University of Macedonia
  • Thessaloníki, Region of Central Macedonia, Greece
Recent publications
Technical 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.
The paper validates a framework which is based on EFQM and associates quality criteria to results and outputs, reflecting the organization readiness and efficiency. The paper studies the views of school principals of Kindergarten and Elementary Schools in Greece. A nationwide survey provides a representative sample of 231 school principals who were administered an online questionnaire. The questionnaire was constructed by applying the EFQM framework as the basic pattern. Based on previous approaches which use PLS models in the education context, the paper provides findings on how key criteria of EFQM associate to each other when applied to the elementary education context in Greece. Findings provide evidence that EFQM criteria are indeed associated both directly and indirectly. The validation of the proposed model supports that the particular instrument can be used as a tool for continually assess, measure and improve the management procedures of elementary education organizations in order to have improved measures of key results criteria.
Towards the transition to blended and remote education, evaluating the levels of students’ digital competence and designing educational programs to advance them is of paramount importance. Existing validated digital competence scales usually ignore either important digital skills needed or new socio-technological innovations. This study proposes and validates a comprehensive digital competence scale for students in higher education. The suggested instrument includes skills of online learning and collaboration, social media, smart and mobile devices, safety, and data protection. The scale was evaluated on a sample of 156 undergraduate and postgraduate students just before and at the beginning of the COVID-19 crisis. The final scale is composed of 28 items and six digital competence components. The evaluation study revealed valid results in terms of model fit criteria, factor loadings, internal validity, and reliability. Individual factors like the students’ field of study, computer experience and age revealed significant associations to the scale components, while gender revealed no significant differences. The suggested scale can be useful to the design of new actions and policies towards remote education and the digital skills’ development of adult learners.
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.
We consider a Cournot duopoly consisting of two geographically separated firms, each associated with a local environmental-friendly trade union that exhibits climate solidarity. In the basic model, firms choose abatement technologies prior to bargaining over wages and employment with the unions. We show that wage demanded is decreasing as the union's degree of climate solidarity increases, providing additional incentives for firms to adopt greener technology, hence improving the social welfare. In the alternative model, where trade unions decide the wages prior to the firms' abatement and employment decisions, the firms choose the dirtiest available technology implying that the union's climate solidarity has no effect on the firm's abatement decisions. These results suggest that establishing climate solidarity as a norm across trade unions can, depending on the timing of the environmental technological choice, become a powerful instrument in battling climate change, critically supplementing the as yet ineffective international policy framework.
Open Educational Resources (OER) provide learning opportunities for all. Usually, OER and links to OER are curated in Repositories of OER (ROER) for open access and use by anyone, including people with disabilities, at any place at any time. This study analyzes the reputation/ authoritativeness, usage, and accessibility of thirteen popular ROER for teaching and learning using three Web Analytics and five Web Accessibility tools. A high difference among the ROER was observed in almost every metric. Millions of users visit some of these ROER every month and on average stay 2–26 min per visit and view 1.1–8.5 pages per visit. Although in many ROER most of their visitors come from the country where the ROER hosting institute operates, other ROER (such as DOER, MIT OCW, and OpenLearn) have managed to attract visitors from all over the world. In some ROER, their visitors come directly to their website while in a few other ROER visitors are coming after visiting a search engine. Although most ROER are accessible by users with disabilities, the Web Accessibility tools revealed several errors in few ROER. In most ROER, less than one third of the traffic is coming from mobile devices although almost everyone has a mobile phone nowadays. Finally, the study makes suggestions to ROER administrators such as interconnecting their ROER, collaborating, exchanging good practices (such as Commons and MIT OCW), improving their website accessibility and mobile-optimized design, as well as promoting their ROER to libraries, educational institutes, and organizations.
The aim of the paper is to propose the construction of an index that captures the economic complexity of cities over the globe, as well as to explore whether it is a good predictor for a range of city-level economic outcomes. This index aspires to mitigate data scarcity for cities and to provide policy makers with the tools for monitoring the evolving role of cities in the global economy. Analytically, we implement the economic complexity methodology on data for the ownership, location and economic activities of the world’s 3,000 largest firms and their subsidiaries to propose a new indicator that quantifies the network of the largest cities worldwide and the economic activities of their globalized firms. We first show that complex cities are the highly diversified cities that host non-ubiquitous economic activities of firms with global presence. Then, in a sample of EU cities, we show that complex cities tend to be more prosperous, have higher population, and are associated with more jobs, human capital, innovation, technology and transport infrastructure. Last, using OLS methodology and accounting for several other confounders, we show that a higher ECI, at the city level, enhances the resilience of cities to negative economic shocks, i.e., their ability to bounce back after a shock. Specifically, we find that the expected increase of the ratio of employment in 2012 over 2006 is 0.01 (mean: 0.992; standard deviation: 0.081) when the ECI increases by 1 unit (mean: 0.371; standard deviation: 1.094), i.e., a satisfactory pace of recovery, in terms of employment. The ability to diversify in the presence of a shock, the reallocation of factors of production to other sectors and the ability to extract rents associated with those diversified activities, uncovers the mechanics of the ECI index.
Traffic sign recognition and autonomous vehicles computing are a few of the innovative applications which are emerging in the domain of mobile edge computing. Distributed machine learning in the form of Federated Learning (FL) has been applied to mobile edge computing through a range of methodologies and techniques for intelligent feature classification approaches. The challenges that research on such FL methods is facing is twofold: identify an optimal distributed architecture and algorithm components to each side to meet the demand of heavy data processing, and enhance the algorithm components with heuristics that fit to the problem domain and optimize the key parameters of the algorithms. In this prospect, we present a Federated Learning implementation based on a neural network architecture with emphasis to traffic sign image recognition. Our benchmark was tested with two FL strategies seeking an optimal performance model and in reference to a corresponding data set. We present the results of this work while we define the scope of future improvements to our model. Keywords— Federated Learning, Mobile Edge Computing, Traffic Sign Image Recognition, Convolutional Neural Networks
The global trend of credentialism as well as increased mobility in studies and work bring new requirements regarding the verification of academic qualifications. So far, traditional nostrification processes face challenges related to high process cycle times, elevated fees, and fraud incidents. In the higher education domain, blockchain-based approaches have emerged to revitalize the verification of academic qualifications, but they mostly rely on closed concepts offering their facility to a limited circle of entities. As a result, the verification of degrees is not publicly available to any third party. Moreover, in most cases, a concise process-aware approach describing the intended functionality with appropriate models is missing; this absence raises ambiguity and trust issues in the implemented application. This paper presents the design and early implementation of the VerDe (Verified Degrees) platform; a proposed blockchain-based application for registration and verification of academic qualifications. The novelty of the approach presented in this paper is the implementation of VerDe as a decentralized application utilizing Business Process Model & Notation (BPMN). This work demonstrates that usage of BPMN constructs provides an efficient method in addressing blockchain usability and complexity issues and facilitates the design and implementation of blockchain-based applications. The benefits of deploying VerDe in conjunction with BPMN are an efficient and transparent facility able to handle mobility consequences, detect fraud, and overcome administrative barriers by offering the verification capability to any third-party, and employing a custom-made token that emulates the European Credit Transfer and Accumulation System (ECTS).
The purpose of this study is twofold: firstly, to provide a literature review of sustainable supply chain management (SSCM) critical factors, practices and performance; and secondly, to develop a comprehensive and testable model of SSCM in the food industry. The research conducted comprises a literature review and a case study. The literature review findings propose a theoretical framework linking SSCM critical factors, practices and performance. The case study comprises two sustainability leaders in the Greek food supply chain in order to investigate the three SSCM constructs. A new set of pioneering SSCM practices in the Greek food industry is identified, including daily conversation, local sourcing and HR investments. The end result of this research proposes a testable model that sheds light on SSCM in the food industry and is based on a set of propositions.
We 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.
Considering the significant decrease of investment and GDP in Greece, and the goal of achieving a V-shaped post-COVID-19 recovery, inward FDI could be regarded as a source of productive private investment. This study aims to indicate differences in the factors determining inward FDI in Greece before and after the great crisis and the role of the informal economy on Greece's inward FDI. This study explores the perceptions of multinational enterprises' upper management regarding motives of and barriers to locating their activities in Greece, the role of the informal economy and how these perceptions changed before and after the great economic crisis of late 2000s. The results indicate that the relation between inward FDI and the informal economy depends on types of entry and that tax evasion opportunities can impact positively on the motives of foreign investors.
This paper investigates the non-marital fertility evolution in the USA for the period between 1976 and 2016. Beyond the well-known determinants in this framework, we add and test for the Easterlin relative income hypothesis. Easterlin stresses the role of the material aspirations formed in childhood (denominator) relative to the current economic perspectives (numerator) of young men. That ratio defines the relative income. We employ panel dynamic techniques at the state level. We find a negative and statistically significant effect of the relative income in the share of children born out-of-wedlock. Most importantly, relative income is robust to the inclusion of marriage. The latter may imply a socio-economic mobility perspective.
In this article, we statistically examine the effectiveness of non‐pharmaceutical interventions (NPIs) implemented by the national governments of Greece and Cyprus during 2020 to (a) limit the spread of the SARS‐CoV‐2 virus, and (b) mitigate the economic fallout brought about by the Covid‐19 pandemic. Applying a modified health belief model, we hypothesize that behavioral outcomes at the policy level are a function of NPIs, perceived severity, and social context. We employ a Prais‐Winsten estimation in 2‐week averages and report panel‐corrected standard errors to find that NPIs have clear, yet differential, effects on public health and the economy in terms of statistical significance and time lags. The study provides a critical framework to inform future interventions during emerging pandemics. 本文中,我们对2020年希腊和塞浦路斯国家政府实施的非药物干预(NPI)的有效性进行了统计检验,以期(a)限制新冠病毒的传播,并(b)减轻2019冠状病毒病(Covid‐19)大流行带来的经济影响。通过应用一项调整过的卫生信念模型,我们假设认为,行为结果在政策层面上受NPI、感知严重性和社会情境的影响。我们对两周平均值采用Prais‐Winsten估计法,并报告了面板校正标准误,发现NPI在统计显著性和时间滞后方面对公共卫生和经济产生明显且不同的影响。本研究提供了一个关键框架,用于在未来新的大流行期间影响干预措施。 En este artículo, examinamos estadísticamente la efectividad de las intervenciones no farmacéuticas (NPI) implementadas por los gobiernos nacionales de Grecia y Chipre durante 2020 para (a) limitar la propagación del virus SARS‐CoV‐2 y (b) mitigar las consecuencias económicas provocadas por la pandemia de Covid‐19. Al aplicar un modelo de creencias de salud modificado, planteamos la hipótesis de que los resultados de comportamiento a nivel de política son una función de las NPI, la gravedad percibida y el contexto social. Empleamos una estimación de Prais‐Winsten en promedios de dos semanas e informamos los errores estándar corregidos del panel para encontrar que las NPI tienen efectos claros, aunque diferenciales, en la salud pública y la economía en términos de importancia estadística y retrasos. El estudio proporciona un marco crítico para informar futuras intervenciones durante las pandemias emergentes.
The standard linear Granger causality test, based on the vector autoregressive model (VAR), requires stationarity of the time series. A VAR model is fitted to the first-differences of the time series, when they exhibit trends and are not co-integrated. In the case of co-integration, the vector error-correction model (VECM) is used instead. Alternatively, a nonlinear information causality measure is suggested, called partial transfer entropy on rank vectors (PTERV), which uses locally ranked observations. It is model-free and of a more general purpose, as it can be directly applied to the original time series without pre-testing for stationarity or co-integration. The significance test of the PTERV detects effectively the connectivity structure of complex multivariate systems. In particular, the size and power of this test are comparable to that of the standard linear Granger causality approach (VAR or VECM) when applied to systems with only linear causal effects, while the PTERV test outperforms the linear causality test when nonlinear causal effects exist, as long as the sample size is large enough. The application of PTERV to stock market data and interest rates illustrates that it can be a useful tool in the causality analysis of financial time series.
: In the era of personalized medicine, Artificial Intelligence (AI) has emerged as a powerful tool with growing applications in the field of gynaecologic oncology. However, AI applications are encountered several challenges derived from their “black-box” nature, which limits their adoption by clinicians. Surgical decision-making in cytoreductive surgery for epithelial ovarian cancer (EOC) is a complex matter, and an accurate prediction of surgical effort is required to ensure the good health and care of patients. We combined high-performance AI modeling with an eXplainable Artificial Intelligence (XAI) framework to explain feature effects and interactions associated with specific threshold surgical efforts using data from a single public institution. We revealed features not routinely measured in the clinical practice, including human factors that could be responsible for the variation in the surgical effort. A selective decreased surgical effort may be associated with the surgeon’s age. The use of XAI frameworks can provide actionable information for surgeons to improve patient outcomes in gynaecologic oncology.
This paper studies whether excluding the cartel ringleader from “leniency programs” (LPs) hinders collusion. The ringleader’s exclusion from any leniency right: (a) destabilizes cartels by creating asymmetry in the partners’ collusive payoffs; and (b) fosters cartel activity by reducing the ringleader’s payoff from deviation. The discriminatory LP can increase the ringleader’s credibility as loyal partner and weaken firms’ incentive to deviate. A partially discriminatory LP that allows the ringleader to receive leniency only when it denounces a cartel that is not under investigation but not when cooperating in an already launched investigation eliminates (b). By restoring the ringleader’s payoff from deviation at its non-discriminatory level, partial discrimination is more effective in destabilizing collusion compared to both, full- and non-discrimination.
In paper we examine the conditions under which aggregate overall Farrell efficiency decomposes by both observation (i.e., firms) and source (i.e., technical and allocative efficiency) using the same set of aggregation weights. These conditions require firms to produce a single output with a homogenous of degree r>0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r > 0$$\end{document} technology. Then, Farrell decomposition at the firm level is also preserved at the industry level.
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4,822 members
Evangelos Kalampokis
  • Department of Business Administration
Konstantinos Papadopoulos
  • Department of Educational and Social Policy
Ioannis Lefkos
  • Department of Educational and Social Policy
Ioannis Konstantaras
  • Department of Business Administration
Panagiotis D Michailidis
  • Department of Balkan, Slavic and Oriental Studies
Egnatia str. 156, 54006, Thessaloníki, Region of Central Macedonia, Greece
Head of institution
Professor Achilleas Zapranis