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arXiv 2020年

作者： Li, Qing Zhu, Jiasong Liu, Jun Cao, Rui Li, Qingquan Jia, Sen Qiu, Guoping College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Urban Informatics Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Urban Smart Transportation and Safety Maintenance Shenzhen University Shenzhen518060 China Shenzhen TCL Industrial Research Institute Co. Ltd. Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Estimating depth from RGB images can facilitate many photometric computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we first introduce the datasets for depth estimation, and then give a comprehensive introduction of the methods from three perspectives: supervised learning-based methods, unsupervised learning-based methods, and sparse samples guidance-based methods. In addition, downstream applications that benefit from the progress have also been illustrated. Finally, we point out the future directions and conclude the paper. Copyright © 2020, The Authors. All rights reserved.

关键词： Image reconstruction

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A Neural Circuit Theory for Neuroinformatics and Brain-Machine Interactions

A Neural Circuit Theory for Neuroinformatics and Brain-Machi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yingxu Wang Jin Jiu Liu Lin International Institute of Cognitive Informatics & Cognitive Computing Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Canada Beijing National Research Center for Information Sci. & Tech School of Information Sci. and Tech Tsinghua University Beijing China School of Software Tsinghua University Beijing China Tsinghua University Beijing China

It is found that neural circuits are fundamental structures of neural networks and the nervous systems in neurology and neuroinformatics. This paper presents a novel neural circuit theory for analytic neurology and formal neuroinformatics. Three categories of neurons known as the association, sensory, and motor nerves are formally modeled. A set of functional structures of neural circuits are identified including serial, convergent, divergent, parallel, and positive/negative-feedback neural circuits. Applying the basic neural circuits, complex neural networks of the brain can be rigorously modeled, which explain the formation of temporal thinking threads, permanent memory, and knowledge representations in the brain and their interfaces to machines and neurocomputing systems.

关键词： Neurons Integrated circuit modeling Brain modeling Neuroinformatics Mathematical model Neural networks

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Introduction of Fog Computations to Measuring System Collection and Accounting of Data of Distributed Internet of Things

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： V D Munister A L Zolkin T G Aygumov V E Ozhiganov Mariupol State University Department of Enterprise Economics State educational institution of higher professional education "Donetsk National Technical University" Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Department of computer engineering software and automated systems Dagestan State Technical University Federal State Unitary Enterprise RUSSIAN FEDERAL NUCLEAR CENTER All-Russian Research Institute of Experimental Physics FSUE RFNC – VNIIEF

The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device's behaviour at a given time *** practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given.

关键词：

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A Qualitative Study of Variability Management of Control software for Industrial Automation Systems

A Qualitative Study of Variability Management of Control Sof...

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International Conference on software Maintenance (ICSM)

作者： Juliane Fischer Safa Bougouffa Alexander Schlie Ina Schaefer Birgit Vogel-Heuser Institute of Automation and Information Systems Technical University of Munich Munich Germany Institute of Software Engineering and Automotive Informatics Technical University of Braunschweig Braunschweig Germany

software product line engineering (SPLE) provides a systematic approach to manage variants and versions arising throughout the development of software systems. While SPLE is successfully applied for variant management in the domain of software engineering, the approach is still not widely spread in industrial automated production systems (aPS). Previous studies highlight the interdisciplinary nature of aPS as a reason for not applying SPLE, since control software variants and versions also result from changes in other disciplines such as the mechanical engineering department (i.e. exchange of a sensor). Additionally, the software may evolve over decades at the customer site. In order to gain a better understanding of the challenges in the development of aPS and the constraints hindering the use of SPLE, we conducted several interviews with software development engineers from the domain of aPS. The interviews main aim was to get an overview of the current state of variability management and applied planned and unplanned software reuse strategies. Based on these insights, we summarize the main results useable for a transition from currently deployed variability management concepts in aPS to the SPLE approach.

关键词： software Interviews Companies IEC Standards Automation software engineering

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1D-Convolutional capsule network for hyperspectral image classification

arXiv

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arXiv 2019年

作者： Zhang, Haitao Meng, Lingguo Wei, Xian Tang, Xiaoliang Tang, Xuan Wang, Xingping Jin, Bo Yao, Wei School of Software Liaoning Technical University Huludao125105 China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic University 181 Chatham Road South Hung Hom Kowloon Hong Kong

Recently, convolutional neural networks (CNNs) have achieved excellent performances in many computer vision tasks. Specifically, for hyperspectral images (HSIs) classification, CNNs often require very complex structure due to the high dimension of HSIs. The complex structure of CNNs results in prohibitive training efforts. Moreover, the common situation in HSIs classification task is the lack of labeled samples, which results in accuracy deterioration of CNNs. In this work, we develop an easy-to-implement capsule network to alleviate the aforementioned problems, i.e., 1D-convolution capsule network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial and spectral information on spatial and spectral domains, which is more lightweight than 3D-convolution due to fewer parameters. Secondly, 1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce parameter amount and computational complexity of conventional capsule network. Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is verified by three representative HSI datasets. Experimental results demonstrate that 1D-ConvCapsNet is superior to state-of-the-art methods in both the accuracy and training effort Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Spatial sparse subspace clustering for compressive spectral imaging

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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Modularizing code by use cases and tests for better maintainability 17

Modularizing code by use cases and tests for better maintain...

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1st International Conference on the Art, Science and engineering of Programming, Programming 2017

作者： Bystrický, Michal Vranic, Valentino Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Ilkovicova 2 Bratislava Slovakia

No abstract available.

ISBN: (纸本)9781450348362

No abstract available.

关键词： use case modularization user acceptance tests test driven development Cucumber

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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The Carbon Footprint of Bitcoin

SSRN

引用

SSRN 2019年

作者： Stoll, Christian Klaaßen, Lena Gallersdörfer, Ulrich MIT Center for Energy and Environmental Policy Research Massachusetts Institute of Technology CambridgeMA02139 United States TUM Center for Energy Markets TUM School of Management Technical University of Munich Germany TUM School of Management Technical University of Munich Germany TUM Software Engineering for Business Information Systems Department of Informatics Technical University of Munich Germany

Participation in the Bitcoin blockchain validation process requires specialized hardware and vast amounts of electricity, which translate into a significant carbon footprint. Here we demonstrate a methodology for estimating the power consumption associated with Bitcoin’s blockchain based on IPO filings of major hardware manufacturers, insights on mining facility operations, and mining pool compositions. We then translate our power consumption estimate into carbon emissions, using the localization of IP-addresses. We determine the annual electricity consumption of Bitcoin, as of November 2018, to be 48.2 TWh, and estimate that annual carbon emissions range from 23.6 to 28.8 MtCO2. This means that the level of emissions produced by Bitcoin sits between the levels produced by the nations of Jordan and Mongolia. With this article, we aim to gauge the external costs of Bitcoin, and inform the broader debate on the costs and benefits of cryptocurrencies. The externalities we discuss here may help policy-makers in setting the right rules as the adoption journey of blockchain has just started. © 2019, The Authors. All rights reserved.

关键词： Blockchain

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