Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF MASSIVE OPEN ONLINE COURSES UTILIZATION IN EDUCATIONAL PROCESS OF A UNIVERSITY BASED ON SPECIALITY «PRE-SCHOOL EDUCATION»

The article deals with the question of massive open online courses utilization in educational process of higher education institutions; the background for the courses implementation is analysed, advantages and disadvantages of their utilization to provide distant component of educational process are estimated. In the framework of the research on peculiarities of MOOCs implementation in electronic learning courses in Borys Grinchenko Kyiv University a survey was hold for students of the specialty “Pre-school education” of the second (Master’s) level, where 50 respondents, who completed massive open online courses during their study, took part. According to the result of the survey it was defined that the majority of the students (from 68% to 78% depending on the course) were completely satisfied with the format of study and consider it one of the ways for their professional development in the future. The article describes the difficulties faced by the students in the process of work with MOOCs; possible ways to overcome them were defined. Accordance of possibilities provided with massive open online courses with the requests of modern students to the ways of extracurricular learning implementation was analysed. Positive aspects of the courses utilization as a part of electronic learning courses were highlighted including motivational factor about completing corresponding MOOCs. In addition, the question of readiness of academics to use massive open online courses in their professional activities was taken into consideration. Lecturers of Borys Grinchenko Kyiv University were surveyed; the results showed that only 30% of teaching staff had previous experience of using MOOCs in educational process, however, around 80% of respondents are interested in further study and practical utilization of such resources.
Super-Perfect Zero-Knowledge Proofs

We initiate a study of super-perfect zero-knowledge proof systems. Loosely speaking, these are proof systems for which the interaction can be perfectly simulated in strict probabilistic polynomial-time. In contrast, the standard definition of perfect zero-knowledge only requires that the interaction can be perfectly simulated by a strict probabilistic polynomial-time that is allowed to fail with probability at most one half.
Digital Rights Watch submission to Online Safety Legislative Reform consultation

As an initial matter, we believe that the definition of ‘online safety’ used throughout thediscussion paper is too narrow -- and that the objectives of this review cannot be met in isolationwithout deep engagement with other ongoing reviews and consideration of interrelated issues.We appreciate the effort taken in this review to harmonise some aspects of regulation thatapplies to online safety, but we note that regulation in this area could benefit from a much morecomprehensive understanding of online harm...
Mask-Guided Region Attention Network for Person Re-Identification

Person re-identification (ReID) is an important and practical task which identifies pedestrians across non-overlapping surveillance cameras based on their visual features. In general, ReID is an extremely challenging task due to complex background clutters, large pose variations and severe occlusions. To improve its performance, a robust and discriminative feature extraction methodology is particularly crucial. Recently, the feature alignment technique driven by human pose estimation, that is, matching two person images with their corresponding parts, increases the effectiveness of ReID to a certain extent. However, we argue that there are still a few problems among these methods such as imprecise handcrafted segmentation of body parts, and some improvements can be further achieved. In this paper, we present a novel framework called Mask-Guided Region Attention Network (MGRAN) for person ReID. MGRAN consists of two major components: Mask-guided Region Attention (MRA) and Multi-feature Alignment (MA). MRA aims to generate spatial attention masks and meanwhile mask out the background clutters and occlusions. Moreover, the generated masks are utilized for region-level feature alignment in the MA module. We then evaluate the proposed method on three public datasets, including Market-1501, DukeMTMC-reID and CUHK03. Extensive experiments with ablation analysis show the effectiveness of this method.
SAMR TECHNOLOGY OF COMPUTER VISUALIZATION MEANS INTRODUCTION IN THE EDUCATIONAL PROCESS FOR THE FORMATION OF VISUAL AND INFORMATION CULTURE OF FUTURE MATHEMATICS AND COMPUTER SCIENCE TEACHERS

The article substantiates the necessity of introducing computer visualization means in the educational process in order to form a visual and informative culture of future mathematics and informatics teachers by modernizing the system of professional training of such specialists in connection with the tendencies of informatization and visualization of the educational sphere. SAMR technology for implementing such means is described. The proposed technology consists of the following steps. Substitution: computer visualization means replace traditional learning tools. Improvements: computer visualization means replace traditional ones, but its functionality is used more widely. Modification: use of computer visualization means allows redesign the educational process by changing the type of classes, methods and forms of teaching. Rethinking: the use of computer visualization means allows creating the conditions for solving such tasks, which previously could not be solved within traditional approaches. The introduction of computer visualization means into the educational process at the first two stages makes the educational process easier, but does not change it significantly. The improving the quality of educational achievements is possible through the introduction of computer visualization means at the third and fourth stages of SAMR technology. As a result of the introduction of SAMR technology, components of the visual and information culture of future mathematics and informatics teachers are formed, namely, the understanding of the essence of the role of computer visualization means in the educational process and skills of perception, analysis, interpretation, comparison, comparison, creation, integration, formation, formation and the use of visual learning material
Benchmarks in Transition - Webinar Slides

Slides of the webinar on benchmark transition. Described why a transition is required, the transition in overnight benchmarks and the LIBOR fallback plans.
Evaluating Robustness to Input Perturbations for Neural Machine Translation

Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.
Bayesian inference for Pareto distribution with prior conjugate and prior non conjugate

The purpose of this study is to determine the best estimator for estimating the shape parameters of the Pareto distribution with the known scale parameter. Estimation of these parameters is done by using the Gamma distribution as the prior distribution of the conjugate and the Uniform distribution as the non-conjugate prior distribution. A comparison of the two prior distributions is done through simulation studies with various sample sizes. The best estimator net is a method that produces the smallest posterior variance, absolute bias, and Bayes confidence interval. This study proves that the Bayes estimator by using the prior conjugate distribution produces all indicators of the goodness of the model with a smaller value than the non-conjugate prior distribution. Thus it can be concluded that the estimator with prior conjugate will produce a better predictive value than prior non-conjugate.
Visual Attention: Deep Rare Features

Human visual system is modeled in engineering field providing feature-engineered methods which detect contrasted/surprising/unusual data into images. This data is "interesting" for humans and leads to numerous applications. Deep learning (DNNs) drastically improved the algorithms efficiency on the main benchmark datasets. However, DNN-based models are counter-intuitive: surprising or unusual data is by definition difficult to learn because of its low occurrence probability. In reality, DNNs models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this paper, we propose a model called DeepRare2019 (DR) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms. DR 1) does not need any training, 2) it takes less than a second per image on CPU only and 3) our tests on three very different eye-tracking datasets show that DR is generic and is always in the top-3 models on all datasets and metrics while no other model exhibits such a regularity and genericity. DeepRare2019 code can be found at https://github.com/numediart/VisualAttention-RareFamily
Effectiveness Of Educational Comics On Prevention Of Violence In Elementary School Children Penggilingan 09 Morning East Jakarta 2019

This study aims to determine the effect of the Effectiveness of Educational Comics on Prevention of Violence in Elementary School Children Penggilingan 09 Morning East Jakarta 2019. The research design used in this study was a pre-experimental design using the One Group Pre-Post-Test design, the sample in this study was 32 students taken using total sampling, the research instrument used a questionnaire, the data analysis used paired t test. The results showed that there were significant differences in the pretest and posttest after the educational comic intervention. There is a significant influence between the Effectiveness of Educational Comics on Prevention of Violence in State Elementary School Children before the intervention and after the intervention. It is expected that students will be more proactive in finding information about preventing violence in children so that they can apply the information obtained as well as possible
METHODOLOGY OF WRITING AN ARGUMENTATIVE ESSAY ON SOCIAL AND PEDAGOGICAL TOPICS

&#x0D; &#x0D; &#x0D; The article concerns the problems connected with the use of such non- standard creative method of professional training of the future teacher as an argumentative essay, having high potential of independent scientific study of the problem that has clearly expressed personality-oriented priorities. Both the concept and the methodology of preparing and writing an essay on social and pedagogical topics by the student are based on the determined theoretical positions that reflect the essence of the argumentative essay, the logic of its formation, indicating a fundamental difference in relation to functional and content-procedural features of writing a discursive essay.&#x0D; &#x0D; &#x0D;
The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America

Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic newspapers. Over 16 million pages of historic American newspapers have been digitized for Chronicling America to date, complete with high-resolution images and machine-readable METS/ALTO OCR. Of considerable interest to Chronicling America users is a semantified corpus, complete with extracted visual content and headlines. To accomplish this, we introduce a visual content recognition model trained on bounding box annotations of photographs, illustrations, maps, comics, and editorial cartoons collected as part of the Library of Congress's Beyond Words crowdsourcing initiative and augmented with additional annotations including those of headlines and advertisements. We describe our pipeline that utilizes this deep learning model to extract 7 classes of visual content: headlines, photographs, illustrations, maps, comics, editorial cartoons, and advertisements, complete with textual content such as captions derived from the METS/ALTO OCR, as well as image embeddings for fast image similarity querying. We report the results of running the pipeline on 16.3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model, and all source code are placed in the public domain for unrestricted re-use.
The Impact of Project Creation on Learning Mathematics in a Transdisciplinary Setting

&lt;p style="text-align:justify"&gt;This article proposes that the reader be nourished by avant-garde elements for new educational practices in the teaching and learning of mathematics. The writing is based on an investigation that was carried out with high school students from Puerto Rico. From a qualitative paradigm with a research design in action, it was explored how students in higher grades resolve situations within their environment. Emphasis was placed on how they analyze through situations in context, and how they build knowledge through the search for a possible solution to the problem through the Project Based Learning strategy. The emphasis in treatment was for students to experience a transdisciplinary environment. Their impressions from the study were the revealing focus of how students learn when they are presented with a situation in their environment, taking roles themselves, solving them it through a project. As part of the project they used their knowledge of science and mathematics to solve the situation. Similarly, they learned mathematical concepts that are part of the Trigonometry course and that are measured in the academic achievement tests called PR Goals, previously known as Puerto Rican Academic Achievement Tests (PPAA).&lt;/p&gt;
Ground Penetrating Radar Image Denoising via Nonuniform Directional Filter Bank Method

Ground penetrating radar (GPR) is an important technology that is used for imaging target through obstacle.Since it has ultrawide band structure, the signal obtained by receiver contains noise component.This component can corrupt the target signal and make target detection difficult.Thus, there are numerious methods are proposed for denoising purposes in GPR community and multiresolution based methods are one of them.The most popular one is the discrete wavelet transform (DWT) where it decomposes the GPR image into detail subbands then the obtained wavelet coeffcients are thresholded to remove the noise.However, DWT decomposition is very limited and the performance is not satisfying in GPR denoising.Then, the curvelet transform (CT) is proposed which makes more detailed decomposition compared to DWT thus it obtains better results.However, the frequency partitioning of the CT is fixed and it cannot be changed.In this study, we proposed a nonuniform directional filter bank (NUDFB) which has arbitrary frequency partitioning unlike CT.Thus, it gives us ability to make more effective frequency partitioning by preserving the target signal structure and the noise component can be removed more efficiently.The proposed NUDFB method is compared with the other avaiable methods by using our simulated dataset which contains challenging scenarios.Both visual and quantitative results which obtained for the simulated dataset are proved the superiorty of our proposed method.
Deep Collaboration and the Labor of Digital Pedagogy in a Liberal Arts Context

Digital Frontiers Session: Deep Collaboration and the Labor of Digital Pedagogy in a Liberal Arts Context
Obtaining Basic Algebra Formulas with Genetic Programming and Functional Rewriting

In this paper, we develop a set of genetic programming operators and an initialization population process based on concepts of functional programming rewriting for boosting inductive genetic programming. Such genetic operators are used within a hybrid adaptive evolutionary algorithm that evolves operator rates at the same time it evolves the solution. Solutions are represented using recursive functions where genome is encoded as an ordered list of trees and phenotype is written in a simple functional programming language that uses rewriting as operational semantic (computational model). The fitness is the number of examples successfully deduced over the cardinal of the set of examples. Parents are selected following a tournament selection mechanism and the next population is obtained following a steady-state strategy. The evolutionary process can use some previous functions (programs) induced as background knowledge. We compare the performance of our technique in a set of hard problems (for classical genetic programming). In particular, we take as test-bed the problem of obtaining equivalent algebraic expressions of some notable products (such as square of a binomial, and cube of a binomial), and the recursive formulas of sum of the first n and squares of the first n natural numbers.
Investigation of WSi and NbN superconducting single-photon detectors in mid-IR range

Abstract Spectral characteristics of WSi and NbN superconducting single-photon detectors with different surface resistance and width of nanowire strips have been investigated in the wavelength range of 1.3-2.5 μm. WSi structures with narrower strips demonstrated better performance for detection of single photons in longer wavelength range. The difference in normalized photon count rate for such structures reaches one order of magnitude higher in comparison with structures based on NbN thin films at 2.5 μm.
Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning

This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant. We face the challenges of developing robust models on a small dataset where each document may consist of over 10K tokens with full of noise including tabular text and task-irrelevant sentences. We first experiment four types of encoders to empirically decide the best document representation, and then apply reinforcement learning to remove noisy text from the long documents, which models the noise extraction process as a sequential decision problem. Our results show that the old bag-of-words encoder outperforms deep learning-based encoders on this task, and reinforcement learning is able to improve upon baseline while pruning out 25% text segments. Our analysis depicts that reinforcement learning is able to identify both typical noisy tokens and task-specific noisy text.
Deeply Supervised Active Learning for Finger Bones Segmentation

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.
Translation Between Waves, wave2wave

The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem. This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data. Experimental results are shown for two real-life data: earthquake and activity translation. The performance improvements of one-dimensional data was about 46% in test loss and that of high-dimensional data was about 1625% in perplexity with regard to the original seq2seq.
Embedding-based Scientific Literature Discovery in a Text Editor Application.

Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field. Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process. Here, we present a web application that combines text editing and literature discovery in an interactive user interface. The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author's manuscript and his interests. Our application aims to take a step towards more enjoyable and effortless academic writing. The demo of the application (this https URL) and a short video tutorial (this https URL) are available online.
SMS Security on Android Using RC4 Algorithm

SMS plays an important role in mobile communication systems. The sending side is acting like as a server for the receiving side that receives short message service at the receiving side. SMS does not incorporate a procedure to accord security for the text sent as data. A majority of the applications for mobile devices are designed and implemented without taking security into account. SMS messages are not normally encrypted by default. Confidentiality is the notion of making sure that data is not made accessible or exposed to unauthorized people. Encryption is the main approach to confidentiality. Both symmetric and asymmetric encryption can be employed. As confidentiality was the original purpose of cryptology, this chapter is introduced as a data confidentiality approach to SMS on Android. It encompasses SMS network architecture as well as cryptographic protocols as theory background and it also deals with design, implementation, and confidentiality assessment of RC4 stream cipher for SMS data confidentiality on mobile networks.
Investigating Relational Recurrent Neural Networks with Variable Length Memory Pointer

Memory based neural networks can remember information longer while modelling temporal data. To improve LSTM's memory, we encode a novel Relational Memory Core (RMC) as the cell state inside an LSTM cell using the standard multi-head self attention mechanism with variable length memory pointer and call it \(\text {LSTM}_{\textit{RMC}}\). Two improvements are claimed: The area on which the RMC operates is expanded to create the new memory as more data is seen with each time step, and the expanded area is treated as a fixed size kernel with shared weights in the form of query, key, and value projection matrices. We design a novel sentence encoder using \(\text {LSTM}_{\textit{RMC}}\) and test our hypotheses on four NLP tasks showing improvements over the standard LSTM and the Transformer encoder as well as state-of-the-art general sentence encoders.