Reviewerly/exHarmony huggingface dataset:
The Usage of “Quizizz”™ App by Sport Sciences Students in the Bachelor’s Degree Anatomy Lecture and Its Effects on Attitude and Course Success

Purpose: The aim of this study is to examine the effects of technologically assisted learning for anatomy courses in sport science collegiate. Methods: One hundred forty-four first-year students from sport sciences faculty attended a required anatomy course in the syllabus. The participants of this class were composed of two learning groups as Classical Learning Group (n=48), and New Approach Group (n=96) based on the lecture style. Classical anatomy course carried out with instruction-based method via PowerPoint lecture on the anatomy course materials such as textbooks, models, and printed visualizations of anatomical sites whereas &amp;ldquo;Quizizz&amp;rdquo;&amp;trade; app-based one performed interactively within a technology-assisted way. The end of the semester, participants answered a reliable and valid survey named Anatomy Lectures Attitude Questionnaire (ALAQ). Results: There was a significant difference in the mid-term and course success when compared Classical Learning (CL) and New Approach (NA). However, no significant differences observed final examination, ALAQ results, and sub-factors&amp;rsquo;. There was a very low correlation between mid-term, final, course success and ALAQ results in NA group. However, no significantly correlation found between mid-term, final, course success and ALAQ results in CL group. Conclusion: The findings reported here suggest that &amp;ldquo;Quizizz&amp;rdquo;&amp;trade; app can enable improving learning outcomes and contribute to test scores for human anatomy in sport sciences collegiate. We do not conceive the substitute traditional learning method within the educational applications for anatomy courses, but it could be regarded as a supplement method of teaching in higher education.
Computer-Aided Recognition and Analysis of Abnormal Behavior in Video

In intelligent computer-aided video abnormal behavior recognition, pedestrian behavior analysis technology can detect and handle abnormal behaviors in time, which has great practical value in ensuring social safety. We analyze a deep learning video behavior recognition network that has advantages in current research. The network first sparsely sampled the input video to obtain the video frame of each video segment, and then used a two-dimensional convolutional network to extract the characteristics of each video frame, then used a three-dimensional network to fuse them. The method realizes the recognition of long-term and short-term actions in the video at the same time. In order to overcome the shortcoming of the large amount of calculation in the 3D convolution part of the network, this paper proposes an improvement to this module in the network, and proposes a mobile 3D convolution network structure. Aiming at the problem of low utilization of long-term motion features in video sequences, this paper constructs a deep residual module by introducing long and short-term memory networks, residual connection design, etc., to fully and effectively utilize the long-term dynamic features in video sequences. Aiming at the problem of large differences in similar actions and small differences between classes in abnormal behavior videos, this paper proposes a 2CSoftmax function based on double center loss to optimize the network model, which is beneficial to maximize the distance between classes and minimize the distance between classes, so as to realize the classification and recognition of similar actions and improve the recognition accuracy.
Say “Sul Sul!” to SimSim, A Sims-Inspired Platform for Sandbox Game AI

This paper proposes environment design in the life simulation game The Sims as a novel platform and challenge for testing divergent search algorithms. In this domain, which includes a minimal viability criterion, the goal is to furnish a house with objects that satisfy the physical needs of a simulated agent. Importantly, the large number of objects available to the player (whether human or automated) affords a wide variety of solutions to the underlying design problem. Empirical studies in a novel open source simulator called SimSim investigate the ability of novelty-based evolutionary algorithms to effectively generate viable environment designs.
Short-term photovoltaic power prediction based on FVS-KELM method

ABSTRACTFor the highly nonlinear short-term photovoltaic power problems affected by a variety of factors, a novel modeling method FVS-KELM based on feature vectors selection (FVS) algorithm and kernel extreme learning machine (KELM) is proposed. Firstly, FVS algorithm maps the input data to the high-dimensional feature space through kernel trick, considering the geometric features, selecting the relevant data subset to form the base vector of the subspace. Secondly, the input data is projected onto the feature subspace, and the final prediction model is established by the KELM method, which does not need set the number of the hidden layer nodes, and uses the kernel function representing the unknown nonlinear feature mapping of the hidden layer, so it has good generalization ability. In order to verify the effectiveness of the FVS-KELM modeling method, it is applied to the benchmark photovoltaic power prediction example provided by Global Energy Forecasting Competition 2014 (GEFCOM2014). Under the same condition, the FVS-KELM method is compared with extreme learning machine (ELM), support vector machine (SVM) method etc. The experimental results show that the FVS-KELM method can not only reduce the computational complexity, but also has good prediction accuracy and robustness, and the model has good generalization.
Two-Step Phase-Shifting Algorithm Based on the Quadratic Spatial Filtering of Interferograms

By using the quadratic spatial filtering (QSF) operation of interferograms, we propose a fast and accurate phase retrieval algorithm in 2-step phase-shifting interferometry (PSI), in which both the interference signal separation and blind phase shift estimation can be realized. Compared with the existed 2-step PSI algorithms, the proposed QSF algorithm reveals two advantages: First, when the background intensity is not accurately estimated, which is a serious problem in 2-step PSI, the distortion of the retrieved phase can be released. Second, there is no requirement about the fringe density of interference pattern, reflecting the phase shift estimation can be realized even if the fringes density is sparse. The former is a valuable solution to reduce most significant errors in 2-step PSI, and the latter makes the accuracy robust against different fringe patterns. Both the simulation and experimental results demonstrate the excellent performance of the proposed QSF algorithm.
Perception of Kwara State undergraduates on the contributions of online tools towards improvement of reading habit

Background of the study: The study aimed at examining perceptions of undergraduates on the contributions of online tools towards improvement of reading habit in Kwara state in universities. The study identified six (6) research questions. The research population was limited to undergraduates at the University of Ilorin, Kwara State University, Malate and Al-Hikmah University.Purpose: The study adopted descriptive survey design with questionnaire to collect data. The simple random sampling technique was adopted. The researcher used Educational and Psychological Measurement Israel model (2003) sample table to arrive at the actual sample size for this study. Therefore, the sample size for this study at level of ± 5% level of significance for population of 53, 130 students was 397. With total population of 76% for University of Ilorin, 18% for Kwara State University Malate and 6% for Al-Hikmah University, Ilorin.Method: Data collected from the field were analyzed using descriptive method of analysis.Findings: The findings of the study revealed that blog, twitters, E-mail, websites, YouTube, Facebook is the most available online tools that improve undergraduates reading habit. The study also revealed that undergraduates using online tools for social purpose than others.Conclusion: The study concluded that undergraduates have good perception using online tools for their reading. The study also established that undergraduates using online tools for social, political, recreation and religion benefits. It is also established that distraction, fallout from technologies, financial constraints and poor power supply are the major setbacks to the use of online tools for reading.
NEW TECHNOLOGIES AND APPLICATIONS OF MOTION TRACKING IN VIRTUAL REALITY SYSTEMS

This article is centered around the sort of virtual reality (VR) -MoCap -motion capture.VR is utilized as a part of the planning and design procedure of the working environment.Firstly, the work environment is designed and before actual building the various conditions are simulated.Product of this simulation by VR is the working environment/ building.VR is utilized too in the medical treatments.This paper investigates a way to deal with physical rehabilitation using best in class advances in virtual reality and motion tracking.As of present, there has been a boom in the utilization of the Kalman channel in Virtual/ Augmented Reality.This paper presents a brief introduction to the Kalman filter and the development of the utilization of the filter in VR motion tracking.
Information-Theoretic Representation Learning for Positive-Unlabeled Classification

Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, which is a critical bottleneck particularly for high-dimensional data. This problem has been commonly addressed by applying principal component analysis in advance, but such unsupervised dimension reduction can collapse underlying class structure. In this paper, we propose a novel representation learning method from PU data based on the information-maximization principle. Our method does not require class-prior estimation and thus can be used as a preprocessing method for PU classification. Through experiments, we demonstrate that our method combined with deep neural networks highly improves the accuracy of PU class-prior estimation, leading to state-of-the-art PU classification performance.
Network Form Development To Implement Life-Long Education

In modern society, the tendency to an innovative economy transition, knowledge economy, is clearly manifested. This determines the demand for specialists who have a need and are ready to learn throughout their lives, and, accordingly, leads to the educational paradigm transformation. Currently, professional training is faced with the task of modernization in order to bring its substantive and procedural components into line with the observed trends in a changing economy and society. With regard to solving this problem, the authors used a systematic approach. Based on the identified contradictions in the training of personnel for high-tech areas of production, a system simulation of the network form for the continuing professional education (CPE) implementation has been carried out. The model is designed to identify the complex of organizational and pedagogical conditions necessary for the continuing professional education in the context of digitalization effective implementation. The study established and substantiated the integrative functions of the network form system of continuing professional education implementation, carried out in accordance with the developed model. Using concrete examples from the nano industry and mechanical engineering field, it is shown that the system functional qualities make it possible to put into practice the ideas of dual training, through which universities are given access to modern production technologies, software and hardware systems and commercial software, and enterprises are able to purposefully influence the educational programs content implemented by universities, use the personnel potential of universities for the modernization of production and innovative developments evaluation.
HUBUNGAN KECERDASAN NATURALISTIK DAN MINAT BELAJAR DENGAN HASIL BELAJAR BIOLOGI SISWA SMA DI KOTA LUWUK

The success of any learning objective is measured from the students’ learning outcomes, which could be influenced by internal and external factors. Internal factors capable of affecting students’ learning outcomes include naturalistic intelligence and learning interest. Hence, the purpose of this study was to describe the relationship between naturalistic intelligence and learning interest, with biology learning outcomes of students in Luwuk City. This was an ex post facto study with a research population of eleventh grade students of Mathematics and Natural Sciences High School in Luwuk City in the 2019/2020 academic year. A proportional random sampling method was used to select 235 out of the 566 students of the grade, as the research subjects. The data collection techniques employed include online questionnaires and documentation, while the analysis techniques used were descriptive and inferential statistics. Based on the results, there was a very low relationship between naturalistic intelligence and biology learning outcomes, there was a low relationship between learning interest with biology learning outcomes, as well as between naturalistic intelligence and learning interest with biology learning outcomes of high school students in Luwuk City.
INFLUENCE OF THE NEUTRAL SHADOWING FORCE ON THE PAIR CORRELATION FUNCTION OF THE DUSTY PLASMA UNDER CRYOGENIC CONDITIONS

In the course of recent years, progresses in sensor innovation has lead to increments in the interest for automated strategies for investigating seismological signals. Fundamental to the comprehension of the components creating seismic signals is the information on the phases of seismic waves. Having the option to indicate the kind of wave prompts better performing seismic forecasting frameworks. In this article, we propose another strategy for the characterization of seismic waves quantification from a three-channel seismograms. The seismograms are isolated into covering time windows, where each time-window is mapped to a lot of multi-scale three-dimensional unitary vectors that portray the direction of the seismic wave present in the window at a few physical scales. The issue of arranging seismic waves gets one of ordering focuses on a few two-dimensional unit circles. We take care of this issue by utilizing kernel based machine learning that are remarkably adjusted to the geometry of the circle. The grouping of the seismic wave depends on our capacity to gain proficiency with the limits between sets of focuses on the circles related with the various kinds of seismic waves. At each signal scale, we characterize a thought of vulnerability connected to the order that considers the geometry of the dissemination of tests on the circle. At long last, we join the grouping results acquired at each scale into a unique label.
Digital Technologies In Online Education

Currently online education is widely in demand in society. It is actively promoted by educational institutions and it is an object of interest of the state, contributing to the development of educational services market and increasing the level of accessibility of education for all society members. Modern digital technologies play an important role in the transformation of education in digital economy. The article deals with modern standards of online learning used for creating personalized adaptive online courses. It is noted that to meet the requirements of adaptability and develop an individual learning trajectory, it is necessary that all educational objects are located in a special repository, which is an online library for storing, managing and sharing educational resources. The system makes a decision on the choice and sequence of inclusion of educational objects in the online course based on the collection of information about the educational activity of students. The use of an object approach to knowledge structuring makes it possible to effectively organize the educational process: creating flexible, personalized learning technologies, changing the content and scenarios of pedagogical work of the teachers. The introduction of modern information technologies in online education will allow to respond ahead of time to the needs of society for qualified staff in the context of the formation of digital economy, the development of breakthrough and promising technologies and their impact on business.
Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

&lt;p&gt;Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in&lt;strong&gt; &lt;/strong&gt;developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver operating characteristic ROC). Dataset was downloaded from UCI ml repository; it is composed of 9 attributes and 699 samples. The findings are clearly showing that the RBF NN classifier is the best in prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC (area under Receiver Operating Characteristic ROC) among all other models.&lt;/p&gt;
Digital slow : Brahmanisms, Zetetic Wild Sciences and Pedagogics

Our digital society and education systems are produced simultaneously constrained within powerful political discourses. In Thomas Pikettyâ€™s latest book Capital et IdÃ©ologie (2019) he claims that the brahmanization of left-wing parties and policies is one of the most prominent features of our times. Brahmans forward middle class values and draw voters from the well-educated middleclass. They reduce focus on distribution issues and instead focus on values anchored in the self-assertive asceticism of the educational elite, ultimately turning into carriers of a middleclass moralism estranged from the working classes. Left-wing right-wing divisions in politics therefore no longer express divisions between classes. Rather, a rivalry between money elites and educational elites. &nbsp; This article is a critique of discursive production of policies and offers a view of digitalization and/of education produced through zetetic or curious wild science, productive doubts and slow scholarships. Focus is the becoming child infused with immanent life. Pragmatic work and pedagogics within different discourses through zetetic inquiry and affect. We tend to think that digitalization and digitalized systems of Machine Learning (ML) and Artificial Intelligence (AI) are effective, saving time and resources. I claim that they actually require more. Require more resources, efforts and time, more and profound relations and care, more and other connectivities, more collaboration and communication, more and other pedagogics. Requiring new norms of iterative difference that is, as all there is always, but as something eternal affectively traversing time and place. Something imperceptible, affirmative, particular and universal. Instead, of asking how we work with and use digital tools and programs ask why and when we work with and use such tools? Such norms and pedagogics cannot be written in traditional conceptual languages of logics and causality.&nbsp; They need concepts and genres that are vibrating with potentialities, not positivist representation. &nbsp
ANALISIS KOMPETENSI GURU DALAM PENGUASAAN TEKNOLOGI INFORMASI DAN KOMUNIKASI PADA PEMBELAJARAN DI SEKOLAH DASAR NEGERI 2 PURBALINGGA LOR

This research focuses on teacher competencies in the use and utilization of information and communication technology in learning. The purpose is to analyze and describe teacher competencies in the use of information and communication technology in learning in a school. The study was conducted using a qualitative descriptive approach. This research was conducted using observation through direct interviews with informants consisting of school principals, class teachers and students. Data validity uses triangulation and reflection. The results show that teachers at SDN 02 Purbalingga Lor have been able and able to use, utilize and collaborate information and communication technology with learning in the classroom. Although in addition there are also constraints both internal and external. Support from related agencies was immediately felt both by the students and by the teachers as users. Principals with their full support also continue to encourage teachers to always innovate, especially in the use of information and communication technology in learning, both in the classroom and outside the classroom.Keywords: Teacher competence, information and communication technology, elementary school.
PRACTICE IN THE DISTANT

Публикация посвящена специфике организации и итогам производственной практики студентов в дистанционном режиме. Изложена методика разработки заданий на практику в условиях дистанта, варианты взаимодействия с руководителями от баз практики, формы представления отчетных материалов обучающимися. Также приведен анализ способов освоения знаний, умений и навыков, предусмотренных компетентностным подходом на основе анализа юридических документов. The publication is devoted to the specifics of the organization and the results of practical training of students in remote mode. The methodology for developing assignments for practice in a distance learning environment, options for interacting with managers from practice bases, forms of presentation of reporting materials by students are described. It also provides an analysis of the ways of mastering knowledge, skills and abilities provided for by the competence-based approach based on the analysis of legal documents.
Extraction of itemsets frequents

Frequent model extraction is the most important step in association rules.The time required for generating frequent itemsets plays an important role.This paper provides a comparative study of algorithms Eclat, Apriori and FP-Growth.The performance of these algorithms is compared according to the efficiency of the time and memory usage