Institutional Repository of Academic Research University of Macedonia
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Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction
No full textSoftware security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F2-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.24565
Femvertising practices on social media: a comparison of luxury and non-luxury brands
No full textPurposeThe purpose of this study is to examine how luxury and non-luxury brands portray women in social media advertising shedding light on their femvertising practices.Design/methodology/approachQuantitative content analysis and multiple correspondence analysis are used to examine female representations in the advertising of personal care products on social media. The sample includes brand posts from 15 brands on two social media platforms.FindingsThe results demonstrate that non-luxury brands use femvertising to a greater extent compared to luxury brands. In particular, this study shows that luxury brands rely more on stereotyped gender expressions and use more sexualisation in their advertising, relative to non-luxury brands.Research limitations/implicationsThis study provides an analysis of luxury and non-luxury brands’ femvertising practices on social media. In doing so, this study extends the study of femvertising to the context of luxury and social media, which is currently underexplored. In terms of practical implications, this study sheds light on the extent of the application of femvertising across luxury and non-luxury brands on social media.Practical implicationsThe findings drive a number of suggestions for luxury marketers, including the use of more independent gender roles and more racial diversity in their social media advertising and the lessening of unrelated sexuality.Originality/valueTo the best of the authors’ knowledge, this study is the first to compare femvertising practices of luxury and non-luxury brands on social media, delineating different facets of femvertising (e.g. gender roles, diversity, etc.) and extending scholarly understanding of the possible facets of this concept.3181285130
General variable neighborhood search for the parallel machine scheduling problem with two common servers
No full textWe address in this paper the parallel machine scheduling problem with a shared loading server and a shared unloading server. Each job has to be loaded by the loading server before being processed on one of the available machines and unloaded immediately by the unloading server after its processing. The objective function involves the minimization of the overall completion time, known as the makespan. This important problem raises in flexible manufacturing systems, automated material handling, healthcare, and many other industrial fields, and has been little studied up to now. To date, research on it has focused on the case of two machines. The regular case of this problem is considered. A mixed integer programming formulation based on completion time variables is suggested to solve small-sized instances of the problem. Due to its NP-hardness, we propose two greedy heuristics based on the minimization of the loading, respectively unloading, server waiting time, and an efficient General Variable Neighborhood Search (GVNS) algorithm. In the computational experiments, the proposed methods are compared using 120 new and publicly available instances. It turns out that, the proposed GVNS with an initial solution-finding mechanism based on the unloading server waiting time minimization significantly outperforms the other approaches
Hackathons for Driving Service Innovation Strategies: The Evolution of a Digital Platform-Based Ecosystem
No full textDespite the fact that hackathons and digital innovation contests have emerged as substantial intermediaries in open innovation and entrepreneurship, knowledge about how hackathons and digital innovation contests impact innovation in cities is restricted. There is also a scarcity of models that aid in the organization of digital innovation contests. Based on the existing frameworks for contest organizations, the aim of this article is to present a case study which develops a framework for hosting and evaluating open data hackathons. The hackathon framework is developed from the organizer’s viewpoint, and it has been executed in three digital innovation competitions in Thessaloniki. The suggested scheme adds new knowledge to the field of open data and digital innovation competitions while also providing practitioners with opportunities to host digital contests. Moreover, this framework offers hackathon organizers with regulations and resources to help them plan innovation contests that contribute to the betterment of an open data ecosystem.8311
Internal auditing in the public sector: a systematic literature review and future research agenda
No full textPurposeThis study reviews post-2009 literature on public sector internal auditing (IA) and addresses three interrelated research questions (RQ): How is research on the public sector IA being developed? What are the focus and criticisms of the literature on public sector IA? What is the future of public sector IA research?Design/methodology/approachWe adopt a systematic literature review approach and analyze 78 peer-reviewed journal articles published between 2010 and 2019. We evaluate five criteria to identify the development of public sector IA research (RQ1), namely level of government, academic discipline, number of countries, geographic areas and MSCI country classification. Similarly, we use four criteria to present the focus and criticisms of the literature (RQ2), namely, type of organizational respondent, research instrument, theories and research theme examined. Finally, we use two criteria to propose new directions for future research (RQ3), namely, the directions resulted from RQ1 and RQ2 and the directions highlighted by the 10 most cited studies in the IA literature (i.e. out of the 78 papers identified).FindingsWe find an increase of publications up to 2017, most of which are single country–focused, particularly on emerging markets. Moreover, we note that IA has been studied at all government levels, most often at the local government level. Although we identify multiple research themes examined in the literature, most studies emphasize "governance" and "operational effectiveness" using quantitative analysis, without reference to any theory. By analyzing these key features, we critically interpret the challenges as well as the skepticism that may surface by researchers. Finally, considering implications from this stream of research and analyzing the most influential studies, we recommend new avenues for investigation such as comparative studies among countries and different markets that provide further evidence on the international and regional levels and studies on the effect of cultural, institutional and demographical characteristics in IA.Practical implicationsOur results will help researchers, practitioners and consultants to identify the key issues related with IA.Originality/valueThis study is the first to provide a systematic literature review on public sector IA. Furthermore, it develops insights, critical reflections and avenues for future research in this field.34218920
Limits and benefits of using telepresence robots for educational purposes
No full textThe continuing spread of the COVID19 virus shows that adequate prepara-tion for telepresence scenarios such as teleteaching is elementary for struc-tured teaching in secondary education. There should be no negative impact on teaching quality, either in times of general crisis or simply as a measure to ensure institutional stability and individual flexibility in an increasing-ly digital world. State-of-the-art telepresence approaches include the possi-bility to use telerobotic systems or telepresence robots (TR). These systems are configured with an immersive interface such that users feel present in a remote environment, projecting their presence through the remote robot. While many professional tasks can be shifted away from the workplace ra-ther easily, social aspects gain particular significance in the context of learning and education. By enabling physical and spatial interaction far be-yond the possibilities of mere video conferencing, the high degree of social presence provided by TR can assist better learning experiences. TR can compensate for the lack of mobility or restricted travel options of students, educators or staff. TR can foster language learning and intercultural ex-change, and TR can prepare students for the workspaces of tomorrow.25th International Conference on Interactive Collaborative Learning (ICL
Machine Learning for Technical Debt Identification
No full textTechnical Debt (TD) is a successful metaphor in conveying the consequences of software inefficiencies and their elimination to both technical and non-technical stakeholders, primarily due to its monetary nature. The identification and quantification of TD rely heavily on the use of a small handful of sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue we use 18 metrics pertaining to source code, repository activity, issue tracking, refactorings, duplication and commenting rates of each class as features for statistical and Machine Learning models, so as to classify them as High-TD or not. As a benchmark we exploit 18,857 classes obtained from 25 Java projects, whose high levels of TD has been confirmed by three leading tools. The findings indicate that it is feasible to identify TD issues with sufficient accuracy and reasonable effort: a subset of superior classifiers achieved an F 2 -measure score of approximately 0.79 with an associated Module Inspection ratio of approximately 0.10. Based on the results a tool prototype for automatically assessing the TD of Java projects has been implemented.48124892490
Market and welfare valuation of the economic burden of diseases attributable to air pollution exposure in the Western Balkans
No full textAim: The population in the Western Balkans is exposed to high air pollution concentrations, among the highest in Europe, causing death and disability. Research, however, on the resulting economic cost in the region is still limited. We estimate the economic cost of the adverse health effects from air pollution exposure, including fine particulate matter (ambient and household) and ambient ozone air pollution in the region.Methods: We employ both market and welfare-oriented methods. According to the Cost-of-Illness (COI) approach, we estimate both the direct (healthcare expenditure) and indirect cost (mortality and morbidity cost). Against the shortcomings of a market-based valuation, the Willingness to Pay (WTP) approach is also used. The most recent data from the Global Burden of Disease Study 2019 are used.Results: Under the COI approach, total economic cost is estimated at PPP 76.7 billion. The monetary amount associated with the disease burden of air pollution is significant.Conclusion: Public health policies should include monitoring of the adverse health effects of air pollution. Abatement policies should aim at reducing ambient air pollution as well as the dependence on polluting household energy usage. The reduced economic cost can be accompanied by benefits associated with climate change mitigation and an overall improvement in population’s health status, an important aspect given the current COVID-19 pandemic.1
On the volatility of cryptocurrencies
No full textWe perform a large-scale analysis to evaluate the performance of traditional and Markov-switching GARCH models for the volatility of 292 cryptocurrencies. For each cryptocurrency, we estimate a total of 27 alternative GARCH specifications. We consider models that allow up to three different regimes. First, the models are compared in terms of goodness-of-fit using the Deviance Information Criterion and the Bayesian Predictive Information Criterion. Next, we evaluate the ability of the models in forecasting one-day ahead conditional volatility and Value-at-Risk. The results indicate that for a wide range of cryptocurrencies, time-varying models outperform traditional ones.6210172
Parents' perceived stress and children's adjustment during the COVID‐19 lockdown in Italy: The mediating role of family resilience
No full textObjectiveThis study aimed to explore the role of family resilience in the relationship between parents' psychological stress and their perceptions of children's emotional and behavioral symptoms during the COVID-19 lockdown in Italy.BackgroundThe COVID-19 lockdown threatened the well-being of parents, with a potentially cascading effect on children's adjustment. However, the negative impact of parents' stress on children's well-being may be attenuated in resilient families.MethodDuring the Italian lockdown, an online survey was administered to 649 parents of at least one child aged between 5 and 17 years. Respondents completed the survey themselves and their child(ren). The Perceived Stress Scale, the Walsh Family Resilience Questionnaire, and the Strengths and Difficulties Questionnaire were administered to parents.ResultsResults show that family resilience is a key mechanism in the association of parents' perceived stress with their perceptions of children's emotional symptoms, prosocial behavior, and hyperactivity and that only parents' marital status moderates this relationship.ConclusionThe intervening role of family resilience emphasizes the need to empower parents and families during the pandemic crisis.ImplicationsBy strengthening family resilience, family resources maybe strengthened to meet new challenges more effectively.72172