharing (SBS) for automatic loss-aware model compression. To this end, we consider the network pruning as a special case of quantization and provide a unified view for model pruning and quantization. We then introduce a single-path model to encode all candidate compression configurations, where a high bitwidth value will be decomposed into the sum of a lowest bitwidth value and a series of re-assignment offsets. Relying on the single-path model, we introduce learnable binary gates to encode the choice of configurations and learn the binary gates and model parameters jointly. More importantly, the configuration search problem can be transformed into a subset selection problem, which helps to significantly reduce the optimization difficulty and computation cost. In this way, the compression configurations of each layer and the trade-off between pruning and quantization can be automatically determined. Extensive experiments on CIFAR-100 and ImageNet show that SBS significantly reduces computation cost while achieving promising performance. For example, our SBS compressed MobileNetV2 achieves

关键词： Encoding (symbols)

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Position: Topological Deep Learning is the New Frontier for Relational Learning

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Birdal, Tolga Bronstein, Michael Carlsson, Gunnar Curry, Justin Gao, Yue Hajij, Mustafa Kwitt, Roland Liò, Pietro Di Lorenzo, Paolo Maroulas, Vasileios Miolane, Nina Nasrin, Farzana Ramamurthy, Karthikeyan Natesan Rieck, Bastian Scardapane, Simone Schaub, Michael T. Veličković, Petar Wang, Bei Wang, Yusu Wei, Guo-Wei Zamzmi, Ghada Department of Mathematics The University of Manchester Manchester United Kingdom Department of Computing Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics Stanford University Stanford United States BlueLightAI Inc United States University at Albany New York United States School of Software Tsinghua University Beijing China University of San Francisco San Francisco United States Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville United States Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara United States Department of Mathematics University of Hawai‘i at Mānoa HI United States IBM Corporation New York United States Helmholtz Munich Munich Germany Technical University of Munich Munich Germany RWTH Aachen University Aachen Germany Google Deep-Mind United Kingdom School of Computing University of Utah Utah United States Computer Science and Engineering Department University of California San Diego San Diego United States Department of Mathematics Michigan State University East LansingMI United States University of South Florida Florida United States

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Stability Analysis of Singular Networked Control Systems with Short Time-Varying Delay

Stability Analysis of Singular Networked Control Systems wit...

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World Conference on Mechanical engineering and Intelligent Manufacturing (WCMEIM)

作者： Hongyan Zhou Hua Li Zhao Zhang Xuebo Chen School of Electronics and Information Engineering University of Science and Technology Liaoning Anshan China School of Business Administration University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

In this article, the problem of stability and stabilization for singular networked control systems (SNCSs) with short time-varying delay is investigated. The SNCS is modeled into a time-invariant discrete time system with norm-bounded uncertainty by using the robust control technique and the nominal point method. Based on the linear matrix inequality (LMI) technique and the Lyapunov stability theorem, the existence condition of a state feedback controller is obtained, which makes the SNCS asymptotically stable, and the controller designed method is also suggested. Finally, a numerical example is delivered to verify the feasibility of this article.

关键词： Robust control State feedback Uncertainty Networked control systems Stability analysis Delays Linear matrix inequalities

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An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

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International Journal of Automation and computing 2019年第6期16卷 786-799页

作者： Jiao Yin Jinli Cao Siuly Siuly Hua Wang Department of Computer Science and Information Technology La Trobe UniversityVictoria 3083Australia School of Software Engineering Chongqing University of Arts and SciencesChongqing 402160China Institute for Sustainable Industries&Liveable Cities Victoria UniversityVictoria 3083Australia

Aiming to differentiate between mild cognitive impairment(MCI) patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state electroencephalogram(EEG) recordings. This framework firstly eliminates noise by employing stationary wavelet transformation(SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional(3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the support vector machine(SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-nearest neighbors(KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delaying cognitive decline and dementia onset.

关键词： Electroencephalogram(EEG) dementia early detection mild cognitive impairment(MCI) stationary wavelet transformation(SWT) support vector machine(SVM)

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Retraction Note: COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

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Soft computing 2024年第SUPPL 1期28卷 65页

作者： Alaa S Al-Waisy Shumoos Al-Fahdawi Mazin Abed Mohammed Karrar Hameed Abdulkareem Salama A Mostafa Mashael S Maashi Muhammad Arif Begonya Garcia-Zapirain Communications Engineering Techniques Department Information Technology Collage Imam Ja'afar Al- Sadiq University Baghdad Iraq. Computer Science Department Al-Ma'aref University College Anbar Iraq. College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq. College of Agriculture Al-Muthanna University Samawah 66001 Iraq. Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia Batu Pahat Johor 86400 Malaysia. Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 11451 Saudi Arabia. School of Computer Science Guangzhou University Guangzhou China. eVIDA Lab The University of Deusto Avda/Universidades 24 Bilbao 48007 Spain.

[This retracts the article DOI: 10.1007/s00500-020-05424-3.].

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2023年 363卷 vii-viii页

作者： Sivakumar Reddy, V. Wang, Jiacun Kamakshi Prasad, V. Dasari, Naga Mallikarjuna Rao Malla Reddy University Hyderabad India Department of Computer Science and Software Engineering Monmouth University West Long BranchNJ United States Department of CSE JawaharlalNehru Technological University Hyderabad Hyderabad India Computer Science and Information Technology Federation University Mawson LakesSA Australia

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Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot2API: Recommending Graphic API from Plot via Semantic Pa...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Zeyu Wang Sheng Huang Zhongxin Liu Meng Yan Xin Xia Bei Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely *** the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API *** collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

关键词： Deep learning Visualization Semantics Neural networks Data visualization software Task analysis

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