Reviewerly/exHarmony huggingface dataset:
Pixel Histogram based Background Modeling for Moving Target Detection

Existing moving target detection methods mainly include inter-frame differences, background differences, optical flow and so on. For the recognition of human motions in the process of human-computer collaboration, existing algorithms are usually difficult to meet the requirements of real-time processing and easily interfered by lighting or image noises. In this paper, a method for establishing a static background model based on pixel histogram is proposed. The effect of moving targets and noises on the background model is excluded due to the selectivity of the new algorithm to the gray values, so it can detect the real background more reliably. Compared with other moving target detection methods, this method has the characteristics of fast speed, strong anti-interference ability, and the ability to identify human body movement quickly and accurately.
The Failed Promise of Development

This chapter examines the promise of human capital investment on economic growth and its consequences for developing countries. Education investment as a way of increasing the stock of human capital remains a key part of the World Bank’s approach to policy reform for so-called developing countries. The chapter argues that such accounts fail to understand the changing relationship between education, employment, and economic growth. Notwithstanding the rapid economic advances made by some emerging economies, most notably China and India, the experience of development has left many people within these countries stranded in the rubble of modernization, while others, including those with a tertiary education, struggle to find meaningful and sustainable employment. Despite continuous national investment in education and economic liberalization, many developing countries are falling further behind. The chapter describes some of the tensions and contradictions of development in emerging economies and how they are fueled by the failed promise of orthodox theory.
AUTOMATION OF DEVELOPING PLAN FOR EXTRA-CURRICULAR ACTIVITIES USING INTELLIGENT INFORMATION TECHNOLOGIES

The article analyzes the problem of organizing extracurricular activities, which, along with the organization of the learning process, is important in modern education characterized by the person integrated development and individualization of training and education programs. The important problems need to be addressed at various stages of the organization of extracurricular activities, from its methodological study to organizational and technical planning. There has been offered a review of the directions of extracurricular activities, the forms in which it can be organized, as well as the specifics of its implementation at various levels of education. In order to reduce the complexity of time costs, ability to choose forms of extracurricular activities in suitable areas, taking into account the time available for its implementation, as well as the ability to generate plans based on individual preferences, an intelligent information system has been developed. A mathematical model of method optimization aimed to solve the problem of decision support, functional and software-architectural models of the developed web service and examples of its work at various stages of generating the extracurricular activities plan in the optimization statement have been presented. The proposed approach can be widely used in the educational institutions of various levels interested in reducing the costs of organizing extracurricular activities, automatic check for compliance with the requirements of the Federal State Educational Standard and generating a working plan taking into account the opinions of various categories of experts.
Implementing Bilinear Interpolation on Quantum Images

In this paper, we present an interpolation scheme for FRQI images based on bilinear interpolation. To accomplish this, we formulated several quantum modules, i.e., assignment module, increment module, and quarter module, and suffused them into our proposed quantum image interpolation circuit. The concrete quantum circuits to accomplish up-scaling and down-scaling based on bilinear for FRQI images are designed and the network complexities of them are analyzed. Finally, to validate the proposed method, simulation experiments to enlarge and reduce the test images are executed, whose results are compared with the nearest neighbor interpolation for FRQI images. The up-scaled images by using proposed interpolation algorithm achieve satisfactory results, and both PSNR and SSIM values are better than those of the nearest neighbor method.
E-Training Of The Cybersecurity For The Senior High School Teachers

Obstacles In Conventional Learning Or Face-To-Face Forcing Some Teachers To Switch To Using The Online Learning Process. The Development of the Covid-19 Virus Outbreak Has Changed the Way of Learning Nationally in Indonesia, including High Schools in Riau Province. Teachers Are Asked To Implement Online Learning Processes Using A Variety Of Available Applications, Either Applications That Are Available For Free Or Applications That Are Paid For. The Use of Applications by Some Teachers, Accompanied by the Development of Information Systems Security Issues, Which Causes Some Teachers to Be Doubtful and Fear of Using Available Applications. The Online Training is conducted in the form of counseling and workshops for participants who are teachers in several high schools / equivalent in Riau Province. Online Training That Has Been Held Has Been Giving Benefits To Teachers In Knowing And Sorting Correct News And Lying News, And An Increased Ability And Skills Of Teachers Regarding Information Systems Security.&#x0D;
EML System Description for VoxCeleb Speaker Diarization Challenge 2020

This technical report describes the EML submission to the first VoxCeleb speaker diarization challenge. Although the aim of the challenge has been the offline processing of the signals, the submitted system is basically the EML online algorithm which decides about the speaker labels in runtime approximately every 1.2 sec. For the first phase of the challenge, only VoxCeleb2 dev dataset was used for training. The results on the provided VoxConverse dev set show much better accuracy in terms of both DER and JER compared to the offline baseline provided in the challenge. The real-time factor of the whole diarization process is about 0.01 using a single CPU machine.
Development test measure of basic school cognitive ability

Admission of new students at the level of basic education in Indonesia has not been based on abilities as a benchmark. In general the only parameter is age, namely a minimum age of 6 years. This is in accordance with government regulations and the Education Law that requires all children to take education at the Basic Education Level. Admission of new students is through a system that has been regulated by the government but it does not use a measuring device in the form of a school entrance test to determine the ability of pre-academic students. This research intends to arrange a measurement device that can measure students’ basic cognitive abilities (pre-academic) in the field of arithmetic. Measuring devices for pre-academic abilities are few and not all are standardized. Dimensions to be measured in the domain of basic capabilities in the field of arithmetic are: classification, sequence or series, correspondence, and conservation. The study used an experimental research method prioritizing research development. Development research emphasizes continuous improvement in products, in this case, standardized test instruments. This study used a test to measure basic skills (pre-academic) totaling 61 items consisting of five dimensions. Respondents were students of 6 or 7 years of age in primary school grade 1 or kindergarten. The results of this study are in the form of a set of tests in the field of basic arithmetic skills that are in accordance with the provisions of good test preparation.
Meningkatkan Ketrampilan Guru dalam Menyusun Perangkat Pembelajaran melalui Training Perangkat Pembelajaran

The purpose of this study is to improve teacher skills in developing learning tools. This research method is classroom action research. The results of the first cycle to further improve teacher skills in developing learning tools. In the first cycle, the teacher had begun to understand and was able to arrange the learning kit even though it was not maximal because the scores obtained from observations were 106 with a maximum score criterion 168 The completeness criterion was 75% of the total score which was 126 which meant the teacher's skills in preparing the learning device were sufficient well. In cycle II, the teacher has begun to understand and is able to compile the learning tools and is maximized because the score obtained from observations is 163 with a maximum score criterion of 168, the completeness criterion is 75% of the total score of 126. It was concluded that the teacher's skills in compiling the devices learning has been very good in compiling syllabi, compiling annual programs, and semester programs.&#x0D; Keywords: Teacher Skills, Training, Learning Tools
A Comparative Study on the Music Textbooks, ‘Music Textbook’(Daegun Secondary School, Daegu) and ‘Secondary School Music Textbook’(Dae-Sang(An Korean Acronym for the Daegu Public Commercial Secondary School), Daegu), Published during the Post-Liberation Period

본 논문은 해방공간 비검인정 중등 음악교과서의 양상 및 특징 고찰에 목적이 있다. 이를 위하여 교과서 두 종의 편저자, 인쇄, 발행, 악보, 구성, 노랫말 등을 비교 분석하는 방법을 사용하였다. 그 결과, 편저자가 명시되지 않은 점, 인쇄용지로 마분지가 사용된 점, 악보에서 다양한 문제가 발견되는 점, 수록 악보에 앞서 악전이 배치된 점, 수록곡 중 한국곡보다 외국곡의 비중이 큰 점, 다른 교과서의 수록곡 차용 및 짜깁기적 양상을 드러내는 점 등의 공통점이 발견되었으며, 반면 인쇄 방식(등사/옵셋), 발행처(미상(학교 추정)/출판사), 맞춤법 등에서는 차이점이 발견되었다. 결론적으로 이 두 종의 비검인정 중등 음악교과서는 편저 방식, 인쇄 발행 관련 사항, 사보 수준, 구성 등에서 유사점도 보이지만, 교과서 제작 예산, 편저자의 역량 또는 교육상 주안점 등에 따라 각각 다른 양상을 드러냄을 알 수 있었다.This Study aims to examine the aspects and characteristics of the music textbooks which were published in an un-authorized way during the Post-Liberation Period. For this purpose, this study used the methods of comparing and analyzing two kinds of middle school music textbooks, especially in printing, publishing, writing the notes, compiling, and lyrics of the songs. In conclusion, these two middle school music textbooks published during the Post-Liberation Period, are similar in the style of compiling, the condition of printing and publishing, and the level of transcript, etc., but they are also different in their compilers’ level of understanding of music, in putting their emphasis on secondary school music education, etc.
SPACE-TIME CHARACTERISTICS OF SOCIAL AND PEDAGOG-ICAL ACTIVITY

One of aspects of a research, within the scientific project of the Russian Federal Property Fund, directed on study-ing of specifics of space-time characteristics of imple-mentation of social and pedagogical activity in modern sociocultural conditions is presented in article.
On Convergence and Generalization of Dropout Training

We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the data distribution with a positive margin, we show that dropout training with logistic loss achieves $ε$-suboptimality in test error in $O(1/ε)$ iterations.
Quaternion-Valued Variational Autoencoder

Deep probabilistic generative models have achieved incredible success in many fields of application. Among such models, variational autoencoders (VAEs) have proved their ability in modeling a generative process by learning a latent representation of the input. In this paper, we propose a novel VAE defined in the quaternion domain, which exploits the properties of quaternion algebra to improve performance while significantly reducing the number of parameters required by the network. The success of the proposed quaternion VAE with respect to traditional VAEs relies on the ability to leverage the internal relations between quaternion-valued input features and on the properties of second-order statistics which allow to define the latent variables in the augmented quaternion domain. In order to show the advantages due to such properties, we define a plain convolutional VAE in the quaternion domain and we evaluate it in comparison with its real-valued counterpart on the CelebA face dataset.
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) is believed to have higher sample efficiency compared with model-free reinforcement learning (MFRL). However, MBRL is plagued by dynamics bottleneck dilemma. Dynamics bottleneck dilemma is the phenomenon that the performance of the algorithm falls into the local optimum instead of increasing when the interaction step with the environment increases, which means more data can not bring better performance. In this paper, we find that the trajectory reward estimation error is the main reason that causes dynamics bottleneck dilemma through theoretical analysis. We give an upper bound of the trajectory reward estimation error and point out that increasing the agent's exploration ability is the key to reduce trajectory reward estimation error, thereby alleviating dynamics bottleneck dilemma. Motivated by this, a model-based control method combined with exploration named MOdel-based Progressive Entropy-based Exploration (MOPE2) is proposed. We conduct experiments on several complex continuous control benchmark tasks. The results verify that MOPE2 can effectively alleviate dynamics bottleneck dilemma and have higher sample efficiency than previous MBRL and MFRL algorithms.
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution

State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train, putting them out of reach for the larger research community. On the other hand, GAN-based image super-resolution models, such as ESRGAN, can not only upscale images to high dimensions, but also are efficient to train. In this paper, we present not-so-big-GAN (nsb-GAN), a simple yet cost-effective two-step training framework for deep generative models (DGMs) of high-dimensional natural images. First, we generate images in low-frequency bands by training a sampler in the wavelet domain. Then, we super-resolve these images from the wavelet domain back to the pixel-space with our novel wavelet super-resolution decoder network. Wavelet-based down-sampling method preserves more structural information than pixel-based methods, leading to significantly better generative quality of the low-resolution sampler (e.g., 64x64). Since the sampler and decoder can be trained in parallel and operate on much lower dimensional spaces than end-to-end models, the training cost is substantially reduced. On ImageNet 512x512, our model achieves a Fréchet Inception Distance (FID) of 10.59 -- beating the baseline BigGAN model -- at half the compute (256 TPU-v3 cores).
Ranking User Attributes for Fast Candidate Selection in Recommendation Systems

Many recommendation systems use users' attributes to retrieve documents before ranking. Instead of using all attributes, this work explores algorithms that choose a subset, in order to achieve higher precision. We propose a model that forecasts the relevance of documents matched by each individual attribute. By restricting to top-K attributes based on the forecast, we observed 50% reduction in latency at 99th percentile on LinkedIn's job recommendation system, as well as increased users' engagements.
Description and Categorization of Balti Tense Markers

The current study aims at describing and categorizing the possible tense markers of Balti language, spoken in the Baltistan region of Gilgit-Baltistan. As for linguistics exposure is concerned, Balti is one of the less explored languages. Balti has a handful amount of traditional pieces of literature in the form of books (Traditional Grammars, stories, and history) and even the available Balti literature have not been documented within the proper paradigm of linguistics. To conduct this particular study, 200 Balti root words (verbs) have been collected from the corpus data using both the naturalistic and documented sources. The selected 200 Balti root words (verbs) were critically described, analyzed, and categorized within the paradigm of inflectional morphemes of tense markers using the qualitative research design. The result shows that there are 11 tense markers, i.e., “-ed", "-en", "-set", "-s", "-uk", "-nuk", "-tuk", "-ik", "-in", "-se" and "-e" in Balti language. These tense markers are added to the respective root verbs to mark present indefinite, present participle, past participle, future indefinite, and future perfect tenses. This study will hopefully encourage future researchers to conduct research works on the various aspects of Balti language.
A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns Analysis

This paper presents first benchmark corpus of Sanskrit Pratyaya (suffix) and inflectional words (padas) formed due to suffixes along with neural network based approaches to process the formation and splitting of inflectional words. Inflectional words spans the primary and secondary derivative nouns as the scope of current work. Pratyayas are an important dimension of morphological analysis of Sanskrit texts. There have been Sanskrit Computational Linguistics tools for processing and analyzing Sanskrit texts. Unfortunately there has not been any work to standardize &amp; validate these tools specifically for derivative nouns analysis. In this work, we prepared a Sanskrit suffix benchmark corpus called Pratyaya-Kosh to evaluate the performance of tools. We also present our own neural approach for derivative nouns analysis while evaluating the same on most prominent Sanskrit Morphological Analysis tools. This benchmark will be freely dedicated and available to researchers worldwide and we hope it will motivate all to improve morphological analysis in Sanskrit Language.
Understanding the effect of self-regulated learning on music performance anxiety through collaborative learning

본 연구의 목적은 협력학습을 통한 자기조절학습이 연주불안에 미치는 영향을 알아보기 위한 것이다. 이를 위해 경기도 A중학교 25명의 학생들이 자유학기제에서 기악 앙상블 학습을 하면서 자기조절학습 능력이 향상됨에 따라 연주불안에 어떠한 변화가 생기는지를 알아보았다. 또한 자기조절학습 능력 요인들의 향상점수와 연주불안 향상점수 간에 어떠한 상관이 있는지 알아보았다. 본 연구의 결과는 다음과 같다. 첫째, 학생들의 사전&#8228;사후 검사를 t 검증으로 분석한 결과 기악 앙상블을 통한 협력학습이 자기조절학습 능력의 향상에 영향을 미치는 것으로 나타났다. 그리고 기악 앙상블 학습에서 연주불안이 상승하였으나 유의미하지는 않았다. 둘째, 자기조절학습 능력의 하위요인인 자기효능감이 연주불안과 유의미한 상관이 있는 것으로 나타났다. 자기효능감이 연주불안과 정적 관련이 있는 결과로 보아 기악 앙상블에서의 자기효능감이 연주불안에 영향을 미치는 것을 알 수 있다. 이러한 결과는 기악 앙상블을 통해 다른 사람들과 비교되며 연주불안이 생길 수 있지만 자기조절학습은 연주불안을 극복하는 데 효과적이라는 것을 시사한다. 협력학습에서 자기효능감이 연주불안에 조절효과가 있는지를 연구하는 실증적 연구가 후속된다면 보다 구체적인 결론을 얻을 수 있을 것이다.The purpose of this study was to find out the effect of self-regulated learning on music performance anxiety through collaborative learning. For this purpose, 25 students at A middle school in Gyeonggi-do were attended in the instrumental ensemble class at the free semester in 2017, and it was examined that how the level of music performance anxiety was changed as their self-regulated learning ability improved. Through the correlation analysis, it was examined that how the improved scores of self-regulated learning ability was correlated with the improved scores of music performance anxiety. The results of this study are as follows. First, the result of t-test analysis of pretest and posttest of self-regulated learning ability and music performance anxiety level shows that collaborative learning through the instrumental ensembles affects the improvement of self-regulating learning ability. Also it was not statistically significant that the degree of music performance anxiety was increased during the instrumental ensemble learning. Second, there was a significant correlation between the self-efficacy and the level of music performance anxiety. This indicates that the improvement of self-efficacy in instrumental ensembles affects the level of music performance anxiety. These results suggest that self-regulated learning is effective in overcoming music performance anxiety, although performance anxiety can be appeared by comparing with other people through instrumental ensembles. A more specific conclusion can be obtained if a empirical study follows on whether self-efficacy through collaborative learning has a effect on music performance anxiety.
Prediction of heavy metal content in multivariate chaotic time series based on LSTM

Accurate mid-short term predictions of heavy metal content in the watershed is of practical significance as people pay more attention to the surrounding living environment.Traditional longterm short-term memory (LSTM) time series prediction models can only rely on existing data, but heavy metal content is affected by many factors.Therefore, the authors constructed multi-chaotic phase space by combining the heavy metal content of Daxia River Basin with temperature, daily runoff, and pH, presenting the complex realistic environment of heavy metal content in the Daxia River Basin.Subsequently, we optimized the input data of the LSTM prediction model and achieved more accurate mid-short-term prediction.The experiment results show that compared with the Volterra series one-step prediction and radial basis function neural network prediction, the LSTM model RMSE of phase space optimization is 0.0927, and MAE is 0.2102, which are superior to the other two prediction models.Therefore, this model has a better prediction effect on more complex system predictions.
Preface for the Number-Theoretic Methods in Cryptology conferences

Abstract Number-Theoretic Methods in Cryptology (NutMiC) is a bi-annual series of conferences that waslaunched in 2017. Its goal is to spur collaborations between cryptographers and number-theorists and to encourage progress on the number-theoretic hard problems used in cryptology. The publishing model for the series is also mixing the traditions of the cryptography and number theory communities. Articles were accepted for presentation at the conference by a scientific commitee and werereviewed again at a slower pace for inclusion in the journal post-proceedings. In 2019, the conference took place at the Institut de Mathématiques de Jussieu, Sorbonne University,Paris. The event was organized in collaboration with the international association for cryptologic research (IACR) and supported by the European Union’s H2020 Program under grant agreement number ERC-669891. This support allowed us to have low registration costs and offer easy access to all interested researchers. We were glad to have the participation of five internationally recognized invited speakers who greatly contributed to the success of the conference. Nutmic 2019 Co-Chairs, Antoine Joux and Jacek Pomykała