Reviewerly/exHarmony huggingface dataset:
Design of E-Commerce Learning Media with Blended Learning Method in Vocational Higher Education

E-Commerce has experienced very rapid development in Indonesia in recent years. Its practical, making shopping easier and even cheaper, makes people interested in connecting with the world of e-commerce. Given the importance of students' alacrity in dealing with the world of e-commerce, important material related to e-commerce is taught to students. Seeing from these problems, it is appropriate to build a learning media that can help students in their efforts to understand the e-commerce business. The learning media used are based on blended learning, where the learning process is carried out through two main elements, namely classroom learning (classroom lessons ) and learning with online learning. In this study, the first thing to do is to describe in full and detail any material that will be delivered to students in E-Commerce courses, referring to SAP and Silabi. The next step is to make observations in class and distribute questionnaires to students to map in detail which materials are easy, medium and which are difficult. From these results, an E-Commerce course learning flow was made which represents the learning model of blended learning which will then be implemented as a learning media
PENGARUH PENERAPAN STRATEGI PEMBELAJARAN INDEX CARD MATCH TERHADAP PRESTASI HASIL BELAJAR SISWA PADA MATA PELAJARAN AQIDAH AKHLAK

The lack of learning media used by teachers in delivering material. Besides that the learning methods used by teachers are still conventional or lecture. So we need one of the learning strategies that can be used, namely the Index Card Match learning strategy. This study aims to determine the effect of learning Index Match cards on the learning outcomes of fifth grade MI students. This type of research is quantitative with correlational research designs. Methods of collecting observational data, questionnaires and documentation. Data analysis using Product Moment Correlation. The results of this study are 1) The implementation of the Index Card Match learning strategy in the high category there are 15 respondents with a percentage of 60%, then in the medium category there are 10 respondents with a percentage of 40%, and those included in the low category there are no respondents with a percentage of 0%. 2) Student achievement in Aqidah Morals in the high category there are 7 students with a percentage of 28%, then in the medium category there are 13 students with a percentage of 52%, and those included in the low category there are 5 students with a percentage of 20%. 3) There is an effect of the application of the Index Card Match learning strategy to the learning achievement of Aqeedah Moral students in class V MI. This is based on the analysis results obtained r = 0.597 then consulted with r table at a significant level of 5% and N = 25 is 0.396 so that r count is greater than r table that is 0.597> 0.396 which means there is an influence. The level of influence is strong, because the analysis results obtained value of r_xy = 0.597 if consulted with Sugiono's interpretation is between 0.40-0.599, which means it is moderate.
Robustness Augmentation of Deep Learning Model Based on Pixel Change

Deep learning has been widely used in many fields.A large number of images can be quickly recognized by the deep learning models to provide information.How to improve the robustness of deep learning applications has become the focus of research.Unfortunately, the recognition ability of the existing deep learning model has been greatly threatened, many images can cause recognition errors in a well-trained model.Although data augmentation is an effective method, the existence of adversarial examples shows that traditional data augmentation methods have no obvious effect on minor pixel changes.After analyzing the impact of pixel changes on model recognition accuracy, a data augmentation method based on a small number of pixel changes is proposed.Our method can optimize the corresponding classification boundary and improve the recognition robustness of the model.Finally, a simple evaluation method to measure the robustness of model recognition is proposed.Our experiments prove the threat of a small number of pixels and the effectiveness of our data augmentation method.Moreover, the data augmentation method has strong generalization ability and can be applied to image recognition in many different fields.
IMPLEMENTASI ZONING DAN FITUR ARAH SEBAGAI EKSTRAKSI FITUR PADA PENGENALAN TULISAN TANGAN AKSARA BALI

Character recognition is one of the most researched fields in computer science. Combining the field of digital image processing and pattern recognition is a challenge in determining the most optimal method combination to complete character recognition. Balinese script is one of the regional scripts used in Balinese literary. The challenge with Balinese script is that some of its characters have a degree of similarity. So far, several methods of feature extraction that have been studied for Balinese script are modified direction feature, template matching, image centroid zone and zone centroid zone, local binary pattern. In this research, we combine methods based on zoning and directional features. The methods used are ICZ, ZCZ and freeman chain code to find the characteristics of Balinese script handwriting. The addition of chain code method aims to determine the value around the foreground point. The results of feature extraction will be used as input in the Support Vector Machine for the classification process. The test result shows that the combination of the ICZ, ZCZ and freeman chain code methods produces an accuracy of 89.09%, while the combination of ICZ and ZCZ produces 88.06% of accuracy. The SVM kernels compared use linear kernels.
Factor Screening using Bayesian Active Learning and Gaussian Process Meta-Modelling

In this paper we propose a data-efficient Bayesian active learning framework for factor screening, which is important when dealing with systems which are expensive to evaluate, such as combat simulations. We use Gaussian Process meta-modelling with the Automatic Relevance Determination covariance kernel, which measures the importance of each factor by the inverse of their associated length-scales in the kernel. This importance measures the degree of non-linearity in the simulation response with respect to the corresponding factor. We initially place a prior over the length-scale values, then use the estimated posterior to select the next datum to simulate which maximises the mutual entropy between the length-scales and the unknown simulation response. Our goal-driven Bayesian active learning strategy ensures that we are data-efficient in discovering the correct values of the length-scales compared to either a random-sampling or uncertainty-sampling based approach. We apply our method to an expensive combat simulation and demonstrate the superiority of our approach.
Simulating Hi-Lo count in MATLAB

Blackjack is a card game that usually played in casino where one or several players bet against a dealer. Each player and the dealer are given a pair of cards where both cards received by the player are facing up (known as the upcard) and the dealer has one card facing up and another facing down (known as the hole card). The rule is simple, the players must accumulate points to beat the dealer. The points of each card are the number that appears on the card, with cards like J, Q and K are counted 10 points. An A can be counted as 1 or 10 points at the player’s preference. Additional cards (maximum 5 cards) can be drawn be players and the dealer and both parties must not exceed 21 points (busting). This game can be played instinctively with luck but mathematicians had found a systematic way to increase the player’s advantage in Blackjack; which is known as card counting. There are many card counting techniques such as Hi-Low count, Hi-Opt II, Red 7, etc. In this study, we will determine whether the Hi-Low count technique will affect the winning probabilities of a player as well as the profit gained by a player in Blackjack. We simulate Blackjack for 10, 000 and 100, 000 trials in MATLAB 2018a using the Hi-Low count and compare the result against the trials without card counting. Furthermore, we will also vary the number of decks used in the trials as this to see how it affects the efficiency of card counting.
Copyright Protection of 3D Images Using Digital Watermarking

The Depth-Image-Based-Rendering (DIBR) method has been developed as an effective method to represent 3D images. In this paper, we describe a digital watermarking that has been developed to enforce copyright protection in DIBR 3D images. The algorithm is blind and robust and has been developed in such a way that watermarking is done by applying the rendering operation at the sender's side as well as at the receiver's side. This is in contrast to published algorithms where no use was made of the rendering process at the sender's side. The performance of the proposed algorithm was extensively evaluated with respect to robustness against Gaussian noise. The experimental results show that the algorithm achieves the preset goals of high robustness.
Optimization design method of human computer interaction mode for basic Japanese teaching software

Abstract In order to solve the problems of less teacher-student interaction and large amount of exercises in modern teaching process, the optimization design method of human-computer interaction mode of basic Japanese teaching software is optimized. Firstly, the overall structure and function of the basic Japanese teaching software are analyzed, including the parameter selection in the chain drive design; secondly, the basic Japanese teaching software is designed in detail, and the innovation of the software in the design process is analyzed; finally, the functions of the designed software are realized. The running example of the system shows that the designed man-machine interactive chain drive assistant teaching software can improve the man-machine interactive experience and is accepted by teachers and students.
Vitiligo Disease Diagnosis Expert System using The Web-Based Fuzzy Mamdani Method

Vitiligo is a disease that causes skin color to fade. Besides being able to attack any skin area on the body, this fading can also occur on the inside of the mouth, eyes, and hair. In this study, an expert system for diagnosing vitiligo has been built, in order to make it easier for patients to find out the level of vitiligo that is being suffered. The problem of this study is how to build an expert system to diagnose vitiligo by implementing Fuzzy Logic using the Mamdani Method. Fuzzy logic is an approach that uses several specific stages. The system is built using a structured approach with use cases, sequences, and activity diagrams as tools for designing the system. The results of this study are in the form of an expert system that is able to produce a risk level for vitiligo.
DETECTING MIS-INFORMATION ON SOCIAL MEDIA USING MACHINE LEARNING

Due to social media people are becoming more exposed to the fake news. It promotes the spread of negativity in the society. Thus fake news detection is becoming one of the major part of the IT industry. Fake news Detection is the non-trivial task, which requires multi-source information such as news content, social context, and dynamic information. Fake news cannot be detected simply based on news contents. In addition to news content, user engagement and social behaviour should also be explored. For example a credible users signal that “this is a fake news” is enough to determine the authenticity. Certain other information like social behaviour of news , the way it has been used by user etc are also important factors. Thus Dataset which contain news content, social context and dynamic information could help in fake news detection. So In our system we provide a way to user in which fake news can be detected. The system uses data mining which provides a way to user to easily detect fake news by using various data mining algorithms
A Human-Centered Interpretability Framework Based on Weight of Evidence.

In this paper, we take a human-centered approach to interpretable machine learning. First, drawing inspiration from the study of explanation in philosophy, cognitive science, and the social sciences, we propose a list of design principles for machine-generated explanations that are meaningful to humans. Using the concept of weight of evidence from information theory, we develop a method for producing explanations that adhere to these principles. We show that this method can be adapted to handle high-dimensional, multi-class settings, yielding a flexible meta-algorithm for generating explanations. We demonstrate that these explanations can be estimated accurately from finite samples and are robust to small perturbations of the inputs. We also evaluate our method through a qualitative user study with machine learning practitioners, where we observe that the resulting explanations are usable despite some participants struggling with background concepts like prior class probabilities. Finally, we conclude by surfacing design implications for interpretability tools
Pengembangan Modul Matematika Siswa Berbasis Pendekatan PMRI Pada Materi Bangun Datar Kelas IV Sekolah Dasar

Student-centred learning requires teachers to provide learning materials that can support learning activities. This is so students can actively learn about learning resources and group discussions to gain knowledge. The obstacle issued by the teacher in making teaching materials themselves is the time required to create teaching materials. The development of modules based on PMRI learning provides a new colour in learning activities and the provision of teachers and activities in learning that are oriented towards student-centred learning. The research used is development research (development research) that uses a device development model design, according to Thiagarajan, namely defining, designing, developing, and disseminating. Based on the validity test from the experts and filling in the student questionnaire when the trial was limited, the score obtained was a percentage of 90.14% so that it can be concluded that the student module was based on PMRI's valid and practical approach. Based on the learning outcomes that have been done, the final average of students is 74%, which means that the learning outcomes of students are in a good category, where there are 8 students (47,058%) excellent category, 7 students (41,176%) good category, 1 student (5,882) %) enough categories and 1 student (5.882%) failed category. Based on an analysis of student learning outcomes, it can be concluded that modules that have been developed have an effect on learning outcomes.
Abstract No. 486 Augmented reality registration of three-dimensional models: a multicenter assessment using the next-generation HoloLens 2

Comparison of registration methods on HoloLens 2 for aligning 3D holographic models onto a CT grid amongst two academic centers to assess efficacy and reproducibility. A 3D holographic model of an abdominal CT phantom (CIRS 071B) with an overlying CT grid (Beekley Medical 117) was segmented using ITK-SNAP and projected using Microsoft HoloLens 2. Custom code was developed in Unity and C# for model registration using four methods: 1) one-handed gestures, 2) optional two-handed gestures, 3) Xbox controller, and 4) computer vision with automated image detection using Vuforia SDK. Code was shared and deployed across institutions. Registration times for aligning the model to the CT grid were recorded until visual satisfaction and alignment errors were < 1 cm in the x-, y-, and z-planes. Eighteen users from two academic centers (3 attendings, 3 trainees, and 3 medical students at each center) attempted each registration method using HoloLens 2 three times. Comparisons were made with paired t-tests within each center, F-tests, and unpaired t-tests between centers. Center 1 had mean manual registration times of 22.6s, 25.2s, and 77.2s, and Center 2 had times of 32.7s, 23.4s, 68.5s for one-handed gestures, two-handed gestures, and Xbox controller, respectively. Both centers showed faster registration times using one- and two-handed hand gestures compared to controller (P < 0.001 at both centers). Between centers, there was no significant difference in registration times for two-handed gestures and controller, but one-handed gestures were faster at Center 1 compared to Center 2 by 31% (P < 0.05). Both centers had fastest registration times with automated computer vision. Center 2’s mean automatic registration time was 3.4s versus Center 1’s time of 7.2s (52% reduction, P < 0.001). Registration times demonstrated similar trends within each center but showed variability in certain registration methods between centers. Prior exposure with the HoloLens may impact hologram interaction and ambient lighting conditions may affect computer vision detection. These findings encourage cross-institutional development and reinforce multicenter evaluation for new technologies, like augmented reality, in the IR suite.
Larger-Context Tagging: When and Why Does It Work?

The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks.However, a relatively less discussed topic is what if more context information is introduced into current top-scoring tagging systems.Although several existing works have attempted to shift tagging systems from sentence-level to document-level, there is still no consensus conclusion about when and why it works, which limits the applicability of the larger-context approach in tagging tasks.In this paper, instead of pursuing a state-of-the-art tagging system by architectural exploration, we focus on investigating when and why the larger-context training, as a general strategy, can work.To this end, we conduct a thorough comparative study on four proposed aggregators for context information collecting and present an attribute-aided evaluation method to interpret the improvement brought by largercontext training.Experimentally, we set up a testbed based on four tagging tasks and thirteen datasets.Hopefully, our preliminary observations can deepen the understanding of larger-context training and enlighten more follow-up works on the use of contextual information.
EDUCATION THROUGH CULTURE AND CULTURAL CONSUMPTION IN FORMAL EDUCATION, IN PRIMARY EDUCATION: doi>10.24250/jpe/1/2021/DRC

This study aims to determine the extent to which online cultural activities can develop interest in authentic values, well-being, knowledge and creative expression of culture in the instructional-educational process. Knowing that cultural activities are a complement to curricular actions, these being actions exercised by adults on the young generation in the form of an interpersonal relationship in order to form the good skills necessary for a harmonious social life, I believe that the rope of cultural activities can intertwine with the obtained intellectual results, with the formation of the adult with an educational potential involved in the instructive-educational process, thus resulting the development of the psychosocial competencies of those involved, facilitating at the same time their social integration and assuming the role of belonging to the community group.
Actions developed at the Instituto de Medicina Integral Prof. Fernando Figueira to confront the COVID-19 pandemic/ Ações desenvolvidas no Instituto de Medicina Integral Prof. Fernando Figueira para enfrentamento à pandemia da COVID-19

Abstract The COVID-19 pandemic has brought countless challenges to the health institutions around the world, especially those located in countries such as Brazil, with large territorial dimensions and many social and economic differences. This technical report aims to publish the actions carried out and the products developed at the Instituto de Medicina Integral Prof. Fernando Figueira (IMIP) before and during the pandemic - from January 31st to September 4th, 2020 - facing it efficiently and effectively, seeking institutional sustainability. The mobilization of the professional staff at the institution was fundamental to create protocols ofas-sistance, adapt the physical structures in the hospital and outpatient care, care for the health professionals, offer teaching and research activities in the distance mode, articulate management members to make decisions based on systematically collected data on the pandemic situationat real time. All actions were carried out with a single objective of assisting all the patients affected by COVID-19 admitted at the institution.
Expert System Toxoplasma Gondii To Detect Disease In Women With Web Based Methods Chaining Forward: Expert System Toxoplasma Gondii To Detect Disease In Women With Web Based Methods Chaining Forward

Cases of Toxoplasmosis in humans in Indonesia ranged from 43 to 88%, while in animals ranged from 6 to 70% depending on the type of animal and its region. Many people do not know about this disease because of lack of knowledge possessed. Based on these problems, in this study the expert system uses a web-based forward chaining method to find out information on disease symptoms and to detect early Toxoplasma Gondii in women, so as to reduce the risk of being infected with the Toxoplasma Gondii parasite. This system produces the output that the knowledge material used is based on expert knowledge. In addition, the accuracy of the results of testing this system gets a percentage of 100%.
Analisis Kualitas Perangkat Lunak Penilaian Kurikulum 2013 Menggunakan Standar ISO/IEC 9126-1:2001

The 2013 curriculum assessment software is a software that helps facilitate the processing of student grades. This is a routine activity that must be done by teachers to calculate student grades in school. So far there has not been any measurement and analysis of the quality of this assessment software. This study aims to measure and analyze the quality of the 2013 curriculum assessment software based on ISO 9126. The sub-criteria for assessment in the quality attribute are adjusted to 3 external quality criteria of ISO 9126, including functionality, usability and reliability. The sub-criteria and their attributes in each criterion are adjusted to the appearance and procedure of using the 2013 curriculum assessment software and are also based on research that uses user views or perspectives in evaluating the quality of software, especially those of the Software Application type. This study shows the results that the quality characteristics of Functionality have a very strong quality interpretation where the 2013 curriculum assessment software has a product capability level that provides functions that exist in the system and can be implemented and run as expected. For the characteristics of the quality of Usability and Reliability, it shows that the results of quality interpretation are sufficient, it means that the 2013 curriculum assessment Software Operators still find it difficult to understand, operate the software.