Reviewerly/exHarmony huggingface dataset:
Syllabus Mining for Analysis of Searchable Information

Writing an effective syllabus is critically important for instructors to provide effective education at universities. However, little is known about how to create a well-written syllabus. It is necessary to elucidate what kind of information must be included in a syllabus. To achieve this goal, we focus on the searchable information in syllabi and analyze an actual syllabus collection that includes 6,493 syllabus documents of a national university in Japan. First, we investigate syllabus classification and syllabus search by using established text mining methods and an information retrieval method. The results of our experiments demonstrate that (i) knowledge discovery from syllabus documents is a challenging and non-trivial task, and (ii) just adding one particular word can already increase the searchability in syllabus search. Next, we investigate methods that provide word suggestions using deep learning approaches and large text corpora. In this experiment, we used a bibliographic database of university libraries in Japan, which contains 3,990,646 bibliographic entries, and a version of Japanese Wikipedia, which contains 2,351,545 articles. The results indicate that (iii) a vocabulary from a bibliographic database of university libraries is effective to ameliorate the efficacy measured by the mean reciprocal rank, and (iv) a wide range of vocabulary is essential in improving the recall in word suggestions.
Making hypothesis testing work with the central limit theorem

In discussing hypothesis testing, we focused on the general process of testing and how we can frame questions and decisions in terms of probabilities, parameters, and data. In this chapter, we make this connection more explicit, looking at how our data is able to help answer our questions. The main connecting point is the Central Limit Theorem which tells us the sampling distribution of our sample mean and proportion. By making some simple assumptions, we can calculate the probability that we observe more extreme data given our assumed state of the world based on this sampling distribution and our alternative hypothesis. Additionally, we consider how R can help us get these probabilities with single lines of code.
Neural Organizations are Registering Frameworks with Interconnected Hubs

Neural organizations, otherwise called fake neural organizations or reproduced neural organizations, are a subset of AI and are at the core of profound learning calculations. Their name and construction are motivated by the human cerebrum, imitating the way that organic neurons sign to each other. A neural organization is a progression of calculations that undertakings to perceive fundamental connections in a bunch of information through an interaction that mirror the manner in which the human cerebrum works. In this sense, neural organizations allude to frameworks of neurons, either natural or fake in nature. Neural organizations are registering frameworks with interconnected hubs that work similar as neurons in the human cerebrum. Utilizing calculations, they can perceive covered up examples and connections in crude information, group and characterize it, and after some time ceaselessly learn and improve.
Extending Dronacharyas Ball Retrieval method to collect Exam Results announced online After Revaluation

After the announcement of UG / PG course Exam results by any University, it is routine that some of the students will apply for Revaluation for some of the subjects attempted by them.The number of subjects applied for Revaluation will vary from student to student.So, after Revaluation the University will again announce results online.Here, we will discuss "how a class teacher can collect the revaluation results announced online in an excel file?" and "how to update the same in the old marks excel file?" by considering VTU exam results as an example.
Chapter-2 Fourier Series

I take great pleasure in presenting this book of engineering mathematics to the students of Engineering colleges. It is prepared in accordance with the syllabus of Bachelor’s degrees in Engineering and polytechnic colleges. It has been prepared by keeping the modern method of education in mind as well as the aptitude and attitude of the students to participate in various competitive examinations. In this book, the concepts are explained in a lucid manner that makes the teaching and learning process more easy and effective. Each chapter has been prepared with strenuous efforts to present the principles of the subject in the easiest manner to understand and to work out the sum of each topic of the book.Similarly, each chapter has been started with an introduction, definitions, theorems, explanation and solved examples for the better understanding of concepts.I hope that this book serves the purpose of keeping in mind the changing needs of the society to make it lively and vibrating.
Breast Cancer Detection Using Neural Network Model and Transform Based Function

The accurate detection of breast cancer saves the lives of women worldwide. Breast cancer is a worldwide leading cause of women's death in the current scenario. The automated diagnoses and image classification give the second opinion to doctor to plan treatment against breast cancer. The advancement of biomedical engineering provides the capacity for natural image classification and detection. The natural image classification and detection is part of an artificial neural network. The artificial neural network imparts various models such as supervised and unsupervised classification processes for breast cancer images. Better classification algorithms predict the better result of breast cancer cells for treatments. Transform function plays a vital role in natural image processing for recognition and decomposition of medical image data; in this paper, the study of breast cancer detection using the transform-based function with neural network models. The nature of the transform function is continuous and discrete applied to the breast cancer image's nature.  This paper details investigation of breast cancer imagery data classification with different neural network models. The classification process also estimates the limitation and divergence of vector factors to control the error rate during the process of classification. Analyzed the neural network models with MATLAB software and reputed image dataset of breast cancer.
Generative Models in Deep Learning

So far, we have mostly discussed discriminative machine learning models that aim to solve a supervised problem, i.e., learn a conditional distribution of the target variable conditioned on the input. In this chapter, we consider generative models whose purpose is to learn the entire distribution of inputs and be able to sample new inputs from this distribution. We will go through a general introduction to generative models and then proceed to generative models in deep learning. First, we will discuss explicit density models that model distribution factors with deep neural networks and their important special case, normalizing flows, and explicit density models that approximate the distribution in question, represented by variational autoencoders. Then we will proceed to the main content, generative adversarial networks, discuss various adversarial architectures and loss functions, and give a case study of style transfer with GANs that is directly relevant to synthetic-to-real transfer.
중국 대학도서관 비형식 학습공간의 연대별 변화에 관한 연구

In recent years, with the change of learning methods, the theory of informal learning has risen. The study of informal learning spaces has also become a topic of concern to scholars in various fields. This article mainly studies whether the internal space of Chinese university libraries as a representative informal learning space has regularity. Through literature review, comparative analysis and case studies, the development status and trends of informal learning and library space planning are discussed. Based on the theoretical investigation, a new library space composition benchmark based on the informal learning space theory is proposed. Through the space syntax, floor plans of 33 target Chinese university libraries were analyzed to determine the changes in the informal learning space of Chinese libraries. By analyzing the data, it can be seen that with the development of time, there are differences and correlations in the connectivity, total depth, integration, and choice of informal learning spaces in Chinese university libraries. This type of change is specifically explained in the conclusion section of the article.
A Theory of Heuristic Learnability

Which concepts can we learn efficiently on average? In this paper, we investigate the capability of a natural average-case learning framework, heuristic PAC (heurPAC) learning to answer this and some other related questions. Roughly speaking, we say that a concept class is heurPAC learnable if there exists a learning algorithm that given $n, s\in\mathbb{N}$ and $\epsilon,\delta,\eta\in(0,1]$ as input, learns all but $\eta$ fraction of $n$-input target functions represented as $s$-bit strings in the class from passively collected examples and then outputs an $\epsilon$-close hypothesis with failure probability at most $\delta$ in polynomial-time in $n$, $s$, $\epsilon^{-1},\delta^{-1}$, and $\eta^{-1}$, where each example is generated according to some example distribution. First, we establish a positive learnability result. Specifically, we show that a simple Fourier-based algorithm heurPAC learns $\Omega(\log n)$-junta functions on the uniform distribution, which is a central open question in the original PAC learning model. Our technical contribution is to introduce the notion of elusive functions that captures hard-to-learn cases and to establish a polynomial relation between the running time and the fraction of such elusive functions. Second, we present clear relations between heurPAC learnability and cryptography. Particularly, we show that for any efficiently evaluated class $\mathscr{C}$, (1) if $\mathscr{C}$ is not heurPAC learnable, then an auxiliary-input one-way function (AIOWF) exists; (2) if $\mathscr{C}$ is not heurPAC learnable on the uniform distribution, then an infinitely-often one-way function (io-OWF) exists. As a corollary, we also present new characterizations for AIOWF and io-OWF based on heurPAC learnability, which is conceptually stronger than the previous ones that are based on average-case learnability for fixed parameters. These results show that our framework might yield heuristic learners with theoretical guarantees for broader classes than the usual PAC learning framework, and any efficiently evaluated class has a potential for such a heuristic learner or a secure cryptographic primitive. Through this paper, we suggest further research toward the win-win ``learning vs. cryptography'' paradigm.
Robotics 2019: Using robotics programming in primary education - Hiroko Kanoh - Yamagata University

Robots for instructive purposes likewise accompany a more modest size and in an ease market, for example, the Wonder Workshop Dash Robot, Ozobot, SPHERO, BB-8, Wow Wee COJI The Coding Robot Toy, mBot, Transformable DIY Programmable Robot Kit, Clementoni My First Robot, LEGO, Bocco, Plen, Chip, Damian, Hicolor, Kamigami Robots Spot the Ladybug, DOBBY, Robi, Robohon, Roboactor, OHaNAS, Zoomer, Mip, KINGBOT, DIY Iron Bot Robot, Premaid, light sensor programing vehicle, the elated robot programming set, the Puchi little robot, block robot taste and Tama robot, and so on For what reason are there countless robots created for programming instruction? They invigorate understudies through body sensation. In preceding the advancement learning point at 9 years of age, contacting different articles and seeing in genuine items are indispensably significant .So, our attention is on the arranging of the usage of robot programming as essential understudies in Japan, in where programming instruction is as yet in an underlying stage, understudies should likewise be more keen on programming through genuine and obvious automated developments than in electronic ones. Kanoh maintains the guidance proposition of sustaining approaches to learn and consider the data characterized by Kanoh and her gathering and the usage of the programming instruction proposition in the solid controlling time of Piaget, J's improvement hypothesis is additionally recommended.
Herding and Information Asymmetry Review

The paper contains literature review of herding and information asymmetry. It is a part of academic project which contains work of researchers which has already been done on this topic
Sistem Informasi Perpustakaan Berbasis Web Pada Sma Negeri 1 Tegallalang

The system which is applied at the library of SMA Negeri 1 Tegallalang is still manual, where students have to search or look for the books that they need one by one. In their system, the officers will record the books borrowed by the students in a notebook. All data of the book collections in the library is also recorded in a book. For the visitor report, out book reports, book returned reports, and book data report will be recap by handwriting.This web will make the students easier to search for titles of books that they want to read or borrow. Therefore, the students no longer need to look for the title of the book they want to borrow by searching one by one on the bookcase. This system will be described in the design of Data Flow Diagrams, Conceptual Data Model, and userinterface is made for it.The results of the problem above is a library information system. This system will be able to assist students in the process of borrowing books. Moreover, it can assist the officers to record the data , reporting, bookkeeping, and the calculation of fines.
THE EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY AS A FACTOR IN THE DEVELOPMENT OF SUPRA-PROFESSIONAL COMPETENCIES OF FUTURE TEACHERS

In this work, the authors have realized the goal of determining the key components and characteristics of the educational environment of the university as a factor of influence on the supra-professional competencies of future teachers. In the empirical part of the study, the authors conducted an online survey of students of the pedagogical university (n=157) to confirm the research hypothesis. As a result, the article argues that a multicomponent educational environment (social, technological, spatially-subject components) should have such characteristics as openness, variability, redundancy and orderliness. The results obtained show the high importance of the formation of supraprofessional competencies of future teachers and the average level of student satisfaction with the possibilities of strengthening them, provided today by the modern educational environment of the university. The results of the study will be useful for the administrative and managerial and teaching staff of the university, who are focused on the development of curricula in accordance with the needs of modern educational institutions and the requirements for teacher training
Activating Audiences: How spatial music can help us to listen

This article discusses the importance of active listening when engaging new audiences with experimental and unfamiliar musical formats. Spatial music is examined as a physically immersive medium in which the audience is implicated as an active component in the execution of its performance. A brief account of the historic context of spatial music will be presented alongside speculation for the trajectory of its future; particularly its potential as a model for audience engagement. This article will first consider how spatially immersive performances have the capacity to activate listeners and how can this help to engage new audiences with new ways of listening. It will also question the notion of inhabiting spatial music, with an investigation of the multiple ways in which spatial music relates to physical space and the terms of its inhabitation. The concept of virtual listening will be discussed in response to trends towards passive hearing, as driven by recent technological developments in acoustic software and hardware, and the resultant abstraction of the spatial and social dynamics of sound in virtual space. The physiological and psychological differences between listening and hearing will also be examined as a means of establishing fundamental differences in the ways that we interact with music, and questioning what our listening habits tell us about audience engagement in the context of experimental music performance. This article will also question the individual roles of the musician, composer, architect/designer and audience in the ongoing responsibility to improve audience engagement in new, or unfamiliar musical works. Importantly, this article will also explicitly examine who we are referring to when we use the term ‘new audiences’. Major developments in acoustic technology during the last few decades have somewhat confused the diagram between music, space and listener. The understanding of which elements are active and which are passive is especially ambiguous at a time when ambisonic and binaural technologies have become developed enough to provide accurate simulations of the abstract, acoustic qualities of spaces, but on virtual terms. Architects, composers, musicians, engineers and audiences are at a crossroads in the development of new music and experimental, spatiosonic practice. ‘Spatiosonic’ is a hybrid term which is used throughout this article to describe work and phenomena which regard space (spatio) and sound (sonic) as equal, interactive partners. This article considers some of the opportunities and limitations at stake in current techniques of composition, performance and listening.
The Difficulties in Writing Descriptive Text Faced by the Eighth Grade Students of SMP Negeri 7 Palangka Raya

The research was conducted using descriptive qualitative method to analyze the difficulties in writing descriptive text especially in generic structure and language features, faced by the eighth grade students of SMP Negeri 7 Palangka Raya. An essay test was given to 20 students of the eighth grade of SMP Negeri 7 Palangka Raya to obtain the data about their difficulties in writing descriptive text. The result concluded that there were 10 students (50%) faced the difficulties in generic structures. They sometimes did not write ‘identification’ or ‘description’, or they did not write it completely and clearly. They had difficulties to analyze them that they placed ‘description’ in the ‘identification’. Some of them even wrote sentences which were not really related. On grammar, there were 8 students (40%) who faced difficulties and on mechanics there were 8 students (40%) who faced the difficulties. The difficulties were in using present tense ‘to be’, ‘pronoun’, and ‘adjective’; and in mechanics they had difficulties in using ‘capitalization’ and ‘punctuation’, in which sometimes they forgot to capitalize the first alphabet in the beginning sentence. All of that means that they were stated as having difficulties in writing good descriptive text.
A NEO-EVOLUTIONARY INTERPRETATION OF THE RELATIONSHIP BETWEEN GENIUS AND CULTURE

The article explores the neo-evolutionist interpretation of the relationship between genius and culture. The reasons for the idealization of genius and its transformation into a symbol of the “prime mover” of cultural progress, a person who can singlehandedly change the course of history, are considered. The author demonstrates the inconsistency of the idea of genius as a “prime mover” of cultural evolution. The conclusion is substantiated that genius is a “tool” of culture, with the help of which it creates its wealth that the epoch, and not an individual person, appears to be the real author of a genius creation, that the flourishing of culture “expresses” itself in brilliant people.
보건교사의 COVID-19상황 대응 경험

Purpose: This study was conducted to describe the experience of school health teachers in regard to the COVID-19 pandemic in South Korea. Methods: We conducted a qualitative study using content analysis. Ten school health teachers were recruited from 6 elementary schools, 2 middle schools and 2 high schools in Seoul, using purposive sampling. They participated in semi-structured in-depth interviews in person or using an online communication system from January to February, 2021. Interviews were transcribed verbatim and analysed using qualitative content analysis. Results: Three main categories and nine generic categories emerged from the analysis. Firstly, it was discovered that school health teachers had psychological and physical stress to cope with COVID-19 due to the 1) fear of the unexpected infectious disease, 2) burden of having to deal with it alone, 3) breakdown of personal life and physical exhaustion and 4) heavy duty as a health teacher caused by the lack of an organic cooperation system with institutions related to school infectious diseases. Secondly, school health teachers had an increased sense of empowerment in regard to infectious disease management as a result of 1) feeling rewarded and appreciated and 2) gaining confidence and trust in infectious disease management. Finally, school health teachers experienced the urgent need for an effective response strategy for infection control because of the 1) confusion over infectious disease response due to lack of practical manuals applicable to the field as well as training, 2) disappointing response system without an expert response team dedicated to managing infectious diseases in schools, and 3) growing awareness of the need for change. Conclusion: It is expected that school health teachers' experience of COVID-19 will be used as important data for building effective and advanced school infectious disease response systems.
Main Menu Music V2.5.1

Set with high priority. You will hear music in the game menu. Mod Version: 2.5.1 Verified on version:
A Practical Hands-on for Learning Graph Data Communities on Manifolds

Learning graph-structured data with hyperbolic geometry has received considerable attention in the last years. This paper reviews recent approaches based on hyperbolic embeddings, Riemannian K-means and Expectation Maximisation algorithms for supervised and unsupervised learning problems on graphs. The presentation is aimed to provide a practical and technical support for users interested in this topic. It is focused on illustrative examples, visualisations and Pytorch codes which are commented in details (The package implementing the algorithms is available online from https://github.com/tgeral68/HyperbolicGraphAndGMM ).
Classification of Images using Transfer Learning

For better accuracy and faster convergence, image classification using Transfer Learning and deep feed-forward neural networks (DFNN) is important. We have used existing models in Transfer Learning that save training time, improve neural network performance, and do not require a lot of data. A number of image classification models have been developed so far that articulates the major issue concerned with recognition accuracy. Image recognition is the most critical problem encountered in the areas applying practical applications of Computer Vision. The attribute of Object Recognition governed by autonomous vehicles with robotic influences, obstacle or pedestrian detection system, etc are few of the practical examples dealing with recognition accuracy. Machine learning, especially neural networks like the (DFNN) that won image classification competitions, has received a lot of attention. Such a DFNN architecture model should be researched and investigated, according to this article. Using new image datasets to see if Transfer Learning will perform better in terms of precision and productivity.There are some similarities to state-of-the-art approaches. The DFNN can be tweaked by changing the total number of hidden layers, hidden neurons in each hidden layer, and the number of connections made in between layers.