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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Sava, Cezar Deaconescu, Razvan Kroning, Martin Monti, Antonello Turcanu, Dinu POLITEHNICA of Bucharest Computer Science and Engineering Department Bucharest Romania RWTH Aachen University Institute for Automation of Complex Power Systems Aachen Germany Fraunhofer Center Digital Energy Fraunhofer FIT Aachen Germany University of Moldova Department of Software Engineering and Automatics Technical Chişinǎu Moldova

ISBN: (纸本)9798331540388

Educational operating systems are crucial for facilitating learning in the digital age, as they exist at the intersection of technology and education. While their technical role is well understood, improving their educational value requires enhancing traditional approaches. This paper explores ways to enrich educational operating systems not just through technological advancement, but also by integrating pedagogical principles. In an ever-changing world, the need for an adaptive and responsive educational operating system to meet diverse student needs is crucial. Through case studies, we analyze how design, interactivity, and accessibility can transform these systems into effective and appealing learning environments. We propose a series of recommendations for the future development of educational operating systems, focusing on enhancing the learning experience and promoting student autonomy, thereby offering a fresh perspective on how technology and education can blend harmoniously and efficiently. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Taming Cross-Tool Traceability in the Wild 31

Taming Cross-Tool Traceability in the Wild

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31st IEEE International Requirements engineering Conference, RE 2023

作者： Ratiu, Cosmina Cristina Mayr-Dorn, Christoph Assuncao, Wesley K. G. Egyed, Alexander Institute of Software Systems Engineering Johannes Kepler University Linz Linz Austria

ISBN: (纸本)9798350326895

Along the process of engineering a safety-critical system, software engineers produce various artifacts, ranging from requirements and change requests to source code and test cases. In order to aid the development of the system and to adhere to the complex safety regulations and standards in place, engineers are often required to maintain bidirectional and consistent traceability between the produced artifacts. However, such artifacts are rarely maintained in one single tool. Because of that, the cross-tool bidirectional traces have to frequently be manually maintained, which can easily become a very time-consuming or infeasible task. Through interviews and observations at our industry partners in regulated domains, we observed that a number of different strategies are used to deal with this challenge. The use of naming conventions, querying, or URL links is observed in the industry. However, they have their shortcomings and hinder engineers from realizing the full potential that traceability can offer. Knowing the challenges in the industry, we explored existing literature. A range of approaches in the literature aims at dealing with traceability, but often they are context-specific and not easily transferable into practice. Given this gap between the state-of-the-art and industry needs, we performed interviews with our industry partners and analyzed tertiary studies from the literature to obtain a better understanding of what traceability properties are needed to unleash the potential of traces. We identified properties that represent the shared challenges between the related work and the industry requirements: discoverability, type checks, flexibility, navigability, and extensibility. While each property is addressed by a subset of the available solutions, we propose a novel traceability approach to support all of them in a single tool. © 2023 IEEE.

关键词： Engineers

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Approaches And Research Directions For Adapting Rapid Prototyping In Industrial Service Development: A Systematic Literature Review 57

Approaches And Research Directions For Adapting Rapid Protot...

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18th IFAC Workshop on Time Delay systems, TDS 2024

作者： Hefft, Daniel Große, Nick Kreyenborg, Alexander Kiklhorn, David Henke, Michael Fraunhofer Institute for Material Flow and Logistics Joseph-von-Fraunhofer-Str. 2-4 Dortmund44227 Germany TU Dortmund University August-Schmidt-Str. 4 Dortmund44227 Germany Fraunhofer Institute for Software and Systems Engineering Speicherstr. 6 Dortmund44147 Germany

Services offer companies unique opportunities to expand their portfolio with customer-oriented solutions leading to competitive advantages due to their uniqueness. Nevertheless, companies struggle with the development and successful implementation of industrial services. The main reason for this lies in a lack of suitable development approaches and procedures for industrial services, which primarily originate from product development. Rapid prototyping (RP) aims to enhance service development (SD) by considering the need for highly iterative and fast development with simultaneous involvement of partners and customers. Against that backdrop, this paper explores use-cases, frameworks, and applications of RP to contribute to restructuring industrial SD. Utilizing a systematic literature review (SLR), RP approaches in the various disciplines of engineering are collected. After extracting important features and aggregated dimensions, the authors compare their findings to meta-theoretical foundations of Service-Dominant Logic (SDL) to give an outlook on future research on adapting RP in SD. © 2024 The Authors.

关键词： Service industry

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of Computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Control Action Types - Patterns of Applied Control for Self-adaptive systems (Summary)

Control Action Types - Patterns of Applied Control for Self-...

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2024 software engineering, SE 2024

作者： Kröher, Christian Gerling, Lea Schmid, Klaus University of Hildesheim Institute of Computer Science Software Systems Engineering Universitätsplatz 1 Hildesheim31141 Germany

ISBN: (纸本)9783885797371

This paper summarizes [KGS23], which received the Best Paper award at the 18th Symposium on software engineering for Adaptive and Self-Managing systems (SEAMS23) in 2023. A Self-adaptive System (SaS) modifies its behavior according to changes in its environment autonomously. Distributed control and central control represent two complementary paradigms to establish this capability. Both of these approaches come with respective advantages and disadvantages. A promising approach to minimize these negative aspects is to combine them to achieve the best of both paradigms. In this paper, we present patterns for integrating distributed and central control, which we call Control Action Types (CATs). Each CAT describes a unique type of interaction between a central controller and a distributed controlled SaS to achieve a desired adaptation. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Decentralized control

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An Empirical Examination of the Technical Aspects of Data Sovereignty

An Empirical Examination of the Technical Aspects of Data So...

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19th International Conference on software Technologies, ICSOFT 2024

作者： Pampus, Julia Heisel, Maritta Institute for Software and Systems Engineering ISST Dortmund Germany Paluno - The Ruhr Institute for Software Technology University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9789897587061

Self-determination and autonomy in data sharing, in recent research also referred to as data sovereignty, arouses increasing interest in the context of industrial ecosystems. Its practical implementation considers organisational, regulatory, legal, and particularly technical aspects. Previous work has not yet focused on the structured analysis of technical characteristics of systems used in data sharing concerning data sovereignty. In this paper, we therefore elicit what system requirements help the data sovereignty of a data sharing participant, starting from privacy protection goals, FAIR principles, and ISO/IEC 25010:2011. To address this, we conducted a qualitative study in the form of an online questionnaire. We asked 18 domain experts to evaluate selected system criteria for their impact and relevance to the implementation of data sovereignty. Our work has resulted in a set of 22 functional requirements that can be used for designing data sharing systems. Subsequently, we discuss our findings, compare them with related work, and address further research. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Data consistency

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Supporting Changes in Digital Ownership and Data Sovereignty Across the Automotive Value Chain with Catena-X

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Procedia Computer Science 2025年 253卷 374-383页

作者： Marvin Manoury Theresa Riedelsheimer Malte Hellmeier Tom Meyer Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstrasse 8-9 10587 Berlin Germany Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraße 6 44147 Dortmund Germany

Considering the concept of a Circular Economy, which entails several life cycles of, e.g., vehicles, their components, and materials, it is important to investigate how the respective Digital Twins are managed over the lifecycle of their physical assets. This publication presents and compares three approaches for managing Digital Twins in industrial use cases. For the aggregation of these approaches, a user-centered design approach has been used in the research project Catena-X which focuses on the automotive industry. The results are analyzed qualitatively based on aspects such as data ownership and data sovereignty. It was identified, that all three approaches have limitations and benefits which make them useful in different scenarios. An overview of their strengths and weaknesses is presented to select an appropriate approach In similar setups. The results described in this publication can be used to select the best update approach in data spaces where multiple stakeholders are able to update information on a single asset.

关键词： Digital Twins Ownership Circular Economy Data Sovereignty Supply Chain Catena-X

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On Characteristics and Process Requirements of Artificial Intelligence Projects

On Characteristics and Process Requirements of Artificial In...

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2024 Projektmanagement und Vorgehensmodelle, PVM 2024 - 2024 Project Management and Process Models, PVM 2024

作者： Krieg, Alexander Theobald, Sven Brandt, Sarah Guckenbiehl, Pascal nextOrange GmbH Geschäftsführung Stadtdeich 2-4 Hamburg20097 Germany Fraunhofer-Institute for Experimental Software Engineering Data Science Fraunhofer-Platz 1 Kaiserslautern67663 Germany Fraunhofer-Institute for Experimental Software Engineering Smart City Design Fraunhofer-Platz 1 Kaiserslautern67663 Germany TOPdesk Deutschland GmbH Carl-Euler-Straße 8 Kaiserslautern67663 Germany

ISBN: (纸本)9783885797470

For computer scientists around the world, Artificial Intelligence (AI) is not a new topic. Most people use AI unconsciously in their everyday life, for example when they use their smartphones. Since the release of ChatGPT in 2022, AI hit the interest of industry and society. The expectations on AI projects are high, companies hope to reduce costs and increase efficiency by inventing AI solutions in their companies. The aim of this paper was to better understand the char-acteristics of AI projects as well as the differences to classic software projects. We also wanted to investigate what kind of process model and project management framework is most suitable to suc-cessfully lead AI projects. We conducted structured interviews with four AI experts to gain an initial overview. The results provide insights into AI projects and suggest ideas for future research on soft-ware engineering for AI projects. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Project management

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