Informatics, Volume 11, Issue 1 (March 2024) – 12 articles

One of the main obstacles in software development projects is requirement volatility (RV), which is defined as uncertainty or changes in software requirements during the development process. Therefore, this research tries to understand the underlying factors behind the RV and the best practices to reduce it. The methodology used for this research is based upon qualitative research using interviews with 12 participants with experience in agile software development projects. The participants hailed from Austria, Nigeria, the USA, the Philippines, Armenia, Sri Lanka, Germany, Egypt, Canada, and Turkey and held roles such as project managers, software developers, Scrum Masters, testers, business analysts, and product owners. Our findings based on our empirical data revealed six primary factors that cause RV and three main agile practices that help to mitigate it. Theoretically, this study contributes to the body of knowledge relating to RV management. Practically, this research is expected to aid software development teams in comprehending the reasons behind RV and the best practices to effectively minimize it. Full article

As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important role in transforming multidimensional data into interpretable formats for decision support systems. To address this problem, the paper presents an analysis of dimensionality reduction and visualization techniques that embrace complex data representations and are useful inferences for decision systems. A novel framework is proposed, utilizing a Siamese neural network with a triplet loss function to analyze multidimensional data encoded into images, thus transforming these data into multidimensional embeddings. This approach uses dimensionality reduction techniques to transform these embeddings into a lower-dimensional space. This transformation not only improves interpretability but also maintains the integrity of the complex data structures. The efficacy of this approach is demonstrated using a keystroke dynamics dataset. The results support the integration of these visualization techniques into decision support systems. The visualization process not only simplifies the complexity of the data, but also reveals deep patterns and relationships hidden in the embeddings. Thus, a comprehensive framework for visualizing and interpreting complex keystroke dynamics is described, making a significant contribution to the field of user authentication. Full article

The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field. Full article

This paper describes the Integration of Archives and Cultural Places (IaCuP) project, which aims to integrate information about a historical cemetery, including its map and grave inventory, with genealogical and documentary knowledge extracted from relevant historical archives. The integrated data are accessible to cemetery visitors through an interactive mobile application, enabling them to navigate a graphical representation of the cemetery while exploring comprehensive visualizations of genealogical data. The basic idea stems from the desire to provide people with access to the rich context of cultural sites, which have often lost their original references over the centuries, making it challenging for individuals today to interpret the meanings embedded within them. The proposed approach leverages large language models (LLMs) to extract information from relevant documents and Web technologies to represent such information as interactive visualizations. As a practical case study, this paper focuses on the Jewish Cemetery in Pisa and the Historical Archives of the Jewish Community in Pisa, working on the genealogical tree of one of the most representative families resting in the cemetery. Full article

As social media (SM) becomes increasingly prevalent, its impact on society is expected to grow accordingly. While SM has brought positive transformations, it has also amplified pre-existing issues such as misinformation, echo chambers, manipulation, and propaganda. A thorough comprehension of this impact, aided by state-of-the-art analytical tools and by an awareness of societal biases and complexities, enables us to anticipate and mitigate the potential negative effects. One such tool is BERTopic, a novel deep-learning algorithm developed for Topic Mining, which has been shown to offer significant advantages over traditional methods like Latent Dirichlet Allocation (LDA), particularly in terms of its high modularity, which allows for extensive personalization at each stage of the topic modeling process. In this study, we hypothesize that BERTopic, when optimized for Twitter data, can provide a more coherent and stable topic modeling. We began by conducting a review of the literature on topic-mining approaches for short-text data. Using this knowledge, we explored the potential for optimizing BERTopic and analyzed its effectiveness. Our focus was on Twitter data spanning the two years of the 117th US Congress. We evaluated BERTopic’s performance using coherence, perplexity, diversity, and stability scores, finding significant improvements over traditional methods and the default parameters for this tool. We discovered that improvements are possible in BERTopic’s coherence and stability. We also identified the major topics of this Congress, which include abortion, student debt, and Judge Ketanji Brown Jackson. Additionally, we describe a simple application we developed for a better visualization of Congress topics. Full article

Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments. Full article

Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on information collected from surveys of university alumni. Five machine learning (ML) algorithms were used for data analysis, including the decision tree, random forest, gradient boosting, support vector machine, and neural network models. To achieve optimal prediction performance, we utilized the Bayesian optimization method to fine-tune the parameters of the five ML algorithms. The five ML models were compared with logistic and ordinal regression. Then, to extract the most important features of the best predictive model, we employed the SHapley Additive exPlanations (SHAP), a novel methodology for extracting the significant features in ML. The results indicated that gradient boosting is a marginally superior predictive model, with 2–3% higher accuracy and area under the receiver operating characteristic curve (AUC) compared to logistic and ordinal regression. Interestingly, concerning low career satisfaction, those with the worst scores for the phrase “how frequently applied knowledge, skills, or technological tools from the academic training” were less satisfied with their careers. To summarize, career satisfaction is related to academic training, alumni satisfaction, employment status, published articles or books, and other factors. Full article

This study presents an augmented reality application for training chest electrocardiography electrode placement. AR applications featuring augmented object displays and interactions have been developed to facilitate learning and training of electrocardiography (ECG) chest lead placement via smartphones. The AR marker-based technique was used to track the objects. The proposed AR application can project virtual ECG electrode positions onto the mannequin’s chest and provide feedback to trainees. We designed experimental tasks using the pre- and post-tests and practice sessions to verify the efficiency of the proposed AR application. The control group was assigned to learn chest ECG electrode placement using traditional methods, whereas the intervention group was introduced to the proposed AR application for ECG electrode placement. The results indicate that the proposed AR application can encourage learning outcomes, such as chest lead ECG knowledge and skills. Moreover, using AR technology can enhance students’ learning experiences. In the future, we plan to apply the proposed AR technology to improve related courses in medical science education. Full article

It is prudent to take a unified approach to exploring how contextual social determinants of health (SDoH) relate to COVID-19 occurrence and outcomes. Poor geographically represented data and a small number of contextual SDoH examined in most previous research studies have left a knowledge gap in the relationships between contextual SDoH and COVID-19 outcomes. In this study, we linked 199 contextual SDoH factors covering 11 domains of social and built environments with electronic health records (EHRs) from a large clinical research network (CRN) in the National Patient-Centered Clinical Research Network (PCORnet) to explore the relation between contextual SDoH and COVID-19 occurrence and hospitalization. We identified 15,890 COVID-19 patients and 63,560 matched non-COVID-19 patients in Florida between January 2020 and May 2021. We adopted a two-phase multiple linear regression approach modified from that in the exposome-wide association (ExWAS) study. After removing the highly correlated SDoH variables, 86 contextual SDoH variables were included in the data analysis. Adjusting for race, ethnicity, and comorbidities, we found six contextual SDoH variables (i.e., hospital available beds and utilization, percent of vacant property, number of golf courses, and percent of minority) related to the occurrence of COVID-19, and three variables (i.e., farmers market, low access, and religion) related to the hospitalization of COVID-19. To our best knowledge, this is the first study to explore the relationship between contextual SDoH and COVID-19 occurrence and hospitalization using EHRs in a major PCORnet CRN. As an exploratory study, the causal effect of SDoH on COVID-19 outcomes will be evaluated in future studies. Full article

Addressing the critical challenges of resource inefficiency and environmental impact in the agrifood sector, this study explores the integration of Internet of Things (IoT) technologies with IOTA’s Tangle, a Distributed Ledger Technology (DLT). This integration aims to enhance sustainable agricultural practices, using rice cultivation as a case study of high relevance and reapplicability given its importance in the food chain and the high irrigation requirement of its cultivation. The approach employs sensor-based intelligent irrigation systems to optimize water efficiency. These systems enable real-time monitoring of agricultural parameters through IoT sensors. Data management is facilitated by IOTA’s Tangle, providing secure and efficient data handling, and integrated with MongoDB, a Database Management System (DBMS), for effective data storage and retrieval. The collaboration between IoT and IOTA led to significant reductions in resource consumption. Implementing sustainable agricultural practices resulted in a 50% reduction in water usage, 25% decrease in nitrogen consumption, and a 50% to 70% reduction in methane emissions. Additionally, the system contributed to lower electricity consumption for irrigation pumps and generated comprehensive historical water depth records, aiding future resource management decisions. This study concludes that the integration of IoT with IOTA’s Tangle presents a highly promising solution for advancing sustainable agriculture. This approach significantly contributes to environmental conservation and food security. Furthermore, it establishes that DLTs like IOTA are not only viable but also effective for real-time monitoring and implementation of sustainable agricultural practices. Full article

Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and syn-chronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit. Full article

Vocabulary acquisition and retention is an essential part of learning a foreign language and many learners use flashcard applications to repetitively increase vocabulary retention. However, it can be difficult for learners to remember new words and phrases without any context. In this paper, we propose a system that allows users to acquire new vocabulary with media which gives context to the words. Theoretically, this use of multimedia context should enable users to practice with interest and increased motivation, which has been shown to enhance the effects of contextual language learning. An experiment with 46 English as foreign language learners showed better retention after two weeks with the proposed system as compared to ordinary flashcards. However, the impact was not universally beneficial to all learners. An analysis of participant attributes that were gathered through surveys and questionnaires shows a link between personality and learning traits and affinity for learning with this system. This result indicates that the proposed system provides a significant advantage in vocabulary retention for some users, while other users should stay with traditional flashcard applications. The implications of this study indicate the need for the development of more personalized learning applications. Full article