Reviewerly/exHarmony huggingface dataset:
DECISION SUPPORT SYSTEMS FOR ASESSING THE QUALITY OF TEACHING MATERIALS IN E-LEARNING-BASED LEARNING AND DESIGNING TEACHING MATERIALS MANAGEMENT USING LEARNING STYLE AND HTSC (HIGH TECHNOLOGY SPIRITUAL COMMUNICATION) METHODS

In the lecture system, it is necessary to have e-Learning based learning in the form of video which includes learning elements that are in accordance with predetermined conditions. High Technology Spiritual Communications is a communication system that was first introduced by the late Harvard University graduate, Indrawan Hatoguan Siregar, Ph.D, who is also a permanent lecturer at Panca Budi Development University. This research discusses the use of the internet and LMS (Learning Management System) as the communication media and the application of HTSC (Hight Technology Spiritual Communications). The advantage in this application is that students can watch videos of teaching materials anytime, anywhere without having to spend special time reading, even students can listen to them while working. The result of this research is that it can divide the learning group into 3 groups, average value, weighting criteria, have a decision matrix, and get the most ideal ranking results.
Video Frame Interpolation via Structure-Motion based Iterative Fusion

Video Frame Interpolation synthesizes non-existent images between adjacent frames, with the aim of providing a smooth and consistent visual experience. Two approaches for solving this challenging task are optical flow based and kernel-based methods. In existing works, optical flow based methods can provide accurate point-to-point motion description, however, they lack constraints on object structure. On the contrary, kernel-based methods focus on structural alignment, which relies on semantic and apparent features, but tends to blur results. Based on these observations, we propose a structure-motion based iterative fusion method. The framework is an end-to-end learnable structure with two stages. First, interpolated frames are synthesized by structure-based and motion-based learning branches respectively, then, an iterative refinement module is established via spatial and temporal feature integration. Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation. Experimental results on three typical benchmarks show that the proposed method achieves superior performance on all evaluation metrics over the state-of-the-art methods, even when our models are trained with only one-tenth of the data other methods use.
A low-rank representation for unsupervised registration of medical images

Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled datasets, which is time-consuming and sometimes impractical due to data sharing issues. Unsupervised image registration algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations. These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training. However, these methods become less effective when the image quality varies, e.g., some images are corrupted by substantial noise or artifacts. In this work, we propose a novel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem. We project noisy images into a noise-free low-rank space, and then compute the similarity between the images. Based on the low-rank similarity measure, we train the registration network to predict the dense deformation fields of noisy image pairs. We highlight that the low-rank projection is reformulated in a way that the registration network can successfully update gradients. With two tasks, i.e., cardiac and abdominal intra-modality registration, we demonstrate that the low-rank representation can boost the generalization ability and robustness of models as well as bring significant improvements in noisy data registration scenarios.
Wavelet method of edge detection in images with high-noise level

In the paper a mathematical model addressed to non-sharp edges in the images is proposed. This model is based on and integral transform with Haar-Gauss wavelet and matching algorithm of bandwidth, such model is used to detection of the edges in images with high-level noises, both in the x plane and the frequency domains. There is shown that applying the integral Haar-Gaussian transformation the detection of single and double edges is possible. Demonstrated in the paper results confirm that wavelet transform supported by the matching wavelet algorithm of wavelength bandwidth make an important exploration tool of the images with the edges possessing a large depth of sharpness.
Unknown-box Approximation to Improve Optical Character Recognition Performance

Optical character recognition (OCR) is a widely used pattern recognition application in numerous domains. There are several feature-rich, general-purpose OCR solutions available for consumers, which can provide moderate to excellent accuracy levels. However, accuracy can diminish with difficult and uncommon document domains. Preprocessing of document images can be used to minimize the effect of domain shift. In this paper, a novel approach is presented for creating a customized preprocessor for a given OCR engine. Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module. Experiments with two datasets and two OCR engines show that the presented preprocessor is able to improve the accuracy of the OCR up to 46% from the baseline by applying pixel-level manipulations to the document image. The implementation of the proposed method and the enhanced public datasets are available for download.
Context based multi-source feature aware event recommendation algorithm

In the research, EBSN event recommendation algorithm is usually based on the user's simple relationship or social relationship. However, there are many factors that affect users' choice in EBSN, such as group based social information in EBSN context information and event text information, location information, time information, tag information. In order to enhance the effect of recommendation, fundamentally solve the problem of data sparsity and cold start of recommendation, this paper proposes an event recommendation algorithm based on context multi-source feature perception, establishes the scoring model of multi-source features, applies Random Forest algorithm to realize the weight calculation and extraction of multi-source features, and finally generates the event recommendation list. The experimental results are compared with other three ranking based recommendation algorithms. In the standard of NDCG@10, the MFA-RF algorithm proposed in this paper has obvious advantages, and solves the problems of data sparsity and cold start in the recommendation.
Penerapan Model Pembelajaran Role Playing pada Materi Debat untuk Meningkatkan Keaktifan dan Prestasi Belajar Siswa Kelas X MIPA 3 MAN 2 Bantul

This study aims to improve the activeness and achievement of learning in Indonesian language debate materials by using role-playing learning models in grade X students of MIPA 3 MAN 2 Bantul even semester of the 2019/2020 academic year. This research was conducted from January to April 2020. This study's subject was a Grade X student of MIPA 3 MAN 2 Bantul, who numbered 25 students. The indicators examined in this study are the activeness and learning achievement of students. The research design uses a class action research model. The study consisted of 2 cycles, each of which consisted of 3 meetings. The data collection method uses observation method, questionnaires, and written tests. The study results can be concluded that applying the Role-Playing learning model can increase the activeness and achievement of learning in the field of study of Bahasa Indonesia debate material in students grade X MIPA 3 MAN 2 Bantul even semester 2019/2020. Student activity in the first cycle of 61.50% increased in the second cycle to 88.50%. In the first cycle, student learning achievement based on an average score of 65.55% increased in the second cycle to 82.28%. Similarly, the number of students completed in the first cycle by 36% rose to 80%. The successful use of Role-Playing learning models can be used as an alternative learning model to improve students' activeness and learning achievement.
BigCQ: A large-scale synthetic dataset of competency question patterns formalized into SPARQL-OWL query templates

Competency Questions (CQs) are used in many ontology engineering methodologies to collect requirements and track the completeness and correctness of an ontology being constructed. Although they are frequently suggested by ontology engineering methodologies, the publicly available datasets of CQs and their formalizations in ontology query languages are very scarce. Since first efforts to automate processes utilizing CQs are being made, it is of high importance to provide large and diverse datasets to fuel these solutions. In this paper, we present BigCQ, the biggest dataset of CQ templates with their formalizations into SPARQL-OWL query templates. BigCQ is created automatically from a dataset of frequently used axiom shapes. These pairs of CQ templates and query templates can be then materialized as actual CQs and SPARQL-OWL queries if filled with resource labels and IRIs from a given ontology. We describe the dataset in detail, provide a description of the process leading to the creation of the dataset and analyze how well the dataset covers real-world examples. We also publish the dataset as well as scripts transforming axiom shapes into pairs of CQ patterns and SPARQL-OWL templates, to make engineers able to adapt the process to their particular needs.
Testing a Point Null Hypothesis against One-Sided for Non Regular and Exponential Families: The Reconcilability Condition to P-values and Posterior Probability

Testing a Point Null Hypothesis against One-Sided for Non Regular and Exponential Families: The Reconcilability Condition to P-values and Posterior Probability
Development Of The Children’s Socio-Cultural Experience As A Problem Of Modern Education

The social and digital reality determines the development of the children’s social-cultural experience. It is essential for educational institutions to establish educational content that developing personal communication skills, effective nurture methods, interaction technologies between school and family. The article refers to the problem of the nurture of students on the literature lessons, emphasizes the need to introduce traditional values to students and to develop their moral sphere. The acquisition of the socio-cultural experience depends on the teachers readiness to talk with a child about the questions of his/her concern, to discuss literary works in the modern conditions and to strengthen the students interest to literature. In the article, the level of schoolchildren’s socio-cultural development and their ideas about significant values are shown and the conclusions about the development of new nurture methods are made. We emphasize the specifics of the development of children’s socio-cultural experience as the goal and the result of studying religious cultures. This is conditioned by cultural characteristics and needs of families, the circle of a child’s communication, the local socio-cultural environment and the opportunity of studying religious cultures at school. The article presents quantitative data on the characteristics of parental experience (raising one or two children) which is significant to the child’s socio-cultural development, such as the attitudes towards the difficulties in the upbringing of children and towards digitalization of the child’s life. We discuss the strategies for helping teachers to create trustful relations with the parents considering their parental experience.
다문화사회의 세계시민교육을 위한 국어과 교수·학습 방안 -2015 개정교육과정을 중심으로-

This paper aims to develop Korean language teaching and learning plan for global citizenship education in a multicultural society. Since the 1990s, Korea has rapidly changed into a multicultural society. By establishing a multicultural education policy, it helped to adapt students from multicultural families to school. Korea's multicultural education policy does not provide general education for multicultural understanding. It has many things in common between global citizenship education and multicultural education. The 2015 revised curriculum contains many elements for global citizenship education. In addition, the characteristics of global citizenship education can be found in the nature of Korean language subjects. Therefore, it is proposed to apply global citizenship education for multicultural society understanding education. We expect that global citizenship education will cultivate multicultural sensitivity. (Busan Sung-ji High School)
Application of Ordinary Differential Equation in Practice

Among relationships between variables reflecting movement process in natural, there are many mathematical models that satisfy ordinary differential equation (ODE). Generally, as an important tool for solving many problems in practice, ODE is used to understand property of an unknown function. This paper describes four practical cases that apply ODE, i.e. identification of death time in criminal investigation; Malthusian population model; archaeology of Mawangdui Han Dynasty Tomb in Changsha (Hunan Province); and authentication of oil painting, etc.
Graph Attention Networks with Positional Embeddings

Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighboring nodes having similar features and labels--, and therefore may not be at their full potential when dealing with non-homophilic graphs. In this work, we focus on addressing this limitation and enable Graph Attention Networks (GAT), a commonly used variant of GNNs, to explore the structural information within each graph locality. Inspired by the positional encoding in the Transformers, we propose a framework, termed Graph Attentional Networks with Positional Embeddings (GAT-POS), to enhance GATs with positional embeddings which capture structural and positional information of the nodes in the graph. In this framework, the positional embeddings are learned by a model predictive of the graph context, plugged into an enhanced GAT architecture, which is able to leverage both the positional and content information of each node. The model is trained jointly to optimize for the task of node classification as well as the task of predicting graph context. Experimental results show that GAT-POS reaches remarkable improvement compared to strong GNN baselines and recent structural embedding enhanced GNNs on non-homophilic graphs.
CREPO: An Open Repository to Benchmark Credal Network Algorithms

Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. Despite the NP-hardness of the (exact) task, a number of algorithms is available to approximate credal network inferences. In this paper we present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models. A Python tool is also delivered to load these data and interact with CREMA, thus making extremely easy to evaluate and compare existing and novel inference algorithms. To demonstrate such benchmarking scheme, we propose an approximate heuristic to be used inside variable elimination schemes to keep a bound on the maximum number of vertices generated during the combination step. A CREPO-based validation against approximate procedures based on linearization and exact techniques performed in CREMA is finally discussed.
Arithmetic Using Compression on Elliptic Curves in Huff’s Form and Its Applications

In this paper for elliptic curves provided by Huff's equation $H_{a,b}: ax(y^2-1) = by(x^2-1)$ and general Huff's equation $G_{\overline{a},\overline{b}} : {\overline{x}}(\overline{a}{\overline{y}}^2-1)={\overline{y}}(\overline{b}{\overline{x}}^2-1)$ and degree 2 compression function $f(x,y) = xy$ on these curves, herein we provide formulas for doubling and differential addition after compression, which for Huff's curves are as efficient as Montgomery's formulas for Montgomery's curves $By^2 = x^3 + Ax^2 + x$. For these curves we also provided point recovery formulas after compression, which for a point $P$ on these curves allows to compute $[n]f(P)$ after compression using the Montgomery ladder algorithm, and then recover $[n]P$. Using formulas of Moody and Shumow for computing odd degree isogenies on general Huff's curves, we have also provide formulas for computing odd degree isogenies after compression for these curves. Moreover, it is shown herein how to apply obtained formulas using compression to the ECM algorithm. In the appendix, we present examples of Huff's curves convenient for the isogeny-based cryptography, where compression can be used.
EDUCATIONAL FACTOR OF LOCAL DEVELOPMENT STRATEGY (ON THE EXAMPLE OF A NETWORK OF SUPPORTING EDUCATIONAL INSTITUTIONS)

The purpose of the study is to identify the features of the impact of networks of educational institutions on local development through the mechanism of their formation. Research methodology. The study used general scientific methods, including analysis, scientific synthesis, analytical method, methods of comparison and generalization. The structure of basic educational institutions as an element of the social system can be considered in component-functional and territorial-functional aspects. Accordingly, the need to identify opportunities for network development requires scientific justification, taking into account the specifics of geographical understanding. Research results. The article examines the impact of the educational factor through the formation of educational institutions on local development. The mechanisms of formation of basic educational institutions, which are one of the elements of the mechanism of reforming the system of general secondary education and streamlining the school network, rational and efficient use of resources, and designed for equal access of children to the educational process. The scientific novelty of the work is the disclosure of the mechanism of formation of directions of local development of OTG through creation of basic educational institutions. The practical significance of the work is that the article develops the mechanism of influence of educational institutions on local development, taking into account territorial differences, opportunities for training competitive workers, creating an effective system of quality education in basic schools and their branches, improving their funding, involvement and taking into account the opinion of civil society, taking into account the trends of demographic and migration processes in the region.
Perancangan Dan Pembuatan Sistem Informasi Akuntansi Pada Kedai Kopi Mitra Bersama

Micro, Small, and Medium Enterprises (MSMEs) do not require high skills and are easily outperformed by competitors, as is the case with Kedai Kopi Mitra Bersama. Kopi Mitra Bersama doesn’t have an accounting recording system. Financial transaction was recorded manually on cash receipts and disbursements on a daily basis without any data on inventories used, what makes the entity’s profit or loss can’t be known with certainty. After conducting observation and interview method, writer has designed a simple accounting information system that can produce financial reports in accordance to SAK EMKM on the basis of Microsoft Office Access to solve problems with business financial recording and reporting. The designed system has been implemented and can assist the owner in knowing business conditions and financial information. In addition, the recommendation for the next writer is to add cash flow statements feature so that the owner can know directly the cash inflow and outflow that occur in a certain period.
Power Grid Dispatching Operation and Analysis System Based on Data Mining Technology

Power grid operation data containing considerable information concerns not only grid operation but also safety in production. How to effectively dispatch such type of data has become a hot issue in all walks of life. This paper, from the perspective of power plant, discusses the application of relevant technologies based on a brief introduction to data mining technology. The discussion starts from such aspects as dispatch operation and dispatch data mining in the hope of providing assistance or inspiration for others.
Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach

Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable. Specifically, the challenge includes very low resolution of the objects, MPEG artifacts due to compression and a large searching area with many hard negatives. Tracking is equally difficult because of the unreliable appearance, and the unreliable motion estimation. Luckily, we found that by combining this two challenging tasks together, there will be mutual benefits. Following the idea, in this paper, we present a neural network model called the Recurrent Correlational Network, where detection and tracking are jointly performed over a multi-frame representation learned through a single, trainable, and end-to-end network. The framework exploits a convolutional long short-term memory network for learning informative appearance changes for detection, while the learned representation is shared in tracking for enhancing its performance. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements in detection performance over deep single-frame detectors and existing motion-based detectors. Furthermore, our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on a bird image dataset.
Horizontal Identification of Lindi Distribution Using Monitoring Wells

Most of the waste is collected and managed in a landfill (TPA). The Pakusari Jember Final Processing Site (TPA) is an example of a TPA that implements the Open Dumping system. This TPA is one of the TPAs located in the City of Jember. This TPA service covers all waste in the city and its surroundings. Most of the waste disposed of in this place is organic waste originating from markets. This causes waste to decompose faster and produce pollutants that can pollute groundwater. Leachate water (leachate) that enters groundwater or rivers will cause dangerous pollution for the surrounding residents. If this is allowed, there will be wider problems for the population. For surveillance measures, it is necessary to conduct a survey to determine the types of pollutants and how wide they are spread. By monitoring 11 research wells and 1 pollutant source site, then the permeability test was carried out at several points, the distribution of leachate content was obtained up to 400 m to the southeast. It can be concluded that the Pakusari TPA has been polluted by leachate waste. Some of the content in the monitoring well is heavy metals such as lead (Pb), cadmium (Cd), copper (Cu) and iron (Fe). With a permeability value (K) = 2.61298E-07 cm / second, there has been a leachate flow propagation of 0.08127406 m / year with a leachate distribution area of 1.478.54 m 2 .