Reviewerly/exHarmony huggingface dataset:
Indeterminacy in Latent Variable Models: Characterization and Strong  Identifiability

Most modern latent variable and probabilistic generative models, such as the variational autoencoder (VAE), have certain indeterminacies that are unresolvable even with an infinite amount of data. Recent applications of such models have indicated the need for \textit{strongly} identifiable models, in which an observation corresponds to a unique latent code. Progress has been made towards reducing model indeterminacies while maintaining flexibility, most notably by the iVAE (arXiv:1907.04809 [stat.ML]), which excludes many -- but not all -- indeterminacies. We construct a full theoretical framework for analyzing the indeterminacies of latent variable models, and characterize them precisely in terms of properties of the generator functions and the latent variable prior distributions. To illustrate, we apply the framework to better understand the structure of recent identifiability results. We then investigate how we might specify strongly identifiable latent variable models, and construct two such classes of models. One is a straightforward modification of iVAE; the other uses ideas from optimal transport and leads to novel models and connections to recent work.
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime

We provide quantitative bounds measuring the $L^2$ difference in function space between the trajectory of a finite-width network trained on finitely many samples from the idealized kernel dynamics of infinite width and infinite data. An implication of the bounds is that the network is biased to learn the top eigenfunctions of the Neural Tangent Kernel not just on the training set but over the entire input space. This bias depends on the model architecture and input distribution alone and thus does not depend on the target function which does not need to be in the RKHS of the kernel. The result is valid for deep architectures with fully connected, convolutional, and residual layers. Furthermore the width does not need to grow polynomially with the number of samples in order to obtain high probability bounds up to a stopping time. The proof exploits the low-effective-rank property of the Fisher Information Matrix at initialization, which implies a low effective dimension of the model (far smaller than the number of parameters). We conclude that local capacity control from the low effective rank of the Fisher Information Matrix is still underexplored theoretically.
Finding and learning multiword units

There is now plenty of evidence that the learning of multiword units can occur across the four strands of meaning-focused input, meaning-focused output, language-focused learning, and fluency development, although the largely unresearched strand with regard to multiword units is fluency development. There are now many useful well-researched lists of multiword units of various kinds. Multiword units tend to be acquired late and are one of the signs of high proficiency in a language. This chapter looks at various ways of classifying multiword units according to their form, meaning, and storage. It has many practical suggestions for supporting the learning of multiword units. These include learning through input, learning through output, consciousness raising, using flash cards, using mnemonic tricks such as alliteration and considering their origins, looking for patterns, using concordances, and fluency development.
Investigating situation awareness via visual exploration in high-intensity multi-agent activity using wearable technology

Introduction: In team-based high-performance environments, such as combat and team sports, bouts of high- and low-intensity activity are interspersed, with bouts of high-intensity activity being central to key goal related and attacking events. Hence, an adequate distribution of energy expenditure is imperative to complete performance tasks effectively. This requires agents to possess strong situational awareness and to make important choices about what actions to prioritise1. Our research in team sport highlights that the decisions agents make are closely associated with the affordances that surround them2 and the visual exploratory actions (also termed scanning) that are required to establish awareness of these affordances3.
Comprehensive Sarcasm Detection using Classification Models and Neural Networks

Natural Language Processing has come a long way ahead in the research field and has been widely treated by researchers as an interesting topic. NLP related models try to calculate the perfect meaning of what is explained in a conversation in order to give high accuracy. The sentiment analysis includes sarcasm detection which too in a way is very difficult to portray without any processing. People convey the exact opposite message to convey a sarcasm. In this paper we will be discussing on preprocessing the dataset and the various models like SVM, KNN, Decision Tree and Random Forest and Neural network models like CNN and LSTM and comparing their performances and determining the best approach to detect and deal with sarcasm.
ORGANIZATION OF EXPERIMENTAL EXAMINATION OF THE EFFECTIVENESS OF THE METHODICAL SYSTEM OF TEACHING UKRAINIAN LANGUAGE FOR PROFESSIONAL PURPOSES

The article focuses on the issue of effective organization of experimental research with the use of the methods of mathematical statistics. The relevance of the topic is evidenced by the necessity of improvement of the process of preparation and conduction of a pedagogical experiment, as well as examination of the objectivity and validity of the performed measurements. The aim of the article is elaboration of the algorithm of theoretical and practical preparation for conducting a pedagogical experiment. The methodical system of teaching Ukrainian Language for Professional Purposes to future pedagogy specialists, which has been developed by the author, serves as the object of experimental examination. The descriptive, systemic and structural, mathematical and statistical methods, as well as the theoretical methods of generalization and particularization, have been applied in the research process. As a result of the study, the main pedagogical conditions for organization of experimental and exploratory learning have been formulated, the specificity of preparatory work before conducting an experiment has been elucidated, the aim and tasks of the experiment have been detailed, and the methodical support has been determined. The fact that the methodical system of teaching Ukrainian Language for Professional Purposes to students of pedagogical institutions of higher education has to be based on the foundations of research, competence, communicative and activity-oriented, linguistic and extralinguistic, professionally oriented, systemic and synergistic, text-centric, functional and stylistic approaches has been taken into account in the process of organization of the experimental work. Components of the general ability of future experts to perform complex specialized tasks and solve practical problems in their professional and pedagogical activity include the discourse, documentation, general scientific, informational and technologic, entrepreneurial, editorial, and terminological competences. For the purpose of objectivity in conducting pedagogical measurements, the sizes of general and random samples have been formed considering the regional and statistical aspects. Determination of a minimal required size of a sample as a micromodel of a general sample is carried out by using the limiting error formula to ensure the accuracy and reliability of research results.
Abnormality of Air Temperature in the Early Decade over Lalitpur, Nepal

The observation of temperature above Lalitpur was studied using the maximum air temperature at 12 PM and minimum air temperature at 3 AM. The data are taken from the Department of Hydrology and Metrology from 2011 to 2020. The observation, plot, and analysis for this work are based on an Excel plot. In addition, observation shows that the maximum air temperature was recorded in 2016 above Lalitpur up to 32.2 oC on April 29 and June 16, 2016). The minimum temperature was recorded up to -2.4 oC on January 9, 2013. The observation of temperature shows the fluctuation, this is because of several factors like clouds, air pollution, aerosol particles, rainfall, fog, seasonal variations, etc.
StarGraph: A Coarse-to-Fine Representation Method for Large-Scale  Knowledge Graph

Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in neighbor entities. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to get better entity representations. The core idea is to divide the neighborhood information into different levels for sampling and processing, where the generalized coarse-grained information and unique fine-grained information are combined to generate an efficient subgraph for each node. In addition, a self-attention network is proposed to process the subgraphs and get the entity representations, which are used to replace the entity embeddings in conventional methods. The proposed method achieves the best results on the ogbl-wikikg2 dataset, which validates the effectiveness of it. The code is now available at https://github.com/hzli-ucas/StarGraph
Improve the Recognition Accuracy of Sign Language Gesture

Abstract: Image classification is one of classical issue of concern in image processing. There are various techniques for solving this issue. Sign languages are natural language that used to communicate with deaf and mute people. There is much different sign language in the world. But the main focused of system is on Sign Language (SL) which is on the way of standardization in that the system will concentrated on hand gestures only. Hand gesture is very important part of the body for exchange ideas, messages, and thoughts among deaf and dumb people. The proposed system will recognize the number 0 to 9 and alphabets from American Sign Language. It will divide into three parts i.e. preprocessing, feature extraction, classification. It will initially identify the gestures from American Sign language. Finally, the system processes that gesture to recognize number with the help of classification using CNN. Additionally we will play the speech of that identified alphabets. Keywords: Hybrid Approach, American Sign Language, Gesture Recognition. Feature Extraction
Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based Traversability Analysis method that combines geometric features with appearance-based features in a hybrid approach based on a SVM classifier. In particular, we show that integrating a new set of geometric and visual features and focusing on important implementation details enables a noticeable boost in performance and reliability. The proposed approach has been compared with state-of-the-art Deep Learning approaches on a public dataset of outdoor driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying complexity, demonstrating its effectiveness and robustness. The method runs fully on CPU and reaches comparable results with respect to the other methods, operates faster, and requires fewer hardware resources.
Designing and Implementing Accommodation Management System: ASHAMS as Case Analysis

Most Bangladeshi schools, colleges and universities rehearse the old conventional and manual accommodation management procedures. Accommodation management in a manual way is a tedious paperwork process since it involves unnecessary time consumption and lots of unwanted errors. This manual procedure lingers the seat management process (allocation-deallocation, room shifting (reallocation), etc.) and slows down the overall work speed for both the hall managerial bodies and students. We have explored the feasibility studies and requirement analysis considering all the manual accommodation management processes. We have proposed and designed a web-based Abdus Salam Hall Accommodation Management System (ASHAMS) according to the outcome obtained. Tools used to implement the system are Microsoft Visual studio and ASP.net framework as the front-end and SQL Server as the back-end server database. We proposed ASHAMS as a pilot project, and further implementation depends on the success of this project. We collected data from Bhasha Shahid Abdus Salam Hall, Noakhali Science and Technology University (NSTU), Bangladesh, for the entire development purpose of ASHAMS. Using ASHAMS, Hall (dormitory/hostel) authority can easily manage the hall details, room details, seat management process and reduce human errors. Hopefully, ASHAMS will overcome the shortcomings of conventional accommodation management procedures; improve the service quality, productivity, personnel efficiency, reliability, and transparency in the organization.
Garden-Path Traversal within GPT-2

In recent years, massive language models consisting exclusively of transformer decoders, led by the GPT-x family, have become increasingly popular. While studies have examined the behavior of these models, they tend to only focus on the output of the language model, avoiding analyzing their internal states despite such analyses being popular tools used within BERTology to study transformer encoders. We present a collection of methods for analyzing GPT-2's hidden states, and use the model's navigation of garden path sentences as a case study to demonstrate the utility of studying this model's behavior beyond its output alone. To support this analysis, we introduce a novel dataset consisting of 3 different types of garden path sentences, along with scripts to manipulate them. We find that measuring Manhattan distances and cosine similarities between hidden states shows that GPT-2 navigates these sentences more intuitively than conventional methods that predict from the model's output alone.
Introduction

This chapter discusses the so-called runtime object method to develop and build professional data-driven applications. It aims to insert, update and delete data from an Oracle database—Oracle 18c XE. Most or all of them need professional technicians or engineers to develop and build database-related applications, instead of building database management and design systems, to meet their normal business-running requirements. However, most of these books are specially designed for database programming engineers or programmers, with a lot of abstract database programming strategies and huge blocks of code, and they are terrible headaches for college students or beginning programmers who are new to Java database programming– related tools such as the Apache NetBeans IDE, Java Development Kit, Java Database Connection, Java Beans, JavaServer Pages, Java Servlet, Java Web Applications, Java Web Services and the JavaServer Faces, as well as database programming–related applications.
A geometric statistic for deep learning model confidence and adversarial defense

Deep learning models are pervasive for a multitude of tasks, but the complexity of these models can limit interpretation and inhibit trust in their estimates of confidence. For the classification task, we investigate the induced geometric relationships between the class conditioned data distributions with the deep learning models’ output weight vectors. We propose a simple statistic, which we call Angular Margin, to characterize the “confidence” of the model given a new input. We compare and contrast our statistic to Angular Visual Hardness and Softmax outputs. We demonstrate that Angular Margin provides a superior statistic for detecting minimum-perturbation adversarial attacks and/or misclassified images than standard Softmax predictions.
Puppets as an Educational Resource in Early Childhood Education: Art, Therapy, and Literature

The paper underlines the importance of puppets in the child’s development in different areas. The puppet provides the child a kind of cover or disguise to hide behind. So also a timid child finds the courage to speak, to express his/her own emotions and to open his/her secrets to his puppet and through it to his/her audience. Thus the puppet helps the child to communicate much more spontaneously, avoiding stressed relations, especially to adults. The puppet is an authority, selected by the child himself. Contemporary attempts to use narration as a method of rediscovering and stabilisation of the child’s personality are supported by using puppets. Regarding the fact that a child is not able to verbally express all his feelings, puppet heroes help him find words and more: another point of view or perspective. In addition children, accustomed to using puppets in their everyday conversations, have richer a vocabulary, they are able to understand the semiotic-symbolic value of visual signs and the language of non-verbal communication, which is important in identifying their real abilities and progress in important areas of development - cognition, sensation, motor co-ordination, social skills…and least but not last - language expression. Through my experience in working with puppets I believe in the magical power of the puppet in all kinds of communication with children, enlightening their talents and different forms of their creativity
Hybrid Image Encryption Algorithm for Grey and Color Medical Images

As these pictures are communicated through the organization, they need a significant degree of insurance. Assuming that the information in these images is at risk for unapproved use, this might prompt serious issues. There are various techniques for getting pictures. Perhaps the most productive method for achieving clinical pictures is to make an encryption process. Disarray and dissemination are the couple primary advances utilized in the process of encryption algorithms. This project offers one more encryption computation for scrambling both dim and shading clinical pictures. Another image separating methodology brings into account picture blocks presented. Then, the image cubes are blended through a jumble model, turn, and subjective stage. Then, a wild essential aide delivers a key to diffuse the blended picture. Of late, diagnosing hardships utilizing clinical pictures got colossal. As these photographs are passed on through the affiliation, they need a basic degree of security. If the information in these photographs is in peril for an unapproved use, this may incite unbelievable issues. There are various methods for getting pictures. Possibly the best frameworks for getting clinical pictures are encryption. Disarray and dispersal are the two fundamental advances utilized in encryption assessments.
Future Tasks and Prospects for Korean Religion in the Post Covid 19 era, focused on the Viewpoint of Religious Education in Korea

본 연구는 포스트 코로나 시대에 종교교육의 관점에서 한국종교의 과제와 전망이라는 주제 로 ‘과연 종교교육의 과제는 무엇인가 ‘라는 성찰과 전망을 설정해 보는데 그 목적을 두고 있다. 이를 위해 종교교육을 담당하는 교사, 종교교육이 이뤄지는 공간. 종교교육의 내용, 종교교육의 방법, 종교 교육의 목적 및 종교교육 매개체 등을 중심으로 검토해 보았다. 그 결과를 제시하면 다음과 같다. 첫째, 종교교육의 관점에서 한국종교는 종교교육을 위한 구성원 간(학생, 교사, 종단, 학부모, 정부 등)협업의 지혜를 발휘하길 기대한다. 둘째, 새로운 의사소통 매체의 연구와 개발을 요구받는다. 그동안 종교교육의 주요한 매개체로는 국가수준의 교육과정, 교과서, 교사였다. 그러나 이제 포스트 코로나 시대의 비대면 체제에서 이 매개체들은 새로운 변화를 요구받는다. 셋째, 종교를 위한 종교보다는 인간을 위한 종교로의 거듭남이다. 그동안 한국의 종교교육은 한국이 처한 종교적 현실 속에서 종교단체의 주도하에 전개되다 보니 교육적이기보다는 종교적이라는 경향이 지배적이었다. 포스트 코로나 시대에는 인간의 생명, 환경, 조화와 협력 등이 더욱 절실해질 전망이기 때문에 인간중심의 종교교육이 더욱 중요하다. 끝으로 종교교육이 한국종교, 국민과 함께 공유되는 대상이 되려면 종교교육과 관련하여 다양한 주체들과 함께 공동 연구와 개발(R&D)을 통해 새롭고 창의적 출구를 마련하는 데 더욱 노력해야 할 것이다. This paper aims to set tasks and prospects for Korean religion in the post Covid 19 era based upon the perspective of Korean Religious Education. The paper examines teachers who teach religion in schools, spaces where religion is taught, contents, methods, the purpose of religious education, and religious education text files. The paper suggests tasks and prospects for Korean religion based on Religious Education in Korea as follows: First, I hope that for the development of Religious Education, much more to display wisdom how to cooperate with each members around(students, teachers, Religious orders, Parents, Government). basically based on Religious Education. In the Post Covid 19 era, it need disorganization of one’s province and much more communication, cooperation each others, because of come across one’s Religious order, it is necessary take an earnest reflections among Religious societies. Second, It is necessary to research and development of new communication mechanism. During past, The main agencies of Religious Education dominated by National Curriculum,Textbook, Teacher. However, under the Post Covid 19 era, It would be difficult that such agencies could occupy the status as before. Textbook in paper also would be shifted by digitalized contents by way of Research and Development new mechanisms. Further more, we can prospect that for the Liberal Arts education in school, National Curriculum, Teacher training for Religious Education system should be reorganized. Third, It will be easy to prospect that the Religion should be rebirth as a Religion for Human kind than for Religion itself. So far, Korean Religious Education more dominated by religious pattern rather than education However in the Post Covid 19 era, it would be pressing prospect that Korean religion much more cooperate and harmonize with Human Life, Environment than before. Finally for more share with citizens as a Korean Religion, It is necessary to make an effort to create new way out by a joint R&D with members related Religious education. For this it is necessary try to find a way how to cope with those problems, by more cooperate with concernd parties: Academic societeis, Religious Order, Parents, Schools, Government.
An Analysis of The Intriguing Murder Mystery in Stephen King’s The Outsider

There is no such thing as a perfect crime. In every case, sooner or later the culprit will be found and he or she shall face the law. Even in a case that can be seen as a perfect crime there will be some piece of information left by the criminal. The solving of the case depends upon the ability of the investigator to find out that hint and put it into investigation. But what happens when the crime is not done by an ordinary human and the police officials have arrested an innocent man? Stephen King’s The Outsider is all about such an inhuman being who has done several crimes especially the Raping and Murdering of innocent children. We can’t even think about a situation when we are blamed or even arrested for another man’s crime. But what happens when one experiences a scenario where the innocent is also guilty? This contradiction is portrayed in The Outsider as it follows the story of a detective whose search for the absolute truth leads him to discover evil in its pure form.&#x0D; Detective Ralph Anderson is all set for arresting Terry Maitland who is an esteemed sports trainer and high school educator for the killing and confrontation of an eleven year old boy, Frank Peterson. All the clues are against Terry Maitland. Ralph Anderson commands to arrest him at a local baseball game in front of his household and the whole horde. But Maitland had a mighty justification, he was in the company of some educators hundreds of miles away and there is video authentication to confirm it. His fingerprints are also there on a book where he claimed to be. How can he be at two places at the same time? However the Intriguing murder mystery is solved at last by the brave efforts of Ralph Anderson and his teammates.