Reviewerly/exHarmony huggingface dataset:
MULTICULTURAL FACTORS AND THE REPRESENTATION OF ACID ATTACK SURVIVORS IN INDIAN SOCIETY BASED ON MULTIMODAL DISCOURSE ANALYSIS

Women considered to the goddess nature and praised in the form of Land and Rivers, but struggle to buy bread and basics in day today life. Women face violence everywhere in every form such as domestic, gang rape, acid throwing, and sexual violence at work place, dowry death and forced abortion. Acid Attack is worldwide and it is considered to be the most abominable form of gender based violence against women. According to India Today Data Intelligence Unit (DIU) the statistics released by National Crime Records Bureau shows between 2014 and 2018, states that there have been 1,483 victims of acid attack happened in the country. The Victims are taunted, shamed and disfigured for no fault on theirs. It is the need of the hour to examine the gender based violence against Women in India; its cause and consequence, as this is an untold tale in the subparts of the country. On the other hand, Indian Cinema is an effective mass communication medium and continues to evolve. There is always a strong bonding between cinema and Indian Society as it is a cultural role player for Indian Audience as well as immigrants where it promotes the uniqueness of multiculturalism of Indian Society.. The role of Women in Indian Cinema acted between the dichotomy of passive subject and Pleasurable Object. Indian New wave made a shift in the screen and contributed dialogues to women‟s role and position. Women became a subject on lens. The representation of women in Indian cinema still endures with controversy and characterized by diverse interpretations in our Multicultural land. This paper aims to examine the Representation of acid attack survivors in Indian Cinema qualitatively with the help of case studies and Multimodal discourse analysis by interpreting with the Interactive and compositional meaning.
A Fast Positioning Technology for Compensation Hole of Automobile Brake Master Cylinder

The compensation hole of the automobile brake master cylinder is an important structural part for adjusting the reservoir and pressure chamber of the brake master cylinder. Its detection accuracy is strictly controlled. However, because the compensation hole is located on the inner wall of the blind hole, the existing detection method cannot meet the testing needs. Therefore, this paper introduces the SSD model into the detection of the compensation hole of the brake master cylinder, and realizes the rapid positioning of the compensation hole by means of network fine-tuning. The compensation hole positioning detection is carried out on the self-developed automobile brake master cylinder compensation hole detector. The entire detection process time is about 5s, and the positioning accuracy is high. We apply the fine-tuning SSD model to the detection of the compensation hole of automobile brake master cylinder, which replaces the traditional method based on human-computer interaction to determine the position of the compensation hole. It has better detection accuracy and faster detection speed, and lays the foundation for the subsequent detection of the size of the compensation hole.
Recent Study on Breast Cancer Prediction Based on Deep Neural Network Model Implemented AWS Machine Learning Platform

Breast cancer is one of the most dangerous cancers in women, with developing breast tissue leading to death. Surgery, radiation, chemicals in conjunction with hormone therapy, and biological therapy are some of the current therapies for breast cancer that have made significant progress. The Deep Neural Network (DNN) model is implemented on the AWS machine learning framework in this work, as well as a comparison with other machine learning techniques such as XGBoost and Random Forest on a public dataset. &nbsp;The plot of model accuracy for the training and validation sets, as well as performance assessment metrics to evaluate the model, show that breast cancer prediction based on DNN model with Hyperparameter tuning has the best results.
A COMPACT OPTIMAL LEARNING MACHINE

Artificial neural networks (ANNs) have been developed and applied to a variety of problems, such as pattern recognition, clustering, function approximation, forecasting, optimization, etc. However, existing ANNs have a high computational cost, since their learning methods are mostly based on a parameter tuning approach. Extreme learning machine (ELM) is a state-of-the-art method that generally dramatically reduces the computational cost. An analysis of the ELM method reveals that there are unsolved key factors, including inefficient hidden node construction, redundant hidden nodes, and unstable results. Therefore, we describe a new learning machine based on analytical incremental learning (AIL) in conjunction with principal component analysis (PCA). This learning machine, PCA-AIL, inherited the advantages from the original one and solved the unsolved key factors of ELM, and also extended AIL capability to serve a multiple-output structure. PCA-AIL was implemented with a single-layer feed-forward neural network architecture, used an adaptive weight determination technique to achieve a compact optimal structure and also used objective relations to support multiple output regression tasks. PCA-AIL has two steps: objective relation estimation and multiple optimal hidden node constructions. In the first step, PCA estimated the objective relations from multiple-output residual errors. In the second step, the multiple optimal nodes were obtained from objective relations and added to the model. PCA-AIL was tested with 16 multiple-objective regression datasets. PCA-AIL mostly outperformed other methods (ELM, EM-ELM, CP-ELM, DP-ELM, PCA-ELM, EI-ELM) in terms of fast testing speed – 0.0017 second, a compact model – 19.9 nodes, an accurate performance – RMSE 0.11261, and a stable result – S.D. of RMSE 0.00911: reported in averaged.
Characterisation and level assessment of schoolchildren’s digital literacy

Relevance. The main trend in the development of the digital society involves active penetration of digital technologies in all spheres of human life. These conditions give rise not only to new opportunities for people, modern technologies, communication methods, problem solution, but also to previously unknown risks and threats. The formation of digital literacy of schoolchildren is becoming an important issue. In these terms, a special role is assigned to the school computer science course. This involves the need for descriptive characteristics of digital literacy and indicators for its qualitative assessment. The authors deem the use of praxeological characteristics of activity to be a promising direction in the solution of this problem from the standpoint of assessing the “correctness” of the use of digital technologies by the learners, their expediency, reasonableness, efficiency, safety and environmental friendliness. The purpose of the article is to identify the characteristic features of digital literacy of schoolchildren that help to assess its level – basic, intermediate and advanced – in the context of the praxeological approach. Methodology and techniques. The systemic and praxeological approaches to assessing the learners’ digital literacy are used as the methodological basis of the research. The Russian and foreign experience of its level-specific characteristics is analysed. When developing the indicators of schoolchildren’s digital competence, praxeological characteristics of human activity were used in the aspect of its efficiency, viability, maintainability, reasonableness, environmental friendliness, precision. The research results are presented by detailed description of the digital literacy level assessment indicators (basic, intermediate, advanced); fixing the specific characteristics of its pragmatistic component based on the praxeological principles of activity assessment and with regard for the priority ideas of the modern digital educational environment. The learner’s individual actions are assessed in seven areas of digital literacy: basic knowledge of hardware and software, information literacy, communication and collaboration, content creation, security, problem solving, career competencies. The scientific novelty is rooted in the context of addressing the problem of evaluating schoolchildren’s digital literacy from the standpoint of the praxeological approach that emphasises the pragmatistic format of digital skills manifestation in practice. The practical significance of the presented research results lies in the possibility of their use in educational institutions of secondary general level in order to improve the systems for assessing the academic achievements of learners in mastering the school-based computer science with regard for evaluation of praxeological characteristics of activity and the choice of most optimal formats for digital literacy development in accordance with the individual level of formed digital skills.
Adiabatic Decoupling and Time Evolution of Coherent States for Multi-component Systems

Coherent states are well adapted to understand adiabatic decoupling for multi-components systems like the Dirac operator or systems obtained by reduction in the Born-Oppenheimer approximation [58]. Hagedorn [130] has done an accurate analysis of this using a variant of coherent states. The new fact for systems is that now states are vectors valued in \(\mathbb C^m\) for \(m\ge 2\) and the eigenvalues of the matrix of the system may cross so that one cannot diagonalize it smoothly. In the case when the eigenvalues are not crossing one can construct an adiabatic modification of the spectral projectors on the modes and compute the time evolution of coherent states following the same strategy as in the scalar case. Here we shall consider only non crossing cases where we can prove an adiabatic decoupling between the modes. For the more difficult crossing case we refer to the literature [130] where non-adiabatic transitions can be computed [101, 129] for more recent results.
Predicting Opioid Overdose Risk of Patients with Opioid Prescriptions Using Electronic Health Records Based on Temporal Deep Learning

Background: The US is experiencing an opioid epidemic, such that opioid overdose is causing more than 100 deaths per day. Identifying patients at high risk of Opioid Overdose (OD) can help to make early clinical interventions to reduce the risk of OD. We aim to build a deep learning model that can predict the patients at high risk for opioid overdose and identify most relevant features.Method: The study included the information of 5,231,614 patients from the Health Facts database with at least one opioid prescription between January 1, 2008 and December 31, 2017. Potential predictors (n = 1185) were extracted to build a feature matrix for prediction. A Long Short-Term Memory (LSTM) based model was built to predict overdose risk in the next hospital visit. Prediction performance was compared with other machine learning methods assessed using machine learning metrics.Findings: Our temporal deep learning model built upon LSTM outperformed the other methods on opioid overdose prediction. It achieved the highest F-1 score (F-1 score: 0.8065, AUCROC: 0.8885). The model is also able to reveal top ranked predictive features, including medications and vital signs.Interpretation: This study demonstrates that a temporal deep learning based predictive model can achieve promising results on identifying risk of opioid overdose of patients using the history of electronic health records. It provides an alternative informatics-based approach to improving clinical decision support for possible early detection and intervention to reduce opioid overdose.Funding: This work was funded partially by the Stony Brook University OVPR Seed Grant 1158484-63845-6.Declaration of Interests: The authors do not have any conflicts of interest to disclose.Ethics Approval Statement: All the data used in the study are de-identified, and the study is approved by Stony Brook University.
An Intelligent System for Allocating Times to the Main Activities of Managers

Correctly allocating times to the main activities of a manager is a crucial task that directly affects the possibility of success for any company. Decision support based on state-of-the-art methods can lead to better performance in this activity. However, allocating times to managerial activities is not straightforward; the decision support should provide a flexible recommendation so the manager can make a final decision while ensuring robustness. This paper describes and assesses a novel approach where a search for the best distribution of the manager’s time is performed by an intelligent decision support system. The approach consists of eliciting manager preferences to define the value of the manager’s main activities and, by using a portfolio-like optimization based on differential evolution, obtaining the best time allocation. Aiming at applicability in practical scenarios, the approach can deal with many activities, group decisions, cope with imprecision, vagueness, ill-determination, and other types of uncertainty. We present evidence of the approach’s applicability exploiting a real case study with the participation of several managers. The approach is assessed through the satisfaction level of each manager.
The Effect Of ABA English Applications On Students’ Vocabulary Mastery At Eighth Grade Of MTs Al Khoir Mananti Padang Lawas

The aim of this study is to answer the following questions: when they taught withthe ABA English Application without the ABA English Application, the students' reportcards in vocabulary mastery are better. The subjects in this study were 25 students ofClass VIII-B MTs Swasta Al-Khoir Mananti Padang Lawas. The results revealed that:ABA English Application shows that in test implementation, particularly in vocabularymastery, the use of applications in learning can produce good scores. It can be seenfrom the grades of the students. The assessment of test outcomes focuses on the learners'vocabulary mastery of the students. The assessment of test results is focused on students’vocabulary mastery in the learners. The normality and homogeneity test was used forthe interpretation of the requirements test, while the t-test formula was used for dataanalysis.The authors continued after the data was analyzed; (1) The learning outcomesof students’ understanding of new vocabulary using the ABA English applicationreached 80.24 and a standard deviation of 7.102. (2) The learning outcomes of students'understanding of new vocabulary obtained did not use the 68.4 application and thestandard deviation of 6,244 t test values were 167.72 and ttable 1.708. So, the ttest is higherthan the ttable. So that, the alternative hypothesis (Ha) proposed is accepted and zero(Ho) is rejected. This means that both the use of the ABA English Application for studentachievement in mastering new vocabulary based on the results of this study, thesuggestion is that the ABA English application can be used as an alternative medium inlearning to vocabulary mastery.
A Study On Face Recognition Using Laplacianfaces

A facial recognition system called Laplacianfaces describes human face appearance-based representation. Using the location preserving projections, face images are assigned to subspace of faces to examine. It is not exactly equivalent to the main component. The evaluation PCA & the discriminant linear analysis LDA that observes only the structure of facial space. This find entering the information from gaining a face subspace that better recognizes the complex structure of the main face. The Laplacianfaces are the perfect approximations directed to function and   managing of Laplace in the facial complex. Therefore, the annoying faces that arise due to changes in lighting, external appearance and posture can be detected. Speculative examination shows that PCA as well as LDA along with LPP can be derived from various models of the graphic. Let's consider proposal Focus Laplacian face along with the Eigen face and also Fisher face systems. The test results suggest that Laplacian face proposed approach offers unmatched representation and gets less failure rates of face affirmation.
The Junction Transistor

Bardeen and Brattain’s failure to acknowledge the work done by others can be explained by human nature—pride, arrogance, ignorance, plain self-interest, or in this case, by jealousy that Shockley’s ability to solve the problems of solid-sate physics was better and faster.
Daria Kazarinova-Pylnova: Dividing the Momentary and the Eternal

Contemporary art has always posed many questions for the viewer regarding form and content, ways of representation and perception. Over the past 150 years, artists have increasingly complicated the task of the viewer — abandoning any rules and laws developed by many generations of their predecessors, proposing new approaches to composition, working with color, transforming the very concept of the work of art. However, until recently, even the most avant-garde paintings and graphics had one key characteristic that united them: they physically existed. Digital art, which has gained more and more popularity in recent years, poses difficult questions for the public. What should be considered as original? What about a work of art that can be reproduced an infinite number of times? How to perceive a picture that does not have the artist’s energy, and does not contain a part of his soul? Since 2016 Moscow artist Daria Kazarinova-Pylnova has been working in the touchscreen technique (drawing with fingers on the tablet’s screen) using tools such as Artboard, sketchGuru and similar art applications. Despite the fact that the Russian public is somewhat wary of digital art, the works of Daria Kazarinova-Pylnova enjoy noticeable popularity among the widest audience. The article examines the problems of the existence and perception of digital works of art on the example of her works. The artist’s reflections on the role, functions and objectives of art make possible to recognize that the technique of creating a work of art is not a determining factor in its perception.
LibGuides: IED-PK | How to Get Copyright Clearance: Copyright Acts

The purpose of this guide will assist you to get permissions for learning materials and their fair use.
Chapter 16. Methodological and Epistemological Issues in Science Education Problem-solving Research: Linear and Nonlinear Paradigms

This chapter examines methodological and epistemological issues concerning research in science education problem solving. It presents some constructive annotations through a short review focusing on specific features of collective research that appeared in the last decades, which are characterized by certain theoretical and methodological traits. The definition of problem and problem solving within the science-learning context is extended to any cognitive challenge. In the quest for a theory, psychological neo-Piagetian constructs provided effective explanatory frameworks for students’ performance in chemistry and physics education. Epistemological problematization brought into discussion the limitation of linear approaches. The paradigm shift, initiated in the behavioral and social sciences, has affected science education research, and a series of applications based on complexity theory and nonlinear dynamics was realized and reported. The catastrophe theory model provided empirical evidence for nonlinear phenomena, modeling and explaining overload effects and students’ failures. The chapter includes a persuasive discussion on the relationship between problem solving and conceptual understanding and the nature of the corresponding latent variables is examined based on empirical evidence provided by contemporary state-of-the-art methodologies. The later endeavors on problem solving in chemistry and physics education founded a new area of investigation promising to provide new answers to old everlasting questions.
A Missiological Study on the Abingdon Bible Commentary Affair in the 1930s

In order to celebrate the 50th anniversary of Protestant Mission in Korea, few foreign missionaries and many Korean pastors from both Methodist and Presbyterian churches participated in the translation of The Abingdon Bible Commentary, and Hyungki J. Lew led this meaningful affair. However, the general assembly of the Korean Presbyterian Church rejected The Abingdon Bible Commentary, because the book denied the biblical inerrancy and verbal inspiration. Also, the Presbyterian Church adjured that the Presbyterian writers request for withdrawal of their translations in the next publication of the commentary. The impact via Presbyterian Church’s objection at that time seems to have not been so great. Rather, the commentary was sold a lot. This paper deals with the dispute of The Abingdon Bible Commentary in the 1930s from the viewpoint of missiological perspectives.
Attention feature matching for weakly-supervised video relocalization.

Localizing the desired video clip for a given query in an untrimmed video has been a hot research topic for multimedia understanding. Recently, a new task named video relocalization, in which the query is a video clip, has been raised. Some methods have been developed for this task, however, these methods often require dense annotations of the temporal boundaries inside long videos for training. A more practical solution is the weakly-supervised approach, which only needs the matching information between the query and video. Motivated by that, we propose a weakly-supervised video relocalization approach based on an attention-based feature matching method. Specifically, it recognizes the video clip by finding the clip whose frames are the most relevant to the query clip frames based on the matching results of the frame embeddings. In addition, an attention module is introduced to identify the frames containing rich semantic correlations in the query video. Extensive experiments on the ActivityNet dataset demonstrate that our method can outperform several weakly-supervised methods consistently and even achieve competing performance to supervised baselines.