Reviewerly/exHarmony huggingface dataset:
READINESS OF A HIGHER SCHOOL STUDENT'S PERSONALITY FOR ACTION FOCUSED ON A SOCIALLY SIGNIFICANT OBJECT

Russia is at the next stage of its development: the future of the country depends on the values which would be attractive to young people in the 21st century. The value orientations of young people are determined by the behavior of the Russian elite, norms, and ideals that are proposed in the process of higher education. Currently, a new generation of the Russian intellectuals is being formed in the process of professional training in higher education. This generation will determine the priority socially significant values and the humanitarian component of the country in the first half of the 21st century. The purpose of the research is to study and analyze the results of diagnostics of socio-psychological attitudes of a higher school student aimed at a stable predisposition and readiness of the individual to action focused on a socially significant object. We used a systematic approach that makes it possible to explain the way any system develops and functions. The use of such a method becomes possible when the research is faced with the task of explaining the integrative properties of an object that is not the result of a simple summation of parts, and whose properties cannot be identified from the features of its constituent elements. We were based on the theory of socialization and the theory of the professional development of the individual. The empirical part of the article is the result of the diagnosis of socio-psychological attitudes of a higher school student aimed at a stable predisposition and readiness of the individual to action focused on a socially significant object that contributes to social development. We have reached the following conclusions:1. Students attracted the attention of researchers from various scientific schools in connection with the development of the education system in Russia. The interest in the problems of value orientations of students is primarily caused by dynamic changes in society, the need to have an idea of the value system, aspirations, and needs of young people in the new socio-economic conditions in Russia. These issues are reflected in the works of Russian and foreign scientists.2. The values that developed among young people at the beginning of the 21st century were mainly individual and personal. They were associated with an increased role of the material factor and awareness of that circumstance. That is why, such values as one's health and readiness to do anything, as long as it is well paid, came out in the first place. 3. Diagnostics of socio-psychological attitudes of a high school student were conducted using a questionnaire consisting of 40 questions (the author is O. V. Potemkin) and aimed at identifying the orientation of the individual to freedom, power, work, and money. The study was conducted in three universities in Yekaterinburg. The number of respondents was 732 people. As a result, two large groups were identified: "social anarchists" and "social altruists".
EVELOPMENT OF NON-UNIFORM RATIONAL TYPE-2 FUZZY B-SPLINE CURVE MODELING

Type-2 fuzzy set offers an opportunity to define a fuzzy control point with type-2 levels of uncertainty. We discuss the development of non-uniform B-spline curve model based on type-2 fuzzy set theory and also a type-2 level of NURBS curve model. First we define type-2 fuzzy control points and type-2 fuzzy weight using a type-2 fuzzy number. Then we introduce fuzzy NURBS curve model with type-2 fuzzy control points and type-2 fuzzy weight. We discuss the model with type-2 fuzzy data points and type-1 fuzzy reduction. The defuzzification of the model is also discussed at the end of this paper.
Real-Time Vehicle Counting Using Computer Vision

With the rapid development of intelligent video analysis, traffic monitoring has become a key technique for collecting information about traffic conditions. Using the traditional sensors such as loop detectors, ultrasonic sensors may cause damage to the road surface. Meanwhile, many of these sensors need to be installed in urban areas, the cost of this work is high. Surveillance video cameras are commonly used sensors in the traffic monitoring, which can provide video stream for vehicle detection and counting. Vehicle counting process provides appropriate information about traffic flow, vehicle crash occurrences and traffic during the peak times in roadways. An acceptable technique to achieve these goals is by using digital image processing methods on roadways. Our project describes the methodology used for image processing or video processing for traffic flow counting with real time videos using a programming language.
A Collaborative Approach for Course Recommendation System

Graduate attributes are the competencies and capabilities set by the college network as benchmark for its students during their time with the organization. The principle point of the paper is to assist students with their scholastic choices, by suggesting elective courses dependent on the graduating attributes scores. Students rate the significance of each course in improving their graduating qualities toward the end of every semester. The elective courses taken by peer students and rated similarly are identified by a collaborative filtering algorithm and prescribed to students. This paper predicts the rating for each course and graduating attribute. The elective courses with the greatest expected ratings are shortlisted for proposal. Also, the aggregated GA score is determined for each student at end of every semester. The shortlisted courses that can improve the feeble GA scores are at prescribed to students.
Examining phonetic trends in the speech of transgender YouTubers

In LGBTQ research, transgender speech is understudied compared to gay and lesbian speech. Fundamental frequency (F0) and vowel space have both shown as useful in determining speaker gender (Simpson, 2009; Gaudio, 1994). Sibilants are also used to determine gender and sexuality (Zimman, 2017; Munson, 2007). This analysis of transgender YouTubers examines F0, vowel space, and sibilants and how their speech aligns with cisgender speakers. Videos of seven transgender YouTubers were transcribed and force-aligned. Measurements were taken comparing speakers within each group and with cisgender speakers. All transgender speakers produced F0 commensurate with cisgender speakers. Vowel space was plotted, showing transwomen produce an expanded vowel space when compared to transmen. Direct comparison to cisgender speakers was not undertaken. Differences in sibilant duration between groups was not borne out in the data. Spectral mean was also measured and transwomen showed ranges similar to ciswomen. Transmen were more variable in their production of sibilants. This study shows that transgender speakers produce speech that mostly aligns with cisgender speakers for the sounds measured. Though not representative of all trans people, these speakers show gendered speech is not simply physiological and further study may aid in efforts toward greater transgender equality.
Irregularity Detection of Daily Behavior Patterns Based on Unsupervised Learning

The irregularity detection of daily behaviors has received lots of attention, especially in homecare. An IRregularity Detection (IRD) algorithm is proposed to identify the irregular behavior patterns using the unsupervised learning. The distance and similarity between daily behavior patterns are designed as important features to build up the irregularity detection model. Experiments demonstrate that the proposed algorithm exceeds the existing unsupervised machine learning algorithms in terms of the numbers of False Negative and False Positive.
21st Century Skills and Competences

It is remarkable how quickly social and technological transformations have been occurring since the end of the 20th century, evolving in different stages and subsequently expanded in this 21st century. These changes have been promoting new social and behavioral structures and require different skills and competences to deal with the impacts and challenges that result from them. The chapter presents a brief discussion on the importance and relevance of what skills and competencies are required in the 21st century, the definitions and concepts found in the literature, and proposes a three-dimensional view that consists of the dimensions of information, communication, and ethics and their social impact in the digital world.
Towards robust and low-complexity radiometric fingerprint

Authentication is challenging in the IoT because most advanced cryptographic algorithms are difficult to afford by constrained devices expected to run many months or years. Radiometric signatures have been used effectively to identify wireless devices based on imperfections in electronic circuits. This technique is also known as Radio frequency (RF) fingerprinting. Previous work proves the feasibility of this technique but mainly considered static channel conditions. In our current work, we systematically and experimentally study the impact of dynamic and complex channel conditions on the radiometric signatures. The results show a threat to identification accuracy for modulation error-based fingerprinting that was considered channel-resilient. Next step, my research aims at improving the robustness of RF fingerprint systems and make this authentication solution ready for widespread implementation.
Text Summarization of Indonesian Folklore with Word Frequency Concept

Storytelling is a fun activity, especially for telling a story to children which can affect the character of our children based on the story. Story from the internet resource can be a long story or a short story. For the former, it needs to make a summary to become shorter. When it is a long one, it is more difficult to tell our kids and it is more relevant to make a summary. For creating a summary, it will be easier to proceed with a computer, consequently, it needs a computer application that automatically provides a short overview of the folklore. The Indonesian folklore is summarized by several processes in a web application with a python programming language as a development tool. The process of summarization begins with processing the text that is entered by the user and it is eliminated in all of the special characters. Then the result is processed in the form of tokenizing to get a set of words. From this process, all of the set's elements that are belong to the stop word list are removed and then the rest of them are counted each for the word frequency that they appear. After finding the word frequency, the sentences are scored by the accumulation of word frequency that they have and are sorted based on this score. Lastly, the sentences are sorted based on each of them appear from the original version and make them be a single paragraph. The result of this research is that text summarization can be carried out with the computer application and python programming languages in the form of a website. The number of sentences that are determined can be selected by the user in the program. Several Indonesian folklores are applied to the application and rated by the reader. Moreover, the application delivers 87% of reader acceptance and it is almost similar to several existing online websites that generate text summary.
Preprocessing method for improving the robustness of blind watermarking technology

With the development of information technology, many image, video and audio resources are rapidly spreading on the Internet, which raises great privacy concerns. As a result, copyright protection has attracted more and more attention. Blind watermarking which is commonly known as a useful technique for copyright protection aims to embed the information into the host imperceptibly and extract the information from the host after unknown attacks during the transmission. Therefore, the embedded watermark's invisibility and robustness to attacks are two important metrics to evaluate a watermarking algorithm's performance. This paper studies the influence of the watermark properties and the host image on the robustness and invisibility of the watermarking method and brings up general rule-of-thumb to improve the performance. Based on the above, a novel watermark preprocessing method which can enhance blind watermarking's robustness is proposed and applied to the robust embedding algorithm based on DWT-DCT (Discrete wavelet transform - discrete cosine transform). Experiments show that the proposed watermark preprocessing method can significantly enhance the watermarking algorithms' robustness and generalization ability.
The Sattriya Dance Ground Exercise Video Dataset for Dynamic Dance Gesture Recognition

Dynamic gesture recognition is a very active research area in computer vision for the last few decades. Feature selection and extraction is one of the most important phases in gesture recognition since it greatly affects the recognition. In this work it is aimed to focus on some discriminate video features that lead to good recognition of dance gestures considering full-body movement of the dancer. Since human body is a highly articulated structure, it is an important issue to extract features that best describe the articulation. In this paper, a novel full body gesture video dataset is introduced, containing 560 video sequences of 28 ground exercises of Sattriya dance as well as annotations of those sequences and class label of every ground exercise. The purpose of creation of this dataset is to develop a computer vision system to classify each ground exercise. As well as this dataset can be useful for benchmarking a variety of computer vision and machine learning methods designed for dynamic dance gesture recognition.This paper also presents a method for dynamic gesture recognition on the Sattriya dance dataset that we have developed.
Comprehensive Evaluation Model of College Physical Education Efficiency Combining Multivariate Decision-making Mathematical Model

In physical education (PE) specialty of higher normal colleges, college PE is an integral part of the entire PE work. Based on the actual experimental PE education, based on the literature review, expert consultation, and other methods, the existing teaching evaluation experience is referenced in this paper. The multivariate decision-making mathematical model is used to establish a comprehensive evaluation model for PE experimental teaching efficiency. It is of important practical significance for enhancing the management of experimental teaching and promoting the improvement of experimental teaching quality.
Comparing Three Data Representations for Music with a Sequence-to-Sequence Model

The choices of neural network model and data representation, a mapping between musical notation and input signals for a neural network, have emerged as a major challenge in creating convincing models for melody generation. Music generation can inspire creativity in artists and the general public, but choosing a proper data representation is complicated because the same musical piece can be presented in a range of expressive ways. In this paper, we compare three different data representations on the task of generating melodies with a sequence-to-sequence model, which generates melodies with flexible length, to explore how they affect the performance of generated music. These three representations are: a monophonic representation, playing one note each time, a polyphonic representation, indicating simultaneous notes and a complex polyphonic representation, expanding the polyphonic representation with dynamics. The influences of three data representations on the generated performance are compared and evaluated by mathematical analysis and human-cantered evaluation. The results show that different data representations fed into the same model endow the generated music with various features, the monophonic representation makes the music sound more melodious to humans’ ears, the polyphonic representation provides expressiveness and the complex-polyphonic representation guarantees the complexity of the generated music.
Informativeness of visual models

Abstract The purpose of the work is to study the informativeness of visual models. The article reveals the features of the visual perception of images. We described the factors that distort the concept of informativeness as an objective value. The article proves that the perception of visual images cannot be equated with the collection of information using technical devices. We described the content of reception, perception and apperception in visual perception of visual models or spatial images The article introduces a systemic model of informativeness, which shows that the use of pixels as image elements does not evaluate the informativeness, but evaluate the information volume of the image. In order to evaluate the informativeness of visual models, it is recommended to use a visual cluster model. The article introduces a system of key indicators to evaluate the informativeness in the information visual processing.
The Relationship Between The Level Of CALIS Knowledge And Satisfaction Among International Students

This research is about the relationship between the level of CALIS knowledge and satisfaction among international students: a case study at Library of Central China Normal University (CCNU). This research used quantitative approach that divided in two analyses such as descriptive statistic and inferential statistic. In the descriptive statistic, this study aims to describe the demographic information such as gender, age, major, country, and level of education. In the inferential statistic, the study obtains the relationship between knowledge of users and users satisfaction, then it obtains the effect of users knowledge and users satisfaction. The finding of this study has showed CALIS in CCNU library need to be further introduced to the international students. International students lacked knowledge of CALIS, therefore they didn’t know how to properly access CALIS. After the author conducted a survey to international students, the author found the students have the basic level of knowledge of CALIS, but when it comes to accessing the thesis, they have a constraint due to the Chinese language. Therefore, the information that they needed was not quickly available. In conclusion, the results of user accessibility indicated when knowledge of CALIS increased, the level of user satisfaction also increased. Likewise, when the user lacked knowledge, the level of user satisfaction also lacked.
Expediting the UUSS's Response to the Soda Springs, Idaho Aftershock Sequence

In principle, a goal of any regional seismic network (RSN) is simple - detect and review seismicity in the network’s region of interest. Vigorous aftershock sequences however present a unique challenge to an RSN’s mission in that they can overwhelm automated real-time and analyst-driven post-processing systems. This inundation with seismicity can the lead to earthquake catalogs that are incomplete and erroneous in the area around the aftershock sequence. In response to this challenge, this abstract explores integrating machine learning as a tool to expedite the University of Utah Seismogram Station’s (UUSS)’s response to the September, 2017 Soda Springs event in southeast Idaho. In the months following the event the UUSS was able to locate over 1000 aftershocks and was catalog complete to approximately 2.5 M_L. Owing to the fact that a temporary deployment of 6 six-channel and 2 strong-motion instruments were deployed to the area shortly after the event, it is anticipated that many more high-quality locations can be obtained without resorting to template matching. Therefore, it is our goal to attempt to recover the existing UUSS catalog with machine learning enabled workflows that can produce analyst-grade picks for location and subsequent magnitude estimation. Should this be successful then the anticipated consequence is that analysts can quickly screen and approve more events and by extension lower the network’s magnitude completeness during the aftershock sequence.
Toward Response-Type Identification for the Real Time

The present study focused on applying machine learning techniques to recognize the student’s responses provided in the educational technology survey in higher educational institutions. The present survey was piloted to investigate the differential analysis in two universities residing in India and Hungary. The survey’s dataset was comprised of 37 features and 331 instances spotting technological questions awareness among students. The responses of students were a hybrid type such as binary, ordinal and nominal. The authors trained and tested the four datasets separately with four machine learning classifiers using two testing techniques. Both the binary and multiclassification problems are solved to identify the response type. To identify the student’s response type, the strength of the classifiers is also compared with the statistical T-test at the 0.05 confidence level. To support the real-time implementation of the predictive models, the CPU user training time is also estimated and compared with the T-test. The cross-validation method significant enhanced the prediction accuracy of each classifier. To identify the response type, it is proved that the multinomial regression (MLR) outperformed others with the uppermost prediction accuracy of 98.3% and prediction time (lowest 0.03 and the highest 0.07 s) is computed. The present study may help to social science students to predict the response type of the respondents in the real time with different features.
Use of Artificial Intelligence and Machine Learning in Games

With modern video games surpassing every set of expectations in terms of graphics, game play, mechanics and hardware support, Artificial Intelligence in video games has also come a long way, from when it was first implemented in 1951. Although every set of games has an AI unique to itself, many of the algorithms are now developed such that they can be implemented in various games without any major changes in coding. But this could lead to the players exploiting AI in a single game to break the other games as well. Though this could be easily fixed by changing some minor fragments of algorithms, it would very well be an efficient way of developing complex AI for many games at once. This paper focuses on providing a cost-efficient way to implement AI algorithms that would benefit most of the upcoming and future games that will depend on AI to make themselves more dynamic to the players. This is done by taking the examples of various AI algorithms implemented in games like Pacman, Dota2, Tom Clancy's- The Division and many more.
Automatic Detection of Sensitive Information in Educative Social Networks.

Detecting sensitive information with privacy in mind is a relevant issue on Social Networks. It is often difficult for users to manage the privacy associated with their posts on social networks taking into account their possible consequences. The main objective of this work is to provide users information about the sensitivity of the information they will share when they decide to publish a message in online media. For this purpose, an assistant agent to detect sensitive information based on different types of categories detected in the message (i.e., location, personal data, health, personal attacks, emotions, etc.) is proposed. Entity recognition libraries, ontologies, dictionaries, and sentiment analysis will be used to detect the different categories. This agent is integrated into the social network Pesedia, aimed for children and teenagers, and through a soft-paternalism mechanism provides information to users about the sensitivity of certain content and help them in making decisions about its publication. The agent decision process will be evaluated with a dataset elaborated from messages of the social network Twitter.