Reviewerly/exHarmony huggingface dataset:
Virtual Reality in Information Transfer

The article is devoted to technologies and virtual reality systems as comprehensive solutions for immersion of the user into virtual reality using specialized devices and interfaces. Technologies for wire/wireless transfer of audio-visual and parametric information in virtual reality systems are discussed. The aim of the article is to analyze and summarize advantages and disadvantages of modern facilities for multimedia and parametric information transfer used in virtual reality systems. A search method for research materials in Russian and international journals included in scientific citation databases was used. The paper analyzes the features (advantages and disadvantages) of using virtual reality in conditions of optical information transfer, wireless protocols (WiFi, Bluetooth, Wireless USB, LIDAR, ZigBee), and wire interfaces (Display Port, HDMI, USB) that provide user communication with virtual reality system. Virtual reality forms a new artificial real world transferred to the user via various wire/wireless (WiGig (802.11ad), WiFi 6 (802.11ax), WiHD (802.15.3c), and Display Port 2.0) interfaces, taking into account physiological, physical and psychometric indicators. Modern technical solutions should give impetus to the creation of adaptive virtual reality with a total immersion effect, when the user cannot distinguish a virtual world from real events.
Enhance Clustering Algorithm Using Optimization

Unsupervised learning can reveal the structure of datasets without being concerned with any labels, K-means clustering is one such method. Traditionally the initial clusters have been selected randomly, with the idea that the algorithm will generate better clusters. However, studies have shown there are methods to improve this initial clustering as well as the K-means process. This paper examines these results on different types of datasets to study if these results hold for all types of data. Another method that is used for unsupervised clustering is the algorithm based on Particle Swarm Optimization. For the second part this paper studies the classic K-means based algorithm and a Hybrid K-means algorithm which uses PSO to improve the results from K-means. The hybrid K-means algorithms are compared to the standard K-means clustering on two benchmark classification problems. In this project we used Kaggle dataset to with different size (small, large and medium) for comparison PSO, k-means and k-means hybrid.
How Visual Perception of the Inside of Things Creates the Impossible Dovetail

Here, we consider a well-known wooden puzzle known as the impossible dovetail. We argue that an intriguing form of amodal completion, dealing with spontaneous interpretations of the inside of objects is the key to understanding why people find it difficult to see how the impossible dovetail is indeed possible.
Development of underwater 3D shape measurement system with improved radiation tolerance

When performing remote tasks using robots in nuclear power plants, a 3D shape measurement system is advantageous in improving the efficiency of remote operations by easily identifying the current state of the target object for example, size, shape, and distance information. Nuclear power plants have high-radiation and underwater environments therefore the electronic parts that comprise 3D shape measurement systems are prone to degradation and thus cannot be used for a long period of time. Also, given the refraction caused by a medium change in the underwater environment, optical design constraints and calibration methods for them are required. The present study proposed a method for developing an underwater 3D shape measurement system with improved radiation tolerance, which is composed of commercial electric parts and a stereo camera while being capable of easily and readily correcting underwater refraction. In an effort to improve its radiation tolerance, the number of parts that are exposed to a radiation environment was minimized to include only necessary components, such as a line beam laser, a motor to rotate the line beam laser, and a stereo camera. Given that a signal processing circuit and control circuit of the camera is susceptible to radiation, an image sensor and lens of the camera were separated from its main body to improve radiation tolerance. The prototype developed in the present study was made of commercial electric parts, and thus it was possible to improve the overall radiation tolerance at a relatively low cost. Also, it was easy to manufacture because there are few constraints for optical design.
Screen Images in Contemporary Opera: Concerning the Issue of the Genre’s Renewal at the Turn of the 20th and 21st Centuries

At the turn of the 20th and the 21st centuries opera theater went through a set of succession of fermentation, which is connected with the inclusion into the opera genre of new themes, subjects and new regularities in the construction of opera librettos, with the formation of specific dramaturgical principles and stylistic discoveries. The article accentuates attention towards the issue of the changes which have occurred in the genre of opera under the influence of new media and performances. The author dwells in great detail on illuminating the question of what changes have occurred within the opera genre under the influence of the media, and what do composers and theater producers wish to achieve by bringing in screen depictions in opera production. For her research material the author has chosen operatic compositions in which innovations are connected with an erosion of the boundaries between art and reality, with a rejection of the regular laws of drama, with the strengthening of the role of the audience during the process of constructing the production’s conception. The article examines operas by Louis Andriessen, Steve Reich and Philip Glass from the positions of manifestation in them of new dramaturgical regularities. As a result of analytical observations the author arrives at the conclusion that the events instilling into the dramaturgy of the opera action by means of screen images carry out meaning-generating functions connected with the creation of the effect of suggestion and estrangement. Keywords: contemporary opera, new media, screen video images, performance.
High-Speed and Secure Elliptic Curve Cryptosystem for Multimedia Applications

The last few years witnessed a rapid increase in the use of multimedia applications, which led to an explosion in the amount of data sent over communication networks.Therefore, it has become necessary to find an effective security solution that preserves the confidentiality of such enormous amount of data sent through unsecure network channels and, at the same time, meets the performance requirements for applications that process the data.This research introduces a high-speed and secure elliptic curve cryptosystem (ECC) appropriate for multimedia security.The proposed ECC improves the performance of data encryption process by accelerating the scaler multiplication operation, while strengthening the immunity of the cryptosystem against side channel attacks.The speed of the encryption process has been increased via the parallel implementation of ECC computations in both the upper scaler multiplication level and the lower point operations level.To accomplish this, modified version of the Right to Left binary algorithm as well as eight parallel multipliers (PM) were used to allow parallel implementation for point doubling and addition.Moreover, projective coordinates systems were used to remove the time-consuming inversion operation.The current 8-PM Montgomery ECC achieves higher performance level compared to previous ECC implementations, and can reduce the risk of side channel attacks.In addition, current research work provides performance and resources-consumption analysis for Weierstrass and Montgomery elliptic curve representations over prime field.However, the proposed ECC implementation consumes more resources.Presented ECCs were implemented using VHDL, and synthesized using the Xilinx tool with target FPGA.
Diarization Based on Identification with X-Vectors

In this paper, we describe a diarization of mono channel telephone recordings from The Language Consulting Center providing the Czech language consultancy service. In our proposed approach to a diarization, we use information about the known identity of one speaker (the language counsellor) acquired from the text transcription at the beginning of the conversation. In the state-of-the-art diarization based on the x-vectors clustering, we replace the clustering step by the identification of each segment of the recording against the counsellor’s identity x-vector and the general x-vector model that represents the client. Our proposed diarization without resegmentation step can be used as an online approach. Because of the uniqueness of our data, we compare our results with the Kaldi diarization as the baseline system.
«SELF-BOOK» OF PERSONAL AND PROFESSIONAL GROWTH OF THE FUTURE TEACHER

In the article, we substantiate the theoretical and practical aspects of the application of "Self-book" of personal and professional growth of the future teacher. In scientific exploration, we reveal the essence and content of the concept of "Self-book", which is presented as an individual diary (book, notepad or glider) of a student, in which he captures his own achievements in personal and professional growth, determines the trajectory of self-knowledge, self-development and self-improvement also monitors the dynamics of educational outcomes through proposed diagnostic techniques. The content and methodological support of "Self-book", which allows to track the dynamics of educational results and personal and professional development of students throughout the entire period of study at a higher education institution, is characterized. We have shown that personal and professional development is a complex multifaceted process, which aims at the formation of future teachers not only general and professional competences, but also envisages the development of their personal meanings. The study identified the main components of personal and professional development of future educators, namely: personal (value-personal and professional settings; self-knowledge and self-development; professional elevation); 21st century skills (critical thinking and problem-solving skills; communication and collaboration skills; digital skills), research and development (ability to highlight the research problem; work in a team; design stages of the research; select the necessary tools to solve the research task; analyze, organize, summarize the information received; to acquire new knowledge for their own use in their professional activity; to create and present their own educational products), reflective (self-concept, personal qualities and results of their own activity; self-analysis, self-knowledge, self-diagnostics, self-assessment, self-development, self-improvement; ability to detect mistakes, disadvantages of their own professional activity, and correct them). This article discusses the advantages and disadvantages of using Self-book in the personal-professional development of a future educator.
Efficient Clustering of Water Distribution Network Using Affinity Propagation

Clustering on graph data is one of the popular clustering techniques in the field of Graph mining.An accurate and fast clustering algorithm which attains high quality clusters in comparison with traditional data-mining or graph mining clustering-algorithms is Affinitypropagation (A-P).A large part of the real-world data falls under complex networks enhancing its applicability to wide sectors of research in computer science.In this paper, we consider a real-world complex infrastructure network, Water Distribution Network (WDN), consisting of node properties namely elevation, base demand, head, pressure and demand and the edge properties flow, velocity, roughness, length, diameter, friction factor and unit head loss.A-P algorithm initially develops a similarity-matrix by using data points of the real-world dataset.Secondly, responsibility and availability matrices are developed iteratively until constant responsibility and availability values are obtained.This is performed by using vectorized implementation.Finally, criterion matrix is developed as the sum of responsibility and availability to generate cluster centers.The WDN is sparse and complex; A-P gives strong clusters with potential cluster centers with time complexity of O(N 2 ).In addition, data points of a cluster formed are at same geographical location.An experimental result shows the novelty of A-P on WDN.
Smart Goal Keeper Prototype using Computer Vision and Raspberry Pi

This paper presents the prototype of a smart goalkeeper that defends goals. The developed solution is ideal for training football players. Color tracking is used with the synchronization of the servo motors and computer vision to detect football. The mathematical calculations performed are linked with positioning the servo to mark the position for the goalkeeper. The developed solution is perfect for the training of football players while hitting the goals. The robot goalkeeper, through its accurate observations, will be adequate and up to date in putting every effort to save the goal. The limits of the goal are defined concerning the size and movement. The Raspberry Pi 3B+ is used with the webcam for tracking the target, which is the ball in our case.
Research on the prediction algorithm about the number of workers returning to work based on the COVID-19

In order to make rational use of the resources about epidemic prevention such as masks, and prevent the leaders of enterprises from falsely reporting the number of workers back to work, the evaluation of the number of workers back to work in enterprises is transformed into the prediction of the number of workers back to work under different distribution about the number of workers back to work.Based on the analysis of the existing historical data, this paper predicts the number of people who return to work through intelligent algorithm, so that the government can prepare and distribute epidemic prevention materials.Based on the analysis of the daily power consumption data of the enterprise, combined with the existing number of enterprises returning to work, this paper constructs a prediction model of the number of enterprises returning to work based on the power consumption, and completes the prediction of the number of enterprises returning to work in the future.The experimental results based on the simulation data of the number of enterprises returning to work with different distributions show that the intelligent algorithm can effectively predict the number of enterprises returning to work under the background of COVID-19.
A Cloud-based Smart Doorbell using Low-Cost COTS Devices

The design of products and services such as a Smart doorbell, demonstrating video analytics software/algorithm functionality, is expected to address a new kind of requirements such as designing a scalable solution while considering the trade-off between cost and accuracy; a flexible architecture to deploy new AI-based models or update existing models, as user requirements evolve; as well as seamlessly integrating different kinds of user interfaces and devices. To address these challenges, we propose a smart doorbell that orchestrates video analytics across Edge and Cloud resources. The proposal uses AWS as a base platform for implementation and leverages Commercially Available Off-The-Shelf (COTS) affordable devices such as Raspberry Pi in the form of an Edge device.
The Development of MCQ generating system based on Ontology Concepts

Education system needs an effective method to evaluate students in a competent way on their major concepts learned from their studies.Generating Multiple Choice Questions (MCQ) process is cumbersome and needs a lot of effort especially among novice instructors.The current MCQ generators do not automatically generate questions but only randomly display questions from the question bank.However, it is not easy to load a massive number of questions in the question banks where it is normally done in stages to have massive question collection in the question bank.Therefore, this paper proposes an automatic question generator specifically for MCQ to enable a massive number of questions generated in just a few seconds.In addition, it is useful for students who have to do self-learning for their tutorial exercises especially during the COVID -19 pandemic crisis where online learning is widely used to replace the traditional face-to-face sessions.The generator has adopted an ontological approach for question generation strategies, and it is implemented using rule-based reasoning.There are six modules proposed and discussed in this paper including the concept extraction from ontology, concept, and question stem mapping, generating appropriate answer options using ontology relation information and reordering of answer options.The functionality and validation test showed that Multiple Choice Question Generator (MCQ-G) can generate MCQ with appropriate answer options.This work will be extended in the future using the difference domain ontology and question types.
Introduction to Web Servers

This chapter introduces web servers, the services they provide, and how they work. This information is essential to web developers so they can make proper use of their web server and provide the best web experience to their users.
Object Extraction and Encoding for Video Monitoring Through Low-Bandwidth Networks

Surveillance cameras in smart cities generate a dramatic amount of data each day, that must be transferred through communication channels. Most of this information is useless, just images of static scenarios that must be observed by thousands of monitor people. Even though there are plenty of algorithms that helps to automatically classify this information, the computational effort is still concentrated on transporting (coding/decoding) those images.This work aims to present the first results of applying an efficient software analyzer and encoder for static cameras that struggles to reduce computational effort and data generation. For this purpose, a combination of background segmentation and classification algorithms are used, which separate the moving objects from background and tag them using convolutional neural networks. For storing, a hyper-image organization structure is proposed, used for describing such objects in a minimal way. To have efficient algorithms, GPUs and multi-core processors are used. Several tests were carried out, on long video sequences, showing the amount of bandwidth that is reduced compare to other traditional encoders.
Indikator Kesejahteraan di Provinsi Bali: Suatu Pendekatan Analisis Biplot

The goal of this reseach is to get the representation of welfare in Bali province based on the indicators represented by People Development Indexs (PDI). PDI is the indexs is a latent variable that can not be measured like measurement variable, so to measure PDI have to be measure by several indicators. In tgeneral, PDI is measured by three indicators that is a long and Healthy life as the Health Indicators, Education Indicators and decent standard of living as Economic Indicators. In Practice, Health Indicators is measured by mean expectation of life. Mean of long school and expectation of long school are measurement of Education Indicators. Whereas Economic Indicators is represented by purchasing power parity.&#x0D; In Staistics methods, to represent or analize multivariate variable there are many methods can be used. One of the methods is Biplot. In Biplot analysis we can represent information about row matrix and coloum matrix simultaneous. In this cases we can get information about object ( 9 districs or city in Bali) and Welfare Indicators represented by PDIsimultanous.&#x0D; The result of biplot analysis in this research is two dimension graphic that represent Euclid distance of object and correlation of indicators so that we can get information of the grouping of object and the characteristic variables of the certain group.
Deep Learning in IoT

The internet of things (IoT), big data analytics, and deep learning (DL) applications in the mechanical internet are expanding. The current digital era has various sensory devices for a wide range of fields and applications, which all generate various sensory data. DL is being applied for handling big data and has achieved great success in the IoT and other fields. The applications for data streams to discover new information, predict future insights, and make control decisions are crucial processes that make the IoT a worthy paradigm for businesses and a quality-of-life improving technology. This chapter provides a detailed account of the IoT domain, machine learning, and DL techniques and applications. The IoT that consists of DL with intelligence backgrounds is also discussed. Recent research on DL in the IoT within the big data domain is also discussed. Current challenges and potential areas for future research are discussed.
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices

Wearable devices have strict power and memory limitations. As a result, there is a need to optimize the power consumption on those devices without sacrificing the accuracy. This paper presents AdaSense: a sensing, feature extraction and classification co-optimized framework for Human Activity Recognition. The proposed techniques reduce the power consumption by dynamically switching among different sensor configurations as a function of the user activity. The framework selects configurations that represent the pareto-frontier of the accuracy and energy trade-off. AdaSense also uses low-overhead processing and classification methodologies. The introduced approach achieves 69% reduction in the power consumption of the sensor with less than 1.5% decrease in the activity recognition accuracy.
Correction to: A computationally efficient and scalable key management scheme for access control of media delivery in digital Pay-TV systems

Tables 1, 3, 4, 5, 6, 7, 8 and the body texts in the original publication contain incorrect symbols. The original article article has been corrected.
FACTORS AFFECTING INDONESIAN YOUNG LEARNERS’ ENGLISH PROFICIENCY LEVEL

This study was aimed at investigating the influencing factors on students’ English proficiency levels based on Cambridge English Tests for Children and Young Learners. A preliminary survey was undertaken to select the sample of elementary schools that offered English as a subject. As many as 157 students who sat in Grades 4, 5, and 6 from nine elementary schools participated in this study. The findings revealed that the average of students’ scores was at Starters/Movers Level, which is equal to A1 in the Common European Framework of Reference (CEFR). Through multiple regression analysis, three out of five investigated factors were detected to be significantly related to students’ English proficiency levels, namely gender, grade level, and school accreditation rank. While rich literature has found gender and grade level as one of the affective variables in language learning, none has studied whether school accreditation status influences young learners’ English achievement. It is recommended that schools and related stakeholders consider these factors to ensure effective English teaching to young learners. It is also suggested that future researchers conduct further study on how and under what conditions the factors can contribute to students’ English achievement.