Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
A Unifying Categorical View of Nondeterministic Iteration and Tests
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2024-09) Goncharov, Sergey; Uustalu, Tarmo; Majumdar, Rupak; Silva, Alexandra; Department of Computer Science
We study Kleene iteration in the categorical context. A celebrated completeness result by Kozen introduced Kleene algebra (with tests) as a ubiquitous tool for lightweight reasoning about program equivalence, and yet, numerous variants of it came along afterwards to answer the demand for more refined flavors of semantics, such as stateful, concurrent, exceptional, hybrid, branching time, etc. We detach Kleene iteration from Kleene algebra and analyze it from the categorical perspective. The notion, we arrive at is that of Kleene-iteration category (with coproducts and tests), which we show to be general and robust in the sense of compatibility with programming language features, such as exceptions, store, concurrent behaviour, etc. We attest the proposed notion w.r.t. various yardsticks, most importantly, by characterizing the free model as a certain category of (nondeterministic) rational trees.
ABA Argument Graphs with Constraints
(IOS Press BV, 2024-08-27) Thompson, Jeff; Thórisson, Kristinn R.; Reed, Chris; Thimm, Matthias; Rienstra, Tjitze; Department of Computer Science
Explainable Learning Analytics : Assessing the stability of student success prediction models by means of explainable AI
(2024-07) Tiukhova, Elena; Vemuri, Pavani; Flores, Nidia López; Islind, Anna Sigridur; Óskarsdóttir, María; Poelmans, Stephan; Baesens, Bart; Snoeck, Monique; Department of Computer Science
Beyond managing student dropout, higher education stakeholders need decision support to consistently influence the student learning process to keep students motivated, engaged, and successful. At the course level, the combination of predictive analytics and self-regulation theory can help instructors determine the best study advice and allow learners to better self-regulate and determine how they want to learn. The best performing techniques are often black-box models that favor performance over interpretability and are heavily influenced by course contexts. In this study, we argue that explainable AI has the potential not only to uncover the reasons behind model decisions, but also to reveal their stability across contexts, effectively bridging the gap between predictive and explanatory learning analytics (LA). In contributing to decision support systems research, this study (1) leverages traditional techniques, such as concept drift and performance drift, to investigate the stability of student success prediction models over time; (2) uses Shapley Additive explanations in a novel way to explore the stability of extracted feature importance rankings generated for these models; (3) generates new insights that emerge from stable features across cohorts, enabling teachers to determine study advice. We believe this study makes a strong contribution to education research at large and expands the field of LA by augmenting the interpretability and explainability of prediction algorithms and ensuring their applicability in changing contexts.
The current role and contribution of ‘forensic clinical psychologists’ (FCPs) to criminal investigation in the United Kingdom
(2024-06-19) Sigurdardóttir, Tinna Dögg; West, Adrian; Gudjonsson, Gisli Hannes
Purpose: This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research. Design/methodology/approach: The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided. Findings: Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%). Practical implications: The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work. Originality/value: To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.
Exquisitor at the Lifelog Search Challenge 2024 : Blending Conversational Search with User Relevance Feedback
(Association for Computing Machinery, Inc, 2024-06-18) Khan, Omar Shahbaz; Sharma, Ujjwal; Zhu, Hongyi; Rudinac, Stevan; Jónsson, Björn Pór; Department of Computer Science
The past decade has seen a rapid expansion of personal and interpersonal multimedia collections. These collections offer a wealth of information about individuals, including their interests, health, and significant life events. While automated techniques can assist in structuring and organizing these collections, they often have limitations in helping users effectively navigate and find relevant items within such large datasets. The Lifelog Search Challenge (LSC) provides a valuable benchmark for evaluating interactive retrieval systems designed for personal multimedia collections. Exquisitor utilizes a large-scale user relevance feedback (URF) approach for searching through large collections. To address challenges in highly descriptive retrieval tasks where the relevance feedback model may fail to identify essential elements, we have enhanced Exquisitor with conversational search capabilities powered by a Vision Language Model (VLM) and refined the features underlying the URF model. Furthermore, Exquisitor has been updated with a streamlined user interface that enables seamless switching between conversational search and URF modes.
Flokkar í Opnum vísindum
Veldu flokk til að skoða.
- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts