Reviewerly/exHarmony huggingface dataset:
An Investigation of the Relationship between the Benefits of Recreational Activity Participation and Social Commitment from the Perspective of University Students

The purpose of the study is to examine the relationship between the benefits of recreational activity participation and social commitment in terms of university students.In this direction, 314 university students (164 female and 150 male students) studying at different departments of Karamanoğlu Mehmetbey University and Necmettin Erbakan University, which were determined by the appropriate sampling method due to the pandemic conditions, participated in the study voluntarily.In addition to the demographic data form, the Recreation Benefit Scale (Akgül et al., 2018) and the Social Engagement Scale (Duru, 2007) were used in the study.In the analysis of the data obtained, skewness and kurtosis values of the data were examined with the Kolmogorov Smirnov test to determine the distribution of the data, as well as the descriptive statistics (percentage and frequency).After it was determined that the data were suitable for the normal distribution parameters, ANOVA, T-Test and Tukey HSD test were used to determine the variables from which the differences were originated.In addition, correlation analysis was performed in order to determine the relationship between the data.As a result of the studies and analyzes, no significant difference could be determined between the benefits of participating in recreational activities and their social commitment according to the age of the participants, the adequacy of leisure time and the class variables they studied; It has been revealed that there is a significant difference in the participants' perceived welfare level, age, and difficulty in evaluating leisure time, according to their recreational benefit status.As a result of the correlation analysis, it was revealed that there is a positive and significant relationship between recreational benefit and social commitment.
Introduction to Apache Spark Streaming

Spark Streaming was added to Apache Spark in 2013 as a scalable, fault-tolerant, real-time streaming processing extension of the core Spark API. Spark Streaming natively supports both batch and streaming workloads and uses micro-batching to ingest and process streams of data passing the results to permanent storage or a dashboard for online data analysis.
Spark Structured Streaming

Nowadays, in the big data world, more and more business processes and daily used applications require the analysis of real-time or near-real-time information at scale. Real-time data analysis is commonly associated with processes that require decisions to be taken quickly and without delay. Therefore, infrastructures capable of providing instant analytics, management of continuously flowing data, and fault tolerance and handling stragglers or slow components are necessary.
Contextual Modeling in Context-Aware Conversation Systems

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation.Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning.Some studies concatenate the current question with previous conversation sentences and use it as input for response generation.Another approach is to use an encoder to store all previous utterances.Each time a new question is encountered, the encoder is updated and used to generate the response.Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context.This results in different encoding models for the question and the context, capturing the specificity of each.In this way, we have access to the entire context when generating the response.To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question.This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response.We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network.Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.
Satisfiability Problem in Interval FP-logic

The article investigates the interval modal logic, in which an action of the modal operator ◊ is limited by the boundaries of an interval. In addition, the language of modal logic is extended by the operator 𝐷(𝛼, 𝛽), the truth of which is determined qualitatively: it is true only if the number of points on the interval [𝑐𝑖, 𝑐𝑖+1] where the formula 𝛼 is true is strictly less than the number of points in this segment where the formula 𝛽 is true. The problem of satisfiability of formulas is solved, and as a consequence, the decidability of logic.
Analisis kepuasan pemustaka terhadap kualitas layanan website perpustakaan di masa pandemi COVID-19

Introduction: Government policies in suppressing the spread of the COVID-19 pandemic creates a bigger challenge to provide better library services. Library websites become one important service in social distance. This study aims to find out the level of user satisfaction in the utilization of library websites during the COVID-19 pandemic. &#x0D; Data Collecttion Methods: The paper used a quantitative approach with surveys. Questionnaires are managed based on webqual variables.&#x0D; Data analysis: The survey data analysis used descriptive statistics with mean value. The mean value is based on the interpretation guidelines.&#x0D; Results and discussion: Library web statements showed the highest score while library web appearance showed the lowest. The average score of user satisfaction on the utilization of library websites is at a satisfactory level. The highest score is 28,04 on the information quality variable. The lowest score of 18,76 on the usability quality variable and interaction quality showed a score of 23,74. &#x0D; Conclusions: Based on the results of data analysis on the webqual variable, the level of user satisfaction in the utilization of library website during the COVID-19 pandemic is found in the satisfactory level.
12 Space traffic management: a simulationbased educational app for learners

Due to the swift creation of human-centric applications and technologies, educational and training programs are progressively transforming toward newer ecosystems of modernization through the adoption of augmented and virtual reality technologies. Simulations and gamifications in engineering education and training could play a huge role in imparting knowledge with entertainment and amusement thereby bringing in positive and effortless understanding of concepts among the students. Foreseeing this, a game based on virtual reality named “Space Traffic Management” has been incepted with the purpose to disseminate space flight and astrodynamics learning environment in the current education scenario. The game, chosen in the national level toy hackathon event (Toycathon 2021), is a typical simulation of realistic space traffic schema with infotainment factor for helping to simplify space education among aspiring students and bring value in their practical understanding. The game helps in stimulating strategic understanding among the players by providing them an invigorating environment consisting of animations, graphics, sound effects, and music. This chapter reports the inception of this game prototype with the mathematical theory related to its creation.
The Effects of Mindfulness Based Cognitive Therapy (MBCT) Group Counseling Program to Enhance Flow in Playing Music of Music Students

This study is the quasi-experimental research. Its purposes were to 1) comparing the flow in playing music of students majoring in musiac between before and after attending mindfulness based cognitive therapy group counseling program; and 2) comparing the flow in playing music of students majoring in music between the experiment group, who attended a group
On the Identification and Suppression of Hate Speech in Online Contexts

This paper focuses the issue of identifying indirect hate speech on digital platforms. In previous studies, the authors have addressed automated flagging and suppression of hate speech in YouTube material. We found that such operations are characterized both by problems pertaining to the vagueness of the hate speech concept, as well as a problem compounding
English Teaching based on "Internet +" is Deeply Integrated with Information Education

The purpose of "Internet+ education" is to use Internet information technology(IT) to promote the development of education network informatization in China, so that excellent education resources can be shared, so as to achieve the universality of education information dissemination in the real sense, and further narrow the education gap between urban and rural areas.Based on the background of "IP" education, this paper deeply integrates english teaching (ET) and information education (IE), and analyzes their teaching effects.This paper briefly analyzes the significance of the deep integration(DI) of ET and IE and the process of ET mode under the IT environment; Through the integration of ET and IT in the English class of Grade One in School A, test the students' interest in English learning and whether the learning efficiency is improved; The experimental results show that students' interest in English learning and learning efficiency have been significantly improved under the DI technology, which verifies the effectiveness of the DI of ET and IE.
RDA20 Plenary: A Decade of Data 2013-2023. Celebrating 10 years of the Research Data Alliance (RDA)

Gothenburg, Sweden played host to the Research Data Alliance (RDA) RDA20 plenary from March 20th to 24th 2023. A hybrid event with most sessions recorded and made available via the Whova app for registered participants. Recordings will be made available on Monday, 1 May 2023 to the wider community. For the first in-person plenary since lockdown,
Opportunities to use educational resources in the context of distance learning

The article describes the importance and necessity of using Internet resources and digital educational resources in the context of distance learning. The content of the concepts “Internet resources”, “educational platform”, “educational resource”, etc. is given, the effectiveness of their use in teaching students is revealed. Definitions of electronic resources, digital educational resources, Internet resources are given. The importance of distance learning today is emphasized. The authors classify the most widely used educational platforms in Kazakhstani schools and focus on their capabilities. To educate an independence-oriented person, you need not only a fairly large amount of information, but also a large variability of information that reflects different approaches to solving the same problems. Only in this case, a person has a subject to search for truth, facts, and evidence to support or refute a particular idea, point of view or task. The effectiveness of the special course “Methods of using digital educational resources in primary school” is considered in the preparation of future primary school teachers for the effective use of digital educational resources
Education for Sustainable Development in Modern Conditions of Digitalization

The purpose of the article is to substantiate the tools for the formation of skills and competencies necessary for sustainable development through the disciplines of the field “Artificial Intelligence.” The article considers the possibilities of developing skills to achieve the goals of sustainable development in the disciplines of artificial intelligence and the possibilities of artificial intelligence technologies for education for sustainable development. When writing the article, the authors used the following methods: praximetric (study of regulatory documentation); theoretical (analysis of domestic and foreign literature, generalization, comparison, forecasting, and pedagogical modeling). Approaches to the role of education in achieving sustainable development goals are analyzed. The possibilities of artificial intelligence technologies for education for sustainable development are considered. The theoretical significance of the work is a generalization of the experience of integrating vendor courses into the educational process. The practical significance of the work lies in presenting the experience of developing skills for sustainable development through the disciplines of the field of “Artificial Intelligence,” implemented in the engineering and pedagogical areas of study at the Minin Nizhny Novgorod State Pedagogical University (Minin University), as well as in the framework of additional education at Minin University. The results of the study are updating the educational programs of engineering and pedagogical specialties and the inclusion of educational content for the formation of digital competencies.
Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches. Several natural language processing (NLP) tools are commonly used to examine the large quantity of data generated online. Particularly, sentiment analysis (SA) is an effective way of classifying the data into different classes of user opinions or sentiments. The latest advances in machine learning (ML) and deep learning (DL) approaches offer an intelligent way of analyzing sentiments. In this view, this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis (WIESFO-DLSA) technique. The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data. The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features. At the same time, bidirectional long short term memory (BiLSTM) model is applied for classification of sentiments into different class labels. Moreover, an enhanced sunflower optimization (ESFO) algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model. A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures.
Content-Based Recommender System For An Online Advertising Platform

This study proposes a content-based recommendation system utilizing Latent Dirichlet Allocation (LDA) topic modeling for a classified advertising website.The system dynamically adapts the user's profile based on implicit and explicit feedback by analyzing the user's past behavior with respect to the ads encountered.This approach ensures that the system can provide personalized recommendations that reflect the user's evolving preferences.An A/B test was conducted to evaluate the system's effectiveness, which showed a significant increase in key performance factors, including conversion rate, average session time, and the number of views.Overall, the study demonstrates the effectiveness of content-based recommendation systems using LDA topic modeling and emphasizes the importance of dynamic user profiling in personalized recommendation systems.
CANCER TUMOR DETECTION USING MACHINE LEARNING

Breast cancer-related tumours can form in breast tissue. It is one of the leading causes of death for women worldwide and the most common type of cancer in females. This article contrasts the detection of breast cancer using data mining, machine learning, and deep learning. However, each technique has a unique accuracy rate that varies based on the circumstances, resources, and datasets used. Many researchers have sought to enhance breast cancer detection and prognosis. Our main objective is to evaluate and contrast the many Machine Learning and Data Mining approaches that are currently in use in order to determine which strategy is the most efficient and will support the massive dataset with the highest prediction accuracy. This article provides all the information a novice needs to comprehend machine learning algorithms and lay a strong foundation for deep learning. The main objective of this study is to highlight all the earlier research on machine-learning algorithms that have been used to detect breast cancer.
Sign Language to Speech Translation

Sign language is a way of communicating using hand gestures, movements, and facial expressions, instead of spoken words. It is the medium of communication used by people who are deaf or have hearing impairments to exchange information between their own community and with normal people. In order to bridge the communication gap between people with hearing and speaking disabilities and people who do not use sign language, a lot of research work using machine learning algorithms has been done. Hence, Sign language translator came into picture. Sign Language Translators are generally used to interpret signs and gestures from deaf and hard hearing people and convert them into text. Key Word: Sign language, Hand Gestures, Helping Impaired People, Machine Learning, Deep Learning.
MINERAL EXPLORATION IN THE VICINITY OF THE LATE EDIACARAN BOUTEGLIMTAPLITE AND PEGMATITE DIKES INIMITER INLIER USING ASTER DATA: PRELIMINARY RESULTS

In the southern part of the Imiter inlier, the early Ediacaran basement of the Saghro Group is crosscut by an important swarm of aplitic and pegmatitic dikes that display an extension of several meters and which can exceed 10 in thickness. They are distributed according to two main directions: NE-SW and NW-SE. Petrographic analysis reveals that the pegmatites and aplite primarily consist of quartz, plagioclase, K-feldspar, muscovite, biotite, allanite, and sphene. The BouTeglimt pegmatites, related to the large BouTeglimt granitic and granodioritic plutons, are exterior pegmatites type, hosted in the metamorphic country rocks. Pegmatite zoning is recognized at two scales: internal zoning, representing variations in mineralogy and texture within individual pegmatite bodies, and regional zoning, characterized by increased mineralogical complexity with distance from the granitic source. Using the ASTER sensor, the study investigated significant clayey, phyllitic, and propylitic alterations of the pegmatites, aplite, and their host rocks in the Imiter mine area. Field investigations and mineralogical characterization highlight four new mineralized zones in base and precious metals. These zones are commonly situated at the peripheries of granites or at the contact between the SG series and the late Ediacaran rocks of the Ouarzazate Group. The mineralization is observed in steeply-dipping veins along major E-W faults parallel to the Imiter fault, as well as in stockwork ore within tuffs, lapilli tuff, rhyolite, and disseminated mineralization within various Ouarzazate Group pyroclastic rocks. The ore mineralogy comprises sulfides and oxide minerals. Furthermore, the muscovite-rich BouTeglimt pegmatite and aplite can be considered an excellent exploration indicators due to their occurrence in fertile granites and rare-element pegmatites. They may hold potential as indicators of tantalum mineralization.
Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy

With the advancement of ubiquitous computing, smartphone sensors are generating a vast amount of unlabeled data streams ubiquitously. This sensor data can potentially help to recognize various behavioral contexts in the natural environment. Accurate behavioral context recognition has a wide variety of applications in many domains like disease prevention and independent living. However, despite the availability of enormous amounts of sensor data, label acquisition, due to its dependence on users, is still a challenging task. In this work, we propose a novel context recognition approach i.e., Dissimilarity-Based Query Strategy ( DBQS ). Our approach DBQS leverages Active Learning based selective sampling to find the informative and diverse samples in the sensor data to train the model. Our approach overcomes the stagnation problem by considering only new and distinct samples from the pool that were not previously explored. Further, our model exploits temporal information in the data in order to further maintain diversity in the dataset. The key intuition behind the proposed approach is that the variations during the learning phase will train the model in diverse settings and it will outperform when assigned a context recognition task in the natural setting. Experimentation on a publicly available natural environment dataset demonstrates that our proposed approach improved overall average Balanced Accuracy(BA) by 6% with an overall 13% less training data requirement.
Differences in Approaches to Achieving Inclusiveness between Leading and Regional Universities in Russia

This chapter aims to explore the legal dimension of the differences in approaches to achieving inclusiveness between the leading and regional universities in Russia. The research results reveal the essence and specificity of approaches to achieving inclusiveness in leading and regional universities in Russia. The compiled institutional model for ensuring the inclusiveness of universities in Russia reveals the legal aspect of this process, which consists of the regulatory and legal consolidation of different forms of inclusiveness in higher education and science and different requirements for universities, depending on their target forms of inclusiveness. The chapter contributes to the development of scientific provisions of the Theory of Inclusiveness of Universities through the disclosure of the legal aspect of managing the inclusiveness of universities (using the strategic academic leadership program “Priority 2030” in Russia as an example) and through clarifying the classification of universities by the criterion of approach to achieving inclusiveness with the separation of leading and regional universities. The theoretical significance of this chapter is that it has clarified the causal links of the achievement of inclusiveness by universities, showing how universities can and should meet today’s requirements for inclusiveness. The novelty of this chapter is that it substantiates the central role of regulators in ensuring the inclusiveness of public universities in developing countries. The practical significance of this chapter lies in the fact that the author’s conclusions have strengthened the scientific and methodological support of the “Priority 2030” program and can be used to accelerate and most effectively achieve Russia’s strategic academic leadership.
Low-loss polarization control in fiber systems for quantum computation

Optical quantum information processing requires low loss interference of quantum light. Also, when the interferometer is composed of optical fibers, degradation of interference visibility due to the finite polarization extinction ratio becomes a problem. Here we propose a low loss method to optimize interference visibility by controlling the polarizations to a crosspoint of two circular trajectories on the Poincaré sphere. Our method maximizes visibility with low optical loss by using fiber stretchers as polarization controllers on both paths of the interferometer. We also experimentally demonstrate our method, where the visibility was maintained basically above 99.9% for three hours using fiber stretchers with an optical loss of 0.02 dB (0.5%). Our method makes fiber systems promising for practical fault-tolerant optical quantum computers.