Informatics, Volume 8, Issue 3 (September 2021) – 22 articles

Social media addiction has undergone a remarkable transformation among regular users, but limited research has been conducted on exploring the antecedents of addiction. The purpose of this study is to investigate the relationship between continuance intention and addiction. Moreover, it investigates the moderating role of satisfaction and trust on the relationship between continuance intention and addiction. The developed conceptual model suggests that continuance intention is the antecedent of addiction, while satisfaction and trust act as moderators between continuance intention and addiction. The antecedents of continuance intention are emotional, informational, social, and hedonic values. A survey was conducted to collect data from 572 voluntary participants, and the analysis was performed using SPSS and AMOS. The statistical result showed the effects of emotional, informational, and hedonic values on Facebook use continuance intention, but the effect of social value was not supported. Results also confirmed a significant effect of continuance intention on Facebook addiction. Additionally, it confirmed the moderating role of satisfaction on the impact of continuance intention on Facebook addiction, but the moderating role of trust was not supported. The results of this study provide insight for Facebook users, managers, and policymakers regarding treatment and intervention for Facebook addiction. It discusses several theoretical and practical implications. In this research, we proposed a new model based on extending the associations between perceived value and continuance behaviours theory. Full article

As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality. Full article

Virtual Reality (VR) technology is frequently applied in simulation, particularly in medical training. VR medical training often requires user input either from controllers or free-hand gestures. Nowadays, hand gestures are commonly tracked via built-in cameras from a VR headset. Like controllers, hand tracking can be used in VR applications to control virtual objects. This research developed VR intubation training as a case study and applied controllers and hand tracking for four interactions—namely collision, grabbing, pressing, and release. The quasi-experimental design assigned 30 medical students in clinical training to investigate the differences between using VR controller and hand tracking in medical interactions. The subjects were divided into two groups, one with VR controllers and the other with VR hand tracking, to study the interaction time and user satisfaction in seven procedures. System Usability Scale (SUS) and User Satisfaction Evaluation Questionnaire (USEQ) were used to measure user usability and satisfaction, respectively. The results showed that the interaction time of each procedure was not different. Similarly, according to SUS and USEQ scores, satisfaction and usability were also not different. Therefore, in VR intubation training, using hand tracking has no difference in results to using controllers. As medical training with free-hand gestures is more natural for real-world situations, hand tracking will play an important role as user input for VR medical training. This allows trainees to recognize and correct their postures intuitively, which is more beneficial for self-learning and practicing. Full article

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that can take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state of the art published in each of those subsets between the years of 2014 and 2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data. Full article

In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6). Full article

Compartmental models have long been used in epidemiological studies for predicting disease spread. However, a major issue when using compartmental mathematical models concerns the time-invariant formulation of hyper-parameters that prevent the model from following the evolution over time of the epidemiological phenomenon under investigation. In order to cope with this problem, the present work suggests an alternative hybrid approach based on Machine Learning that avoids recalculation of hyper-parameters and only uses an initial set. This study shows that the proposed hybrid approach makes it possible to correct the expected loss of accuracy observed in the compartmental model when the considered time horizon increases. As a case study, a basic compartmental model has been designed and tested to forecast COVID-19 hospitalizations during the first and the second pandemic waves in Lombardy, Italy. The model is based on an extended formulation of the contact function that allows modelling of the trend of personal contacts throughout the reference period. Moreover, the scenario analysis proposed in this work can help policy-makers select the most appropriate containment measures to reduce hospitalizations and relieve pressure on the health system, but also to limit any negative impact on the economic and social systems. Full article

The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with the aim of summarising the existing methodologies that apply machine learning methods on patient-reported outcome measures datasets for predicting clinical outcomes to support further research and development within the field. We identify 15 articles published within the last decade that employ machine learning methods at various stages of exploiting datasets consisting of patient-reported outcome measures for predicting clinical outcomes, presenting promising research and demonstrating the utility of patient-reported outcome measures data for developmental research, personalised treatment and precision medicine with the help of machine learning-based decision-support systems. Furthermore, we identify and discuss the gaps and challenges, such as inconsistency in reporting the results across different articles, use of different evaluation metrics, legal aspects of using the data, and data unavailability, among others, which can potentially be addressed in future studies. Full article

Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much information and proof as possible to help a computer program to predict bladder cancer treatment side effects and support the oncologist’s decision. This will help in deciding treatment options for patients with bladder malignancies. Bladder cancer knowledge is complex and requires simplification before any attempt to represent it in a formal or computerized manner. In this work we rely on the capabilities of OWL ontologies to seamlessly capture and conceptualize the required knowledge about this type of cancer and the underlying patient treatment process. Our ontology allows case-based reasoning to effectively predict treatment side effects for a given set of contextual information related to a specific medical case. The ontology is enriched with proofs and evidence collected from online biomedical research databases using “web crawlers”. We have exclusively designed the crawler algorithm to search for the required knowledge based on a set of specified keywords. Results from the study presented 80.3% of real reported bladder cancer treatment side-effects prediction and were close to really occurring adverse events recorded within the collected test samples when applying the approach. Evidence-based medicine combined with semantic knowledge-based models is prominent in generating predictions related to possible health concerns. The integration of a diversity of knowledge and evidence into one single integrated knowledge-base could dramatically enhance the process of predicting treatment risks and side effects applied to bladder cancer oncotherapy. Full article

A first aim of the present work is the determination of the actual sources of the “finite precision error” generation and accumulation in two important algorithms: Bernoulli’s map and the folded Baker’s map. These two computational schemes attract the attention of a growing number of researchers, in connection with a wide range of applications. However, both Bernoulli’s and Baker’s maps, when implemented in a contemporary computing machine, suffer from a very serious numerical error due to the finite word length. This error, causally, causes a failure of these two algorithms after a relatively very small number of iterations. In the present manuscript, novel methods for eliminating this numerical error are presented. In fact, the introduced approach succeeds in executing the Bernoulli’s map and the folded Baker’s map in a computing machine for many hundreds of thousands of iterations, offering results practically free of finite precision error. These successful techniques are based on the determination and understanding of the substantial sources of finite precision (round-off) error, which is generated and accumulated in these two important chaotic maps. Full article

Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures in which convolutional layers are successively applied to the input data. In general, the performance of neural networks has improved as the depth of CNNs has increased. However, an increase in the depth of a CNN is not always accompanied by an increase in the accuracy of the neural network. This is because the gradient vanishing problem may arise, causing the weights of the weighted layers to fail to converge. Accordingly, the gradient flows of the VGGNet, ResNet, SENet, and DenseNet models were analyzed and compared in this study, and the reasons for the differences in the error rate performances of the models were derived. Full article

The local press has always played a central role in the Portuguese society. Recently, new innovative technological projects to develop mobile applications and focus on local journalism in Portugal have emerged. These initiatives allow the development of better and more appealing services for local users. However, due to the important social role of the local press, this also brings along some responsibilities. Our main research goal is to study the accessibility issues in local journalism in Portugal. To this end, we first describe the current situation of local journalism in Portugal and some accessibility issues raised by the appearance of mobile applications. We then develop a simple checklist that allows the assessment of whether these applications have prevented social exclusion and facilitated the access of local information to a wide range of users, including disabled citizens. This tool provides the regional news publisher with information to improve its democratization of access to local information in Portugal. Using the cognitive walkthrough method, we illustrate the proposed framework by presenting case studies of five mobile applications in Portuguese local and regional press. This study concludes that despite the great potential that mobile applications showcase, several accessibility issues have not been properly addressed. Full article

COVID-19 pandemic consequences are tragic, and many problems will persist after the health problem ends. Some studies have focused on mental health issues, reporting worrying percentages. It is known that there is a bidirectional relationship between mental health and sleep quality, and it would be expected that sleep would be affected by the pandemic. In order to know the Portuguese people’s habits before and during the confinement, we carried out a survey of 188 people aged 13 to 84 (38 ± 15) to find out the most frequent sleep patterns, habits and disorders before and during confinement. With this survey it was also intended to measure the most frequent changes in sleep patterns, habits, and disturbances on the general population and according to demographic data (gender, age group and professional status), sleep disorders arise, perceptions about sleep during confinement and if Portuguese think that in the future the sleep patterns will be the pre or during outbreak. Results indicate that, comparing before and during confinement, there is a slight correlation between gender and sleep disorders (before vs. during), a correlation between age group and professional status time to wake up, and between professional status and sleep disorders, and a strong correlation between the professional situation and changes in the invigorated feeling level (p < 0.001). Support for mental health and interventions to improve sleep quality should be offered to the population in general, and, according to our study, the Portuguese population. Full article

This research was aimed at constructing a thesaurus of the ethnic groups in the Mekong River Basin that is a compilation of controlled vocabularies of both Thai and English language, with a digital platform that enables semantic search and linked open data. The research method involved four steps: (1) organization of knowledge content; (2) construction of the thesaurus; (3) development of a digital thesaurus platform; and (4) evaluation. The concepts and theories used in the research comprised knowledge organization, thesaurus construction, digital platform development, and system evaluation. The tool for developing the digital thesaurus was the Tematres web application. The research results are: (1) there are 4273 principle words related to the ethnic groups that have been compiled and classified by the terms for each of the eight deep levels, 2596 were found to have hierarchical relationships, and 6858 had associative relationships; (2) the digital thesaurus platform was able to manage the controlled vocabularies related to the Mekong ethnic groups by storing both Thai and English vocabularies. When retrieved, the vocabulary, details of the broader term, narrow term, related term, cross reference, and scope note are displayed. Thus, semantic search is viable through applications, linked open data technology, and web services. Full article

In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems. Full article

Digital contact tracing applications (CTAs) have been one of the most widely discussed technical methods of controlling the COVID-19 outbreak. The effectiveness of this technology and its ethical justification depend highly on public acceptance and adoption. This study aims to describe the current knowledge about public acceptance of CTAs and identify individual perspectives, which are essential to consider concerning CTA acceptance and adoption. In this scoping review, 25 studies from four continents across the globe are compiled, and critical topics are identified and discussed. The results show that public acceptance varies across national cultures and sociodemographic strata. Lower acceptance among people who are mistrusting, socially disadvantaged, or those with low technical skills suggest a risk that CTAs may amplify existing inequities. Regarding determinants of acceptance, eight themes emerged, covering both attitudes and behavioral perspectives that can influence acceptance, including trust, privacy concerns, social responsibility, perceived health threat, experience of and access to technologies, performance expectancy and perceived benefits, and understanding. Furthermore, widespread misconceptions about the CTA function are a topic in need of immediate attention to ensure the safe use of CTAs. The intention-action gap is another topic in need of more research. Full article

Regional rainfall forecasting is an important issue in hydrology and meteorology. Machine learning algorithms especially deep learning methods have emerged as a part of prediction tools for regional rainfall forecasting. This paper aims to design and implement a generic computing framework that can assemble a variety of machine learning algorithms as computational engines for regional rainfall forecasting in Upstate New York. The algorithms that have been bagged in the computing framework include the classical algorithms and the state-of-the-art deep learning algorithms, such as K-Nearest Neighbors, Support Vector Machine, Deep Neural Network, Wide Neural Network, Deep and Wide Neural Network, Reservoir Computing, and Long Short Term Memory methods. Through the experimental results and the performance comparisons of these various engines, we have observed that the SVM- and KNN-based method are outstanding models over other models in classification while DWNN- and KNN-based methods outstrip other models in regression, particularly those prevailing deep-learning-based methods, for handling uncertain and complex climatic data for precipitation forecasting. Meanwhile, the normalization methods such as Z-score and Minmax are also integrated into the generic computing framework for the investigation and evaluation of their impacts on machine learning models. Full article

This article deals with the multicriteria programming model to optimize the time of completing home assignments by school students in both in-class and online forms of teaching. To develop a solution, we defined 12 criteria influencing the school exercises’ effectiveness. In this amount, five criteria describe exercises themselves and seven others the conditions at which the exercises are completed. We used these criteria to design a neural network, which output influences target function and the search for optimal values with three optimization techniques: backtracking search optimization algorithm (BSA), particle swarm optimization algorithm (PSO), and genetic algorithm (GA). We propose to represent the findings for the optimal time to complete homework as a Pareto set. Full article

The capabilities of the people, processes, and technology are important factors to consider when exploring continuous use to create value. Multiple perceptions and attitudes towards self-service systems lead to various usage levels and outcomes. With complex analytical structures, organizations need a better understanding of IS value and users’ satisfaction. Incompatibility reduces the purpose of self-service analytics, decreasing its value and making it obsolete. In a qualitative, single case study, 20 interviews in a major digital Scandinavian marketplace were explored using the expectation–confirmation theory of continuous use to explore the mechanisms influencing the sustainability of self-service value. Two main mechanisms were identified: the personal capability reinforcement mechanism and the environment value reinforcement mechanism. This study contributes to the post-implementation and continuous use literature and self-service analytics literature and provides some practice implications to the related industry. Full article

The epidemiological situation caused by the COVID-19 pandemic led to efforts to mitigate the transmission of the infection, forcing workers and students to stay at home. Universities closed, as did many businesses, forcing education and work to adapt to the new situation. While for some people it was a positive experience, for others it was not. This article analyzes the responses of 89 respondents, over 18 years old, in teleworking or enrolled in university online education, in a survey at the beginning of 2021, when Portugal was in a new state of emergency. Variables such as gender, age and parenthood were used, as well as ownership of equipment, own workspace, and quality of internet, comparing distance learning/work with traditional methods and measuring levels of satisfaction and willingness to adopt this model in the future. These results suggest an association of gender and parenting in the valuation of telework/distance education; women were more positive than men and participants with children were more positive than participants without children. The same was the case for respondents with their own workspace and better-quality internet. There is a strong relation between paternity and the preference for the distance model, in the sense of valuing the distance model, as well as a relation between those who have their own work space and the appreciation of the distance model. In general, respondents to our survey showed that they are not fond of adopting telework/distance learning in the future. Full article

With the universal use of mobile computing devices, there has been a notable increase in the number of mobile applications developed for educational purposes. Gamification strategies offer a new set of tools to educators and, combined with the location services provided by those devices, allow the creation of innovative location-based mobile learning experiences. In this literature review, we conduct an analysis of educational mobile location-based games. The review includes articles published from January of 2010 to October of 2020, and from 127 records screened, 26 articles were analysed in full-text form. This analysis allowed us to answer the following six predefined research questions: Who are the target audiences for location-based games? In which subjects are location-based games most used? Which strategies are implemented with mobile devices to improve the student’s learning process? What are the main impacts of location-based games on students’ learning? What are the main challenges to the development of location-based games for education? What future tendencies and research opportunities can be identified from the analysis of the current state of the art? Full article

In higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. These large datasets can be accessed using an array of data analysis tools that may lead users to assume that data sources conflict with one another. As users identify new ways of accessing and analyzing these data, they deviate from existing work practices and sometimes create their own databases. This study investigated the needs of end users who are accessing these seemingly fragmented databases. Analysis of a survey completed by eighteen users and ten semi-structured interviews from five colleges and universities highlighted three recurring themes that affect work practices (access, understandability, and use), as well as a series of challenges and opportunities for the design of data gateways for higher education. We discuss a set of broadly applicable design recommendations and five design functionalities that the data gateways should support: training, collaboration, tracking, definitions and roadblocks, and time. Full article

The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems. Full article