Computer Science - Journal Articles
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Item The insider’s dilemma: employed open source developers’ identification imbalance and intentions to leave(Taylor & Francis, 2025) Schaarschmidt, Mario; Stol, Klaas-Jan; Fitzgerald, Brian; Science Foundation IrelandIn corporate-sponsored open source software development, company-employed developersbecome “insiders” to the OSS community, and therefore have two roles: they serve asa representative of their employing company, but may also identify as a member of theopen source community. This study investigates what happens when identification with thecompany exceeds identification with the community (and vice versa), and also focuses onconsequences when these insider roles come in conflict. Informed by social identity theory andorganization-profession conflict theory, we report on two studies that predict identificationimbalance to affect company turnover intention. Our first study is based on a survey ofemployed Linux kernel developers and uses polynomial regression to assess the effect ofidentification imbalance (and congruence) on company turnover intention. The second studyextends our investigation beyond Linux and demonstrates that the effect of identificationimbalance on turnover intention is mediated by role conflict. The findings suggest that turn-over intention is lowest, when company and community identification match at high ratherthan low levels. We also find developers’ company career ambition influences how role conflictrelates to company turnover intention. This study holds implications for theory and formanagers in companies who engage with OSS communities.Item Artificially intelligent nursing homes: a scoping review of palliative care interventions(Frontiers Media, 2025-02-11) Ronan, Isabel; Tabirca, Sabin; Murphy, David; Cornally, Nicola; Saab, Mohamad M.; Crowley, Patrice; Science Foundation Ireland; University College CorkIntroduction: The world’s population is aging at a rapid rate. Nursing homes are needed to care for an increasing number of older adults. Palliative care can improve the quality of life of nursing home residents. Artificial Intelligence can be used to improve palliative care services. The aim of this scoping review is to synthesize research surrounding AI-based palliative care interventions in nursing homes. Methods: A PRISMA-ScR scoping review was carried out using modified guidelines specifically designed for computer science research. A wide range of keywords are considered in searching six databases, including IEEE, ACM, and SpringerLink. Results: We screened 3255 articles for inclusion after duplicate removal. 3175 articles were excluded during title and abstract screening. A further 61 articles were excluded during the full-text screening stage. We included 19 articles in our analysis. Studies either focus on intelligent physical systems or decision support systems. There is a clear divide between the two types of technologies. There are key issues to address in future research surrounding palliative definitions, data accessibility, and stakeholder involvement. Discussion: This paper presents the first review to consolidate research on palliative care interventions in nursing homes. The findings of this review indicate that integrated intelligent physical systems and decision support systems have yet to be explored. A broad range of machine learning solutions remain unused within the context of nursing home palliative care. These findings are of relevance to both nurses and computer scientists, who may use this review to reflect on their own practices when developing such technology.Item A machine learning approach to 3D protein structure sonification(Studio Musica Press, 2024-01) Ronan, Isabel; Mi, Yanlin; Yallapragada, Venkata Vamsi Bharadwaj; Ó Nuanáin, Cárthach; Tabirca, Sabin; Science Foundation Ireland; Munster Technological UniversityProteins are intricate structures that can be analysed by biologists and presented to the public using visualisations. However, with an increase in the amount of readily available protein-related information, new forms of data representation are needed. Sonification offers multiple advantages when conveying large amounts of complex data to interested audiences. Previous attempts have been made to sonify protein data; these techniques mainly focus on using amino acid sequences and secondary structures. This paper proposes a novel protein sonification algorithm involving atomic coordinates, B-factors, and occupancies to investigate new ways of displaying 3D protein structure data. This study culminates in creating a cultural showcase involving some of nature's most significant molecular structures. Results of both musical analysis and the showcase indicate that protein sonification has the potential to act as a helpful outreach and engagement tool for biologists while also helping experts in the field glean new insights from complex data.Item DashReStreamer: Framework for creation of impaired video clips under realistic network conditions(Association for Computing Machinery (ACM), 2024-12-16) Hodžić, Kerim; Cosovic, Mirsad; Mrdovic, Sasa; Quinlan, Jason J.; Raca, DarijoThe continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modeling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an .mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM, and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform YouTube. As a demonstration of framework usage, we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.Item A heuristic method for perishable inventory management under non-stationary demand(Elsevier Ltd., 2025-01-07) Gulecyuz, Suheyl; O’Sullivan, Barry; Tarim, S. Armagan; Science Foundation IrelandOur study considers a perishable inventory system under a finite planning horizon, periodic review, non-stationary stochastic demand, zero lead time, FIFO (first in, first out) issuing policy, and a fixed shelf life. The inventory system has a fixed setup cost and linear ordering, holding, penalty, and outdating costs per item. We introduce a computationally-efficient heuristic which formulates the problem as a network graph, and then calculates the shortest path in a recursive way and by keeping the average total cost per period at minimum. The heuristic firstly determines the replenishment periods and cycles using the deterministic-equivalent shortest path approach. Taking the replenishment plan constructed in the first step as an input, it calculates the order quantities with respect to the observed inventory states as a second step. We conduct numerical experiments for various scenarios and parameters, and compare them to the optimal stochastic dynamic programming (SDP) results. Our experiments conclude that the computation time is reduced significantly, and the average optimality gap between the expected total cost and the optimal cost is 1.87%.