Informatics

Recent technology implementation in learning has inspired language educators to employ various e-learning techniques, strategies, and applications in their pedagogical practices while aiming at improving specific learning efficiencies of students. The current study attempts to blend e-learning activities, including blogging, video making, online exercises, and digital storyboarding, with English language teaching and explores its impact on engineering cohorts at a public university in Malaysia. The longitudinal research study used three digital applications—Voyant Tools, Lumos Text Complexity Analyzer, and Advanced Text Analyzer—to analyze the data collected through a variety of digital assignments and activities from two English language courses during the researched academic semesters. Contributing to the available literature on the significance of integrating technology innovation with language learning, the study found that implementing e-learning activities can provide substantial insights into improving the learners’ different linguistic competencies, including writing competency, reading comprehension, and vocabulary enhancement. Moreover, the implementation of such innovative technology can motivate students to engage in more peer interactivity, learning engagement, and self-directed learning. Full article

Website publishers cannot monetize the ad impressions that are prevented by ad-blockers. Publishers can then employ anti-ad-blockers that force users to choose between either accepting ad impressions by whitelisting the website in the ad-blocker, or leaving the website without accessing the content. This study delineates the mechanisms of how willingness to whitelist/leave the website are affected by the request’s sensitivity to recipients as well as the users’ psychological reactance and evaluation of the website advertising. We tested the proposed relationships using an online panel sample of 500 ad-blocker users, who were asked about their willingness to whitelist/leave their favorite online newspaper after receiving a hypothetical anti-ad-blocker request—four alternative requests with different sensitivity levels were created and randomly assigned to the participants. The results confirmed that (a) the request’s sensitivity can improve the recipient’s compliance, (b) users’ psychological reactance plays an important role in explaining the overall phenomenon, and (c) a favorable evaluation of the website advertising can improve willingness to whitelist. These findings help to better understand user response to anti-ad-blockers and may also help publishers increase their whitelist ratios. Full article

Mobile learning is a global trend, which has become more widespread in the post-COVID-19 pandemic era. However, with the adoption of mobile learning comes new assessment approaches to evaluate the understanding of the acquired information and knowledge. Nevertheless, there is scant knowledge of how to enhance assessment information integrity in mobile learning assessments. Due to the importance of assessments in evaluating knowledge, integrity is the sine qua non of online assessments. This research focuses on the strategies universities could use to improve assessment information integrity. This research adopts a qualitative design, employing interviews with academics as well as teaching and learning support staff for data collection. The findings reveal five strategies that academics and support staff recommend to enhance assessment information integrity in mobile learning. The theoretical and practical implications are discussed, as well as future research directions. Full article

Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field. Full article

The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human accounts and how this will affect the rumor diffusion process. This paper aims to present a new information diffusion model that characterizes the role of bots in the rumor diffusion process in OSNs. The proposed $S{I}_h{I}_{b}R$ model extends the classical SIR model by introducing two types of infected users with different infection rates: the users who are infected by human $\left(I_h\right)$ accounts with a normal infection rate and the users who are infected by bot accounts $\left(I_b\right)$ with a different diffusion rate that reflects the intent and steadiness of this type of account to spread the rumors. The influence of fake accounts on human accounts diffusion rate has been measured using the social impact theory, as it better reflects the deliberate behavior of bot accounts to spread a rumor to a large portion of the network by considering both the strength and the bias of the source node. The experiment results show that the accuracy of the $S{I}_h{I}_{b}R$ model outperforms the SIR model when simulating the rumor diffusion process in the existence of fake accounts. It has been concluded that fake accounts accelerate the rumor diffusion process as they impact many people in a short time. Full article

This study aimed to examine the use of light and color in digital paintings and their effect on audiences’ perceptions of environmental issues. Five digital paintings depicting environmental issues have been designed. Digital painting techniques created black-and-white, monochrome, and color images. Each image used utopian and dystopian visualization concepts to communicate hope and despair. In the experiment, 225 volunteers representing students in colleges were separated into three independent groups: the first group was offered black-and-white images, the second group was offered monochromatic images, and the third group was offered color images. After viewing each image, participants were asked to complete questionnaires about their emotions and cognitions regarding environmental issues, including identifying hope and despair and the artist’s perspective at the end. The analysis showed no differences in emotions and cognitions among participants. However, monochromatic images were the most emotionally expressive. The results indicated that the surrounding atmosphere of the images created despair, whereas objects inspired hope. Artists should emphasize the composition of the atmosphere and the objects in the image to convey the concepts of utopia and dystopia to raise awareness of environmental issues. Full article

Companies try to acquire the finest advantages and techniques in a technologically advanced and end-to-end market to have a stronger foothold there. Although empirical research on this topic links IT to a decline in vertical integration, corporations are increasingly using this corporate strategy. The goal of this study is to show how over the past 22 years, scientific literature has changed with regard to how information technology (IT) affects vertical integration, one of the main types of corporate strategies. The findings demonstrated that vertical integration has been evolving in a balanced manner in a technological environment. Three categories—information technology, innovation, and processes—help explain this association and were discovered through cluster analysis. The direction of operational integration, the degree of industry concentration, demand unpredictability, and innovation should all be considered while making integration decisions. Full article

The modern conception of industrial production recognizes the increasingly crucial role of maintenance. Currently, maintenance is thought of as a service that aims to maintain the efficiency of equipment and systems while also taking quality, energy efficiency, and safety requirements into consideration. In this study, a new methodology for automating the fan maintenance procedures was developed. An approach based on the recording of the acoustic emission and the failure diagnosis using deep learning was evaluated for the detection of dust deposits on the blades of an axial fan. Two operating conditions have been foreseen: No-Fault, and Fault. In the No-Fault condition, the fan blades are perfectly clean while in the Fault condition, deposits of material have been artificially created. Utilizing a pre-trained network (SqueezeNet) built on the ImageNet dataset, the acquired data were used to build an algorithm based on convolutional neural networks (CNN). The transfer learning applied to the images of the spectrograms extracted from the recordings of the acoustic emission of the fan, in the two operating conditions, returned excellent results (accuracy = 0.95), confirming the excellent performance of the methodology. Full article

This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate outcomes was derived from a private educational institution survey. The original dataset contains information on 17,898 graduates and approximately 148 features. Three machine learning algorithms, namely, decision trees, random forest, and gradient boosting, were used for data analysis. These three machine learning models were compared with ordinal regression. The results indicate that gradient boosting is the best predictive model, which is 6% higher than the ordinal regression accuracy. The SHapley Additive exPlanations (SHAP), a novel methodology to extract the significant features of different machine learning algorithms, was then used to extract the most important features of the gradient boosting model. Current salary is the most important feature in predicting job levels. Interestingly, graduates who realized the importance of communication skills and teamwork to be good leaders also had higher job positions. Finally, general relevant features to predict job levels include the number of people directly in charge, company size, seniority, and satisfaction with income. Full article

The effectiveness of the e-tax system in encouraging tax compliance has been largely unexplored. Thus, the current study aims to examine the interrelationship between technological predictors in explaining tax compliance intention among certified tax professionals. Based on the literature on information system success and tax compliance intention, this paper proposed an expanded conceptual framework that incorporates convenience and perception of reduced compliance costs as predictors and satisfaction as a mediator. The data were collected from 650 tax professionals who used e-Filing and 492 who used e-Form through an online survey and analyzed using hierarchical multiple regression. The empirical results suggest that participants’ perceived service quality of e-Filing services and perceptions of reduced compliance costs positively influence users’ willingness to comply with tax regulations. The latter predictor is also, and only, significant among e-Form users. The empirical results also provide statistical evidence for the mediating role of satisfaction in the relationship between all predictors and tax compliance intention. This study encourages tax policymakers and e-tax filing providers to improve their services to increase user satisfaction and tax compliance. Full article

Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient diagnosis, low latency, and low energy consumption, etc., are advantages confirmed according to the achieved experimental results. Full article

The authors of the present study explored how ICT devices used in P.E. lessons determine psychomotor performance, perceived motivational climate, and motivation. The students were allowed to use ICT devices (smartphone, webpages, Facebook) during a four-week intervention. In the course of the research project aimed to assess the impact of the application of ICT devices on performance and motivation, the participants were divided into two test groups and one control group. The sample consisted of secondary school students including 21 males and 64 females with the Mage = 16.72 years. The results showed that in groups where ICT devices were used, performance (p = 0.04) and task orientation (p = 0.00) significantly improved. Meanwhile, in the group in which ICT devices were not used, the intervention resulted in improved performance (p = 0.00) and by the end of the project, this trend was coupled with increased Ego orientation (p = 0.00) and higher rate of amotivation (p = 0.04). It can be concluded that the use of ICT tools has a positive impact on performance and motivation. Full article

The importance of business analytics (BA) in driving knowledge generation and business innovation has been widely discussed in both the academic and business communities. However, empirical research on the relationship between knowledge orientation and business analytics capabilities in driving business model innovation remains scarce. Drawing on the knowledge-based view and dynamic capabilities theory, this study develops a model to investigate the interplay between knowledge orientation and BA capabilities in driving business model innovation. It also explores the moderating role of industry type on this relationship. To test the model, data were collected from a cross-sectional sample of 207 firms (high-tech and non-high-tech industries). Descriptive and structural equation modeling (SEM) were used to test the hypotheses. The findings showed that knowledge orientation and BA capabilities are significantly and positively related to business model innovation. Knowledge commitment, shared vision, and open-mindedness are significantly and positively related to BA perception and recognition capabilities and BA integration capabilities. BA capabilities mediated the relationship between knowledge orientation and business model innovation. The path mechanism of knowledge orientation → BA capabilities → business model innovation shows that industry type has a moderating effect on knowledge orientation and BA capabilities, as well as BA capabilities and business model innovation. This study provides empirically proven insights and practical guidance on the dynamics and mechanisms of BA and organizational knowledge capabilities and their impact on business model innovation. Full article

Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules”. Community detection is an interesting and valuable approach to discovering the structure of the community in a complex network, revealing the internal organization of the nodes, and has become a leading research topic in the analysis of complex networks. This work investigates the link between biological modules and network communities in test-case biological networks that are commonly used as a reference point and which include Protein–Protein Interaction Networks, Metabolic Networks and Transcriptional Regulation Networks. In order to identify small and structurally well-defined communities in the biological context, a hybrid immune metaheuristic algorithm Hybrid-IA is proposed and compared with several metaheuristics, hyper-heuristics, and the well-known greedy algorithm Louvain, with respect to modularity maximization. Considering the limitation of modularity optimization, which can fail to identify smaller communities, the reliability of Hybrid-IA was also analyzed with respect to three well-known sensitivity analysis measures (NMI, ARI and NVI) that assess how similar the detected communities are to real ones. By inspecting all outcomes and the performed comparisons, we will see that on one hand Hybrid-IA finds slightly lower modularity values than Louvain, but outperforms all other metaheuristics, while on the other hand, it can detect communities more similar to the real ones when compared to those detected by Louvain. Full article

This work proposes a tool to predict the risk of road accidents. The developed system consists of three steps: data selection and collection, preprocessing, and the use of mining algorithms. The data were imported from the Portuguese National Guard database, and they related to accidents that occurred from 2019 to 2021. The results allowed us to conclude that the highest concentration of accidents occurs during the time interval from 17:00 to 20:00, and that rain is the meteorological factor with the greatest effect on the probability of an accident occurring. Additionally, we concluded that Friday is the day of the week on which more accidents occur than on other days. These results are of importance to the decision makers responsible for planning the most effective allocation of resources for traffic surveillance. Full article

This paper examines the efficacy of telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPAs). Retrospective electronic patient health records (ePHR) with type 2 diabetes mellitus (T2DM) were examined from 1 January 2019 to 30 June 2021. Multiple linear regression models indicated that T2DM patients with uncontrolled diabetes utilizing TM were similar to traditional visits in lowering hemoglobin (HbA1c) levels. The healthcare service type significantly predicted HbA1c % values, as the regression coefficient for TM (vs. F2F) showed a significant negative association (B = −0.339, p < 0.001), suggesting that patients using TM were likely to have 0.34 lower HbA1c % values on average when compared with F2F visits. The regression coefficient for female (vs. male) gender showed a positive association (B = 0.190, p < 0.034), with HbA1c % levels showing that female patients had 0.19 higher HbA1c levels than males. Age (B = −0.026, p < 0.001) was a significant predictor of HbA1c % levels, with 0.026 lower HbA1c % levels for each year’s increase in age. Black adults (B = 0.888, p < 0.001), on average, were more likely to have 0.888 higher HbA1c % levels when compared with White adults. Full article

In this paper, a measure is proposed that, based on the trajectories of moving objects, computes the speed limit rate in each of the cells in which a region is segmented (the space where the objects move). The time is also segmented into intervals. In this way, the behavior of moving objects can be analyzed with regard to their speed in a cell for a given time interval. An implementation of the corresponding algorithm for this measure and several experiments were conducted with the trajectories of taxis in Porto (Portugal). The results showed that the speed limit rate measure can be helpful for detecting patterns of movement, e.g., in a day (morning hours vs. night hours) or on different days of the week (weekdays vs. weekends). This measure might also serve as a rough estimate for congestion in a (sub)region. This may be useful for traffic analysis, including traffic prediction. Full article

Digital twin (DT) has gained significant recognition among researchers due to its potential across industries. With the prime goal of solving numerous challenges confronting the construction industry (CI), DT in recent years has witnessed several applications in the CI. Hence, researchers have been advocating for DT adoption to tackle the challenges of the CI. Notwithstanding, a distinguishable set of barriers that oppose the adoption of DT in the CI has not been determined. Therefore, this paper identifies the barriers and incorporates them into a classified framework to enhance the roadmap for adopting DT in the CI. This research conducts an extensive review of the literature and analyses the barriers whilst integrating the science mapping technique. Using Scopus, ScienceDirect, and Web of Science databases, 154 related bibliographic records were identified and analysed using science mapping, while 40 carefully selected relevant publications were systematically reviewed. From the review, the top five barriers identified include low level of knowledge, low level of technology acceptance, lack of clear DT value propositions, project complexities, and static nature of building data. The results show that the UK, China, the USA, and Germany are the countries spearheading the DT adoption in the CI, while only a small number of institutions from Australia, the UK, Algeria, and Greece have established institutional collaborations for DT research. A conceptual framework was developed on the basis of 30 identified barriers to support the DT adoption roadmap. The main categories of the framework comprise stakeholder-oriented, industry-related, construction-enterprise-related, and technology-related barriers. The identified barriers and the framework will guide and broaden the knowledge of DT, which is critical for successful adoption in the construction industry. Full article

We review the emergence of digital weather information, the history of human embodied knowing about weather, and two perspectives on cognition, one of which is symbolic (amodal, abstract, and arbitrary) and the other being embodied (embodied, extended, embedded, and enacted) to address the question: Beyond the general weather information they provide, to what extent can digital devices be used in an embodied way to extend a person’s pick-up of weather information? This is an interesting question to examine because human weather information and knowledge has a long past in our evolutionary history. Our human ancestors had to pick-up immediate information from the environment (including the weather) to survive. Digital weather information and knowing has a comparatively short past and a promising future. After reviewing these relevant topics, we concluded that, with the possible exception of weather radar apps, nothing currently exists in the form of digital products than can extend the immediate sensory reach of people to alert them about just-about-to-occur weather—at least not in the embodied forms of information. We believe that people who are weather salient (i.e., have a strong psychological attunement to the weather) may be in the best position going forward to integrate digital weather knowing with that which is embodied. Full article