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arXiv 2020年

作者： Guan, Yanran Liu, Han Liu, Kun Yin, Kangxue Hu, Ruizhen van Kaick, Oliver Zhang, Yan Yumer, Ersin Carr, Nathan Mech, Radomir Zhang, Hao School of Computer Science Carleton University Canada Electronic Arts United States Department of Computer Science and Technology Nanjing University China College of Computer Science and Software Engineering Shenzhen University China Uber ATG United States Adobe Systems United States School of Computing Science Simon Fraser University Canada

We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D objects for data augmentation, constrained modeling, as well as open-ended exploration to possibly inspire new designs. Starting with an initial population of 3D objects belonging to one or more functional categories, we evolve the shapes through part recombination to produce generations of hybrids or crossbreeds between parents from the heterogeneous shape collection. Evolutionary selection of offsprings is guided both by a functional plausibility score derived from functionality analysis of shapes in the initial population and user preference, as in a design gallery. Since cross-category hybridization may result in offsprings not belonging to any of the known functional categories, we develop a means for functionality partial matching to evaluate functional plausibility on partial shapes. We show a variety of plausible hybrid shapes generated by our functionality-aware model evolution, which can complement existing datasets as training data and boost the performance of contemporary data-driven segmentation schemes, especially in challenging cases. Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels. At the same time, unexpected yet functional object prototypes can emerge during open-ended exploration owing to structure breaking when evolving a heterogeneous collection. Copyright © 2020, The Authors. All rights reserved.

关键词： 3D modeling

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Towards a Circular Economy of Industrial software

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Procedia CIRP 2020年 90卷 37-42页

作者： Vladimir Kutscher Sebastian Ruland Patrick Müller Nathan Wasser Malte Lochau Reiner Anderl Andy Schürr Mira Mezini Reiner Hähnle Department of Computer Integrated Design TU Darmstadt Otto-Berndt-Str. 2 Darmstadt 64287 Germany Real-Time Systems Lab TU Darmstadt Magdalenenstr. 4 64289 Darmstadt Germany Software Technology Group TU Darmstadt Hochschulstr. 10 Darmstadt 64287 Germany Software Engineering Group TU Darmstadt Hochschulstr. 10 Darmstadt 64287 Germany

software has become an indispensable part of industrial production and thus influences the life cycle of manufacturing systems, as many of these systems have to be replaced or evolved due to changing requirements. software adaptation through continuous evolution extends the service time of these systems and thus saves valuable resources. In this paper we present an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime. Our proposed approach systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code. The approach is evaluated on a CNC machine as a special type of industrial system.

关键词： software reengineering Industrial software Digital twin Industry 4.0 Legacy system computerized numerical control Circular economy

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The impact of industry 4.0 on automation and information technology

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AIP Conference Proceedings 2024年第1期3102卷

作者： A. L. Zolkin T. G. Aygumov T. B. Matvievskaya A. S. Bityutskiy V. V. Dragulenko Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala Russia Department “International financial and management accounting” Russian University of Transport (MIIT) Moscow 127994 Russia "Invent Technology" LLP Almaty A10E5P4 Kazakhstan Department of tractors automobiles and technical mechanics Federal State Budgetary Educational Institution of Higher Education “Kuban State Agrarian University named after I.T. Trubilin” Kalinina Street 13 Krasnodar 350044 Russia

Industry 4.0 is transforming the manufacturing industry through the integration of advanced technologies such as automation and information technology. This paper provides a comprehensive review of the impact of Industry 4.0 on automation and information technology in industry. It examines the potential benefits of Industry 4.0, including improved efficiency, productivity, flexibility, and customization, as well as the challenges businesses face in implementing these technologies. The paper discusses the need for careful planning, collaboration, and investment to ensure that Industry 4.0 investments align with business goals and provide a return on investment. Also, this paper highlights the need to prioritize cyber security and training to mitigate the risks associated with integrating advanced technologies. It identifies the importance of reskilling and upskilling the workforce to prepare for new roles and responsibilities and highlights the need to prioritize cybersecurity in Industry 4.0 strategies. The paper highlights the opportunities that Industry 4.0 offers businesses to create new business models and transform their manufacturing operations. The use of advanced technologies such as 3D printing, artificial intelligence and the Internet of Things enables businesses to create products on demand and tailor them to specific customer needs. This can increase customer satisfaction and create new business opportunities. Alongside this, this paper presents an analysis of the impact of Industry 4.0 on various industries and the growth in adoption of Industry 4.0 technologies over time.

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Incorporating a Virtual Reality Environment in the Teaching of Analysis of software Requirements

Incorporating a Virtual Reality Environment in the Teaching ...

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Frontiers in Education (FIE) Conference

作者： Omar Ochoa Adam Babbit Department of Electrical Computer Software & Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL USA

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This Work in Progress paper in the Innovative Practice Category presents an approach to incorporate the use of a virtual reality environment in the teaching of analysis of software requirements through modeling. Common models include use case diagrams, state charts, data flow diagrams, etc., all serving their own role in defining different perspectives of a system. These different perspectives of the same system can be a powerful technique to facilitate the analysis of requirements about the system. Using a virtual reality environment allows for the linking of multiple perspectives from the separate analysis models to be viewed in three dimensions. This approach can be particularly beneficial to students in learning how to analyze models by providing a mechanism to examine the visual relationship between the perspectives. Using this approach allows the student to view, create, manipulate, and alter models within the native three-dimensional space, which can provide an indepth experience better suited for learning. The virtual reality environment also allows models to be nested to provide better abstractions and layered to provide a level of complexity impossible to replicate in the traditional two-dimensional space. This paper explores a prototype virtual reality environment to facilitate the use of software requirement models for undergraduate software engineering students. As part of this work, students will be exposed to the tool and tasked with using the virtual reality environment to analyze the models of systems. Assessment of the use of the approach will be conducted and results will be presented including feedback from the students, benefits found, insights and potential ways to evolve the approach for other areas such as design or testing.

关键词： Solid modeling software Analytical models Virtual reality Data models Education

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An Adaptive Robustness Evolution Algorithm with Self-Competition for Scale-Free Internet of Things

An Adaptive Robustness Evolution Algorithm with Self-Competi...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Tie Qiu Zilong Lu Keqiu Li Guoliang Xue Dapeng Oliver Wu College of Intelligence and Computing Tianjin University Tianjin China School of Software Dalian University of Technology Dalian China School of Computing Information and Decision Systems Engineering Arizona State University Tempe AZ USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology for increased resistance against malicious attacks is an NP-hard problem. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the evolution of the population. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. Moreover, a self-competitive mechanism is used to ensure convergence. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

关键词： Sociology Statistics Robustness Network topology Topology Convergence Measurement

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Construction and analysis of mathematical models of granular aggregates

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AIP Conference Proceedings 2023年第1期2758卷

作者： Lilia H. Zagorodnyuk Vladislav D. Ryzhikh Gleb A. Syitov Dmitry A. Sumskoy Department of Building materials science Products and structures Belgorod State Technological University named after V.G. Shukhov Kostyukov St. 46 Belgorod 308012 Russia Department of Software engineering for computers and computer-based systems Belgorod State Technological University named after V.G. Shukhov Kostyukov St. 46 Belgorod 308012 Russia

The task of developing effective granular aggregates, as well as the creation of a mathematical model and management of this process, is the most promising and relevant. The paper deals with the construction of mathematical models based on the results of experimental studies of granular aggregates (GA). We present the processing of the results of experimental studies on the production of granular aggregates using the classical method with the use of the high-level general-purpose Python programming language. The processing of research results is fully object-oriented. This approach to the generation of the obtained results allowed us to automate the processing of the array of data for the planning of the experiment. The results of mathematical modeling and nomograms reflecting the variability of the developed compositions, which allowed to obtain samples with the required technological, physical-mechanical and operational characteristics are presented. The paper calculates the values of reproducibility dispersions: for sand fraction ≤0.16 mm - 0.889; for sand fraction ≤0.315 mm - 0.967; for sand fraction ≤0.63 mm - 0.981. The analysis of nomograms has shown that the greatest strength values have the compositions with the sand fraction of 0.16 mm and the minimum content of sand (as an aggregate). For mixtures with sand fractions ≤0.315 mm it is established that the strongest will be the mixture with the minimum content of aggregate. It is revealed that when using sand fraction ≤0.63 mm the strength is reduced.

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An Arithmetic Differential Privacy Budget Allocation Method for the Partitioning and Publishing of Location Information

An Arithmetic Differential Privacy Budget Allocation Method ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yan Yan Xin Gao Adnan Mahmood Yang Zhang Shuang Wang Quan Z. Sheng School of Computer & Communication Lanzhou University of Technology Lanzhou China Department of Computing Macquarie University Sydney Australia Telecommunications Software and Systems Group WIT Waterford Republic of Ireland School of Information Management Wuhan University Wuhan China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the ε-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.

关键词： Privacy Differential privacy Publishing Web and internet services Big Data Resource management Usability

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Modern trend of automation and digitalization in law application

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AIP Conference Proceedings 2023年第1期2910卷

作者： O. Karpenko T. Aygumov A. Zolkin A. Gureva I. Poskryakov JSC Samaragorenergosbyt Samara 443079 Russia Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala Russia Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia Transport college Federal State Budgetary Institution of Higher Education “Admiral Ushakov Maritime State University” Novorossiysk 353918 Russia Department «Economics and management of transport» Russian University of Transport (MIIT) Obraztsova Street 9 str. 9 Moscow 127994 Russia

The main points and manifestations of the modern trend of automation and digitalization in law application are considered in the article. The positive and negative aspects of the considered phenomena are highlighted. Attention is paid to informatization of court activities, optimization, introduction of new technologies, videoconferencing, provision of legal Internet services, audio recording, etc. Informatization in the field of legal relations and law application is designed to meet the modern needs of all subjects of legal relations. It affects lawmaking, law application and law enforcement activities, expert practice, legal education and increasing the legal literacy of the population, and includes government agencies, the judiciary system. It also enables citizens to access up-to-date modern legal information.

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A reinforcement learning approach for the autonomous assembly of in-space habitats and infrastructures in uncertain environments 70

A reinforcement learning approach for the autonomous assembl...

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70th International Astronautical Congress, IAC 2019

作者： Moser, Joshua Cooper, John Neilan, James Chapin, Liam Glassner, Samantha Komendera, Erik Department of Mechanical Engineering Virginia Polytechnic Institute State University United States Autonomous Integrated Systems Research Branch NASA Langley Research Center Flight Software System Branch NASA Langley Research Center Department of Computer Science Virginia Polytechnic Institute State University United States Department of Mechanical Engineering Northeastern University

NASA Langley Research Center has developed a robotic unit, the Assembler, for use in the domain of autonomous in-space assembly, capable of reconfiguration and collaboration. A known part of space mission scheduling and the autonomous collaborative assembly problem is task assignment. This problem can be thought of as a Markov decision process framing of a job shop scheduling problem which allows for a reinforcement learning solution to approximate the optimal policies for task assignment. The research presented here initiates the evaluation of reinforcement learning to that end. Simulation results show that the policies determined through reinforcement learning performed better than random and greedy policies. These promising results will support further research into the applicability and optimality of reinforcement learning in increasingly complex scenarios. Copyright © 2019 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Reinforcement learning

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A Hybrid RSA algorithm in support of IoT greenhouse applications 2

A Hybrid RSA algorithm in support of IoT greenhouse applicat...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Aledhari, Mohammed Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond College of Computing and Software Engineering Kennesaw State University GA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

ISBN: (纸本)9781728129778

Internet of Things (IoT) is being utilized in a plethora of applications, many of which aim to improve system performance. IoT nodes suffer from several limitations, such as power supply, computational capability, and information security. The current state of IoT information and the potential for security breaches represent a significant untoward condition, especially regarding the organizational necessity for confidentiality and privacy. In this paper, we propose a strong, simple and energy conserving three-stage data encryption algorithm with a focus on securing IoT data in support of greenhouse applications. The stages include: (1) a novel implementation of the K-Map substitution functions;(2) the utilization of a chaotic equation to generate a sequence of random numbers, which are added to the result of the first stage;(3) the third stage incorporates the Rivest, Shamir, and Adelman (RSA) algorithm, performed on feeds from the output of the second stage, resulting in encrypted data, requiring private key decryption. The proposed algorithm eliminates the handshaking of the traditional RSA to exchange the keys (private and public) between IoT nodes and the cloud (server), then reduce the transmission time by 30%. The proposed cryptography algorithm is implemented and tested using two evaluation methods: a single micro-controller (standalone) and on a server (cloud). The algorithm is tested in both directions up/down link, and provides an acceptable and stable performance with 1.3 faster than the original RSA. © 2019 IEEE.

关键词： Internet of things

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