Reviewerly/exHarmony huggingface dataset:
Designing a Real-Time-Based Optical Character Recognition to Detect ID Cards

This research 0aims to design a Real-time ID card detection based on Optical Character Recognition (OCR). OCR detects and records information into CSV files using a camera. Hopefully, it can become one of the administrative solutions in Indonesia by using existing identity cards using OCR in real time. This research method was carried out independently in August 2021 using ID cards as objects. The tool involved was a 320x320 pixel webcam camera on an HP Intel Core i5 7th Gen notebook. The software used by Easy OCR was Pytorch-based. ID cards were detected using an algorithm by TensorFlow object detection with SSD MobileNet V2 FPNLite 320x320 as the pre-trained model of Tensorflow. The researchers collected ID card images using a webcam with various light conditions and orientations and label them using labeling. The researchers trained it with only 20 photos. After 3000 training steps, the researchers obtained about 0.17 loss and 0.95. Thus, the ID card detection tool using OCR runs well.
UUV 3D Path Planning Based on PSO-ACO Fusion Algorithm

In order to solve the problems of difficult convergence and local optimal solution of ant colony optimization (ACO) algorithm, and low convergence accuracy of particle swarm optimization (PSO) algorithm, a particle swarm optimization ant colony optimization (PSO-ACO) fusion algorithm is proposed to deal with the three-dimensional (3D) path planning problem of unmanned underwater vehicle (UUV). In this algorithm: based on the idea of spatial stratification, a 3D grid model is established to build underwater environment model; PSO algorithm is used to pre search the path and quickly obtain the solution, which is used as the initial pheromone increment of ACO algorithm; the pheromone global updating method of ACO algorithm is improved: an adjusting factor is added to the pheromone global update equation to accelerate the convergence speed of ACO algorithm; the state transition equation of ACO algorithm is also improved, so that the algorithm has a greater probability to select the point with the largest weighted product of pheromone and heuristic information as the next path point. Experimental results show that the fusion algorithm effectively improves the global search ability and shortens the search time.
Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation

Image segmentation is one of the major challenges in real-world computer vision applications. Context-embedded network models proposed for image segmentation have outperformed context-free models. However, optimized values of many parameters need to consider for such a complex network. The manual parameter selection process is ineffective and produces suboptimal performance for such a model. Therefore, we propose a context-based genetically optimized network model for image segmentation in this paper. Genetic algorithms enhance the performance of the deep network model by determining the best parameter values. The proposed three-level deep network is adaptable to image context by extracting visual and context-rich features and optimally integrating them to obtain final pixel labels for scene images. The genetic algorithm ensures optimal parameter values in all three levels to obtain a globally optimized network model to achieve the best segmentation results.
Pengembangan Mobile Learning Berbasis Android Sebagai Media Pembelajaran Program Aplikasi MYOB Accounting

The purpose of this research is to develop student learning media by applying Android-based mobile learning to computer accounting subjects with the help of the MYOB application program on trading company materials at SMKN 6 Surabaya. The results of the analysis used are the results of the feasibility test of learning media, and are guided by the results of the assessment of learning media taken from media experts and material experts as well as student responses. This study uses a 4D model from Thiagarajan, but in this study it is limited to only three stages, namely Define, Design and Develop. The type of method chosen in this research is quantitative. In collecting data through media validation sheets, material expert validation sheets and student response questionnaires. This media experiment was conducted on 20 students of class XI Accounting and Finance at SMKN 6 Surabaya. Launching the research results, it can be stated that this android-based learning media is very feasible with the overall results having an average component value of 88.2%. This figure includes the feasibility of content, presentation, and media. The results of student responses indicate that this learning media is very feasible. The results of the students' responses show that this learning media is very good. According to the results of the feasibility of this product, it can be concluded that android-based learning media is very feasible to be used as a learning medium
Development of Educational Game-Based Learning Media in Natural Science Subjects at Madrasah Ibtidaiyah Jayapura

This research aimed to address the issue of educational technology in madrasah ibtidaiyah by producing an educational game-based learning media in the field of natural science in MI in Jayapura, Papua. The development method used in this research was the modified 4D Thiagarajan model. This research consisted of three stages: Define, Design, and Develop. The educational game-based learning media, named GALAKSI Application, was evaluated by validators to determine the feasibility of the learning media to be used in MI. The instrument used was a checklist questionnaire. The data obtained was qualitative data that was converted into quantitative data and then tabulated and analyzed descriptively to determine the quality of the developed product. The research results show that the educational game-based learning media GALAKSI for natural science subjects has been developed based on the assessment of validators. The assessment results show that the aspect of display quality obtained a percentage of 85%, the aspect of software engineering obtained a percentage of 81%, the aspect of curriculum obtained a percentage of 82%, the aspect of the material presentation obtained a percentage of 95%, the aspect of implementation obtained a percentage of 97%, the aspect of evaluation obtained a percentage of 93%, and the aspect of language obtained a percentage of 92%. Based on these data, it is classified as very good (SB). The results show that the GALAKSI Application educational game-based learning media can be used for self-learning by students.
Sistem Pakar Pendeteksi Penyakit Gigi Dan Mulut Dengan Menggunakan Metode Forward Chaining

Diseases of the teeth and mouth are one of the many health problems that often interfere with the activities of sufferers. Diseases attack indiscriminately Reed both children and adults. Dental and oral health is an important thing that needs to be considered. Problems that occur in the teeth and mouth must be handled properly so as not to cause other problems that occur due to improper handling. Doctors are required to provide decisions quickly, and accurately in diagnosing diseases suffered by their patients. But to make such a diagnosis is not easy, considering that each disease has more than one symptom, and several different diseases have almost the same symptoms. Decision - making in diagnosing the disease carefully and appropriately must be done quickly for the treatment process of the disease. Especially for young doctors and all doctors if they find cases that are difficult to diagnose, they will generally be guided by books. This will certainly take time and effort. Treatment for this problem is to build an expert system application that functions to diagnose diseases of the teeth and mouth. This expert system application is expected to help doctors in deciding to diagnose diseases quickly or become a matter of consideration or reference.
Residual balanced attention network for real-time traffic scene semantic segmentation

&lt;span lang="EN-US"&gt;Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.&lt;/span&gt;
Deep learning in phishing mitigation: a uniform resource locator-based predictive model

&lt;span lang="EN-US"&gt;To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses pre-processing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learning-based model of convolution neural network with bi-directional long short-term memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.&lt;/span&gt;
Convolutional neural network based key generation for security of data through encryption with advanced encryption standard

Machine learning techniques, especially deep learning, are playing an increasingly important role in our lives. Deep learning uses different models to extract information from the data. They have already had a huge impact in areas such as health (i.e., cancer diagnosis), self-driving cars, speech recognition, and data encryption. Recently, deep learning models, including convolutional neural networks (CNN), have been proven to be more effective in the security field. Moreover, the National Institute of Standards and Technology (NIST) recommends the advanced encryption standard (AES) algorithm as the most often utilized encryption method in several security applications. In this paper, a crypt-intelligent system (CIS) capable of securing data is proposed. It is based on the combination of the performance of CNN with the AES, by substituting the key expansion unit of AES with a CNN architecture that performs the key generation. Our CIS is described using very high-speed integrated circuit (VHSIC) hardware description language (VHDL), simulated by ModelSim, synthesized, and implemented with Xilinx ISE 14.7. Finally, the Airtex-7 series XC7A100T device has achieved an encryption throughput of 965.88 Mbps. In addition, the CIS offers a high degree of flexibility and is supported by reconfigurability, based on the experimental results, if sufficient resources are available, the architecture can provide performance that can satisfy cryptographic applications.
Optimal interdigitated electrode sensor design for biosensors using multi-objective particle-swarm optimization

&lt;p&gt;Interdigitated electrodes (IDEs) are commonly employed in biological cellular characterization techniques such as electrical cell-substrate impedance sensing (ECIS). Because of its simple production technique and low cost, interdigitated electrode sensor design is critical for practical impedance spectroscopy in the medical and pharmaceutical domains. The equivalent circuit of an IDE was modeled in this paper, it consisted of three primary components: double layer capacitance, C&lt;sub&gt;dl&lt;/sub&gt;, solution capacitance, C&lt;sub&gt;Sol&lt;/sub&gt;, and solution resistance, R&lt;sub&gt;Sol&lt;/sub&gt;. One of the challenging optimization challenges is the geometric optimization of the interdigital electrode structure of a sensor. We employ metaheuristic techniques to identify the best answer to problems of this kind. multi-objective optimization of the IDE using multi-objective particle swarm optimization (MOPSO) was achieved to maximize the sensitivity of the electrode and minimize the Cut-off frequency. The optimal geometrical parameters determined during optimization are used to build the electrical equivalent circuit. The amplitude and phase of the impedance versus frequency analysis were calculated using EC-LAB® software, and the corresponding conductivity was determined.&lt;/p&gt;
Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling

&lt;span lang="EN-US"&gt;The investigation of a deep neural network for pedestrian classification using transfer learning methods is proposed in this study. The development of deep convolutional neural networks has significantly improved the autonomous driver assistance system for pedestrian classification. However, the presence of partially occluded parts and the appearance variation under complex scenes are still robust to challenge in the pedestrian detection system. To address this problem, we proposed six transfer learning models: end-to-end convolutional neural network (CNN) model, scratch-trained residual network (ResNet50) model, and four transfer learning models: visual geometry group 16 (VGG16), GoogLeNet (InceptionV3), ResNet50, and MobileNet. The performance of the pedestrian classification was evaluated using four publicly datasets: &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;Institut National de Recherche en Sciences et Technologies du Numérique&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; (INRIA), Prince of Songkla University (PSU), CVC05, and Walailak University (WU) datasets. The experimental results show that six transfer learning models achieve classification accuracy of 65.2% (end-to-end CNN), 92.92% (scratch-trained ResNet50), 97.15% (pre-trained VGG16), 94.39% (pre-trained InceptionV3), 90.43% (pre-trained ResNet50), and 98.69% (pre-trained MobileNet) using data from Southern Thailand (PSU dataset). Further analysis reveals that the deeper the ConvNet architecture, the more specific information of features is provided. In addition, the deep ConvNet architecture can distinguish pedestrian occluded patterns while being trained with partially occluded parts of data samples.&lt;/span&gt;
El aprendizaje basado en juegos como revulsivo didáctico para mejorar la competencia comunicativa en el aula de lengua extranjera: una revisión de la literatura

The search for the ideal teaching paradigm has, since ancient times, been one of the great challenges facing didactics in any of its areas. However, game-based learning has not enjoyed the necessary focus considering its enormous potential. This article offers a review of the literature on this technique with the aim of addressing, from a global perspective, both, its dimension and its pedagogical scope. In it, we will analyze its nature, its elements and we will detail the time needed for its correct application. Likewise, we will summarize the guidelines to optimize its use, providing, in addition, different tools for developing teaching resources that can be used in the foreign language classroom.
Nonlinear Fuzzy Control of Position and Velocity Using Crisp Outputs Implemented in a General-Purpose Microprocessor

A line follower robot is a classic prototype in automatic control learning and quite applicable in the industry for example in cargo transport, the common control strategy in this type of device is a PD linear controller since it produces a fairly stable response and also for its ease of tuning. The current work presents the design, analysis and implementation of a nonlinear fuzzy control strategy, which generates several operational benefits compared to PD control. For the analysis, the responses of the two types of controllers are compared through simulation algorithms and experimental tests, where parameters such as the response time or the effort in the system actuators are evaluated. In general, the fuzzy controller non-linearity generates some confidence when choosing a control tool for this type of device. Finally, a speed control strategy easily adaptable to the fuzzy position controller is presented in such a way that the travel time is further optimized.
A Task-Resource Joint Optimization Model in Distributed Computing

Distributed computing decomposes the user tasks into many small tasks and distributes them to multiple servers for processing to save the overall computing time. There is an important challenge that how to efficiently divide the tasks to achieve the goal. In this paper, aiming at solving the complex relationship between multiple tasks and multiple servers, we propose a resource-task joint optimization model in a distributed computing environment. The optimization goal of this model is to minimize the total computation time, while considering the transmission time and execution time of task data. Based on the task size, intensity, and computing power of the server, we propose a task-resource joint optimization algorithm to solve the proposed model. Numerical simulation verified the feasibility of the model and the correctness of the algorithm.
FACTORS INFLUENCE THE ACQUISITION OF VOCABULARY BY YOUNG LEARNERS

This research focuses on the factors that influence vocabulary acquisition by young learners. This study analyses how internal and external factors influence vocabulary or language acquisition. A person's abilities are influenced by various factors that exist in individuals and the environment, as well as vocabulary abilities. In learning and having adequate vocabulary skills, it is also influenced by several factors that must be considered so that these vocabulary abilities also help students' language and speech development. The data in this study were ten students at Horray School. The data collection technique for this research used observation and questionnaire methods. In this case, observations were made for three weeks, and then a post-test was given to students at the last meeting every week. There is also a questionnaire to be distributed and filled in by parents of students to obtain information on the factors influencing students in mastering English or vocabulary. This study's results indicate several factors that influence students' vocabulary mastery. There are three internal factors: age, experience, and learning style. The three external factors are motivation, instruction, and access to native speakers. These factors come from within the individual or internal and in the environment or external. The environment, in this case, also influences children's vocabulary skills because it is a place for children to adapt and develop their abilities, skills and understanding of various things, including vocabulary mastery.
Elementary School Teachers’ Perceptions of Indonesian Elementary School Textbooks: A Case Study

Teaching materials are one of the important tools in the learning process. But unfortunately, there are still many teachers who misunderstand about teaching materials, especially Indonesian. This research aimed to analyze the need for teaching materials based on the teacher’s perception in the context of communication, reasoning, and reading interest. This research used a mixed research method. Data were collected through a questionnaire form given to 211 teachers and interview results to 20 teachers. Then the data were analyzed using a data triangulation approach. The results of this research indicated that the need for teaching materials for elementary school students to solve reading difficulties includes grammar, material, and an attractive appearance, making it easier for teachers to convey the material.
Problems of Competency Approach in Developing Students' Creativity Qualities for Creating a Database

Today, the training of personnel with high qualifications in information technologies is the need of the hour, it is very important to form not only skills but also professional competence, independent thinking, and decision-making skills. Many countries of the world, in their demands for their personnel, definitely require competencies in working with information and using information and communication technologies. The main purpose of writing this article was to study the problems of their creative approach to creating a database, educational methods based on the competence approach to developing their individual qualities, and the effective use of pedagogical technologies in the process of providing students with knowledge about databases in the higher education system. During the study, scientific research conducted by many eminent scientists of the world was studied. Based on observation, analysis, synthesis, and other methods, effective methods were selected for forming students' creative qualities in creating a database. As a result of the research, effective results were achieved through the Real project, mixed education methods. The obtained results and recommendations can be applied in the higher education system's database training process. The practical significance of the research results is that in the process of creating a database of future specialists, it will serve to form their professional competence, to achieve the qualification of creating a database based on an independent and creative approach.
The Uneasy Case for a Ransom Tax

Abstract The goal of our paper is to demonstrate the potential effects of a tax on paying a ransom on the incentives of stakeholders involved: both the perpetrators (the attackers placing the ransomware) as well as the potential victim. We do think that there is a case for a ransom tax, but we do also realise that it is not easy to make that case, and hence we express this doubt in our title. A tax could stimulate ex ante cybersecurity and also (when price elasticity is not too low) reduce ex post ransom payments. In addition, a tax in combination with a smartly designed subsidy could have benefits.
MANAJEMEN WEBSITE DI MA NEGERI 2 SURAKARTA SEBAGAI MEDIA INFORMASI DAN PROMOSI

&lt;table border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="369"&gt;&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This study aims to determine whether the MAN 2 Surakarta website is used as a medium of information and promotion. The method used in this research is qualitative research. This research was carried out in October 2022. Data collection was carried out by interviews and observations. How to examine it by observing the MAN 2 Surakarta website media and conducting direct interviews with relevant sources. Then look at several sources from the MAN 2 Surakarta website. The validity of the data in this study used source triangulation and technique triangulation. The sample used in this study is the MAN 2 Surakarta website. According to the research results, at MAN 2 Surakarta, it shows that the website used functions as an information and promotional medium. If schools ignore media websites, they will lose students. Schools need to use website marketing principles to attract parents of students to want to send their children to these institutions.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;