Reviewerly/exHarmony huggingface dataset:
Using Concordance to Decode the Ideological Weight of Lexis in Learning Narrative Literature: A Computational Approach

In learning narrative literature, students find difficulty in comprehending and approaching the ideological messages beyond the usage of recurrent lexical items in literary narrative texts.This problem comes as a result of the huge number of lexical items literary texts, particularly the narrative, abound in.The use of the computer and of computational linguistics work makes it possible to process and examine large data for a variety of purposes and to investigate questions that could not feasibly be answered if the analysis was carried manually.This paper, therefore, investigates the relevance of using concordance to decode the ideological weight of lexis in narrative literature.The main objective of the paper is to explore the extent to which certain ideologies and themes are decoded in literary narrative texts undergone a computational concordance analysis.This is conducted by means of a computational concordancing that is intended for providing two verifiable inputs: Frequency Distribution (FD) and Key Word in Context (KWIC).The paper is grounded on an experimental study, where 39 majoring English students attending one novel course at Prince Sattam bin Abdulaziz University, were voluntarily involved in an optional course addressing the study of a narrative literary text: Animal Farm.Participants were divided into two groups: an experimental group and a control group.The former was allowed to use concordance, whereas the latter was only permitted to use conventional methods of studying narrative texts (mere reading).Both groups are assigned to find out the themes and the ideological meanings inferred from a selected list of 13 words from the novel.Results show that the experimental group manages, by applying concordance, to decode the ideological weight of the selected words in a more accurate, credible and faster way than the control group.This in turn facilitates the process of determining the intended message addressed in the novel, either at the character-character level of discourse or at the author-readers one.
Classification of Maturity Level of the Mangosteen using the Convolutional Neural Network (CNN) Method

Mangosteen (Garcinia Mangostana L.) is a plant that grows in tropical area and originally from Indonesia which becomes the prime commodity of the national trade and has a very big export potential.Mangosteen is a climacteric food so it can be ripen in its storage period.Exported mangosteen needs to pass through the output process, the output process still uses the manual mode .the identification process that uses a person's eyes visually is still having some limits.The process needs more capacity to sort the fruit.With the developed technology nowadays the process itself can be done digitally.This research applied the Deep Learning with Convolution Neural Network (CNN) method which give the 97.1% accuration result.
Algorithm of detection, classification and gripping of occluded objects by CNN techniques and Haar classifiers

The following paper presents the development of an algorithm, in charge of detecting, classifying and grabbing occluded objects, using artificial intelligence techniques, machine vision for the recognition of the environment, an anthropomorphic manipulator for the manipulation of the elements. 5 types of tools were used for their detection and classification, where the user selects one of them, so that the program searches for it in the work environment and delivers it in a specific area, overcoming difficulties such as occlusions of up to 70%. These tools were classified using two CNN (convolutional neural network) type networks, a fast R-CNN (fast region-based CNN) for the detection and classification of occlusions, and a DAG-CNN (directed acyclic graph-CNN) for the classification tools. Furthermore, a Haar classifier was trained in order to compare its ability to recognize occlusions with respect to the fast R-CNN. Fast R-CNN and DAG-CNN achieved 70.9% and 96.2% accuracy, respectively, Haar classifiers with about 50% accuracy, and an accuracy of grip and delivery of occluded objects of 90% in the application, was achieved.
EMPLOYING DIGITAL LEARNING FOR FOSTERING INNOVATIVE CREATIVITY

Abstract – Digital learning is a current mode of education today, triggered by the rapid development of information and communication technology in the era of Industrial Revolution 4.0. This mode of learning has the capasity to develop the potential and abilities of students,  not only in knowledge but also in life skills, especially in terms of innovative creativity which is an important capital in the era of  21st Century. This article discusses the concept of digital learning,  readiness for usage, and its role in developing students' innovative creativity. Keywords – Digital learning, creativity, innovation  Abstrak – Pembelajaran digital merupakan modus pendidikan masa kini yang dipengaruhi oleh perkembangan pesat teknologi komunikasi dan informasi pada era Industrial Revolution 4.0. Modus ini mempunyai potensi untuk mengembangkan potensi dan kemampuan anak didik bukan saja dalam hal pengetahuan tetapi juga dalam life skills, khususnya dalam hal kreativitas inovatif yang menjadi modal penting dalam kehidupan era Abad 21. Artikel ini membahas tentang pengertian pembelajaran digital serta kesiapan untuk menggunakan, dan perannya dalam pengembangan kreativitas inovatif mahasiswa. Kata Kunci – Pembelajaran digital, kreativitas, inovasi
Role and Influence of Social Media on the PR Industry: Uses and Opportunities in UAE

The industry of public relations (PR) within the United Arab Emirates (UAE) is lagging corresponding to the advancements which are happening in the external world.In this context, social media has been regarded to be the primary catalyst in terms of pushing the PR industry of the UAE to cope with the continuous alterations of the external environment.The research initially included secondary research where qualitative data has been collected for drawing hypotheses regarding the impact of social media on the public relations industry of the UAE.The Mixed method of data collection has been done using face to face interviews and surveys based on questionnaires sets.Based on the findings, the MODE method has been implemented for finding out the central tendencies of the responses, and thematic analysis has been performed.It has been analyzed and concluded that social media does impact in a highly positive manner in the industry of public relations of the UAE.
A STUDYING OF WEB CONTENT MINING TOOLS

The web today has become an archive of information in any structure such content, sound, video, designs, and multimedia, with the progression of time overall web, the world wide web is now crowded with different data making extraction of virtual data burdensome process, web utilizes various information mining strategies to mine helpful information from page substance and web hyperlink. The fundamental employments of web content mining are to gather, sort out, classify, providing the best data accessible on the web for the client who needs to get it. The WCM tools are needful to examining some HTML reports, content and pictures at that point, the outcome is using by the web engine. This paper displays an overview of web mining categorization, web content technique and critical review and study of web content mining tools since (2011-2019) by building the table's a comparison of these instruments dependent on some important criteria
Resource Provision Method of Cloud Platform for Power Distribution System via Workload Prediction

With the development of smartness of power distribution system (PDS), PDS is confronted with multiple events and its operation state changes greatly and frequently. The consequent applications vary over time leading to dynamic workload, which challenges resource provision in cloud platform for PDS. In this paper, we propose a resource provision method for cloud platform in PDS via workload prediction. Starting from the causation of workload change, we forecast the operation state of DS by Markov Chain. We introduce the knowledge-based system (KBS) that contains logic relationship with operation states, events and applications, by which we predict the future workload under different operation states. We proposed a proactive dynamic computing resource provision model considering the predicted workload aiming at high-speed execution and economic operation in the cloud. The optimal solution is obtained from Pareto sets by technique for order preference by similarity to an ideal solution (TOPSIS). Results show that our proposed approach can not only improve the execution speed for DS application but also utilize the resources efficiently and economically.
The ACT-R Cognitive Architecture and Its pyactr Implementation

In this chapter, we introduce the ACT-R cognitive architecture and the Python3 implementation pyactr we use throughout the book. We end with a basic ACT-R model for subject-verb agreement.
CUSTOMER REVIEW ANALYSIS - MULTI-LABEL CLASSIFICATION AND SENTIMENT ANALYSIS

In this paper we focus on the hotel sectors and help them process these huge chunks of data in the form of customer reviews and help them derive useful information.The data pre-processing involves the scrapping of reviews from different sites and storing them and also check the correctness of the regular expression of the reviews.Our modelling employed includes three machine learning algorithms namely Naive Bayes, Support vector machine (svm) and Logistic regression.These three models improve the accuracy of the model as well as its robustness.The main idea of using these models are that the reviews are labelled so that the hotel management need not waste loads of time reading all the reviews.Instead the important reviews can be arranged based on their polarity and the important topic discussed in the review can be highlighted.So that it is easy for the management to analyse both the positive as well as the negative reviews.Sentiment polarity is incorporated to arrange the reviews based on the sentiment the review establishes.This paper helps the world to properly analyse the feedbacks and the reviews given by the customers.
The Use of Scramble Word Game to Develop Students Competence in Pronoun at MTsN 3 Sidoarjo

Students with different intellectual have limitations to specify to be with a subject in Pronoun.That is why they feel difficult to arrange the sentences.In this research, classroom action research is designed to develop students by implementing scramble word game.This research aims 1) to describe the scramble word game can develop students competence in Pronoun or not 2) to identify the situations when scramble word game implement in the class.This research used classroom action research was done in the students of seventh C grade of MTsN 3 Sidoarjo.The teacher implemented a scramble game method to develop students' competence in Pronoun.This research provided pre-test before treatment and post-test after the treatment.The researcher obtained the data through observation and documentation.The findings of this research There were 26 students got less score than 60 in cycle 1, and the number decreased to 10 students in cycle 2.Besides, there were four students got scores more than 60 in cycle 1, and it increased to 32 students in cycle 2.It increased on the number of students who achieved equal to or more than 60 as many as 28 students from pre-test or cycle 1 to post-test or cycle 2.It meant that the result had met the criteria of success.Thirty-two students got the expected score.If we compared to the result in pre-cycle 1, there was a difference.In cycle 2, there was an unexpected significant improvement.At the end of this research, it is expected to be implemented other researcher to achieve a better teaching and learning process.
Concepts of political leadership: рhilosophical-anthropological and socio-cultural measurements

The article reveals a conclusion that a significant number of approaches to understanding the phenomenon of a political leadership have been formed based on the presence of a significant number of studies on the problem of political leadership. They take into account a large number of factors that characterize the personality of a leader in the political, socio-economic, legal, religious and cultural spheres.The article analyzes the problem of political leadership in the works of such scientists as: N. Machiavelli, J. Dewey, F. Nietzsche, G. Storozheva, A. Priyatelchuk, S. Siegele, G. Lebon, G. Tard, Z. Freud, L. Edinger, Y. Jennings, S. Hook, M. Weber, T. Adorno, G. Marcuse, E. Fromm, K. Friedrich, G. Page, M. Samimi, B. Buyak, G. Grinlif, K. Hodgkinson, G. Fairholm, T., S. Kuchmarskikh, G. Hilmen, T. Fittssimen, A. Morozov, N. Gorbacheva, B. Bujak, A. Traverse, A. Pakhareva, N. Tkach and others.Despite the variety and variability of methodological approaches to understanding leadership, it is still necessary to conceptualize them.It is revealed that one of the most productive approaches to understanding the essence of political leadership is its interpretation based on value and socio-cultural orientations in the development of the individual and society, in general.It is clarified that the study of the category "personality"plays an important role within the socio-cultural approach to understanding of leadership. In addition, socio-cultural approach provides research on leadership through the prism of identifying individual, social communities and society as a whole in special forms of activity.The relevance of the socio-cultural approach is also explained by the fact that it allows the use of interdisciplinary research, opening a wide field for the possibility of developing a synthetic theory of political leadership.
Nature of Probability-Based Proof Number Search

Probability-based Proof Number Search (PPN-Search) is a best-first search algorithm that possesses a unique nature.It combines two kinds of information from a tree structure, namely, information from visited nodes and yet to be visited nodes.Information coming from visited nodes is determined based on winning status.On the other hand, information from yet to be visited (unexplored) nodes is determined by employing playout technique in leaf nodes.All of the information is combined into a value called probability-based proof number (PPN).In this paper, PPN-Search is employed to solve randomly generated Connect Four positions.Its results are compared to two other well-known best-first search algorithms, namely Proof Number Search and Monte-Carlo Proof Number Search.The limitation of PPN-Search related to the use of real numbers is identified based on the experiment.To increase the performance of PPN-Search while preserving its strength, an improvement technique using precision rate is introduced.Analysis from further experiments shows that the addition of the precision rate value accentuates the nature of PPN-Search, especially in its ability to combine information into PPN, which leads to increased performance.It is marked by reduced number of nodes needed to be explored up to 57% compared to implementation without precision rate.
A Kernelized Correlation Filter Tracker with Random Sampling Haar-Like Feature

Correlation filters tracking algorithm, one of the famous tracking algorithm, has been widely explored in visual tracking domain because of its high accuracy. In this work, we proposed a new tracking algorithm by learning effective correlation filter with random sampling Haar-like feature. In order to accurate use the color information of the object, the structured multi-channel Haar-like features are extracted from the transformed color space. Furthermore, the high-dimensional Haar-like feature are randomly projected into a low-dimensional subspace. Moreover, the non-Linear regression is used in kernelized correlation filter to obtain the object state in the next frame. Finally, extensive experiments on the OTB100 demonstrate that the proposed tracker shows excellent tracking performance.
Embedding Additional Natural Language Processing Tools into the TXM Platform

In our work we present a description of integration of natural language processing tools (pseudostem extraction, noun phrase extraction, verb government analysis) in order to extend analytic facilities of the TXM corpora analysis platform. The tools introduced in the paper are combined into a single software package providing TXM platform with an effective specialized corpora preparation tool for further analysis.
Some results related with type of family of sets

In this paper, we will discuss the concept of fuzzy set and its complement , and also we will study new types of fuzzy families such as the fuzzy -field ,fuzzy - -field ,fuzzy -field ,fuzzy - -field ,and fuzzy -field and the relationship between them
The Impact of Knowledge Management on the Marketing Performance of Small and Medium Enterprises in the State of Kuwait

The study aimed to measure the Impact of Knowledge Management on the Marketing Performance of Small and Medium Enterprises in the State of Kuwait. The population of the study comprised of the owners of small and medium enterprises in the State of Kuwait. An appropriate sample of 150 respondents was used and 124 valid questionnaires were retrieved for analysis. &#x0D; &#x0D; The study reached an effect of knowledge management has been found on the marketing performance in small and medium projects in the State of Kuwait. This reflects the importance of attention to diversifying the sources of obtaining knowledge from the environment surrounding the project in a manner that can be used to its advantage in order to improve the level of its marketing performance. The study also highlighted that the opinions of the study sample on the impact of knowledge management on the marketing performance in small and medium enterprises in the State of Kuwait do not differ according to their practical experiences and scientific qualifications.
Combining Effects in a Music Programming Language based on Patterns

HMusic is a domain specific language based on music patterns that can be used to write music and live coding. The main abstractions provided by the language are patterns and tracks. Code written in HMusic looks like patterns and multi-tracks available in music sequencers, drum machines and DAWs. HMusic provides primitives to design and combine patterns generating new patterns. The objective of this paper is to extend the original design of HMusic to allow effects on tracks. We describe new abstractions to add effects on individual tracks and in groups of tracks, and how they influence the combinators for track composition and multiplication. HMusic allows the live coding of music and, as it is embedded in the Haskell functional programming language, programmers can write functions to manipulate effects on the fly. The current implementation of the language is compiled into Sonic Pi [1], and we describe how the compiler’s back-end was modified to support the new abstractions for effects. HMusic can be and can be downloaded from [2].
Metadata Action Network Model for Cloud Based Development Environment

Cloud-based software development solutions (entitled as Platform-as-a-Service, Low-Code platforms) have been promoted as a game changing paradigm backed by model-driven architecture and supported by various cloud-based services. With the engagement of a sheer number of platform users (experienced, novel, or citizen developers) these platforms generate invaluable data and that can be considered as user metadata actions. As cloud-based development solutions provide novice users with a new development experience (performing data actions that altogether leads to a successful software app), users often times face with uncertainty about development performance; how good or complete is app development? Thus, the issue addressed in this research is how to measure user performance by using digital trace data generated on the cloud platform from a Network Science perspective. This research proposes a novel approach to leveraging digital trace data on Platform-as-a-Service (PaaS) from a Network Science perspective. The proposed approach considers the importance of digital trace data as metadata actions on PaaS and introduces a network model (so-called Metadata Action Network), which is claimed to be the result of reconstruction of events of developer’s actions. We show suitability of the proposed approach to better understanding of real-world digital trace data on PaaS solution and elaborate basic performance analytics on a PaaS solution with research and practical implications.
Library Management in the Implementation of School Literacy Movement in Primary Schools

The role of library as the heart of school has not been fully understood by the school community.The lack of skill in information literacy among the librarians and teachers become the main consideration for this research.The aim of this research is to observe the library management in supporting the Gerakan Literasi Sekolah/GLS (School Literacy Movement).The benefit of the research results is to make the school management able to make a technical effort to improve the information literacy in terms of library management by creating a program for the improvement of collaboration of teachers and librarians for achieving an optimal output of GLS in stimulating the reading interest among students.This research was conducted in Magelang Regency with 5 primary schools as the research subjects, on balance, representing the characterics of: type of primary schools (private school and state school), being equipped with library, implementing the GLS program.The instruments used in this research included observation, interview and documentation and the method in data processing used the data reduction, data presentation and drawing conclusions.Meanwhile, the data validity used the data triangulation.The results of this research showed that first the synergy of library roles with the literacy movement was pragmatic meaning to move and distribute the books in each class through the Reading Corner activitity; second, the Guidelines for SNP library management had been fulfilled by the school as the administrative obligation; third, awareness of information literacy for teachers and principal in library management was categorized into the low level and fourth, the comprehension of the implementation of technical instructions of GLS by the school communities were still limited only in the phase 1.
Novel Survey on Power and Latency aware Cloudlet Selection Strategy in Multi-Cloudlet Atmosphere

Nowadays the cell cloud computing is a promising place in the field of studies. And the interest in using cellular cloud computing(MCC) for compute-intensive cellular devices jobs consisting of multimedia applications has been growing and these applications requires high computing supply in both the processing and communication and execution of these applications within the cloud impacts the quality of service, latency delay and power consumption so the cloudlets are introduced. Cloudlets are nothing but a set of computers located at the threshold of the internet and provides the same services as a cloud. The cloudlets can be implemented incorporation as a corporate cloudlet, home cloudlet and users can share their resources. As we know that cellular devices are unable to process all the users request so the request is offloaded to optimum cloudlet and receive results later. There are many approaches have been discussed by authors for improvement of cellular devices by minimizing the power consumption and response time. Here we propose a survey paper which gives the brief introduction about cloudlet and its architecture and features. It also discusses about computation offloading process which means offloading the execution to other rich systems with better performance .computational offloading frameworks and their contributions and the critical issues which exists in MCC. The case study about various proposed schemas used for reducing the power and latency.