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 qualitative analysis of the experience of gambling harm and accessing support among United Kingdom Armed Forces personnel
(2025-12) Champion, Hannah; Biggar, Blair; Jones, Matthew; Larcombe, Justyn; Fossey, Matt; Dymond, Simon; Department of Psychology
Background: Military personnel (both currently serving and veterans) are vulnerable to harm from gambling, yet many are reluctant to seek help. The aims of this study were to explore the lived experience of gambling and gambling harm in currently serving members of the UK Armed Forces and to seek to improve military-specific gambling harm information and support. Methods: Semi-structured interviews were undertaken with self-selected currently serving personnel from the UK Armed Forces. Interview questions focus on lived experience of gambling harm, motivators and triggers around gambling, and awareness of gambling information and support. Results: Thematic analysis identified four main themes: (1) sociocultural pathways to gambling harm; (2) influencing factors unique to military life; (3) obstacles to early intervention and support; and (4) facilitators of help and support. Conclusions: Findings showed that the nature and extent of gambling harm within the UK Armed Forces may not be fully acknowledged, and that currently serving personnel face barriers accessing safer gambling information and support. Specifically, there was a lack of education around the nature of gambling harm, identifying it, how to go about seeking help, and from whom. The normalisation of potentially harmful behaviour, stigmatising attitudes, and concerns around anonymity serve as further barriers to help-seeking. There is a need to raise awareness, reduce stigma, and enhance support for gambling harm within the UK Armed Forces.
AppCon : Mitigating evasion attacks to ML cyber detectors
(2020-04-01) Apruzzese, Giovanni; Andreolini, Mauro; Marchetti, Mirco; Colacino, Vincenzo Giuseppe; Russo, Giacomo; Department of Computer Science
Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature. We address this problem by presenting AppCon, an original approach to harden intrusion detectors against adversarial evasion attacks. Our proposal leverages the integration of ensemble learning to realistic network environments, by combining layers of detectors devoted to monitor the behavior of the applications employed by the organization. Our proposal is validated through extensive experiments performed in heterogeneous network settings simulating botnet detection scenarios, and consider detectors based on distinct machine-and deep-learning algorithms. The results demonstrate the effectiveness of AppCon in mitigating the dangerous threat of adversarial attacks in over 75% of the considered evasion attempts, while not being affected by the limitations of existing countermeasures, such as performance degradation in non-adversarial settings. For these reasons, our proposal represents a valuable contribution to the development of more secure cyber defense platforms.
A novel mobile application to examine impaired vigilance through digital means
(2025-12) Pahari, Purbanka; Schmitz, Lisa; Richert, Elena; Jóhannsdóttir, Kamilla Rún; Kristbergsdóttir, Hlín; Korkalainen, Henri; Töyräs, Juha; Leppänen, Timo; Nikkonen, Sami; Arnardottir, Erna Sif; Islind, Anna Sigridur; Department of Engineering; Department of Psychology; Department of Computer Science
The psychomotor vigilance task (PVT) is a three to thirty-minute test widely used to measure alertness and vigilance. However, only a few comprehensively validated PVTs are currently available through digital means, e.g., mobile devices. Thus, the present study aimed to investigate the usefulness of a novel three-minute PVT (PVTSR) delivered through a touchscreen mobile device. The present study comprised 41 healthy or undiagnosed with obstructive sleep apnea participants recruited through local advertisement in Reykjavik, Iceland between 2021 and 2022. First, participants completed an in-lab ten-minute standard PVT (PVTstd) on a computer with the Inquisit software at Reykjavik University Sleep Institute. Later, the same participants completed a three-minute PVTSR in the Sleep Revolution mobile application at home. With a paired sample t-test, differences between the PVT outputs, i.e., mean reaction time (RT) and median RT, were compared between PVTstd and PVTSR. In addition, effect sizes and Pearson correlations between the PVTstd and PVTSR outcomes were evaluated. The average mean RT (PVTstd: 350.2 ms; PVTSR: 424.1 ms) and median RT (PVTstd: 334.4 ms; PVTSR: 415.2 ms) were significantly (p < 0.001) shorter in PVTstd compared to PVTSR. The effect sizes for mean RT and median RT were 0.70 and 0.79, respectively. In addition, the correlations of mean RT (r = 0.96, 95% confidence interval (CI): 0.92–0.98) and median RT (r = 0.94, 95% CI: 0.89–0.97) between PVTstd and PVTSR were high. Although median and mean reaction times were systematically higher when measured with the mobile application, there was a strong correlation between the methods’ outcomes. After accounting for this systematic increase, the novel PVTSR approach shows potential for evaluating impaired vigilance. With future fine-tuning to adjust for the systematic delay, this readily available three-minute test could enable long-term follow-up of impaired vigilance in participants’ homes.
An inner-and outer-fed dual-arm archimedean spiral antenna for generating multiple orbital angular momentum modes
(2019-02) Wang, Lulu; Chen, Huiyong; Guo, Kai; Shen, Fei; Guo, Zhongyi; Department of Engineering
Orbital angular momentum (OAM) beams have attracted great attention owing to their excellent performances in imaging and communication. In this paper, a dual-arm Archimedean spiral antenna (DASA) is proposed to generate multiple OAM states with positive and negative values by feeding at the inner and outer ends, respectively. The topological charge of radiated vortex waves is reconfigurable by tuning the operating frequency. Dual-mode OAM states are generated at different working frequencies (l = ±1 at 3 GHz, l = ±2 at 4 GHz, and l = ±3 at 4.8 GHz). Both the simulation and measurement results demonstrate that OAM beams can be generated effectively by the DASA. In addition, a conical cavity is used to increase the gain of the proposed DASA for more than 5 dBi in comparison to the traditional cylindrical cavity. Furthermore, the qualities of the generated OAM modes by the proposed DASA have been evaluated at different operating frequencies of 3 GHz, 4 GHz, and 4.8 GHz, respectively. The OAM modes purities of l = −1, −2, −3, 1, 2, and 3 are predominate with the proportion of about 81%, 70%, 74%, 78%, 77%, and 75%, respectively. Our results demonstrate that the proposed DASA has great potentials in OAM multiplexing communication systems.
An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis
(2025-08) Correra, Simona; Gunnarsson, Arnar Evgení; Recenti, Marco; Mercaldo, Francesco; Nardone, Vittoria; Santone, Antonella; Jónsson, Halldór; Gargiulo, Paolo; Department of Engineering
Objective: This study introduces an explainable, radiomics-based machine learning framework for the automated classification of sarcoma tumors using MRI. The approach aims to empower clinicians, reducing dependence on subjective image interpretation. Methods: A total of 186 MRI scans from 86 patients diagnosed with bone and soft tissue sarcoma were manually segmented to isolate tumor regions and corresponding healthy tissue. From these segmentations, 851 handcrafted radiomic features were extracted, including wavelet-transformed descriptors. A Random Forest classifier was trained to distinguish between tumor and healthy tissue, with hyperparameter tuning performed through nested cross-validation. To ensure transparency and interpretability, model behavior was explored through Feature Importance analysis and Local Interpretable Model-agnostic Explanations (LIME). Results: The model achieved an F1-score of 0.742, with an accuracy of 0.724 on the test set. LIME analysis revealed that texture and wavelet-based features were the most influential in driving the model’s predictions. Conclusions: By enabling accurate and interpretable classification of sarcomas in MRI, the proposed method provides a non-invasive approach to tumor classification, supporting an earlier, more personalized and precision-driven diagnosis. This study highlights the potential of explainable AI to assist in more secure clinical decision-making.
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