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Conference on software engineering Education & Training (CSEE&T)

作者： Michael Vierhauser Iris Groher Tobias Antensteiner Clemens Sauerwein Department of Computer Science University of Innsbruck Innsbruck Austria Institute of Business Informatics - Software Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM Computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas.

关键词： Systematics Large language models Learning (artificial intelligence) Chatbots Market research Artificial intelligence engineering education

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Expert-sourcing Domain-specific Knowledge: The Case of Synonym Validation

arXiv

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arXiv 2023年

作者： Unterkalmsteiner, Michael Yates, Andrew Software Engineering Research Lab Sweden Blekinge Institute of Technology Karlskrona Sweden Max Planck Institute for Informatics Saarbrücken Germany

One prerequisite for supervised machine learning is high quality labelled data. Acquiring such data is, particularly if expert knowledge is required, costly or even impossible if the task needs to be performed by a single expert. In this paper, we illustrate tool support that we adopted and extended to source domain-specific knowledge from experts. We provide insight in design decisions that aim at motivating experts to dedicate their time at performing the labelling task. We are currently using the approach to identify true synonyms from a list of candidate synonyms. The identification of synonyms is important in scenarios were stakeholders from different companies and background need to collaborate, for example when defining and negotiating requirements. We foresee that the approach of expert-sourcing is applicable to any data labelling task in software engineering. The discussed design decisions and implementation are an initial draft that can be extended, refined and validated with further application. © 2023, CC BY-NC-SA.

关键词： Supervised learning

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Transformers for Detection of Distressed Cardiac Patients with an ICD Based on Danish Text Messages

Transformers for Detection of Distressed Cardiac Patients wi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Weimar Andersen, Julie Dittmann Jensen, Marcus Lomstein Wiil, Uffe Kock Skovbakke, Soren Skov, Ole Pedersen, Susanne S. Peimankar, Abdolrahman Ebrahimi, Ali University of Southern Denmark Sdu Software Engineering The Maersk Mc-Kinney Moller Institute Odense5230 Denmark University of Southern Denmark Sdu Health Informatics and Technology The Maersk Mc-Kinney Moller Institute Odense5230 Denmark University of Southern Denmark Department of Psychology Odense5230 Denmark University of Southern Denmark Sdu Health Informatics and Technology The Maersk Mc-Kinney Moller Institute Odesne5230 Denmark

ISBN: (纸本)9798350337488

Cardiac patients with implantable cardioverter defibrillator devices frequently exhibit signs of anxiety and depression (termed 'Distressed'). Early detection of these patients is vital for evaluation, intervention, and prevention against relapse. Considering the growing datasets relevant to distress, coupled with the evolution of machine learning methodologies, there exists a promising prospect to develop intelligent systems for the detection of distressed cardiac patients through written materials. In this context, data from two sources were collected: a questionnaire and text communication messages, acquired through the randomized ACQUIRE-ICD study of 168 participants. These textual messages were labelled as either Distressed or Non-Distressed based on questionnaire responses. Following preprocessing, the dataset facilitated the development of transformer-based classification models, including mBERT, XLM-RoBERTa, ÆLÆCTRA, and RoBERTa, as well as a hard voting ensemble method to classify patients into Distressed and Non-Distressed categories. To address imbalances in class distribution and dataset scarcity, a data augmentation method was employed. Results indicated the superior performance of the proposed hard voting ensemble, recording weighted metrics of 80% precision, 67% recall, 73% F1-score, and 75% accuracy. Notably, this ensemble correctly identified 67% of Distressed samples, while the most efficient base transformer, mBERT, identified 63% of Distressed samples. © 2023 IEEE.

关键词： Distress Ensemble Machine Learning Mental Heath Transformers

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Utilizing Organizational Patterns to Build High-Performing Remote Teams 23

Utilizing Organizational Patterns to Build High-Performing R...

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Kristián Toldy Waheedullah Sulaiman Khail Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien und Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private.

关键词： agile communication distributed teams meeting organizational patterns remote work

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A study of media texts in the Slovak language

A study of media texts in the Slovak language

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Given Igor Stupavský Given Valentino Vranić Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Brastislava 4 Slovakia

ISBN: (数字)9781665483742

ISBN: (纸本)9781665483759

Nowadays, it is difficult to determine the veracity of the information obtained. Communication takes place through websites, discussions, social networks, and the like. Using various anti-social behavior tips, such as fake news, their disseminates want to gain various benefits or convince the recipient of “their truth.” The theoretical part of the work is focused on different approaches to the detection of antisocial behavior. In the experimental part of this work, we will deal with the analysis of the statuses of opinion-forming dailies in the Slovak language, which we obtained from the social network Facebook. NLP message analysis is not accurate enough, and we create an algorithm that is better identifies POS tags.

关键词： Technological innovation Social networking (online) Conferences Data collection Tagging Behavioral sciences Fake news

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software Process as a Service: Towards A software Process Ecosystem

arXiv

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arXiv 2025年

作者： Greulich, Oliver Knieke, Christoph Rafie, Bassel Rausch, Andreas Kuhrmann, Marco Technische Universität Clausthal Institute for Software and Systems Engineering Clausthal-Zellerfeld Germany Reutlingen University Faculty of Informatics Herman Hollerith Center Böblingen Germany

In large-scale projects operated in regulated environments, standard development processes are employed to meet strict compliance demands. Since such processes are usually complex, providing process users with access to their required process, which should be tailored to a project’s needs is a challenging task that requires proper tool support. In this paper, we present a process ecosystem in which software processes are provided as web-based services. We outline the general idea, describe the modeling approach, and we illustrate the concept’s realization using a proof-of-concept case based on a large software process line that is mandatory to use for IT projects in the German public sector. The suitability is evaluated with three experts that valued the improved accessibly and usability of the process and the end-user support tool. © 2025, CC BY.

关键词： software prototyping

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Self-Reported Learning Strategies and Preferences in Health informatics

Self-Reported Learning Strategies and Preferences in Health ...

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34th Medical informatics Europe Conference, MIE 2024

作者： Rohani, Narjes Gallagher, Michael Gal, Kobi Banas, Kasia Manataki, Areti Usher institute University of Edinburgh United Kingdom Moray House School of Education and Sport University of Edinburgh United Kingdom School of Informatics University of Edinburgh United Kingdom Dept. of Software and Information Systems Engineering Ben-Gurion University Israel School of Computer Science University of St Andrews United Kingdom

ISBN: (纸本)9781643685335

Despite the proliferation of educational programmes in Health informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education. © 2024 The Authors.

关键词： Contrastive Learning

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Options Evaluator With an Artificial Intelligence-Based Volatility Model

Options Evaluator With an Artificial Intelligence-Based Vola...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Árpád Rigó Balázs Tusor John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

The subject of this paper is an options modeling system, which aims to provide the most accurate profit forecast possible for options portfolios in a comprehensible form, as software on the market will misrepresent this in the absence of accurate implied volatility data, which can put trading success at risk. The software determines future implied volatility from 1-year historical options on VXX stock with 6 samples per trading day using statistics and a deep neural network (Long short-term memory LSTM). Using this statistical approach and the trained volatility model, the system calculates the profit/loss curve, thus providing a more accurate picture of the possible future outcomes of a given portfolio.

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Constellation Recognition on Digital Images

Constellation Recognition on Digital Images

引用

International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Zsuzsanna Molnár Dániel Kiss John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

In this paper, we present an optimization-based approach for star constellation recognition. The main components of the proposed procedure are the processing of digital images and the minimization of the error in point cloud matching by parameter optimization. In the optimization phase, the Nelder– Mead algorithm, the simulated annealing algorithm, and an evolutionary algorithm were used. Also, the behavior of these methods is evaluated and compared in the paper based on their average error values for different test images. Results on our test dataset showed that the Nelder–Mead simplex algorithm was performing the best in solving the recognition task.

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Parameter Optimization of a Cellular Automaton Model in Distributed Environment

Parameter Optimization of a Cellular Automaton Model in Dist...

引用

International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Attila Jancsi Dániel Kiss John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

We present a distributed software environment to search for optimal parameters of the Cellular Potts Model using data from cell culture experiments. The software architecture follows a master-worker pattern. The worker nodes are executing simulations of the model with given parameter sets and send the computed objective function values back to the master. The master node implements an optimization method, a genetic algorithm in our case, and a task scheduler to distribute the generated simulation tasks among the workers through the network. Results showed that an optimal combination of parameters can be found for the model, and the efficiency scales linearly with the number of workers.

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