Informatics

Making smart and informed decisions often requires the integration and analysis of large amounts of data. However, integrating these data is rarely straightforward, mainly because of heterogeneities in data structure and format. In this study, we focus on two widely used data formats by municipalities to store digital maps of their infrastructure: Computer-Aided Design (CAD) and Geographic Information Systems (GIS). While most municipalities still maintain infrastructure data in CAD format, many have started converting them to GIS since GIS includes geographical coordinates. However, the inherent differences between these two formats pose challenges to accurately converting information from CAD to GIS. The main goal of this study is to develop a procedure to help municipalities to perform CAD-to-GIS conversion. To that end, potential problems in CAD-to-GIS conversion were first identified through interviews with practitioners at different U.S. municipalities and through a literature review. Taken together, we propose the C2G framework to streamline the conversion process while minimizing information loss. The framework consists of five stages, and the execution of this framework and tasks involved in each stage are explained. Moreover, we apply the framework to real-world underground stormwater infrastructure data obtained from the University of Illinois at Chicago (UIC) to illustrate the framework’s applicability. The case study explains details about the technical difficulties we encountered in the process and provides recommendations to circumvent those difficulties. The results from the case study showed that the C2G framework was able to successfully convert CAD data to GIS data. Although the framework is developed specific to the needs of CAD/GIS practitioners in the US municipalities, it can be adopted in most CAD-to-GIS conversion situations. The information learned during the interviews supports the need for a standard CAD-to-GIS conversion process. The contribution of this study is to fill this gap by developing a generalized framework to carry out CAD-to-GIS conversion which only requires basic knowledge of CAD and GIS. Full article

Social media is the most popular canvas to engage with art. In this study, we provide a different angle, on how an artistic installation on a world-renowned monument—such as Paris’ Arc de Triomphe—can emotionally affect viewers and potentially increase the popularity of the artwork. We collected N = 7078 Instagram and N = 3776 Twitter posts of the Arc de Triomphe as wrapped (installation) and unwrapped using APIs. As engagement indicators for several supervised machine learning experiments, we chose the total number of likes, comments, shares, text sentiment, and so on. Our findings revealed that people were captivated by the poetic installation. Based on the results, we discovered that the sentiments of triumph and surprise prevailed in datasets of the Arc de Triomphe as unwrapped. The same sentiments of triumph and surprise were most prevalent in datasets as wrapped, as well, but with higher scores. Furthermore, we have provided evidence of public art experience and engagement in the social media era. This research, we believe, will be useful in future studies of social media through the lens of public art and popularity. We hope that our findings will stimulate future research in the fields of art curatorship, cultural heritage management, marketing and communication, aesthetics, and culture analytics. Full article

Low back pain is a leading cause of disability worldwide, putting a significant strain on individual sufferers, their families, and the economy as a whole. It has a significant economic impact on the global economy because of the costs associated with healthcare, lost productivity, activity limitation, and work absence. Self-management, education, and adopting healthy lifestyle behaviors, such as increasing physical activity, are all widely recommended treatments. Access to services provided by healthcare professionals who provide these treatments can be limited and costly. This evaluation study focuses on the application of the MyRelief serious game, with the goal of addressing such challenges by providing an accessible, interactive, and fun platform that incorporates self-management, behavior change strategies, and educational information consistent with recommendations for managing low-back pain, based on self-assessment models implemented through ontology-based mechanics. Functional disability measured using the Oswestry Disability Questionnaire showed the statistically significant (p < 0.001) improvement in subjects’ self-evaluation of their health status. System Usability Scale (SUS) test score of 77.6 also suggests that the MyRelief serious game can potentially influence patient enablement. Full article

The complexity in collaborative work is mainly related to the difficulty in social interaction, which generates low levels of understanding among participants about what they should do and about the problem to be solved, resulting in problems in the motivation to generate true collaboration. Therefore, in the search to improve collaborative work and encourage this collaboration, it is necessary to implement strategies that promote the construction of shared understanding and obtain better group results. However, building it becomes a challenge due to the factors that influence it and how little is known about its construction. In this sense, to improve collaborative work, as a result of a research process, the THUNDERS process is proposed, which provides a set of elements to build shared understanding in problem-solving activities and with heterogeneous group formation. Specifically, this paper presents the results of the statistical validation of THUNDERS through the Student’s t-test, which was used in an exploratory study in the educational field in two Colombian universities, where learning styles were considered for the formation of groups; having groups that used the process and other control groups that did not use it, the collaborative activity consisted of determining the scope of a process line simulating a software development company. According to the results obtained in the context of this study, it can be considered that THUNDERS encourages and improves shared understanding when people in a group work collaboratively to solve a problem. In addition, elements for improvement were identified that should be incorporated in further stages of this research so that the process allows for an easy and guided construction of shared understanding in any application context. Full article

Data integration is one of the most challenging tasks for digital collections whose data are stored across various repositories. Data integration across digital repositories has several challenges. First, data heterogeneity in terms of data schema and data values usually occurs across diverse data sources. Second, heterogeneity in data representation and semantic issues are among the problems. The same data may appear in different repositories with varied data representations, i.e., metadata schema. Recent research has focused on matching several related metadata schemas. In this paper, a metadata integration framework is proposed to support digital repositories in socio-cultural anthropology at the Princess Maha Chakri Sirindhorn Anthropology Centre (SAC), Thailand. The proposed framework is defined based on the Metadata Lifecycle Model (MLM). It utilizes non-procedural schema mappings to express data relationships in diverse schemas. A case study of metadata integration over the SAC digital repositories was conducted to validate the framework. The SAC common metadata schema was designed to support data mapping across 13 digital repositories. The SAC “One Search” system was developed to exemplify the system implementation of the framework. Evaluation results showed that the proposed metadata integration framework can support domain experts in socio-cultural anthropology in unified searching across the repositories. Full article

Based on many years of experience as a management consultant in different industries and corporate structures and cultures, the motivation to use digital transformation in connection with variable corporate goals—such as fluctuating workloads, agile response to customer inquiries, and ecological and economic sustainability—results in a process or a product to be developed that intelligently adapts to market requirements and requires forward-looking leadership. Using an AI-based methodical analysis and synthesis approach, the high consumption of economic and human resources is to be continuously monitored and optimization measures initiated at an early stage. The necessary information technology with its infrastructure and architecture is the starting point to accompany the agility and changeability of corporate goals. Researching the relevant documents begins with writing the panorama or the state of knowledge on the topic. This article is about the IT infrastructure based on the requirements for an architecture and behavior that a versatile, agile company needs to accompany the constantly changing framework conditions of the market. The technology used and the available resources, including the human resources, need to be adapted as early as possible. Data now represent the most valuable asset on Earth and future industrial manufacturing systems must maximize the opportunity of data usage. Low-level data must be transformed to make them useful in supporting intelligent decision-making, for example. Furthermore, future manufacturing systems must be highly productive, adaptable, absent of error, and kind to the environment and to local communities. The all-important design should minimize the waste of material, capital, energy, and media. Herein, we discuss the fulfilling of agile customer requirements involving adaptable and modulated production processes (related to the ‘agile manufacturing’ and ‘digital transformation’ perspectives). Full article

The contemporary urban environment faces such challenges as overloaded traffic, heavy pollution, and social problems, etc. The concept of the “smart city” allows solving some of these issues. One of the opportunities provided by the smart city is the development of micro-mobility and sharing services; contributing to the optimization of transport flows and decreasing carbon footprints. This study investigates the factors affecting the development of e-scooter sharing services and the attitudes of young urban residents towards using these services. The research applied a PLS-SEM (partial least squares structural equation modeling) analysis performed in SmartPLS3.7 software. The data were collected via focus groups and surveying a population aged 18–35. The authors partially based the research on the UTAUT model (the unified theory of acceptance and use of technology), taking such constructs as “intention to use”, “anxiety”, “attitude toward use”, “effort expectancy”, and “social influence”; they also introduced the new unique variables “internal uncertainty”, “e-scooter design”, “experience”, “perceived safety”, “infrastructure quality”, and “motivation to physical activity”. The main finding of the study was determining that the latent variables attitude towards sharing, anxiety, internal uncertainty, JTBD (jobs to be done), and new way of thinking have a direct or indirect effect on the intention to ride e-scooters in the future and/or to use sharing services. The obtained results permit making recommendations to businesses, municipal authorities, and other stakeholders on developing e-scooter sharing services as a contribution to the advancement of the smart city. Full article

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility. Full article

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field. Full article

Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, planning and driving policies, and control, which have diverse requirements and goals. It is, therefore, challenging to prototype new RL projects with different simulators, especially when there is a need to examine several reward functions at once. This paper introduces a modified simulator based on the Udacity simulator, made for autonomous cars using RL. It creates reward functions, along with sensors to create a baseline implementation for RL-based vehicles. The modified simulator also resets the vehicle when it gets stuck or is in a non-terminating loop, making it more reliable. Overall, the paper seeks to make the prototyping of new systems simple, with the testing of different RL-based systems. Full article

The aim of this study is to synthesize the rapidly increasing literature on privacy and security risk of digital payment. By reviewing 591 studies, the literature on this topic was evaluated using a bibliographical approach to highlight the intellectual development of the field and recommend potential research directions in this still-emerging field. According to our assessment, academics have continued to focus on perceived privacy and security, while more multigroup analyses based on subdimensions of risk are needed. In addition, the vast majority of studies have not considered the inter-relationship between risk attributes. We analyse the potential causes of the lack of research diversity and provide additional suggestions to improve digital payment research in the future. This study will be valuable for academics, analysts, regulators, practitioners, and investors. Full article

This study validates and extends the latest unified theory of acceptance and use of technology (UTAUT2) with the privacy calculus model. To evaluate the adoption of healthcare and e-government applications, researchers have recommended—in previous literature—the application of technology adoption models with privacy, trust, and security-related constructs. However, the current UTAUT2 model lacks privacy, trust, and security-related constructs. Therefore, the proposed UTAUT2 with the privacy calculus model is incorporated into four constructs: privacy concern, perceived risk, trust in the smart national identity card (SNIC), and perceived credibility. Results from a survey data of 720 respondents show that habit, effort expectancy, performance expectancy, social influence, hedonic motivation, and price value are direct determinants that influence behavioral intentions to use. Results also revealed that behavioral intentions, facilitating conditions, habits, perceived risks, and privacy concerns are direct predictors of ‘use behavior’. The authors also analyzed the interrelationships among the research constructs. The extended model may lead toward establishing better innovative e-health services to cover the desires of the citizens through the use of health information applications embedded in an all-in-one card. Full article

We review the progress in developing a science of interdependence applied to the determinations and perceptions of risk for autonomous human–machine systems based on a case study of the Department of Defense’s (DoD) faulty determination of risk in a drone strike in Afghanistan; the DoD’s assessment was rushed, suppressing alternative risk perceptions. We begin by contrasting the lack of success found in a case study from the commercial sphere (Facebook’s use of machine intelligence to find and categorize “hate speech”). Then, after the DoD case study, we draw a comparison with the Department of Energy’s (DOE) mismanagement of its military nuclear wastes that created health risks to the public, DOE employees, and the environment. The DOE recovered by defending its risk determinations and challenging risk perceptions in public. We apply this process to autonomous human–machine systems. The result from this review is a major discovery about the costly suppression of risk perceptions to best determine actual risks, whether for the military, business, or politics. For autonomous systems, we conclude that the determinations of actual risks need to be limited in scope as much as feasible; and that a process of free and open debate needs to be adopted that challenges the risk perceptions arising in situations facing uncertainty as the best, and possibly the only, path forward to a solution. Full article

Network security encloses a wide set of technologies dealing with intrusions detection. Despite the massive adoption of signature-based network intrusion detection systems (IDSs), they fail in detecting zero-day attacks and previously unseen vulnerabilities exploits. Behaviour-based network IDSs have been seen as a way to overcome signature-based IDS flaws, namely through the implementation of machine-learning-based methods, to tolerate new forms of normal network behaviour, and to identify yet unknown malicious activities. A wide set of machine learning methods has been applied to implement behaviour-based IDSs with promising results on detecting new forms of intrusions and attacks. Innovative machine learning techniques have emerged, namely deep-learning-based techniques, to process unstructured data, speed up the classification process, and improve the overall performance obtained by behaviour-based network intrusion detection systems. The use of realistic datasets of normal and malicious networking activities is crucial to benchmark machine learning models, as they should represent real-world networking scenarios and be based on realistic computers network activity. This paper aims to evaluate CSE-CIC-IDS2018 dataset and benchmark a set of deep-learning-based methods, namely convolutional neural networks (CNN) and long short-term memory (LSTM). Autoencoder and principal component analysis (PCA) methods were also applied to evaluate features reduction in the original dataset and its implications in the overall detection performance. The results revealed the appropriateness of using the CSE-CIC-IDS2018 dataset to benchmark supervised deep learning models. It was also possible to evaluate the robustness of using CNN and LSTM methods to detect unseen normal activity and variations of previously trained attacks. The results reveal that feature reduction methods decreased the processing time without loss of accuracy in the overall detection performance. Full article

The COVID-19 pandemic has brought massive online activities and increased cybersecurity incidents and cybercrime. As a result of this, the cyber reputation of organisations has also received increased scrutiny and global attention. Due to increased cybercrime, reputation displaying a more important role within risk management frameworks both within public and private institutions is vital. This study identifies key factors in determining reputational damage to public and private sector institutions through cyberattacks. Researchers conducted an extensive review of the literature, which addresses factors relating to risk management of reputation post-cyber breach. The study identified 42 potential factors, which were then classified using the STAR model. This model is an organisational design framework and was suitable due to its alignment with organisations. A qualitative study using semi-structured and structured questions was conducted with purposively selected cybersecurity experts in both public and private sector institutions. Data obtained from the expert forum were analysed using thematic analysis, which revealed that a commonly accepted definition for cyber reputation was lacking despite the growing use of the term “online reputation”. In addition, the structured questions data were analysed using relative importance index rankings. The analysis results revealed significant factors in determining reputational damage due to cyberattacks, as well as highlighting reputation factor discrepancies between private and public institutions. Theoretically, this study contributes to the body of knowledge relating to cybersecurity of organisations. Practically, this research is expected to aid organisations to properly position themselves to meet cyber incidents and become more competitive in the post-COVID-19 era. Full article

The article considers the attitude of smart city residents towards the use of web applications in everyday life. It is very important for many stakeholders since it affects the involvement of people in all processes of urban life and contributes to the implementation of the smart city concept. The goal of the research is to study the factors influencing the intention and use of web applications in a smart city. Based on the results of surveying the residents of Riga, the UTA UT model was applied with the employment of partial least squares structural equation modeling in Smart PLS. The traditional constructs of the UTAUT model—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), as well as Attitude towards the use of Applications (ATA)—had a direct or indirect positive relationship with the intention to use technologies (Behavioral Intention: BI) and/or with usage of these technologies (Use Behavior: UB). Anxiety indirectly via ATA showed a negative effect on UB. The influence of Age, Gender and Education on BI and UB as moderators was also investigated. Only Age as a moderator negatively affected the relationship between FC and PE and SI. The results showed that in order to involve in full scope of the population of Riga in the use of communication technologies and the implementation of the smart city concept, it is necessary to create the appropriate conditions for residents, in particular by teaching people on a permanent basis. Some of the obtained results were different from similar studies’ results, which emphasizes that city authorities and other stakeholders should make decisions on the involvement of citizens in smart process based on the local peculiarities, which supports the slogan of smart cities—think globally, act locally. Full article

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. Image-based saliency estimation being participant independent, the estimation from EEG could take into account the subject specificity. For this purpose, a set of signals has been recorded, and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. Being able to predict a visual saliency map from EEG could help in research studying the relationship between brain activity and visual attention. It could also help in various applications: vigilance assessment during driving, neuromarketing, and also in the help for the diagnosis and treatment of visual attention-related diseases. For the sake of reproducibility, the codes and dataset considered in this paper have been made publicly available to promote research in the field. Full article

This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-based hole-punching architecture supported by the Transmission Control Protocol (TCP) creates the optimum alerting conditions. Furthermore, the results provide clear evidence to show that the decentralised EEW system architecture can outperform the centralised EEW architecture and can save valuable seconds when generating EEW, leading to a longer warning time for the end-user. This paper contributes to the EEW literature by proposing a novel EEW network architecture. Full article

Despite the numerous advantages of microchip implants, their adoption remains low in the public sector. We conducted a cross-sectional survey to identify concerns and expectations about microchip implants among potential users. A total of 179 United States adults aged 18–83 years responded to two qualitative questions that were then analyzed using the thematic analysis technique. The identified codes were first categorized and then clustered to generate themes for both concerns and expectations. The prevalence of each theme was calculated across various demographic factors. Concerns were related to data protection, health risks, knowledge, negative affect, ease of use, metaphysical dilemmas, monetary issues, and negative social impact. Expectations included medical and non-medical uses, dismissal of microchips, technical advances, human enhancement, regulations, and affordability. The prevalence of concerns and benefits differed by immigration status and medical conditions. Informed by our findings, we present a modification to the Technology Acceptance Model for predicting public’s behavioral intention to use subcutaneous microchips. We discuss the five newly proposed determinants and seven predictor variables of this model by surveying the literature. Full article