Reviewerly/exHarmony huggingface dataset:
COMPUTER-ORIENTED RE-TESTING OF STUDENTS AND POSTGRADUATE STUDENTS STUDY RESULTS IN MOODLE

The urgency of the research problem is due to the need to find new opportunities for computer-based testing, especially in the current context of the COVID-19 pandemic, when most higher education institutions operate in a distance learning environment. The aim of the article is to study the impact of retesting on the performance of students and postgraduate students on the example of studying some pedagogical disciplines in a classical university. The author used the following research methods: theoretical - in order to understand the features of computer-based testing of students in high school, formulating the conclusions of the study, etc. (analysis and synthesis, comparison, generalization, etc.); empirical – to develop didactic tests and identify indicators of initial and re-testing of students and postgraduate students (online survey, pedagogical observation); mathematical – to process the results using the computer program IBM SPSS Statistics 23. The author obtained the following specific results: according to an online survey of 1211 respondents (during 2020), almost 67% of undergraduate students, 70% of undergraduates and 83% of university students study in Moodle most disciplines during the COVID-19 pandemic. The Moodle system is most prevalent in distance learning during the COVID-19 pandemic at the university. The coefficients of internal consistency (Cronbach’s alpha) of the developed tests are equal to 0,80-0,94 for different disciplines. Students and postgraduate students took the mandatory first test at Moodle, and they also had the opportunity to retake the test of their choice. Retesting helps to improve the performance of students and postgraduate students. The average increase in test score (%) between the test and re-test was in the range of 2 -16 for various disciplines (2-14 for bachelor’s students, 11-16 for master’s students, 3-5 for postgraduate students).
KOMPETENSI PUSTAKAWAN DALAM MELAKUKAN SELEKSI KOLEKSI PERPUSTAKAAN

This study aims to determine the librarians’ competencies in selecting library collection and how to improve these competencies. The method used in this research is literature study. Based on this research, it can be explained that the librarians’ competencies in selecting library collection consist of having knowledge of several things, namely their institutional programs, the collection development policies, the needs of their users, the quality of various types of collections, the credibility of publishers and providers of electronic information sources, the ability of the authors, the use of various selection tools, as well as having knowledge about information technology development plus having skills in communicating and in using various types of collection. Furthermore, to improve the competencies, librarians need to do these things: follow several trainings; read more books to increase knowledge; conduct user studies; learn how to use the selection tools; study the quality of library materials; learn everything about book publishing; seek knowledge about book editors and producers of audio-visual materials; discover what publishers who produce the best material for libraries by researching their catalogs and advertisements; and examine the national bibliography and the book trade bibliographies.
Educational Game “Mr. Foodi" Healthy and Nutritious Food for Children

Children really like food that makes them interested in trying it, but the food that makes them interested is likely to be one of them. Source of problems for his health. Food and drinks that do not know the manufacturing process have an impact on children's development. Therefore, an educational game was created to get to know healthy and nutritious food. This is so that children can know which food is good for them and which food is not good for consumption. In its implementation, game development can use Construct2. Construct2 is a device that is used specifically for making games for platforms 2D. Games that use construct2 can be applied to educational games about healthy and nutritious food using the method finite state machine. FSM is a game smart agent development in the field of artificial intelligence. Methods Finite state machine (FSM) is a control system design methodology that describes the behavior or the working principle of the system by using the three things, namely, state, event and action.
Asynchronous Stochastic Gradient Descent for Extreme-Scale Recommender Systems

Recommender systems are influential for many internet applications. As the size of the dataset provided for a recommendation model grows rapidly, how to utilize such amount of data effectively matters a lot. For a typical Click-Through-Rate(CTR) prediction model, the amount of daily samples can probably be up to hundreds of terabytes, which reaches dozens of petabytes at an extreme-scale when we take several days into consideration. Such data makes it essential to train the model parallelly and continuously. Traditional asynchronous stochastic gradient descent (ASGD) and its variants are proved efficient but often suffer from stale gradients. Hence, the model convergence tends to be worse as more workers are used. Moreover, the existing adaptive optimizers, which are friendly to sparse data, stagger in long-term training due to the significant imbalance between new and accumulated gradients. To address the challenges posed by extreme-scale data, we propose: 1) Staleness normalization and data normalization to eliminate the turbulence of stale gradients when training asynchronously in hundreds and thousands of workers; 2) SWAP, a novel framework for adaptive optimizers to balance the new and historical gradients by taking sampling period into consideration. We implement these approaches in TensorFlow and apply them to CTR tasks in real-world e- commerce scenarios. Experiments show that the number of workers in asynchronous training can be extended to 3000 with guaranteed convergence, and the final AUC is improved by more than 5 percentage.
Numerical Solution of the Cauchy Problem for the Laplace Equation: A Deterministic and Bayesian Approach

We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation. The a priori model is built up from Gaussian Markov random fields. Different precision matrices for the Cauchy problem are introduced. We take advantage of the relationship between the a priori distribution and traditional Tikhonov regularization to propose different models where smooth and non-smooth regularization is possible. A low range analysis allow us to estimate the optimal dimension of data and its relation to the the unknown.
News Category Classification using Support Vector Machine Algorithm

Nowadays many have used web-based systems to convey information and news in real time. However, in dividing news into these categories, some are still done manually, so it takes a long time. Of the several existing techniques, the technique most often used for classification of news content is the Support Vector Machine (SVM). In complex problems or problems with many parameters, this method is very good to use. The SVM algorithm performs supervised learning classifications or has inputs and outputs that have been formed into a mathematical relationship model that can classify and predict existing data. There are 2224 datasets and 5 categories with 70% of the data being trained and 30% of the data being tested. This study produces text classifications in the form of technology, business, sports, entertainment, and political categories from digital news content. The classification results obtained an accuracy value of 98.35% with an average precision of 90%, a recall of 98%, an F1-score of 98% and a Support of 668.
원격수업에 대한 대학원생의 요구분석

The purpose of this study is to analyze various demands in online classes of graduate students, and to establish teaching strategies accordingly. In this study, Witkine & Altschuld's three-stage model was used. three stages are as follows: First, satisfaction with on-line lessons was investigated. In the second stage, data was collected and analyzed through written inquiries and phone calls of graduate students currently taking classes as on-line lectures. In the third step, teaching strategies for utilization were presented. The teaching strategy based on the results of the demand analysis is as follows. First, in order to help learners continue their learning to the end, it is necessary to establish a unique online teaching strategy such as management of learners for active participation, use of various cases for tension and concentration, and transfer of practical knowledge. Second, in order to meet the demand for question/answer with professors and sharing opinions with fellow learners, communication strategies such as online chatting using SNS should be introduced in the course of class introduction and it is necessary to establish strategies for students to actively participate in class and promote immersion. Third, technical support for a smooth distance lecture will be required for instructors.
JURNAL NASIONAL: BAHAN AJAR E-BOOK PENDIDIKAN AGAMA ISLAM: STUDI PADA SEKOLAH MENENGAH PERTAMA NEGERI 1 MUARA TELANG

This research is motivated because there is a problem that there are many students whose interest in learning is lacking, especially for textual learning. Students prefer to learn instantaneously using internet facilities. done by the teacher. Whereas on the other hand, there is a problem of students not daring to express their opinions in class because of the lack of knowledge of the students themselves. This research uses qualitative methods. data using observation, interviews, and documentation. Data analysis uses qualitative data analysis techniques. The results showed that the implementation of e-books in PAI learning at SMP Negeri 1 Muara Telang ran smoothly because it was supported by communication and interaction factors between individual students and teachers well established, the characteristics of teachers and students who had the ability to use e-books, attitudes and the mindset of the teacher and students to work together towards progress together and the financial situation of parents who support, the good habits of parents, students and the environment from the socio-cultural aspects and a conducive school climate cooperate with each other for school progress. The obstacles in implementation are the unprepared attitude of students and the stateof equipment that is not supportive.
Penerapan Algoritma Convolutional Neural Network dalam Klasifikasi Telur Ayam Fertil dan Infertil Berdasarkan Hasil Candling

Fertile chicken eggs are eggs that can hatch because these eggs have a development in the form of dots of blood and blood vessels or can be called an embryo, while infertile chicken eggs are a type of egg that cannot be hatched because there is no embryo development in the hatching process. Inspection of infertile chicken eggs must be carried out especially for breeders who will carry out the selection and transfer of fertile chicken eggs and infertile chicken eggs. However, currently, the selection of fertile and infertile chicken eggs is still using a less effective way, namely only by looking at the egg shell or called candling, this process is certainly less accurate to classify which eggs are fertile and infertile eggs because not all breeders are able to see the results of the eggs properly. candling so that the possibility of prediction errors. Therefore, in this study, a classification of fertile chicken eggs and infertile chicken eggs will be carried out based on candling results using the Convolutional Neural Network method. From the results of the classification carried out, the percentage of accuracy obtained for the classification of fertile and infertile chicken eggs is 98% and an error of 5%.
Defeasible Reasoning via Datalog$^\neg$

We address the problem of compiling defeasible theories to Datalog$^\neg$ programs. We prove the correctness of this compilation, for the defeasible logic $DL(\partial_{||})$, but the techniques we use apply to many other defeasible logics. Structural properties of $DL(\partial_{||})$ are identified that support efficient implementation and/or approximation of the conclusions of defeasible theories in the logic, compared with other defeasible logics. We also use previously well-studied structural properties of logic programs to adapt to incomplete Datalog$^\neg$ implementations.
Elements that Contribute to the Efficiency of Communication Between Teacher and Student

Abstract Communication skills are one of the most important educational elements today. Current education has already included communication competencies as independent objectives in the education of young generations. Communication is a multidisciplinary existential basis and that is why its understanding is not easy, and must be approached scientifically, conceptually and structurally. This paper aims to emphasize the importance of communication in the teaching-learning process and its direct correlation with school success but also with success in life. Teachers need communication more than anyone. Their verbal communication occupies 70% of a class lesson (Haslett, 1987). The appeal to the elements of emotional intelligence, to the motivation / interests of the students, becomes a necessity and at the same time they are constituted as factors that can improve and make efficient the didactic communication. In this paper we will analyze the main functions of teacher-student communication (guidance, information and thought challenge). There are described other elements that can also contribute to better communication in the classroom: active presentation of topics, providing summaries, resumption of main ideas, clarity of presentation (sentence logic, examples and explanations), assessment of comprehension deficiencies and their correction, frequently asked questions. All these aspects have proved, through the studies carried out, that they are positively correlated with the students’ results. The verbal communication of the educator is always accompanied by the elements of non-verbal communication that have an important role in achieving an effective instructive-educational process. In this regard, we can mention a few essential components: posture, gestures, facial expressions, style of dressing, tone of voice, all of which can incite or divert the listener’s attention. Another important element in effectiveness of the didactic communication is the knowledge of the cultural, economic and social environment from which the students come, their specific characteristics. The interaction will be more plausible, accessible to the student, when well known, culturally familiar aspects are inserted in the complex process of teacher-student communication. The integration of cultural diversity in didactic communication has become a stringent requirement of modern education.
Functional and Approaches of Multi Cloud Performance as a Service Model

Cloud networking provides different tools and applications sharing capabilities to fulfil consumer computers' needs. It is a model from which consumers can access computational tools as internet providers (cloud services). Cloud vendors provide a wide variety of services Asymmetrical cloud services with heterogeneous functionality, making it hard for consumers Find the right provider to meet your needs. Many cloud services study studies were conducted Discovery, and many frameworks and solutions have been proposed that have implemented various techniques. This paper seeks to present state-of-the-art discoveries for cloud resources through The methods, strategies and templates that are present. In addition, it offers a cloud taxonomy Approaches to operation exploration. An integrative analysis methodology was used to investigate the associated aspects. literature. Literature. Analysis of current discovery solutions for cloud services and a taxonomy of discoveries Approaches focused on many experiences, including the climate for exploration and Process techniques for exploration. The taxonomy suggested enables easy classification and comparison Discovery solutions to cloud providers. In addition, problems and discrepancies in analysis will be revealed. Present fresh developments into the discovery of increasingly advanced and efficient cloud platforms.
VideoLightFormer: Lightweight Action Recognition using Transformers

Efficient video action recognition remains a challenging problem. One large model after another takes the place of the state-of-the-art on the Kinetics dataset, but real-world efficiency evaluations are often lacking. In this work, we fill this gap and investigate the use of transformers for efficient action recognition. We propose a novel, lightweight action recognition architecture, VideoLightFormer. In a factorized fashion, we carefully extend the 2D convolutional Temporal Segment Network with transformers, while maintaining spatial and temporal video structure throughout the entire model. Existing methods often resort to one of the two extremes, where they either apply huge transformers to video features, or minimal transformers on highly pooled video features. Our method differs from them by keeping the transformer models small, but leveraging full spatiotemporal feature structure. We evaluate VideoLightFormer in a high-efficiency setting on the temporally-demanding EPIC-KITCHENS-100 and Something-Something-V2 (SSV2) datasets and find that it achieves a better mix of efficiency and accuracy than existing state-of-the-art models, apart from the Temporal Shift Module on SSV2.
Privacy-Utility Trade-Offs Against Limited Adversaries

We study privacy-utility trade-offs where users share privacy-correlated useful information with a service provider to obtain some utility. The service provider is adversarial in the sense that it can infer the users' private information based on the shared useful information. To minimize the privacy leakage while maintaining a desired level of utility, the users carefully perturb the useful information via a probabilistic privacy mapping before sharing it. We focus on the setting in which the adversary attempting an inference attack on the users' privacy has potentially biased information about the statistical correlation between the private and useful variables. This information asymmetry between the users and the limited adversary leads to better privacy guarantees than the case of the omniscient adversary under the same utility requirement. We first identify assumptions on the adversary's information so that the inference costs are well-defined and finite. Then, we characterize the impact of the information asymmetry and show that it increases the inference costs for the adversary. We further formulate the design of the privacy mapping against a limited adversary using a difference of convex functions program and solve it via the concave-convex procedure. When the adversary's information is not precisely available, we adopt a Bayesian view and represent the adversary's information by a probability distribution. In this case, the expected cost for the adversary does not admit a closed-form expression, and we establish and maximize a lower bound of the expected cost. We provide a numerical example regarding a census data set to illustrate the theoretical results.
Controlling Internet of Things with different platforms: Security and Privacy challenges

The Internet of Things (IOT) is one of the most important topics these days. It contains huge numbers of devices connected to each other with a very accurate access control through advanced and trusted control. IOT aims to use easy and achieve high security for the users. Based on that, this paper surveys recent research related to controlling three generations of network types have passed through the IOT which services the client in accessing or controlling their resources. In addition, this work aims to describe centralized framework (such as cloud computing), decentralized system (like edge computing), distributed network (Blockchain technology), and smart contract in IOT with the description of their security and privacy challenges
การพัฒนาหลักสูตรฝึกอบรมการรังวัดที่ดินด้วยระบบโครงข่ายดาวเทียมแบบจลน์ “RTK GNSS Network” ด้วยวิธีผสมผสาน

AbstractThe purpose of this research was to develop the training course for land surveying using a kinetic satellite network GNSS Network by the integrated methodology. The sample group in this study included 30 students Higher Vocational Certificate (Diploma) 1st Academic year 2020 Department of Construction Lopburi Technical College. The research instruments were. 1) Context Evaluation : the department head of engineering company need person, who have knowledge about land surveying using the kinetic satellite network system GNSS Network. The students majority of the students indicated that they needed training for land surveying with a kinetic satellite network GNSS Network. 2) Input Evaluation : the training program can be categorized into two groups of topics : online training (2 sub topic),and technical workshop training (5 sub topics). Conformity assessment by experts, Assess consistency between topics and behavioral objectives. 3) Process Evaluation : Training courses that are evaluated by experts to try out and assess the achievement of the exercises and tests. 4) Product Evaluation : the result showed that the students were satisfied with the training course development for land surveying using the GNSS Network. Keywords: Training Course, Land Surveying, RTK GNSS Network, Mixed Method
BCH codes over $\gf(q)$ with length $q^m+1$

BCH codes are an interesting class of cyclic codes due to their efficient encoding and decoding algorithms. In many cases, BCH codes are the best linear codes. However, the dimension and minimum distance of BCH codes have been seldom solved. Until now, there have been few results on BCH codes over $\gf(q)$ with length $q^m+1$, especially when $q$ is a prime power and $m$ is even. The objective of this paper is to study BCH codes of this type over finite fields and analyse their parameters. The BCH codes presented in this paper have good parameters in general, and contain many optimal linear codes.
Machine learning methods for geological support in geosteering

Summary In order to more accurately predict lithology and the nature of saturation, as well as minimize the time to perform interpretation, it was decided to test machine learning algorithms. For solving such problems, the gradient boosting algorithm (GBoosting) showed the best efficiency.
Hierarchical Graph Capsule Network

Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data. However, existing GNNs suffer from limited capability in capturing the hierarchical graph representation which plays an important role in graph classification. In this paper, we innovatively propose hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies. Specifically, disentangled graph capsules are established by identifying heterogeneous factors underlying each node, such that their instantiation parameters represent different properties of the same entity. To learn the hierarchical representation, HGCN characterizes the part-whole relationship between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the structure information among the parts. Experimental studies demonstrate the effectiveness of HGCN and the contribution of each component.