Reviewerly/exHarmony huggingface dataset:
THE URGENCY OF THE DEVELOPMENT OF MULTIPLEINTELLIGENCE-BASED PLAYING ACTIVITY MODELS IN DISASTERAREAS

So far, playing activities programs in disaster areas are still partial. The research entitled Multiple-Intelligence-BasedPlaying Activity Models in Disaster Area in Central Java has brought about multiple-intelligence-based playing activityprogram models, especially in disaster areasThe research aims at: 1) describing multiple- intelligence base playing activity models from traditional games withlocal wisdom; 2) designing the multiple-intelligence-based playing activity models which can be practically applied indisaster areas; 3) empirically applying the multiple-intelligence-based playing activity models.The research employed action research method followed with action/ implementation with stages: 1) identifying andmapping the existence of multiple-intelligence-based playing activity models in Central Java by using desk analysismethod, survey, and Focus Group Discussion (FGD), 2) designing multiple-intelligence-based playing activity modelswith descriptive and regression method, and 3) formulating and applying the multiple-intelligence-based playing activitymodels for Child-Friendly Schools in disaster area as well as the guidelines for its application.The result of the research is the mapping of playing activity models in disaster areas. The driving factors of thesuccess of multiple-intelligence-based playing activity models application includes: everyone’s awareness to get back totraditional games, minimized technology- based playing activities, parent’s guidance in children’s playing activities, andgovernment’s support to multiple-intelligence-based playing activities.
Teaching with Commentaries

Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In this paper, we take steps towards extending the scope of teaching. We propose a flexible teaching framework using commentaries, meta-learned information helpful for training on a particular task or dataset. We present an efficient and scalable gradient-based method to learn commentaries, leveraging recent work on implicit differentiation. We explore diverse applications of commentaries, from learning weights for individual training examples, to parameterising label-dependent data augmentation policies, to representing attention masks that highlight salient image regions. In these settings, we find that commentaries can improve training speed and/or performance and also provide fundamental insights about the dataset and training process.
SISTEM INFORMASI TRY OUT MANDIRI UNBK BERBASIS WEB MENGGUNAKAN METODE LINEAR CONGRUENT PADA KELOMPOK KERJA MADRASAH (KKM) MTS N 2 DEMAK

Abstract The try out process is an exam simulation exercise every year before the National Examination conducted by school institutions. The main objective of this exercise is as an effort of school institutions to prepare students to get maximum scores on the National Examination. Madrasah Working Group (KKM) of MTs 2 Demak is a body within the Ministry of Religion which fosters coordinative cooperative relations between Demak MTs 2 and private Madrasahs in the area of Demak 2 MTs. So far the Try Out system at the KKM level still uses manual procedures and is still off line using paper. As a result of this system, there are still delays in the distribution of questions and reports at the central level. In making Try Out questions, randomization of questions only uses the package method questions A, B and C, so there are still students who give each other answers. LCM (linear congruent method) is a method of generating random numbers on the Try Out. The principle of this method is where the form of the LCM number as the scrambler of the question, including increament (c), modulus (m), multiplier (a), and initial value (Z0), which has provisions: c = 0 ≤ c &lt;m, m = 0 &lt;m, a = 0 &lt;a &lt;m, and Z0 = 0 ≤ Z0 &lt;m. Making this application using the PHP programming language and database used is MySql with the R &amp; D system development method according to Borg and Gall, including: Research and information collecting, Planning, Develop prelminary form of product, Preliminary field testing, Main product revision, Main field testing , Operational product revision, Operational field testing, Final product revision, Dissemination and implementation. Madrasah Working Group (KKM) Demak MTs 2 in conducting a try out system still uses the manual offline method and the problem scrambler is only limited to packages A, B, and C, then with the Try Out Information System the web-based KKM level is expected to facilitate parties KKM and tighten students to avoid fraud.
독립유공자 포상과 역사인식 - 1958년과 1962년 독립유공자 포상 명단 분석 -

This article aims to explain the relation between the political context and the country’s official perception of history at the time inherent in the conferment of state decorations on independence movement activists in 1962, from the point of view that the conferment is a social construct that reveals the country’s perception of history. For the explaining, this article provides a comparative analysis between the lesser-known conferment plan of state decorations on independence movement activists in 1958 and the conferment in 1962. The conferment plan in 1958, promoted by the Syngman Rhee administration, did not lead to an official conferment even after selecting the recommended recipients, but used as the basic data for the conferment in 1962. This article attempts to clarify the historical context of the conferment in 1962 by examining the continuity and discontinuity between the conferment plan in 1958 and the conferment in 1962. This article also tries to show the country’s official perception of history that intervened in the recommended list of the conferment in 1958 and 1962 by analyzing the internal hierarchy of the independence movement, which is represented by the grades of the decoration. The last purpose of this article is to clarify how the selection of independence patriots and the political intentions of the military regime are interconnected through the case of Kim Ch’angsuk, the most symbolic person in the conferment in 1962.
Digitalisasi Layanan Sistem Informasi Pemasaran dan Laporan Persediaan Barang

Abstract Companies require a management information sistem that is good to making work efficiency and effectiveness and for improve service to customers. The increasing competition will force companies that are growing and that has been developed to provide effective and efficient services, so that the goals and objectives can be achieved by proper and eventually the company can compete in maintaining business continuity. Technological advancements encourage every type of business to keep abreast of developments and continue to improve their ability to manage data and information needed. This paper will discuss how to design, develop and implementation services as well as analyzing digital information sistem fatherly sales and inventory reports. With this method of digitizing sistems through information sistem service is possible to awaken a reliable information sistem, so that data can be stored with appropriate and practical, the information needed can be searched quickly and accurately as well as the necessary reports can be viewed in an easy and detailed.
Power grid middle platform data anomaly detection based on two-stream convolutional neural network

With the rapid development of the Internet, network anomaly detection has increasingly become a problem worthy of attention. To effectively detect anomalies in the network, this paper describes a two-stream convolutional neural network (TS-CNN) anomaly data detection method. First, TS-CNN uses multiple 1-D convolutional layers to extract features from 1-D network data, and generate 1-D feature vectors through pooling; at the same time, TS-CNN preprocesses 1-D network data to generate 2-D grayscale images. Multiple 2-D convolutional layers are used to extract features from grayscale images, and 1-D feature vectors are generated through a pooling layer and a fully connected layer; finally, the two feature vectors are spliced through a fully connected layer to output the detection results. Experimental results show that the detection method based on TS-CNN can effectively detect network abnormal data.
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning

Backward locking and update locking are well-known sources of inefficiency in backpropagation that prevent from concurrently updating layers. Several works have recently suggested using local error signals to train network blocks asynchronously to overcome these limitations. However, they often require numerous iterations of trial-and-error to find the best configuration for local training, including how to decouple network blocks and which auxiliary networks to use for each block. In this work, we propose a differentiable search algorithm named SEDONA to automate this process. Experimental results show that our algorithm can consistently discover transferable decoupled architectures for VGG and ResNet variants, and significantly outperforms the ones trained with end-to-end backpropagation and other state-of-the-art greedy-leaning methods in CIFAR-10, Tiny-ImageNet and ImageNet. Thanks to improved parallelism by local training, we also report up to 2× speedup over backpropagation in total training time.
A New Method for a Micromouse to Find its Way Through a Maze Unaided

A Micromouse is a robot that is designed to find its way through any maze with a black line on a white map. The white map acts as a white background for the maze. Since its external environment is not known, the robot requires some intelligence or decision-making capabilities to find its way through the maze. Therefore, this paper covers on the “decision making algorithm”. It is an electro-mechanical robot that consists of 3 main sub systems: the drive system, an array of sensors, and the control system. This paper also covers algorithm and programming logic to solve the maze. Then gradually improves the algorithm to accurately solve the maze on a oneway path with extra intelligence. Our aim is to allow the Micro mouse to reach the end of the maze.
Visualizing Recommendations for Points of Interest

Workshop ECAI. European Conference on Artificial Intelligence (24th. Santiago de Compostela. 2020)
An Advanced Neighbourhood approach of recommending movies on Netflix data by the combination of KNN and XGBoost

A Recommendation System (RS) is a special kind of intelligent system that exploits existing users preferences i.e. ratingson items or auxiliary information provided to make recommendations on items towards the users. Generally RS has wider range ofapplications in e-commerce services, social networking etc. Collaborative Filtering (CF) is the most utilized technique inrecommending services, but still it suffers from other issues like recommendation accuracy, whether the user is recommended withcorrect suggestions. This paper proposes a Hybrid Collaborative Filtering that encourages Item-Item similarity matrix and User-Usersimilarity matrix along with developing the model with advanced feature engineering techniques. The vital problem encountered inrecommender systems is the data sparsity. The proposed work utilizes Shrunk based Pearson Similarity measure calculation to getsimilarity values which leads to overcome data sparsity issues. The paper further addresses the computational and memory issueswith the proposed K-Nearest Neighbours (KNN) and eXtreme Gradient Boost (XGBoost) approach an optimized neighbourhoodtechnique, which is a combination of the KNN and XGBoost formulations. The results outperformed well compared to the existingtraditional collaborative filtering technique resulting in reduced error rates as RMSE value 1.07153 and MAPE value around34.4460.
Dynamically rational judgment aggregation

Judgment-aggregation theory has always focused on the attainment of rational collective judgments. But so far, rationality has been understood in static terms: as coherence of judgments at a given time, understood as consistency, completeness, and/or deductive closure. By contrast, this paper discusses whether collective judgments can be dynamically rational, so that they change rationally in response to new information. Formally, a judgment aggregation rule is dynamically rational with respect to a given revision operator if, whenever all individuals revise their judgments in light of some information (a learnt proposition), then the new aggregate judgments are the old ones revised in light of this information, i.e., aggregation and revision commute. We prove a general impossibility theorem: if the propositions on the agenda are sufficiently interconnected, no judgment aggregation rule with standard properties is dynamically rational with respect to any revision operator satisfying some mild conditions (familiar from belief revision theory). Our theorem is the dynamic-rationality analogue of some well-known impossibility theorems for static rationality. We also explore how dynamic rationality might be achieved by relaxing some of the conditions on the aggregation rule and/or the revision operator.
Research on the Application of Blended Teaching Model in College English Teaching

With the increasing integration of information technology and foreign language teaching, the Chinese college English teaching reform is more and more focused on applying the mixed teaching model to daily foreign language teaching. According to the current teaching situation, the influence of the traditional teaching model is still deep-rooted, it is difficult to jump out of the inherent circle built by years, the roles of teachers and students have not been changed after years of teaching, the teaching concept has not been changed and innovative, and the teaching method is single. In view of these situations, the application of mixed teaching mode in college English is analyzed, and it is demonstrated that the use of online teaching resources can achieve better teaching results, thereby promoting the development of college English teaching reform activities.
Regulating Algorithms in The European Data- Driven Economy: The Role of Competition Law and Civil Liability

While showing the potential to make the market more competitive and efficient, algorithms are acknowledged to pose a challenge to competition law enforcement. This is because algorithmic tacit collusion does not amount to an outright agreement but is something more than mere market parallelism, which is normal in competitive markets. This essay reviews the economic and legal scholarship, the national and supranational case law and the supranational policy debate on this issue to explore if and how competition law can play a role in clarifying such a grey area, without discouraging technological innovation and economic development. In this regard, this essay finds that, while the case law has already addressed algorithms implementing explicit anticompetitive agreements under Article 101 TFEU, scholars fail to agree on how to tackle algorithmic tacit collusion. This has come under the radar of ongoing policy initiatives, such as the European Commission’s New Competition Tool initiative. Waiting for innovative regulatory and competition law solutions to better tackle algorithmic collusion, this essay proposes to use, as an alternative to Article 101 TFEU, the notion of collective abuse of dominant position under Article 102 TFEU. Finally, this essay considers how civil liability and private enforcement may contribute to competition law enforcement against algorithmic collusion.
Kontribusi Pola Asuh, Kondisi Sosial Ekonomi Orang Tua dan Kompetensi Guru terhadap Motivasi Belajar Siswa Kelas VII SMP Negeri 2 Juwiring Klaten

The success of education can be seen from the output generated from an educational institution. Student motivation is an important part of the learning process. In fact, 7th grade students of Junior High School 2 Juwiring Klaten have low learning motivation. This study aims to analyze and test the contribution of: 1) parenting, socioeconomic conditions of parents and teacher competence toward student’s learning motivation at 7th Grade of Junior High School 2 Juwiring Klaten; 2) parenting toward students' learning motivation at 7th Grade of Junior High School 2 Juwiring Klaten; 3) parents 'socioeconomic conditions toward student’s learning motivation at 7th Grade of Junior High School 2 Juwiring Klaten; 4) teacher competence toward student’s learning motivation at 7th Grade of Junior High School 2 Juwiring Klaten.This type of research is quantitative research. Respondents in this study were 132 students. Data collection techniques using a questionnaire. Data analysis techniques used multiple linear regression analysis.The results of this study indicate: 1) simultaneous parenting, socioeconomic conditions of parents, and teacher competence together have a contribution to student’s learning motivation with a significance of an F test less than 0.05 which is 0,000; 2) there is a significant contribution between parenting and student’s learning motivation with a significance value 0.009 and t arithmetic 2.648; 3) there is a significant contribution between parents' socioeconomic conditions and student’s learning motivation with a significance value 0,000 and t arithmetic 4,686; 4) there is a significant contribution between teacher competency and student’s learning motivation with a significance value 0,000 and t arithmetic 5,498. The conclusions of this study are: 1) parenting, socio-economic conditions of parents and teacher competence together have a contribution to the student’s learning motivation at 7th Grade of Junior High School 2 Juwiring Klaten with a significance level of α = 5%. The value of the coefficient of determination (R2) is 0.379; 2) parenting gives an effective contributions 3.87%; 3) parents' socioeconomic conditions gives an effective contributions 15.18%; 4) teacher competencies gives effective contributions 18.88%.
ANALISIS FAKTOR LINGKUNGAN TERHADAP KEBIASAAN BACA SISWA KELAS I SD 3 DEMAAN KUDUS

The research objective was to determine the environmental factors that affect the reading habits of grade I students in Elementary School 3 Demaan Kudus. This research used a qualitative descriptive study with eleven informants consisting of the one class teacher, five students, and five students’ parents. The data sources are primary and secondary. Data collection techniques by observation, interviews, and literature study. Data analysis techniques with data reduction, data presentation, and concluding. The validity test used the source triangulation technique. The results showed that the reading habits of first-grade students at Elementary School 3 Demaan Kudus were influenced by the school environment and the home environment. School environmental factors such as social influence from friends, the class environment, rarely going  to the school library, and not a special reading curriculum. Home environmental factors, namely the lack of parental attention, lack of parental motivation, and parents when at home have not provided an example to cultivate reading.
STUDI ANALISIS TRANSISI WEB APPLICATION MENJADI MOBILE APPLICATION UNTUK KEBUTUHAN DATA MANAGEMENT DI INDONESIA

Abstract:Technology has developed rapidly, which is now entering the fourth industrial revolution. The fourth revolution uses smartphones as an important device in business. That’s why the transition of web applications is very rapidly to mobile application. This research method uses qualitative research, with a variety of phenomenon and literature study. The observed phenomenon is technological development. Until now, smartphones have become important devices in business because the transition of web applications to mobile applications is very rapid. From this research, it can be concluded that following the development of technology can help business development.
FORMING GEOGRAPHICAL COMPETENCE OF STUDENTS IN CONDITIONS OF DISTANCE TEACHING

The article considers topical issues related to the process of forming student’s geographical competence, which they will receive by digital technologies through distance learning. Modern man lives in an information society, and for this he must have all the necessary tools, because a person who does not have information technology is deprived of one of the adaptive mechanisms of a dynamic society. Today the problem of education concerns not only the formation of subject competencies in schoolchildren, but also the development of human information culture through the formation of digital competence. The article describes the conducted pedagogical research, which revealed the advantages and disadvantages of distance learning of geography in modern conditions of long-term quarantine caused by covid-19. Among the specific socio-pedagogical problems, the central one of them is the contradiction between the growth rate of knowledge in society and the limited opportunities for their assimilation by the individual. The answer is the content of the article.
A Study on the Environmental Elements for Physical Computing Game Development

본 연구는 가상세계에서 물리적 성질을 부여해 인터렉션하는 피지컬 컴퓨팅의 개념을 게임 개발에 적용함에 있어 중요한 환경적 요소에 관한 것이다. 이를 위해 피지컬 컴퓨팅 환경과 기존의 피지컬 컴퓨팅게임, 디지털 아트와 정보교육 분야의 연구 사례 분석을 통해 환경적 요소를 선별했다. 도출된 요소들은 델파이 조사방법으로 합의를 검증했다. 개발 환경 측면, 관련 사례 측면, 개발 서비스 측면으로 구분하여 27개의 요소를 선정했으며 피지컬 컴퓨팅의 환경 측면의 중요도가 가장 높게 나타났다. 세부 영역별로는 가상세계의 물리적 성질부여, 구현 기술의 적합성, 현실의 플레이이어와 가상의 플레이어와의 일치성 순으로 나타났다. In this study, we derive the important environmental elements for developing physical computing game. Environmental elements are selected based on the analysis of the physical computing environment in the area including physical computing itself, existing physical computing games, digital art, and information education. The derived elements are verified by Delphi's investigation methodology. The 27 factors are chosen in terms of development environment, development case, and development service. Among them the environmental aspects of physical computing are ranked as the highest importance.
Robust Offline Reinforcement Learning from Low-Quality Data

In practice, due to the need for online interaction with the environment, deploying reinforcement learning (RL) agents in safety-critical scenarios is challenging. Offline RL offers the promise of directly leveraging large, previously collected datasets to acquire effective policies without further interaction. Although a number of offline RL algorithms have been proposed, their performance is generally limited by the quality of dataset. To address this problem, we propose an Adaptive Policy constrainT (AdaPT) method, which allows effective exploration on out-of-distribution actions by imposing an adaptive constraint on the learned policy. We theoretically show that AdaPT produces a tight upper bound on the distributional deviation between the learned policy and the behavior policy, and this upper bound is the minimum requirement to guarantee policy improvement at each iteration. Formally, we present a practical AdaPT augmented Actor-Critic (AdaPT-AC) algorithm, which is able to learn a generalizable policy even from the dataset that contains a large amount of random data and induces a bad behavior policy. The empirical results on a range of continuous control benchmark tasks demonstrate that AdaPT-AC substantially outperforms several popular algorithms in terms of both final performance and robustness on four datasets of different qualities.