Reviewerly/exHarmony huggingface dataset:
Research on Segmentation and Recognition Algorithm of Moving Human Image Based on Deep Learning

With the improvement of living standards, people have higher and higher requirements for the quality of life and continue to pursue a green and healthy life. Video surveillance system has more and more security requirements in the fields of transportation, banking, security, power and military facilities. Therefore, video surveillance has been widely used in all aspects of people's production and life. The image segmentation algorithm predicts a discrete semantic label for each pixel of the image, which can be used to extract the target object with hard boundary in the image and synthesize it into a new background. But this does not satisfy all needs. Most of the current methods of human image segmentation can only guarantee one of the indicators, which makes the practical application of human image segmentation greatly limited. According to the difference between different areas in the video image, the video image is divided into motion area, background area and occlusion area, and the edge detection operator is used to detect the edge of the moving object, and finally the moving object in the video image is segmented. The algorithm is characterized by simplicity and high efficiency, and is suitable for monitoring systems with limited resources such as embedded systems. Experiments show that this algorithm can segment moving human images in different scenes, and it has good robustness and accuracy, reaching a practical level. This algorithm has practical significance for automatic control of lens aiming at moving human in surveillance system.
Intelligent model description language and algorithm generation based on deep learning

In recent years, with the continuous development of Internet technology and the emergence of large-scale data sets, deep learning has achieved great success in many traditional computer vision tasks due to its excellent computing power. Deep learning technology widespread application cannot leave the convolutional neural network algorithm and circulating neural network algorithm, nowadays, most of the research is to divide the image into one or more discrete label, can describe the image of the object categories, but did not describe the relationship between each object in the image and the image of what is happening. To sum up, this paper studies the image description algorithm based on deep learning and puts forward a series of improvement schemes for the defects of existing models. Experimental results show that compared with traditional methods, the proposed image description algorithm can effectively improve the performance of the image description algorithm, solve the defects of the previous image description algorithm, and generate the image description text more accurately.
Formation of the information and methodological competence of students in the process of distance learning

The article discusses the issues of distance learning, which has become popular with the advent of the Internet, opening up new development opportunities for residents of remote communities and business people with busy work schedules, is perceived only as an additional way to gain knowledge or prepare for exams, provides an opportunity full-fledged distance courses and advanced training programs at prestigious universities, commercial and non-profit companies from different countries, anywhere in the world, at a convenient time and pacing. Thus, distance education was an important problem for Kazakhstan even before the pandemic, which made the issue even more pressing. The authors also discuss the differences and provide examples of distance and online learning. The essence of online learning and distance learning concepts is revealed. Ways of providing students with information and methodological materials in distance learning are considered. The authors carried out a theoretical and methodological analysis of distance learning, distance education concepts. The paper identifies and analyzes the mechanisms of establishment of distance education. The types of international and national online platforms are shown. For illustrative examples of the resources used, the authors describe “on-screen” copies of the lectures of the online conference program zoom.us and idl.ksu.kz — platforms for distance learning of Karaganda University of the name of academician E.A. Buketov. The paper summarizes the problems of distance education, including the organization of distance learning of the University, outlines the prospects for further scientific developments.
Colorization of Grayscale Images: An Overview

The problem of image colorization is a well-known and important topic for different reasons in the field of image processing. From statistical models using feed-forward neural network, to using convolutional neural networks (CNNs), and generative adversarial networks (GANs), there are many state-of-the-art methods for colorizing images and videos. In this paper, we have done a comprehensive study, implementation, and analysis of different techniques that convert grayscale images into colorful ones. Comparison has been done based on preprocessing of image data, network architecture, loss functions, results, etc. We have implemented these methods on common datasets and compared the results visually as well as based on different metrics.
APPLICATION OF A PSEUDO-RANDOM NUMBER GENERATOR TO INCREASE THE EFFICIENCY OF SMART DUST TECHNOLOGY IN SMART HOME CONTROL

The article is devoted to the use of pseudo-random number generators to improve the efficiency of Smart Dust technology in the context of smart home management. The article examines the problem of ensuring reliable and safe interaction between Smart Dust technology and the Internet of Things (IoT) in the context of smart home management. To solve this problem, it is proposed to use generators of pseudo-random numbers. The article provides a detailed overview of Smart Dust and IoT technology, including their application in smart home management. The advantages and disadvantages of Smart Dust and IoT technology are analyzed, their possible problems with security and reliability of interaction are indicated. Next, the article describes in detail the principles of pseudorandom number generators and their use in Smart Dust technology. In particular, research is presented on how the use of pseudo-random numbers can help improve the security and reliability of the interaction between Smart Dust technology and IoT. The article also presents a mathematical model of using Smart Dust technology using pseudo-random number generators in smart home management. The algorithm of operation of the Smart Dust technology using a generator of pseudo-random numbers in a smart house has been built. The article provides an analysis of the effectiveness of using pseudorandom number generators for Smart Dust technology in smart home management. For this, experiments were conducted that showed that the use of pseudorandom number generators increases the efficiency of Smart Dust technology and reduces the risk of errors and incorrect system operation. The prospects of using a sequence of pseudo-random numbers in Smart Dust technology are characterized.
THE EFFECTS OF THE APPLICATION OF INDIVIDUALIZED INSTRUCTION IN TEACHING PROSE TEXTS

This paper discusses the possibility of teaching prose literary texts in the first cycle of primary education using individualized instruction in order to highlight its effectiveness in helping students grasp this type of learning content. The conducted experimental research aimed to examine how the application of individualized teaching to the process of teaching prose literary works in the lower grades of primary school contributes to their more complete understanding. The results obtained have shown that the experimental group, which learned the planned teaching units by using the individualized instruction approach achieved significantly better results compared to the control group, which worked in the usual way.
Sorting Strings

This chapter discusses the limitations incurred by the sorters of atomic items when applied to sort variable-length items (aka strings). It then introduces a simple, yet effective comparison-based lower bound, which is eventually matched by means of an elegant variant of QuickSort, named Multi-key QuickSort, properly designed to deal with strings. The structure of this string sorter will also allow us to introduce an interesting, powerful, and dynamic data structure for string indexing, the ternary search tree, which supports efficient prefix searches over a dynamic string dictionary that fits in the internal memory of a computer. The case of large string dictionaries that cannot be fit into the internal memory of a computer is discussed in Chapter 9.
Comparison of Algorithms for Automatic Terminology Extraction on Material of Educational Texts on Biology

Two methods of automatic extraction of terms are considered in the research. An experiment on the basis of educational texts from biology textbooks, that are used in state schools of Russia, is carried out. The way of evaluation of the proposed methods for correspondence of analyzed parameters to validation values is offered.
Photonic Interconnect Based Neural Network Simulator

This paper introduces a photonic interconnect simulator that supports architecture simulation and design on a large enough scale to facilitate design-space exploration of optical neural networks on a C++ implementation with PyTorch integration.
Research Trend Analysis with SATI on Hybrid-learning in ESL/EFL Since COVID-19

In the era of internet+, hybrid learning is bound to become a trend, while the Covid-19 pandemic acts as an accelerator of hybrid learning. This research selected 1773 articles from WOS and opted SATI as the major platform to do the bibliographic information processing. The results of word frequency analysis and co-word analysis were then interpreted to identify the current trend and hotspots of research in the fields of hybrid learning in ESL/EFL. The purpose is to seek more effective teaching mode reform and innovation in the future and make new technology really serve teaching.
Chatra Paryesana (Student Enquiry System)

Abstract: In today's technological era, chatbots not only mimic human conversation but can also perform sophisticated jobs like purchasing theatre tickets. RASA is a free and open-source programme that may be used to run various versions of the NLU and DIET models. It has the capacity for natural language processing, reinforcement learning, and API/database integration. nervous system. This project incorporates a number of rasa's fundamental principles to allow for user interaction in its execution of tasks. Using the Rasa platform, we developed a chatbot specifically for universities. You can use this chatbot to obtain student information, view grades, and verify attendance. By integrating with Telegram, users no longer need to download and run a separate programme to use this bot. This method is far more interesting than the standard web page
Searching Strings by Prefix

The present and following chapter extend the treatment of the dictionary problem to the case of more sophisticated forms of key matching, namely prefix match and substring match between a variable-length pattern string and all strings of an input dictionary. In particular, this chapter addresses the former problem, which occurs in many real-life applications concerned, first and foremost, with key-value stores and search engines. Discussion starts with very simple array-based solutions for internal and external memory (i.e. disks), and then moves to evaluate their time , space, and I/O complexities, which motivates the introduction of more advanced solutions for string compression (i.e. front coding and locality-preserving front coding), and data-structure design for prefix string search (i.e. compacted tries and Patricia tries). The chapter is concluded with a discussion on the management of dynamic and very large string dictionaries, which leads to the description of String B-trees. As for all previous chapters, the algorithmic discussion is enriched with pseudocodes, illustrative figures, and many running examples.
The Dictionary Problem

This is one of the most studied problems in computer science, which is just touched in basic algorithm and data structure courses offered at the undergraduate level.This chapter makes a step forward by first introducing universal hashing, which solves some negative issues that basic hash functions experience, and then moves on to describe several advanced approaches to hashing, such as perfect hashing, Cuckoo hashing, minimal ordered perfect hashing, and finally Bloom filters. The theoretical analysis and algorithm description is enriched by figures and pseudocodes, plus several running examples that drive the reader to a better understanding of these important and advanced algorithmic concepts and tools.
AR Mobile Application for Human Anatomy

Abstract: Anatomy is the main topic of human biology. The study of interior body organs, from cells to organ systems, is known as human anatomy. Studying the 2D model of the internal structure is difficult and tiresome. The purpose of this study is to develop a 3D Augmented Reality anatomy of human learning system. When learning anatomy through textbooks, puppets, or 2D models, it might be difficult to understand the internal structure. One solution that has been developed is an Android app that uses augmented reality. An interactive menu in the program allows you to scan an image and see a 3D version of a model in actual space. This study uses Augmented Reality technology to create a human body learning system. It is hoped that by using this approach, students will be able to visualize the human body's parts in 3D with ease. Students can learn more quickly with the use of AR technology.
F2SD: a dataset for end-to-end group detection algorithms

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.
Two-stage semantic segmentation in neural networks

Semantic segmentation consists of classifying each pixel according to a set of classes. This process is particularly slow for high-resolution images, which are present in many applications, ranging from biomedicine to the automotive industry. In this work, we propose an algorithm targeted to segment high-resolution images based on two stages. During stage 1, a lower-resolution interpolation of the image is the input of a first neural network, whose low-resolution output is resized to the original resolution. Then, in stage 2, the probabilities resulting from stage 1 are divided into contiguous patches, with less confident ones being collected and refined by a second neural network. The main novelty of this algorithm is the aggregation of the low-resolution result from stage 1 with the high-resolution patches from stage 2. We propose the U-Net architecture segmentation, evaluated in six databases. Our method shows similar results to the baseline regarding the Dice coefficient, with fewer arithmetic operations.
Token Sparsification for Faster Medical Image Segmentation

Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is still unknown how to perform segmentation from sparse tokens. To this end, we reformulate segmentation as a sparse encoding $$\rightarrow $$ token completion $$\rightarrow $$ dense decoding (SCD) pipeline. We first empirically show that naïvely applying existing approaches from classification token pruning and masked image modeling (MIM) leads to failure and inefficient training caused by inappropriate sampling algorithms and the low quality of the restored dense features. In this paper, we propose Soft-topK Token Pruning (STP) and Multi-layer Token Assembly (MTA) to address these problems. In sparse encoding, STP predicts token importance scores with a lightweight sub-network and samples the topK tokens. The intractable topK gradients are approximated through a continuous perturbed score distribution. In token completion, MTA restores a full token sequence by assembling both sparse output tokens and pruned multi-layer intermediate ones. The last dense decoding stage is compatible with existing segmentation decoders, e.g., UNETR. Experiments show SCD pipelines equipped with STP and MTA are much faster than baselines without token pruning in both training (up to 120% higher throughput) and inference (up to 60.6% higher throughput) while maintaining segmentation quality. Code is available here: https://github.com/cvlab-stonybrook/TokenSparse-for-MedSeg .
Semantic Macroroles

This chapter offers an in-depth discussion of semantic macroroles and macrorole assignment in Role and Reference Grammar (RRG). In the first part, the RRG theory of thematic relations is discussed in the context of a brief history of theta roles and generalized semantic roles. The second part turns to the role of Aktionsart, semantic and syntactic transitivity, and causativity in macrorole assignment. Although the focus of the chapter is on standard RRG, the third part of the chapter discusses alternative approaches, concerning the number of macroroles that should be postulated and the semantic features that are relevant to macrorole assignment.
Digitalization of Education

The vector of state policy, which is aimed at intensifying the processes of digitalization of higher education, as well as the risks of the COVID-19 pandemic, actualizes the importance of research on this topic. During the work on the article, the authors used qualitative and quantitative research methods. Quantitative methods include a questionnaire survey of Russian university students (N=1553 people) conducted in February-April 2020 based on the Google platform. The authors also conducted focus groups with the participation of full-time and part-time students. Additionally, the methods of correlation analysis were applied during the research. The most optimistic assessments of students relate to the issues of organizing training and assessing knowledge in the context of digitalization: 74.1% believe that more understandable requirements for the work performed are provided, and 72.9% believe that the control system is becoming clearer. Aspects related to interactions with teachers are rated somewhat lower: only 61.6% believe that in the context of digitalization, the presentation of the material becomes more understandable, and 49.6% of respondents believe that “communication with teacher becomes more accessible.” The third part of the respondents (30.2%) believes that digital technologies contribute to the overload of students in the learning process. Among students with a low level of digital literacy, the proportion of such respondents increases (38.8%), which is 8.6% higher than the average values for the sample. During the focus groups, it was noted that only a small number of students had direct experience of interaction with teachers using advanced digital technologies (Mentimeter, Kahoot, Quizizz, Socrative, and Padlet) in the educational process to increase its intensification and interactivity. Today’s youth have a superficial understanding of digitalization processes, replacing its content with the primary attributes of informatization: online teaching of disciplines, electronic personal account of students, and electronic educational materials. Digitalization assessments are primarily based on subjective factors – understanding their experience of remote learning during the COVID-19 pandemic. Correlation analysis has presented the existence of a relationship between the level of digital literacy of students and the perception of the digitalization processes of education.
The Effects of Online Havruta Discussion Based AI(Artificial Intelligence) Application Education: Focusing Pre-service Teachers' Creative Teaching Efficacy

Objectives The purpose of this study was to investigate the effects of online Havruta discussion-based AI application education on pre-service teachers' creative teaching efficacy. Methods For this purpose, 48 college students taking education method and educational engineering courses at K University in Seoul were randomly assigned to either experimental or control group, and eight topics on AI application education were conducted for eight weeks. The experimental group conducted online Havruta discussion activities in AI application education, and the students in the control group conducted a general online teaching method in AI education. Pre- and post-surveys were conducted in the control group and the experimental group, and covariance analysis was performed on the collected data to find out the difference in creative teaching efficacy of pre-service teachers between the two groups. Results It was found that there was a statistically significant difference in creative teaching efficacy between the experiment and control group. The experimental group showed higher scores in creative teaching strategy efficacy and creative thinking efficacy, which are sub-elements of creative teaching efficacy than the control group, which showed statistically significant differences. Conclusions This online Havruta discussion-based AI application education promoted high-quality discussions through continuous questions and answers to pre-service teachers, and improved creative teaching efficacy. This study, which applied the online Havruta discussion activities, is meaningful in that it presented specific ways to promote social interaction in an online educational environment and specific strategies for teachers to effectively apply artificial intelligence education in the authentic educational environment.