Reviewerly/exHarmony huggingface dataset:
Analysis of Elearning Quality Measurement With Webqual Method at Politeknik MBP Medan

The development of information technology today has also changed the lifestyle and way of life of many people. By using information technology, many people can explore the world without being limited by space and time through the internet. Information technology has become a tool that has the power as a driving force that changes business, economics, socio-politics and other fields without limits. One of the things that has experienced major changes in lifestyle and way of life in the field of education is teaching and learning activities. The Politeknik MBP Medan college, which was also affected by Pandami, had to change the teaching-learning method using the Learning Management System service which is the right teaching-learning method to provide learning materials to students, in this case called students. In order to obtain the quality of this service, first, the satisfaction level of service users is measured based on the standard of comparison (gap) between reality and the expectations of users of the service. Based on the results of quality measurements from e-learning at the Politeknik MBP Medan for the academic year 2020/2021 in the even semester using the Webqual method, it was concluded that the Webqual method along with its attributes can provide analytical results to be used in improving the performance quality of e-learning. The results of measuring the quality of the data processing of respondents' answers are obtained that the User Satisfaction Level Score is -0.18, this indicates that the quality of e-learning as a whole is not in line with the expectations of users. The results of the analysis of 22 statement attributes from the Webqual method show that all attributes get the results of the Tingkatkan pattern analysis and none of the statement attributes get the Pertahankan pattern.
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks

Deep neural networks have achieved outstanding performance over various tasks, but they have a critical issue: over-confident predictions even for completely unknown samples. Many studies have been proposed to successfully filter out these unknown samples, but they only considered narrow and specific tasks, referred to as misclassification detection, open-set recognition, or out-of-distribution detection. In this work, we argue that these tasks should be treated as fundamentally an identical problem because an ideal model should possess detection capability for all those tasks. Therefore, we introduce the unknown detection task, an integration of previous individual tasks, for a rigorous examination of the detection capability of deep neural networks on a wide spectrum of unknown samples. To this end, unified benchmark datasets on different scales were constructed and the unknown detection capabilities of existing popular methods were subject to comparison. We found that Deep Ensemble consistently outperforms the other approaches in detecting unknowns; however, all methods are only successful for a specific type of unknown. The reproducible code and benchmark datasets are available at https://github.com/daintlab/unknown-detection-benchmarks .
Partial Label Learning Based on Fully Connected Deep Neural Network

The ambiguity of training samples in the partial label learning framework makes it difficult for us to develop learning algorithms and most of the existing algorithms are proposed based on the traditional shallow machine learn- ing models, such as decision tree, support vector machine, and Gaussian process model. Deep neu- ral networks have demonstrated excellent perfor- mance in many application fields, but currently it is rarely used for partial label learning frame- work. This study proposes a new partial label learning algorithm based on a fully connected deep neural network, in which the relationship between the candidate labels and the ground- truth label of each training sample is established by defining three new loss functions, and a regu- larization term is added to prevent overfitting. The experimental results on the controlled U- CI datasets and real-world partial label datasets reveal that the proposed algorithm can achieve higher classification accuracy than the state-of- the-art partial label learning algorithms.
THE CLUSTER APPROACH IMPLEMENTATION FOR ORGANIZING THE ACTIVITIES OF GENERAL-STUDIES DEPARTMENTS IN FEDERAL UNIVERSITIES OF RUSSIAN FEDERATION

Information about the paper titled "THE CLUSTER APPROACH IMPLEMENTATION FOR ORGANIZING THE ACTIVITIES OF GENERAL-STUDIES DEPARTMENTS IN FEDERAL UNIVERSITIES OF RUSSIAN FEDERATION" at IATED Digital Library
3DVNet: Multi-View Depth Prediction and Volumetric Refinement.

We present 3DVNet, a novel multi-view stereo (MVS) depth-prediction method that combines the advantages of previous depth-based and volumetric MVS approaches. Our key idea is the use of a 3D scene-modeling network that iteratively updates a set of coarse depth predictions, resulting in highly accurate predictions which agree on the underlying scene geometry. Unlike existing depth-prediction techniques, our method uses a volumetric 3D convolutional neural network (CNN) that operates in world space on all depth maps jointly. The network can therefore learn meaningful scene-level priors. Furthermore, unlike existing volumetric MVS techniques, our 3D CNN operates on a feature-augmented point cloud, allowing for effective aggregation of multi-view information and flexible iterative refinement of depth maps. Experimental results show our method exceeds state-of-the-art accuracy in both depth prediction and 3D reconstruction metrics on the ScanNet dataset, as well as a selection of scenes from the TUM-RGBD and ICL-NUIM datasets. This shows that our method is both effective and generalizes to new settings.
MEDIA LITERACY FOR PRIMARY SCHOOL CHILDREN

T. Fisenko, R. Segol, O. Baliun, O. HusakNational Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UKRAINE)
EDUCATIONAL CHATBOT USING A HYBRID-RESPONSE APPROACH

In the classical classroom setting, an educator is tasked with transferring knowledge to multiple students. An immediately noticeable flaw, however, is that when learning, students may need additional attention, however, that is not always possible when there is a class of twenty-five students but only one educator. Our Educational Chatbot system makes use of AI (Artificial Intelligence) to engage in a dialogue with the students and help them learn Mathematics by providing explanations similar to those provided by the educator. The novelty of our work lies in the fact that we are moving away from traditional systems. In fact, such systems tend to fall into two main categories. The first makes use of hard-coded responses which are handcrafted specifically for this task. The second uses dynamic responses which are still built upon a well-defined script. Our chatbot uses a hybrid approach. The underlying responses or scripts are not hardcoded. Our AI uses the explanations which the educator provides to the children and generalizes over them using the T5 transformers model and the GPT2 dataset. So in essence, no special format is required to teach the AI since it is capable of processing the notes and create dynamic responses out of them automatically. In so doing, the educator or the AI expert does not need to set up the system since it is done automatically. We tested the system with around 100 people that are involved in education and the results are very promising. In general, the system was well received with 73% of respondents indicating that they enjoyed using the chatbot. Furthermore, 77% of them claimed that the chatbot helped them understand Mathematics better. Although the system is far from being perfect, the results are very positive and encourage us to keep on improving our automated hybrid approach.
(0,1)-matrices and Discrepancy

Let $m$ and $n$ be positive integers, and let $R =(r_1, \ldots, r_m)$ and $S =(s_1,\ldots, s_n)$ be nonnegative integral vectors. Let $A(R,S)$ be the set of all $m \times n$ $(0,1)$-matrices with row sum vector $R$ and column vector $S$. Let $R$ and $S$ be nonincreasing, and let $F(R)$ be the $m \times n$ $(0,1)$-matrix where for each $i$, the $i^{th}$ row of $F(R,S)$ consists of $r_i$ 1's followed by $n-r_i$ 0's. Let $A\in A(R,S)$. The discrepancy of A, $disc(A)$, is the number of positions in which $F(R)$ has a 1 and $A$ has a 0. In this paper, we investigate the possible discrepancy of $A^t$ versus the discrepancy of $A$. We show that if the discrepancy of $A$ is $\ell$, then the discrepancy of the transpose of $A$ is at least $\frac{\ell}{2}$ and at most $2\ell$. These bounds are tight.
Crisis and Creativity - How COVID-19 impacted Social Networks’ uses in the Artistic sector

As the COVID-19 outbreak hits the world, Malaysia's government has taken precautionary measures immediately, by enforcing the Movement Control Orders (MCO). During the enforcement of MCO, many intriguing stories begin and one of it is, various untold hidden talents have been exposed indirectly by Malaysians. These talents have been uploaded through some of their Social Media (SM) accounts themselves and have been shared by hundreds and thousands of their followers through SM as well. This study seeks to identify the untold hidden talent through SM account among Malaysian society during the unprecedented time of the pandemic, furthermore, it occurs during the enforcement of MCO. The qualitative research approach has been carried out for this study; thus, an in-depth interview has been used as the main method to gather all the data involving talented respondents' personal experience. The empirical results of the study discovered that their action is to avoid suffering from depression, also, it is to dumbfound the mental health issue. Meanwhile, SM has impacted the growth of their talent, indirectly increasing the number of viewers and subscribers or followers on their SM account such as YouTube and Instagram. Finally, their surrounding contacts have positively encouraged for the talent to be practised and been written in a journal for general reference. Indeed, this study is expected to contribute to the SM field and health science and well-being studies to motivate future generations on how to overcome depression and mental health during a global pandemic.
The effect of beauty vocational training institutions on the satisfaction of trainees

Background/Objectives: This study was intended to examine the impact of the educational environment on satisfaction among trainees of beauty vocational training institutions.The educational performance of beauty vocational training institutions was set to the satisfaction level of students and the influence of factors such as educational environment, teaching method, and instructor capacity was to be verified.Methods/Statistical analysis: In order to establish theoretical concepts through the study of literature on prior research and to identify the variables in the educational environment and verify their impact on satisfaction, a research model and research theory will be established and an empirical analysis will be conducted through the questionnaire.We would like to analyze the relationship between independent variables such as educational environment and dependent variables such as teaching method and instructor competency's influence on trainee satisfaction.The survey was conducted on 180 beauty trainees in Seoul and Gyeonggi-do, and the collected data was analyzed using AMOS 22.0 Findings: The results of this study are as follows.The educational environment has been shown to have a significant effect on teaching methods and teaching competencies.The teaching method has been shown to have a significant definition effect on trainee satisfaction.In the competency section of the instructor, it was shown to have a significant negative effect on trainee satisfaction.Improvements/Applications: This study confirmed that the educational environment factors and teaching skills are variables that affect the satisfaction of trainees.Therefore, in order to improve the satisfaction level of trainees at beauty training institutions, the quality of education should be improved first by improving the quality of education environment and developing various teaching methods.
The assessment and analysis of materials availability

The availability of library materials is central to the effective running of a library. Does the library have what its clients want? Can they find or access it? And if not, why not? The analysis of materials availability can thus provide a fundamental measure of performance, and can also deliver valuable information to determine strategies for improvement to library services. The main theoretical work in this area was done in the 1970s and focused on print resources and on searches by library clients for known items. There have, however, been relatively few attempts to transfer the principles of this early work to the electronic information universe. Recent work at Curtin University Library has suggested that the current discovery environment is too complex to be captured within a single survey instrument. This chapter explores a mixed-methods approach to collecting data on materials availability from multiple sources, addressing different aspects of the problem. These include survey and focus group data, and analysis of discovery systems logs, document delivery requests and catalogue problem reports, which can be combined to create a holistic view of interactions between clients and library collections, and to provide a basis for a sustainable performance measure.
AWAKENING THE INTEREST IN COMPUTER SCIENCE THROUGH THE USE OF ARDUINO - A CASE STUDY WITH HIGH SCHOOL GIRLS

G. O. Mattos, J. Aires Moreira, R. A. Medeiros, A. F. Aragão, T. G. M. Andrade, L. L. A. O. Campos, M. L. M. Calixto, V. G. L. PessoaFederal University of Paraiba (BRAZIL)
HIGHER EDUCATION, ARTIFICIAL INTELLIGENCE AND PERSONALIZED KNOWLEDGE: THE VALUES OF 'DYNAMIC INTELLIGENCE CULTURAL EDUCATION' (DICE)

In the fourth industrial revolution higher education organizations are under pressure from the pandemic crisis, 5G mobile networks, artificial intelligence and robots. Artificial Intelligence algorithms have affected higher education structures globally, changing how we communicate, educate and connect with one another. The info-communication connections and practice draw out questions on the forms of power and control of algorithms may have on higher education systems. Artificial Intelligence (AI) means different things to different people in the era of digital transformation. The paper examines the 'paradigm shift’ is, from the detached paradigm in traditional higher education content environment to the unitary paradigm in digital education platforms, so-called the ‘Info-communication Content Algorithms Paradigm’ (ICAP). It analyses a) the role of academics and administrators of higher education organizations in the pandemic crisis, b) the role of artificial intelligence content algorithms in developing personalized knowledge, c) the new value in digital education systems i.e. the vales of 'Dynamic Intelligence Cultural Education' (DICE) model in order to regulate the higher education systems and to evaluate academics and administrators in the era of digital transformation. Finally, it suggests to administrators of higher education organizations to use new digital business values i.e. the values of the 'Dynamic Intelligence Cultural Education' (DICE) to motivate academics and activate personalized knowledge in higher education organizations.
Model for Bayesian Networks Conversion from Summary-Based Health Ontologies

Ontologies allow knowledge representation easily interpreted by both humans and computers. Bayesian networks (BNs) support uncertain reasoning, providing a way to treat uncertainty existent in the health area. The construction of BNs is difficult and arduous, involving extensive manual interaction. On the other hand, there is a huge number of ontologies available and describing health knowledge. These ontologies can be applied as sources for the creation of BNs, easing the difficulty observed in this task. This work presents a new model for the semi-automatic extraction of BNs from health ontologies using the ontology summarization as a basis. The main contribution of this work is the use of conversion rules, created from a critical analysis of the context of ontologies and an approach for ontologies summarization, which provides greater precision in the data conversion. The text describes the theoretical basis, the formulated hypotheses, the developed model and the evaluation experiment. The model was implemented to support a Pedagogical Simulator dedicated to foster health education, the Health Simulator project. Therefore it is a real case of generation of BNs for clinical cases and is integrated to a publisher of BNs. Through it, is possible to generate the knowledge structure of the Health Simulator project, and advance its clinical case simulator based on BNs. The results indicate good possibilities in the generation of BNs, with advances concerning the state of the art.
PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK (LKPD) BERBASIS REALLOTMENT ACTIVITIES MENGGUNAKAN APLIKASI CANVA PADA MATERI SEGIEMPAT

The roles and functions and responsibilities of education greatly determine the quality of human resources, mathematics learning which emphasizes the planting of concepts based on understanding because understanding is more effective as an alternative to learning mathematics than memorizing, in accordance with one of the missions of learning mathematics, namely directing the understanding of mathematical concepts needed to complete math problem faced. This study aims to develop student worksheets based on reallotment activities using the Canva application on quadrangular material for grade VII SMP. This research is a type of research and development (R&D). The data in the study were collected by means of a questionnaire and documentation. The research instruments used were the media expert validation sheet, the material expert validation sheet and the linguist validation sheet using the Method of Summated Ratings (MSR) to measure the validity of the LKPD. The results showed that LKPD based on reallotment activities using the Canva application on quadrilateral material for class VII SMP met the criteria. very valid, namely 91.57%.
Echocardiogram Image Compression based on Wavelet Tree

Image compression is important in echocardiogram images to preserve the quality of images for storing and transmission of images. Existing methods have limitations of lower efficiency in preserve the edge and quality of images. In this research, the Wavelet Tree was proposed to improve the efficiency of image compression to preserve image edge. The compression performance and quality parameters were assessed using echocardiogram images. The proposed Wavelet Tree was applied for compression and reconstruction of images to preserve edges of images. The Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Edge Preservation Index (EPI) metrics were used to evaluate the performance of the proposed method. In the proposed Wavelet Tree method PSNR, SSIM and EPI values were 33.7 dB, 0.77 and 0.54 respectively. The proposed Wavelet Tree method has better performance in preserving the edge of images.
The Compound Prediction Analysis of Information Network Security Situation based on Support Vector Combined with BP Neural Network Learning Algorithm

In order to solve the problem of low security of data in network transmission and inaccurate prediction of future security situation, an improved neural network learning algorithm is proposed in this paper. The algorithm makes up for the shortcomings of the standard neural network learning algorithm, eliminates the redundant data by vector support, and realizes the effective clustering of information data. In addition, the improved neural network learning algorithm uses the order of data to optimize the "end" data in the standard neural network learning algorithm, so as to improve the accuracy and computational efficiency of network security situation prediction.MATLAB simulation results show that the data processing capacity of support vector combined BP neural network is consistent with the actual security situation data requirements, the consistency can reach 98%. the consistency of the security situation results can reach 99%, the composite prediction time of the whole security situation is less than 25s, the line segment slope change can reach 2.3% ,and the slope change range can reach 1.2%,, which is better than BP neural network algorithm.
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning

Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. However, previous studies suggest that beyond class-imbalance, intrinsic data difficulty factors like overlapping, noise, and small disjuncts also play critical roles. To handle them, many solutions have been proposed (e.g., noise removal, borderline sampling, hard example mining) but are still confined to a specific factor and cannot generalize to broader scenarios, which raises an interesting question: how to handle both class-agnostic difficulties and the class-imbalance in a unified way? To answer this, we consider both class-imbalance and its orthogonal: intra-class imbalance, i.e., the imbalanced distribution over easy and hard samples. Such distribution naturally reflects the complex influence of class-agnostic intrinsic data difficulties thus providing a new unified view for identifying and handling these factors during learning. From this perspective, we discuss the pros and cons of existing IL solutions and further propose new balancing techniques for more robust and efficient IL. Finally, we wrap up all solutions into a generic ensemble IL framework, namely DuBE (Duple-Balanced Ensemble). It features explicit and efficient inter-\&intra-class balancing as well as easy extension with standardized APIs. Extensive experiments validate the effectiveness of DuBE. Code, examples, and documentation are available at https://github.com/AnonAuthorAI/duplebalance and https://duplebalance.readthedocs.io.
PAINTING WITH PASTELS – A POSSIBILITY TO REDUCE EMOTIONAL STRESS AND STIMULATE A CREATIVE THINKING FOR STUDENTS IN INTEREST CLASSES A GROUP FOR ALL-DAY ORGANIZATION OF THE SCHOOL DAY

If you capture all students attention with something that activates all their senses, imagination and their bodies – then they would be able to perceive and assimilate better, to follow easily. Painting with pastels is a unique way to reduce emotional stress among students and stimulate the process of learning and creative thinking. Drawing make students think outside the box and allows students to be bold and creative in solving problems, it lets them discover new paths and improve their current ways of thinking and acting.