Blekinge Institute of Technology
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Simplicity of Non-associative Skew Laurent Polynomial Rings
No full textIn 1903, Hilbert [4] introduced a ring of formal Laurent series with a skewed or twisted multiplication to show the existence of a non-commutative ordered division ring. Nowadays, the rings and their corresponding multiplication are thus referred to as skew or twisted Laurent series rings and Hilbert’s twist [6], respectively. Thirty years later, Ore [12] initiated the study of what he called ‘non-commutative polynomial rings’, today more commonly known as Ore extensions. Since their introductions, skew Laurent series rings, Ore extensions and the closely related skew Laurent polynomial rings have been studied quite extensively (see e.g. [3, 6, 7] for comprehensive introductions). Moreover, some years ago, Nystedt, Öinert and Richter [10] introduced a non-associative generalization of Ore extensions. We introduce non-associative skew Laurent polynomial rings and characterize when they are simple. Thereby, we generalize results by Jordan, Voskoglou, and Nystedt and Öinert
Ranking approaches for similarity-based web element location
No full textContext: GUI-based tests for web applications are frequently broken by fragility, i.e. regression tests fail due to changing properties of the web elements. The most influential factor for fragility are the locators used in the scripts, i.e. the means of identifying the elements of the GUI. Objective: We extend a state-of-the-art Multi-Locator solution that considers 14 locators from the DOM model of a web application, and identifies overlapping nodes in the DOM tree (VON-Similo). We augment the approach with standard Machine Learning and Learning to Rank (LTR) approaches to aid the location of web elements. Method: We document an experiment with a ground truth of 1163 web element pairs, taken from different releases of 40 web applications, to compare the robustness of the algorithms to locator weight change, and the performance of LTR approaches in terms of MeanRank and PctAtN. Results: Using LTR algorithms, we obtain a maximum probability of finding the correct target at the first position of 88.4% (lowest 82.57%), and among the first three positions of 94.79% (lowest 91.86%). The best mean rank of the correct candidate is 1.57. Conclusion: The similarity-based approach proved to be highly dependable in the context of web application testing, where a low percentage of matching errors can still be accepted
Governing the commons : code ownership and code-clones in large-scale software development
No full textContext: In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. This creates diversity both in the knowledge of, and in the responsibility for, individual components. Objective: Our objective is to understand how and why different teams introduce technical debt in the form of code clones as they change different components. Method: We collected data about change size and clone introductions made by ten teams in eight components which was part of a large industrial software system. We then designed a Multi-Level Generalized Linear Model (MLGLM), to illustrate the teams’ differing behavior. Finally, we discussed the results with three development teams, plus line manager and the architect team, evaluating whether the model inferences aligned with what they expected. Responses were recorded and thematically coded. Results: The results show that teams do behave differently in different components, and the feedback from the teams indicates that this method of illustrating team behavior can be useful as a complement to traditional summary statistics of ownership. Conclusions: We find that our model-based approach produces useful visualizations of team introductions of code clones as they change different components. Practitioners stated that the visualizations gave them insights that were useful, and by comparing with an average team, inter-team comparisons can be avoided. Thus, this has the potential to be a useful feedback tool for teams in software development organizations that employ weak or collective ownership. © The Author(s) 2024
Affective Aspects of Screening for Intimate Partner Violence : The Impact of Emotions on the Implementation of Routinely Asking Questions About Violence in Women’s Health Care
No full textThere are both facilitating and hindering factors when it comes to screening for intimate partner violence (IPV). While research indicates that health-care providers’ emotions regarding screening serve as an influencing factor, there is little scholarly work that has systematically considered the role of emotions in inquiring about IPV. Addressing this research gap, the article explores the affective aspects of routinely asking questions about violence in women’s health care. The findings show that emotions serve as both antecedents and consequences of routine inquiry, indicating that the role of emotions should be viewed as integral to any effort to improve screening practices for IPV
A License Management System for Collaborative AI Engineering
No full textThe AI marketplace ecosystem accelerates multiple modules of the AI engineering pipeline by fostering collaboration between stakeholders. However, marketplace collaborators often face a dilemma in striking a balance between sharing artifacts and protecting intellectual property (IP) rights. Thus, there is a need for a license management system within the AI marketplace to facilitate the exchange of artifacts in a trusted and secure manner. This work shares experiences while building such a license management system within the Bonseyes marketplace (BMP), a functional crowdsourcing AI marketplace that specializes in deploying real-time applications on edge devices. The BMP was developed, and its applicability is proven through the European H2020 project by a series of open calls and workshops, for gathering stakeholders and orchestrating the marketplace operations. The main contributions of this work are (i) implementation of an end-to-end license management system that deals with selecting license templates, license agreement interaction between seller and buyer, and the generation and enforcement of human- and machine-readable license files, and (ii) introduction of "Synchronization licenses'' concept from the music industry to the AI marketplace context where consumers acquire a license to integrate the artifact into another application, and a respective BMP use-case for collaborative AI engineering. dAIEDGE: HORIZON-CL4-2022-HUMAN-02-0
Fine-tuning Large Language Models for Software Supply Chains Threats Mitigation
No full textThe growing complexity and interconnectivity of software supply chains have elevated the risks of security threats, demanding innovative solutions. This thesis investigates the fine-tuning of Large Language Models (LLMs), particularly Microsoft Phi-2, to enhance their ability to identify and mitigate software supply chain vulnerabilities. Using advanced techniques such as Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA), the Phi-2 model was trained on a domain-specific dataset comprising incident reports, threat intelligence data, and best practices. The methodology encompasses a rigorous evaluation process using quantitative metrics, including ROUGE, BERTScore, and BLEURT, supplemented by qualitative insights derived from semi-structured interviews with cybersecurity experts. The in[1]terviews revealed valuable perspectives on the practical applicability of the fine-tuned model in addressing real-world threats such as compromised third-party components, open-source dependency vulnerabilities, and emerging attack patterns. The fine-tuned model exhibited significant improvements in generating contex[1]tually relevant, precise, and actionable threat mitigation strategies compared to its baseline. The findings demonstrate that domain-specific fine-tuning of LLMs is a vi[1]able approach for advancing automated threat detection and response capabilities in software supply chains. This research provides a robust framework for integrating AI[1]driven solutions into the software development lifecycle, contributing to the fields of software engineering and cybersecurity by improving resilience against supply chain attacks
Very good gradings on matrix rings are epsilon-strong
No full textWe investigate properties of group gradings on matrix rings (Formula presented.), where R is an associative unital ring and n is a positive integer. More precisely, we introduce very good gradings and show that any very good grading on (Formula presented.) is necessarily epsilon-strong. We also identify a condition that is sufficient to guarantee that (Formula presented.) is an epsilon-crossed product, i.e. isomorphic to a crossed product associated with a unital twisted partial action. In the case where R has IBN, we provide a characterization of when (Formula presented.) is an epsilon-crossed product. Our results are illustrated by several examples.
Att möta glömskan: Sjuksköterskans erfarenheter av att vårda personer med demenssjukdom på somatisk vårdavdelning : En allmän litteraturöversikt
No full textBakgrund: Demenssjukdom är ett samlingsnamn för olika sjukdomar som karaktäriseras av kognitiva nedsättningar. Varje år insjuknar allt fler individer på grund av den ökade livslängden hos människor. Det är en obotlig sjukdom som gör att individen progressivt förlorar färdigheter som individen tidigare besuttit. Omvårdnad kan ses som den mest optimala behandlingen för demenssjukdomar och omvårdnad är sjuksköterskans huvudområde. Trots detta blir omvårdnad komplext vid vård av personer med demenssjukdom på grund av de symptom som individen upplever. Syfte: Syftet var att beskriva sjuksköterskors erfarenheter av att vårda personer med demenssjukdom på somatisk vårdavdelning. Metod: En allmän litteraturöversikt bestående av kvalitativa artiklar genomfördes. Sökningar genomfördes i databaserna Cinahl och Pubmed vilket resulterade i nio vetenskapliga artiklar. Artiklarna analyserades med Fribergs fyrstegsmodell som grund för att sammanställa ett resultat. Resultat: Resultatet presenterades i två huvudkategorier; Sjuksköterskors erfarenheter av förutsättningar som skapar en god vård och Sjuksköterskors erfarenheter av hinder för en god vård. Vidare delades huvudkategorierna in i tre respektive fyra underkategorier; erfarenheter av ett personcentrerat förhållningssätt, erfarenheter av samverkan i team och erfarenheter av kunskap om demenssjukdom samt erfarenheter av kommunikationssvårigheter, erfarenheter av brist på tid, erfarenheter av miljön som hinder och erfarenheter av känslomässig påverkan. Slutsats: Resultatet visar att vårdandet av personer med demenssjukdom är ett komplext arbete. Arbetet kräver anpassning och tid som inte alltid finns att tillgodose. Vårdavdelningar är sällan anpassade för denna typ av vård vilket försvårar arbetet ytterligare. Kompetens kring ämnet ansågs vara av vikt och särskild utbildning om demenssjukdomar kan vara välgörande både för sjuksköterskor och patienter
Enhancing Peak-Hour Connectivity in Urban Ride-Sharing Platforms through Dynamic Graph Theory Analysis : A Simulation-Based Approach
No full textThis thesis explores the application of dynamic graph theory to optimize urban ridesharing platforms, particularly during peak-hour traffic congestion. By integrating real-time traffic data with dynamic routing algorithms, this research aims to improve ride-sharing efficiency, reduce congestion, and enhance urban mobility. The study investigates the effectiveness of combining Dijkstra’s and A* algorithms, focusing on the dynamic adjustment of routes based on real-time traffic conditions. Further, the study leverages a simulation of the city of Gothenburg, Sweden’s road network, this the result from this demonstrates that the combination of these algorithms significantly reduces travel time and congestion compared to traditional static routing methods. The results of this study contribute to the development of more efficient, adaptive, and scalable ride-sharing systems, with implications for urban transportation planning and policy. The findings emphasize the importance of integrating real-time traffic data into ride-sharing platforms to improve service delivery and reduce congestion during peak hours
Prediction of Parkinson’s Disease : A comparative analysis of supervised machine learning algorithms using voice and speech signal data
No full textBackground: People irrespective of race and place, get affected by Parkinson’sDisease (PD). Approximately, 1% of the world’s population gets affected by thisdisease after reaching the age of 60. PD is a neural disorder that causes uncontrollable shaking of the legs or whole body. People with this disease might also developsymptoms such as insomnia, memory-related issues, depression, and changes in behavior. Parkinson’s Disease PD is a neurodegenerative disorder that progressivelyaffects both motor skills and other functions. Detecting it early is essential for better disease management. One of the earliest signs of PD is changes in speech, asindividuals often experience alterations in their voice, pronunciation, and fluency. Itis very important to predict the chance of falling victim to the disease by taking afew attributes into account. This can be done with the help of machine learningtechniques. Machine learning algorithms such as Logistic Regression (LR), RandomForest (RF), and K Nearest Neighbours (KNN) were used to predict if someone isat risk of getting diagnosed with this disorder. Objectives: The objective of this research work is to build machine learning modelsby training machine learning algorithms and to find out which algorithm is the mostaccurate in predicting Parkinson’s Disease. Methods: Two research methodologies were used in this research. We have used Literature review and Experimentation to complete the objectives. We have reviewedmany existing research papers published on PD and its prediction using machinelearning. Finally, we have used experimentation to build three machine learningmodels by training their respective algorithms with a data set collected from theweb. Results: By performing a Literature review, algorithms like LR, RF, and KNNwere selected. In the experimentation part, models have been built by training thealgorithms with the data set "Parkinson’s Disease" and KNN is the best-performingalgorithm with the highest accuracy. Conclusions: Three machine learning models, LR, RF, and KNN were built bytraining their respective algorithms using the data set "Parkinson’s Disease". Thisresearch has concluded by saying that KNN is the best-performing algorithm as itachieved the highest accuracy in the prediction of PD