Journal Description
Computers
Computers
is an international, scientific, peer-reviewed, open access journal of computer science, including computer and network architecture and computer–human interaction as its main foci, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
- Journal Rank: CiteScore - Q2 (Computer Networks and Communications)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.8 (2022);
5-Year Impact Factor:
2.6 (2022)
Latest Articles
Exploring the Connection between the TDD Practice and Test Smells—A Systematic Literature Review
Computers 2024, 13(3), 79; https://doi.org/10.3390/computers13030079 - 18 Mar 2024
Abstract
Test-driven development (TDD) is an agile practice of writing test code before production code, following three stages: red, green, and refactor. In the red stage, the test code is written; in the green stage, the minimum code necessary to make the test pass
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Test-driven development (TDD) is an agile practice of writing test code before production code, following three stages: red, green, and refactor. In the red stage, the test code is written; in the green stage, the minimum code necessary to make the test pass is implemented, and in the refactor stage, improvements are made to the code. This practice is widespread across the industry, and various studies have been conducted to understand its benefits and impacts on the software development process. Despite its popularity, TDD studies often focus on the technical aspects of the practice, such as the external/internal quality of the code, productivity, test smells, and code comprehension, rather than the context in which it is practiced. In this paper, we present a systematic literature review using Scopus, Web of Science, and Google Scholar that focuses on the TDD practice and the influences that lead to the introduction of test smells/anti-patterns in the test code. The findings suggest that organizational structure influences the testing strategy. Additionally, there is a tendency to use test smells and TDD anti-patterns interchangeably, and test smells negatively impact code comprehension. Furthermore, TDD styles and the relationship between TDD practice and the generation of test smells are frequently overlooked in the literature.
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(This article belongs to the Special Issue Selected Papers from 18th Iberian Conference on Information Systems and Technologies (CISTI'2023))
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Pedestrian Collision Avoidance in Autonomous Vehicles: A Review
by
Timothé Verstraete and Naveed Muhammad
Computers 2024, 13(3), 78; https://doi.org/10.3390/computers13030078 - 16 Mar 2024
Abstract
Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community
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Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community over the last ten years. For this purpose, we propose a classification of studies in the literature in terms of the following: (i) pedestrian detection methods, (ii) collision avoidance approaches, (iii) actions, (iv) computing methods, and (v) test methods.
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(This article belongs to the Special Issue Recent Advances in Autonomous Vehicle Solutions)
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Open AccessEditorial
Special Issue on Advances in Database Engineered Applications
by
Richard Chbeir, Mirjana Ivanovic, Yannis Manolopoulos and Claudio Silvestri
Computers 2024, 13(3), 77; https://doi.org/10.3390/computers13030077 - 14 Mar 2024
Abstract
The 27th International Database Engineering and Applications Symposium (IDEAS-2023) was held in Heraklion, Crete, Greece, on 5–7 May 2023 [...]
Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
Open AccessArticle
Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model
by
Eduard Puerto, Jose Aguilar and Angel Pinto
Computers 2024, 13(3), 76; https://doi.org/10.3390/computers13030076 - 12 Mar 2024
Abstract
Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction
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Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach.
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(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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Open AccessArticle
A Rule-Based Algorithm and Its Specializations for Measuring the Complexity of Software in Educational Digital Environments
by
Artyom V. Gorchakov, Liliya A. Demidova and Peter N. Sovietov
Computers 2024, 13(3), 75; https://doi.org/10.3390/computers13030075 - 11 Mar 2024
Abstract
Modern software systems consist of many software components; the source code of modern software systems is hard to understand and maintain for new developers. Aiming to simplify the readability and understandability of source code, companies that specialize in software development adopt programming standards,
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Modern software systems consist of many software components; the source code of modern software systems is hard to understand and maintain for new developers. Aiming to simplify the readability and understandability of source code, companies that specialize in software development adopt programming standards, software design patterns, and static analyzers with the aim of decreasing the complexity of software. Recent research introduced a number of code metrics allowing the numerical characterization of the maintainability of code snippets. Cyclomatic Complexity (CycC) is one widely used metric for measuring the complexity of software. The value of CycC is equal to the number of decision points in a program plus one. However, CycC does not take into account the nesting levels of the syntactic structures that break the linear control flow in a program. Aiming to resolve this, the Cognitive Complexity (CogC) metric was proposed as a successor to CycC. In this paper, we describe a rule-based algorithm and its specializations for measuring the complexity of programs. We express the CycC and CogC metrics by means of the described algorithm and propose a new complexity metric named Educational Complexity (EduC) for use in educational digital environments. EduC is at least as strict as CycC and CogC are and includes additional checks that are based on definition-use graph analysis of a program. We evaluate the CycC, CogC, and EduC metrics using the source code of programs submitted to a Digital Teaching Assistant (DTA) system that automates a university programming course. The obtained results confirm that EduC rejects more overcomplicated and difficult-to-understand programs in solving unique programming exercises generated by the DTA system when compared to CycC and CogC.
Full article
(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)
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Open AccessArticle
Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
by
Hibatul Azizi Hisyam Ng and Toktam Mahmoodi
Computers 2024, 13(3), 74; https://doi.org/10.3390/computers13030074 - 10 Mar 2024
Abstract
Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse
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Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.
Full article
(This article belongs to the Special Issue Emerging Trends and Challenges of Software-Defined Networking (SDN) Technologies)
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Open AccessArticle
Rapid Experimental Protocol for PMSM via MBD: Modeling, Simulation, and Experiment
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Mingyuan Hu, Hyeongki Ahn, Hyein Kang, Yoonuh Chung and Kwanho You
Computers 2024, 13(3), 73; https://doi.org/10.3390/computers13030073 - 09 Mar 2024
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As control algorithms evolve, their enhanced performance is often accompanied by increased complexity, reaching a point where practical experimentation becomes unfeasible. This situation has led to many theoretical studies relying solely on simulations without experimental verification. To address this gap, this study introduces
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As control algorithms evolve, their enhanced performance is often accompanied by increased complexity, reaching a point where practical experimentation becomes unfeasible. This situation has led to many theoretical studies relying solely on simulations without experimental verification. To address this gap, this study introduces a rapid experimentation protocol (REP) for applying field-oriented control (FOC) strategies to permanent magnet synchronous motors (PMSMs) based on model-based design (MBD) and automated code generation. REP is designed to be user-friendly and straightforward, offering a less complex and more accessible alternative to DSP toolboxes. Its excellent hardware compatibility is conducive to code porting and development. With this protocol, users can quickly conduct FOC strategy experiments with reduced dependency on the complex automated code generation tools often associated with toolboxes. Centered around the PMSM model, this method utilizes only the fundamental modules of MATLAB2023b/Simulink, greatly simplifying the user experience. To demonstrate the feasibility and efficiency of the protocol, models for both sensor-based and sensorless control are developed. The practicality of REP, including sensor-based and sensorless experiments, is successfully validated on an arm-cortex-M4-based GD32 microcontroller.
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Open AccessArticle
The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain
by
Nino Adamashvili, Nino Zhizhilashvili and Caterina Tricase
Computers 2024, 13(3), 72; https://doi.org/10.3390/computers13030072 - 08 Mar 2024
Abstract
The study presents a comprehensive examination of the recent advancements in the field of wine production using the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain Technology (BCT). The paper aims to provide insights into the implementation of these technologies in the
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The study presents a comprehensive examination of the recent advancements in the field of wine production using the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain Technology (BCT). The paper aims to provide insights into the implementation of these technologies in the wine supply chain and to identify the potential benefits associated with their use. The study highlights the various applications of IoT, AI, and BCT in wine production, including vineyard management, wine quality control, and supply chain management. It also discusses the potential benefits of these technologies, such as improved efficiency, increased transparency, and reduced costs. The study concludes by presenting the framework proposed by the authors in order to overcome the challenges associated with the implementation of these technologies in the wine supply chain and suggests areas for future research. The proposed framework meets the challenges of lack of transparency, lack of ecosystem management in the wine industry and irresponsible spending associated with the lack of monitoring and prediction tools. Overall, the study provides valuable insights into the potential of IoT, AI, and BCT in optimizing the wine supply chain and offers a comprehensive review of the existing literature on the study subject.
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(This article belongs to the Special Issue Blockchain Technology—a Breakthrough Innovation for Modern Industries)
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A Low-Cost Deep-Learning-Based System for Grading Cashew Nuts
by
Van-Nam Pham, Quang-Huy Do Ba, Duc-Anh Tran Le, Quang-Minh Nguyen, Dinh Do Van and Linh Nguyen
Computers 2024, 13(3), 71; https://doi.org/10.3390/computers13030071 - 08 Mar 2024
Abstract
Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the
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Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the price of the products. To address this issue, in this work we first propose a low-cost grading system for cashew nuts by using the off-the-shelf equipment. The most important but complicated part of the system is its “eye”, which is required to detect and classify the nuts into different grades. To this end, we propose to exploit advantages of both the YOLOv8 and Transformer models and combine them in one single model. More specifically, we develop a module called SC3T that can be employed to integrate into the backbone of the YOLOv8 architecture. In the SC3T module, a Transformer block is dexterously integrated into along with the C3TR module. More importantly, the classifier is not only efficient but also compact, which can be implemented in an embedded device of our developed cashew nut grading system. The proposed classifier, called the YOLOv8–Transformer model, can enable our developed grading system, through a low-cost camera, to correctly detect and accurately classify the cashew nuts into four quality grades. In our grading system, we also developed an actuation mechanism to efficiently sort the nuts according to the classification results, getting the products ready for packaging. To verify the effectiveness of the proposed classifier, we collected a dataset from our sorting system, and trained and tested the model. The obtained results demonstrate that our proposed approach outperforms all the baseline methods given the collected image data.
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(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)
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Open AccessArticle
User-Centered Pipeline for Synthetic Augmentation of Anomaly Detection Datasets
by
Alexander Rosbak-Mortensen, Marco Jansen, Morten Muhlig, Mikkel Bjørndahl Kristensen Tøt and Ivan Nikolov
Computers 2024, 13(3), 70; https://doi.org/10.3390/computers13030070 - 08 Mar 2024
Abstract
Automatic anomaly detection plays a critical role in surveillance systems but requires datasets comprising large amounts of annotated data to train and evaluate models. Gathering and annotating these data is a labor-intensive task that can become costly. A way to circumvent this is
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Automatic anomaly detection plays a critical role in surveillance systems but requires datasets comprising large amounts of annotated data to train and evaluate models. Gathering and annotating these data is a labor-intensive task that can become costly. A way to circumvent this is to use synthetic data to augment anomalies directly into existing datasets. This far more diverse scenario can be created and come directly with annotations. This however also poses new issues for the computer-vision engineer and researcher end users, who are not readily familiar with 3D modeling, game development, or computer graphics methodologies and must rely on external specialists to use or tweak such pipelines. In this paper, we extend our previous work of an application that synthesizes dataset variations using 3D models and augments anomalies on real backgrounds using the Unity Engine. We developed a high-usability user interface for our application through a series of RITE experiments and evaluated the final product with the help of deep-learning specialists who provided positive feedback regarding its usability, accessibility, and user experience. Finally, we tested if the proposed solution can be used in the context of traffic surveillance by augmenting the train data from the challenging Street Scene dataset. We found that by using our synthetic data, we could achieve higher detection accuracy. We also propose the next steps to expand the proposed solution for better usability and render accuracy through the use of segmentation pre-processing.
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(This article belongs to the Special Issue Selected Papers from Computer Graphics & Visual Computing (CGVC 2023))
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How to Develop Information Systems to Improve Accessible Tourism: Proposal of a Roadmap to Support the Development of Accessible Solutions
by
Pedro Teixeira, Celeste Eusébio and Leonor Teixeira
Computers 2024, 13(3), 69; https://doi.org/10.3390/computers13030069 - 07 Mar 2024
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The right to tourism has become a crucial aspect of society. Through more accessible tourism, it is possible to improve travel conditions for people with disabilities. Nonetheless, barriers still exist, with the lack of information about accessibility conditions representing a main obstacle. Information
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The right to tourism has become a crucial aspect of society. Through more accessible tourism, it is possible to improve travel conditions for people with disabilities. Nonetheless, barriers still exist, with the lack of information about accessibility conditions representing a main obstacle. Information systems can help overcome these hurdles. However, it is verified that methodologies to support the development of accessible IS are currently very scarce. Thus, this study intends to develop an accessible IS for accessible tourism and propose a roadmap to support the creation of accessible IS solutions. To obtain the intended accessible tourism solution, an action research methodology was followed, which involved adapting already established frameworks, that combine Agile development and user-centered design techniques. Following the methodology, a web application named access@tour by action was created. This mobile solution is capable of improving information management within the accessible tourism market. From this experimental study, a proposal for a methodological roadmap emerged. This roadmap helps to better understand how to develop accessible IS by demonstrating techniques for gathering accessibility requirements and validating them. The roadmap is adaptable and suitable for IS projects involving accessibility. Both results provide a better perspective on how to integrate accessibility during the development of IS, possibly supporting future researchers in creating accessible solutions.
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Open AccessArticle
Inverse Trigonometric Fuzzy Preference Programming to Generate Weights with Optimal Solutions Implemented on Evaluation Criteria in E-Learning
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Emi Iryanti, Paulus Insap Santosa, Sri Suning Kusumawardani and Indriana Hidayah
Computers 2024, 13(3), 68; https://doi.org/10.3390/computers13030068 - 07 Mar 2024
Abstract
Nielsen’s heuristics are widely recognized for usability evaluation, but they are often considered insufficiently specific for assessing particular domains, such as e-learning. Currently, e-learning plays a pivotal role in higher education because of the shift in the educational paradigm from a teacher-centered approach
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Nielsen’s heuristics are widely recognized for usability evaluation, but they are often considered insufficiently specific for assessing particular domains, such as e-learning. Currently, e-learning plays a pivotal role in higher education because of the shift in the educational paradigm from a teacher-centered approach to a student-centered approach. The criteria utilized in multiple sets of heuristics for evaluating e-learning are carefully examined based on the definitions of each criterion. If there are similarities in meaning among these criteria, they are consolidated into a single criterion, resulting in the creation of 20 new criteria (spanning three primary aspects) for the evaluation of e-learning. These 20 new criteria encompass key aspects related to the user interface, learning development, and motivation. Each aspect is assigned a weight to facilitate prioritization when implementing improvements to evaluate e-learning, which is especially beneficial for institutions with limited resources responsible for the relevant units. In terms of weighting, there is room for enhancement to attain more optimal weighting outcomes by employing a Fuzzy Preference Programming method known as Inverse Trigonometric Fuzzy Preference Programming (ITFPP). The higher the assigned weight, the greater the priority for implementing improvements.
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(This article belongs to the Topic Innovation, Communication and Engineering)
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Open AccessArticle
Integrating the Internet of Things (IoT) in SPA Medicine: Innovations and Challenges in Digital Wellness
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Mario Casillo, Liliana Cecere, Francesco Colace, Angelo Lorusso and Domenico Santaniello
Computers 2024, 13(3), 67; https://doi.org/10.3390/computers13030067 - 06 Mar 2024
Abstract
Integrating modern and innovative technologies such as the Internet of Things (IoT) and Machine Learning (ML) presents new opportunities in healthcare, especially in medical spa therapies. Once considered palliative, these therapies conducted using mineral/thermal water are now recognized as a targeted and specific
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Integrating modern and innovative technologies such as the Internet of Things (IoT) and Machine Learning (ML) presents new opportunities in healthcare, especially in medical spa therapies. Once considered palliative, these therapies conducted using mineral/thermal water are now recognized as a targeted and specific therapeutic modality. The peculiarity of these treatments lies in their simplicity of administration, which allows for prolonged treatments, often lasting weeks, with progressive and controlled therapeutic effects. Thanks to new technologies, it will be possible to continuously monitor the patient, both on-site and remotely, increasing the effectiveness of the treatment. In this context, wearable devices, such as smartwatches, facilitate non-invasive monitoring of vital signs by collecting precise data on several key parameters, such as heart rate or blood oxygenation level, and providing a perspective of detailed treatment progress. The constant acquisition of data thanks to the IoT, combined with the advanced analytics of ML technologies, allows for data collection and precise analysis, allowing real-time monitoring and personalized treatment adaptation. This article introduces an IoT-based framework integrated with ML techniques to monitor spa treatments, providing tailored customer management and more effective results. A preliminary experimentation phase was designed and implemented to evaluate the system’s performance through evaluation questionnaires. Encouraging preliminary results have shown that the innovative approach can enhance and highlight the therapeutic value of spa therapies and their significant contribution to personalized healthcare.
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(This article belongs to the Special Issue Sensors and Smart Cities 2023)
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Bus Driver Head Position Detection Using Capsule Networks under Dynamic Driving Conditions
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János Hollósi, Áron Ballagi, Gábor Kovács, Szabolcs Fischer and Viktor Nagy
Computers 2024, 13(3), 66; https://doi.org/10.3390/computers13030066 - 03 Mar 2024
Abstract
Monitoring bus driver behavior and posture in urban public transport’s dynamic and unpredictable environment requires robust real-time analytics systems. Traditional camera-based systems that use computer vision techniques for facial recognition are foundational. However, they often struggle with real-world challenges such as sudden driver
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Monitoring bus driver behavior and posture in urban public transport’s dynamic and unpredictable environment requires robust real-time analytics systems. Traditional camera-based systems that use computer vision techniques for facial recognition are foundational. However, they often struggle with real-world challenges such as sudden driver movements, active driver–passenger interactions, variations in lighting, and physical obstructions. Our investigation covers four different neural network architectures, including two variations of convolutional neural networks (CNNs) that form the comparative baseline. The capsule network (CapsNet) developed by our team has been shown to be superior in terms of efficiency and speed in facial recognition tasks compared to traditional models. It offers a new approach for rapidly and accurately detecting a driver’s head position within the wide-angled view of the bus driver’s cabin. This research demonstrates the potential of CapsNets in driver head and face detection and lays the foundation for integrating CapsNet-based solutions into real-time monitoring systems to enhance public transportation safety protocols.
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(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)
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A First Approach to Co-Design a Multimodal Pedagogic Conversational Agent with Pre-Service Teachers to Teach Programming in Primary Education
by
Diana Pérez-Marín, Raquel Hijón-Neira and Celeste Pizarro
Computers 2024, 13(3), 65; https://doi.org/10.3390/computers13030065 - 29 Feb 2024
Abstract
Pedagogic Conversational Agents (PCAs) are interactive systems that engage the student in a dialogue to teach some domain. They can have the roles of a teacher, student, or companion, and adopt several shapes. In our previous work, a significant increase of students’ performance
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Pedagogic Conversational Agents (PCAs) are interactive systems that engage the student in a dialogue to teach some domain. They can have the roles of a teacher, student, or companion, and adopt several shapes. In our previous work, a significant increase of students’ performance when learning programming was found when using PCAs in the teacher role. However, it is not common to find PCAs used in classrooms. In this paper, it is explored whether pre-service teachers would accept PCAs to teach programming better if they were co-designed with them. Pre-service teachers are chosen because they are still in training, so they can be taught what PCAs are and how this technology could be helpful. Moreover, pre-service teachers can choose whether they integrate PCAs in the teaching activities that they carry out as part of their degree’s course. An experiment with 35 pre-service primary education teachers was carried out during the 2021/2022 academic year to co-design a robotic PCA to teach programming. The experience validates the idea that involving pre-service teachers in the design of a PCA facilitates their involvement to integrate this technology in their classrooms. In total, 97% of the pre-service teachers that stated in a survey that they believed robot PCA could help children to learn programming, and 80% answered that they would like to use them in their classrooms.
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(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques
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Dragos Alexandru Andrioaia, Vasile Gheorghita Gaitan, George Culea and Ioan Viorel Banu
Computers 2024, 13(3), 64; https://doi.org/10.3390/computers13030064 - 29 Feb 2024
Abstract
Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of
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Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.
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(This article belongs to the Special Issue Recent Advances in Autonomous Vehicle Solutions)
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Open AccessArticle
Proposed Fuzzy-Stranded-Neural Network Model That Utilizes IoT Plant-Level Sensory Monitoring and Distributed Services for the Early Detection of Downy Mildew in Viticulture
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Sotirios Kontogiannis, Stefanos Koundouras and Christos Pikridas
Computers 2024, 13(3), 63; https://doi.org/10.3390/computers13030063 - 28 Feb 2024
Abstract
Novel monitoring architecture approaches are required to detect viticulture diseases early. Existing micro-climate decision support systems can only cope with late detection from empirical and semi-empirical models that provide less accurate results. Such models cannot alleviate precision viticulture planning and pesticide control actions,
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Novel monitoring architecture approaches are required to detect viticulture diseases early. Existing micro-climate decision support systems can only cope with late detection from empirical and semi-empirical models that provide less accurate results. Such models cannot alleviate precision viticulture planning and pesticide control actions, providing early reconnaissances that may trigger interventions. This paper presents a new plant-level monitoring architecture called thingsAI. The proposed system utilizes low-cost, autonomous, easy-to-install IoT sensors for vine-level monitoring, utilizing the low-power LoRaWAN protocol for sensory measurement acquisition. Facilitated by a distributed cloud architecture and open-source user interfaces, it provides state-of-the-art deep learning inference services and decision support interfaces. This paper also presents a new deep learning detection algorithm based on supervised fuzzy annotation processes, targeting downy mildew disease detection and, therefore, planning early interventions. The authors tested their proposed system and deep learning model on the grape variety of protected designation of origin called debina, cultivated in Zitsa, Greece. From their experimental results, the authors show that their proposed model can detect vine locations and timely breakpoints of mildew occurrences, which farmers can use as input for targeted intervention efforts.
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(This article belongs to the Special Issue Artificial Intelligence in Industrial IoT Applications)
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Horizontal Learning Approach to Discover Association Rules
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Arthur Yosef, Idan Roth, Eli Shnaider, Amos Baranes and Moti Schneider
Computers 2024, 13(3), 62; https://doi.org/10.3390/computers13030062 - 28 Feb 2024
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Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of
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Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from to in the worst case).
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Suicide-Related Groups and School Shooting Fan Communities on Social Media: A Network Analysis
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Anastasia Peshkovskaya, Sergey Chudinov, Galina Serbina and Alexander Gubanov
Computers 2024, 13(3), 61; https://doi.org/10.3390/computers13030061 - 27 Feb 2024
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As network structure of virtual communities related to suicide and school shooting still remains unaddressed in scientific literature, we employed basic demographics analysis and social network analysis (SNA) to show common features, as well as distinct facets in the communities’ structure and their
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As network structure of virtual communities related to suicide and school shooting still remains unaddressed in scientific literature, we employed basic demographics analysis and social network analysis (SNA) to show common features, as well as distinct facets in the communities’ structure and their followers’ network. Open and publicly accessible data of over 16,000 user accounts were collected with a social media monitoring system. Results showed that adolescents and young adults were the main audience of suicide-related and school shooting fan communities. List of blocked virtual groups related to school shooting was more extensive than that of suicide, which indicates a high radicalization degree of school shooting virtual groups. The homogeneity of followers’ interests was more typical for subscribers of suicide-related communities. A social network analysis showed that followers of school shooting virtual groups were closely interconnected with their peers, and their network was monolithic, while followers of suicide-related virtual groups were fragmented into numerous communities, so presence of a giant connected component in their network can be questioned. We consider our results highly relevant for better understanding the network aspects of virtual information existence, harmful information spreading, and its potential impact on society.
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Open AccessSystematic Review
Cyber Threat Intelligence on Blockchain: A Systematic Literature Review
by
Dimitrios Chatziamanetoglou and Konstantinos Rantos
Computers 2024, 13(3), 60; https://doi.org/10.3390/computers13030060 - 26 Feb 2024
Abstract
Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address
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Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.
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(This article belongs to the Special Issue BLockchain Enabled Sustainable Smart Cities (BLESS 2022))
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