Reviewerly/exHarmony huggingface dataset:
STTG-net: a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

Abstract In recent years, human motion prediction has become an active research topic in computer vision. However, owing to the complexity and stochastic nature of human motion, it remains a challenging problem. In previous works, human motion prediction has always been treated as a typical inter-sequence problem, and most works have aimed to capture the temporal dependence between successive frames. However, although these approaches focused on the effects of the temporal dimension, they rarely considered the correlation between different joints in space. Thus, the spatio-temporal coupling of human joints is considered, to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network (GCN) (STTG-Net). The temporal transformer is used to capture the global temporal dependencies, and the spatial GCN module is used to establish local spatial correlations between the joints for each frame. To overcome the problems of error accumulation and discontinuity in the motion prediction, a revision method based on fusion strategy is also proposed, in which the current prediction frame is fused with the previous frame. The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods. The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.
INDUCED \(nK_{2}\) DECOMPOSITION OF INFINITE SQUARE GRIDS AND INFINITE HEXAGONAL GRIDS

The induced \(nK_2\) decomposition of infinite square grids and hexagonal grids are described here. We use the multi-level distance edge labeling as an effective technique in the decomposition of square grids. If the edges are adjacent, then their color difference is at least 2 and if they are separated by exactly a single edge, then their colors must be distinct. Only non-negative integers are used for labeling. The proposed partitioning technique per the edge labels to get the induced \(nK_2\) decomposition of the ladder graph is the square grid and the hexagonal grid.
Speed Estimation of Video Target Based on Siamese Convolutional Network and Kalman Filtering

In recent years, computer vision-based moving target state detection and motion parameter estimation have become a hot topic. Aiming at the problem that the accurate speed of moving target cannot be calculated from the target tracking results, in this paper, a speed estimation method using Siamese convolutional network and Kalman filtering is proposed and it is validated by single pendulum experimental data. Firstly, an improved Siamese convolutional network is integrated to detect the position of a moving ball in the single pendulum video. Then, these position coordinates are integrated to estimate the speed of the moving ball by using the information from the previous and current frame through a Kalman filter. The experimental results show that this method can achieve a sound on the speed estimation of moving objects in videos.
Aplikasi Belajar Asik Berbasis Android sebagai Media Pembelajaran Matematika

&lt;p class="15cKeywordsBInggris"&gt;&lt;span&gt;The current condition of learning is carried out online based on government regulations to prevent the spread of the COVID-19 virus. Therefore, it is necessary to have android-based learning media (Belajar Asik) to make it easier for independent learning in their respective homes. This study aims to develop and find out the feasibility and user responses of Belajar Asik media in mathematics subjects. This type of research is Research and Development (R&amp;amp;D) using the ADDIE development model with five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. This research is limited to the implementation stage using limited tests and has not yet arrived at a test on a wide scale. Validation is carried out by material experts and media experts as many as two people each. The subjects in this study were 27 students, with data analysis techniques using a quantitative description. The findings are in the form of android-based cool learning media in mathematics learning. Validation results based on material experts obtained a very-feasible category (92%), while results from media experts obtained a feasible category (78%). Meanwhile, the results of students' responses to this media received a very positive response category (68%). Therefore, thus this application is feasible to use and can be applied as a learning medium in mathematics subjects’ trigonometric competencies class XI MA NW Kabar.&lt;/span&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Panorama mapping of the colon – a phantom study with robotic endoscopy

Abstract When documenting colonoscopic examinations, the challenge arises, that the exact location of the detected lesions (such as polyps) can usually only be determined with low precision. In order to improve the image-based documentation of such lesions with respect to their location and context, a system is currently investigated and developed, allowing the creation of a panoramic map of the interior of the colon’s lumen during an endoscopic examination. To evaluate the proposed algorithms, a 3D-printed colon phantom with known geometry has been developed. Furthermore, to obtain image sequences with precise and known motion during a simulated examination for test purposes, a robot was used to perform the insertion and withdrawal of the endoscope with constant speed and a steady viewing angle. The thus obtained image data is used to generate an ideal panoramic map using our the proposed stitching algorithm. Hence, it is possible to obtain reference maps of the phantom, which can be used to evaluate the quality of the algorithm.
Aplicação da Decomposição em Valores Singulares para compressão de imagens

The objective of this paper is to present the Singular Value Decomposition and develop a computational application for compression and reconstruction of digital images, which can be stored and transmitted using less information than the original image.For this, the RGB color system is considered.The image is described using three matrices, one for each channel of these colors, and decomposition is applied for each matrix.For grayscale images, only one matrix is required.Then, the Eckart-Young Theorem ( 1937) is applied to image reconstruction.Computational programs using Matlab are presented, with numerical examples and applications in images, which are compact and displayed in color and grayscale, requiring a much smaller amount of values to be stored.
APLIKASI SISTEM PAKAR UNTUK MENDIAGNOSA LEBIH DINI PENYAKIT KOLERA PADA ANAK MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN)

Cholera is a disease that can be dangerous if not treated immediately. The main symptoms are diarrhea, shock, and seizures. Children with cholera need serious treatment from medical personnel. The problem that often occurs is that there are not many doctors who are experts in this field, plus for people who are far from urban areas such as people who live in mountainous areas or remote villages it is very unlikely to be able to consult a doctor due to distance, cost and time factors. This study aims to build an expert system application that is able to diagnose cholera in children early by applying the K-Nearest Neighbor (KNN) method, so that people, especially those who are far from urban areas, can find out more about cholera in children so that it can be treated more quickly. The KNN method can be implemented in a system that adopts the ability of experts to diagnose cholera in children. In applying the KNN method, symptom initialization is carried out by entering the density value and looking for the combination confidence value to get the diagnostic result. From this research, it can be concluded that the application that was built can be used to replace experts in helping to diagnose cholera in children early.
Research on Intelligent Recognition and Analysis Framework of Japanese Katakana in the Background of Big Data and Fuzzy Recognition

With the increase of communication methods, more and more foreign words appear in Japanese communication. Since most of the syllables of these words do not conform to the cognitive schema and usage habits of Japanese katakana, the Japanese vocabulary system is not suitable for some words with more syllables. Vocabulary is abbreviated into katakana abbreviations and romaji abbreviations. The continuous template matching technology is used to analyze the distributed data structure of big data, the information fusion processing of cloud service combined big data is combined with the matching correlation detection method, and the frequent item detection and association rule feature extraction are performed on the high-dimensional fusion data. The method has a prediction accuracy of up to 7.3% and a small error of 6.2%.
Structured Query Language (SQL) based College Management System

This work focuses on online approval of requests in universities and digitization like leave requests, out pass requests, on-duty approval, and university admission management system. The most difficult challenge that college students face is getting permissions and leaves granted. For all sorts of approval, many colleges still employ manual and archaic methods. Maintaining attendance and reports for students is also a difficult process and a lot of manpower is required. To make it easier and simpler, an app is created and published among universities to make all the processes digital and online. The main objective of this work is to develop an application that solves the difficulties of the traditional method. The application is developed from a hyper-automation development platform designed for all business sizes. Structured Query Language (SQL) is used for creating tables and managing data in the database. This application will be useful for teachers and students to become automated in permissions, leaves requests, and related reports and records.
Cervical Cancer Cell Segmentation and Classification using ML Approach

In the current era, the developers are focusing on the adequacy of Ensemble Learning (EL) and Transfer Learning (TL) techniques in the field of cervical histopathology photograph determination. However, there is not many examinations which have described the separation stages in cervical histopathological photos. Accordingly, on this study, we advise an Ensembled Transfer Learning (ETL) shape which explains clearly the separated cervical histopathological photos. In addition, we’ve created Xception, Inception-V3, Resnet-50 and VCG-16 models in the setup. At that factor, to support the order of execution, a weighted EL method is presented. From that factor onward, to proceed with the proposed calculation, a dataset comprising of around 500 photos, recolored through 3 immunohistochemistry techniques (Vascular endothelial growth factor-VEGF, Hypoxia-Inducible Factor-HIF and Aquaporins-AQP) is used. On testing, we gather the maximum accuracy over 97.03% and 98.61% on recoloring AQP pictures and recoloring VEGF pictures, separately. At last, a further test in the classification of harmless cell from a dangerous one is finished at the Herlev dataset and receives a popular exactness accuracy of 98.37%.
Prospect of Intelligent English Translation Algorithm based on Neural Machine Translation and Internal Chain Network Modeling

Prospect of the intelligent English translation algorithm based on the neural machine translation and internal chain network modeling is studied in the paper. Due to the limitation of the scale of thesaurus, neural network translation models often cannot deal with the translation of people's names well, which is prone to missing translation or wrong translation, and the translation quality of people's names often has a great impact on the quality of the final translation. The preprocessing of bilingual corpus in machine translation is a prerequisite for obtaining good translations. In order to enable the neural network to better learn the semantic information in the bilingual corpus and the contextual relationship of each word, high-quality word segmentation is particularly important. Hence, with the integration of the neural machine translation and deep neural network, this research work has designed and developed an intelligent English translation algorithm. Through the experimental analysis, the simulations are conducted and comparison analysis is presented.
An Adaptive FLV Steganography Approach Using Simulated Annealing

Steganography is not only the art of hiding secret messages in cover media but also a process of communication and secure data transfer. Secret messages can be sent over the Internet with security by using several steganography techniques, but all of them present challenges in steganalysis. This study proposes a new secure technique called flash video (FLV) file steganography that keeps the frame video quality and is difficult to detect. The technique can hide any type of secret message inside a given FLV file. The secret message is divided into packets of the same length, reordered packet, and encrypted bytes before being hidden at the end of a selected video tag. A simulated annealing (SA) approach to select tags for steganography is presented to reduce or avoid the challenge of steganalysis. The proposed method uses SA as supporting framework to deal with the FLV file as a host for different types of secret messages. The system determines the minimum path within the host FLV file by using SA and hides the message bits inside each pixel in the minimum computed path. Analysis of the host FLV file cannot be performed without proper knowledge on the transformation process. Thus, the existence of the secret message is difficult to detect by steganalysis. Knowledge is represented by the key of finding the minimum path in the host FLV file, key of secret message length, key of additional bytes, key of message packets reordering and key of message extension. Experimental results show that the proposed technique satisfies the main requirements of steganography with regard to visual appearance, capacity, undetectability, and robustness against extraction.
User Review Analysis of Mobile English Vocabulary Learning

More and more language learners today rely on mobile assisted vocabulary learning to expand their grasp of words. Against this background, reviews left by target users are impactful in a language learning app market because online reviews play roles as a decision-making tool used by prospects before committing to any service provider and as a channel through which a mobile app developer understand users’ current expectations and their using and learning experience in depth. The aim of this study is to explore the issues users concern the most in app-based vocabulary learning and provide suggestions to mobile app developers, hoping they leverage the advice to perfect apps’ performance and gain a competitive edge in a booming app market.
Comparison of Multiple Machine Learning Approaches and Sentiment Analysis in Detection of Spam

Currently, all of our communications are made through various electronic communication mediums. While it has made communication between people from different parts of the world very easy, things like spamming have made life difficult for many people. Spammers are found in almost every electronic communication platform like email, mobile SMS, social networking sites, etc. So, with time detecting spam messages and filtering them out from our important messages have become more and more important. For many years, Natural Language Processing (NLP) researchers have proposed different techniques to detect spam messages. In this paper, our objective is to detect spam messages in a dataset using vectorization along with various machine learning algorithms and compare their results to find out the best classifier for detecting spam messages.
Development of blended learning model integrated nursing leadership training using web-based in Indonesia

Nurse managers are the frontline leaders in the ward that directly impact the quality of care services. Many nurse managers in Indonesia were inadequately prepared to fulfill this role, especially during this crisis. The objective was to develop blended learning an Integrated Nursing Leadership Training (INLeT) using web based and test the effectiveness.This research is a research development using modified Borg and Gall by researcher, namely, Planning, Redesign, Intervention, Modify, and Evaluation. The INLET model was validated by nine experts in health and media technology design. Data were tested using quasi-experiment research in Sanglah General Hospital and Wangaya Public Hospital in Bali.The results of analysis of the content validity of all items valued V index 0.96 (high category) from design experts and V Index 0.92 (high category from health experts. The experiment group obtained N Gain scored 0.5603 at medium level, and the control group result of N Gain Score was 0.2845 in the low-level category. Using t-test, both the experiment and control group showed significant improvement of score test with P = 0.000 (P < 0.005). Bivariate analysis using independent t-test result P = 0.000 (P < 0.005) means significant difference score test compared between experiment and control group.Using the web, this blended learning INLeT Model is effectively applied for nurse manager professional development; therefore, this study can prepare nurse managers into a leadership role by integrating moodle web-based into nursing training.
Students' Attitude towards the Implementation of the School Literacy Movement

The low interest in reading Indonesian students moves the government to issue a school literacy movement (GLS) program. GLS is a program in which one of the activities is reading non-learning books for 15 minutes before KBM starts. The purpose of this study is to find out how students' attitudes towards the implementation of GLS in Yogyakarta. This type of research is a quantitative study with survey approach, the population in this study were all students of SMA Negeri 5 Yogyakarta, the sample of this study were 205 students. Data collection techniques in this study used nontest techniques with data collection instruments in the form of measurement scales. Analysis of the data in this study uses descriptive statistical analysis aided by the Microsoft Excel program. The results obtained are the attitudes of students towards the implementation of GLS in the category of agreed attitude with the following explanation: (a) 131 students (63.90%) are in the agreed category; (b) 48 students (23.41%) are in the category of strongly agree; (c) 25 students (12.20%) were in the category of disagreeing, and (d) 1 student (0.49%) in the category of strongly disagreeing. Given the importance of this literacy activity, the researcher hopes that the school can improve the quality of its collaboration with various parties, increasing the variety of literacy activities carried out, and schools can provide comfortable reading rooms and bookshelves in each class so that students can place their literacy books and journals on these shelves.
An Analysis of Students’ Difficulties in Writing Descriptive Texts in the Tenth Grade of SMA Kristen Palangka Raya

Most students had problems writing a descriptive text when they were assigned their text. Many students were not able to describe themselves or they had difficulties in writing descriptive text. Some students think that writing is the most difficult skill in English. Some students also could not finish their writing at the end of the time set due to limited ideas and vocabulary. This research aimed to describe the students’ difficulties in writing descriptive texts and find out the causes underlining those frequent difficulties. This research uses the qualitative descriptive method. The research subjects were X MIPA students of SMA Kristen Palangka Raya who had difficulties when writing descriptive text. This research involved 17 students as the research subjects. The data of this research were the results of students’ worksheets and the student's answers to the given questionnaires. The researcher employed a writing test and questionnaires to gain descriptive data. As a result, the factors that cause the students’ difficulties in writing descriptive text were students’ lack of vocabulary, difficulties to translate, and lack of competence in understanding English tenses.
Teaching Vocabulary Using Fly Swatter Game at the Seventh Graders of Secondary School

Vocabulary is one of the important elements in learning English. In fact, many students still face difficulties in mastering English vocabulary. This research is about to find out the effect of teaching vocabulary using Fly Swattter game at the seventh graders of secondary school. The method used in this research was a quasi-experiment by using one group pretest and posttest design. For the sample, the researcher took 32 students of the seventh grade of MTs Nurul Hidayah Mintin at Pulang Pisau Regency in the Central Kalimantan Province. Test and questionnaire were instruments used by the researcher to collect the data needed. The result shows that there is a significant effect of teaching English vocabulary using Fly Swatter game. This is also supported by students’ positive responses from questionnaire given. Therefore, the game has succeeded in improving students’ vocabulary of English.
Developing e-Book of Reading for Intensive Course in Virtual Classroom: Students’ Need Analysis

This research clarifies the demands of students through need analysis concerning the content of e-book of reading for Intensive Course in virtual classroom, and the questionnaire was distributed to one hundred and twelve (112) students of English Study Program of FKIP University of Palangka Raya to explore the necessity. The results showed that most students require: first, reading texts as instructional materials which are accompanied by English grammar and English expressions to lead students to expose both oral and written English in daily life communication with other people using ICT (100%). Second, reading texts consist of 200-250 words (87.50%) which covered the topics of daily life and Dayak culture themes (.90%). Third, reading activity is to do reading a text and looking for detailed information (88.39%), and speaking activity is to discuss the exercises and tasks with peers (88.39%). Forth, writing input is about the explanation of the sentence structure used in the text, and writing activity that led them doing writing practice to write a text similar to the example given using their own sentence (82.84%). Fifth, learning activity on vocabulary and grammar cover pairing words with their meaning (89.28%), and write sentences according to the given pattern (100%). Last, the lecturer’s role acts as motivator and learners’ role are as responsive participants, and setting takes in virtual classroom or online learning. These signify student’s needs most which are as the basis for designing and developing e-book later.
Student- Teacher’s Digital Competency Readiness During COVID- 19

The COVID-19 crisis changed the traditional method into online transformation among teachersand they should need digital competency in order to teach creative and innovative through digital platforms. Digital readiness is nothing but preparedness or act of readiness to do something in meaningful way. The purpose of the present study is to find out the level digital competency readiness among student - teachers during covid 19.The researcher used self developed questionnaire, it has 20 items about technical readiness, pedagogical readiness and readiness regarding time management. The result showed that there is no significant difference between male and female student teachers digital competency readiness and there is significant difference with respect to student – teacher’s locality and major subjects.