Computers

13 pages, 2463 KiB

Open AccessArticle

You Look like You’ll Buy It! Purchase Intent Prediction Based on Facially Detected Emotions in Social Media Campaigns for Food Products

by Katerina Tzafilkou, Anastasios A. Economides and Foteini-Rafailia Panavou

Computers 2023, 12(4), 88; https://doi.org/10.3390/computers12040088 - 21 Apr 2023

Cited by 4 | Viewed by 2304

Abstract

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent [...] Read more.

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent through web analytics and sales historical data. Here, the use of facially expressed emotions is suggested to infer the purchase intent of online consumers while watching social media video campaigns for food products (yogurt and nut butters). A FaceReader Online^TM multi-stage experiment was set, collecting data from 154 valid sessions of 74 participants. A set of different classification models was deployed, and the performance evaluation metrics were compared. The models included Neural Networks (NNs), Logistic Regression (LR), Decision Trees (DTs), Random Forest (RF,) and Support Vector Machine (SVM). The NNs proved highly accurate (90–91%) in predicting the consumers’ intention to buy or try the product, while RF showed promising results (75%). The expressions of sadness and surprise indicated the highest levels of relative importance in RF and DTs correspondingly. Despite the low activation scores in arousal, micro expressions of emotions proved to be sufficient input in predicting purchase intent based on instances of facially decoded emotions. 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, 6757 KiB

Open AccessArticle

Data-Driven Solution to Identify Sentiments from Online Drug Reviews

by Rezaul Haque, Saddam Hossain Laskar, Katura Gania Khushbu, Md Junayed Hasan and Jia Uddin

Computers 2023, 12(4), 87; https://doi.org/10.3390/computers12040087 - 21 Apr 2023

Cited by 6 | Viewed by 2846

Abstract

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial [...] Read more.

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial adverse drug reactions, and machine learning (ML) approaches such as sentiment analysis (SA) can be employed to derive valuable insights. However, given the sheer volume of comments, it is often impractical for consumers to manually review all of them before determining a purchase decision. Therefore, drug assessments can serve as a valuable source of medical information for both healthcare professionals and the general public, aiding in decision making and improving public monitoring systems by revealing collective experiences. Nonetheless, the unstructured and linguistic nature of the comments poses a significant challenge for effective categorization, with previous studies having utilized machine and deep learning (DL) algorithms to address this challenge. Despite both approaches showing promising results, DL classifiers outperformed ML classifiers in previous studies. Therefore, the objective of our study was to improve upon earlier research by applying SA to medication reviews and training five ML algorithms on two distinct feature extractions and four DL classifiers on two different word-embedding approaches to obtain higher categorization scores. Our findings indicated that the random forest trained on the count vectorizer outperformed all other ML algorithms, achieving an accuracy and F1 score of 96.65% and 96.42%, respectively. Furthermore, the bidirectional LSTM (Bi-LSTM) model trained on GloVe embedding resulted in an even better accuracy and F1 score, reaching 97.40% and 97.42%, respectively. Hence, by utilizing appropriate natural language processing and ML algorithms, we were able to achieve superior results compared to earlier studies. 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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15 pages, 6484 KiB

Open AccessProject Report

Impacts of Topology and Bandwidth on Distributed Shared Memory Systems

by Jonathan Milton and Payman Zarkesh-Ha

Computers 2023, 12(4), 86; https://doi.org/10.3390/computers12040086 - 21 Apr 2023

Viewed by 2496

Abstract

As high-performance computing designs become increasingly complex, the importance of evaluating with simulation also grows. One of the most critical aspects of distributed computing design is the network architecture; different topologies and bandwidths have dramatic impacts on the overall performance of the system [...] Read more.

As high-performance computing designs become increasingly complex, the importance of evaluating with simulation also grows. One of the most critical aspects of distributed computing design is the network architecture; different topologies and bandwidths have dramatic impacts on the overall performance of the system and should be explored to find the optimal design point. This work uses simulations developed to run in the existing Structural Simulation Toolkit v12.1.0 software framework to show that for a hypothetical test case, more complicated network topologies have better overall performance and performance improves with increased bandwidth, making them worth the additional design effort and expense. Specifically, the test case HyperX topology is shown to outperform the next best evaluated topology by thirty percent and is the only topology that did not experience diminishing performance gains with increased bandwidth. Full article

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15 pages, 5196 KiB

Open AccessArticle

Subdivision Shading for Catmull-Clark and Loop Subdivision Surfaces with Semi-Sharp Creases

by Jun Zhou, Jan Boonstra and Jiří Kosinka

Computers 2023, 12(4), 85; https://doi.org/10.3390/computers12040085 - 21 Apr 2023

Viewed by 2199

Abstract

Coarse meshes can be recursively subdivided into denser and denser meshes by dividing their faces into several smaller faces and repositioning the vertices according to carefully designed subdivision rules. This process leads to smooth surfaces, such as in the case of Catmull-Clark or [...] Read more.

Coarse meshes can be recursively subdivided into denser and denser meshes by dividing their faces into several smaller faces and repositioning the vertices according to carefully designed subdivision rules. This process leads to smooth surfaces, such as in the case of Catmull-Clark or Loop subdivision, but often suffers from shading artifacts near extraordinary points due to the lower quality of the normal field there, typically corresponding to only tangent-plane (and not higher) continuity at these points. The idea of subdivision shading is to apply the same subdivision rules that are used to subdivide geometry to also subdivide the normals associated with mesh vertices. This leads to smoother normal fields, which can be used for shading purposes, and this in turn removes the shading artifacts. However, the original subdivision shading method does not support sharp and semi-sharp creases, which are desired ingredients in subdivision surface modelling. We present two approaches to extending subdivision shading to work also on models with (semi-)sharp creases, and demonstrate this in the cases of Catmull-Clark as well as Loop subdivision. Full article

(This article belongs to the Special Issue Selected Papers from Computer Graphics & Visual Computing (CGVC 2022))

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14 pages, 4986 KiB

Open AccessArticle

Design and Simulation-Based Optimization of an Intelligent Autonomous Cruise Control System

by Milad Andalibi, Alireza Shourangizhaghighi, Mojtaba Hajihosseini, Seyed Saeed Madani, Carlos Ziebert and Jalil Boudjadar

Computers 2023, 12(4), 84; https://doi.org/10.3390/computers12040084 - 20 Apr 2023

Cited by 1 | Viewed by 2092

Abstract

Significant progress has recently been made in transportation automation to alleviate human faults in traffic flow. Recent breakthroughs in artificial intelligence have provided justification for replacing human drivers with digital control systems. This paper proposes the design of a self-adaptive real-time cruise control [...] Read more.

Significant progress has recently been made in transportation automation to alleviate human faults in traffic flow. Recent breakthroughs in artificial intelligence have provided justification for replacing human drivers with digital control systems. This paper proposes the design of a self-adaptive real-time cruise control system to enable path-following control of autonomous ground vehicles so that a self-driving car can drive along a road while following a lead vehicle. To achieve the cooperative objectives, we use a multi-agent deep reinforcement learning (MADRL) technique, including one agent to control the acceleration and another agent to operate the steering control. Since the steering of an autonomous automobile could be adjusted by a stepper motor, a well-known DQN agent is considered to provide the discrete angle values for the closed-loop lateral control. We performed a simulation-based analysis to evaluate the efficacy of the proposed MADRL path following control for autonomous vehicles (AVs). Moreover, we carried out a thorough comparison with two state-of-the-art controllers to examine the accuracy and effectiveness of our proposed control system. Full article

(This article belongs to the Special Issue Recent Advances in Digital Twins and Cognitive Twins)

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12 pages, 565 KiB

Open AccessReview

Crossing the AI Chasm in Neurocritical Care

by Marco Cascella, Jonathan Montomoli, Valentina Bellini, Alessandro Vittori, Helena Biancuzzi, Francesca Dal Mas and Elena Giovanna Bignami

Computers 2023, 12(4), 83; https://doi.org/10.3390/computers12040083 - 19 Apr 2023

Cited by 2 | Viewed by 2532

Abstract

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial [...] Read more.

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process. Full article

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

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17 pages, 653 KiB

Open AccessArticle

Bound the Parameters of Neural Networks Using Particle Swarm Optimization

by Ioannis G. Tsoulos, Alexandros Tzallas, Evangelos Karvounis and Dimitrios Tsalikakis

Computers 2023, 12(4), 82; https://doi.org/10.3390/computers12040082 - 17 Apr 2023

Cited by 1 | Viewed by 1847

Abstract

Artificial neural networks are machine learning models widely used in many sciences as well as in practical applications. The basic element of these models is a vector of parameters; the values of these parameters should be estimated using some computational method, and this [...] Read more.

Artificial neural networks are machine learning models widely used in many sciences as well as in practical applications. The basic element of these models is a vector of parameters; the values of these parameters should be estimated using some computational method, and this process is called training. For effective training of the network, computational methods from the field of global minimization are often used. However, for global minimization techniques to be effective, the bounds of the objective function should also be clearly defined. In this paper, a two-stage global optimization technique is presented for efficient training of artificial neural networks. In the first stage, the bounds for the neural network parameters are estimated using Particle Swarm Optimization and, in the following phase, the parameters of the network are optimized within the bounds of the first phase using global optimization techniques. The suggested method was used on a series of well-known problems in the literature and the experimental results were more than encouraging. Full article

(This article belongs to the Special Issue Uncertainty-Aware Artificial Intelligence)

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26 pages, 13704 KiB

Open AccessArticle

Prototype of an Emergency Response System Using IoT in a Fog Computing Environment

by Iván Ortiz-Garcés, Roberto O. Andrade, Santiago Sanchez-Viteri and William Villegas-Ch.

Computers 2023, 12(4), 81; https://doi.org/10.3390/computers12040081 - 16 Apr 2023

Cited by 7 | Viewed by 2596

Abstract

Currently, the internet of things (IoT) is a technology entering various areas of society, such as transportation, agriculture, homes, smart buildings, power grids, etc. The internet of things has a wide variety of devices connected to the network, which can saturate the central [...] Read more.

Currently, the internet of things (IoT) is a technology entering various areas of society, such as transportation, agriculture, homes, smart buildings, power grids, etc. The internet of things has a wide variety of devices connected to the network, which can saturate the central links to cloud computing servers. IoT applications that are sensitive to response time are affected by the distance that data is sent to be processed for actions and results. This work aims to create a prototype application focused on emergency vehicles through a fog computing infrastructure. This technology makes it possible to reduce response times and send only the necessary data to cloud computing. The emergency vehicle contains a wireless device that sends periodic alert messages, known as an in-vehicle beacon. Beacon messages can be used to enable green traffic lights toward the destination. The prototype contains fog computing nodes interconnected as close to the vehicle as using the low-power whole area network protocol called a long-range wide area network. In the same way, fog computing nodes run a graphical user interface (GUI) application to manage the nodes. In addition, a comparison is made between fog computing and cloud computing, considering the response time of these technologies. Full article

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

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36 pages, 11052 KiB

Open AccessArticle

Real-Time Self-Adaptive Traffic Management System for Optimal Vehicular Navigation in Modern Cities

by Youssef Benmessaoud, Loubna Cherrat and Mostafa Ezziyyani

Computers 2023, 12(4), 80; https://doi.org/10.3390/computers12040080 - 14 Apr 2023

Cited by 3 | Viewed by 3694

Abstract

The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of [...] Read more.

The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of the most optimal roads and nodes using instances of road graphs at different timeslots to help minimize congestion for actual drivers in urban areas. The first phase of the study involved reducing traffic congestion in one city. The data were collected using a mobile application installed on more than 10 taxi drivers’ phones, capturing data at different timeslots. Based on the results, the shortest path was proposed for each timeslot. The proposed technique was effective in reducing traffic congestion in the city. To test the effectiveness of the proposed technique in other cities, the second phase of the study involved extending the proposed technique to another city using a self-adaptive system based on a similarity approach regarding the structures and sub-regions of the two cities. The results showed that the proposed technique can be successfully applied to different cities with similar urban structures and traffic regulations. The proposed technique offers an innovative approach to reducing traffic congestion in urban areas. It leverages dynamic calculation of the shortest route and utilizes instances of road graphs to optimize traffic flow. By successfully implementing this approach, we can improve journey times and reduce fuel consumption, pollution, and other operating costs, which will contribute to a better quality of urban life. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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26 pages, 3522 KiB

Open AccessReview

Developing Resilient Cyber-Physical Systems: A Review of State-of-the-Art Malware Detection Approaches, Gaps, and Future Directions

by M. Imran Malik, Ahmed Ibrahim, Peter Hannay and Leslie F. Sikos

Computers 2023, 12(4), 79; https://doi.org/10.3390/computers12040079 - 14 Apr 2023

Cited by 12 | Viewed by 4403

Abstract

Cyber-physical systems (CPSes) are rapidly evolving in critical infrastructure (CI) domains such as smart grid, healthcare, the military, and telecommunication. These systems are continually threatened by malicious software (malware) attacks by adversaries due to their improvised tactics and attack methods. A minor configuration [...] Read more.

Cyber-physical systems (CPSes) are rapidly evolving in critical infrastructure (CI) domains such as smart grid, healthcare, the military, and telecommunication. These systems are continually threatened by malicious software (malware) attacks by adversaries due to their improvised tactics and attack methods. A minor configuration change in a CPS through malware has devastating effects, which the world has seen in Stuxnet, BlackEnergy, Industroyer, and Triton. This paper is a comprehensive review of malware analysis practices currently being used and their limitations and efficacy in securing CPSes. Using well-known real-world incidents, we have covered the significant impacts when a CPS is compromised. In particular, we have prepared exhaustive hypothetical scenarios to discuss the implications of false positives on CPSes. To improve the security of critical systems, we believe that nature-inspired metaheuristic algorithms can effectively counter the overwhelming malware threats geared toward CPSes. However, our detailed review shows that these algorithms have not been adapted to their full potential to counter malicious software. Finally, the gaps identified through this research have led us to propose future research directions using nature-inspired algorithms that would help in bringing optimization by reducing false positives, thereby increasing the security of such systems. Full article

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18 pages, 618 KiB

Open AccessArticle

Enhancing JWT Authentication and Authorization in Web Applications Based on User Behavior History

by Ahmet Bucko, Kamer Vishi, Bujar Krasniqi and Blerim Rexha

Computers 2023, 12(4), 78; https://doi.org/10.3390/computers12040078 - 13 Apr 2023

Cited by 7 | Viewed by 7616

Abstract

The rapid growth of the web has transformed our daily lives and the need for secure user authentication and authorization has become a crucial aspect of web-based services. JSON Web Tokens (JWT), based on RFC 7519, are widely used as a standard for [...] Read more.

The rapid growth of the web has transformed our daily lives and the need for secure user authentication and authorization has become a crucial aspect of web-based services. JSON Web Tokens (JWT), based on RFC 7519, are widely used as a standard for user authentication and authorization. However, these tokens do not store information about the user’s behavior history. To address this issue, this paper presents a solution to enhance the trustworthiness of user authentication in web applications based on their behavior history. The solution considers factors such as the number of password attempts, IP address consistency, and user agent type and assigns a weight or percentage to each. These weights are summed up and stored in the user’s account, and updated after each transaction. The proposed approach was implemented using the .NET framework, C# programming language, and PostgreSQL database. The results show that the proposed solution effectively increases the level of trust in user authentication. The paper concludes by highlighting the strengths and limitations of the proposed solution. Full article

(This article belongs to the Special Issue Innovative Authentication Methods)

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32 pages, 3612 KiB

Open AccessArticle

Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions

by Carolin Wienrich, Astrid Carolus, André Markus, Yannik Augustin, Jan Pfister and Andreas Hotho

Computers 2023, 12(4), 77; https://doi.org/10.3390/computers12040077 - 13 Apr 2023

Cited by 4 | Viewed by 5662

Abstract

Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time [...] Read more.

Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions. 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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17 pages, 4969 KiB

Open AccessArticle

SmartWatcher©: A Solution to Automatically Assess the Smartness of Buildings

by Yu Ye, Alfonso P. Ramallo-González, Valentina Tomat, Juan Sanchez Valverde and Antonio Skarmeta-Gómez

Computers 2023, 12(4), 76; https://doi.org/10.3390/computers12040076 - 12 Apr 2023

Viewed by 2100

Abstract

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the [...] Read more.

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the large number of existing buildings could exhaust resources, but some organisations are requesting this regardless (such as the smart readiness indicator of the European Union). To tackle this issue, this work describes a tool that was created to find connected devices to automatically evaluate smartness. The tool, which was given the name SmartWatcher, uses a design-for-purpose natural language processing algorithm that converts verbal information into numerical information. The method was tested on real buildings in four different geographical locations. SmartWatcher is shown to be powerful, as it was capable of obtaining numerical values from verbal descriptions of devices. Additionally, a preliminary comparison of values obtained using the automatic engine and clipboard assessments showed that although the results were still far from being perfect, some visual correlation could be seen. This anticipates that, with the addition of appropriate techniques that refine this algorithm, or with the addition of new ones (with other more advanced natural language processing methods), the accuracy of this tool could be greatly increased. 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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16 pages, 32019 KiB

Open AccessArticle

A Comparative Study of Safety Zone Visualisations for Virtual and Physical Robot Arms Using Augmented Reality

by Yunus Emre Cogurcu, James A. Douthwaite and Steve Maddock

Computers 2023, 12(4), 75; https://doi.org/10.3390/computers12040075 - 10 Apr 2023

Cited by 2 | Viewed by 2277

Abstract

The use of robot arms in various industrial settings has changed the way tasks are completed. However, safety concerns for both humans and robots in these collaborative environments remain a critical challenge. Traditional approaches to visualising safety zones, including physical barriers and warning [...] Read more.

The use of robot arms in various industrial settings has changed the way tasks are completed. However, safety concerns for both humans and robots in these collaborative environments remain a critical challenge. Traditional approaches to visualising safety zones, including physical barriers and warning signs, may not always be effective in dynamic environments or where multiple robots and humans are working simultaneously. Mixed reality technologies offer dynamic and intuitive visualisations of safety zones in real time, with the potential to overcome these limitations. In this study, we compare the effectiveness of safety zone visualisations in virtual and real robot arm environments using the Microsoft HoloLens 2. We tested our system with a collaborative pick-and-place application that mimics a real manufacturing scenario in an industrial robot cell. We investigated the impact of safety zone shape, size, and appearance in this application. Visualisations that used virtual cage bars were found to be the most preferred safety zone configuration for a real robot arm. However, the results for this aspect were mixed for a virtual robot arm experiment. These results raise the question of whether or not safety visualisations can initially be tested in a virtual scenario and the results transferred to a real robot arm scenario, which has implications for the testing of trust and safety in human–robot collaboration environments. Full article

(This article belongs to the Special Issue Selected Papers from Computer Graphics & Visual Computing (CGVC 2022))

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17 pages, 3160 KiB

Open AccessArticle

Proposal for a System for the Identification of the Concentration of Students Who Attend Online Educational Models

by William Villegas-Ch., Joselin García-Ortiz, Isabel Urbina-Camacho and Aracely Mera-Navarrete

Computers 2023, 12(4), 74; https://doi.org/10.3390/computers12040074 - 6 Apr 2023

Cited by 4 | Viewed by 2645

Abstract

Currently, e-learning has revolutionized the way students learn by offering access to quality education in a model that does not depend on a specific space and time. However, due to the e-learning method where no tutor can directly control the group of students, [...] Read more.

Currently, e-learning has revolutionized the way students learn by offering access to quality education in a model that does not depend on a specific space and time. However, due to the e-learning method where no tutor can directly control the group of students, they can be distracted for various reasons, which greatly affects their learning capacity. Several scientific works try to improve the quality of online education, but a holistic approach is necessary to address this problem. Identifying students’ attention spans is important in understanding how students process and retain information. Attention is a critical cognitive process that affects a student’s ability to learn. Therefore, it is important to use a variety of techniques and tools to assess student attention, such as standardized tests, behavioral observation, and assessment of academic achievement. This work proposes a system that uses devices such as cameras to monitor the attention level of students in real time during online classes. The results are used with feedback as a heuristic value to analyze the performance of the students, as well as the teaching standards of the teachers. Full article

(This article belongs to the Special Issue Present and Future of E-Learning Technologies)

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19 pages, 3445 KiB

Open AccessArticle

Pedagogical Implications and Methodological Possibilities of Digital Transformation in Digital Education after the COVID-19 Epidemic

by Zoltán Szűts, György Molnár, Réka Racsko, Geoffrey Vaughan and Tünde Lengyelné Molnár

Computers 2023, 12(4), 73; https://doi.org/10.3390/computers12040073 - 4 Apr 2023

Cited by 5 | Viewed by 2248

Abstract

In the context of digital pedagogy, internet communication platforms, digital media interfaces, applications, and info-communication tools, best practices are stepping up in the educational process and forming a methodology. The focus areas targeted by the questionnaire presented in this study are digital transformation; [...] Read more.

In the context of digital pedagogy, internet communication platforms, digital media interfaces, applications, and info-communication tools, best practices are stepping up in the educational process and forming a methodology. The focus areas targeted by the questionnaire presented in this study are digital transformation; the smart use of digital tools; characteristics of the learning environment; classroom activities, learning organization, and methodology; content; and curriculum sharing. During the research, the authors asked what were the online communication channels and digital platforms that teachers have effectively used in terms of learning efficiency in distance learning ordered as a result of the COVID-19 emergency in Hungary. The empirical research goal was to explore the conditions among Hungarian teachers with the help of a questionnaire and a semi-structured online interview. Seven thousand teachers were contacted through email, and a return rate of 10.7% was achieved. The questionnaire was filled in online by n = 751 primary teachers. According to the respondents, the most effective tools in the transformation of education are self-made tutorial videos and real-time written and video-based chat. 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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18 pages, 4038 KiB

Open AccessReview

Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation

by Hamed Taherdoost and Mitra Madanchian

Computers 2023, 12(4), 72; https://doi.org/10.3390/computers12040072 - 31 Mar 2023

Cited by 30 | Viewed by 23377

Abstract

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working [...] Read more.

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working has highlighted the shortcomings in current procedures. These gaps will be filled by artificial intelligence (AI), which will also alter how KM is transformed and knowledge is handled. This article analyzes studies from 2012 to 2022 that examined AI and KM, with a particular emphasis on how AI may support businesses in their attempts to successfully manage knowledge and information. This critical review examines the current approaches in light of the literature that is currently accessible on AI and KM, focusing on articles that address practical applications and the research background. Furthermore, this review provides insight into potential future study directions and improvements by presenting a critical evaluation. 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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19 pages, 455 KiB

Open AccessArticle

Pain Detection in Biophysiological Signals: Knowledge Transfer from Short-Term to Long-Term Stimuli Based on Distance-Specific Segment Selection

by Tobias Benjamin Ricken, Peter Bellmann, Steffen Walter and Friedhelm Schwenker

Computers 2023, 12(4), 71; https://doi.org/10.3390/computers12040071 - 31 Mar 2023

Cited by 2 | Viewed by 1875

Abstract

In this study, we analyze a signal segmentation-specific pain duration transfer task by applying knowledge transfer from short-term (phasic) pain stimuli to long-term (tonic) pain stimuli. To this end, we focus on the physiological signals of the X-ITE Pain Database. We evaluate different [...] Read more.

In this study, we analyze a signal segmentation-specific pain duration transfer task by applying knowledge transfer from short-term (phasic) pain stimuli to long-term (tonic) pain stimuli. To this end, we focus on the physiological signals of the X-ITE Pain Database. We evaluate different distance-based segment selection approaches with the aim of identifying individual segments of the corresponding tonic stimuli that lead to the best classification performance. The phasic domain is used to train the classification model. In the first main step, we compute class-specific prototypes for the phasic domain. In the second main step, we compute the distances between all segments of the tonic samples and each prototype. The segment with the lowest distance to the prototypes is then fed to the classifier. Our analysis includes the evaluation of a variety of distance metrics, namely the Euclidean, Bray–Curtis, Canberra, Chebyshev, City-Block and Wasserstein distances. Our results show that in combination with most of the metrics used, the distance-based selection of one individual segment outperforms the naive approach in which the tonic stimuli are fed to the phasic domain-based classification model without any adaptation. Moreover, most of the evaluated distance-based segment selection approaches lead to outcomes that are close to the classification performance, which is obtained by focusing on the respective best segments. For instance, for the trapezius (TRA) signal, in combination with the electric pain domain, we obtained an averaged accuracy of 68.0%, while the naive approach led to 66.0%. For the thermal pain domain, in combination with the electrodermal activity (EDA) signal, we obtained an averaged accuracy of 59.6%, outperforming the naive approach, which led to 53.2%. Full article

(This article belongs to the Special Issue Multimodal Pattern Recognition of Social Signals in HCI)

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19 pages, 4060 KiB

Open AccessArticle

Optimization of a Fuzzy System Used to Characterize the Factors That Affect Drivers on Urban Roads

by Lilian Astrid Bejarano, Carlos Enrique Montenegro and Helbert Eduardo Espitia

Computers 2023, 12(4), 70; https://doi.org/10.3390/computers12040070 - 30 Mar 2023

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Abstract

This document seeks to model the behavior of drivers on urban roads considering different environmental factors using a Mamdani-type fuzzy system. For this, a leader-following traffic model and a fuzzy logic system are used to characterize the behavior of drivers. Real data are [...] Read more.

This document seeks to model the behavior of drivers on urban roads considering different environmental factors using a Mamdani-type fuzzy system. For this, a leader-following traffic model and a fuzzy logic system are used to characterize the behavior of drivers. Real data are obtained using a camera in the roads under consideration, and these data and an optimization process are employed to fit the fuzzy model. For the optimization process, the fuzzy logic system used to model the driver’s behavior is incorporated into a dynamic vehicle tracking model where the fuzzy system allows considering different environmental factors in the traffic model simulation. After carrying out the optimization process, it is possible to assign linguistic labels to the fuzzy sets associated with the output. In this way, the interpretability of the proposed fuzzy system is achieved by assigning labels (concepts) to the fuzzy sets. The results show that the proposed model fits the real data, and the fuzzy sets are adjusted according to the measured data for the different considered cases. Full article

(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)

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13 pages, 16987 KiB

Open AccessArticle

Depth-Aware Neural Style Transfer for Videos

by Eleftherios Ioannou and Steve Maddock

Computers 2023, 12(4), 69; https://doi.org/10.3390/computers12040069 - 27 Mar 2023

Cited by 2 | Viewed by 2422

Abstract

Temporal consistency and content preservation are the prominent challenges in artistic video style transfer. To address these challenges, we present a technique that utilizes depth data and we demonstrate this on real-world videos from the web, as well as on a standard video [...] Read more.

Temporal consistency and content preservation are the prominent challenges in artistic video style transfer. To address these challenges, we present a technique that utilizes depth data and we demonstrate this on real-world videos from the web, as well as on a standard video dataset of three-dimensional computer-generated content. Our algorithm employs an image-transformation network combined with a depth encoder network for stylizing video sequences. For improved global structure preservation and temporal stability, the depth encoder network encodes ground-truth depth information which is fused into the stylization network. To further enforce temporal coherence, we employ ConvLSTM layers in the encoder, and a loss function based on calculated depth information for the output frames is also used. We show that our approach is capable of producing stylized videos with improved temporal consistency compared to state-of-the-art methods whilst also successfully transferring the artistic style of a target painting. Full article

(This article belongs to the Special Issue Selected Papers from Computer Graphics & Visual Computing (CGVC 2022))

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Computers, Volume 12, Issue 4 (April 2023) – 20 articles

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