Informatics, Volume 8, Issue 4 (December 2021) – 26 articles

Useful information can be provided by 2.5D maps that can take advantage of the additional dimension. However, aside from stereoscopic landmarks, optimal methods for presenting other essential information is unclear. Two experiments were conducted to explore how the presentation of 2.5D maps can effectively increase wayfinding performance. First, analysis was performed to understand the effects of 2.5D maps on wayfinding behavior and map reading. Then, a 2.5D map design was proposed and verified to optimize the 2.5D map presentation of urban environments. The results showed that compared with users of low view angle maps, those using high view angle maps orientated more easily with elements of the map during wayfinding tasks. High view angle maps allowed superior performance, and including transparency and lines improved wayfinding performance. The participants using maps that were opaque and with lines exhibited the most confusion and hesitation. The participants who used maps that were transparent and had lines exhibited the least confusion and hesitation. Highlighting buildings at intersections can help map users use the intersections as references and increase their intuitive spatial ability. Full article

The evolution of technology in clinical environments increases the level of precision in patient care, as well as optimizes the management of healthcare centers. However, the need to have information systems that are more sophisticated and require interoperability between them means that a great deal of effort has to be made to assume the maintenance and scalability of the systems. Therefore, a proposal for a standard information model for the integration of clinical systems in a healthcare environment is presented. In order to elaborate the model, an analysis of the functional needs of the different clinical areas of a clinical environment is made based on the information systems that make up the system and application map. An evaluation of the technical requirements and the technological solutions that can satisfy these requirements is also carried out, delving into the different technical alternatives that allow the exchange of information. From the analysis carried out, an integration model capable of covering the needs that arise in clinical environments with a high level of complexity is obtained, also allowing the continuous evolution of the systems that make up the model, along with the incorporation of new systems. Although the model presented may fully cover the expectations raised, the rapid evolution in terms of both functional needs and technical aspects makes it necessary to continuously monitor and evaluate the model, in order to adapt it to the needs that arise. Full article

Cybersecurity is a never-ending battle against attackers, who try to identify and exploit misconfigurations and software vulnerabilities before being patched. In this ongoing conflict, it is important to analyse the properties of the vulnerability time series to understand when information systems are more vulnerable. We study computer systems’ software vulnerabilities and probe the relevant National Vulnerability Database (NVD) time-series properties. More specifically, we show through an extensive experimental study based on the National Institute of Standards and Technology (NIST) database that the relevant systems software time series present significant chaotic properties. Moreover, by defining some systems based on open and closed source software, we compare their chaotic properties resulting in statistical conclusions. The contribution of this novel study is focused on the prepossessing stage of vulnerabilities time series forecasting. The strong evidence of their chaotic properties as derived by this research effort could lead to a deeper analysis to provide additional tools to their forecasting process. Full article

The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)—in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment. Full article

In this paper, we study the controllability of an Expressive TTS system trained on a dataset for a continuous control. The dataset is the Blizzard 2013 dataset based on audiobooks read by a female speaker containing a great variability in styles and expressiveness. Controllability is evaluated with both an objective and a subjective experiment. The objective assessment is based on a measure of correlation between acoustic features and the dimensions of the latent space representing expressiveness. The subjective assessment is based on a perceptual experiment in which users are shown an interface for Controllable Expressive TTS and asked to retrieve a synthetic utterance whose expressiveness subjectively corresponds to that a reference utterance. Full article

The purpose of this study is to investigate students’ intention to continue using online learning platforms during face-to-face traditional classes in a way that is parallel to their usage during online virtual classes (during the pandemic). This investigation of students’ intention is based on a conceptual model that uses newly used external factors in addition to the technology acceptance model (TAM) contrasts; hence, it takes into consideration users’ satisfaction, the external factor of information richness (IR) and the quality of the educational system and information disseminated. The participants were 768 university students who have experienced the teaching environments of both traditional face-to-face classes and online classes during the pandemic. A structural equation modelling (SEM) test was conducted to analyse the independent variables, including the users’ situation awareness (SA), perceived ease of use, perceived usefulness, satisfaction, IR, education system quality and information quality. An online questionnaire was used to explore students’ perceptions of their intention to use online platforms accessibly in a face-to-face learning environment. The results showed that (a) students prefer online platforms that have a higher level of content richness, to be able to implement the three dimensions of users’ situation awareness (perception, comprehension and projection); (b) there were significant effects of TAM constructs on students’ satisfaction and acceptance; (c) students are in favour of using a learning platform that is characterised by a high level of educational system quality and information quality and (d) students with a higher level of satisfaction have a more positive attitude in their willingness to use the online learning system. Full article

The aim of this study was to analyze the content, context, and structure of folktales from the Mekong River Basin, and to develop a metadata schema for data description and folktale storage. The research was conducted using the MAAT metadata lifecycle model, which comprises the following four steps: (1) conducting an information content analysis; (2) creating metadata requirements, (3) developing a metadata schema; and (4) carrying out a metadata service and evaluation. The folktale analysis, based on Anne Gilliland’s information object analysis, revealed the following: (1) the folktale content consists of types of tales, and the morals, beliefs, and parts they incorporate; (2) the folktale context consists of and names distributors, characters, scenes, magical objects, ethnic groups, languages, countries, relationships between tales, and their sources; (3) the folktale structure includes verbal, non-verbal, and mixed forms. The metadata schema development adopted the functional requirements for bibliographic records concepts and existing metadata standards, resulting in metadata with the following 18 elements: identifier, title, creator, contributor, description, relation, language, medium, sources, date, rights, keyword, character, moral, ethnic group, motif, place, and country. The metadata elements were described using the categories: name, definition, format, example, and note. Full article

Inefficient and ineffective practices in the construction industry have hindered productivity even though it is considered as one of the largest sectors in any county. One best solution to overcome these inherent problems in the construction industry is to move forward with digital technologies. For that, organizational structure, technical aspects, and, most importantly, human factors need to be considered. The aim of this research is to find out human behaviors that affect the digital transformation of the construction industry based on the well-accepted model Unified Theory of Acceptance and Use of Technology (UTAUT). An in-depth literature review was carried out using fifty-five journal papers to develop a conceptual model for the acceptance of digital transformation, and it was validated and further reviewed using ten expert interviews. The model consists of seven constraints: Personal Benefits, Perceived Usefulness, Perceived Risk, Facility Conditions, Attitudes, and Subjective Norms. The analytical hierarchy process (AHP) was carried out to rank these seven factors according to individual priorities in the construction industry. Further, the model was extended and modified using factors derived from literature review and expert feedback. It is proved that “Perceived Personal Benefits” is the major consideration of an individual who is willing to move towards digital transformation. This research fulfills the lack of knowledge in the digitalization of the construction industry as per a human perspective, and it provides a prerequisite to finding the solutions for the issues which emerged within the industry towards digitalization. Further, the framework developed in the research can be used to systematically adopt the human factor for the digital transformation of the construction industry. In addition, this enables the analysis of changing demands for humans in digitally transformed environments, such as Industry 4.0 environments, and contributes towards a successful digital transformation that avoids the pitfalls of innovation performed without attention to human factors. The paper concludes by highlighting future research directions on the human factor in digital transformation as well as managerial implications for successful application in practice. Full article

The main objective of this research is to investigate the role of social media campaigns (the type of social media platform, type of message, and message source sender) in raising public health awareness and behavioral change during (COVID-19) as a global pandemic across national selected countries (Poland and Jordan). The research utilizes a quantitative method with an exploratory and descriptive design to accumulate the initial data from a research survey given to the respondents from Jordan and Poland. A total of 1149 web questionnaires were collected from respondents in the two countries (Poland 531 and Jordan 618). In addition, multiple regression analysis was used to test the study hypotheses. The findings showed positive relationships between the components of a social media campaign, public health awareness, and behavioral change during (COVID-19) in the two countries at the same time. However, the preferred type of social media platforms, the message types and type of source sender significantly differ among the respondents due to their countries. This is the first study that examines the role of social media campaigns (the type of social media platform, type of message and message source sender) in public health awareness and behavioral change during (COVID-19) as a global pandemic in across national selected countries (Poland and Jordan). Full article

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization. Full article

As social media has shifted from traditional to modern technical patterns, organizations have sought to take advantage of the presence of beneficiaries on social networks. They may serve customers, display ads, and respond to queries on social media accounts such as Twitter. The implementation of these services required a scientific study considering: (1) how to attract beneficiaries, (2) attraction times, and (3) measurement of the impact of that attraction. This study aimed to address these three points through an analysis of data from an educational organization’s Twitter account. We found that the interaction rates with tweets increased in the evening, and we identified the best times for the organization to reach more followers. We examined five months of data (an entire semester), analyzing thousands of tweets and their associated impressions, types of responses, integration ratio, and account usage. We also discovered that the quality of tweets had an impact on attracting new followers, particularly when tweeting media such as photos, videos, and other types of content. Finally, this research serves as a resource for educational organizations on new ways to publish accounts and foster organizational growth through electronic media. Full article

Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings. Full article

Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities. Full article

This article aims to improve the secondary distribution process in a mass consumer company implementing technologies, such as transport management system (TMS) to achieve the objectives set by the company. A DMAIC based methodology is proposed to define and solve structured problems related to secondary distribution, following the performance of the process based on critical to logistics (CTL) factors. The methodology prioritized the design of a master plan for the secondary distribution and the characterization of the secondary distribution process, defining the principal technologies that should compose the business architecture of the secondary distribution, with emphasis on the TMS due to its significant impact and relevance for planning, execution, and control of the distribution process. This study replaces the control component of the DMAIC with the assess component to perform the economic and productivity evaluation of the implementation of a TMS since the improvement proposals were formulated and evaluated. The results show that TMS allows the reduction of delivery time variability, order processing time, voided invoices, distribution costs, the increase in customer service and efficiency in the distribution operation and generates profitability for the medium and long term. Full article

Data generation is currently expanding at an astonishing pace, and the function of marketing is becoming increasingly sophisticated and customized. Companies seek to understand their internal corporate environment and externalities and to exponentially enhance their marketing power. This study aims to understand the influence of Big data analysis on digital marketing. The methodologies used to approach this issue were: (a) a systematic literature review based on articles dated between 2014 and 2020; and (b) a bibliometric analysis of articles dated between 2000 and 2020 using the software VOSviewer. The literature review allowed us to conclude that in the next decades, the business world in general, and marketing in particular, will define more oriented strategies based on a more profound knowledge of consumer behavior. Artificial intelligence agents driven by machine learning methods, technology, and Big data will be a conditioning factor in defining these strategies. Full article

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders. Full article

The fight against the COVID-19 pandemic still involves many struggles and challenges. The greatest challenge that most governments are currently facing is the lack of a precise, accurate, and automated mechanism for detecting and tracking new COVID-19 cases. In response to this challenge, this study proposes the first blockchain-based system, called the COVID-19 contact tracing system (CCTS), to verify, track, and detect new cases of COVID-19. The proposed system consists of four integrated components: an infection verifier subsystem, a mass surveillance subsystem, a P2P mobile application, and a blockchain platform for managing all transactions between the three subsystem models. To investigate the performance of the proposed system, CCTS has been simulated and tested against a created dataset consisting of 300 confirmed cases and 2539 contacts. Based on the metrics of the confusion matrix (i.e., recall, precision, accuracy, and F1 Score), the detection evaluation results proved that the proposed blockchain-based system achieved an average of accuracy of 75.79% and a false discovery rate (FDR) of 0.004 in recognizing persons in contact with COVID-19 patients within two different areas of infection covered by GPS. Moreover, the simulation results also demonstrated the success of the proposed system in performing self-estimation of infection probabilities and sending and receiving infection alerts in P2P communications in crowds of people by users. The infection probability results have been calculated using the binomial distribution function technique. This result can be considered unique compared with other similar systems in the literature. The new system could support governments, health authorities, and citizens in making critical decisions regarding infection detection, prediction, tracking, and avoiding the COVID-19 outbreak. Moreover, the functionality of the proposed CCTS can be adapted to work against any other similar pandemics in the future. Full article

Today’s computing is based on the classic paradigm proposed by John von Neumann, three-quarters of a century ago. That paradigm, however, was justified for (the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!). It led to catastrophic performance losses in computing systems, from the operating gate level to large networks, including the neuromorphic ones. The model is perfect, but the paradigm is applied outside of its range of validity. The classic paradigm is completed here by providing the “procedure” missing from the “First Draft” that enables computing science to work with cases where the transfer time is not negligible apart from the processing time. The paper reviews whether we can describe the implemented computing processes by using the accurate interpretation of the computing model, and whether we can explain the issues experienced in different fields of today’s computing by omitting the wrong omissions. Furthermore, it discusses some of the consequences of improper technological implementations, from shared media to parallelized operation, suggesting ideas on how computing performance could be improved to meet the growing societal demands. Full article

Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy. Full article

As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks. Full article

Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machine learning algorithms rather than on the selection of suitable datasets. In fact, most publications consider a single, usually non-public, benchmark. More benchmarks are needed to design and test the generality of the proposed approaches. This paper is the first to define the requirements on these benchmarks. It highlights that there are only two benchmarks that can be used for supervised learning among the six publicly available ones we found in the literature. We also illustrate how such a benchmark can be used with deep learning to successfully train and evaluate a failure prediction model. We raise several perspectives for research. Full article

This study examines the experience of marketing departments to become fully data-driven decision-making organizations. We evaluate an organic approach of departmental sensemaking and an administered approach by which top management increase the influence of analytics skilled employees. Data collection commenced with 15 depth interviews of marketing and analytics professionals in the US and Europe involved in the implementation of big data analytics (BDA) and was followed by a survey data of 298 marketing and analytics middle management professionals at United States based firms. The survey data supports the logic that BDA sensemaking is initiated by top management and is comprised of four primary activities: external knowledge acquisition, improving digitized data quality, big data analytics experimentation and big data analytics information dissemination. Top management drives progress toward data-driven decision-making by facilitating sensemaking and by increasing the influence of BDA skilled employees. This study suggests that while a shift toward enterprise analytics increases the quality of resource available to the marketing department, this approach could stymie the quality of marketing insights gained from BDA. This study presents a model of how to improve the quality of marketing insights and improve data-driven decision-making. Full article

To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a high accuracy child monitoring system for locating students if they are inside or outside a school bus using the Internet of Things (IoT) via Bluetooth Low Energy (BLE) which is suitable for a signal strength indication (RSSI) algorithm. The in/out-bus child tracking system alerts a driver to determine if there is a child left on the bus or not. Distance between devices is analyzed for decision making to affiliate the zone of the current children’s position. A simplified and high accuracy machine learning of least mean square (LMS) algorithm is used in this research with model-based RSSI localization techniques. The distance is calculated with the grid size of 0.5 m × 0.5 m similar in size to an actual seat of a school bus using two zones (inside or outside a school bus). The averaged signal strength is proposed for this research, rather than using the raw value of the signal strength in typical works, providing a robust position-tracking system with high accuracy while maintaining the simplicity of the classical trilateration method leading to precise classification of each student from each zone. The test was performed to validate the effectiveness of the proposed tracking strategy which precisely shows the positions of each student. The proposed method, therefore, can be applied for future autopilot school buses where students’ home locations can be securely stored in the system used for references to transport each student to their homes without a driver. Full article

This study employs a novel 3D engineered robotic eye system with dielectric elastomer actuator (DEA) pupils and a 3D sculpted and colourised gelatin iris membrane to replicate the appearance and materiality of the human eye. A camera system for facial expression analysis (FEA) was installed in the left eye, and a photo-resistor for measuring light frequencies in the right. Unlike previous prototypes, this configuration permits the robotic eyes to respond to both light and emotion proximal to a human eye. A series of experiments were undertaken using a pupil tracking headset to monitor test subjects when observing positive and negative video stimuli. A second test measured pupil dilation ranges to high and low light frequencies using a high-powered artificial light. This data was converted into a series of algorithms for servomotor triangulation to control the photosensitive and emotive pupil dilation sequences. The robotic eyes were evaluated against the pupillometric data and video feeds of the human eyes to determine operational accuracy. Finally, the dilating robotic eye system was installed in a realistic humanoid robot (RHR) and comparatively evaluated in a human-robot interaction (HRI) experiment. The results of this study show that the robotic eyes can emulate the average pupil reflex of the human eye under typical light conditions and to positive and negative emotive stimuli. However, the results of the HRI experiment indicate that replicating natural eye contact behaviour was more significant than emulating pupil dilation. Full article

A conceptual artificial intelligence (AI)-enabled framework is presented in this study involving triangulation of various diagnostic methods for management of coronavirus disease 2019 (COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AI-enabled framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based molecular diagnostics, radiological image-based assessments, and end-user provided information for the detection of COVID-19 cases and management of symptomatic patients. It will support self-data capture, clinical risk stratification, explanation-based intelligent recommendations for patient triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable communication with end-users in local languages through cheap and accessible means, such as WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19 cases and general support for pandemic recovery. We intend to test the feasibility of implementing the proposed framework through community engagement in sub-Saharan African (SSA) countries where many people are living with pre-existing comorbidities. A multimodal approach to disease diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential services across the continuum of COVID-19 care. Full article