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Data spaces as enablers for sustainability

Data spaces as enablers for sustainability

2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Hoppe, Christoph Schmelzer, Robert Möller, Frederik Schoormann, Thorsten Fraunhofer Institute for Software and Systems Engineering Industrial Manufacturing Speicherstraße 6 Dortmund44147 Germany Technische Universität Chemnitz Business Informatics Straße der Nationen 62 Chemnitz09111 Germany Technische Universität Braunschweig Institute for Business Informatics Rebenring 58a Braunschweig38106 Germany Universität Hildesheim Department for Information Systems Samelsonplatz Hildesheim31141 Germany

ISBN: (纸本)9783885797319

One of our society's most fundamental challenges is promoting sustainable development. Data sharing across organizations is one way to spur innovation and address the Sustainable Development Goals (SDGs) through optimizing resource utilization, fostering circular supply chains, and producing accurate information about CO2 emissions. However, organizations often hesitate to share data given a range of concerns, including the fear of data misappropriation or the lack of control over what others do with their data. Data spaces as a novel artifact seek to tackle these issues by providing an integrated data management approach upholding data sovereignty. These spaces can boost sustainable development by being a secure and trusted digital infrastructure for organizations to share data for sustainable purposes. To disclose how this digital solution fosters sustainability, we present a set of 65 potentials of data spaces along with the dimensions of ecological, economic, and social sustainability. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Sustainable development

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Stochastic MPC with Situation-Aware Dynamic Risk Assessment for Autonomous Driving

Stochastic MPC with Situation-Aware Dynamic Risk Assessment ...

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International Conference on Intelligent Transportation

作者： Jonas Ulmen Marc Wellstein Christoph Mark Ganesh Sundaram Daniel Görges Jan Reich University of Kaiserslautern-Landau Kaiserslautern Germany Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany

In this paper, we propose a novel interplay between stochastic model predictive control and situation-aware dynamic risk assessment (SINADRA) for autonomous driving. With the help of SINADRA a Bayesian network is engineered which infers a probabilistic behavior classification for a surrounding actor. This classification is considered in the chance constraints of the model predictive controller (MPC). The MPC can guarantee no constraint violations up to a chosen probability. If a constraint violation occurs, the system is handled by an emergency braking assistant (EBA) ensuring the safety of the overall system. The approach of combining MPC with SINADRA results in a less conservative driving behavior while not introducing additional safety violations. The probability of interference by the EBA can be used as a design parameter, that can be tuned by the manufacturer or possibly by the end-consumer through a safe interface.

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Development of Load Balancing Method in a Microservice Architecture System

Development of Load Balancing Method in a Microservice Archi...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Aung Kyaw Myo Evgeni M. Portnov Alexander S. Alekseev Institute of Systems and Software Engineering and Information Technology National Research University of Electronic Technology (MIET) Zelenograd Moscow Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

This paper presents a novel load balancing method for microservice architectures, focusing on optimizing resource utilization and enhancing system performance. The proposed approach employs a dynamic load balancer that continuously monitors microservice instances, distributing incoming requests based on real-time workload metrics. By integrating queuing theory and simulation modeling, the method maximizes the output stream intensity of processed requests while minimizing rejection rates. Comparative analysis demonstrates a 15% improvement in processing speed compared to traditional uniform distribution methods. The solution addresses key challenges such as uneven request distribution and service failures, ensuring high availability and fault tolerance. The developed algorithm is validated through simulation models, showcasing its effectiveness in real-world scenarios. This research contributes to advancing efficient data distribution in microservice-based systems, offering practical insights for scalable web applications.

关键词： Fault tolerance System performance Heuristic algorithms Fault tolerant systems Microservice architectures Load management Real-time systems Resource management Queueing analysis Load modeling

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Guidance on Dataspaces: Development of a Learning Environment for Industrial SMEs

Guidance on Dataspaces: Development of a Learning Environmen...

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International Conference on Control and Robots (ICCR)

作者： Lick Jonas Lamarz Jessica Dohmann Friederike Kulkarni Pranav Jayant Zerbin Julian Koldewey Christian Department for Digital Transformation Fraunhofer Institute for Mechatronic Systems Design Paderborn Germany Department for Corporate Innovation Fraunhofer Institute for Mechatronic Systems Design Paderborn Germany Research group for Advanced Systems Engineering University of Paderborn - Heinz Nixdorf Institute Paderborn Germany

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

The rapid advancements in digital transformation have led to the emergence of dataspaces as a pivotal element for industry-wide and cross-industry data integration and interoperability across various businesses. Despite their potential, the adoption and effective utilization of dataspaces by business stakeholders remain challenging. This paper aims to address this gap by developing a comprehensive learning environment tailored for business stakeholders. Through an interview study and an analysis of the current state of research, we identify problem fields and derive key requirements for the development of the learning environment. The proposed environment includes a demonstrator and a training concept designed to enhance stakeholders' understanding and capabilities in managing and leveraging dataspaces. Our findings contribute to the body of knowledge by providing practical guidance through learning environments for the deployment of dataspaces in business contexts and highlighting areas for future research.

关键词： Training Service robots Digital transformation Data integration Stakeholders Interviews Interoperability Business

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Parallel Bounded Search for the Maximum Clique Problem

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Journal of Computer Science & Technology 2023年第5期38卷 1187-1202页

作者：江华白珂刘海姣李初民 Felip Manya 付樟华 Engineering Research Center of Cyberspace Yunnan UniversityKunming 650500China School of Software Yunnan UniversityKunming 650500China Laboratory of Modeling Information and SystemsUniversity of Picardie Jules VerneAmiens 80039France Artificial Intelligence Research Institute Spanish National Research CouncilCatalonia 08193Spain Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000China Institute of Robotics and Intelligent Manufacturing Chinese University of Hong KongShenzhen 518000China

Given an undirected graph,the Maximum Clique Problem(MCP)is to find a largest complete subgraph of the *** is NP-hard and has found many practical *** this paper,we propose a parallel Branch-and-Bound(BnB)algorithm to tackle this NP-hard problem,which carries out multiple bounded searches in *** search has its upper bound and shares a lower bound with the rest of the *** potential benefit of the proposed approach is that an active search terminates as soon as the best lower bound found so far reaches or exceeds its upper *** describe the implementation of our highly scalable and efficient parallel MCP algorithm,called PBS,which is based on a state-of-the-art sequential MCP *** proposed algorithm PBS is evaluated on hard DIMACS and BHOSLIB *** results show that PBS achieves a near-linear speedup on most DIMACS instances and a superlinear speedup on most BHOSLIB ***,we give a detailed analysis that explains the good speedups achieved for the tested instances.

关键词： Branch-and-Bound(BnB) maximum clique problem(MCP) parallel search

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Facilitating Skill-Based Robot Programing Using the Asset Administration Shell

Facilitating Skill-Based Robot Programing Using the Asset Ad...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Moritz Hofer Alwin Hoffmann Christian Eymüller XITASO GmbH Research & Innovation Augsburg Germany University of Augsburg Institute for Software & Systems Engineering Augsburg Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

This paper presents an approach to abstract capabilities of hardware components, such as a robot, and to store the necessary data for accessing that capability in Asset Administration Shells (AAS). It uses an ontology based on the Capability, Skills, and Services (CSS) model, enhanced with the possibility to describe physical relations between components. Capabilities describe abstract abilities realized through technology-neutral skills, exemplified in Docker containers using a standardized interface to communicate. The case study, bin picking, illustrates the system's application and requirements. Two user types are identified: the hardware manufacturers who can abstract and store component capabilities in an AAS, and the user who can load these shells in order to orchestrate the process program. This paper details the system's design, implementation, and evaluation, demonstrating its functionality through the case study.

关键词： Semantics Robot vision systems Standardization Containers Ontologies Programming Hardware software Robots System analysis and design

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques 13

State-based Sandbox Tool for Distributed Malware Detection w...

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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Rehida, Pavlo Markowsky, George Sachenko, Anatoliy Savenko, Oleg Khmelnytskyi National University Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine Kennesaw State University Department of Software Engineering and Game Development Marietta United States Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

ISBN: (纸本)9798350396119

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described. © 2023 IEEE.

关键词： anti-emulation techniques malware detection obfuscation techniques sandbox

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Learning Simulation-Based Digital Twins for Discrete Material Flow systems: A Review

Learning Simulation-Based Digital Twins for Discrete Materia...

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Simulation Winter Conference

作者： Christian Schwede Daniel Fischer University of Applied Science and Arts Bielefeld Bielefeld NRW GERMANY Fraunhofer Institute for Software and System Engineering (ISST) Dortmund NRW GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Digital Twins (DTs) play a crucial role in the fourth industrial revolution. In the context of discrete material flow systems, companies under constant competitive pressure seek solutions to minimize costs and maximize performance. Simulation-based DTs can help make optimal decisions in the design, planning, and control of these systems. Such DTs are until now created and updated by domain experts producing costs and are often not considering the advances made in machine learning to improve prediction quality. Learning DTs out of data could be the solution for a broader application. A lot of work has already been done that contributes to this endeavor, yet relevant building blocks originate from different scientific areas resulting in the use of different terminology. Thus we present a holistic review of relevant work and analyze the state of the art based on a new classification scheme deriving relevant building blocks and gaps for future research.

关键词： Costs Reviews Terminology Machine learning Companies Control systems Digital twins Planning Fourth Industrial Revolution

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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