Journal of Universal Computer Science最新文献

AI-Based Adaptive Learning: A Systematic Mapping of the Literature 基于人工智能的自适应学习:文献的系统映射

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-10-28 DOI: 10.3897/jucs.90528

Aymane Ezzaim, Aziz Dahbi, Abdelfatteh Haidine, Abdelhak Aqqal

引用次数: 0

Survey on Integration of Consensus Mechanisms in IoT-based Blockchains 基于物联网的区块链共识机制整合研究

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-10-28 DOI: 10.3897/jucs.94929

Anderson Melo de Morais, Fernando Antonio Aires Lins, Nelson Souto Rosa

{"title":"Survey on Integration of Consensus Mechanisms in IoT-based Blockchains","authors":"Anderson Melo de Morais, Fernando Antonio Aires Lins, Nelson Souto Rosa","doi":"10.3897/jucs.94929","DOIUrl":"https://doi.org/10.3897/jucs.94929","url":null,"abstract":"While IoT systems are increasingly present in different areas of society, ensuring their data&rsquo;s privacy, security, and inviolability becomes paramount. In this direction, Blockchain has been used to protect the security and immutability of data generated by IoT devices and sensors. At the heart of Blockchain solutions, consensus algorithms are crucial in ensuring the security of creating and writing data in new blocks. Choosing which consensus algorithms to utilise is critical because of a fundamental tradeoff between their security strength and response time. However, recent surveys of consensus mechanisms for IoT-based Blockchain focused on individually using and analysing these algorithms. Investigating the integration between these algorithms to address IoT-specific requirements better is a promising approach. In this context, this paper presents a literature review that explains and discusses consensus algorithms in IoT environments and their combinations. The review analyses eight dimensions that help understand existing proposals: ease of integration, scalability, latency, throughput, power consumption, configuration issues, integrated algorithms, and adversary tolerance. The final analysis also suggests and discusses open challenges in integrating multiple consensus algorithms considering the particularities of IoT systems.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face Plastic Surgery Recognition Model Based on Neural Network and Meta-Learning Model  基于神经网络和元学习模型的面部整形手术识别模型

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-10-28 DOI: 10.3897/jucs.98674

Rasha R. Atallah, Ahmad Sami Al-Shamayleh, Mohammed A. Awadallah

{"title":"Face Plastic Surgery Recognition Model Based on Neural Network and Meta-Learning Model&nbsp;","authors":"Rasha R. Atallah, Ahmad Sami Al-Shamayleh, Mohammed A. Awadallah","doi":"10.3897/jucs.98674","DOIUrl":"https://doi.org/10.3897/jucs.98674","url":null,"abstract":"Facial recognition is a procedure of verifying a person's identity by using the face, which is considered one of the biometric security methods. However, facial recognition methods face many challenges, such as face aging, wearing a face mask, having a beard, and undergoing plastic surgery, which decreases the accuracy of these methods. This study evaluates the impact of plastic surgery on face recognition models. The motivation for conducting the research in that aspect is because plastic surgery treatments do not only change the shape and texture of any face but also have increased rapidly in this era. This paper proposes a model based on an artificial neural network with model-agnostic meta-learning (ANN-MAML) for plastic surgery face recognition. This study aims to build a framework for face recognition before and after undergoing plastic surgery based on an artificial neural network. Also, the study seeks to clarify the collaboration between facial plastic surgery and facial recognition software to determine the issues. The researchers evaluated the proposed ANN-MAML's performance using the HDA dataset. The experimental results show that the proposed ANN-MAML learning model attained an accuracy of 90% in facial recognition using Rhinoplasty (Nose surgery) images, 91% on Blepharoplasty surgery (Eyelid surgery) images, 94% on Brow lift (Forehead surgery) images, as well as 92% on Rhytidectomy (Facelift) images. Finally, the results of the proposed model were compared with the baseline methods by the researchers, which showed the superiority of the ANN-MAML over the baselines.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"60 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control of a Spherical Robot Rolling Over Irregular Surfaces 球形机器人在不规则表面上滚动的控制

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-10-28 DOI: 10.3897/jucs.89703

Sergio-Daniel Sanchez-Solar, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza

{"title":"Control of a Spherical Robot Rolling Over Irregular Surfaces","authors":"Sergio-Daniel Sanchez-Solar, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza","doi":"10.3897/jucs.89703","DOIUrl":"https://doi.org/10.3897/jucs.89703","url":null,"abstract":"Pendulum-Driven Spherical Robots are a type of spherical robot whose motion is achieved by controlling two motors for longitudinal and lateral motion. This configuration makes the robot a non-holonomic system, which impedes it from navigating directly towards a target. In addition, controlling its motion on inclined irregular surfaces is also an issue that has not received much attention. In this work, we addressed these two issues by proposing a methodology to con-trol both motors using PID controllers. However, we propose tuning the controller&rsquo;s gains using stochastic signals for the longitudinal controller because by varying the motor&rsquo;s torque, the robot is more susceptible to destabilization in combination with a classical gain tuning methodology for the second controller. Our results indicate that this enables the robot to perform motion on inclined irregular surfaces. We also propose using semicircular trajectories to plan the robot&rsquo;s motion to reach a target successfully even when moving on inclined irregular surfaces. We have carried out experiments in the Webots simulator, showing that our approach does not overshoot while reaching a settling time of almost 0. These results outperform the Ziegler-Nichols PID controller.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"294 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Naive Fracterm Calculus 朴素分形微积分

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-09-28 DOI: 10.3897/jucs.87563

Jan Bergstra, John V. Tucker

引用次数: 0

Perceptual Learning Modules (PLM) in CS1: a Negative Result and a Methodological Warning 知觉学习模块(PLM)在CS1中的负面结果和方法论警告

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-09-28 DOI: 10.3897/jucs.96347

Ricardo Caceffo, Jacques Wainer, Guilherme Gama, Islene Garcia, Rodolfo Azevedo

{"title":"Perceptual Learning Modules (PLM) in CS1: a Negative Result and a Methodological Warning","authors":"Ricardo Caceffo, Jacques Wainer, Guilherme Gama, Islene Garcia, Rodolfo Azevedo","doi":"10.3897/jucs.96347","DOIUrl":"https://doi.org/10.3897/jucs.96347","url":null,"abstract":"Perceptual Learning Modules (PLMs) is a variation of Perceptual Learning based on multiple-choice questionnaires. There exists successful research of the use of PLMs in math and flight training. The possibility of designing and adopting PLMs in Introductory Programming Courses (CS1) is still an open area of study. The goal of this study is to test whether students that received a PLM training on recognising segments of programs will perform better at writing programs. Two PLM interventions were administered to students. The first intervention was a nonrandom controlled experiment, in which students opted to answer the PLM questionnaire (N=40), while the control group consisted of students that did not answer it (N=629). The second intervention was a randomized controlled experiment with a placebo, in which students were randomly assigned to perform either the PLM questionnaire (N=51) or another a placebo activity (N=51). The different forms of analysis of the first experiment results yielded Cohen&rsquo;s d ranging from 0.23 to 0.34 in favor of the PLM intervention. For the second experiment, the effect size was d = -0.11 against the PLM intervention, but the two results were significant. We believe that the cautious conclusion is that there is a null effect in using a PLM activity as part of a CS1 course. The paper is also of interest because of the methodological decisions and techniques used.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PlantKViT: A Combination Model of Vision Transformer and KNN for Forest Plants Classification PlantKViT:一种用于森林植物分类的视觉变压器和KNN组合模型

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-09-28 DOI: 10.3897/jucs.94657

Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, Pham Thi Kim Thoa

{"title":"PlantKViT: A Combination Model of Vision Transformer and KNN for Forest Plants Classification","authors":"Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, Pham Thi Kim Thoa","doi":"10.3897/jucs.94657","DOIUrl":"https://doi.org/10.3897/jucs.94657","url":null,"abstract":"The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called &lsquo;plant id&rsquo; and &lsquo;Danangplant&rsquo; on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-case learning analytics: Feasibility of a human-centered analytics approach to support doctoral education 个案学习分析:以人为中心的分析方法支持博士教育的可行性

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-09-28 DOI: 10.3897/jucs.94067

Luis P. Prieto, Gerti Pishtari, Yannis Dimitriadis, María Jesús Rodríguez-Triana, Tobias Ley, Paula Odriozola-González

{"title":"Single-case learning analytics: Feasibility of a human-centered analytics approach to support doctoral education","authors":"Luis P. Prieto, Gerti Pishtari, Yannis Dimitriadis, María Jesús Rodríguez-Triana, Tobias Ley, Paula Odriozola-González","doi":"10.3897/jucs.94067","DOIUrl":"https://doi.org/10.3897/jucs.94067","url":null,"abstract":"Recent advances in machine learning and natural language processing have the potential to transform human activity in many domains. The field of learning analytics has applied these techniques successfully to many areas of education but has not been able to permeate others, such as doctoral education. Indeed, doctoral education remains an under-researched area with widespread problems (high dropout rates, low mental well-being) and lacks technological support beyond very specialized tasks. The inherent uniqueness of the doctoral journey may help explain the lack of generalized solutions (technological or otherwise) to these challenges. We propose a novel approach to apply the aforementioned advances in computation to support doctoral education. Single-case learning analytics defines a process in which doctoral students, researchers, and computational elements collaborate to extract insights about a single (doctoral) learner's experience and learning process. The feasibility and added value of this approach are demonstrated using an authentic dataset collected by nine doctoral students over a period of at least two months. The insights from this exploratory proof-of-concept serve to spark a research agenda for future technological support of doctoral education, which is aligned with recent calls for more human-centred approaches to designing and implementing learning analytics technologies.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Fall Detection by Enhanced SVM with Fuzzy Logic Membership Function 基于模糊隶属函数的增强支持向量机智能跌倒检测

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-09-28 DOI: 10.3897/jucs.91399

Mohammad Kchouri, Norharyati Harum, Hussein Hazimeh, Ali Obeid

{"title":"Smart Fall Detection by Enhanced SVM with Fuzzy Logic Membership Function","authors":"Mohammad Kchouri, Norharyati Harum, Hussein Hazimeh, Ali Obeid","doi":"10.3897/jucs.91399","DOIUrl":"https://doi.org/10.3897/jucs.91399","url":null,"abstract":"Falling is a critical issue for disabled people, and it leads to potentially serious injuries and death. Smart fall detection is a technology that depends on sensors and auxiliary devices that seek to improve the quality of life and enhance the lifestyle of disabled people. So far, the most widely used fall prediction methods collect data from inertial measurement unit (IMU) sensors. In addition, they use thresholds to identify falls based on artificial experiences or machine learning (ML) algorithms. Nonetheless, these approaches still require extensive classification and calibration. In this paper, we suggest a new technique to detect falls by combining Fuzzy Logic (FL) and Support Vector Machine (SVM). The FL model is built by using a fuzzy membership function along with the input dataset to obtain the intermediate output. Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. Besides, the hyperplane of the SVM is used as the separating plane to replace the traditional threshold method for detecting falling Activities of Daily Living (ADLs) on a comprehensive dataset containing simulated falling ADLs, non-falling ADLs, and scripted ADLs, including falling ADLs and unscripted ADLs performed by volunteers with our designed device. The results show that no false-positive rate had been triggered, and 100% specificity was achieved for ADL. An overall accuracy of about 99.87% in detecting the fall function was obtained. Furthermore, the overall sensitivity of 100% with no false negative rate obtained was achieved by implementing the proposed method. The attained results validate that our introduced method can effectively learn from features extracted from a multiphase fall model.&nbsp;","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Supervised Semantic Segmentation for Identification of Irrelevant Objects in a Waste Recycling Plant 基于半监督语义分割的垃圾回收厂无关目标识别

4区计算机科学

Journal of Universal Computer Science Pub Date : 2023-05-28 DOI: 10.3897/jucs.87643

César Domínguez, Jónathan Heras, Eloy Mata, Vico Pascual, Lucas Fernández-Cedrón, Marcos Martínez-Lanchares, Jon Pellejero-Espinosa, Antonio Rubio-Loscertales, Carlos Tarragona-Pérez

{"title":"Semi-Supervised Semantic Segmentation for Identification of Irrelevant Objects in a Waste Recycling Plant","authors":"César Domínguez, Jónathan Heras, Eloy Mata, Vico Pascual, Lucas Fernández-Cedrón, Marcos Martínez-Lanchares, Jon Pellejero-Espinosa, Antonio Rubio-Loscertales, Carlos Tarragona-Pérez","doi":"10.3897/jucs.87643","DOIUrl":"https://doi.org/10.3897/jucs.87643","url":null,"abstract":"In waste recycling plants, measuring the waste volume and weight at the beginning of the treatment process is key for a better management of resources. This task can be conducted by using orthophoto images, but it is necessary to remove from those images the objects, such as containers or trucks, that are not involved in the measurement process. This work proposes the application of deep learning for the semantic segmentation of those irrelevant objects. Several deep architectures are trained and compared, while three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) are proposed to take advantage of non-annotated images. In these experiments, the U-net++ architecture with an EfficientNetB3 backbone, trained with the set of labelled images, achieves the best overall multi Dice score of 91.23%. The application of semi-supervised learning methods further boosts the segmentation accuracy in a range between 1.31% and 2.59%, on average.","PeriodicalId":54757,"journal":{"name":"Journal of Universal Computer Science","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135831710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0