Reviewerly/exHarmony huggingface dataset:
METODE PEMBELAJARAN BERBASIS GAME UNTUK MENINGKATKAN KETRAMPILAN MEMBACA DAN MENULIS AKSARA JAWA

The purpose of this study is to find out how effective game-based learning is for improving Javanese script reading and writing skills. This research method is qualitative with the sources of data from this study are caregivers, foster children, and documents. The target in this study was the Al-Maun orphanage located on Margo Noyo street, Ngajum Village, Ngajum District, Malang Regency, East Java Province, Indonesia. Data collection techniques used were interviews, observation, documentation and tests. The data analysis technique used is an interactive model. From the results of this study it can be concluded that the game-based learning method is very effective in improving the ability to read and write Javanese script among children. Judging from the results the average value of children - children increased and only 8% are below the average (75).
Missing data imputation in cardiometabolic risk assessment: A solution based on artificial neural networks

A common problem when working with medical records is that some measurements are missing. The simplest and the most common solution, especially in machine learning domain, is to exclude records with incomplete data. This approach produces datasets with reduced statistical power and can even lead to biased or erroneous final results. There are, however, many proposed imputing methods for missing data. Although some of them, such as multiple imputation, are mature and well researched, they can be prone to misuse and are not always suitable for building complex frameworks. This paper explores neural networks as a potential tool for imputing univariate missing laboratory data during cardiometabolic risk assessment, comparing it to other simple methods that could be easily set up and used further in building predictive models. We have found that neural networks outperform other algorithms for diverse fraction of missing data and different mechanisms causing their missingness.
Implementasi Machine Learning dalam Penentuan Rekomendasi Musik dengan Metode Content-Based Filtering

The industry that is experiencing significant development is the music industry. An example of its development is the many online music service providers of application platforms. The amount of data stored makes it difficult to analyze existing data, the presence of Machine Learning is felt to be able to answer these challenges. Improving user experience is important to attract users to use the applications they have. The recommendation system is one way to improve that. This research aims to create a system that can present music recommendations according to user preferences so that the user's comfort level will increase. The system developed in this research uses the Extreme Programming method with several stages, namely planning, design, coding, and testing. This research utilizes Machine Learning in searching for data patterns and Content-Based Filtering (CBF) methods in finding recommendations. The recommendation system with the CBF method can produce a song similarity level of up to 0.6684, as well as the value of precision reaching 0.125 and 0.200 at recall. The results of Performance Testing and System Testing obtained stated that the recommendation system can run well with an average response time 3.5 seconds. The conclusion of this research is that the recommendation system using the CBF method can produce recommendations that are in accordance with user preferences, but with not too much data. More effective algorithms are needed for larger data.
Enhanced DBSCAN with Hierarchical Tree for Web Rule Mining

Like other mining, web mining is also necessary to increase the power of web search engine to identify the intended web page and web document. While processing with large datasets, there arises several issues associated with space availability, similarity relationships between different webpage’s and running time. Hence, this paper intends to develop an enhanced web mining model based on two contributions. At first, the hierarchical tree is framed, which produces different categories of the searching queries (different web pages). Next, to hierarchical tree model, enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique model is developed by modifying the traditional DBSCAN. This technique results in proper session identification from raw data. Moreover, this technique offers the optimal level of clusters necessitated for hierarchical clustering. After hierarchical clustering, the rule mining is adopted. The traditional rule mining technique is generally based on the frequency; however, this paper intends to enhance the traditional rule mining based on utility factor as the second contribution. Hence the proposed model for web rule mining is termed as Enhanced DBSCAN-based Hierarchical Tree (EDBHT). It benefits in providing the search results depending on high level information (e.g., location), so that the ability of search engine in providing the interesting association rules can be improved. Next, to the implementation, the performance of proposed EDBHT is found to be enhanced when compared over several traditional models.
The Analysis of Network Expressions Under the Positive Energy Ecology of Real Society

At the end of the 20th century, with the rapid development of modern science and technology, the world has entered an Internet era, and a new mainstream medium --network language has emerged.In this Internet age, Chinese Internet language has gone through such a process: from extensive to standard and orderly, from transmitting negative energy to advocating positive energy, and from immature at the beginning to mature today.A review of the "top ten Internet expressions of the year" released by the China language resources monitoring and research center from 2015 to 2019 shows that most of these Internet buzzwords are closely related to social life, and the number of cyber languages that embody core socialist values has also increased significantly.For example, the "top 10 cyber languages" in 2019 reflects this feature.In terms of its annual network buzzwords, much of which comes from the social life and political events, positive energy is the main melody, it has showed people not only pay attention to own life, also pay attention to the social life, and the thought and behavior also gradually return to unity, made the network language toward the right direction of healthy and orderly development.Of course, cyber language has also brought some bad effects, but we should not give up eating for fear of choking, we should give correct guidance, because it is still in the process of constant development and change, so the country will always be on the road to its regulatory norms.
Sample Complexity Bounds for RNNs with Application to Combinatorial Graph Problems (Student Abstract)

Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurrent neural networks (RNNs) are particularly suited for this task and can even outperform state-of-the-art heuristics. However, the theoretical framework for estimating real-valued RNNs is understood only poorly. As our primary contribution, this is the first work that upper bounds the sample complexity for learning real-valued RNNs. While such derivations have been made earlier for feed-forward and convolutional neural networks, our work presents the first such attempt for recurrent neural networks. Given a single-layer RNN with a rectified linear units and input of length b, we show that a population prediction error of ε can be realized with at most Õ(a4b/ε2) samples.1 We further derive comparable results for multi-layer RNNs. Accordingly, a size-adaptive RNN fed with graphs of at most n vertices can be learned in Õ(n6/ε2), i.,e., with only a polynomial number of samples. For combinatorial graph problems, this provides a theoretical foundation that renders RNNs competitive.
Adabot: Fault-Tolerant Java Decompiler (Student Abstract)

Reverse Engineering has been an extremely important field in software engineering, it helps us to better understand and analyze the internal architecture and interrealtions of executables. Classical Java reverse engineering task includes disassembly and decompilation. Traditional Abstract Syntax Tree (AST) based disassemblers and decompilers are strictly rule defined and thus highly fault intolerant when bytecode obfuscation were introduced for safety concern. In this work, we view decompilation as a statistical machine translation task and propose a decompilation framework which is fully based on self-attention mechanism. Through better adaption to the linguistic uniqueness of bytecode, our model fully outperforms rule-based models and previous works based on recurrence mechanism.
Multiple-Environment Markov Decision Processes: Efficient Analysis and Applications

Multiple-environment Markov decision processes (MEMDPs) are MDPs equipped with not one, but multiple probabilistic transition functions, which represent the various possible unknown environments. While the previous research on MEMDPs focused on theoretical properties for long-run average payoff, we study them with discounted-sum payoff and focus on their practical advantages and applications. MEMDPs can be viewed as a special case of Partially observable and Mixed observability MDPs: the state of the system is perfectly observable, but not the environment. We show that the specific structure of MEMDPs allows for more efficient algorithmic analysis, in particular for faster belief updates. We demonstrate the applicability of MEMDPs in several domains. In particular, we formalize the sequential decision-making approach to contextual recommendation systems as MEMDPs and substantially improve over the previous MDP approach.
Face of Negotiation theory:A Critical Analysis of Hijab (Veil) Among Muslim Women In Pakistan

The practice of hijab (veiling) among Muslim women has triggered a great deal of scholarly exploration and debate. This research puts forward empirical evidence to clarify the perception of people about appearance and veil in Pakistani context and highlights the conflicting meanings and purposes of veil among people belonging to different strata of society. With the increasing popularity of veil among Muslim women not only motivated religious scholars to get into this matter holistically but also prompted sociolinguists to explore the reality. Informed by face negotiation theory and social semiotics, this study unveils the ideological implications of wearing hijab. To dissect the reality, the research has been carried out at two levels. First, it examines the perspective of observers through questionnaire and second, it investigates the ideology of women who do/don’t wear hijab through short interviews. Specifically hijab is assumed to draw a line among different strata of Pakistan on one hand while on other, in strict sense it creates a gap between exclusion and inclusion of women in Islamic/Muslim community
Systematization of the Scientific and Pedagogical Heritage of Boris Korotyaev

The article is dedicated to the scientific and pedagogical heritage of the outstanding scientist Boris Korotyaev. It provides a clear justification for the appropriateness of our scientific intelligence on this issue. A significant number of scholars citing Boris Korotyaev testifies to the relevance of the principles and the use of his pedagogical ideas by modern scholars. The article presents the systematization of the scientifically-pedagogical works of a scientist in chronological sequence according to the following criteria: content and problems. According to the content, the scientific and pedagogical heritage of Boris Korotyaev is classified into monographs, study guides, methodological recommendations, and scientific articles. The problems that are addressed in the scientific works of Boris Korotyaev relate to general pedagogy, didactics, the theory of education, the organization of work of secondary and higher schools, and since the 2000s, he has covered the topic of pedagogical philosophy in his books.
PENGARUH PROJECT BASED LEARNING TERHADAP MOTIVASI BELAJAR SEKOLAH DASAR NEGERI 167 PEKANBARU

This study aims to study: the differences between students who take part in spending with a project-based learning model and students who follow learning with conventional models of student learning motivation. This study was a quasi-experimental study with nonequivalent control group design. The population in this study were fifth grade students of Public Elementary School 167 Pekanbaru in 2018/2019. Data collection using tests to determine critical thinking skills and questionnaires to learn learning motivation. Data analysis using t-test and Manova with a significance level of 5%. The results of this study indicate that there are differences between students who take part in spending with a project-based learning model with students who take learning with the conventional model on student learning motivation with the results of data calculations also understand Thitung at 17.412 and P = 0.00.
COMPONENTS OF PUBLIC SPEAKING: THE FACTORS THAT MAKE STUDENTS INTERESTED IN ENGLISH SPEECH AND TEACHING

Abstract: This study tries to find out which components of Public Speaking actually make the students interested in English speech and English teaching. This is a descriptive qualitative study where the data for the research were collected through a self-administered questionnaire distributed to all students of the fourth semester at English Study Program of State Polytechnic of Bengkalis – Riau. The technique used to collect the data was done by asking the students to watch a video of a speech and then answer the questionnaire given. The questionnaire was set based on relevant literature review and also focused on condition of the students based on daily observation done by the writer during his teaching Public Speaking subject to the students. After collecting the data, the writer sorted the data by summarizing and choosing the specific category, displaying and verifying the data analysis and making the conclusion from the finding data. The findings of this research are hoped to give another point of view to English lecturers to realize certain parts of teaching that actually can make it more interesting for the students. The result of this study shows that Story Telling and Body Language performed by a speaker or a lecturer are among the most interesting aspects that make them interested in an English speech or teaching. Beside, this study also reveals that lack of Pauses and Visual Aids performed by the lecturers make their teaching become less interesting.
Perancangan Chatbot di Universitas Proklamasi 45

Customer Support is a person who serves prospective student questions appropriately and quickly. However, the conventional Customer Support requires high operating costs and has limited working hours. Moreover, in busy times, sometimes Customer Support is slow in responding to questions. In this study, a Chatbot system will be built to answer basic questions from prospective students, so it can improve the efficiency and effectiveness of Customer Support work at the Proklamasi 45 University. The more complex questions will still be directed to conventional Customer Support by the system. The Chatbot system in this study was built using the waterfall method, which consists of the requirements specification, design, implementation, testing, and implementation stages. The intent recognition will be performed using the StarSpace algorithm and Count Vectorizer by the Chatbot System. The frequency of occurrence of words in a sentence will be a reference to determine the purpose of a sentence. Testing is performed by Black box method by sending questions and evaluating the responses given by the system. The test results show that the Chatbot can provide answers with an accuracy of 97.75%. So it can be concluded that the Chatbot system can substitute the role of Customer Support. Accuracy can be improved by adding more data variations to the training data
Tolerance, Empathy, and Aggression as Factors in Compliance with Rules of Online Communication by Russian Adolescents, Young Adults, and Parents

Background.Internet psychology has changed its research focus from describing the Internet as a separate space, with continuous interaction between offline and online communication, to exploring socialization in the world of mixed online/offline reality.This paper deals with the psychological and user activity factors of communication on the Internet in comparison with offline communication.Objective.To differentiate the role of user activity, difficulties with regulating and expressing aggression, empathy and tolerance in compliance with online communication rules.Design.The study included 1,029 adolescents aged 14-17, 525 adolescents aged 12-13, 736 young adults aged 17-30, and 1,105 parents of adolescents aged 12-17.Participants assessed how likely they are to follow communication rules online and offline, and reported their user activity level; they filled out the Chen Internet Addiction Scale, Buss-Perry Aggression Questionnaire, Davis Multidimensional Empathy Questionnaire, and Tolerance Index.Results.It was shown that adolescents in general are a "risk group" for noncompliance with communication rules ("Internet etiquette"), but this is due to their general propensity not to follow any rules.Both in adults and in adolescents, failure to follow online communication rules is related to difficulties with aggression regulation, tolerance, empathy, and a low level of propensity for Internet addiction. Conclusion.A difference between online and offline communication is related not to difficulties with regulation of aggression (anger and hostility), but to a lack of empathy and tolerance, and signs of Internet addiction.
A Bandwidth-Aware Video Segments Request Strategy to Optimize User’s QoE in Connected Vehicle Networks

In ITS environment, the driver-assistant services usually demand delivering video content to vehicular users, which is very useful in the intervention of emergency services, and reminds the drivers the traffic jam in ahead road. Nowadays for online video delivery in connected vehicle networks, HTTP adaptive streaming (HAS) is one of the most promising technologies, where a video is encoded into multiple bitrates and then split into small segments. It is crux for ITS service providers how to improve users' QoE through an adaptive video segments request strategy. From the perspective of adaptive matching among segment bitrate, network bandwidth, and buffer occupancy, we propose a bandwidth-aware video segment request strategy to optimize user's QoE (for short Bw-QoE), which consists of two parts, i.e., available bandwidth estimation, and segment's bitrate selection. First, based on the severity of bandwidth fluctuation, Bw-QoE estimates the available bandwidth with different historical weighted average methods. Then constrained by the estimated available bandwidth and buffer occupancy, the adaptive bitrate selection is formulated as an optimization model to maximize user's QoE of a video streaming session. It is fairly attractive for Bw-QoE to prevent buffer overflow, underflow and unuseful download, and to reduce bitrate switching. Compared with the classic strategy (e.g., LIU's strategy), the simulation results show that the proposed strategy Bw-QoE has higher average video bitrate (about 8%-10%), less interruption (about 100%), less segment bitrate switching (about 15%-17%) and higher buffer occupancy (about 13%-51%), which demonstrate that our strategy effectively improves QoE.
The Impact of Management Information Systems Features on Crisis Management in Jordanian Commercial Banks (Field Study on Jordanian Commercial Banks in the Northern Sector)

This study aims to explore the impact of management information system’s (MIS) features (reliability degree on the system, the availability of computer devices, elasticity degree of the system, and the training programs) on crisis management in the Jordanian Commercial Banks (Northern Region), trying to build theoretical and practical frame for the effect of management information system in fulfilling innovations. And trying to clarify the impact of these features on crisis management. In addition to introduce suggestions and recommendations to the firms, and to the future researches in same topics. Eleven commercial banks have been selected, based on a random judgment sample, (78) respondents were drawn from main commercial bank (northern region) of different administrative levels, and all considered to be valid. The most important research findings that there is positive statistically significant influence among management information system features (reliability degree on the system, the availability of computers’ devices, elasticity degree of the system, and the training programs) on crisis management. The study recommended that problems and constraints that might adversely affect the application of management information systems should be known in the future.
Teaching Undergraduate Artificial Intelligence Classes: An Experiment with an Attendance Requirement

We report on an experiment that we performed when we taught the undergraduate artificial intelligence class at the University of Southern California. We taught it – under very similar conditions – once with and once without an attendance requirement. The attendance requirement substantially increased the attendance of the students. It did not substantially affect their performance but decreased their course ratings across all categories in the official course evaluation, whose results happened to be biased toward the opinions of the students attending the lectures. For example, the overall rating of the instructor was 0.89 lower (on a 1-5 scale) with the attendance requirement and the overall rating of the class was 0.85 lower. Thus, the attendance requirement, combined with the policy for administering the course evaluation, had a large impact on the course ratings, which is a problem if the course ratings influence decisions on promotions, tenure, and salary increments for the instructors but also demonstrates the potential for the manipulation of course ratings.
A study of the possibilities of text mining and machine learning for score evaluation and review content

With the widespread use of the Internet, there are more and more opportunities to purchase a variety of products through online shopping. The opportunities are not only for small products such as books, but also for home appliances. Previously, when purchasing a product, users who wanted to buy a product would visit a store and get expert advice on what to buy. Now, however, customers consider reviews on the Internet to be more important information for considering the products to be purchased. And evaluation page consists of an overall evaluation, an evaluation of each feature, and comments, which are word of mouth. The overall evaluation and the evaluation of each feature is often a score evaluation, and organized information such as the average and the distribution of scores are presented. However, it is difficult to read all the comments that are word of mouth because they are often enumerated as is. Therefore, in this study, we created a system to label which features people commented on in response to the word of mouth comments using data from the TV’s comprehensive evaluation page. 2392 TV evaluation results from Sony.com were used. From the extracted data, text mining was performed on the comments, which are word of mouth, followed by labels of which features are commented on. When 80\% of the test data was prepared and implemented against 20\% of the learning data, the label was predicted with 77\% accuracy. From this study, we used text mining to label the comments, which are customer impression. from the current study, text mining was used to label the comments, which are customer impression. The results and score ratings were used to identify customer trends.