Reviewerly/exHarmony huggingface dataset:
UPAYA MENINGKATKAN HASIL BELAJAR MENGGUNAKAN MODEL TALKING STICK PADA MATA PELAJARAN PAI DI SMP

Abstract This study aims to improve the learning value of class VII A students of SMP Negeri 4 Pontianak. This research was conducted in two cycles, each of which had planning, implementation, observation and reflection. Data anatysis techniques that guide data management use a percentage (%) with a constant size of 100. Learning values are obtained from students test score of formative tests in the evaluation activities of each cycle. The result of study in cycle I showed that the average of learning value of students was 76.8 dan in cycle II showed that the average of learneng value of students increased to 80.9. Keywords : Enhancement, Learning Value, PAI, Talking Stick Model,
Blockchain Governance Challenges: Beyond Libertarianism

This essay considers the ideological context of blockchain technology. This technology is often celebrated for its potential for decentralization, distribution, privacy, and a lack of intermediaries and coordinators for transactions and general governance. Because of these features, blockchain technology, and, in particular, its most famous inauguration—the bitcoin blockchain—is frequently identified with libertarianism. In this essay, we argue that the ideological context of blockchain technology is much more complicated. In addition to unraveling a number of background ideologies and their role in this technology, we raise the ontological issue concerning the relationship of ideology to technology. These matters have implications for, among other things, the approach that should be taken to blockchain's governance, as well as how international lawyers may approach this “foreign”-seeming phenomenon that has its proponents from the European Central Bank to the United Nations (not, however, forgetting the private sector nor the digital underground).
Improving Accuracy of Out-of-Distribution Detection and In-Distribution Classification by Incorporating JSD Consistency Loss

Out-of-distribution (OOD) detection, the classification of samples not included in the training data, is essential to improve the reliability of deep learning.Recently, the accuracy of OOD detection through unsupervised representation learning is high; however, the accuracy of in-distribution classification (IN-D) is reduced.This is due to the cross entropy, which trains the network to predict shifting transformations (such as angles) for OOD detection.Cross entropy loss conflicts with the consistency in representation learning; that is, samples with different data augmentations applied to the same sample should share the same representation.To avoid this problem, we add the Jensen-Shannon divergence (JSD) consistency loss.To demonstrate its effectiveness for both OOD detection and IN-D classification, we apply it to contrasting shifted instances (CSI) based on the latest representation learning.Our experiments demonstrate that JSD consistency loss outperforms existing methods in both OOD detection and IN-D classification for unlabeled multi-class datasets.Improving Accuracy of Out-of-Distribution Detection and In-Distribution Classification by Incorporating JSD Consistency Loss
The Interrelation of Satisfaction with Online Education at the University with the Individual Psychological Characteristics of Students

In the last two years, changes related to the provision of educational services using a virtual educational environment have been taking place more intensively in the field of education, which is reflected in psychological scientific research in various areas of science. The purpose of this study is to identify the relationship of satisfaction with online learning of a university student with their individual psychological characteristics. Students of Irkutsk and Angarsk universities took part in the study. A theoretical analysis of modern psychological literature on the following issues is carried out: directions of digitalization; the concept of satisfaction with learning; satisfaction factors. The methodological basis of the research is the research of A.L. Zhuravlev, I.A. Zimnaya, B.B. Aysmontas. The empirical part of the work includes qualitative (identification of satisfaction factors) and quantitative (survey, testing) methods of analysis. Mathematical and statistical analysis included the method of correlation analysis, the use of the criterion of differences. The results of the study of the structure of satisfaction with learning; the identified relationships of satisfaction and individual psychological characteristics of an individual are presented. As a result of the study, correlations of individual psychological characteristics of students (achievement motivation and psychological qualities-discipline, independence) and components of satisfaction with learning are revealed; psychological characteristics of a student who is satisfied and dissatisfied with learning are presented. The obtained results can be used to develop psychological recommendations for the subjects of the educational process to improve online learning.
Text to Image GANs with RoBERTa and Fine-grained Attention Networks

Synthesizing new images from textual descriptions requires understanding the context of the text.It is a very challenging problem in Natural Language Processing and Computer vision.Existing systems use Generative Adversarial Network (GAN) to generate images using a simple text encoder from their captions.This paper consist synthesizing images from textual descriptions using Caltech-UCSD birds datasets by baselining the generative model using Attentional Generative Adversarial Networks (AttnGAN) and using RoBERTa pre-trained neural language model for word embeddings.The results obtained are compared with the baseline AttnGAN model and conduct various analyses on incorporating RoBERTa text encoder concerning simple encoder in the existing system.Various performance improvements were noted compared to baseline Attention Generative networks.The FID score has decreased from 23.98 in AttnGAN to 20.77 with incorporation of RoBERTa model with AttnGAN.
Development of a Model for Assessing the Activities of Teachers in an Educational Organization

This article reveals the essence of the assessment of teachers’ activities. The authors show the importance of a comprehensive assessment of this activity. Today, comprehensive assessment of the activities of teachers is an urgent problem that determines the purpose of the work - the development of a model for evaluating the activities of teachers in an educational organization. The degree of development of this issue depends on the quality of education in general. Therefore, the authors not only developed a model for assessing the activities of teachers, but also conducted a study that revealed the results of introducing a rating model into the activities of an educational institution. On the basis of the results obtained in the future, work in an educational organization for the evaluation of the activities of teaching staff will be improved.
General Editor' Preface

This first special issue of 2022 is guest edited by two scholars with attested expertise in the use of digital technology in foreign language education, in particular machine translation, and the issues it raises for applied linguistic research and practice.
Analysis of Crime Pattern using Data Mining Techniques

The advancement in Information Technology permits high volume of data to be generated in databases of institutions, organizations, government, including Law Enforcement Agencies (LEAs).Technologies have also been developed to store and manipulate these data to enhance decision making.Crime remains a severe threat to humanity.Criminals currently, exploit highly sophisticated technologies to perform criminal activities.To effectively combat crime, LEAs must be adequately equipped with technological tools such as data mining technology to enable useful discoveries from databases.To achieve this, a Real-time Integrated Crime Information System (RICIS) was developed and mobile phones were used by informants (general public) to capture information about crimes being committed within Southern-East, Nigeria.Each crime information captured is being sent to the LEA responsible for the crime type and the information is stored in the agency database for data analysis.Thus, this study uses data mining algorithms to analyze crime trends and patterns in Southern-Eastern part of Nigeria between 2012 and 2013.The algorithms adopted were Classification and Rule Induction.The data set of 973 were collected from Eleme Police station, PortHarcourt (2012) and Nsukka Police station (2013).The analysis enables identifications of some trends of crimes and criminal activities from various LEAs databases, enhancing crime control and public safety.
Introduction to the Minitrack on Actors, Agents, and Avatars: Visualizing Digital Humans in E-Commerce and Social Media

Agents, and Avatars: Visualizing Digital Humans in E-Commerce and Social Media minitrack focuses on the visualization and application of digital human characters in the context of E-commerce and social media disciplines.As visualization technology advances in recent years, digital humans have been transforming the way we work, live, play, and learn.Realistic digital humans have been deployed in multiple areas, including e-commerce and social media.In social media, human realistic digital agents have become virtual influencers.In electronic commerce, virtual agents have been deployed as digital sales assistants, fashion advisers, financial consultants and personal shoppers.In particular, digital humans will become more relevant as social media is further integrated into the retail customer experience.Comparing with other mini-tracks on artificial intelligence at HICSS, this mini track focuses on the visualization aspect of digital human in particular.We welcome submissions on any technology advancement to create human-realistic avatars and agents.This mini-track also emphasizes the importance of investigating the behavioral and perception outcomes, uncovering the complicated interactive nature among the technology, the end user, the tasks, as well as the organizational environment, and opening the black box of the underlying cognitive processes undertaken by the end user while interacting with digital humans.The adoption and use of digital humans can provide incomparable guidance to the creation of such digital characters.We hope this mini-track bridges academic research with practitioner concerns to create synergy on promoting the creation and usage of digital humans.With the strong support of the HICSS Internet and the Digital Economy Track chairs as well as the contributing authors and quality reviewers, we have launched this minitrack for the first time this year.We positioned this minitrack as a place for researchers or practitioners from diverse background to share their research and ideas.This year, Actors, Agents, and Avatars: Visualizing Digital Humans in
Ways and methods of improving the efficiency of the educational course «Journalism Skills»

The main purpose of scientific intelligence is to identify and analyze the techniques and methods of improving the effectiveness of one of the most popular and pragmatic training courses. «Journalistic skills» is read as a theoretical course with a large number of practical ones, but this is clearly not enough, so we offer original methods of raising the level of journalistic activity, analysis of facts, based on the practice of domestic and foreign media. The article provides a solid historical digression; it is recommended to use the appropriate advice of generations of Ukrainian researchers on the professional discipline. The individual approach to the training and retraining of reporters and analysts in print and online publications is demonstrated by specific examples. The optimal ways of combining a theoretical course and practical work to improve journalistic skills in domestic universities and the media are proposed.
Radiometry Analysis of Alluvial Deposits Samples Collected from Sharm El-Sheikh Area, Southern Sinai. Egypt

At Sharm El-Sheikh area, alluvial deposits derived from younger granites which exhibit association of important minerals (alkali feldspar, plagioclase feldspar, quartz, allanite, zircon, monazite chevkinite and magnetite) were subjected to radiometry measurements using HP-Ge detector.Activity concentrations of the radionuclides were found to be near to each other except 40 K which measure high values.Element content of thorium and uranium (eTh & eU) was calculated to estimate uranium migration (in / out) process for the studied locality.Most of the studied samples were found belong to forbidden zone except samples Yg9 & Yg10.The results indicated migration of uranium in percent ranges from 7.31% to 32.51% for the locality of the collected and studied samples.Detrital 230 Th was calculated to determine the corrected 230 Thd and applied to 230 Th/ 234 U dating which found to range between, 27 to 28 Ky.Migration time was also calculated using uranium original values.
PROGRAM HOLISTIK PEMBANGUNAN PEMBERDAYAAN DESA (PHP2D) OPTIMALISASI BALAI BANJAR SEBAGAI RUMAH BELAJAR PADA MASA COVID 19 DI DESA CEPAKA TABANAN

The Covid 19 pandemic has weakened the economy of the world community. The resulting impact is felt by all levels of rural and urban society; among parents, adults and children. Amid the economic downturn, people must survive. Various alternative efforts were made so that people could still eat and receive education. Especially school children who are very risky if their education is stopped for a relatively long time. This condition motivates the HIMA Accounting and Management of Triatma Mulya University to implement the PHP2D program through the optimization of the Banjar Hall as a Learning Center in the Covid 19 Pandemic Era in Cepaka Village, Tabanan, Bali. used as an arena for monthly meetings. Through this PHP2D program, the Banjar Hall is now equipped with internet facilities, work desks and bookshelves for libraries. Every day at 15-18 wita the children in each banjar; come to study with the help of students and lecturers. The availability of free internet and the presence of 5 students as teachers / mentors in each banjar make a positive contribution to the community and children in Cepaka village. After four months of this activity, a very harmonious relationship was formed between students and school children ranging from elementary and junior high schools. School children, totaling nearly 150 children, are trained in their learning, academic, soft skills and hard skills in order to grow into a superior generation. This activity received a warm welcome from the community, even the Head of Kediri Sub-district was very grateful and made Cepaka Village the first project pilot village to provide a learning house with free wi-fi. It is hoped that in the next period other villages can also be developed, students help children in learning so that they can achieve their goals.
Deep Descriptor Learning with Auxiliary Classification Loss for Retrieving Images of Silk Fabrics in the Context of Preserving European Silk Heritage

With the growing number of digitally available collections consisting of images depicting relevant objects from the past in relation with descriptive annotations, the need for suitable information retrieval techniques is becoming increasingly important to support historians in their work. In this context, we address the problem of image retrieval for searching records in a database of silk fabrics. The descriptors, used as an index to the database, are learned by a convolutional neural network, exploiting the available annotations to automatically generate training data. Descriptor learning is combined with auxiliary classification loss with the aim of supporting the clustering in the descriptor space with respect to the properties of the depicted silk objects, such as the place or time of origin. We evaluate our approach on a dataset of fabric images in a kNN-classification, showing promising results with respect to the ability of the descriptors to represent semantic properties of silk fabrics; integrating the auxiliary loss improves the overall accuracy by 2.7% and the average F1 score by 5.6%. It can be observed that the largest improvements can be obtained for variables with imbalanced class distributions. An evaluation on the WikiArt dataset demonstrates the transferability of our approach to other digital collections.
Trade Law in a Data-Driven Economy

This chapter introduces three cross-cutting themes that illustrate the relationship between artificial intelligence and international economic law (IEL): disruption, regulation, and reconfiguration. We explore the theme of disruption along the trifecta of AI-related technological, economic, and legal change. In this context, the impact of AI triggers political and economic pressures, as evidenced by intensive lobbying and engagement in different governance venues for and against various regulatory choices, including what will be regulated, how to regulate it, and whom should be regulated. Along these lines, we assess the extent to which IEL has already been reconfigured and examine the need for further reconfiguration. We conclude this introduction chapter by bringing the contributions we assembled in this volume into conversation with one another and identify topics that warrant further research. Taken as a whole, this book portrays the interaction between AI and IEL. We have collectively explored and evaluated the impact of AI disruption, the need for AI regulation, and directions for IEL reconfiguration.
PISA reading literacy assessment and senior high school reading literacy assessments: How do they differ?

The Program for International Student Assessment (PISA) is an international program assessment to evaluate and determine the education system's effectiveness by focusing on reading, mathematics, and science. This study aims to analyze the differences between PISA reading literacy assessment and senior high school reading literacy assessment in West Sumatera. Reading literacy is abilities and skills in reading needed in everyday life to develop students' knowledge and potential. The method of this study was comparative method. Data were taken from PISA reading literacy 2018, which OECD published, and reading tests made by English teachers from ten Senior High Schools in West Sumatera. In addition, this study used document analysis to collect the data. The differences between PISA reading literacy assessment and reading literacy assessment at senior high school were seen from eight indicators: cognitive process, form of text, types of text, form of test, situation, types of question, level of question, text medium, and text environment. It was found that PISA reading literacy assessment was more complex compared to reading literacy assessment at senior high school.
Sinkhorn Regression

This paper introduces a novel Robust Regression (RR) model, named Sinkhorn regression, which imposes Sinkhorn distances on both loss function and regularization. Traditional RR methods target at searching for an element-wise loss function (e.g., Lp-norm) to characterize the errors such that outlying data have a relatively smaller influence on the regression estimator. Due to the neglect of the geometric information, they often lead to the suboptimal results in the practical applications. To address this problem, we use a cross-bin distance function, i.e., Sinkhorn distances, to capture the geometric knowledge of real data. Sinkhorn distances is invariant in movement, rotation and zoom. Thus, our method is more robust to variations of data than traditional regression models. Meanwhile, we leverage Kullback-Leibler divergence to relax the proposed model with marginal constraints into its unbalanced formulation to adapt more types of features. In addition, we propose an efficient algorithm to solve the relaxed model and establish its complete statistical guarantees under mild conditions. Experiments on the five publicly available microarray data sets and one mass spectrometry data set demonstrate the effectiveness and robustness of our method.
Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.
Designing and building earthquake warning system with Arduino and web-based SES60 SLP sensor

Indonesia is a country that is crossed by a world ring of fire that has the potential for earthquakes and tsunamis. An earthquake is an occurrence of shaking the ground due to the sudden release of energy into the earth. Since earthquakes occur within an undetermined period, we need a device to detect earthquake vibrations as early as possible within few seconds and announce real-time warning information on the website. The monitor screen displays a flat, a slightly increasing, the alarm displays a sound "weak earthquake”, "moderate earthquake", "strong earthquake", "very strong earthquake”. When there are no earthquake waves, very small shaking, PGA200 gals, PGA400 gals, PGA600 gals and PGA800 gals respectively. The alarm announces watch out (hati-hati) for moderate earthquake PGA200 gals, caution (waspada) PGA400 gals and ask to evacuate for PGA600 gals. The earthquakes detecting devices are useful in reducing their negative impacts, for instance, fatalities and material losses. The development of this system is to provide faster and more accurate information, and we can access the information on the internet network with broad coverage. The system can send the information within 5 second to user.
PERPUSTAKAAN DIGITAL BUDAYA INDONESIA: PERAN MASYARAKAT DAN KOMUNITAS MELINDUNGI DAN MELESTARIKAN BUDAYA INDONESIA

Indonesia is a country with cultural diversity, this diversity has not been able to be identified and registered, so it is feared those cultures will be eroded by globalization or claimed by another party. Perpustakaan Digital Budaya Indonesia with the concept of Virtual Community to collect data on culture which involves people and communities in the preservation of the culture of Indonesia. This article is going to describe how Perpustakaan Digital Budaya Indonesia through budaya-indonesia.org performs cultural preservation with the contribution of all elements of society. This study is using a qualitative approach with web observation and literature study as data collection techniques. The results showed that efforts to conserve the culture of the community by documenting the cultural records and inventory data, access and utilization of cultural data and the benefits of cultural data gathered at the Perpustakaan Digital Budaya Indonesia provide many benefits to Indonesia, among others present in various cultures, record various cultural heritages and minimize claims against Indonesian culture from foreign parties.