Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Decentralized Self-balancing Architecture for the Web of Things

Edge computing is a distributed computing paradigm that relies on computational resources of end devices in a network to bring benefits such as low bandwidth utilization, responsiveness, scalability and privacy preservation. Applications range from large scale sensor networks to IoT, and concern multiple domains (agriculture, supply chain, medicine. . . ). However, resource usage optimization, a challenge due to the limited capacity of edge devices, is typically handled in a centralized way, which remains an important limitation. In this paper, we propose a decentralized approach that relies on a combination of blockchain and consensus algorithm to monitor network resources and if necessary, migrate applications at run-time. We integrate our solution into an application container platform, thus providing an edge architecture capable of general purpose computation. We validate and evaluate our solution with a proof-of-concept implementation in a national cultural heritage building.
Customer satisfaction of urban farming application services: “e-Buruan Sae”

This study evaluates the customer satisfaction model in adopting the Web 2.0-based e-Buruan Sae application. The e-Buruan Sae application is an interactive Web 2.0-based application used by the government and the people of Bandung City to manage urban farming activities. This research is quantitative correlational research and applies a research method based on Design Research Methodology (DRM). The stages of this research are the literature review stage with the output of research objectives, the initial empirical analysis stage with the outputs of the hypothesis and research model, the experimental stage for data collection, and the final empirical analysis stage with the output of the data analysis results. This study uses six variables to measure customer satisfaction: ease of use, service quality, interactivity, trust, customer satisfaction, and IT development. The results of empirical testing show that the ease of use and IT development variables do not affect the customer satisfaction variable. In contrast, the remaining variables have a positive effect on customer satisfaction. This research produces measurements to evaluate customer satisfaction on the Web 2.0-based urban farming application.
Applications, Opportunities, and Current Challenges in the Healthcare Industry

One of the most crucial aspects of human life is healthcare. Individual’s highest goal should be to live a healthy life, since it is directly tied to their quality of life. Hence, healthcare data have become a vital component of industrial healthcare. The healthcare data are used to provide healthcare to the individual from whom we obtain, and they are also utilized for purposes other than healthcare delivery for that individual such as insurance, research, and so on. This paper investigates the sophisticated research solutions for the complete range of healthcare data, analyzes the data and gives guidance for future progress in healthcare practice. It highlights the developments in using of blockchain technology in healthcare domain, emphasizing the need for healthcare data to be shared among various healthcare entities. It then summarizes modern and emerging technologies such as cloud and fog computing in healthcare industry for classifying illness and segmentation, with an emphasis on deep learning and AI and machine learning architectures which have already established as the standard methodology. In conclusion, this study summarizes the different healthcare applications, research challenges and problems related to clinical procedures, fall detection, and security and confidentiality in the healthcare industry.
A Proof Procedure For Separation Logic With Inductive Definitions and Theory Reasoning

A proof procedure, in the spirit of the sequent calculus, is proposed to check the validity of entailments between Separation Logic formulas combining inductively defined predicates denoted structures of bounded tree width and theory reasoning. The calculus is sound and complete, in the sense that a sequent is valid iff it admits a (possibly infinite) proof tree. We show that the procedure terminates in the two following cases: (i) When the inductive rules that define the predicates occurring on the left-hand side of the entailment terminate, in which case the proof tree is always finite. (ii) When the theory is empty, in which case every valid sequent admits a rational proof tree, where the total number of pairwise distinct sequents occurring in the proof tree is doubly exponential w.r.t.\ the size of the end-sequent.
Efficient Robust Training via Backward Smoothing

Adversarial training is so far the most effective strategy in defending against adversarial examples. However, it suffers from high computational costs due to the iterative adversarial attacks in each training step. Recent studies show that it is possible to achieve fast Adversarial Training by performing a single-step attack with random initialization. However, such an approach still lags behind state-of-the-art adversarial training algorithms on both stability and model robustness. In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem. Following this new perspective, we also propose a new initialization strategy, backward smoothing, to further improve the stability and model robustness over single-step robust training methods. Experiments on multiple benchmarks demonstrate that our method achieves similar model robustness as the original TRADES method while using much less training time ($\sim$3x improvement with the same training schedule).
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. Our algorithm, Constraint Sampling Reinforcement Learning (CSRL), incorporates prior domain knowledge as constraints/restrictions on the RL policy. It takes in multiple potential policy constraints to maintain robustness to misspecification of individual constraints while leveraging helpful ones to learn quickly. Given a base RL learning algorithm (ex. UCRL, DQN, Rainbow) we propose an upper confidence with elimination scheme that leverages the relationship between the constraints, and their observed performance, to adaptively switch among them. We instantiate our algorithm with DQN-type algorithms and UCRL as base algorithms, and evaluate our algorithm in four environments, including three simulators based on real data: recommendations, educational activity sequencing, and HIV treatment sequencing. In all cases, CSRL learns a good policy faster than baselines.
Using WhatsApp Group &amp; Google Meet during Online Class, Why Not?

Due to this current outbreak of Covid-19, the process of teaching and learning is now completely held online. For students, every teacher/lecturer must find the appropriate learning platform. Consequently, this article examines the effect of the Covid-19 pandemic on the college students’ perception in 2 English classes in Salatiga (an Islamic university) and Solo (a private university) during online learning. Researchers would respond to theories and facts emerging in the field, as well as answer problems that have not been solved in previous research. This type of research was descriptive qualitative. The data was collected using Google forms. Then, inductive analysis was used to test the data. The findings show that, despite the effects of the Covid-19 pandemic on online English classes, such as unstable signal problems and the need to be constantly in front of gadgets, students can understand the material. They also have a good time in online English classes because the lecturers in WhatsApp Groups (WAG) include games, puzzles, quizzes, and other interesting activities to keep them entertained during online classes. They can also learn the correct pronunciation by using WhatsApp Voice Note and Google Meet. During online learning, both lecturers and students face numerous challenges. They also suggested being creative in class and livening up the atmosphere, despite the fact that it was held online due to a massive pandemic.
Investigating the use of mobile communication technology in professional development: a connectivist approach

In most universities in Zimbabwe, the educators do not possess professional teaching qualifications. Most educators resist institutional interventions aimed at equipping them with appropriate pedagogical skills due to the rigidity of intervention programmes. The advent of mobile communication technology brought various social media platforms that enhance communication. This study was aimed at investigating the experiences of university educators during the use of WhatsApp Messenger to learn about inquiry-based pedagogy as a professional development course. Nine university educators who had no prior training in pedagogy were purposely selected from three Zimbabwean universities. Semi-structured interviews and a focus group discussion were conducted to gather empirical data. The connectivism theoretical framework was used to understand the phenomenon under study. A thematic network analysis was deemed suitable for this study. The findings showed that by learning about inquiry-based pedagogy (IBP) on the WhatsApp Messenger platform, the university educators managed to transform their professional identities as well as improve their pedagogical practices. It may be concluded that WhatsApp Messenger offers a conducive platform for educators’ professional development. It is recommended that universities use WhatsApp Messenger for professional development through online communities of practice.
A Practice and Consideration of AI Model Development via Once-for-All

Once-for-All (OFA) is an AI model development method that allows a model (Supernet), a redundant representation of a base AI model (Base Model), to be trained only once to obtain models (Subnets) that are suitable for various devices in terms of accuracy, processing speed and number of parameters. In this paper, we address a road obstacle detection system consisting of multiple AI models, and apply OFA to each AI model. Finally, we succeed in obtaining the optimal Subnets for the entire system by considering the combination of the obtained Subnets.
Anomaly Detection Using Pretrained Model with Neighbor Features and Anomalous Features

In anomaly detection using deep learning, normal models based on pretrained CNNs using only normal data have become the mainstream. This framework can only use normal data for training and discards valuable information even when abnormal data is available. In addition, PaDiM, one of the representative models in this framework, creates a normal model for each position and thus cannot consider the relationship between each pixel. In this paper, we propose a method to generate a normal model by considering the information of anomalous data and neighborhood information, and achieve an image-level AUROC: 0.984 on MVTec AD.
High Speed Image Matching Method Using Coarse to Fine Search Based on Uniform Performance of Rotation Matching

This paper introduces a fast image matching method using coarse to fine search. For reliable and high-speed matching, it is important to decide on optimal interval on coarse search. In the proposed method, efficient search is realized by making the coarse search interval non-uniform in order to make the performance of rotation matching uniform. Through experiments using 4 types×121 test images, it was confirmed that the proposed method can achieve a speedup of up to 60% compared to the conventional method.
Post-editing Eğitimi, Acemi Post-editörlerin Performansını Etkiliyor mu? Deneysel Bir Çalışma

Machine translation systems led to the creation of a new role for translators: the post-editor. With the birth of neural machine translation systems, the demand for post-editing has been increasing in the recent years, and it has now become a common service given by language service providers and professional translators. Such a change in the landscape of the translation industry might evolve the translation training programs worldwide. It is still heavily discussed whether post-editing and translation skills overlap, and post-editing courses are now included into the curriculum by several translation departments. We set out to investigate whether post-editing training influences the performance of student post-editors in order to explore the necessary background and skills in post-editing tasks. We measured productivity parameters and quality of the final outputs produced by two groups of participants, one of which was previously trained on post-editing. Our results show that, the experimental and control groups did not differ significantly from each other in terms of productivity. There was also little to no difference when we evaluated the post-edited outputs produced by both groups against a reference text using automatic machine translation evaluation metrics. However, we detected a statistical significance between the groups when we analyzed the number of errors in the final output. The post-editors in the experimental group were more aware of the typical errors of machine translation engines.
The Effect of Education Using Leaflet Media About Prevention of Anemia During Menstruation on the Knowledge of Young Girls at SMP 19

ABSTRACT Anemia is a condition where the level of hemoglobin (HB) in the blood is lower than normal. Blood women suffer from anemia from 37.1% in 2013 to 39.1% in 2018. Menstruation is one of the factors that cause anemia at this time women lose a lot. SMP 19 Palu has never received education about preventing anemia during menstruation which affects the knowledge of adolescent girls in an effort to prevent anemia. This study aims to analyze the effect of education using leaflets about preventing anemia during menstruation on the knowledge of adolescent girls at SMP 19 Palu. This research method is Pre Experiment with One Group Pretest Posttest Design, the research population is all students of class VII, VIII, IX, totaling 125 students. The sample in this study were 35 female students. Sampling was done by Simple Random Sampling. The results of the pre-test were only one person with good knowledge and 24 people with sufficient knowledge. In the post test 31 people with good knowledge and 4 people with sufficient knowledge got a score of 17.50 and a p-value of 0.000. can: education using leaflets can improve adolescent girls about preventing anemia during menstruation. It is recommended that the school provide education using leaflet media to increase knowledge of preventing anemia during menstruation.
Perbandingan Naïve Bayes Dan C45 Dalam Klasifikasi Tes Kesehatan Mahasiswa Baru Akbid As-Syifa

Medical test is important to determine the health of a person's body so they are often carried out and have even become one of the requirements for an institution to accept new members such as the As-Syifa Midwifery Academy which conducts medical tests for prospective new students. The problem is that so far the classification system for recapitulation of medical test results is still done manually, thus slowing the performance of the campus and it is feared that there will be damage to the data format. The problem solving technique above is carried out with a data mining process using Naïve Bayes and Decision Tree C45 where the two algorithms are compared to find the 1 best classification algorithm to be implemented in the system. The dataset uses data on the recapitulation of the results of the 2018 new student health tests sourced from the Administration (TU) of Akbid As-Syifa. The comparison uses 4 data testing models and the confusion matrix as the performance evaluation value of the modeling algorithm. The modeling results obtained that the Decision Tree C45 algorithm is superior and suitable to be implemented with an accuracy rate of 100% while Naïve Bayes has a maximum accuracy rate of 96%. The purpose of this study was to help Akbid As-Syifa classify the results of the health test of prospective new students.
Sentiment Analysis of IMDb Movie Reviews

Abstract: Sentiment analysis is an artificial intelligence branch that focuses on expressing human emotions and opinions in the sense of data. We try to focus on our sentiment analysis work on the Stanford University-provided IMDB movie review database in this post. We look at the expression of emotions to determine whether a movie review is positive or negative, then standardize and use these features to train multiple label classifiers to identify movie reviews on the right label. Since there were no solid framework structures in a study of random jargon movies, a standardized N-gram approach was used. In addition, a comparison of the various categories' methods was conducted in order to determine the best classifier for our domain query. Our process, which employs separation techniques, has an accuracy of 83 percent.
A Corpus-Based Study on Politeness Used by L1 Thai EFL Learners

This research aimed to identify politeness super-strategies, the strategies under each category of super-strategy, and common lexical patterns in each strategy when performed by L1 Thai intermediate-level EFL learners in English conversation. It attempted to use the corpus-based approach to examine the pattern of how L1 Thai intermediate-level EFL learners use politeness in English conversation and, on the other hand, to examine the existing problems with L1 Thai intermediate-level EFL learners’ use of politeness super-strategies based on the findings. In total, 30 L1 Thai intermediate-level EFL learners were involved in this research. A learner corpus was built to investigate the use of politeness super-strategies and the frequencies of lexical patterns. The findings illustrated that L1 Thai intermediate-level EFL learners tended toward using the negative politeness super-strategy, while no off record super-strategy was found. Moreover, L1 Thai intermediate-level EFL learners used a limited number of politeness strategies under the super-strategy categories identified in this research, with a limited selection of lexical patterns in each strategy. The findings reveal that L1 Thai intermediate-level EFL learners adopt only a small range of politeness strategies, thereby resulting in a discrepancy in the performance of politeness in English conversation.
Contextualized and Localized Supplementary E-Learning Materials in Science 8 Physics

This study is in response to the current situation in the Philippines that aims to support students' learning needs even from the comfort of their own homes, as well as to improve the teaching and learning process through the use of contextualized and localized supplementary e-learning materials for students. This study aimed to develop, evaluate, and determine the effectiveness of contextualized and localized supplementary e-learning materials for Grade 8 students in Science (Physics) at a public national high school in Antipolo City during the school year 2021-2022. The study utilized both descriptive and experimental research designs. The developed materials were evaluated by twenty science experts and twenty science teachers from the City Schools Division of Antipolo. The participants of the study were thirty Grade 8 students. The data gathering instruments used in this study were the questionnaire checklist, pretest, and posttest. The study showed that the developed contextualized and localized supplementary e-learning materials in science 8 (Physics) helped in the improvement of the teaching and learning process. It is recommended that the materials be used accordingly and constantly modified for improvement.
Analysis of Ability to Understand Text Reading on Students of English Education Study Program

This research was conducted with the title Analysis of the second semester students' ability of the English Education Study Program in understanding reading texts. The purpose of this research is to examine the students' ability to understand the text. This study uses a qualitative method. The population used is the second semester students of HKBP Nommensen Pematangsiantar University for the 2020/2021 academic year. The sample taken is 32 people. The research data were collected from the given text. During the test, 30 questions were given. The type of test given is an objective test. After collecting the data, the writer then analyzed the students' scores. From these students, it was found that almost all students could not understand the text. There are 19 students (59.37%) who score below 65 and that makes them still need more attention than to help them improve their ability to understand the text.
Teka-Teki Silang dan Video Animasi Meningkatkan Pengetahuan Gizi Seimbang pada Anak Usia Sekolah

Nutritional problems in school-age children can occur because of less knowledge. School-age period is the right time for provide nutrition education. Nutrition education needs the interesting and fun media to make it easier to understand, such as crossword puzzles and animation videos. This study aims is to determine the influence of nutrition education with crossword puzzles and animation videos on balanced nutrition knowledge in school-age children. The methods that used is a quasi-experimental with a pretest and posttest group design. The 54 students aged 10 to 12 from fifth graders were selected by stratified random sampling and divided into two media groups. The statistical tests utilized are the independent and paired sample t-tests. The majority of responders were male (63%) and 11 years old (79.6%). The results showed that there were differences in the influence of nutrition education before and after being given crossword puzzles (p = 0.003) and animation video (p = 0.000) on balanced nutrition knowledge. The results also showed that there was no difference in the effectiveness of nutrition education between crossword puzzles and animated videos on balanced nutrition knowledge (p = 0.987). Crossword puzzles and animation videos have an impact for given balanced nutrition education on school-aged children. Animation videos is more effective in increasing balanced nutrition knowledge on school-age children compared to crossword puzzles. It is recommended that the government and schools implement nutrition education initiatives using engaging and entertaining media, such as animation videos.