Examensarbeten för masterexamen
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- PostADHD, Mental Health and Smart Devices - Exploring Preferences and Perceptions of Smart Watches and Devices and their Potential Role in Supporting Adults with ADHD(2024) Salomonsson Stridsberg, Evelina; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Fjeld, Morten; Mylonopoulou, VasilikiThis thesis explores the potential of wearable technology, specifically smartwatches, to assist individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) in managing daily challenges. ADHD is characterised by inattention, hyperactivity and impulsivity, which can significantly affect executive functioning and overall well-being. The study investigated how individuals with ADHD currently use wearables and other technologies, their preferences, challenges and perceived benefits. It also explored how smartwatches can be tailored to provide support and how these devices can integrate the strategies that individuals with ADHD use to address their everyday challenges. It concludes that smartwatches can be tailored to address these challenges through features like customisable notifications, alarms, timers and reminders that align with the individuals routines, as well as quick note-taking through voice recording. Additionally, it emphasises the importance of providing education, trial opportunities and personalised setup assistance to enhance the adaption and effective use of wearable technology. The findings highlight the need for further research and integration of these tools within healthcare systems or educational institutions to provide additional support for people with ADHD in their daily lives.
- PostDeveloping a one-to-many generation LLM for diverse, accurate and efficient retrosynthesis(2024) Li, Junyong; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Engkvist, Ola; Johansson, RichardOne of the most common applications of deep learning for cheminformatics is retrosynthesis, which is a task of predicting reactants given a chemical product. After transformer was invented, it has been widely used for retrosynthesis. Chemformer is a transformer-based model, which was pre-trained using SMILES of chemical molecules first and can be fine-tuned for retrosynthesis. The model achieves stateof- the-art performance on this task. Retrosynthesis task expects multiple predictions of reactants. Chemformer uses beam search or multinomial search to get multiple predictions, which results in a lack of diversity, accuracy and efficiency of the model. In this project, the sphere projection strategy, which is a one-to-many generation strategy, was applied to Chemformer to enable it to generate multiple predictions. The sphere projection achieves one-to-many generation by introducing variations of source embedding of encoder and combining those variations with a single-prediction sampler, such as greedy search and multinomial search (multinomial size = 1). By comparing the modified Chemformer with sphere projection strategy to the baseline Chemformer, it was shown that the strategy can improve diversity, accuracy and efficiency by 197%, 7% and 4% respectively for beam search, and 101%, 2% and 17% respectively for multinomial search.
- PostAI and ML for Software Product Management: A Framework for Emerging Challenges(2024) JÖNMARK, JULIA; SÖDERSTRÖM, HANNA; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Knauss, Eric; Bosch, JanIn the rapidly evolving landscape of software product management (SPM), the integration of artificial intelligence (AI) and machine learning (ML) presents both unprecedented opportunities and significant challenges. This thesis investigates the impact of AI and ML on SPM practices and develops a comprehensive framework tailored to address the emerging needs of this dynamic field. Utilizing a mixedmethod approach, the study first conducts a systematic literature review to identify the current utilization and challenges of AI and ML within SPM. This is followed by empirical data collection through interviews with professionals in the field, ensuring a robust foundation for framework development. The research findings reveal that while AI and ML can significantly enhance decision-making and efficiency in SPM, they also introduce complexities related to integration, ethics, and management. In response, this thesis proposes a novel SPM framework that incorporates how SPM should use AI and ML components and tools effectively, focusing on enhancing SPM and aligning with digital transformation and digitalization goals. The framework is validated through a workshop and an interview with experts in the field. This study aims to bridge a crucial gap in academic literature and to also offer practical insights for individuals and organizations aiming to leverage AI and ML for enhanced SPM strategies, ensuring both competitive advantage and alignment with evolving technological landscapes.
- PostCybersecurity requirements identification using LLMs - A design science study(2024) Linde, Filip; Sanner, Oscar; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Horkoff, Jennifer; Fotrousi, FarnazContext: Threat analysis and risk assessment (TARA) is a widely used approach for conducting cybersecurity analysis in the automotive industry. The process is initiated early in the development process and continuously iterated. Problems: Automotive systems continue to rely more on software. Additionally, the National Vulnerability Database (NVD) show that more vulnerabilities are found each year. As a result, much time has to be spent continuously ensuring that systems have updated TARA analysis. Method: We designed a Large Language Model (LLM) based artifact to help security engineers by automatically identifying attack paths and security requirements. The artifact achieved this via a combination of prompt engineering and grounding in both the Common Vulnerabilities and Exposures (CVE) database, and the Automotive Information Sharing and Analysis Center (Automotive-ISAC) Automotive Threat Matrix (ATM). Result: The artifact could define security requirements which met the expected standards of practitioners and were correct based on the attacks they were generated to mitigate. However, challenges were identified in the generation of attacks paths, where the generated output was less consistent in how well it met expectations. Experts perceived it to be able to generate appropriate requirements for an initial TARA analysis, however future work is needed to determine how more complex paths and requirements could be identified automatically.
- PostParallel and Distributed Motif Discovery in Temporal Networks - A feasibility study applying thread parallelism and community structure(2024) Marton, Stefan; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Damaschke, Peter; Hassan, Ahmed Ali-EldinTemporal networks are used to model complex systems in topics such as epidemiology, finance, and computer networks. Motifs are subgraphs of a directed graph that are representative of the structure of that particular graph. Motifs have been extended to temporal networks. Motif discovery is a computationally hard problem; in fact sub-problems are NP-hard problems. In this thesis, we explore state-of-theart temporal network motif discovery algorithms, and how they can be parallelized on a multi-threaded system and distributed across multiple systems. We select Kovanen’s definition of temporal network motif. We implement a simple approach to thread parallelism to demonstrate the potential for parallelism of the algorithm, and find that the parallelizable proportion p > 0.89, which implies great potential for parallelism. We utilize community structure of the graph the temporal network represents to divide the network into work packages for distributed computation. In doing so, we encounter and report numerous challenges in distribution of the exact solution approach.