Reviewerly/exHarmony huggingface dataset:
How “Tools” Produce “Data”: Searching in a Large Digital Corpus of Audiovisual Holocaust Testimonies

The field of Jewish Studies is facing many new challenges as a result of ongoing digitization. This chapter focuses on digital oral histories of the Holocaust. Following the digital revolution in oral history, many institutions now provide access to multiple collections at once. One of the new challenges is thus related to the simultaneous availability of several archives, as well as various search engines which apply different methods to browse their content. The aim of this chapter is to identify and describe participants’ practices for working with a large corpus of audiovisual Holocaust testimonies, especially in terms of locating relevant results within the collection by using three different search systems. We have conducted an empirical study in an experimental setting designed to emulate work with various search engines. Three pairs of novice users solved ten tasks over video-conferencing software, utilizing three different search “tools” (USC Shoah Foundation’s Visual History Archive, Amalach, and Pixla). Our main findings consist of formulating a fundamental structure and elements of participants’ collaborative work, composed of three complementary actions: testing, sharing, and implementing. Furthermore, users obtained the search results by two main approaches: aggregation and query refinement. Interestingly, they did not upgrade the searching skills progressively, but rather used the current “best knowledge” for all the tasks and search engines at once. The participants’ emergent competence was continuously developed on the basis of collaborative work with the search engines and the results obtained so far through their work on the previous tasks.
References

An R Companion for The Fundamentals of Social Research offers students the opportunity to delve into the world of R using real data sets and statistical analysis techniques directly from Paul M. Kellstedt, Guy D. Whitten, and Steven A. Tuch's new textbook. Workbook sections parallel chapters in the main text, giving students a chance to apply the lessons and techniques learned in each chapter in a statistical software setting. Detailed chapters teach students to reproduce results presented in the textbook, allowing them to become comfortable performing statistical analyses for evaluating causal claims through repeated practice. Step-by-step instructions for using R are provided, along with command lines and screenshots to demonstrate proper use of the software. Instructions for producing the figures and tables in the main text are integrated throughout the workbook. End-of-chapter exercises encourage students to formulate and evaluate their own hypotheses.
Transformers

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Topic-Sensitive Intentional Modals

Abstract This chapter introduces two-place Topic-Sensitive Intentional Modals (TSIMs): variably strict, topic-sensitive modal operators of the form ‘Xφψ’, which are to represent attitude ascriptions—generic reading: ‘One Xs (thinks, believes, imagines, etc.) that ψ, given φ’. It presents a basic possible worlds semantics for a simple propositional language including them. The key twofold clause, giving the truth conditions for the TSIMs, has it that, for Xφψ to be true, we require (1) that ψ be true throughout a set of worlds, selected via an accessibility relation or function indexed to φ; and (2) that ψ be fully on-topic with respect to φ. (1) makes of the TSIM variably strict modal operators, that is, variably strict quantifiers over possible worlds. (2) makes them topic-sensitive. The semantics validates various inferences involving conjunction; invalidates various inferences involving disjunction; and makes the TSIM operators non-monotonic (in particular, Xφψ does not entail Xφ&amp;Xψ) and hyperintensional: capable of differentiating between necessarily or intensionally equivalent contents. Such validities and invalidities are defended for interpretations of X that range across a variety of attitudes. Finally, the chapter introduces a number of constraints on the selection functions. These will give us stronger logics for the Xφψs, as well as different plausible readings for them, to be explored in the three following chapters.
Stress Fields Extraction in Multi-Polarized Photoelasticity Images Using Deep Convolutional Neural Networks

Digital photoelasticity requires demodulating stress fields, wrapped into color fringe patterns. As an alternative to traditional methods, deep convolutional neural networks are trained to recover stress maps from isochromatic images related to different orientations of a polarized camera, reaching high precision in different analytical models.
Exploring Structural Information for Semantic Segmentation of Ultrasound Images

Biomedical semantic segmentation aims to automat-ically label each pixel of a medical image with a corresponding class, such as lung and heart. Such a technique significantly facilitates clinical diagnosis and thus has attracted increasing attentions both in industry and academia. Although promising, existing methods such as U-Net and MSRF, may suffer from the following two issues when they are applied to ultrasound images: 1) few ultrasound datasets are publicly available due to the regulation of patient's privacy, while U-NET/MSRF models are data-hungry and are trained on tens of thousands of images; 2) the low-quality ultrasonic images used in training procedure may lead to inaccurate segmentation in the evaluations. To fill this gap, this paper presents structured multi-scale residual fusion network (SMNet), a novel method for better semantic segmentation of ultrasonic images by exploring rich interactions between the organs. Equipped with such structured information, our SMNet can properly tackle the above two challenging issues. Experiments on a real-world dataset show the effectiveness of our approach, yielding significant improvement compared with the sate-of-the-art models.
What is the best level to report from a hierarchical classifier?

In a multi-level (coarse to fine) classifier, decisions along with their estimated uncertainties can be reported at any or all levels. We define the concept of the “best” level from the standpoint of a user who seeks to make a single class call that seeks to maximize specificity and minimize the error in making a mistake.
A Comparative Analysis of Research Trends of Education Policy in Korea from the Perspective of Education Administration and Politics of Education

본 연구는 전통적인 교육행정학의 정치-행정 이원론을 극복하기 위해 창립된 교육정치학회가 교육행정학회와 갖는 차별성을 탐구하기 위해 교육정치학회 출범 이후 두 학회의 연구동향을 분석하는 데 목적을 두었다. 이를 위해 교육정치학회의 학술지인 「교육정치학연구」와 교육행정학회의 학술지인 「교육행정학연구」의 교육정책 관련 논문 각각 192편, 389편을 대상으로 연구주제, 연구목적, 연구방법, 이론 및 모형의 근거 등 네 개 범주에 대해 연구동향을 비교ㆍ분석하였다. 분석 결과, 교육정치학회는 정체성이 확립되어 가는 과정인 ‘발전준비기’와 ‘정체성 확립기’ 사이에 자리해 있음이 확인되었다. 두 학문분야 간 학문 공동체의 공유로부터 비롯되는 학문적 유사성이 발견되기도 했으며, 이는 교육정치학회의 정체성 확립을 위한 경계 짓기의 필요성과 방법에 대한 논의의 필요성을 시사한다.The purpose of this study is to inductively and empirically analyze the academic boundaries and paradigms shared by the academic community of Politics of Education. This research focuses on the comparison of research trends in 'The Journal of Politics of Education' (192 articles) with ‘The Journal of Educational Administration’ (389 articles) of the studies in education policy in the last 25 years, after the Korean Society of The Politics of Education (KSPE) was founded. Especially taking the consequences of the politics-administration dichotomy into account - underpinned in the objective of the foundation of KSPE, the research intended to examine whether they gradually find academic identity in terms of research topic, research objective, research methodology, and theoretic framework. The results implied that KSPE is at the stage between ‘preparation for development’ and ‘identity Establishment’. However, in the case of similarity and redundancy that appear in the two academic fields, further discussion is required on the necessity and method of boundary building among their shared academic community.
Time-dependent metrics to assess performance prediction systems

Purpose The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners. Design/methodology/approach The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the key earliness points in the prediction process. Authors propose an algorithm for computing earliness. Furthermore, the authors propose using an earliness-stability score (ESS) to investigate the relationship between the earliness of a classifier and its stability. The ESS is used to examine the trade-off between only time-dependent metrics. The aim is to compare its use to the earliness-accuracy score (EAS). Findings Stability and accuracy are proportional when the system's accuracy increases or decreases over time. However, when the accuracy stagnates or varies slightly, the system's stability is decreasing rather than stagnating. As a result, the use of ESS and EAS is complementary and allows for a better definition of the point of earliness in time by studying the relation-ship between earliness and accuracy on the one hand and earliness and stability on the other. Originality/value When evaluating the performance of PPS, the temporal dimension is an important factor that is overlooked by traditional measures current metrics are not well suited to assessing PPS’s ability to predict correctly at the earliest, as well as monitoring predictions stability and evolution over time. Thus, in this work, the authors propose time-dependent metrics, including earliness, stability and the trade-offs, with objective to assess PPS over time.
An insight into 110 years of influence of Principle of Literary Warrant on Dewey Decimal Classification system

The present article is an endeavour to understand the one hundred and ten years of influence of the Principle of Literary warrant as introduced by E. Wyndham Hulme on the revision of Dewey Decimal Classification (DDC) through different editions in the field of library classification theory and practice. During this study, a comparison with other warrants/consensus has been undertaken whenever and wherever deemed necessary. The literary exploration was based on all the editions of DDC which are available on Internet Archive platform. The paper divides different editions of DDC into two distinct groups: one in which only veiled references to literary warrant were found corroborating Dewey‟s thinking about applicability of different consensus angle and the other where declared references to the concept by Editorial Policy Committee were encountered. The article concludes that the principle has extended its influence on the web environment of DDC as well.
Recommendation Engine: Challenges and Scope

On the internet, with the increased number of users, its use for recommending products and services is also increasing. But there is a need to filter that data and provides only relevant recommendations. Recommendation systems help in solving this problem by providing personalized recommendations from a large pool of data. This paper provides an overview of the recommendation system along with its various filtering techniques. The paper also discusses various challenges faced by the current recommendation systems and the possible research areas in this field that can improve its efficiency.
Secure Image Steganography Approach for Hiding Compressed Data

Abdul-Zaher, Khadija M. El-MagdSewesy, Adel Abo Mohamed, Marghany HassanSteganography is the technique of hiding a secret message inside a normal, non-confidential file, or message to avoid detection. The main challenge in steganography is to build a balanced system that resists attacks and hides a large amount of data while maintaining high quality. To overcome these challenges, many algorithms have been developed, some of which are better than others in a certain direction. In this chapter, an improved steganography approach is proposed where more than one method is combined to obtain a more secure algorithm with high quality and capacity. First, Gzip compression technique is used to compress the mystery message which increases the capacity of the proposed algorithm. Next, the p-Fibonacci method is used to randomize the locations of the pixels that hide the ambiguous message which improves the security of the proposed scheme. Then, the pixel value modification (PVM) technique with modulus function is used to embed the secret message raising the embedding capacity and quality. This approach overcomes the limitations of the previous methods because it is providing a secure with high embedding capacity system. To estimate the security of the proposed scheme, we used histogram, entropy, and chi-squared analysis to apply statistical attacks on the stego-image. Later, the experimental results show that the proposed methods provide high embedding capacity while maintaining good performance with large payload. For a payload of 204,800 B, we achieved a PSNR of 55.91 DB. The drawbacks of the previous methods are completely eliminated. The proposed scheme is more effective than previous steganography schemes in terms of quality, capacity, and security.
Reference Path Planning for UAVs Formation Flight Based on PH Curve

Formation flight of Unmanned aerial vehicles (UAVs) has become a research hotspot in recent years. How to plan a flyable path for UAV formation when flying with a given configuration to the destination safely is an important technology. Therefore, this paper proposes a path planning method based on Pythagorean Hodograph (PH) curves and Delaunay triangulation to generate a flyable reference path for UAV formation. Firstly, the kinematic constraints of the formation path are derived, while the formation moves. Secondly, the Delaunay triangulation and the Warshall-Floyd algorithm are used to obtain the best waypoints array from starting point to destination. Thirdly, the PH curves are applied to connect each two neighbour waypoints for meeting the kinematic constraints. The multi-population hybrid particle swarm genetic algorithm is proposed to generate an optimal flyable formation path. Finally, simulations are carried out considering a formation with three UAVs in a complicated environment. The simulation results show that the paths planned are connected by several PH curves, and all the paths can meet the kinematic constraints, avoid the obstacles and threat zones. Furthermore, the single population particle swarm genetic algorithm is also applied in the same simulation, and the simulation results show that the multi-population hybrid particle swarm genetic algorithm proposed in this paper has faster convergence speed and better stability.
BERT ile Kazak Haber Veri Kümesinden Anahtar Kelime Çıkarımı

Keywords provide a concise and precise description of the document's content. Due to the importance of the keyword and the difficulty of manual markup, automatic keyword extraction makes this process easy and fast. In this paper, Keyword Extraction from Kazakh News Dataset was presented. Model performance results were obtained by using the BERT base - uncased and BERT-base-multilingual-uncased pre-trained language model for the newly compiled Kazakh News Dataset-KND. Compiled Kazakh news data set consists of 7060 data. Data were collected from the web pages anatili.kazgazeta.kz, Bilimdinews.kz, and zhasalash.kz using the BeautifulSoap and Requests libraries. These web pages mostly contain news, history, and literary texts. The dataset includes the publication name or news title, the author of the publication or news subject, and the URL of the Kazakh news site. In the evaluation of the training results, it was observed that the BERT base-multilingual-uncased F-score performance was higher than the BERT model.
THE IMPACT OF COVID-19 ON EDUCATION IN KYRGYZSTAN

This article presents that the world has been fighting against the COVID-19 outbreak and its impact on education is of great importance. According to the statistics more than 190 countries have closed schools nationwide, impacting nearly 90% of students globally and it also concerns to the education system of Kyrgyzstan. The Ministry of Education and Science of the Kyrgyz Republic has designed special recommendations for online education. From the source of the International webinar on the exchange of knowledge between participating countries of the project "Development of human capital", July 29,2020: There were considered a strategy for responding to COVID-19 in education which included educational portal, maximum access to pre-school and school education for children in all regions of the country; video- lessons for TV broadcasts of academic content; assessment of educational results of students; methodical support; instructions and video for teachers prepared by in- service teacher training institute; successes and challenges of remote learning; short-term plans for the future; long-term continuity plans educational process
Classes and Inheritance

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
A Data Analysis Study of Code Smells within Java Repositories

Although code smells are not categorized as a bug, the results can be long-lasting and decrease both maintainability and scalability of software projects.This paper presents findings from both former and current industry individuals, aiming to gauge their familiarity with such violations.Based on the feedback from these individuals, a collection of smells were extracted from a sample size of 100 Java repositories in order to validate some of the smells that are typically encountered.After analyzing these repositories, the smells typically encountered are Long Statement, Magic Number, and Unutilized Abstraction.The results of this study are applicable for developers and researchers who require insight on the frequencies of code smells within a typical repository.
An Efficient Algorithm for Visualization and Interpretation of Grounded Language Models

Contemporary approaches to grounded language communication accept an utterance and current world representation as input and produce symbols representing the meaning as output. Since modern approaches to language understanding for human-robot interaction use techniques rooted in machine learning, the quality or sensitivity of the solution is often opaque relative to small changes in input. Although it is possible to sample and visualize solutions over a large space of inputs, naïve application of current techniques is often prohibitively expensive for real-time feedback. In this paper we address this problem by reformulating the inference process of Distributed Correspondence Graphs to only recompute subsets of spatially dependent constituent features over a space of sampled environment models. We quantitatively evaluate the speed of inference in physical experiments involving a tabletop robot manipulation scenario. We demonstrate the ability to visualize configurations of the environment where symbol grounding produces consistent solutions in real-time and illustrate how these techniques can be used to identify and repair gaps or inaccuracies in training data.
Atomic Formulation of the Boolean Curve Fitting Problem

Boolean curve fitting is the process of finding a Boolean function that takes given values at certain points in its Boolean domain. The problem boils down to solving a set of ‘big’ Boolean equations that may or may not be consistent. The usual formulation of the Boolean curve fitting problem is quite complicated, indeed. In this paper, we formulate the Boolean curve fitting problem using the technique of atomic decomposition of Boolean equations. This converts the problem into a set of independent switching equations. We present the solution of these switching equations and express the solution in very simple and compact forms. We also present the consistency and uniqueness conditions for this problem again in very compact forms. A few illustrative examples are given. These examples clearly pinpoint the simplicity gained by the Boolean-equation solving step within the overall Boolean-fitting procedure. The method presented here can be applied to the design of Boolean functions for cryptographic systems.
On combining denoising with learning-based image decoding

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captured images. More recently, learning-based solutions have shown impressive results in image enhancement in general, and in image denoising in particular. In this paper, we review multiple novel solutions for image denoising in the compressed domain, by integrating denoising operations into the decoder of a learning-based compression method. The paper starts by explaining the advantages of such an approach from different points of view. We then describe the proposed solutions, including both blind and non-blind methods, comparing them to state of the art methods. Finally, conclusions are drawn from the obtained results, summarizing the advantages and drawbacks of each method.