Informatics, Volume 7, Issue 4 (December 2020) – 20 articles

In this work, a mobile application is developed to assist patients suffering from chronic obstructive pulmonary disease (COPD) or Asthma that will reduce the dependency on hospital and clinic based tests and enable users to better manage their disease through increased self-involvement. Due to the pervasiveness of smartphones, it is proposed to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing COPD or asthma in a reliable and cost effective manner. Data collected using an experimental setup consisting of an airflow source, an anemometer, and a smartphone is used to develop a mathematical model that relates exhalation frequency to air flow rate. This model allows for the computation of two key parameters known as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) that are used in the diagnosis of respiratory diseases. The developed platform has been validated using data collected from 25 subjects with various conditions. Results show that an excellent match is achieved between the FVC and FEV1 values computed using a clinical spirometer and those returned by the model embedded in the mobile application. Full article

Hyperscanning is a technique which simultaneously records the neural activity of two or more people. This is done using one of several neuroimaging methods, such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The use of hyperscanning has seen a dramatic rise in recent years to monitor social interactions between two or more people. Similarly, there has been an increase in the use of virtual reality (VR) for collaboration, and an increase in the frequency of social interactions being carried out in virtual environments (VE). In light of this, it is important to understand how interactions function within VEs, and how they can be enhanced to improve their quality in a VE. In this paper, we present some of the work that has been undertaken in the field of social neuroscience, with a special emphasis on hyperscanning. We also cover the literature detailing the work that has been carried out in the human–computer interaction domain that addresses remote collaboration. Finally, we present a way forward where these two research domains can be combined to explore how monitoring the neural activity of a group of participants in VE could enhance collaboration among them. Full article

The COVID-19 pandemic has revealed the digital vulnerability of many citizens of the rural world. This article identifies and analyzes the proposals made by academic literature to overcome the digital divide in the European rural world for the five-year period 2016–2020. A scoping review has been carried out according to the PRISMA methodology in the two dimensions of the digital divide: access and connectivity, and use and exploitation. Online databases were used to identify scientific articles from which, after screening, 28 key documents were selected. The results update Salemink systematic review of articles published between 1991 and 2014 on digital and rural development in Western countries and it also intends to go beyond by extracting recommendations. A variety of political, social, educational, technical and economic issues has been exposed, with a common emphasis on the empowerment of rural populations. The findings provide actionable evidence and proposals to facilitate decision-making in current policy information to overcome rural digital divide. From them, seven recommendations that could have a wide and rapid impact to minimize the effects of the COVID-19 pandemic linked to the rural digital divide are synthesized. Three lines of action in the medium term are also proposed: the evaluation of national and regional public policies; the consideration of digital inclusion as a potential instrument to reduce rural depopulation; and the training in advanced digital skills to improve the social communication processes, considered key to promote empowerment and entrepreneurship. Full article

The use of data visualization is increasing; however, there is little empirical explanation for how it supports users. Our goal in this paper is to deepen our understanding of the role of interactive visualizations in a particular context of decision making. Specifically, we attempt to understand the role of the working memory system, which is a concept to understand the mechanism of the processing and temporary storage of information in variety of cognitive tasks. We compared two interfaces, SimulSort and its non-visual counterpart Typical Sorting, with a multi-attribute decision-making problem. Because decision outcomes are known to be affected by the limitations of a person’s working memory, we conducted a crowdsourcing-based user study using SimulSort to understand how working memory, especially the phonological loop, can benefit from the using visualizations. We examined the impact on working memory with a well known dual-task methodology by designing a concurrent task to tap into the main decision-making task. The experiment was conducted with a total of 137 participants and an ordered logistic regression using a proportional odds model was applied to analyze the decision quality. The results supported the hypothesis that when using SimulSort, participants required less working memory than they required with Typical Sorting to accomplish the multi-attribute decision-making task even though SimulSort outperformed Typical Sorting in terms of decision quality. We also provide methodologies to conduct working memory studies by implementing an articulatory suppression task on crowdsourcing platforms in which experimenters have less control over the participants. Full article

Twitter enables millions of active users to send and read concise messages on the internet every day. Yet some people use Twitter to propagate violent and threatening messages resulting in cyberbullying. Previous research has focused on whether cyberbullying behavior exists or not in a tweet (binary classification). In this research, we developed a model for detecting the severity of cyberbullying in a tweet. The developed model is a feature-based model that uses features from the content of a tweet, to develop a machine learning classifier for classifying the tweets as non-cyberbullied, and low, medium, or high-level cyberbullied tweets. In this study, we introduced pointwise semantic orientation as a new input feature along with utilizing predicted features (gender, age, and personality type) and Twitter API features. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa (84%), classifier accuracy (93%), and F-measure (92%) metric. Overall, 40% of the classifiers increased performance in comparison with baseline approaches. Our analysis shows that features with the highest odd ratio: for detecting low-level severity include: age group between 19–22 years and users with <1 year of Twitter account activation; for medium-level severity: neuroticism, age group between 23–29 years, and being a Twitter user between one to two years; and for high-level severity: neuroticism and extraversion, and the number of times tweet has been favorited by other users. We believe that this research using a multi-class classification approach provides a step forward in identifying severity at different levels (low, medium, high) when the content of a tweet is classified as cyberbullied. Lastly, the current study only focused on the Twitter platform; other social network platforms can be investigated using the same approach to detect cyberbullying severity patterns. Full article

(1) Background: In limited-resource settings such as the Dominican Republic, many factors contribute to poor health outcomes experienced by adolescent mothers, including insufficient support and/or health knowledge. In response, we designed a digital educational support group, administered through WhatsApp Messenger, for new adolescent mothers. The purpose of this study was to assess if participation in this digital support group could improve health outcomes and health behaviors. (2) Methods: Participants completed questionnaires with a health literacy screener, demographic items, knowledge questions, the Index of Autonomous Functioning, and five Patient Reported Outcomes Measurement Information System scales before and after the moderator-led intervention. Differences between pre- and post-intervention scores were calculated and perceptions of the intervention were explored through in-depth interviews analyzed with content analysis. Participants’ well-baby visit attendance and contraceptive use were compared to that of controls and a national sample. (3) Results: Participants’ (N = 58) knowledge scores increased (p < 0.05). Participants were 6.58 times more likely to attend well-baby visits than controls (95% CI: 2.23–19.4) and their contraceptive use was higher than that of the national sample (p < 0.05). Participants indicated the intervention was enjoyable and beneficial. (4) Conclusion: This adolescent-centered digital intervention is a promising method to improve health outcomes and health behaviors of young mothers in limited-resource settings. Full article

Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case. Full article

Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy. Full article

Affective computing is a branch of artificial intelligence that aims at processing and interpreting emotions. In this study, we implemented sensors/actuators into a stuffed toy mammoth, which allows the toy to have an affective and cognitive basis to its communication. The goal is for therapists to use this as a tool during their therapy sessions that work with patients with mood disorders. The toy detects emotion and provides a dialogue that would guide a session aimed at working with emotional regulation and perception. These technical capabilities are possible by employing IBM Watson’s services, implemented into a Raspberry Pi Zero. In this paper, we delve into its evaluation with neurotypical adolescents, a panel of experts, and other professionals. The evaluation aims were to perform a technical and application validation for use in therapy sessions. The results of the evaluations are generally positive, with an 87% accuracy for emotion recognition, and an average usability score of 77.5 for experts (n = 5), and 64.35 for professionals (n = 23). We add to that information some of the issues encountered, its effects on applicability, and future work to be done. Full article

In the present study, we analyzed User Generated Content (UGC) to measure the importance of Search Engine Optimization (SEO) for startups. For this purpose, we used several clustering algorithms to identify user communities on Twitter. The dataset contained a total of 67,126 tweets. A three-step UGC analysis process was applied to the data. First, a Latent Dirichlet allocation (LDA) was developed to divide the UGC-sample into topics. Next, a sentiment analysis (SA) with machine-learning was applied to divide the sample of topics into negative, positive, and neutral feelings. Finally, a textual analysis (TA) process with data mining techniques was used to extract indicators related to the SEO technique optimization in startups. The results helped us identify UGC communities in Twitter about SEO for startups and the main optimization indicators according to the feelings expressed in tweets. Our results also demonstrated that Black Hack SEO is not the most relevant strategy of positioning of digital marketing for startups and that, although this strategy is used by the startups, it is predominantly negatively perceived by SEO UGC communities. Full article

The new communication paradigm supported by Information and Communication Technology (ICT) puts end-users at the center of innovation processes, thereby shifting the emphasis from technology to people. Citizen centric approaches such as New Public Governance and Open Government in the public management research suggest that government alone cannot be responsible for creating public value. Traditional approaches to public engagement and governmental reforms remain relevant, however our research is more interested in the ability of a networked society to resolve social problems for itself, i.e., without government intervention. In seeking to gain insights into bottom up co-creation processes, this paper aims to collect and generalize information on the international civic technology platforms by focusing on three dimensions: identification of the objectives (content), classification of main stakeholder groups (actors), and definition of co-creative methods (processes). In view of a paucity of research on Civic Technologies, the content analysis will extend the understanding of this growing field and allow us to identify the patterns in their development. Full article

Technological advances in game-mediated robotics provide an opportunity to engage children with cerebral palsy (CP) and other neuromotor disabilities in more frequent and intensive therapy by making personalized, programmed interventions available 24/7 in children’s homes. Though shown to be clinically effective and feasible to produce, little is known of the subjective factors impacting acceptance of what we term assistive/rehabilitative (A/R) gamebots by their target populations. This research describes the conceptualization phase of an effort to develop a valid and reliable instrument to guide the design of A/R gamebots. We conducted in-depth interviews with 8 children with CP and their families who had trialed an exemplar A/R gamebot, PedBotHome, for 28 days in their homes. The goal was to understand how existing theories and instruments were either appropriate or inappropriate for measuring the subjective experience of A/R gamebots. Key findings were the importance of differentiating the use case of therapy from that of assistance in rehabilitative technology assessment, the need to incorporate the differing perspectives of children with CP and those of their parents into A/R gamebot evaluation, and the potential conflict between the goals of preserving the quality of the experience of game play for the child while also optimizing the intensity and duration of therapy provided during play. Full article

Traditional philological methods in Roman legal scholarship such as close reading and strict juristic reasoning have analysed law in extraordinary detail. Such methods, however, have paid less attention to the empirical characteristics of legal texts and occasionally projected an abstract framework onto the sources. The paper presents a series of computer-assisted methods to open new frontiers of inquiry. Using a Python coding environment, we have built a relational database of the Latin text of the Digest, a historical sourcebook of Roman law compiled under the order of Emperor Justinian in 533 CE. Subsequently, we investigated the structure of Roman law by automatically clustering the sections of the Digest according to their linguistic profile. Finally, we explored the characteristics of Roman legal language according to the principles and methods of computational distributional semantics. Our research has discovered an empirical structure of Roman law which arises from the sources themselves and complements the dominant scholarly assumption that Roman law rests on abstract structures. By building and comparing Latin word embeddings models, we were also able to detect a semantic split in words with general and legal sense. These investigations point to a practical focus in Roman law which is consistent with the view that ancient law schools were more interested in training lawyers for practice rather than in philosophical neatness. Full article

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching. Full article

Drug interactions are an important source of medical error and a topic of particular interest to patient audiences. Patients must be informed to be able to participate in decision-making affecting their health. This paper explores the availability of drug interaction checkers in Scandinavia and the prevalence and characteristics of usability issues preventing patients from benefiting from them. Drug interaction checkers were sampled and evaluated through heuristic evaluations. Issue-based data were analyzed through descriptive statistics, as well as single-case and cross-case qualitative analyses. The findings were interpreted side-by-side using a mixed-methods approach. The results showed a multitude of usability issues. Catastrophic issues indicating the safety of dangerous drug pairings were found in two of the checkers. Results also showed that the checkers lacked adaptive design, patient-oriented content, and adherence to basic design principles. A positive correlation was observed between system complexity and number of usability issues. We suggest that this comes from a lack of systematic design approach. The market for Scandinavian drug interaction checkers was as such characterized by a limited selection of checkers known to be used by patients for their utility, but failing to accommodate them in terms of system quality. Full article

While efforts are underway to create a sound system of electronic health records in Palestinian health institutions, there remain obstacles and challenges. Given modern day demands on health systems, we propose a federated electronic health system based on the clinical document architecture (CDA) that is compliant within the Palestine context. This architecture also brings a normalized electronic health record and a structure of blockchain to enhance interoperability with scalability, fault tolerance, privacy, and security. The new architecture and technologies will enhance services by allowing health care players, patients, and others to have the opportunity to obtain improved access and control of their health services. This may also serve as a useful model for other low-middle income countries. Full article

Background: The occurrence of a fire when implementing anesthesia is a high-risk, low-frequency event. The operating room is a high-stakes environment that has no room for error. Mixed reality simulation may be a solution to better prepare healthcare professionals. The purpose of this quantitative, descriptive study was to evaluate the technical and non-technical skills of student registered nurse anesthetists (SRNAs) who participated in a mixed reality simulation of an operating room fire. Methods: Magic Leap One^TM augmented reality headsets were used by 32 student registered nurse anesthetists to simulate an emergent fire during a simulated tracheostomy procedure. Both technical and non-technical skills were evaluated by faculty members utilizing a checklist. Results: The SRNAs’ overall mean technical skill performance was 18.16 ± 1.44 out of a maximum score of 20, and the mean non-technical skill performance was 91.25% out of 100%. Conclusions: This study demonstrated the utility and limitations in applying novel technology in simulation. Participants demonstrated a strong performance of technical and non-technical skills in the management of a simulated operating room fire. Recommendations for future applications include the use of multiple sensory inputs into the scenario design and including all core team members in the immersive mixed reality environment. Full article

This research work employs theoretical and empirical expert knowledge in constructing an agglomerative parallel processing algorithm that performs spatio-temporal clustering upon seismic data. This is made possible by exploiting the spatial and temporal sphere of influence of the main earthquakes solely, clustering seismic events into a number of fuzzy bordered, interactive and yet potentially distinct seismic zones. To evaluate whether the unveiled clusters indeed depict a distinct seismic zone, deep learning neural networks are deployed to map seismic energy release rates with time intervals between consecutive large earthquakes. Such a correlation fails should there be influence by neighboring seismic areas, hence casting the seismic region as non-distinct, or if the extent of the seismic zone has not been captured fully. For the deep learning neural network to depict such a correlation requires a steady seismic energy input flow. To address that the western area of the Hellenic seismic arc has been selected as a test case due to the nearly constant motion of the African plate that sinks beneath the Eurasian plate at a steady yearly rate. This causes a steady flow of strain energy stored in tectonic underground faults, i.e., the seismic energy storage elements; a partial release of which, when propagated all the way to the surface, casts as an earthquake. The results are complementary two-fold with the correlation between the energy release rates and the time interval amongst large earthquakes supporting the presence of a potential distinct seismic zone in the Ionian Sea and vice versa. Full article

The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions. Full article

Modern scientific visualization is web-based and uses emerging technology such as WebGL (Web Graphics Library) and WebGPU for three-dimensional computer graphics and WebXR for augmented and virtual reality devices. These technologies, paired with the accessibility of websites, potentially offer a user experience beyond traditional standalone visualization systems. We review the state-of-the-art of web-based scientific visualization and present an overview of existing methods categorized by application domain. As part of this analysis, we introduce the Scientific Visualization Future Readiness Score (SciVis FRS) to rank visualizations for a technology-driven disruptive tomorrow. We then summarize challenges, current state of the publication trend, future directions, and opportunities for this exciting research field. Full article