Institutional Repository of Academic Research University of Macedonia
Not a member yet
2215 research outputs found
Sort by
A note on the relative productivity drivers of economists: a probit/logit approach for six European countries
No full textWe examine the drivers of research performance of 1431 economists from six European countries. Data from the Scopus database are collected. We compare the relative performance of economists from three northern European countries: Belgium, Denmark and Germany with three from the south: Greece, Italy and Portugal. Relative performance is measured as the deviation from the country average in both citations and papers. The dependent variables take the value of 1 if the productivity of the researcher is above the country average and 0 if it is below. Probit/logit analysis is employed and marginal effects are estimated to examine the significance of factors like the country of their PhD studies, gender and inbreeding at the national level. A US PhD or a German PhD affects negatively the relative productivity of German economists. Inbreeding at the national level (locally trained economists) reduces productivity among Greek, Italian and Portuguese economists. Gender is significant in the case of Denmark, Germany and Italy, but it does not affect productivity in Belgium, Greece and Portugal.5542171217
Using Knowledge Graphs to provide public service information
No full textPublic authorities all over the world publish an increasing amount of information about the Public Services (PS) they provide to facilitate discovery and use by citizens and businesses. Proper management (storing, processing, querying) of this information is necessary to make it fully exploitable. This includes adopting a common PS data model to facilitate interoperability, such as the Core Public Service Vocabulary (CPSV) developed by the European Union. In addition, the choice of technology is important as it is closely related to the quality of information. The aim of this paper is to investigate the benefits of adopting knowledge graphs to manage PS information that are structured using CPSV data model. For this purpose, we capitalize on previous research enriching CPSV and using RDF to develop a relevant knowledge graph and evaluate its use by employing various usage scenarios. The results suggest the use of knowledge graphs can provide benefits by exploiting domain-specific rules however there is still work to be done before the public sector widely adopts their use.252259DG.O 2022: The 23rd Annual International Conference on Digital Government Researc
Feasibility Study for a Black Sea SDGs Observatory
No full text55The 9th International Conference on Sustainable Developmen
IJV partner relations in emerging markets: the importance of Greek partner's prior IJV experience
No full textPurpose: The purpose of this study is to examine the effect of the Greek partners' prior international joint venture (IJV) experience on partner compatibility, knowledge transfer (KT) and trust in their IJVs. Design/methodology/approach: The authors conducted a primary research study and collected a total of 50 useable questionnaires from Greek firms with IJV participation. Findings: The findings show a positive effect of the Greek partner's prior experience in IJV establishment and management on partner compatibility and on successful KT to the IJV. Practical implications: The results are significant for executives of firms who seek to expand to international markets through IJV formation and for practitioners involved in IJVs, regarding prior IJV experience, partner compatibility and KT to their IJVs. Originality/value: This study uses a sample of Greek firms with IJV participation to examine the effect of their prior IJV experience on IJV partner relations in the region of South East (SE) Europe. Additionally, it enhances the understanding of the effect of prior experience in the IJV establishment and management in emerging markets and sheds light to the antecedents of partner compatibility, which have been neglected by researchers.15455557
A new method of identifying key industries: a principal component analysis
No full textThis article using the principal components analysis identifies key industries and groups them into particular clusters. The data come from the US benchmark input–output tables of the years 2002, 2007, 2012 and the most recently published input–output table of the year 2019. We observe some intertemporal switches of industries both between and within the top clusters. The findings further suggest that structural change is a slow-moving process and it takes time for some industries to move from one cluster to the other. This information may be proved important in the designation of effective economic policies by targeting key industries and also for the stability properties of the economic system.11
A Support Vector Machine model for classification of efficiency: An application to M&A
No full textOne of the main issues in banking and finance sector is measuring the efficiency of mergers and acquisitions (M&A), due to a plethora of key performance indicators (KPI) and variables. In this study, the efficiency of 441 M&A deals is evaluated based on specific inputs and outputs, including the change of environmental and social governance (ESG) scores. Due to presence of negative data, two Data Envelopment Analysis (DEA) and second stage analyses have been applied. The first is a regression model, which examines the impact of control variables on the efficiency of DEA scores. The second is a Support Vector Machine (SVM) model, mapping efficiency based on gender diversity. Results indicate that the performance of M&A deals is positively affected by both gender diversity and relative size whereas is negatively affected by the deal value. The SVM model classification indicates which regions of efficiency and stability are reflected by good or bad representation of women on boards.6110163
An employee perspective of human resource development practices in the public sector: the role of organizational and supervisor support
No full textThis article adopts an employee-level perspective which is currently lacking in the public sector literature and responds to the call for additional research concerning factors that affect public employees’ job attitudes and work behaviors. Based on a survey of civil servants, this study explored the antecedents and outcomes of perceived investment in employee development (PIED). Our research demonstrates the significant role of organizational support (POS) on employees’ perceptions of development. Furthermore, supervisor support (PSS) mediated the relationship between POS and PIED. This finding sheds light on the role of supervisors as agents who represent or personify the organization. Also, consistent with the JD-R model and the social exchange theory, we indicated that public employees within a workplace that provides substantial training and developmental incentives, are more likely to report greater levels of organizational commitment and organizational citizenship behavior (OCB). PIED was found to act as an important mediator between the relationships of POS and employees’ outcomes and PSS and employees’ outcomes.88373975
Applying BERT for Early-Stage Recognition of Persistence in Chat-Based Social Engineering Attacks
No full textChat-based social engineering (CSE) attacks are attracting increasing attention in the Small-Medium Enterprise (SME) environment, given the ease and potential impact of such an attack. During a CSE attack, malicious users will repeatedly use linguistic tricks to eventually deceive their victims. Thus, to protect SME users, it would be beneficial to have a cyber-defense mechanism able to detect persistent interlocutors who repeatedly bring up critical topics that could lead to sensitive data exposure. We build a natural language processing model, called CSE-PersistenceBERT, for paraphrase detection to recognize persistency as a social engineering attacker’s behavior during a chat-based dialogue. The CSE-PersistenceBERT model consists of a pre-trained BERT model fine-tuned using our handcrafted CSE-Persistence corpus; a corpus appropriately annotated for the specific downstream task of paraphrase recognition. The model identifies the linguistic relationship between the sentences uttered during the dialogue and exposes the malicious intent of the attacker. The results are satisfactory and prove the efficiency of CSE-PersistenceBERT as a recognition mechanism of a social engineer’s persistent behavior during a CSE attack.12231235
Decision support for GPU acceleration by predicting energy savings and programming effort
No full textAs the number of heterogeneous embedded systems used in IoT applications increases, there is a lack of software tools to assist developers to meet the challenge of reducing energy consumption. Indeed, there are only few performance prediction tools for heterogeneous systems in the literature and they typically focus on the prediction of speedup by acceleration. In this work, we propose a methodology for analyzing CPU applications in order to estimate the potential Energy gains by offloading a piece of code on an embedded GPU. The proposed methodology provides several features beyond the state of the art of existing predictors, including the combination of static analysis and dynamic instrumentation approaches and the prediction of the programming effort of developing the CUDA kernel of a CPU code, using advanced metrics. The methodology is supported by a tool-flow and it is demonstrated and evaluated on modern heterogeneous embedded systems (Nvidia), where shows classification accuracy above 75%. The results show that the proposed methodology can assist application developers in the early design choice of investing effort to acceleration considering the expected Energy Savings and the Effort required to develop acceleration-specific code.3410063
Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer
No full textComplete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for the R0 resection outcome, apply a methodology to explain the prediction, and evaluate the interpretability by analysing feature interactions. The retrospective cohort finally assessed 571 consecutive advanced-stage EOC patients who underwent cytoreductive surgery. An eXtreme Gradient Boosting (XGBoost) algorithm was employed to develop the predictive model including mostly patient- and surgery-specific variables. The Shapley Additive explanations (SHAP) framework was used to provide global and local explainability for the predictive model. The XGBoost accurately predicted R0 resection (area under curve [AUC] = 0.866; 95% confidence interval [CI] = 0.8-0.93). We identified "turning points" that increased the probability of complete cytoreduction including Intraoperative Mapping of Ovarian Cancer Score and Peritoneal Carcinomatosis Index 4, patient's age < 60 years, and largest tumour bulk < 5 cm in a surgical environment of optimized infrastructural support. We demonstrated high model accuracy for the R0 resection prediction in EOC patients and provided novel global and local feature explainability that can be used for quality control and internal audit.12460