3,158 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Insights into software development approaches: mining Q &A repositories

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    © 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Context: Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective: We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method: We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings: We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions: The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.Peer reviewe

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Emerging Digital Technologies in Patient Care: Dealing with connected, intelligent medical device vulnerabilities and failures in the healthcare sector

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    The integration of the Internet of Medical Things (IoMT) and Artificial Intelligence (AI) into clinical routines is significantly impacting organisational preparedness at the point of care, raising concerns not only about the resilience of the healthcare infrastructure, but also about how physicians, clinicians, and healthcare professionals respond to, manage, and reduce new risks associated with connected and intelligent medical devices in the interest of patient safety and care. The following report summarises findings from the workshop entitled Emerging Digital Technologies in Patient Care: Dealing with Connected, Intelligent Medical Device Vulnerabilities and Failures in the Healthcare Sector, held on 23 February 2023 at Goodenough College, London. The workshop was organised by members of the Reg-MedTech project, funded by the PETRAS National Centre of Excellence in IoT Systems Cybersecurity (EPSRC grant number EP/S035362/1), in collaboration with project partners at the BSI, the UK’s National Standards Body. Since October 2021, the Reg-MedTech project has investigated the extent to which current regulatory frameworks and standards address the critical cybersecurity, data governance, and algorithmic integrity risks posed by connected and intelligent medical devices. A critical finding from its ongoing research has been the need to develop standards, regulations, and policies that are better informed by the experiences of physicians, clinicians, and healthcare professionals dealing with software-based medical devices or software as a medical device (SaMD) in their day-to-day practice

    Learning, future cost and role of offshore renewable energy technologies in the North Sea energy system

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    The pace of cost decline of offshore renewable energy technologies significantly impacts their role in the North Sea energy transition. However, a good understanding of their remains a critical knowledge gap in the literature. Therefore, this thesis aims to quantify the future role of offshore renewables in the North Sea energy transition and assess the impact of cost development on their optimal deployments. The following findings were observed in this thesis, 1) Fixed-bottom offshore wind is well established in the North Sea region and is already competitive with onshore renewables 2) Floating wind is emerging and their current costs are high, but it can reach about 40 EUR/MWh by early 2040 and would require 44 billion EUR of learning investment.3) Grid connection costs will become a major factor as wind farm moves further away. Policy actions and innovation is needed in this space to avoid increasing integration costs. 4) Offshore wind (fixed-bottom and floating) can play a significant role in the North Sea energy system, comprising 498 GW of deployments in 2050 (222 GW of fixed-bottom and 276 GW of floating wind) and contributing up to a maximum of 51% of total power generation in the North Sea power system. 5) The role of the investigated low-TRL offshore renewables, including the tidal stream, wave technology, and bioethanol, was limited in all scenarios considered, as they remain expensive compared to other mature technologies in the system

    Improving Outcomes in Machine Learning and Data-Driven Learning Systems using Structural Causal Models

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    The field of causal inference has experienced rapid growth and development in recent years. Its significance in addressing a diverse array of problems and its relevance across various research and application domains are increasingly being acknowledged. However, the current state-of-the-art approaches to causal inference have not yet gained widespread adoption in mainstream data science practices. This research endeavor begins by seeking to motivate enthusiasm for contemporary approaches to causal investigation utilizing observational data. It explores the existing applications and potential future prospects for employing causal inference methods to enhance desired outcomes in data-driven learning applications across various domains, with a particular focus on their relevance in artificial intelligence (AI). Following this motivation, this dissertation proceeds to offer a broad review of fundamental concepts, theoretical frameworks, methodological advancements, and existing techniques pertaining to causal inference. The research advances by investigating the problem of data-driven root cause analysis through the lens of causal structure modeling. Data-driven approaches to root cause analysis (RCA) have received attention recently due to their ability to exploit increasing data availability for more effective root cause identification in complex processes. Advancements in the field of causal inference enable unbiased causal investigations using observational data. This study proposes a data-driven RCA method and a time-to-event (TTE) data simulation procedure built on the structural causal model (SCM) framework. A novel causality-based method is introduced for learning a representation of root cause mechanisms, termed in this work as root cause graphs (RCGs), from observational TTE data. Three case scenarios are used to generate TTE datasets for evaluating the proposed method. The utility of the proposed RCG recovery method is demonstrated by using recovered RCGs to guide the estimation of root cause treatment effects. In the presence of mediation, RCG-guided models produce superior estimates of root cause total effects compared to models that adjust for all covariates. The author delves into the subject of integrating causal inference and machine learning. Incorporating causal inference into machine learning offers many benefits including enhancing model interpretability and robustness to changes in data distributions. This work considers the task of feature selection for prediction model development in the context of potentially changing environments. First, a filter feature selection approach that improves on the select k-best method and prioritizes causal features is introduced and compared to the standard select k-best algorithm. Secondly, a causal feature selection algorithm which adapts to covariate shifts in the target domain is proposed for domain adaptation. Causal approaches to feature selection are demonstrated to be capable of yielding optimal prediction performance when modeling assumptions are met. Additionally, they can mitigate the degrading effects of some forms of dataset shifts on prediction performance

    Artificial Intelligence-enabled Automation for Compliance Checking against GDPR

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    Requirements engineering (RE) is concerned with eliciting legal requirements from applicable regulations to enable developing legally compliant software. Current software systems rely heavily on data, some of which can be confidential, personal, or sensitive. To address the growing concerns about data protection and privacy, the general data protection regulation (GDPR) has been introduced in the European Union (EU). Organizations, whether based in the EU or not, must comply with GDPR as long as they collect or process personal data of EU residents. Breaching GDPR can be charged with large fines reaching up to up to billions of euros. Privacy policies (PPs) and data processing agreements (DPAs) are documents regulated by GDPR to ensure, among other things, secure collection and processing of personal data. Such regulated documents can be used to elicit legal requirements that are inline with the organizations’ data protection policies. As a prerequisite to elicit a complete set of legal requirements, however, these documents must be compliant with GDPR. Checking the compliance of regulated documents entirely manually is a laborious and error-prone task. As we elaborate below, this dissertation investigates utilizing artificial intelligence (AI) technologies to provide automated support for compliance checking against GDPR. • AI-enabled Automation for Compliance Checking of PPs: PPs are technical documents stating the multiple privacy-related requirements that a system should satisfy in order to help individuals make informed decisions about sharing their personal data. We devise an automated solution that leverages natural language processing (NLP) and machine learning (ML), two sub-fields of AI, for checking the compliance of PPs against the applicable provisions in GDPR. Specifically, we create a comprehensive conceptual model capturing all information types pertinent to PPs and we further define a set of compliance criteria for the automated compliance checking of PPs. • NLP-based Automation for Compliance Checking of DPAs: DPAs are legally binding agreements between different organizations involved in the collection and processing of personal data to ensure that personal data remains protected. Using NLP semantic analysis technologies, we develop an automated solution that checks at phrasal-level the compliance of DPAs against GDPR. Our solution is able to provide not only a compliance assessment, but also detailed recommendations about avoiding GDPR violations. • ML-enabled Automation for Compliance Checking of DPAs: To understand how different representations of GDPR requirements and different enabling technologies fare against one another, we develop an automated solution that utilizes a combination of conceptual modeling and ML. We further empirically compare the resulting solution with our previously proposed solution, which uses natural language to represent GDPR requirements and leverages rules alongside NLP semantic analysis for the automated support

    General Course Catalog [2022/23 academic year]

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    General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp

    Dialogue without barriers. A comprehensive approach to dealing with stuttering

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    The book Dialogue without barriers: A comprehensive approach to dealing with stuttering is the result of Norwegian-Polish cooperation undertaken in the project LOGOLab – Dialogue without barriers. Three partners have been involved in the production of this book, namely, the University of Silesia in Katowice, Poland, the UiT Arctic University of Norway in Tromsø, and the Agere Aude Foundation for Knowledge and Social Dialogue. The project was implemented under the Education Program financed by the EEA Grants (EEA / 19 / K1 / D1 / W / 0031). The EEA Grants represent the contribution of Iceland, Liechtenstein, and Norway towards a green, competitive, and inclusive Europe. The most important goal of the LOGOLab project was to raise the standards of speech-language therapy in stuttering by incorporating the principles of Evidence-based practice, taking into account the assumptions of inclusive education and community-based model of intervention. An essential strategy for achieving this goal has become the dissemination of reliable and up-to-date knowledge about stuttering, and the development of appropriate social attitudes towards stuttering. The improvement of the quality of academic education for speech-language therapy students and of vocational training for certified speech-language therapists should also be mentioned. An additional aim was to provide reliable information for leaders of the self-help movement, who support people with stuttering non-institutionally
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