Computers

18 pages, 1016 KiB

Open AccessReview

Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

by Manuel Ayala-Chauvin, Fátima Avilés-Castillo and Jorge Buele

Computers 2023, 12(7), 146; https://doi.org/10.3390/computers12070146 - 22 Jul 2023

Cited by 2 | Viewed by 3458

Data analysis is increasingly critical in aiding decision-making within public and private institutions. This paper scrutinizes the status quo of big data and data analysis and its applications within Ecuador, focusing on its societal, educational, and industrial impact. A detailed literature review was [...] Read more.

Data analysis is increasingly critical in aiding decision-making within public and private institutions. This paper scrutinizes the status quo of big data and data analysis and its applications within Ecuador, focusing on its societal, educational, and industrial impact. A detailed literature review was conducted from academic databases such as SpringerLink, Scopus, IEEE Xplore, Web of Science, and ACM, incorporating research from inception until May 2023. The search process adhered to the PRISMA statement, employing specific inclusion and exclusion criteria. The analysis revealed that data implementation in Ecuador, while recent, has found noteworthy applications in six principal areas, classified using ISCED: education, science, engineering, health, social, and services. In the scientific and engineering sectors, big data has notably contributed to disaster mitigation and optimizing resource allocation in smart cities. Its application in the social sector has fortified cybersecurity and election data integrity, while in services, it has enhanced residential ICT adoption and urban planning. Health sector applications are emerging, particularly in disease prediction and patient monitoring. Educational applications predominantly involve student performance analysis and curricular evaluation. This review emphasizes that while big data’s potential is being gradually realized in Ecuador, further research, data security measures, and institutional interoperability are required to fully leverage its benefits. Full article

(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)

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20 pages, 1187 KiB

Open AccessArticle

Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination

by Mateo Tobón-Henao, Andrés Marino Álvarez-Meza and Cesar German Castellanos-Dominguez

Computers 2023, 12(7), 145; https://doi.org/10.3390/computers12070145 - 21 Jul 2023

Cited by 1 | Viewed by 1127

Abstract

Brain–computer interfaces (BCIs) from electroencephalography (EEG) provide a practical approach to support human–technology interaction. In particular, motor imagery (MI) is a widely used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, [...] Read more.

Brain–computer interfaces (BCIs) from electroencephalography (EEG) provide a practical approach to support human–technology interaction. In particular, motor imagery (MI) is a widely used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named kernel-based regularized EEGNet (KREEGNet), leveled on centered kernel alignment and Gaussian functional connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intrasubject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multiclass MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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22 pages, 1783 KiB

Open AccessArticle

FGPE+: The Mobile FGPE Environment and the Pareto-Optimized Gamified Programming Exercise Selection Model—An Empirical Evaluation

by Rytis Maskeliūnas, Robertas Damaševičius, Tomas Blažauskas, Jakub Swacha, Ricardo Queirós and José Carlos Paiva

Computers 2023, 12(7), 144; https://doi.org/10.3390/computers12070144 - 21 Jul 2023

Cited by 1 | Viewed by 2831

Abstract

This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding [...] Read more.

This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51). Full article

(This article belongs to the Special Issue Game-Based Learning, Gamification in Education and Serious Games 2023)

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20 pages, 985 KiB

Open AccessArticle

Adaptive Gamification in Science Education: An Analysis of the Impact of Implementation and Adapted Game Elements on Students’ Motivation

by Alkinoos-Ioannis Zourmpakis, Michail Kalogiannakis and Stamatios Papadakis

Computers 2023, 12(7), 143; https://doi.org/10.3390/computers12070143 - 18 Jul 2023

Cited by 6 | Viewed by 5772

Abstract

In recent years, gamification has captured the attention of researchers and educators, particularly in science education, where students often express negative emotions. Gamification methods aim to motivate learners to participate in learning by incorporating intrinsic and extrinsic motivational factors. However, the effectiveness of [...] Read more.

In recent years, gamification has captured the attention of researchers and educators, particularly in science education, where students often express negative emotions. Gamification methods aim to motivate learners to participate in learning by incorporating intrinsic and extrinsic motivational factors. However, the effectiveness of gamification has yielded varying outcomes, prompting researchers to explore adaptive gamification as an alternative approach. Nevertheless, there needs to be more research on adaptive gamification approaches, particularly concerning motivation, which is the primary objective of gamification. In this study, we developed and tested an adaptive gamification environment based on specific motivational and psychological frameworks. This environment incorporated adaptive criteria, learning strategies, gaming elements, and all crucial aspects of science education for six classes of third-grade students in primary school. We employed a quantitative approach to gain insights into the motivational impact on students and their perception of the adaptive gamification application. We aimed to understand how each game element experienced by students influenced their motivation. Based on our findings, students were more motivated to learn science when using an adaptive gamification environment. Additionally, the adaptation process was largely successful, as students generally liked the game elements integrated into their lessons, indicating the effectiveness of the multidimensional framework employed in enhancing students’ experiences and engagement. Full article

(This article belongs to the Special Issue Game-Based Learning, Gamification in Education and Serious Games 2023)

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18 pages, 521 KiB

Open AccessArticle

Efficient Day-Ahead Scheduling of PV-STATCOMs in Medium-Voltage Distribution Networks Using a Second-Order Cone Relaxation

by Oscar Danilo Montoya, Oscar David Florez-Cediel and Walter Gil-González

Computers 2023, 12(7), 142; https://doi.org/10.3390/computers12070142 - 18 Jul 2023

Cited by 1 | Viewed by 783

Abstract

This paper utilizes convex optimization to implement a day-ahead scheduling strategy for operating a photovoltaic distribution static compensator (PV-STATCOM) in medium-voltage distribution networks. The nonlinear non-convex programming model of the day-ahead scheduling strategy is transformed into a convex optimization model using the second-order [...] Read more.

This paper utilizes convex optimization to implement a day-ahead scheduling strategy for operating a photovoltaic distribution static compensator (PV-STATCOM) in medium-voltage distribution networks. The nonlinear non-convex programming model of the day-ahead scheduling strategy is transformed into a convex optimization model using the second-order cone programming approach in the complex domain. The main goal of efficiently operating PV-STATCOMs in distribution networks is to dynamically compensate for the active and reactive power generated by renewable energy resources such as photovoltaic plants. This is achieved by controlling power electronic converters, usually voltage source converters, to manage reactive power with lagging or leading power factors. Numerical simulations were conducted to analyze the effects of different power factors on the IEEE 33- and 69-bus systems. The simulations considered operations with a unity power factor (active power injection only), a zero power factor (reactive power injection only), and a variable power factor (active and reactive power injections). The results demonstrated the benefits of dynamic, active and reactive power compensation in reducing grid power losses, voltage profile deviations, and energy purchasing costs at the substation terminals. These simulations were conducted using the CVX tool and the Gurobi solver in the MATLAB programming environment. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2023)

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19 pages, 8279 KiB

Open AccessArticle

A Deep Learning Network with Aggregation Residual Transformation for Human Activity Recognition Using Inertial and Stretch Sensors

by Sakorn Mekruksavanich and Anuchit Jitpattanakul

Computers 2023, 12(7), 141; https://doi.org/10.3390/computers12070141 - 17 Jul 2023

Cited by 9 | Viewed by 1148

Abstract

With the rise of artificial intelligence, sensor-based human activity recognition (S-HAR) is increasingly being employed in healthcare monitoring for the elderly, fitness tracking, and patient rehabilitation using smart devices. Inertial sensors have been commonly used for S-HAR, but wearable devices have been demanding [...] Read more.

With the rise of artificial intelligence, sensor-based human activity recognition (S-HAR) is increasingly being employed in healthcare monitoring for the elderly, fitness tracking, and patient rehabilitation using smart devices. Inertial sensors have been commonly used for S-HAR, but wearable devices have been demanding more comfort and flexibility in recent years. Consequently, there has been an effort to incorporate stretch sensors into S-HAR with the advancement of flexible electronics technology. This paper presents a deep learning network model, utilizing aggregation residual transformation, that can efficiently extract spatial–temporal features and perform activity classification. The efficacy of the suggested model was assessed using the w-HAR dataset, which included both inertial and stretch sensor data. This dataset was used to train and test five fundamental deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU), along with the proposed model. The primary objective of the w-HAR investigations was to determine the feasibility of utilizing stretch sensors for recognizing human actions. Additionally, this study aimed to explore the effectiveness of combining data from both inertial and stretch sensors in S-HAR. The results clearly demonstrate the effectiveness of the proposed approach in enhancing HAR using inertial and stretch sensors. The deep learning model we presented achieved an impressive accuracy of 97.68%. Notably, our method outperformed existing approaches and demonstrated excellent generalization capabilities. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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12 pages, 2467 KiB

Open AccessArticle

Developing a Sustainable Online Platform for Language Learning across Europe

by Alexander Mikroyannidis, Maria Perifanou and Anastasios A. Economides

Computers 2023, 12(7), 140; https://doi.org/10.3390/computers12070140 - 15 Jul 2023

Cited by 1 | Viewed by 1639

Abstract

In this paper, we present a sustainable approach for addressing the language skills gap among EU citizens, which significantly hinders their mobility across the EU and their participation in education, in training, as well as in youth programmes. Our approach is based on [...] Read more.

In this paper, we present a sustainable approach for addressing the language skills gap among EU citizens, which significantly hinders their mobility across the EU and their participation in education, in training, as well as in youth programmes. Our approach is based on the sustainable design of the OpenLang Network platform, which provides an open and collaborative online learning environment for language learners and teachers across Europe, and addresses the limitations of existing computer-assisted language learning approaches. The OpenLang Network platform is bringing together educators and Erasmus+ mobility participants to improve their language skills and cultural knowledge. To this end, the OpenLang Network platform offers a collection of multilingual Open Educational Resources and language learning services. The paper presents the results from the user evaluation of the platform, which has been conducted with members of its community of language teachers and learners. A mixed methods approach has been adopted in order to collect and analyse both qualitative and quantitative data from users about the sustainable design of the OpenLang Network platform, as well as to measure the user satisfaction levels of the platform’s language learning services. According to the user evaluation results, the platform offers a sustainable online environment and a positive user experience for language learning. The user evaluation has also helped us identify a set of best practices and challenges associated with the long-term sustainability of an online language learning community. Full article

(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)

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23 pages, 2228 KiB

Open AccessArticle

An Experimental Approach to Estimation of the Energy Cost of Dynamic Branch Prediction in an Intel High-Performance Processor

by Fahad Swilim Alqurashi and Muhammad Al-Hashimi

Computers 2023, 12(7), 139; https://doi.org/10.3390/computers12070139 - 11 Jul 2023

Viewed by 1141

Abstract

Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. In this work, extensive experimental methods and procedures were used to assess the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, [...] Read more.

Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. In this work, extensive experimental methods and procedures were used to assess the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, focusing on the dynamic branch predictor (DBP). The investigation relied on the Running Average Power Limit (RAPL) interface from Intel, a software tool for credibly reporting the power and energy based on instrumentation inside the CPU. We used well-known microbenchmarks under various run conditions to explore potential pitfalls and to develop precautions to raise the precision of the measurements obtained from RAPL for more reliable power estimation. The authors discuss the factors that affect the measurements and share the difficulties encountered and the lessons learned. Full article

(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)

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22 pages, 3776 KiB

Open AccessArticle

Preschool Children’s Metaphoric Perceptions of Digital Games: A Comparison between Regions

by Elçin Yazıcı Arıcı, Michail Kalogiannakis and Stamatios Papadakis

Computers 2023, 12(7), 138; https://doi.org/10.3390/computers12070138 - 11 Jul 2023

Cited by 2 | Viewed by 1553

Abstract

Preschoolers now play digital games on touch screens, e-toys and electronic learning systems. Although digital games have an important place in children’s lives, there needs to be more information about the meanings they attach to games. In this context, the research aims to [...] Read more.

Preschoolers now play digital games on touch screens, e-toys and electronic learning systems. Although digital games have an important place in children’s lives, there needs to be more information about the meanings they attach to games. In this context, the research aims to determine the perceptions of preschool children studying in different regions of Turkey regarding digital games with the help of metaphors. Four hundred twenty-one preschool children studying in seven regions of Turkey participated in the research. The data were collected through the “Digital Game Metaphor Form” to determine children’s perceptions of digital games and through “Drawing and Visualization”, which comprises the symbolic pictures children draw of their feelings and thoughts. Phenomenology, a qualitative research model, was used in this study. The data were analyzed using the content analysis method. When the data were evaluated, the children had produced 421 metaphors collected in the following seven categories: “Nature Images, Technology Images, Fantasy/Supernatural Images, Education Images, Affective/Motivational Images, Struggle Images, and Value Images”. When evaluated based on regions, the Black Sea Region ranked first in the “Fantasy/Supernatural Images and Affective/Motivational Images” categories. In contrast, the Central Anatolia Region ranked first in the “Technology Images and Education Images” categories, and the Marmara Region ranked first in the “Nature Images and Value Images” categories. In addition, it was determined that the Southeast Anatolia Region ranks first in the “Struggle Images” category. Full article

(This article belongs to the Special Issue Game-Based Learning, Gamification in Education and Serious Games 2023)

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21 pages, 1054 KiB

Open AccessArticle

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

by Saima Khosa, Arif Mehmood and Muhammad Rizwan

Computers 2023, 12(7), 137; https://doi.org/10.3390/computers12070137 - 08 Jul 2023

Viewed by 1756

Abstract

The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. [...] Read more.

The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. The data are stratified into train and test sets to ensure equal representation of each category. Word embeddings are generated using transformer models, with the last hidden layer selected as the embedding. Mean pooling calculates a single vector representation called sentence embedding, capturing the overall meaning of the news article. The performance of Softmax and Random Forest, as well as the soft voting of both, is evaluated using evaluation measures such as accuracy, F1 score, precision, and recall. The study also contributes by evaluating the performance of Softmax and Random Forest individually. The macro-average F1 score is calculated to compare the performance of different transformer embeddings in the same experimental settings. The experiments reveal that MPNet versions v1 and v3 achieve the highest F1 score of 97.7% when combined with Random Forest, while T5 Large embedding achieves the highest F1 score of 98.2% when used with Softmax regression. MPNet v1 performs exceptionally well when used in the voting classifier, obtaining an impressive F1 score of 98.6%. In conclusion, the experiments validate the superiority of certain transformer models, such as MPNet v1, MPNet v3, and DistilRoBERTa, when used to calculate sentence embeddings within the Random Forest framework. The results also highlight the promising performance of T5 Large and RoBERTa Large in voting of Softmax regression and Random Forest. The voting classifier, employing transformer embeddings and ensemble learning techniques, consistently outperforms other baselines and individual algorithms. These findings emphasize the effectiveness of the voting classifier with transformer embeddings in achieving accurate and reliable predictions for news category classification tasks. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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32 pages, 863 KiB

Open AccessReview

Prioritizing Use Cases: A Systematic Literature Review

by Yousra Odeh and Nedhal Al-Saiyd

Computers 2023, 12(7), 136; https://doi.org/10.3390/computers12070136 - 06 Jul 2023

Cited by 1 | Viewed by 2477

Abstract

The prioritization of software requirements is necessary for successful software development. A use case is a useful approach to represent and prioritize user-centric requirements. Use-case-based prioritization is used to rank use cases to attain a business value based on identified criteria. The research [...] Read more.

The prioritization of software requirements is necessary for successful software development. A use case is a useful approach to represent and prioritize user-centric requirements. Use-case-based prioritization is used to rank use cases to attain a business value based on identified criteria. The research community has started engaging use case modeling for emerging technologies such as the IoT, mobile development, and big data. A systematic literature review was conducted to understand the approaches reported in the last two decades. For each of the 40 identified approaches, a review is presented with respect to consideration of scenarios, the extent of formality, and the size of requirements. Only 32.5% of the reviewed studies considered scenario-based approaches, and the majority of reported approaches were semiformally developed (53.8%). The reported result opens prospects for the development of new approaches to fill a gap regarding the inclusive of strategic goals and respective business processes that support scenario representation. This study reveals that existing approaches fail to consider necessary criteria such as risks, goals, and some quality-related requirements. The findings reported herein are useful for researchers and practitioners aiming to improve current prioritization practices using the use case approach. Full article

(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)

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4 pages, 169 KiB

Open AccessEditorial

Special Issue “Advances in Machine and Deep Learning in the Health Domain”

by Antonio Celesti, Ivanoe De Falco, Antonino Galletta and Giovanna Sannino

Computers 2023, 12(7), 135; https://doi.org/10.3390/computers12070135 - 04 Jul 2023

Viewed by 741

Abstract

Machine and deep learning techniques are fuelling a revolution in the health domain and are attracting the interest of many cross-disciplinary research groups all over the world [...] Full article

(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)

21 pages, 7455 KiB

Open AccessArticle

Technologies Supporting Screening Oculomotor Problems: Challenges for Virtual Reality

by Are Dæhlen, Ilona Heldal and Qasim Ali

Computers 2023, 12(7), 134; https://doi.org/10.3390/computers12070134 - 01 Jul 2023

Cited by 1 | Viewed by 1002

Abstract

Oculomotor dysfunctions (OMDs) are problems relating to coordination and accuracy of eye movements for processing visual information. Eye-tracking (ET) technologies show great promise in the identification of OMDs. However, current computer technologies for vision screening are specialized devices with limited screen size and [...] Read more.

Oculomotor dysfunctions (OMDs) are problems relating to coordination and accuracy of eye movements for processing visual information. Eye-tracking (ET) technologies show great promise in the identification of OMDs. However, current computer technologies for vision screening are specialized devices with limited screen size and the inability to measure depth, while visual field and depth are important information for detecting OMDs. In this experimental study, we examine the possibilities of immersive virtual reality (VR) technologies compared with laptop technologies for increased user experiences, presence, immersiveness, and the use of serious games for identifying OMDs. The results present increased interest in VR-based screening, motivating users to focus better using VR applications free from outside distractions. These limitations currently include lower performance and confidence in results of identifying OMDs with the used HMDs. Using serious games for screening in VR is also estimated to have great potential for developing a more robust vision screening tool, especially for younger children. Full article

(This article belongs to the Special Issue Immersive Virtual Learning Environments: Connecting Spaces, Time, Technologies, Curriculum, Assessment, and Innovation)

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14 pages, 1294 KiB

Open AccessArticle

Mixed Cultural Visits or What COVID-19 Taught Us

by Angeliki Antoniou

Computers 2023, 12(7), 133; https://doi.org/10.3390/computers12070133 - 29 Jun 2023

Cited by 1 | Viewed by 996

Abstract

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the pandemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. [...] Read more.

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the pandemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. Diverse types of mergers between online and onsite visits have already begun to be investigated by researchers, with the purpose of not only avoiding the spread of disease but also of enabling visits of people who were previously excluded, such as persons in remote geographic areas or people with mobility challenges. In fact, over the last three years, there have been rapid developments in mixed visits in cultural heritage sites. The current work takes into account a contextual model of museum learning to define potential use scenarios for visits from people of different cultural backgrounds and offers an evaluation of current practices. The new model that emerges, the contextual model of mixed visits, allows for the further study of the field, as it attempts to describe recent research efforts in four main contexts: mixed visits in the personal context, mixed visits in the socio-cultural context, mixed visits in the physical context, and temporality of mixed visits. Inductive analysis of a literature review allowed the extraction of relevant themes, examples from museums, as well as extraction of guidelines. Full article

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15 pages, 813 KiB

Open AccessReview

Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art

by Ryan Schuerkamp, Luke Liang, Ketra L. Rice and Philippe J. Giabbanelli

Computers 2023, 12(7), 132; https://doi.org/10.3390/computers12070132 - 29 Jun 2023

Cited by 1 | Viewed by 1492

Abstract

Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, [...] Read more.

Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions. Full article

(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)

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25 pages, 4434 KiB

Open AccessArticle

A Novel Dynamic Software-Defined Networking Approach to Neutralize Traffic Burst

by Aakanksha Sharma, Venki Balasubramanian and Joarder Kamruzzaman

Computers 2023, 12(7), 131; https://doi.org/10.3390/computers12070131 - 27 Jun 2023

Cited by 2 | Viewed by 1497

Abstract

Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or [...] Read more.

Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. Full article

(This article belongs to the Special Issue Software-Defined Internet of Everything)

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15 pages, 954 KiB

Open AccessArticle

Stealth Literacy Assessments via Educational Games

by Ying Fang, Tong Li, Linh Huynh, Katerina Christhilf, Rod D. Roscoe and Danielle S. McNamara

Computers 2023, 12(7), 130; https://doi.org/10.3390/computers12070130 - 25 Jun 2023

Viewed by 1554

Abstract

Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic [...] Read more.

Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has some potential to increase the assessment’s validity and reliability. The primary objective of this study is to examine the feasibility of a novel approach for stealthily assessing literacy skills using games in an intelligent tutoring system (ITS) designed for reading comprehension strategy training. We investigated the degree to which learners’ game performance and enjoyment predicted their scores on standardized reading tests. Amazon Mechanical Turk participants (n = 211) played three games in iSTART and self-reported their level of game enjoyment after each game. Participants also completed the Gates–MacGinitie Reading Test (GMRT), which includes vocabulary knowledge and reading comprehension measures. The results indicated that participants’ performance in each game as well as the combined performance across all three games predicted their literacy skills. However, the relations between game enjoyment and literacy skills varied across games. These findings suggest the potential of leveraging serious games to assess students’ literacy skills and improve the adaptivity of game-based learning environments. Full article

(This article belongs to the Special Issue Game-Based Learning, Gamification in Education and Serious Games 2023)

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25 pages, 4249 KiB

Open AccessArticle

Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization

by Giacomo Donati, Federica Zonzini and Luca De Marchi

Computers 2023, 12(7), 129; https://doi.org/10.3390/computers12070129 - 23 Jun 2023

Viewed by 1198

Abstract

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which [...] Read more.

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations. Full article

(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)

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28 pages, 5942 KiB

Open AccessArticle

Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models

by Mina Samir, Nada Sherief and Walid Abdelmoez

Computers 2023, 12(7), 128; https://doi.org/10.3390/computers12070128 - 23 Jun 2023

Cited by 2 | Viewed by 2005

Abstract

Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without [...] Read more.

Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with “black box” AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system’s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history. Full article

(This article belongs to the Special Issue Human Understandable Artificial Intelligence)

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24 pages, 6105 KiB

Open AccessArticle

A Control Framework for a Secure Internet of Things within Small-, Medium-, and Micro-Sized Enterprises in a Developing Economy

by Tebogo Mhlongo, John Andrew van der Poll and Tebogo Sethibe

Computers 2023, 12(7), 127; https://doi.org/10.3390/computers12070127 - 22 Jun 2023

Viewed by 2100

Abstract

Small and medium enterprises (SMEs) play a critical role in the economic growth of a nation, and their significance is increasingly acknowledged. More than 90% of commercial establishments, almost 70f% of jobs, and 55% of the GDP are held by SMEs in mature [...] Read more.

Small and medium enterprises (SMEs) play a critical role in the economic growth of a nation, and their significance is increasingly acknowledged. More than 90% of commercial establishments, almost 70f% of jobs, and 55% of the GDP are held by SMEs in mature economies. Additionally, this sector accounts for 70% of employment possibilities and up to 40% of the GDP in developing countries. Technologically, the Internet of Things (IoT) enables multiple connected devices, i.e., “things”, to add value to businesses, as they can communicate and send messages or signals promptly. In this article, we investigate various challenges SMEs experience in IoT adoption to further their businesses. Amongst others, the challenges elicited include IoT considerations for SMEs, data, financial availability, and challenges related to the SME environment. Having analysed the challenges, a three-tiered solution framework coined the Secure IoT Control Framework (SIoTCF) to address the said challenges is developed and briefly validated through a theoretical analysis of the elements of the framework. It is hoped that the proposed framework will assist with aspects of design, governance, and maintenance in enhancing the security levels of IoT adoption and usage in SMEs, especially start-ups or less experienced SMEs. Future work in this area will involve surveying SME owners and ICT staff to validate the utility of the SIoTCF further. The study adds to the body of knowledge in general by developing a secure IoT control framework. In the field of ICT, this paradigm is expected to be useful for academics, researchers, and students. Full article

(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems 2023)

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