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Informatics, Volume 7, Issue 3 (September 2020) – 8 articles

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Open AccessArticle
Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots
Informatics 2020, 7(3), 28; https://doi.org/10.3390/informatics7030028 (registering DOI) - 31 Jul 2020
Viewed by 297
Abstract
Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing [...] Read more.
Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable. Full article
(This article belongs to the Special Issue Feature Paper in Informatics)
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Open AccessArticle
Building a Persuasive Virtual Dietitian
Informatics 2020, 7(3), 27; https://doi.org/10.3390/informatics7030027 - 30 Jul 2020
Viewed by 210
Abstract
This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and [...] Read more.
This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results. Full article
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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Open AccessArticle
Crossing the Power Line: Using Virtual Simulation to Prepare the First Responders of Utility Linemen
Informatics 2020, 7(3), 26; https://doi.org/10.3390/informatics7030026 - 29 Jul 2020
Viewed by 294
Abstract
Virtual reality (VR) healthcare simulation has helped learners develop skills that are transferable to real-word conditions. Innovative strategies are needed to train workers to improve community safety. The purpose of this pilot project was to evaluate the use of a VR simulation applying [...] Read more.
Virtual reality (VR) healthcare simulation has helped learners develop skills that are transferable to real-word conditions. Innovative strategies are needed to train workers to improve community safety. The purpose of this pilot project was to evaluate the use of a VR simulation applying the International Nursing Association for Clinical Simulation and Learning (INACSL) Standards of Best Practice: SimulationSM Simulation Design with eight power line workers. Six power industry supervisors and educators assisted in facilitating three VR simulations with eight linemen participants. Kotter’s eight steps to leading change and the INACSL Standards of Best Practice: SimulationSM Simulation Design were utilized in working with energy leaders and VR developers to carry out this pilot project. Pre- and post-implementation surveys demonstrated a 28% improvement in participants’ learning outcomes. All three learning objectives were met. This project demonstrated the successful application of a translational framework and the INACSL Standards of Best Practice: SimulationSM in a VR context in the power industry. This process may be helpful to guide or inspire further adoption of VR in unconventional settings. Full article
(This article belongs to the Special Issue Applications of Virtual Simulation and Virtual Reality in Nursing)
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Open AccessArticle
Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review
Informatics 2020, 7(3), 25; https://doi.org/10.3390/informatics7030025 - 25 Jul 2020
Viewed by 330
Abstract
Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic [...] Read more.
Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings. Full article
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Open AccessReview
Modeling of Open Government Data for Public Sector Organizations Using the Potential Theories and Determinants—A Systematic Review
Informatics 2020, 7(3), 24; https://doi.org/10.3390/informatics7030024 - 21 Jul 2020
Viewed by 316
Abstract
Open government data (OGD) has huge potential to increase transparency, accountability, and participation while improving efficiency in operations, data-driven and evidence-based policymaking, and trust in government institutions. Despite its potential benefits, OGD has not been widely and successfully adopted in public sector organizations, [...] Read more.
Open government data (OGD) has huge potential to increase transparency, accountability, and participation while improving efficiency in operations, data-driven and evidence-based policymaking, and trust in government institutions. Despite its potential benefits, OGD has not been widely and successfully adopted in public sector organizations, particularly in developing countries. Therefore, the purpose of this study is to explore the theories/frameworks and potential determinants that influence the OGD adoption in public sector organizations. To ascertain the various determinants of OGD adoption in public sector organizations, this study involved a systematic review of already established theories and determinants addressed in the public sector open data domain. The review revealed that the TOE (technology, organization, environment) framework was dominantly employed over theories in the earlier studies to understand organizational adoption to OGD followed by institutional theory. The results, concerning potential determinants, revealed that some of the most frequently addressed determinants are an organization’s digitization/digitalization capacity, compliance pressure, financial resources, legislation, policy, regulations, organizational culture, political leadership commitment, top-management support, and data quality. The findings will enrich researchers to empirically investigate the exposed determinants and improve the understanding of decision-makers to leverage OGD adoption by taking relevant measures. Full article
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Open AccessArticle
Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms
Informatics 2020, 7(3), 23; https://doi.org/10.3390/informatics7030023 - 20 Jul 2020
Viewed by 342
Abstract
In recent years, security in urban areas has gradually assumed a central position, focusing increasing attention on citizens, institutions and political forces. Security problems have a different nature—to name a few, we can think of the problems deriving from citizens’ mobility, then move [...] Read more.
In recent years, security in urban areas has gradually assumed a central position, focusing increasing attention on citizens, institutions and political forces. Security problems have a different nature—to name a few, we can think of the problems deriving from citizens’ mobility, then move on to microcrime, and end up with the ever-present risk of terrorism. Equipping a smart city with an infrastructure of sensors capable of alerting security managers about a possible risk becomes crucial for the safety of citizens. The use of unmanned aerial vehicles (UAVs) to manage citizens’ needs is now widespread, to highlight the possible risks to public safety. These risks were then increased using these devices to carry out terrorist attacks in various places around the world. Detecting the presence of drones is not a simple procedure given the small size and the presence of only rotating parts. This study presents the results of studies carried out on the detection of the presence of UAVs in outdoor/indoor urban sound environments. For the detection of UAVs, sensors capable of measuring the sound emitted by UAVs and algorithms based on deep neural networks capable of identifying their spectral signature that were used. The results obtained suggest the adoption of this methodology for improving the safety of smart cities. Full article
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Open AccessFeature PaperArticle
Investigation of Women’s Health on Wikipedia—A Temporal Analysis of Women’s Health Topic
Informatics 2020, 7(3), 22; https://doi.org/10.3390/informatics7030022 - 17 Jul 2020
Viewed by 357
Abstract
New health-related concepts, terms, and topics emerge, and the meanings of existing terms and topics keep changing. This study investigated and explored the evolutions of the women’s health topic on Wikipedia. The creation time, page views data, page edits data, and text of [...] Read more.
New health-related concepts, terms, and topics emerge, and the meanings of existing terms and topics keep changing. This study investigated and explored the evolutions of the women’s health topic on Wikipedia. The creation time, page views data, page edits data, and text of historical versions of 207 women-health-related entries from 2010 to 2017 on Wikipedia were collected. Coding, subject analysis, descriptive and inferential statistical analysis, and Self-Organizing Map and n-gram approaches were employed to explore the characteristics and evolutions of the entries for the women’s health topic. The results show that the number of the women-health-related entries kept increasing from 2010 to 2017, and nearly half of them were related to the supports and protection of women’s health. The total number of page views of the investigated items increased from 2011 to 2013, but it decreased from 2013 to 2017, while the total number of page edits stayed stable from 2010 to 2017. Growing subjects were found during the investigated period, such as abuse and violence, and family planning and reproduction. However, the entries related to the economy and politics were diminishing. There was no association between the internal characteristic evolution and the external popularity evolution of the women’s health topic. Full article
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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Open AccessArticle
Expert Refined Topic Models to Edit Topic Clusters in Image Analysis Applied to Welding Engineering
Informatics 2020, 7(3), 21; https://doi.org/10.3390/informatics7030021 - 29 Jun 2020
Viewed by 540
Abstract
This paper proposes a new method to generate edited topics or clusters to analyze images for prioritizing quality issues. The approach is associated with a new way for subject matter experts to edit the cluster definitions by “zapping” or “boosting” pixels. We refer [...] Read more.
This paper proposes a new method to generate edited topics or clusters to analyze images for prioritizing quality issues. The approach is associated with a new way for subject matter experts to edit the cluster definitions by “zapping” or “boosting” pixels. We refer to the information entered by users or experts as “high-level” data and we are apparently the first to allow in our model for the possibility of errors coming from the experts. The collapsed Gibbs sampler is proposed that permits efficient processing for datasets involving tens of thousands of records. Numerical examples illustrate the benefits of the high-level data related to improving accuracy measured by Kullback–Leibler (KL) distance. The numerical examples include a Tungsten inert gas example from the literature. In addition, a novel laser aluminum alloy image application illustrates the assignment of welds to groups that correspond to part conformance standards. Full article
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