Reviewerly/exHarmony huggingface dataset:
Application of Works Visual Basic in Room Café

Visual Basic is a practical course that studies composition and is a compulsory subject for students of graphic design courses at Widyatama University. In studying composition, of course there must be an object to be composed, namely design elements, there must also be a method or method to compose it, namely design principles. Once it can be composed it will produce a contrasting visual perception or harmony. The underlying theory of visual perception is Gestalt theory. Usually in the curriculum of basic course subjects are studied in 2 semesters namely in semester 1 learn the technical ways of composing and in semester 2 learn exploration in shapes and colors. However, in the graphic design study program at Widyatama University, which has vocational education pathways, the second semester basic course has been directed to application or applied, that is, each of the results of his work or work must be applied to real media, one of which is applied to the cafe room. In the application process, the basic visual works are affixed to one of the cafes' wall spaces. Of course, the work must match the character of the room and in the end the result must add to the aesthetic and comfortable atmosphere. In the teaching and learning process in the basic course, an experimental approach is used in processing the composition, of course there is a continuous development process in the process of creating assignments. The purpose of this research is to find a learning method or model that can be applied to students in general as a tutorial.
DEVELOPMENT OF ELECTRONIC LEARNING SYSTEMS FOR SPECIAL NEEDS CHILDREN (AUTISM) AT ELEMENTARY SCHOOL LEVEL IN EFFORTS TO INCREASE COGNITIVE INTELLIGENCE

In an effort to increase cognitive intelligence in children with special needs at the elementary school level, many factors can influence one of the proper learning model specifically for children with autism. In the learning process in the classroom, most of them still use the lecture method, therefore in the learning process students tend to be less interested in the learning process This study aims to develop a learning system using e-learning for children with special needs at the elementary school level using the multimedia to increase cognitive abilities. The research of Learning Management Moodle System is one of the solution that can solve learning problems for students with special needs. Data collection methods used in this study are: observation, interviews, literature study, literature study, questionnaires, and systems development methods. The steps for  systems development method are analyzing data requirements, analyzing process requirements, analyzing software and hardware requirements, analyzing user needs, designing business models, systems designing, systems implementing and system testing. With the existence of an e-learning learning system for children with special needs, it can overcome learning deficiencies in class and students can carry out remote learning anywhere, anytime and repeatedly in a more interactive and fun way, so as to increase student understanding.
Optimal valve slide length for brass instruments

One of the first patents for compensating valve brass instruments by US Letters Patent #457,337 dated 11 August 1891 of Fontaine Besson is studied and optimized to obtain a much better in tune compensating instrument. The variance of intonation for the Besson instrument is 18.86%. An optimized four valve brass instruments is devised that lowers the variance to 4.22%. However, the first valve is flat by 7.39% on both sides of the instrument. Because intonation errors of ±5% are tolerable a spring loaded trigger must be provided to shorten the first valve when used alone. An EXCEL spreadsheet is presented analyzing many different three, four and five valve compensating and noncompensating instruments. Of particular interest is a five valve compensating instrument having a variance of 2.37% and standard deviation of 2.48%. The fifth valve lowers the instrument by 6 semitones. Instead of the fifth valve aforementioned a fifth valve of 0.237 of the length of the fourth valve could be used in combination with the fourth valve making the horn lighter. An Excel spread sheet is presented for the calculations relating to many ordinary and compensating three, four and five valve instruments.
Usability Testing Analysis of Web Portal Online Lecture XYZ University Bandung Indonesia

The XYZ University Online Lecture web portal is an important part of supporting the successof a university. This study aims to examine whether the XYZ University online lecture webportal is in accordance with the acceptability criteria of usability testing. A high level ofusability is closely related to the popularity and high utilization of the system by users to helpthem. The method used is usability testing using the Likert scale measurement of 20questionnaire questions which are grouped into five usability variables, namely Learnability,Efficiency, Memorability, Error and Satisfication. The number of research respondents was84 students who used web portals from various study programs. This study uses a SystemUsability Scale (SUS) questionnaire to measure user satisfaction. The results showed that theXYZ University Online Course web portal was generally good, easy to learn, quite efficient,enough to satisfy users with not many errors. This can be seen from the Learnability value of66%, Efficiency 65%, Memorability 64.8%, Error 61.6% and Satisfication 69.8%. Theadvanced group has a value for the four usability components apart from error, which is upto 8 percent higher than the beginner group.
Telemonitoramento de idosos durante a pandemia COVID-19 realizado por estudantes de Terapia Ocupacional: relato de experiência didático-assistencial

The pandemic due to Covid-19 brought changes in the daily life of the elderly population, mainly starting from the adhesion of social distancing as a prevention strategy. In this context it presented this experience report regarding telemonitoring carried out by Occupational Therapy interns to elderly people who, previously to the context of a pandemic, participated in a group within the scope of health promotion .
Identifikasi Tingkat Kematangan Buah Strawberry Berdasarkan Warna RGB dengan Menggunakan Metode Regionprops

The traditional fruit identification process experiences many obstacles due to human nature which has weaknesses that make the desired results ineffective. Advances in computer technology have touched the world of agriculture from a pre-harvest and post-harvest perspective. Here the problem arises how to recognize the fruit so that it fits the real conditions. The condition of the strawberry fruit is determined by the level of maturity seen from the color side of the strawberry. Farmers usually classify strawberries into ripe, medium ripe and raw categories. Image processing method has the ability to analyze maturity conditions using Red, Green, Blue (RGB) values ​​as a reference. Determination of classification using the Regionprops method which uses the value on the matrix label as a reference. The results in this study amounted to 60%. So it can be concluded that the Regionprops method can be used for the identification process of fruit.
Improved NSGA-II for the minimum constraint removal problem

This paper proposes a comprehensive multi-objective evaluation model to derive a feasible solution to MCR path planning that is from the robot's individual perspective, is driven by the costs and benefits, and takes into account factors such as the minimum constraint set, the route length, and the cost. The feasible solution to the MCR path is evaluated using this model. A typical multi-objective algorithm NSGA-II is applied to the MCR problem. The algorithm test and real scenario test results show that compared with single objective algorithm planning, the NSGA-II-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform the MCR path planning more effectively.
A persistent incremental learning approach for object classification of unseen categories using convolutional neural networks on mobile robots

Neural Networks and especially Convolutional Neural Networks (CNN) show remarkable results in many fields, among others in object classification and recognition. But these networks are limited by the tasks they are trained on, as they are designed to learn all tasks they will need during their lifetime in the beginning and hence are frozen. If now new tasks arrive, the network has to be trained completely new. These networks are therefore usually not able to learn in a continual manner, like humans are capable of. In this work, a novel approach is presented, where a deep neural network is used to continually learn new unseen object categories on images, which can be used in different fields, like mobile robots. First, different architectural strategies are proposed to dynamically adapt the network according to the categories it learns over time. This includes one strategy, where the last layer of our network is adapted and another one where multiple fully-connected layers are created for each new sequence. In order to prevent forgetting, different regularization strategies are shown, including a novel loss function where the classification is replaced by a regression. So, it is ensured that already learned categories are not forgotten by simultaneously enabling the network to learn new categories. Furthermore, the emerging problem of a discrepancy in the output distribution is recognized and different solutions are proposed. This includes a novel regularization strategy, where the outputs are divided by the variance per category. Finally, a novel dataset for continual learning is presented, which is especially suited for object recognition in our mobile robot environment (HOWS-CL-25). It consists of 150,795 synthetic images of 25 different household object categories in a randomly changing environment. Our approach can be classified as online learning, a special variant of incremental learning, where one is limited by the data the network can observe in a specific time step, without the access to previous training examples - also called rehearsal-free. This is a challenging and unsolved problem in comparison to other incremental learning approaches, which also use previous training examples, but as this thesis is focusing on an approach for mobile robots, online learning is more relevant. Our approach is tested on different datasets and compared with other solutions from literature. Additionally, our method was evaluated in the CLVISION workshop at CVPR 2020.
Fuzzy Logic-Based Solar Generation Tracking

Precise solar generation forecasting is dependent on parameters like temperature, radiation, humidity, and the time of day conditions. These conditions when combined play a role in forecasting the output of solar energy system. In this paper, considering the above-mentioned four variables, a simple and accurate method for prediction of solar power generation with the help of fuzzy logic model based on the Mamdani method has been proposed. Simultaneous correlation of the weather-dependent variables has been evaluated, and solar power generation is forecasted at a particular location in Delhi using the proposed methodology. A solar data logger system has been used to log the ambient weather conditions along with the instantaneous solar power output. The fuzzy system is then trained on the logged values with the comparative analysis of the forecasted power versus the real-time data. The fuzzy model has also been trained on the National Renewable Energy Laboratory dataset for further accuracy.
ENHANCING IMBALANCED MULTI CLASS CLASSIFICATION OF DEEP FUZZY C-MEANS CLUSTERING USING OAA-DB ALGORITHM

In this paper enhancing the imbalanced multi class classification of deep fuzzy C-Meansclustering using OAA-DB algorithm. The most important issue in the classification process is imbalance classlearning. In present years, all the researchers have been focused on the multi class classification techniques.Now, the problem of imbalanced class distribution is overcome by using One-Against-All with Data Balancing(OAA-DB) algorithm. This algorithm will enhance the performance of classification in multi class imbalanceddata. Synthetic Minority Over-sampling Technique (SMOTE) is used to resample the imbalance data. In theproposed system, three multi class imbalanced data sets are used to enhance the performance of classification.Hence this technique will improve the accuracy in effective way.
아이트래커를 활용한 읽기 능력 진단 지표 개발

Reading ability is a fundamental and essential ability to lead everyday life. There have been attempts to diagnose and effectively guide learners in their reading ability. However, this was mainly centered on indirect and multiple-choice assessments. Therefore, this study focused on the eye tracker as a tool to supplement the limitations of indirect assessment and the shortcomings of the multiple choice question, and developed a reading ability diagnostic assessment using the eye tracker. For the reading ability diagnostic assessment using eye tracker, a reading ability factors was extracted and developed for each grade level. The assessment questions were revised and statistically verified through multiple test. In addition, based on reading ability, the students were sampled and applied reading assessment using eye tracker. These results were used to derive a reading ability diagnosis indicators. Reading diagnostic instrument that uses eye tracker is able to diagnose reading process. This provides an effective guidance by providing more accurate feedback to improve reading ability.
Prognostics Health Management (PHM) System for Power Transformer Using Kernel Extreme Learning Machine (K-ELM)

A power transformer is one of the most important and valuable components for the power system network. This device is critical to ensure power quality and reliable electricity supply for consumers. When the power transformer could not work properly or out of service in unforeseen ways, it provides a severe impact on power system utilities and customers in term of the expensive of transformer's replacement cost and revenue lost caused by the electrical blackout. To overcome these issues, the proper prognostics health management (PHM) system as a tool for condition monitoring and health assessment of these valuable assets is required. This paper proposed a PHM system based on a kernel extreme learning machine (K-ELM) for power transformer's health assessment. Two sets of variable combinations called Set-1 and Set-2 were considered to examine the robustness and efficacy of the proposed method. In Set-1, the input variables were water content, total acidity, breakdown voltage, dissipation factor, dissolved combustible gases, and 2-furfuraldehyde. While the output of PHM system was the health condition which categorized as good, moderate, and bad circumstances. Set-2 utilized water content, total acidity, breakdown voltage, dissipation factor, and interfacial tension as input variables. Whereas, the PHM system outputs consisted of four categories: normal, good, moderate, and bad. The proposed method with two sets of variables had showed the satisfactory results for transformer's health condition assessment compared to an extreme learning machine (ELM), support vector machine (SVM), and least-square support vector machine (LS-SVM) in terms of learning and testing accuracies and computation time. The proposed PHM system using the Set-1 dataset could assess the transformer health as of 100% while in terms of the testing process, the proposed PHM system has an excellent accuracy result as of 68.67%. Furthermore, the proposed PHM system using the Set-2 dataset had successfully assessed the transformer health as of 100%. In the testing phase, the proposed PHM system model has a rigorous result for its accuracy result as of 93.61%.
Semi Supervised Image Segmentation Based on Markov Random Field and Kernel K-means

Abstract Semi supervised image segmentation uses a small amount of supervised information to improve the performance of image segmentation. In the first part of this paper, we construct a submodular regularization term based on Markov random field (MRF). The regularization term is combined with kernel k-means (KKM), and a semi supervised kernel k-means algorithm (SKKM) is designed based on graph cut technique. In the second part of this paper, we combine SKKM with smooth regularizer to get MRF &amp; SKKM. The experimental results show that, compared with the original SKKM, the segmentation results of MRF &amp; SKKM achieve better results in common evaluation indicators; moreover, compared with the binary image of segmentation results, MRF &amp; SKKM has higher and smoother edge fiting.
ACM SIGMETRICS International Workshop on Distributed Cloud Computing (DCC)

The International Workshop on Distributed Cloud Computing (DCC) is an interdisciplinary forum on distributed systems, algorithms as well as networking and cloud computing. DCC 2020 was co-located with SIGMETRICS 2020, in the week of June 8-12, 2020. The selected topic of DCC 2020 was Machine Learning in distributed environments.
Tibetan-Chinese Machine Translation Technology Combining Linguistic Grammar and Formal Grammar

Open and closed experiments were carried out on the corpus respectively. Machine translation (MT) model based on syntactic information is better than that based on phrase model, which proves that adding Tibetan short syntactic analysis to Tibetan-Chinese MT application system is helpful to improve the performance of Tibetan-Chinese MT system(TCMT). What’s more, it also proves that the ITG is suitable for TCMT and can tackle the problem of inconsistent the order of the words between two languages in machine translation, which improves the performance of the overall TCMT system
Large-Scale Educational Assessment against Classroom Assessment: Pedagogy and Measurement Paradigm in EFL Classroom

While the assessment principles focus on the large-scale educational assessment, the classroom assessment remains underexplored in teaching, learning and assessment paradigm.Classroom assessment with all its individual features is characterized by the unique context that it accompanies.However, because of the differences it is not easy to apply the assessment principles borrowed from the large-scale assessment on classroom assessment (Fulcher & Davidson, 2007).Taylor and Nelon (1996:17) described this as 'misfit of the measurement paradigm'.This essay will note these points of challenges before conducting a literature review that explores how classroom assessment can be utilized as both an assessment and learning medium against the backdrop of largescale assessment principles.The discussion will then be focused on EFL classroom in Saudi Arabia to have a contextual understanding of the topic.In the later part of this essay, we will construct a list of possible activities and exercises based on our new knowledge and findings.
PRACTICAL VOCATIONAL AND EDUCATIONAL TRAINING OF FUTURE TEACHERS OF VOCATIONAL SCHOOLS IN GERMANY: FEATURES OF THE DUAL MODEL

A unique tool that helps to balance the imbalance between the growing needs of young people, the demands of the labor market and educational opportunities in the countries of the European Union is a dual model of vocational education, which has found its application in the field of higher education in Germany. This fact actualizes the need to study the practice of dual training in teacher education in this country. The article describes the structure and content of practical training of future vocational school teachers in Germany, identifies and analyzes the peculiarities of applying a dual approach to its organization. It has been found out that gaining the professional experience, forming necessary practical skills and competences is carried out in the process of industrial and school practices, pedagogical internships. An empirical foundation for higher education is formed during the industrial practice. The school practice is considered to be a form of gaining skills, not a field of activity. The pedagogical internship organized on the principle of duality of places of study helps to form the pedagogical activity competence of a beginner teacher. The unity and interpenetration of theoretical and practical pedagogical training are fully realized during the pedagogical internship. These components of practical vocational training make it possible to combine and arrange theoretical and practical components of studying, to synthesize scientific knowledge and practical skills, to form the own style of professional activity. The prospects of using the positive experience of applying the dual model of practical vocational training of teachers at vocational schools in Germany in the similar practice of pedagogical education in Ukraine are identified in the article: initiation of previous practical activity in manufacturing of a chosen profession as a necessary background for admission into the higher education institution; introduction of pedagogical internship; cooperation of higher education institutions with enterprises, institutions and organizations. Keywords: vocational school teacher, practical vocational training, industrial practice, school practice, pedagogical internship, duality, Germany.
Face Recognition Application Using the Eigenface Method for Employee Attendance at STMIK Budi Darma

Attendance is one of the most important repetitive transactions because it is related to the productivity of employees and employees and is one of the controlling indicators Human resources (HR) which aim to increase the potential of human resources as well used in the framework of efficiency. Current technological developments allow making a system that can assist humans in recognizing a digital image. One of them, a field that is currently being developed is pattern recognition. This technology identifies the special physical characteristics of a person. An example of pattern recognition is an example of face recognition, iris recognition, fingerprint recognition (finger recognition), and others. The face is part of the human body to be the focus of attention in social interactions. The face has a very important role that can show someone's identity; therefore a face can be used as a part of the human body that is used as an indication of knowing someone or face recognition. Attendance is an administrative act regarding attendance and absence attendance of employees. In this facial recognition, research uses a camera to capturing someone's face was then compared to the face that had previously been stored in a specific database. Broadly speaking, the process of facial recognition is the webcam camera captures the face. Then we get an R (read) value, G (Green), B (Blue). Using pre-processing, cropped, RGB to grayscale. After the Grayscale process is carried out, the face processing stage is carried out using the eigenface method. In this eigenface method, there are several core stages namely: converting faces to matrices, calculating Flat Vector averages, determining values eigenface, and perform the face identification process by looking for the eigenface value approaching. One of the ways of facial recognition can be developed into an application absence that can be applied in companies to prevent manipulation of absences by employees or employees.