Reviewerly/exHarmony huggingface dataset:
Extractive Text Summarization for Social News using Hybrid Techniques in Opinion Mining

Presently almost all enterprises are oriented into building text data in abundance savoring the benefits of big data concept but the reality is that it’s not practically possible to go through all this data/documents for decision making because of the time constraint. Here in exists intense need of an approach as an alternative for the actual content which can summarize the complete textual content. By adopting these summarizing approaches, the accuracy in data retrieval of summarized content via search queries can be enhanced compared to performing search over the broad range of original textual content. There are many text summarization techniques formulated having their own pros and cons. The present work focuses on a comprehensive news review of extractive text summarization process methods and also taking into account, data appended dynamically. The existing work recommends a technique of hybrid text summarization that’s a blend of CRF (conditional random fields) and LSA (Latent Semantic Analysis) which being highly adhesive with low redundant summary and coherent and in-depth information. The above hybrid techniques is being extracted in five types that being: Positive and negative, statement, questions, suggestions and comments. The technique of LSA extracts hidden semantic structures within words/sentences that being commonly utilized in the process of summarization. The statistical modeling technique of CRF adopts ML (machine leaning) for offering structured detection and providing multiple options for evaluation of opinion summarization thereby identifying the most appropriate algorithm for news text summarizations considering the heavy volume of datasets.
Peculiarities of the correlation between professional well-being and organizational commitment of information technology specialists

&#x0D; &#x0D; &#x0D; The article analyzes the problem of the correlation between professional well-being and organizational commitment of specialists in the field of IT in Ukraine. The concept of "professional well-being" is defined as the psychological state of the employee associated with the efficiency and productivity of his work; the concept of "organizational commitment" as an emotional attachment, identification with the norms and values of the organization, acceptance of goals, excessive work in the interests of the company. The professional well-being was studied according to the method of "Professional well-being" K. Ruth (indicators: autonomy in professional activity, competence, professional growth, positive relations in the team, professional goals, acceptance of oneself as a professional, general indicator); and organizational commitment according to the method of "Professional demand for personality" O. Kharitonova (indicators: satisfaction with the realization of professional potential, belonging to the professional community, experiencing professional demand, professional competence, professional authority, evaluation of professional results, attitude to others, self- esteem and general indicator of personal demand in the professional plan) and the Questionnaire of J. Meyer and N. Allen "Scale of organizational commitment" (indicators: affective loyalty, normative loyalty, continuance loyalty). The correlation between professional well-being and organizational commitment has been empirically identified by correlation analysis. Loyalty scales have been found to be negatively correlated with professional well-being scales, suggesting that the more competent an IT professional considers himself, the less likely he is to become loyal to an organization. It is concluded that the structure of professional well-being and commitment to the company of IT employees has its own characteristics and requires more detailed further research.&#x0D; &#x0D; &#x0D;
Establishing a Research Experience for Teachers Site to Enhance Data Analytics Curriculum in Secondary STEM Education

This paper introduces the background and establishment of the first Research Experience for Teachers (RET) Site in Arkansas, supported by the National Science Foundation.The Arkansas Data Analytics Teacher Alliance (AR-DATA) program partners with school districts in the Northwest Arkansas region to promote research-driven high school analytics curriculum and education to reach underserved students, such as those from rural areas.At least thirty 9 th -12 th grade mathematics, computer science, and pre-engineering teachers will participate in AR-DATA and work with faculty mentors, graduate students, curriculum coaches, and industry experts in a six-week RET Summer Program and academic-year follow up to develop and disseminate learning modules to enhance current curriculum, attain new knowledge of data analytics and engineering applications, and benefit professionally through the RET program activities.The learning modules developed will reflect current cutting-edge analytics research, as well as the development needs of next-generation analytics workforce.
Special Issue on Emerging Trends and Challenges in Supervised Learning Tasks

With the massive growth of data-intensive applications, the machine learning field has gained widespread popularity [...]
Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based CloudSim toolkit framework.
Cloud Computing Application in Education

The aim of the study is doing the classification of research articles on Cloud computing adoption in education sector through meta-analysis based on number of articles in different geographical location, year of publication, types of methodology, frameworks used and research area covered in last 9 years. In total 143 research articles from 27 peer-reviewed journals from the year 2010 to 2018 were used for meta-analysis. The research findings from the meta-analysis show there is a very little study in the area of cloud computing application in education. The study contributes to the body of knowledge by identifying a classification method for research methodology types, geographical area, articles published in last nine years, types of research framework and research area through meta-analysis.
Development of Augmented Reality for Children Book Lutung Kasarung

Folklore needs to be preserved and developed to improve the nation's emotional intelligence, especially the emotional intelligence of elementary school students who are the nation's next generation. With current technological advances, it is possible to develop an ordinary folklore book into a more interesting folklore book with the addition of Augmented Reality technology in it. By using the System Development Life Cycle (SDLC) method, an Augmented Reality-based application can be created to be able to make Lutung Kasarung children's folklore books can be enjoyed by getting experience that is more than just reading ordinary physical books because there is Augmented Reality technology inside. This Augmented Reality technology has 100% accuracy if it meets the specified requirements, namely a distance of 45 centimeters and a height of 30 centimeters, and in testing at a distance of 50 centimeters, 55 centimeters, 60 centimeters, 65 centimeters with a height of 25 centimeters and 30 centimeters. In addition, with the AR Book, it is also expected to be able to introduce AR technology to both parents and children as an alternative to children's fairy tale books.
AN ONTOLOGY APPROACH TO THE SENTIMENT ANALYSIS OF SOFTWARE SYSTEMS

The article proposes an unorthodox approach to opinion mining for software systems (sentiment analysis) that can be in Russian, English or German languages. The main feature of this approach is an extraction of patterns represented by collocations consisting of one, two or more words and connected by common meaning or grammar. The article gives some examples of patterns in Russian in the area of computer games development. The determining lexical and semantic patterns are included in the ontology of the software system sentiment analysis described formally in the article.
Maze Solving by a Quantum Walk with Sinks and Self-Loops: Numerical Analysis

Maze-solving by natural phenomena is a symbolic result of the autonomous optimization induced by a natural system. We present a method for finding the shortest path on a maze consisting of a bipartite graph using a discrete-time quantum walk, which is a toy model of many kinds of quantum systems. By evolving the amplitude distribution according to the quantum walk on a kind of network with sinks, which is the exit of the amplitude, the amplitude distribution remains eternally on the paths between two self-loops indicating the start and the goal of the maze. We performed a numerical analysis of some simple cases and found that the shortest paths were detected by the chain of the maximum trapped densities in most cases of bipartite graphs. The counterintuitive dependence of the convergence steps on the size of the structure of the network was observed in some cases, implying that the asymmetry of the network accelerates or decelerates the convergence process. The relation between the amplitude remaining and distance of the path is also discussed briefly.
Pre-Processing Filter Reflecting Human Visual Perception to Improve Saliency Detection Performance

Salient object detection is a method of finding an object within an image that a person determines to be important and is expected to focus on. Various features are used to compute the visual saliency, and in general, the color and luminance of the scene are widely used among the spatial features. However, humans perceive the same color and luminance differently depending on the influence of the surrounding environment. As the human visual system (HVS) operates through a very complex mechanism, both neurobiological and psychological aspects must be considered for the accurate detection of salient objects. To reflect this characteristic in the saliency detection process, we have proposed two pre-processing methods to apply to the input image. First, we applied a bilateral filter to improve the segmentation results by smoothing the image so that only the overall context of the image remains while preserving the important borders of the image. Second, although the amount of light is the same, it can be perceived with a difference in the brightness owing to the influence of the surrounding environment. Therefore, we applied oriented difference-of-Gaussians (ODOG) and locally normalized ODOG (LODOG) filters that adjust the input image by predicting the brightness as perceived by humans. Experiments on five public benchmark datasets for which ground truth exists show that our proposed method further improves the performance of previous state-of-the-art methods.
EDUCATIONAL INNOVATION AS A SUBJECT OF STUDY BY UKRAINIAN AND FOREIGN SCHOLARS

World globalization processes require changes in all spheres of human life, especially in the education system. Total innovativeness becomes one of the main features of modern specialist's worldview. Therefore, the central project of the modern education is to educate an "innovative person", an individual with a high adaptive and innovative potential, with developed critical and divergent thinking, capable of non-standard solutions and active creative search. At the same time, modernization of education contributes to the emergence of new terminology and requires comprehension of innovative processes in pedagogical theory. Thus, the purpose of the article is to define the essence of the definition " educational innovation" in the studies of Ukrainian and foreign scientists, to find common and specific in the reasoning of researchers on this phenomenon. In addition, the article determines the prerequisites of the innovation process in Ukraine and the need to implement educational innovations in world practice. Summarizing the scientific views on the essence of the educational innovation defined that the research area of educational innovation are: the laws and regularities of all aspects of the educational innovation process, mechanisms of change management states of education and the relationship between past acquired teaching experience and the design of future educational innovation processes; reorganization of the educational process in particular and the impact on the development of the educational system as a whole; the process of updating the relationship of subjects of education in innovative cooperation; preparing young people for future professional innovative activities, etc. In addition, the relevance of the definition of " educational innovation" and the dynamism of the formation of this definition were established; the procedural nature of the essence, the presence of the personal factor of educational innovation and its value as a necessary component of pedagogical and social development were determined And also the article determines the main tasks of educational innovation as a science. It is proved that educational innovation, exercising direct influence on all participants of the innovation process, is a theoretical foundation for effective changes in the educational system in general
Developing Self-Regulation Skills Of Children In Mathematics Lessons

The study focuses on developing the self-regulation skills of high school students when learning mathematics.The introduction of distance learning brings an urgent focus on the development of students' self-direction, cognitive activity and motivation during the online lesson.The authors analyse the research in this area and identify a list of methodological techniques for developing this skill.The purpose of the study is to assess the effect of methods aimed at developing self-regulated learning, to make a qualitative analysis of students' knowledge and to develop recommendations for the organization of teachers' activities aimed at developing self-regulation skills.The study involved 264 students from secondary schools in Kokshetau, Republic of Kazakhstan.The authors conducted the experiment among 11th-grade high school students and covered the sections of algebra and geometry.The experiment used active methods to assess their impact on the development of self-regulation skills.Due to the organization of distance learning, the authors applied different types of digital technologies and online learning platforms during the experiment.The online lesson series assesses students' beginning, intermediate and final tests according to predetermined assessment criteria.The results of the study showed an increase in students' learning ability and self-study.At the same time, these results point to isolated issues in the organization of online mathematics teaching and highlight gaps in key aspects of learning.This allows the authors to develop the necessary recommendations for the organization of student learning that contribute to the development of self-regulation skills.
Global Context and Enhanced Feature Guided Residual Refinement Network for 3D Cardiovascular Image Segmentation

As an important pre-processing step in clinical applications, automatic and accurate 3D cardiovascular image segmentation has attracted more and more attention. However, cardiovascular structures are often with high diversity, blood pool and myocardium shapes are also with large variability, and ambiguous cardiac borders make the segmentation task very challenging. In this paper, a novel deep neural network to segment the blood pool and myocardium from three dimensional cardiovascular images is introduced by fully exploiting the global context and complementary information encoded in different feature extraction layers, referred to as GCEFG-R2Net briefly. In order to semantically locate the two kinds of regions in a global manner, we design a global context pooling module which can effectively learn context information in a global manner from the deep features extracted from the last two deep layers. Instead of directly using or combining different levels of deep features, we develop an interactive feature aggregation strategy to enhance different levels of deep features by embedding a series of interactive feature aggregation modules. By using the enhanced features, a residual feature refining branch is designed for refining the side outputs in a top-down stream with the guidance of global context features. Finally, the refined side outputs of different layers and the enhanced deep features are combined to generate the final segmentation result by using a feature fusion module. Extensive experiments on two challenge datasets are conducted to demonstrate that the proposed GCEFG-R2Net can obtain appealing segmentation results for the blood pool and myocardium and performs better than other state-of-the-art methods.
Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter

The development of information technology today has experienced very rapid growth. One of the developments in information technology, namely social media such as Twitter, Facebook, and Youtube, are some of the most popular communication media in today's society. Twitter is often used to express emotions about something, either praising or criticizing in the form of emotion. Human emotions can be categorized into five basic emotions, namely love, joy, sadness, anger, and fear. Twitter users' emotional tweets can be known as opinion or sentiment analysis (opinion analysis or sentiment analysis). Sentiment analysis is also carried out to see opinions or tendencies towards a problem or policy, whether they tend to have negative or positive opinions. The COVID-19 vaccine has become one of the discussions with a fairly high intensity on social media. Vaccine-related tweets have increased as government policies evolve. The responses of netizens also varied, ranging from clinical trials of vaccines, free vaccines, vaccine effectiveness, halal vaccines, to the implementation of vaccinations. This research produces a system that can analyze tweet sentiment related to the covid 19 vaccine in Indonesia where the tweet is obtained using the Twitter API. This system uses the Multinominal Naive Bayes method for the classification process.
STEM: Unsupervised STructural EMbedding for Stance Detection

Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion -- we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.
Research Activities In Professional And Personal Development Of A Teacher

The article discusses the actual problem of the research activity development of teachers caused by modern trends of social development and justified in scientific research, which is a condition for increasing the competitiveness of representatives of all professions.It is proved that only a teacher who has a high level of research activity can teach a person to independently formulate problems, search for new ideas and solutions, and instill self-education skills.The structural-functional analysis of professional and teaching activities allowed us to identify the scientific-research component in its composition, which assumes that pedagogical activity is of a research nature, if it is aimed at improving this activity, improving the quality of the results obtained.The process of professional and personal development of a teacher is a sequence of interrelated and successive time stages that begin with the emergence of professional intentions in the individual and end with the teacher's self-realization in professional work.Based on the theoretical research analysis, the development process correlation of research activities with the stages of professional and personal development of the teacher is carried out.The generalization of the existing experience made it possible for each of the seven identified stages of professional and personal development of the teacher to formulate the tasks and orientation of the teacher's research activities.The presented characteristics reveal the tendency of complication, improvement of research activity from stage to stage and prove its development in correlation with the stages of professional and personal development of the teacher.
Overview Of Cloud Storage

Cloud capacity administrations have gotten to be progressively well known. Since of the significance of security, numerous cloud capacity encryption plans have been proposed to ensure information from those who don't have get to. Cloud computing is one of the up and coming advances that will overhaul the era of the web. The information put away within the smartphones is expanded as more applications are sent and executed. The client can store information within the server and recover them at any time and from anyplace. This paper gives the consider on cloud storage. It incorporates the working of cloud storage, its sorts and architecture, characteristics, advantages and impediments.