Reviewerly/exHarmony huggingface dataset:
The oretical and methodical basis of organization student’s self-learning in educational media environment

In this article highlighted actuality of using media resources in self-education and proved effectiveness of organizing self-learning in educational media environment. By the thoughts of author, their use helps to increase the efficiency of students’ self-learning process, increases students’ motivation to learn. It can be concluded that media resources must be used in the self-education process in order to improve the quality of knowledge and skills. Besides that, due to the media resources self-learning process which is conducted in educational media environment will more productive and fascinating.
MODELS AND METHODS OF ARTIFICIAL INTELLIGENCE FOR CREATING A COMPUTER CREATIVITY PRODUCT

Today, information technology is used in all spheres of human activity, it can greatly simplify the processes that require routine, physical, computational tasks in people’s everyday life and in their production activities. Models and methods that form the basis of information technology impacted by continuous improvement, and this gives more and more new opportunities where they could be applied in practice. Particularly in such areas as artificial intelligence, because each person, for example, uses such indisputable and simply necessary assistants as large electrical appliances that have an intellectual component (refrigerators, washing machines, etc.). And the evolution of artificial intelligence does not stop there, and here we must mention such an important area of human life as recreation. This type of pastime means its classic manifestation, without the use of expensive gadgets, watching various programs on TV or the Internet and even not doing sports at home or outdoors. We mean an intellectual holiday, based on the usual favorite art book, which is read with the sounds of a pleasant melody, with beautiful illustrations, and now, by the way, for some time in scientific circles there is talk of using models and methods to create a creative product. by which is meant a written poem, a literary prose work, a painted picture, and so on. That is, the performance of those tasks that are traditionally attributed only to geniuses, artists throughout the history of human existence. In this article it is offered to consider directions in which researches in creation of a creative product by computers (machines) at use of methods of artificial intelligence take place today. Also briefly described approaches that describe the most advanced ideas for realizing the creative potential of computer machines.
Desarrollo de habilidades socioemocionales a través de un curso de tutorías B-Learning para alumnos de nivel Bachillerato de la Universidad Autónoma de Zacatecas “Francisco García Salinas”: Estudio realizado en el Programa de Preparatoria No. XIII

As part of the institutional strategy to prevent failure and school dropout, seeking better academic performance by upper-secondary students, a project is established where the tutorial part is related to the use of technological tools and resources. A course in B-Learning is developed, developing strategies that modify their behavior and performance, said course consists of four units, where strategies are developed to improve attention, identify emotions and the strategies necessary for their concentration, allowing them to develop social-emotional skills achieving better academic performance.
Redescription Model Mining

This paper introduces Redescription Model Mining, a novel approach to identify interpretable patterns across two datasets that share only a subset of attributes and have no common instances. In particular, Redescription Model Mining aims to find pairs of describable data subsets -- one for each dataset -- that induce similar exceptional models with respect to a prespecified model class. To achieve this, we combine two previously separate research areas: Exceptional Model Mining and Redescription Mining. For this new problem setting, we develop interestingness measures to select promising patterns, propose efficient algorithms, and demonstrate their potential on synthetic and real-world data. Uncovered patterns can hint at common underlying phenomena that manifest themselves across datasets, enabling the discovery of possible associations between (combinations of) attributes that do not appear in the same dataset.
Correction to: Unfolding identity of financial institutions in bitcoin blockchain by weekly pattern of network flows

A correction to this paper has been published: https://doi.org/10.1007/s40844-021-00216-2
Proposal of Image generation model using cGANs for sketching faces

The transition from sketches to realistic images of human faces has an important application in criminal investigation science to find criminals as depicted by witnesses. However, due to the difference between the sketch image and the real face image in terms of image detail and color, it is challenging and takes time to transform from hand-drawn sketches to actual faces. To solve this problem, we propose an image generation model using the conditional generative adversarial network with autoencoder (cGANs-AE) model to generate synthetic samples for variable length and multi-feature sequence datasets. The goal of the model is to learn how to encode a dataset that reduces its vector size. Using a vector with reducing the dimension, the autoencoder will have to recreate the image similar to the original image. The autoencoder aims to produce output as input and focus only on the essential features. Raw sketches over the cGANS create realistic images that quickly and easily make the sketch images raw images. The results show that the model achieves high accuracy of up to 75%, and PSNR is 25.5 dB that is potentially applicable for practice with only 606 face images. The performance of our proposed architecture is compared with other solutions, and the results show that our proposal obtains competitive performance in terms of output quality (25.5 dB) and efficiency (above 75%).
Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis

Text does not fully specify the spoken form, so text-to-speech models must be able to learn from speech data that vary in ways not explained by the corresponding text. One way to reduce the amount of unexplained variation in training data is to provide acoustic information as an additional learning signal. When generating speech, modifying this acoustic information enables multiple distinct renditions of a text to be produced. Since much of the unexplained variation is in the prosody, we propose a model that generates speech explicitly conditioned on the three primary acoustic correlates of prosody: $F_{0}$, energy and duration. The model is flexible about how the values of these features are specified: they can be externally provided, or predicted from text, or predicted then subsequently modified. Compared to a model that employs a variational auto-encoder to learn unsupervised latent features, our model provides more interpretable, temporally-precise, and disentangled control. When automatically predicting the acoustic features from text, it generates speech that is more natural than that from a Tacotron 2 model with reference encoder. Subsequent human-in-the-loop modification of the predicted acoustic features can significantly further increase naturalness.
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

News recommendation is important for improving news reading experience of users. Users' news click behaviors are widely used for inferring user interests and predicting future clicks. However, click behaviors are heavily affected by the biases brought by the positions of news displayed on the webpage. It is important to eliminate the effect of position biases on the recommendation model to accurately target user interests. In this paper, we propose a news recommendation method named DebiasGAN that can effectively eliminate the effect of position biases via adversarial learning. We use a bias-aware click model to capture the influence of position bias on click behaviors, and we use a bias-invariant click model with random candidate news positions to estimate the ideally unbiased click scores. We apply adversarial learning techniques to the hidden representations learned by the two models to help the bias-invariant click model capture the bias-independent interest of users on news. Experimental results on two real-world datasets show that DebiasGAN can effectively improve the accuracy of news recommendation by eliminating position biases.
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations

Recent researches show that deep learning model is susceptible to backdoor attacks. Many defenses against backdoor attacks have been proposed. However, existing defense works require high computational overhead or backdoor attack information such as the trigger size, which is difficult to satisfy in realistic scenarios. In this paper, a novel backdoor detection method based on adversarial examples is proposed. The proposed method leverages intentional adversarial perturbations to detect whether an image contains a trigger, which can be applied in both the training stage and the inference stage (sanitize the training set in training stage and detect the backdoor instances in inference stage). Specifically, given an untrusted image, the adversarial perturbation is added to the image intentionally. If the prediction of the model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance. Compared with most existing defense works, the proposed adversarial perturbation based method requires low computational resources and maintains the visual quality of the images. Experimental results show that, the backdoor detection rate of the proposed defense method is 99.63%, 99.76% and 99.91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively. Besides, the proposed method maintains the visual quality of the image as the l2 norm of the added perturbation are as low as 2.8715, 3.0513 and 2.4362 on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively. In addition, it is also demonstrated that the proposed method can achieve high defense performance against backdoor attacks under different attack settings (trigger transparency, trigger size and trigger pattern). Compared with the existing defense work (STRIP), the proposed method has better detection performance on all the three datasets, and is more efficient than STRIP.
An Ecolinguistic Analysis of Metafunctions of Language in Pakistani COVID-19 Advertorials

This study examines the language used in advertorials of Covid 19 from an ecolinguistic perspective. Through the ecolinguistic study of advertorials on Covid 19, the elements of fear and xenophobia are dredged up. Regarding Covid 19, little research has been carried out on advertorials. This study applies Halliday's (1970) metafunctions of language proposed in Systemic Functional Linguistics (SFL) on Covid advertorials from the perspective of ecolinguistics. Fifty Covid 19 related advertorials from ten different Pakistani websites were selected. Five advertorials from each website were chosen. Ecolinguistic analysis of Halliday's three metafunctions was adopted as the analytical framework. The findings of the study indicated that the Covid 19 advertorials are exploited to accentuate and propagate xenophobia and fear in the ecosystem through different lexical choices. The findings of this study provide a guideline of didactic inferences for future researchers in the field.
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation. The multi-scale features from pre-trained CNNs are recently used for the localized Mahalanobis distances with significant performance. However, the increased feature size is problematic to scale up to the bigger CNNs, since it requires the batch-inverse of multi-dimensional covariance tensor. Here, we generalize an ad-hoc method, random feature selection, into semi-orthogonal embedding for robust approximation, cubically reducing the computational cost for the inverse of multi-dimensional covariance tensor. With the scrutiny of ablation studies, the proposed method achieves a new state-of-the-art with significant margins for the MVTec AD, KolektorSDD, KolektorSDD2, and mSTC datasets. The theoretical and empirical analyses offer insights and verification of our straightforward yet cost-effective approach.
Osiris: Automated Discovery of Microarchitectural Side Channels

In the last years, a series of side channels have been discovered on CPUs. These side channels have been used in powerful attacks, e.g., on cryptographic implementations, or as building blocks in transient-execution attacks such as Spectre or Meltdown. However, in many cases, discovering side channels is still a tedious manual process. In this paper, we present Osiris, a fuzzing-based framework to automatically discover microarchitectural side channels. Based on a machine-readable specification of a CPU's ISA, Osiris generates instruction-sequence triples and automatically tests whether they form a timing-based side channel. Furthermore, Osiris evaluates their usability as a side channel in transient-execution attacks, i.e., as the microarchitectural encoding for attacks like Spectre. In total, we discover four novel timing-based side channels on Intel and AMD CPUs. Based on these side channels, we demonstrate exploitation in three case studies. We show that our microarchitectural KASLR break using non-temporal loads, FlushConflict, even works on the new Intel Ice Lake and Comet Lake microarchitectures. We present a cross-core cross-VM covert channel that is not relying on the memory subsystem and transmits up to 1 kbit/s. We demonstrate this channel on the AWS cloud, showing that it is stealthy and noise resistant. Finally, we demonstrate Stream+Reload, a covert channel for transient-execution attacks that, on average, allows leaking 7.83 bytes within a transient window, improving state-of-the-art attacks that only leak up to 3 bytes.
Improving Formality Style Transfer with Context-Aware Rule Injection

Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST). CARI injects multiple rules into an end-to-end BERT-based encoder and decoder model. It learns to select optimal rules based on context. The intrinsic evaluation showed that CARI achieved the new highest performance on the FST benchmark dataset. Our extrinsic evaluation showed that CARI can greatly improve the regular pre-trained models' performance on several tweet sentiment analysis tasks.
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure.

Our goal in this work is to generate realistic videos given just one initial frame as input. Existing unsupervised approaches to this task do not consider the fact that a video typically shows a 3D environment, and that this should remain coherent from frame to frame even as the camera and objects move. We address this by developing a model that first estimates the latent 3D structure of the scene, including the segmentation of any moving objects. It then predicts future frames by simulating the object and camera dynamics, and rendering the resulting views. Importantly, it is trained end-to-end using only the unsupervised objective of predicting future frames, without any 3D information nor segmentation annotations. Experiments on two challenging datasets of natural videos show that our model can estimate 3D structure and motion segmentation from a single frame, and hence generate plausible and varied predictions.
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation

Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference. Previous methods typically tackle RVOS through directly grounding linguistic reference over the image lattice. Such bottom-up strategy fails to explore object-level cues, easily leading to inferior results. In this work, we instead put forward a two-stage, top-down RVOS solution. First, an exhaustive set of object tracklets is constructed by propagating object masks detected from several sampled frames to the entire video. Second, a Transformer-based tracklet-language grounding module is proposed, which models instance-level visual relations and cross-modal interactions simultaneously and efficiently. Our model ranks first place on CVPR2021 Referring Youtube-VOS challenge.
Zero-Cost Operation Scoring in Differentiable Architecture Search

We formalize and analyze a fundamental component of differentiable neural architecture search (NAS): local "operation scoring" at each operation choice. We view existing operation scoring functions as inexact proxies for accuracy, and we find that they perform poorly when analyzed empirically on NAS benchmarks. From this perspective, we introduce a novel \textit{perturbation-based zero-cost operation scoring} (Zero-Cost-PT) approach, which utilizes zero-cost proxies that were recently studied in multi-trial NAS but degrade significantly on larger search spaces, typical for differentiable NAS. We conduct a thorough empirical evaluation on a number of NAS benchmarks and large search spaces, from NAS-Bench-201, NAS-Bench-1Shot1, NAS-Bench-Macro, to DARTS-like and MobileNet-like spaces, showing significant improvements in both search time and accuracy. On the ImageNet classification task on the DARTS search space, our approach improved accuracy compared to the best current training-free methods (TE-NAS) while being over 10$\times$ faster (total searching time 25 minutes on a single GPU), and observed significantly better transferability on architectures searched on the CIFAR-10 dataset with an accuracy increase of 1.8 pp. Our code is available at: https://github.com/zerocostptnas/zerocost_operation_score.
Human Machine Interaction Using Gestures

For the interface between the human and computer the communication is needed, as humans communicate with each other by the means of speech, voice or any other actions so same as in digital world communication is needed so justice can be an important and very useful function for new technology in this era to interact with computer by means of these without touching any of the equipment. So human machine interface which is abbreviated as HMI technology which includes hardware and software that helps in communication or exchange of any data or any information between human beings and electronic gadgets or computer. The Basic goal of HMI is to interact between the user and computer and improve the computer usability and more efficient to use the functions of computer a laptop. So in spite of using any of the input devices like mouse keyboard or joystick pointer or any of these we can use a hand for the means of justice to provide input for the electronic gadgets or computer which which will return the output on the laptop screen. in this project we have used to under Sonic sensors and PF mountain two sensors and laptop screen and provided the input to the laptop or computer / PC by the means of hand gestures and and then the sensor will calibrate a convert the distance and provide to Arduino and further data will proceed with relevant information by using certain libraries in Python and then the output is shown on the screen. There are many functions which can be form using this technology but the functions which we have included in a project is what three main sectors first is for scrolling for shifting the web pages in any of the web browsers and second would be VLC media player by by performing certain actions like increasing the volume and decreasing the volume or playing the video or pause the video and third part would be, to control the actions in any of the game like to swipe either sides of the game or up down jumping in any of the game. So for doing this we have performed the programming in two softwares first is arduino IDE and second would be Python IDLE by including two libraries pyserial and Piautogui. Pyserial library is used personal communication and Piautogui is used to perform actions. And hence we successfully meet a girls by controlling the laptop or computer / PC.
韓國의 開化期 漢文 敎育(1) - 公文式의 文書 表記 規定 再檢討 -

National Classical Chinese and Chinese Characters Education in Korea faced significant change after the Gabo Reform of 1894. While promoting Classical Chinese literacy was traditionally the main goal of official acdemies - hyanggyo [鄕校], sahak [四學], Seonggyun-gwan [成均館] - as well as local ones - seowon [書院], seodang [書堂] -, common and vocational education that the modern school system persued was designed to improve vernacular Korean language ability under the principle of unity of speech and writing. The national institution Hakh-mu-amun [學務衙門] was in charge of establishing and enforcing modern school system and colloquial-style language policy after 1894 reform. This thesis traces the context of transition period to modern education when Classical Chinese as literary language was losing its influence while Korean vernacular was gaining its hegemony in Korean language life, by analyzing main contents of office and employment regulations of Regulations Governing the Bureau of Scrutiny [Jeongoguk jorye 銓考局 條例] that consisted Hakh-mu-amun system. Subsequently, the thesis reviews the notation principles in official document [公文式] to reveal the characteristics and meaning of language policy of Hakh-mu-amun.
ESSENCE OF INTEGRATIVE LEARNING AND POSSIBLE WAYS OF ITS IMPLEMENTATION IN PRIMARY SCHOOL OF THE REPUBLIC OF KAZAKHSTAN

The article discusses the theoretical aspects of the integrative approach in teaching junior schoolchildren, the degree of its study, relevance and significance, reveals the essence and content of integrated learning. The concept of an integrative approach to teaching as a special model for the implementation of the complexity, integrity of knowledge of students, the development of their systemic thinking and scientific worldview are disclosed. The issues of updating the principle of integration in the educational process of primary school students and instilling critical thinking skills in them are considered. The use of innovative teaching methods allows you to accelerate the integration process and at the initial stage to identify the student's ability to learn and study certain sciences. The integrative approach allows you to implement the principle of interconnection in any component of the pedagogical process and affects its integrity and consistency.
TopicsRanksDC: Distance-based Topic Ranking applied on Two-Class Data

In this paper, we introduce a novel approach named TopicsRanksDC for topics ranking based on the distance between two clusters that are generated by each topic. We assume that our data consists of text documents that are associated with two-classes. Our approach ranks each topic contained in these text documents by its significance for separating the two-classes. Firstly, the algorithm detects topics using Latent Dirichlet Allocation (LDA). The words defining each topic are represented as two clusters, where each one is associated with one of the classes. We compute four distance metrics, Single Linkage, Complete Linkage, Average Linkage and distance between the centroid. We compare the results of LDA topics and random topics. The results show that the rank for LDA topics is much higher than random topics. The results of TopicsRanksDC tool are promising for future work to enable search engines to suggest related topics.