Reviewerly/exHarmony huggingface dataset:
CodeLabeller: A Web-Based Code Annotation Tool for Java Design Patterns and Summaries

While constructing supervised learning models, we require labeled examples to build a corpus and train a machine learning model. However, most studies have built the labeled dataset manually, which, on many occasions, is a daunting task. To mitigate this problem, we have built an online tool called CodeLabeller. CodeLabeller is a web-based tool that aims to provide an efficient approach to handling the process of labeling source code files for supervised learning methods at scale by improving the data collection process throughout. CodeLabeller is tested by constructing a corpus of over a thousand source files obtained from a large collection of open source Java projects and labeling each Java source file with their respective design patterns and summaries. Twenty-five experts in the field of software engineering participated in a usability evaluation of the tool using the standard User Experience Questionnaire online survey. The survey results demonstrate that the tool achieves the Good standard on hedonic and pragmatic quality standards, is easy to use and meets the needs of annotating the corpus for supervised classifiers. Apart from assisting researchers in crowdsourcing a labeled dataset, the tool has practical applicability in software engineering education and assists in building expert ratings for software artefacts.
An Exploration of Theatre Rehearsals in Social Virtual Reality

Virtual Reality (VR) offers potential for theatre makers to rehearse remotely in settings which are uniquely immersive. In collaboration with a major drama school in the United Kingdom, a longitudinal diary study was completed to examine the utility of consumer-grade VR for theatre rehearsals. Utilising commonly affordable headsets and general-purpose Social VR applications, 10 experienced students (2 directors, 8 actors) rehearsed scenes in VR over 3 weeks, before performing them in person. Participants detailed their experiences in diary logs and interviews, expressing the ability to work through spatial arrangements (blocking) as a full body avatar to be positively beneficial. Limitations included the absence of facial expressions and gestural nuance. Our overarching conclusion is that low-tech VR can be a useful aid in theatre rehearsals and early stages of production. In conclusion we outline design recommendations for a) using VR in theatre production and b) research and development of Social VR.
Video Object Counting With Scene-Aware Multi-Object Tracking

The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection results, the authors use the multi-object tracking method to assign an independent ID number to each object. From the time the ID tag is obtained until the end of the video, each object is counted only once. However, even minor amounts of image noise can cause irreversible changes in feature information, resulting in severe tracking drifts. This paper introduces the concept of scene awareness and addresses unreasonable ID assignment caused by unreliable feature matching in the context of region division. Through the macro analysis of the scene, the authors define the region (called the transition region) where the number of objects can increase or decrease and require that all ID assignments for new objects and ID deletions for existing objects take place only in the transition region. Because the actual number of objects in the non-transition region is constant, they rematch unmatched objects with existing IDs in the region (called ID relocation) because changes in object ID are caused by feature matching failure. In this paper, the authors create algorithms for dynamically generating transition regions, detecting object increases and decreases, and relocating object IDs. Experimental results show that the method effectively improves the accuracy of video object counting.
Introduction to quantum computing (with the assistance of Edward D. Davis)

Abstract We trace the origins of quantum computing to attempts to set physical limits to the computing power of classical computers. Some ideas of Feynman are discussed and his interesting proof of Bell’s inequality is presented. The circuit model of quantum computing, due essentially to David Deutsch, is described. We give a simple demonstration of quantum error correction. Schor’s famous algorithm for efficient factoring of numbers is derived.
Evaluation of Drainage System on Jalan Slamet Riyadi and the Mahakam IV Bridge Approach Road, Samarinda City

The construction of the Mahakam IV Bridge and the geometric changes to the Slamet Riyadi road in the vicinity have caused inundation problems on the main road section and the Mahakam IV Bridge Approach Road on the Samarinda City side. It is necessary to evaluate the drainage system in that location to prevent inundation that disturbs traffic and causes road damage. Flood discharge is analyzed based on rainfall data and using a Modified Rational Method. The design rain is calculated by a method that satisfies the goodness of fit test. The evaluation results show that the flood discharge to the channel is greater than the capacity channel. An alternative solution is to add a direct flow path to the Mahakam River in the form of long storage. And to speed up the drying on the bridge approach road, it is necessary to increase the number of street inlets and repair the culverts that drain water to the Mahakam River
Automatic extraction of significant terms from the title and abstract of scientific papers using the machine learning algorithm: A multiple module approach

Keyword extraction is the task of identifying important terms or phrase that are most representative of the sourcedocument. Although the process of automatic extraction of keywords from title is an old method, it was mainly forextraction from a single web document. Our approach differs from previous research works on keyword extraction in severalaspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purposeof this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturingresearch trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools.More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how farsignificant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientificarticle corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract theresults. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-expertsof that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be asolution to reduce the labour-intensive manual indexing process.
Online tools for advanced development of the programming skill

The article addresses the issue of training and developing programming competence in students and high school students for performance education and describes some online tools that facilitate this process.
Motivational aspects of digital games in learning process

With the advent of digital games and its rapid evolution, it is almost impossible for a lot of people especially the young children to go a day without coming into contact with them. One of the impacts of these digital games is that it is changing the way these young children think and learn. It is therefore important to carefully examine the influence of digital games on children’s education. The purpose of this research is to identify and examine the factors that motivate children to play digital games and to determine the effect of such games to the children’s learning abilities. Responses from 172 students of ages between 11 and 16 are analysed in this research. A questionnaire is used to capture the children’s motivation towards digital gaming. Also, an intellectual test was carried out to determine the effect of digital games on the children’s learning abilities. The findings have revealed that competitive spirit is the major factor that influences children to play digital games because of the challenge and the competition that comes along with it. Furthermore, a critical view from the results of the intellectual test has shown that the children that play digital games score higher results and were able to finish within a short period of time as against the children that do not play. The outcome of this research could be used to explore the possibilities of using digital games as tools for learning, especially to the young ages.
Online handwriting trajectory reconstruction from kinematic sensors using temporal convolutional network

Handwriting with digital pens is a common way to facilitate human–computer interaction through the use of online handwriting (OH) trajectory reconstruction. In this work, we focus on a digital pen equipped with sensors from which one wants to reconstruct the OH trajectory. Such a pen allows to write on any surface and to get the digital trace, which can help learning to write, by writing on paper, and can be useful for many other applications such as collaborative meetings, etc. In this paper, we introduce a novel processing pipeline that maps the sensor signals of the pen to the corresponding OH trajectory. Notably, in order to tackle the difference of sampling rates between the pen and the tablet (which provides ground truth information), our preprocessing pipeline relies on Dynamic Time Warping to align the signals. We introduce a dedicated neural network architecture, inspired by a Temporal Convolutional Network, to reconstruct the online trajectory from the pen sensor signals. Finally, we also present a new benchmark dataset on which our method is evaluated both qualitatively and quantitatively, showing a notable improvement over its most notable competitor.
CASE REPORT: PHYSIOTHERAPY TELESERVICE IN THE IMMEDIATE POSTOPERATIVE BREAST CANCER SURGERY

Introduction: Breast cancer is one of the most incidents and most common among women; surgical intervention is the main treatment, and physiotherapy plays an important role in the postoperative period, especially in the maintenance of range of motion and prevention/control of lymphedema. In the face of the contemporary scenario in which social isolation has been maintained due to the COVID-19 pandemic, the Federal Council of Physiotherapy and Occupational Therapy in Brazil released forms of remote care for patients who need clinical intervention. Objective: This study aims to report the experience of telecare in physiotherapy in the postoperative period of breast cancer surgery, focusing on aspects, pain, fatigue, mobility of the upper limb, and development of lymphedema. Case report: This is an experience report based on the telecare of a 61-year-old mastectomized patient, with 9 consultations, 8 in the remote format. The participant was evaluated regarding health habits, edema, sensation of pain, and tiredness using the Visual Scale of Pain (VAS), the Borg Rating of Perceived Exertion Scale, and a self-authored mastology questionnaire. In the initial evaluation, we found movement limitation (flexion-90º, extension-30º, and abduction-90º), the sensation of tightness in the scar, the sensation of a heavy limb, and dryness on the side of the surgery. In the final evaluation, we observed improvements when compared to the member opposed to surgery, in the gain of range of motion (flexion-180º, extension-45º, and abduction-180º), reduction of pain (VAS) and fatigue (BORG), and improvements in activities of daily living. Conclusion: The service in physical therapy was effective and obtained results similar to those expected in conventional face-to-face physical therapy treatments.
Blockchain and Cryptography Communication System

Abstract: Blockchain is an innovative technology that overcomes these threats, allowing sensitive operations to be decentralized while maintaining a high level of security. Eliminate the need for trusted intermediaries. The blockchain is accessible to network nodes and keeps track of all transactions that have taken place. The goal of our work is to offer a secure communication solution based on blockchain technology. In this project, we explain why blockchain should secure communications and offer a blockchain-based messaging design pattern that maintains the performance and security of data stored on the blockchain by using a smart contract to protect identity and associated verify public key and validate a user certificate. The system is entirely a combination of blockchain and cryptography for communication systems
THE IMPLEMENTATION OF FINITE-STATES CONTINUOUS TIME MARKOV CHAIN ON DAILY CASES OF COVID-19 IN BANDUNG

Markov chain is a stochastic process to describe a phenomenon in the future based on a previous state. In practice, Markov chains are distinguished by time into two, namely discrete-time Markov chain and continuous-time Markov Chain. This research will discuss the continuous-time Markov chain with finite-state. COVID-19 phenomena can describe and predict using the continuous-time Markov chain. Authors use the data daily cases of COVID-19 in Greater Bandung including Bandung City, Bandung District, West Bandung District, Cimahi City and Sumedang District. Used data came from simulated data of daily cases of COVID-19 in Greater Bandung from August, 2020 until November 14, 2021 that recorded through the website COVID-19 of West Java. In terms of described and predicted the COVID-19 phenomenon in Greater Bandung for long-term probability, authors use stationary distribution and limit distribution. COVID-19 phenomenon is described into two states: state 0 (lower than average of data) and state 1 (higher than average of data). The result of continuous-time Markov chain with finite-state shows that the probability of the daily cases of COVID-19 for five locations in Greater Bandung is state 0 have a larger probability than state 1. It means that COVID-19 in Greater Bandung over the long-term will decrease.
Web version of the protocol of the orofacial myofunctional evaluation with scores: usability and learning

The Orofacial Myofunctional Evaluation with Scores (OMES) protocol has been validated and used in clinical practice and research. The goals of this study were to develop, analyze and improve a version of OMES for the Web and to investigate the relationship between the usability judgments and the prior experience of the evaluators and whether using the interface promotes learning, as shown by the task completion time (TCT).Study steps: 1) inspection of the prototype by the team; 2) evaluation of usability by three experienced speech-language pathologists (SLPs); and 3) evaluation of its usability by 12 SLPs with varying levels of experience in the use of OMES. Participants answered the Heuristic evaluation (HE), the Computer System Usability Questionnaire (CSUQ), and expressed free comments. The TCT was recorded.The OMES-Web reached excellent usability levels, and the participants were highly satisfied. The correlations between the participants' experience and the HE and CSUQ scores were not significant. The TCT decreased significantly throughout the tasks.OMES-Web meets the usability criteria, and participants feel satisfied with the system regardless of their level of experience. The fact that it is easy to learn favors its adoption by professionals.
Performance of SEAGOP (Sea Garbage Obtaining Pump Bin) in Magapo, Mati City, Davao Oriental, Philippines

The main purpose of this is to help address the problem of floating marine debris in the seawater of Magapo-3, Mati City, Davao Oriental, Philippines. This led the researchers to devise a version of Seabin V5 and called it, SEAGOP: Sea Garbage Obtaining Pump-bin. This model was made of light materials which can help maintain its balance and consist of sensors and SMS notification, which notifies the person in charge if the bin was already full. The test of SEAGOP for seven days, yielded 205 pieces of different waste materials, with the five most common floating marine debris: cellophane (31%), plastic wrappers (30%), leaves (8%), plastic bottles (6%), and face masks (5%). Furthermore, the results of the detection and SMS notification rate during the trials were 100% reliable. The device was further evaluated using the standard of ISO 9126 and resulted in an average of 4.68 functionality, 4.3 reliability, 4.9 usability, 4.6 efficiency, 4.46 maintainability, and 4.36 portability out of five. Results showed that SEAGOP performed well in collecting floating marine debris, detecting, and sending SMS notifications when the bin was full. This can be further upgraded and funded by the local government to help minimize floating marine debris in coastal areas of Mati City.
Dimensionality Reduction and Visualization in Public Management Research from the Perspective of Algorithm Recommendation

With the development of computer technology, algorithm recommendation systems have been widely used in intelligent transportation, urban planning, and other fields. Aiming at the current problems in public management in China, this paper proposes a dimensionality reduction calculation method, and uses the improved particle swarm optimization algorithm to analyze and evaluate the dimensionality reduction process. With the expectation of providing some reference value and ideas for solving practical application problems, a corresponding relationship equation is established with the minimum expected value as the objective function to solve the corresponding optimal solution, the evaluation results of system performance indicators and comprehensive scoring criteria under relevant parameters and constraints are given. The test results show that the visualization effect of the dimensionality reduction visualization model for public management research based on the algorithm recommendation perspective is over 90%.
Positive experience and further prospects for online education

The article focuses on the positive aspects of online education and discusses the lessons learned and effective practices that can be brought back into conventional face-to-face classes. Unlike numerous studies pointing out disadvantages of distance education, the authors contend that online education during the pandemic was quite efficient, enabling them to take away a lot of useful findings, techniques, and experiences. The paper describes the results of the study conducted among freshmen and sophomores, as well as lecturers of the Department of the English language and the administration of Baikal International Business School of Irkutsk State University. Using student and teacher questionnaires, a dean interview, and observation, the authors collect multiple psychographic, motivational, and behavioral data allowing to find out preferences and opinions of online education. As a result of their research, the authors come to the conclusion that the main outcomes of online teaching are more collaboration for course development and preparation of student assessment, the integration of MOOCS into conventional courses, the use of numerous interactive technologies and videoconferencing platforms for various purposes, more reflection on teaching and student learning, and more opportunities for professional growth. Overall, teachers report becoming more knowledgeable, better equipped, and more competent. Besides professional benefits, after overcoming the challenges of online teaching, the instructors seem to have become more stress-resistant and resourceful.
PENGEMBANGAN EVALUASI PEMBELAJARAN BERBASIS GAME MENGGUNAKAN PLATFORM WORDWALL

The development of information and communication technology is currently increasingly advanced. Information and communication are more accessible, including in the world of education. This requires educators to do more creativity by utilizing technology as a material to support the success of goals in learning. Mathematics is no exception, which is considered a complicated subject for students. Included in the learning evaluation activities of the lessons that have been given during learning activities. This research aims to create new innovations related to learning evaluation by utilizing educational games from the woodwall platform. The type of research method in this study was Research and Development using the ADDIE model which consists of five stages, namely analysis, design, development, implementation and evaluation. The validation results from media experts and material experts showed that this woodwall game media was very good for use as a learning evaluation medium. In addition to the results of the validation of the media experts and material experts, the questionnaire that was given to the respondents also showed positive results.
Bias-based Denoising Causal Recommendation Algorithm

Traditional recommendation algorithms, such as collaborative filtering, make recommendations by learning the relevant relationships between users and items. However, considering only the relationships without considering the underlying causal mechanisms would be unfair, uninterpretable, and would lead to bias. In this paper, we propose bias-based denoising causal recommendation algorithm (BDCR) . First, the method dynamically transforms the explicit user-item feedback into implicit feedback with an embedded representation. Then, a truncation function based on causal inference is constructed to remove false positive noise. In addition, traditional recommendations and denoised causal recommendations are aggregated to obtain predictive scores. Finally, experimental results on two real datasets show that the BDCR algorithm outperforms the classical algorithm in terms of recall and NDCG metrics.
Enhancing Robustness for Facial Emotion Recognize

Annotating correct labels for a large expression data set is a difficult mission. There are many problems that give rise to label noise in the expression data set, such as the subjectivity of the annotators, the low-quality images, uneven of data, and so on. Due to these problems in the training data set mentioned above, the test accuracy after the convolutional neural network (CNN) training to fail to reach a high standard. This paper starts with a few main problems existing in the expression data set, and uses two methods to improve the accuracy of CNN: 1) In the training phase of the CNN, the method of probability statistics is used to improve the testing accuracy of CNN. 2) Using special data augmentation ways to solve the problem of imbalance sample classification in the training data set. Through the above two methods, the CNN obtained 84.2% accuracy in the FERPlus without pretraining.