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During the Bronze Age, the inhabitants of regions of Crete, mainland Greece, and Cyprus inscribed their languages using, among other scripts, a writing system called Linear A. These symbols, mainly characterized by combinations of lines, have, since their discovery, remained a mystery. Not only is the corpus very small, but it is challenging to link Minoan, the language behind Linear A, to any known language. Most decipherment attempts involve using the phonetic values of Linear B, a grammatological offspring of Linear A, to 'read' Linear A. However, this yields meaningless words. Recently, novel approaches to deciphering the script have emerged which involve a computational component. In this paper, two such approaches are combined to account for the biases involved in provisionally assigning Linear B phonetic values to Linear A and to shed more light on the possible connections of Linear A with other scripts and languages from the region. Additionally, the limitations inherent in such approaches are discussed. Firstly, a feature-based similarity measure is used to compare Linear A with the Carian Alphabet and the Cypriot Syllabary. A few Linear A symbols are matched with symbols from the Carian Alphabet and the Cypriot Syllabary. Finally, using the derived phonetic values, Linear A is compared with Ancient Egyptian, Luwian, Hittite, Proto-Celtic, and Uralic using a consonantal approach. Some possible word matches are identified from each language. [ABSTRACT FROM AUTHOR]

In viticulture, downy mildew is one of the most common diseases that, if not adequately treated, can diminish production yield. However, the uncontrolled use of pesticides to alleviate its occurrence can pose significant risks for farmers, consumers, and the environment. This paper presents a new framework for the early detection and estimation of the mildew's appearance in viticulture fields. The framework utilizes a protocol for the real-time acquisition of drones' high-resolution RGB images and a cloud-docker-based video or image inference process using object detection CNN models. The authors implemented their framework proposition using open-source tools and experimented with their proposed implementation on the debina grape variety in Zitsa, Greece, during downy mildew outbursts. The authors present evaluation results of deep learning Faster R-CNN object detection models trained on their downy mildew annotated dataset, using the different object classifiers of VGG16, ViTDet, MobileNetV3, EfficientNet, SqueezeNet, and ResNet. The authors compare Faster R-CNN and YOLO object detectors in terms of accuracy and speed. From their experimentation, the embedded device model ViTDet showed the worst accuracy results compared to the fast inferences of YOLOv8, while MobileNetV3 significantly outperformed YOLOv8 in terms of both accuracy and speed. Regarding cloud inferences, large ResNet models performed well in terms of accuracy, while YOLOv5 faster inferences presented significant object classification losses. [ABSTRACT FROM AUTHOR]

Businesses affected by the pandemic have realized the importance of incorporating digital transformation into their operations. However, as a result of the market lockdown, they realized that they needed to digitalize their firms immediately and make greater attempts to enhance their economic situation by integrating a greater number of technological components. While there have been numerous studies conducted on the adoption of digital transformation in small–medium enterprises, there has been no research carried out on the implementation of digital transformation in the specific industry of driving schools. This paper investigates the significance of digital transformation, as well as the potential for its application in this industry's business setting and the ways in which it can be utilized to improve innovation capabilities and performance. The data for this study came from 300 driving instructors in Greece and Cyprus. Multivariate regression analysis was used to analyze the data. The outcomes suggest that driving schools have a generally positive reaction to and acknowledgement of the increasing speed of digital transformation. The results also give driving school owners useful information that helps them show how important digital transformation is to their businesses. Using the findings of this study, driving schools will be able to improve their operational capabilities and accelerate their development in the post-COVID era. [ABSTRACT FROM AUTHOR]

The increasing amount of legal information available online is overwhelming for both citizens and legal professionals, making it difficult and time-consuming to find relevant information and keep up with the latest legal developments. Automatic text summarization techniques can be highly beneficial as they save time, reduce costs, and lessen the cognitive load of legal professionals. However, applying these techniques to legal documents poses several challenges due to the complexity of legal documents and the lack of needed resources, especially in linguistically under-resourced languages, such as the Greek language. In this paper, we address automatic summarization of Greek legal documents. A major challenge in this area is the lack of suitable datasets in the Greek language. In response, we developed a new metadata-rich dataset consisting of selected judgments from the Supreme Civil and Criminal Court of Greece, alongside their reference summaries and category tags, tailored for the purpose of automated legal document summarization. We also adopted several state-of-the-art methods for abstractive and extractive summarization and conducted a comprehensive evaluation of the methods using both human and automatic metrics. Our results: (i) revealed that, while extractive methods exhibit average performance, abstractive methods generate moderately fluent and coherent text, but they tend to receive low scores in relevance and consistency metrics; (ii) indicated the need for metrics that capture better a legal document summary's coherence, relevance, and consistency; (iii) demonstrated that fine-tuning BERT models on a specific upstream task can significantly improve the model's performance. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationships among eight ancient scripts from between Greece and India. We used convolutional neural networks combined with support vector machines to give a numerical rating of the similarity between pairs of signs (one sign from each of two different scripts). Two scripts that had a one-to-one matching of their signs were determined to be related. The result of the analysis is the finding of the following three groups, which are listed in chronological order: (1) Sumerian pictograms, the Indus Valley script, and the proto-Elamite script; (2) Cretan hieroglyphs and Linear B; and (3) the Phoenician, Greek, and Brahmi alphabets. Based on their geographic locations and times of appearance, Group (1) may originate from Mesopotamia in the early Bronze Age, Group (2) may originate from Europe in the middle Bronze Age, and Group (3) may originate from the Sinai Peninsula in the late Bronze Age. [ABSTRACT FROM AUTHOR]

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece. [ABSTRACT FROM AUTHOR]

Agritourism is long established and the interest in diversification of agricultural enterprises into tourism has increased. However, many challenges have emerged regarding the lack of appropriate skills, strategic planning, Information Systems (IS), as well as increased costs in production processes. In Greece, the contribution of agritourism to economic growth has increased in the last decade, but the relationship between the agricultural sector and agritourism is a reason for certain people having largely taken advantage from the development of tourism in a specific area. Greek businesses operating in the agritourism sector are seeking long-term sustainability. However, they lack strategic planning and effective use of IS. As the strategic implementation of Information Technology (IT) in Small Medium Enterprises (SMEs) in the agritourism sector is under-researched, this paper aims to investigate how the process of Strategic Information Systems Planning (SISP) affects the success of Greek SMEs in the agritourism industry. IS executives completed the survey and ANOVA analysis was used for data analysis. The findings of the paper indicate that IS executives do not focus on the analysis of the external environment and the evaluation of opportunities for IS development. In addition, the lack of formulation of the IT strategy creates inefficient and unsuccessful IT projects. [ABSTRACT FROM AUTHOR]

The digitalization of agriculture generates a new environment for the actors involved in agrifood production. In such a context, Agricultural Knowledge and Innovation Systems (AKISs) face the challenge of reconsidering their operational paradigms, redefining priorities, and designing strategies to achieve new aims. To do so, the actors participating in AKISs should develop and exploit a set of competencies known as dynamic capabilities, including the aptitude to sense the change in the external environment, the capacity to seize the opportunities that this change creates, and an ability to transform and adapt themselves to the new conditions that digitalization generates. In this study, using as examples the AKISs operating in Greece and Italy, we aimed to uncover if and how actors participating in these systems attempt and manage to deploy such capabilities. Based on a qualitative approach and drawing on data from two workshops, we discovered that seizing the opportunities sensed is a challenging task for AKIS actors. Our results also indicate that knowledge is a pivotal resource for AKISs, allowing actors to enhance their transformative capacity. However, to create a "collective" knowledge base, AKISs should ensure a functional connection between stakeholders and strengthen the roles of actors not actively engaged with the system, like public advisory organizations, universities, and technology providers. [ABSTRACT FROM AUTHOR]

This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the impact of educational software characteristics on software performance through the mediating role of user acceptance. Our approach allows for a deeper understanding of the factors that contribute to the effectiveness of educational software by bridging the fields of educational technology, psychology, and human–computer interaction, offering a holistic perspective on software adoption and performance. This study is based on a sample collected from public and private education institutes in Northern Greece and on data obtained from 236 users. The statistical method employed is structural equation models (SEMs), via SPSS—AMOS estimation. The findings of this study suggest that user acceptance and performance appraisal are exceptionally interrelated in regard to educational applications. The study argues that user acceptance is positively related to the performance of educational software and constitutes the nested epicenter mediating construct in the educational software characteristics. Additional findings, such as computer-familiar users and users from the field of choral music, are positively related to the performance of the educational software. Our conclusions help in understanding the psychological and behavioral aspects of technology adoption in the educational setting. Findings are discussed in terms of their practical usefulness in education and further research. [ABSTRACT FROM AUTHOR]

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there. [ABSTRACT FROM AUTHOR]

Due to COVID-19 restrictions, many restaurants were forced to discontinue in-person service, either by locking down or finding alternative methods of operation. Despite the fact that, in the United States of America, digital restaurants have already been established for many years, in Greece, this phenomenon became popular during the pandemic. These delivery-only companies operate exclusively online, allowing customers to place orders from restaurants without a physical location. This has revolutionized the process of ordering food, as customers can browse digital menus, view images, and utilize other options provided by digital food technology. As a result, customers have had new experiences with food thanks to digital eateries during the pandemic. This research study is quantitative and utilized a questionnaire distributed to 1097 participating consumers over the internet. The sample was selected using straightforward random sampling, where each member of the population had an equal and unique chance of participating in the survey. The data were collected over a period of 2 months. [ABSTRACT FROM AUTHOR]

The aim of this work is to highlight the possibilities of using VR applications in the informal learning process. This is attempted through the development of virtual reality cultural applications for historical monuments. For this purpose, the theoretical framework of virtual and augmented reality techniques is presented, developing as a showcase of the virtual environment of the historical bridge of Arta, in Greece. The bridge model is created through 3D software, which is then imported into virtual world environment by employing the Unity engine. The main objective of the research is the technical and empirical evaluation of the VR application by specialists, in comparison with the real environment of the monument. Accordingly, the use of the application in the learning process is evaluated by high school students. Using the conclusions of the evaluation, the environment will be enriched with multimedia elements and the application will be evaluated by secondary school students as a learning experience and process, using electroencephalography (EEG). The recording and analysis of research results can be generalized and lead to safe conclusions for the use of similar applications in the field of culture and learning. [ABSTRACT FROM AUTHOR]

Over the last decade, there have been many changes in the field of political analysis at a global level. Through social networking platforms, millions of people have the opportunity to express their opinion and capture their thoughts at any time, leaving their digital footprint. As such, massive datasets are now available, which can be used by analysts to gain useful insights on the current political climate and identify political tendencies. In this paper, we present TwiFly, a framework built for analyzing Twitter data. TwiFly accepts a number of accounts to be monitored for a specific time-frame and visualizes in real time useful extracted information. As a proof of concept, we present the application of our platform to the most recent elections of Greece, gaining useful insights on the election results. [ABSTRACT FROM AUTHOR]

Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users' attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system's benefits, while regarding IoT, the high quality of the services is the most important thing. [ABSTRACT FROM AUTHOR]