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PSCS4CPP: A Generative PSCS Implementation for C++ 1

Model-Driven Engineering and Software Development - 9th International Conference, MODELSWARD 2021, and 10th International Conference, MODELSWARD 2022, Revised Selected Papers

作者： Hammer, Maximilian Maschotta, Ralph Wichmann, Alexander Jungebloud, Tino Bedini, Francesco Zimmermann, Armin Systems and Software Engineering Group Computer Science and Automation Department Technische Universität Ilmenau Ilmenau Germany

ISBN: (数字)9783031388217

ISBN: (纸本)9783031388200

Since the early 2000s, Model-driven engineering has aimed to accomplish executable UML models. With fUML and PSCS, the Object Management group (OMG) published standardized specifications of precise semantics for certain parts of UML in the form of metamodels, which form an execution environment. A certain characteristic of these environments is that static information about a model is analyzed and evaluated during runtime. With composite structures being a concept for describing structural properties of a model, the majority of execution semantics specified by PSCS concern analysis and processing of such static information about the model’s fine-grained structure. Thus, the PSCS specification appears suitable for a generative realization approach. Using Model-To-Text-Transformation to generate source code, which serves as an input for the actual execution environment, the runtime level of model executions can be relieved by outsourcing analysis and processing of static information to the level of code generation. By inserting this preprocessing step, the performance of the actual model execution at runtime can be improved. This paper introduces an implementation of the PSCS specification for C++ based on code generation using Model-to-Text-Transformation. This also includes a generative approach to realize an extension mechanism of the fUML and PSCS execution environments by introducing user-defined semantic execution strategies. The proposed PSCS implementation was developed as a part of the MDE4CPP project. Moreover, this paper presents a set of test models validating the correct functionality of the implementation as well as a performance benchmark. Finally, analysis results of an application example for the presented realization are evaluated and discussed. © 2023, Springer Nature Switzerland AG.

关键词： Specifications

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Towards NLP-driven Online-classification of Industrial Alarm Messages 50

Towards NLP-driven Online-classification of Industrial Alarm...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Shatewakasi, Yeersen Vogel-Heuser, Birgit Wilch, Jan Hankemeier, Victoria Höfgen, Josua Technical University of Munich Institute of Automation and Information Systems Garching Germany University of Münster Computer Science Department Autonomous Intelligent Systems Group Münster Germany

ISBN: (纸本)9781665464543

Industrial alarm systems facilitate the reliable and effective management of industrial process operations. However, the manufacturer’s fear of missing a critical fault or lacking process understanding may lead to the implementation of alarm systems that overburden operators by an enormous amount of alarms. Inadequate alarm settings and maintenance, coupled with a high volume of disturbance alarms, necessitate operators making crucial decisions within a limited time. Thus, an alarm management strategy that accurately anticipates the types of incoming alarms is needed, especially in brownfield systems where it is infeasible to simplify the underlying alarming functions. This alleviates the need for human interpretation of alarms upon arrival, allowing operators to address anomalous behaviors early. This study proposes an alarm classification approach, based on active learning, latent dirichlet allocation (LDA) topic modeling and transformer-based deep learning. It focuses on addressing these challenges through a systematic natural language processing (NLP) approach to classify and map fault messages. To demonstrate the validity of this approach, real-world alarm data from an industrial hybrid non-woven manufacturing process is used. © 2024 IEEE.

关键词： Natural language processing systems

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Towards Threat Modelling of IoT Context-Sharing Platforms 22

Towards Threat Modelling of IoT Context-Sharing Platforms

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Goudarzi, Mohammad Shaghaghi, Arash Finn, Simon Stiller, Burkhard Jha, Sanjay School of Computer Science and Engineering UNSW Sydney Australia Australia Communication Systems Group Department of Informatics University of Zurich Switzerland Cisco Australia

ISBN: (纸本)9798331510183

The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on context-sharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployments, making their security essential to ensure the resilience and reliability of these "systems of systems." In this paper, we take the first steps toward systematically and comprehensively addressing the security of IoT context-sharing platforms. We propose a framework for threat modelling and security analysis of a generic IoT context-sharing solution, employing the MITRE ATT&CK framework. Through an evaluation of various industry-funded projects and academic research, we identify significant security challenges in the design of IoT context-sharing platforms. Our threat modelling provides an in-depth analysis of the techniques and sub-techniques adversaries may use to exploit these systems, offering valuable insights for future research aimed at developing resilient solutions. Additionally, we have developed an open-source threat analysis tool that incorporates our detailed threat modelling, which can be used to evaluate and enhance the security of existing context-sharing platforms. ©2024 IEEE.

关键词： Cloud platforms

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ZigBee Network for AGV Communication in Industrial Environments 10

ZigBee Network for AGV Communication in Industrial Environme...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Flak, Jaroslaw Skowron, Tomasz Cupek, Rafal Fojcik, Marcin Caban, Dariusz Domanski, Adam Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering Bergen Norway

ISBN: (纸本)9798350345032

Automated Guided Vehicles (AGVs) are a key component of many modern industrial systems. AGVs are supposed to communicate with each other in real time using wireless networks. In this article, the advantages and disadvantages of the ZigBee wireless network related to the control of AGVs are considered. We analyze the performance of the ZigBee network programmed with both C# and Python libraries to control ZigBee devices. The throughput and signal strength are presented and discussed depending on the transmission speed of the serial port, the payload size, and the presence and distance from the obstacles. The results of the experiments show the effective values of these parameters, the methods of using C# and Python, and the reliability of the throughput up to a certain point in network devices. © 2023 IEEE.

关键词： Python

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Unsupervised Spatio-Temporal Anomalous Thermal Behavior Monitoring of Inside-Built Environments 19

Unsupervised Spatio-Temporal Anomalous Thermal Behavior Moni...

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19th Annual International Conference on distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Khan, Naima Al Hafiz Khan, Md Abdullah Roy, Nirmalya Department of Information Systems United States Umbc Center for Real-time Distributed Sensing and Autonomy United States Kennesaw State University Department of Computer Science United States

ISBN: (纸本)9798350346497

Continual wavering of outside weather degrades the efficiency of inside building envelope over time and leads to additional energy consumption, various structural damages, etc. Frequent monitoring of the indoor built environment with thermal images can assist in identifying the energy-leaking and potentially damage-prone areas. Although in recent years different researches performed deep learning and computer vision based thermal anomaly detection in built environment, several issues related to conducting strategic non-intrusive indoor thermal inspection using temporal thermal images, are still unresolved in uncontrolled environment of residential buildings. In this work, we propose a scalable thermal image-based monitoring approach for building envelopes combining the visual knowledge of structural joint information among different building components and their corresponding temporal thermal status. We collected longitudinal thermal images from indoor scenes of different building components (e.g., door, window, wall) and employed a high-level spatio-temporal graph (st-graph) to represent the structural connection among different building components and their temporal self-changes. Our proposed novel unsupervised spatio-temporal clustering framework assigns the cluster label to nodes in st-graph, combining its structural (the self and neighboring component) and temporal features which achieves better performance in identifying thermal variation compared to other clustering based approaches. We demonstrate thermal variation across the spots which indicates the potential energy leakage areas inside the built environment. The cluster patterns obtained from our proposed model assist in understanding the thermal characteristics of various surfaces at certain conditions, such as sun reflection and airflow in the inside built environment. © 2023 IEEE.

关键词： Potential energy

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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Digitization and Secure Process of Forensic Data Management Using Blockchain

Digitization and Secure Process of Forensic Data Management ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Indhumathi, R. Mathivanan, P. Mahalakshmi, K. Subha, K. Krishna, Nanda S. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Business Systems Coimbatore India Surya Group of Institutions Department of Computer Science and Engineering Villuppuram India

ISBN: (纸本)9798350315127

Cyber threats and data manipulation are the main concerns in this technological society. The investigator finds it challenging to submit evidence to the court because of the information breach as continuity documentation of records is very critical for safeguarding against criminal events. Blockchain-based digital forensic data management systems have been developed to deal with this issue of Privacy preservation for evidence items. Blockchain technology provides authentication, immutability, traceability, resilience, and distributed trust for forensic data. In order to ensure security and ease of use, the system utilizes onion architecture. Data can be stored and analysed whenever required. They provide trust between the examiners and the system. The system is a multi-platform system that provides effective usage where it can be accessed and used from anywhere. The honest information is make sure to follow the crime investigator of the steps. if we use the propose system of block chain, it is can't be altered the evidence during the submission of court. The storing data of each block is verify and added to the blockchian. The creation of safe database was implemented by a decentralised network with bitcoin and networks of cryptocurrency. In proposed system, the blockchain technology helps in can't able to access and altered the data during the investigation periods. This system is used to implements the easily accessed and used in anywhere. © 2023 IEEE.

关键词： Blockchain

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Outlier quantification for multibeam data

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Informatik-Spektrum 2022年第4期45卷 218-222页

作者： Ziolkowski, Tobias Koschmider, Agnes Kröger, Peer Devey, Colin Helmholtz Centre for Ocean Research Kiel Kiel Germany Group Process Analytics Computer Science Department Kiel University Kiel Germany Group Information Systems and Data Mining Computer Science Department Kiel University Kiel Germany

This paper discusses the challenges of applying a data analytics pipeline for a large volume of data as can be found in natural and life sciences. To address this challenge, we attempt to elaborate an approach for an improved detection of outliers. We discuss an approach for outlier quantification for bathymetric data. As a use case, we selected ocean science (multibeam) data to calculate the outlierness for each data point. The benefit of outlier quantification is a more accurate estimation of which outliers should be removed or further analyzed. To shed light on the subject, this paper is structured as follows: first, a summary of related works on outlier detection is provided. The usefulness for a structured approach of outlier quantification is then discussed using multibeam data. This is followed by a presentation of the challenges for a suitable solution, and the paper concludes with a summary. © 2022, The Author(s).

关键词： Data Analytics

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Towards Adaptive Trajectories for Mixed Autonomous and Human-Operated Ships 5

Towards Adaptive Trajectories for Mixed Autonomous and Human...

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5th IEEE International Conference on Autonomic Computing and Self-Organizing systems Companion, ACSOS-C 2024

作者： Pianini, Danilo Tomforde, Sven Alma Mater Studiorum - Università di Bologna Department of Computer Science and Engineering Cesena Italy Intelligent Systems Group Kiel University Kiel Germany

ISBN: (纸本)9798350389760

We are witnessing the rise of autonomous cars, which will likely revolutionize the way we travel. Arguably, the maritime domain lags behind, as ships operate on many more degrees of freedom (thus, a much larger search space): there is less physical infrastructure, and rules are less consistent and constraining than what is found on roads. The problem is further complicated by the inevitable co-existence of autonomous and human-operated ships: the latter may take unpredictable decisions, which require adjustments on the autonomous ones. Finally, the problem is inherently decentralised, there is no central authority, and communication means can be very diverse in terms of communication distance and performance, mandating special care on which information is shared and how. In this work, we elaborate on the challenges of trajectory prediction and adaptation for mixed autonomous and human-operated ships, and we propose initial ideas on potential approaches to address them. © 2024 IEEE.

关键词： Decentralized control

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Joakinator: An Interface for Transforming Body Movement and Perception through Machine Learning and Sonification of Muscle-Tone and Force 23rd

Joakinator: An Interface for Transforming Body Movement and ...

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23rd International Conference on New Interfaces for Musical Expression, NIME 2023

作者： Díaz-Durán, Joaquín R. Vidal, Laia Turmo Tajadura-Jiménez, Ana DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain

Joakinator is a wearable interactive interface that allows users to activate different media materials, such as sound, music, and video, through body gestures. The device, designed in the context of music and performing arts, integrates surface electromyogram, force sensors, and machine learning algorithms with tailored-made software for sonifying muscle-tone and force. This allows the body to reflect expressively the content of the media and the architecture of the device. Recently, we have started to investigate the potential of Joakinator to alter body perception in the context of BODYinTRANSIT, a European Research Council Project focused on the transformations of body perception through the use of interactive sound/haptics technology. At NIME-2023, we will showcase Joakinator and invite visitors to experience the device firsthand. Visitors will have the opportunity to try on the device, observe others using it, and reflect on its capabilities to transform body movement and perception through the sonification of muscle-tone and force. Overall, Joakinator is a technology that pushes the boundaries of bodycomputer interaction and opens new possibilities for human-computer interaction and expression. © 2023, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Machine Perception

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