Digital, Volume 4, Issue 1 (March 2024) – 14 articles

Augmented reality (AR) applications are currently used in many fields for communication and educational purposes. Tourism is also a sector where augmented reality is used for destination marketing and cultural heritage promotion. This study will focus on mobile location-based AR applications and their potential in tourism. Such applications can guide tourists to places of interest and enhance their overall experience. The aim of this paper is to present a mobile application that was created for tourists visiting the region of Western Macedonia, Greece. The application was developed in order to guide the users in the region, entertain them, and educate them about the region’s sights, cultural heritage, and other special characteristics. The paper also aims to present a large set of features that are present in the application, including various types of AR (marker-based, markerless, and location-based) in order to provide designers who wish to create AR applications for tourism with new ideas. The application was validated by a usability test, and its features were evaluated by 39 participants who completed a questionnaire with 29 Likert-scale items. This procedure revealed the level of acceptance for the application features, and valuable feedback was also received during a discussion with the participants about how the application could be upgraded in the future. Full article

Although artificial intelligence technologies have provided valuable insights into the advertising industry, more comprehensive studies that properly examine the applications of AI in advertising using scientometric network analysis are needed. Using publications from journals indexed in the Web of Science, we seek to analyze the emergence of AI through the examination of keyword co-occurrences and co-authorship. Our goal is to identify essential concepts and influential research that have significantly impacted the advertising business. The findings highlight noteworthy patterns, indicating the growing importance of machine learning tools and techniques such as deep learning, and advanced natural language processing methods like word2vec, GANs, and others, as well as their societal impacts as they continue to define the future of advertising practices. Full article

Since the Software Quality Model was defined as an international standard, many quality assurance teams have used this quality model in a waterfall model for software development and quality control. As more software is delivered as a cloud service, various methodologies have been created with an awareness of the link between development productivity and operations, enabling faster development. However, most development methods are development-oriented with awareness of development progress, and there has been little consideration of methods that achieve quality orientation for continuous quality improvement and monitoring. Therefore, we developed a method to visualize the progress of software quality during development by defining quality goals in the project charter using the quality model defined in international standards, classifying each test by quality characteristics, and clarifying the quality ensured by each test. This was achieved by classifying each test by quality characteristics and clarifying the quality ensured by each test. To use quality characteristics as KPIs, it is necessary to manage test results for each test type and compare them with past build results. This paper explains how to visualize the quality to be assured and the benefits of using quality characteristics as KPIs and proposes a method to achieve rapid and high-quality product development. Full article

The paper introduces an innovative methodology that combines photogrammetry and laser scanning techniques to create detailed 3D models of historic mansions within the Kifissia region of Attica, Greece. While photogrammetry excels in capturing intricate textures, it faces challenges such as lighting variations and precise image alignment. On the other hand, laser scanning offers precision in capturing geometric details but struggles with reflective surfaces and large datasets. Our study integrates these methods to leverage their strengths and address limitations, resulting in comprehensive and accurate digital twins of cultural spaces. The methodology section outlines the step-by-step process of integration, emphasizing solutions to specific challenges encountered in the study area. Preliminary results showcase the enhanced fidelity and completeness of the digital twins, demonstrating the effectiveness of the combined approach. The subsequent sections of the paper delve into a detailed presentation of the methodology, provide a comprehensive analysis of obtained results, and discuss the implications of this innovative approach in cultural preservation and broader applications. Full article

This study aspires to contribute some initial results to the growing area of research regarding technology potential in the field of early foreign language literacy. An experiment was conducted to examine very young learners’ alphabet and vocabulary learning and retention in an early foreign language (FL) learning context when implementing augmented reality (AR) applications, while very young learners’ motivation was also assessed. A pilot intervention was implemented in a state school in northern Greece. The participants (n = 26) were primary school first-graders (5.5–6 years old) and were assigned into two groups, experimental (13) and control (13). To examine the effects of the intervention, this current study employed two instruments: (a) a pre-test–post-test model to assess young learners’ alphabet and vocabulary learning during three phases and (b) a questionnaire to assess their motivation during the learning process. The findings of this study reveal that both groups displayed significant improvements in FL alphabet and vocabulary learning; however, there are statistical differences in favor of the experimental group regarding long-term alphabet and vocabulary learning and retention. Furthermore, qualitative results regarding children’s perceptions of the technology used indicate that AR was highly appealing and motivating to participating students. Full article

The COVID-19 pandemic led to widespread restrictions globally, prompting governments to implement measures for containment. Vaccines, while aiding in reducing virus transmission, have also introduced the challenge of identifying vaccinated individuals for the purpose of easing restrictions. The European Union (EU) addressed this through the “digital COVID-19 certification” system, allowing citizens to travel within the EU based on their vaccination, recovery, or negative test status. However, the system’s digital format poses challenges for those who are not digitally proficient, such as seniors and those with low educational or socioeconomic status. This study aims to propose enhancements to the current system, considering the mobility needs of all citizens. The methodology involves reviewing literature on digital literacy, the digital divide, and information systems related to vaccination and certification. The paper presents straightforward recommendations to make the COVID-19 certificate more accessible to digitally excluded individuals. These proposals may serve as a valuable starting point for healthcare executives to evaluate and adapt the certification scheme to be inclusive of a broader range of stakeholders. Full article

The aim of this study is to assess whether social media addiction contributes to the intention to buy; it is based on the model of Hajli (2014) that assesses the relationships between the constructs of social media use, trust, perceived usefulness, and intention to buy in social media sites. To this end, a confirmatory factor analysis was carried out to evaluate whether the Hajli model applied to this sample, as well as multigroup CFA to measure invariance across gender and across following influencers or not. Finally, the path analysis evaluates the intersection of social media addiction with the Hajli model (2014). The results confirmed the Hajli model as well as the inclusion in the model of social media addiction as a variable that contributes to purchase intention on social media. Configural, metric, and scalar invariance were found across genders and across the following influencers or not. Also, the values found for internal consistency and composite reliability, convergent reliability, and discriminant reliability were within the reference values. Full article

This article relies on a combination of digital and analog data to analyze the 2D urban development of al-ʿAqaba and Jallūm districts in the Old City of Aleppo. The dataset consists of vectorized historical maps of the city spanning various historical periods. The oldest map in the collection dates back to the 1900s. Additionally, there are high-resolution orthomosaics created from a 3D model obtained through Terrestrial Laser Scanning (TLS) and Aerial Photogrammetry techniques. Through the analysis and integration of these various data types, the article proposes an analog-digital workflow that tracks the alterations in the urban fabric of the designated study area. The analysis primarily examines the alterations in the city’s two-dimensional layout and the distribution of mass and void. Tracking the changes in the street network of the studied area is the main goal of this research, along with recognizing the spatial changes in the built environment. The article identified changes in both the open spaces and the street layout. Full article

While most published research on COVID-19 focused on a few countries and especially on the second wave of the pandemic and the vaccination period, we turn to the first wave (March–May 2020) to examine the sentiments and emotions expressed by Twitter users in Greece. Using deep-learning techniques, the analysis reveals a complex interplay of surprise, anger, fear, and sadness. Initially, surprise was dominant, reflecting the shock and uncertainty accompanying the sudden onset of the pandemic. Anger replaced surprise as individuals struggled with isolation and social distancing. Despite these challenges, positive sentiments of hope, resilience and solidarity were also expressed. The COVID-19 pandemic had a strong imprint upon the emotional landscape worldwide and in Greece. This calls for appealing to emotions as well as to reason when crafting effective public health strategies. Full article

This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed using the Mostly Basic Python Problems (MBPP) dataset. In addition to the direct assessment of GPT-3.5 and GPT-4, a comparative analysis involving other popular large language models in the AI landscape, notably Google’s Bard and Anthropic’s Claude, was conducted to measure and compare their proficiency in the same tasks. The results highlight the strengths of ChatGPT models in programming tasks, offering valuable insights for the AI community, specifically for developers and researchers. As the popularity of artificial intelligence increases, this study serves as an early look into the field of AI-assisted programming. Full article

In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate defects and their impact on a complex antique wall painting of Giotto. The interferometry data is acquired with a portable off-axis interferometer set-up with a phase-shifted reference beam coupled with the object beam in front of the digital photosensitive medium. A digital holographic speckle pattern interferometry (DHSPI) system is used to register digital recordings of interferogram sequences over time. The surface is monitored for as long as it deforms prior to returning to its initial reference equilibrium state prior to excitation. The attempt to separate the whole vs. local defect complex amplitudes from the interferometric data is presented. The main aim is to achieve isolation and visualization of each defect’s impact amplitude in order to obtain detailed documentation of each defect and its structural impact on the surface for structural diagnosis purposes. Full article

News recommender systems (NRS) are crucial for helping users navigate the vast amount of content available online. However, traditional NRS often suffer from biases that lead to a narrow and unfair distribution of exposure across news items. In this paper, we propose a novel approach, the Contextual-Dual Bias Reduction Recommendation System (C-DBRRS), which leverages Long Short-Term Memory (LSTM) networks optimized with a multi-objective function to balance accuracy and diversity. We conducted experiments on two real-world news recommendation datasets and the results indicate that our approach outperforms the baseline methods, and achieves higher accuracy while promoting a fair and balanced distribution of recommendations. This work contributes to the development of a fair and responsible recommendation system. Full article

This paper presents a study guide and an analysis of its use in the computer programming learning process of an 8-year-old elementary school student through the Scratch program. The research’s objective is to explore and understand how this individual student approaches learning programming skills and tackles challenges within the Scratch environment. An individual case study approach was adopted at home, combining qualitative and quantitative methods to gain a comprehensive insight into the student’s learning process. The study was conducted without grant support, and the researcher actively participated as an educator and observer in the student’s learning sessions. Performance was assessed, and a semi-structured interview was conducted to inquire about the student’s experiences, motivations, and interests regarding programming in Scratch, as well as their feelings after the training. Additionally, the student’s activities during programming sessions were meticulously recorded, and projects created in Scratch were analyzed to assess progress and understanding of concepts. The findings of this research have the potential to contribute to the field of programming education and provide valuable insights into how young elementary school-aged individuals can acquire computer and programming skills in an interactive environment such as Scratch. The results obtained demonstrate that using the proposed guide to introduce elementary school students to programming at home, with parents acting as educators, is feasible. Therefore, it helps facilitate access to this knowledge, which is currently limited for many individuals in an official educational setting. Full article

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation. Full article