Reviewerly/exHarmony huggingface dataset:
The Effect of Innovative Leadership and Creative Leadership to Organizational Learning, Organizational Adaptation and Adaptive Performance

An integrative model of the influence of innovative leadership and creative leadership on adaptive performance was developed and tested. Organizational learning, and organizational adaptation were treated as intermediary variables. To achieve the purpose of the research, the model was developed as indicated and analyzed using SEM with Smart-PLS. The results, suggest that the effect of innovative leadership on adaptive performance is positive and insignificant, this means that the better innovation leadership will tend to increase adaptive performance, even though the increase is not significant. But rest variables have direct effect positive and significant, as well as indirectly effect. Specifically, innovative leadership and creative leadership affect adaptive performance indirect through their effects on organizational learning, and organizational adaptation are positive and significant. In general, findings of the study support major findings of previous studies, extend and integrate previous models, in which they are discussed.
Image Retrieval by Soft Computing Technique and Visual Features

Digital platform-based services increase content on servers and retrieval of relevant information depends on data matching algorithms.Out of different type of data image plays a crucial role for various document proof, study, analysis, diagnosis.Hence retrieval of relevant image as per requirement is very important.This paper has proposed an image retrieval model which takes visual, text query as input and provide relevant images.Work has utilized soft computing genetic algorithm technique for the initial image storage in clusters.Genetic algorithm finds the cluster center images as per visual feature know as co-occurrence matrix and text query.Cluster center images cluster whole image dataset and act as filter to extract relevant image as per user query.Implementation of proposed work was done on Matlab and experiment was performed on real image dataset.Result shows that proposed model has increase the image relevancy as per user requirement.
Pragmatic Analysis Of Anger Expression Used Bysocial Media Users On Conflict Between America And Iran Found In Donald Trump's Facebook Comments

This study aims (1) to clarify the types of anger expression and (2) to elaborate the intention of anger expression which found in Donald Trump’s Facebook comments. This study belongs to descriptive qualitative research in which the data are collected through observation and documentation. The writer uses Tone Analyzer website to clarify the anger comments which showed by social media users especially Facebook users. There are 55 selected data which are analyzed by the writer. The writer uses the theory of Le Mouse (1998) to clarify the types of anger expression and use the theoty of Yule (1996) to elaborate the intention of anger expression which showed by social media users in Donald Trump’s Facebook comments. The results show that (1) there are 5 types of anger expression, they are 48% of verbal anger, 19% of constructive anger, 16% of judgmental anger, 10% of passive anger, and 7% of retaliatory anger. So, It ca be concluded that the social media users especially Facebook users prefer to use verbal anger to express their anger. And (2) there are 8 intention of anger expression, they are 18% of accusing, 18% of swearing, 15% of requesting, 15% of commanding, 15%of suggesting, 10% of threatening, 5% of mocking and 4% of blaming. It means that the social media users especially Facebook users prefer to accuse and swearing in express their anger.
MENINGKATKAN KEMAMPUAN SISWA BERPIDATO MELALUI MODEL SNOWBALL THROWING DI KELAS IV SDN 3 TILONGKABILA KABUPATEN BONEBOLANGO

The Snowball Throwing model can improve the ability of students to make speeches in Class IV SDN 3 Tilong Kabila Bone Bolango Regency. This study aims to improve the ability of students to make speeches through the Snowball Throwing Model in Class IV SDN 3 Tilong Kabila Bone Bolango Regency. This type of research uses action classes, by collecting data, tests, observations and documentation. Based on the results of classroom action research that is before conducting research, researchers conducted initial observations by interviewing Class IV teachers that found in the learning process, especially in the speech material, students were still shy in speaking and did not dare to appear in front of the class. the first cycle of action from 21 students 4 students or 19% Able (M), 9 students or 43% Poor (KM), 8 students or 38% Able (TM). Cycle II there are 21 students 17 students or 81% Able (M), 4 students or 19% less able (KM). based on the results of research conducted it can be concluded that through the snowball throwing model can improve the ability of students to make speeches grade IV SDN 3 Tilong k abila Bone Bola ngo Regency .
Application of Clustering and Quantum Teaching Methods as Efforts to Improve Elementary School Students Writing Ability

The purpose of this research is to provide clear information about what, why, and how to learn to write/write using the clustering method and quantum teaching approach. Writing learning activities with the clustering method and quantum teaching approach were carried out in grade V SDN 2 Lamokato Kolaka, starting from curriculum review, program preparation, program presentation, and evaluation. The results showed that: (a) improving the quality of the student learning process seen from the activeness of students in participating in the learning process can be an indicator of the success of learning assessment; (b) the results of the student learning outcomes that reached an average of 8.03, while in the assessment test the average score was only 5.26. Also, the results of statistical analysis using the t-test showed 21,889 results. While the t table price at α = 0.05 is 2.04. Thus, learning writing using the clustering method and quantum teaching approach in class VI SDN 2 Lamokato has significant results; and (c) learning to write/compose using the clustering method and quantum teaching approach is a learning model that can educate students' emotions, activate the right and left hemispheres of the brain, and be able to provide skills to face life in the future.
The Effect of Pictionary Game on the Students’ Writing Ability at the Tenth Grade of MAS Al-Hasyimiyah Tebing Tinggi.

The research was conducted to know the significant effect of Pictionarygame of students’ writing ability which was observed from the students at the tenthgrade of MAS Al-Hasyimiyah Tebing Tinggi. The research method of this paperwas quantitative research by using quasi-experimental research. The population ofthe research was the tenth grade of MAS Al-Hasyimiyah Tebing Tinggi. In takingthe sample, the researcher used saturated sampling which took all of the students inthe tenth grade. The two classes divided into experimental class and control class.There were 24 students in experimental class and 24 students in control class. Inexperimental class, the researcher taught by using Pictionary game, meanwhile thecontrol class that used lecture method. In experimental class that was taught byPictionary game, the increase of the mean score was 21.17 and in control class wastaught by lecture method, the increase of the mean score was 4.87. The calculationof T-test by using SPSS v.23 in post-test of both classes, the researcher found thatthe value of the Sig. score was 0.000. It means that the Sig. core was smaller than0.005 (0.000 < 0.05). It can be concluded that there were significant differencesbetween the two classes; experimental class and control class. It showed thatPictionary game gave the effect on students’ writing ability at the tenth grade ofMAS Al-Hasyimiyah Tebing Tinggi.
On the Explicit Role of Initialization on the Convergence and Generalization Properties of Overparametrized Linear Networks

Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. A promising direction to explain this phenomenon is the \emph{Neural Tangent Kernel} (NTK), which characterizes the implicit regularization effect of gradient flow/descent on infinitely wide neural networks with random initialization. However, a non-asymptotic analysis that connects generalization performance, initialization, and optimization for finite width networks remains elusive. In this paper, we present a novel analysis of overparametrized single-hidden layer linear networks, which formally connects initialization, optimization, and overparametrization with generalization performance. We exploit the fact that gradient flow preserves a certain matrix that characterizes the \emph{imbalance} of the network weights, to show that the squared loss converges exponentially at a rate that depends on the level of imbalance of the initialization. Such guarantees on the convergence rate allow us to show that large hidden layer width, together with (properly scaled) random initialization, implicitly constrains the dynamics of the network parameters to be close to a low-dimensional manifold. In turn, minimizing the loss over this manifold leads to solutions with good generalization, which correspond to the min-norm solution in the linear case. Finally, we derive a novel O(h−1/2) upper-bound on the operator norm distance between the trained network and the min-norm solution, where h is the hidden layer width.
KG-Agnostic Entity Linking Orchestration.

The domain of Entity Linking (EL) has been researched thoroughly, resulting in numerous approaches and a level of maturity in the field. In this dissertation, we aim at (1) creating an orchestrated, continuously-improving system through addition of approaches both existing and ones that are yet to come. On the same note, our framework shall (2) allow for Knowledge Graph (KG)- and potentially languageagnostic EL processes through cross-KG techniques, enabling further exploration of KG-dependence in the domain of EL. Finally, we will boost the ease of (3) reproducibility and (4) comparison between EL systems, their underlying techniques, as well as orchestration frameworks as a whole.
Egyptian literature funded by STDF: A Bibliometric Study

The study aimed to identify the characteristics of the Egyptian literature funded by STDF and to know the effect of the STDF funding on the quality of this literature. To achieve the objectives of the study, the researcher collected the Egyptian literature funded by the STDF that indexed in the WOS and SCOPUS databases during the period 2007-2019. The found 2,849 research papers. The study showed that the growth rate of research funded by STDF is 55.7%. Public universities are the largest contributor to literature, while the National Research Center is the most important research institution contributing to this literature. The Egypt-America Joint Fund for Science and Technology is the largest foreign funding agency that participated with STDF in funding Egyptian literature. The literature funded by STDF published in 1,313 scientific periodicals, published by 230 publishers, led by the Elsevier, 93% of these periodicals have an impact factor, co-authoring is the common type of authoring. International cooperation between Egyptian researchers and foreign researchers constitute of approximately half of the literature. STDF funds 106 topics and the chemistry is considered as the most frequently funded subject, followed by engineering, then pharmacy and pharmacology. The study showed a positive effect of STDF funding on the quality of Egyptian literature compared to unfunded Egyptian literature, either in the value of the impact factor of the periodicals in which it was published or in the average number of citations and the average number of using.
Supervised Sentiment Analysis Model of Textual Content for Images

Sentiment analysis is a domain in machine learning that tries to analyze people’s emotion, feeling, opinion and attitudes towards particular service or product. It aims to extract feelings and opinion from textual reviews; therefore, it is closely related to natural language processing (NLP). Social media has provided a huge amount of text reviews, which is practically impossible to read and analyze the emotions, attitudes and opinions that were expressed in those textual data. Sentiment analysis is a machine learning concept to classify a textual data according to reviewers’ emotion and attitudes about a service or product, which helps in determine strong or weak production. In this paper work we aim to develop a sentiment analysis model of texts for images. Different machine learning algorithms are tested such as Naive Bays, Logistic Regression and Support Vector Machine (SVM), in order to develop a high accuracy sentiment analysis system. The model is developed to determine whether a text has positive or negative emotion for images. The outcome of the project work shows that SVM algorithm has a better performance for such purpose, while Logistic Regression algorithm shows a faster execution time.
INFORMATION AND METHODOLOGICAL SUPPORT OF STUDENTS’ SELF-GOVERNING INDEPENDENT WORK

The author presents the results of scientific research on informational and educational support of students’ self-organizing independent work, which is complicated and comprehensive, because it includes sequential and consistent execution of large stages of educational and cognitive activity requiring creative approach to decision making. Starting point of this activity is assignment from the lead instructor to study and acquire given learning material. Having received the assignment, the student assesses the individual work effort required to complete it, sets time limits, draws up the schedule, manages the workplace, gets acquainted with information and training materials available and, if necessary, searches for additional required literature. After that, the student is to study educational theoretical material and perform practical exercises and tasks. Finally, he/she carries out self-control of acquired knowledge and skills. Assuming that acquired knowledge and skills do not fully comply with the fund of evaluation assets the student is to reexamine the learning material or correct the mistakes in the practical exercise. The next stage is to submit the accomplished work to the lead instructor. For holistic and figurative appreciation of this complex didactic process pedagogical science uses system modeling. In the present article the author introduces the functional model of students’ self-organizing independent work adapted to distance education. The model under consideration is of practical interest because it can be applied to forecast the process of knowledge acquisition in students’ self-organizing independent work and monitor the influence every link of this work has on the final result of independent work. In the long run, decision is made as to which educational technology proves the best. The efficiency of informational and educational support in the structure of the presented model has been confirmed by the results of the pilot study and final testing.
KEBERADAAN KEBUTUHAN EXISTENCE, RELATEDNESS, GROWTH, DALAM VIDEO GAME HARVEST MOON BACK TO NATURE

Playing video games is basically an activity where it must have an stimulus energy which is commonly referred to as motivation. Without motivation, player will not be interested in playing a video game with a long duration. The Harvest Moon Back to Nature video game as a video game that was quite successful, which made the video game a legendary video game for the audience's audience as early as 1997. It creates interest which in turn encourages researchers to know more about how motivation for human needs is presented by the Harvest Moon Back to Nature video game. This research fundamentally use interpretative analytical descriptive method with narrative approach. Using a view from ERG theory Alderfer, which according to him motivation is related to basic human needs which consist of existence needs, relatedness needs, and growth needs.. From the results of the research conducted using these instruments, it can be concluded that the power of motivation has been presented in the Harvest Moon Back to Nature video game from the fulfillment of existence needs, relatedness needs, and growth needs, both in the context events of farming, raising, and in carrying out other events.
Event Trigger Recognition Based on Positive and Negative Weight Computing and its Application

Event trigger recognition is a sub-task of event extraction, which is important for text classification, topic tracking and so on. In order to improve the effectiveness of using word features as a benchmark, a new event trigger recognition method based on positive and negative weight computing is proposed. Firstly, the associated word feature, the part-of-speech feature and the dependency feature are combined. Then, the combination of these three features with positive and negative weight computing is used to identify triggers. Finally, the text classification is carried out based on the event triggers. Findings from our experiments show that the application of our method achieves ideal results.
Innovative forms for certificating graduates of professional retraining programs: a comparative analysis of the performance

The demand of the industry and employees for training forms and the content of educational programs is being transformed. Upto-date and advanced knowledge is required, as well as flexible skills, and quick ways to master them. The request becomes particularly relevant in the case of professional retraining programs. The traditional format of final certification includes tools that simulate a locally limited situation of activity. In this way, it is difficult to identify the formation of competencies for a wider set of functional tasks. This difficulty can be overcome by introducing a new certification tool – a simulator that simulates the working day of a specialist during which he implements a wide list of functional responsibilities, fixed in the job regulations. The purpose of the article is to evaluate the results of using a professional activity simulator as a means of diagnosing the level of professional readiness. It contains evidence of the feasibility of switching to the use of the simulator for the final certification of HR specialists.
초‧중급 학습자를 위한 교재코퍼스 제작과 이를 활용한 수업 방안 제언

This paper tries to suggest the way of properly using different types of example sentences by the difficulty levels to teach a foreign language, through the development of pedagogic corpus for low-intermediate learners. A pedagogic corpus enables instructors and learners to set up their own learning goals and can collect and create their desired corpus materials. For this, the development of pedagogic corpus tried to focus on collecting different example sentences from a wide range of materials and subdivide types of materials and units or categories containing example sentences in order to allow learners to be exposed to vocabularies or grammar rules they want to learn in various contexts. Besides, different types of proposal of lesson plans using a pedagogic corpus have been made, in that conversation, grammar, and writing are integrated in a single lesson. By pursuing further research mentioned below, this paper will contribute to enhanced educational and academic effectiveness of the pedagogic corpus being currently developed. First, characteristics of example sentences in teaching materials published in Korea will be analyzed to find differences from the educational corpus used in China. Second, it will try to find a way to use the analysis of parts of speech and elements of sentence in each example sentence. The part-of-speech tagging facilitates the easy analysis of method of using words to make a sentence and the sentence structure. The grammar structure analyzed in this way can be the basic data for teaching and learning conversation, writing, and translation.
Fenomeno Speranza: la voce dura della banlieue casertana

Speranza, an Italian-French rapper, in recent years has made a name for himself within the newItalian dialectal rap scene. His music portrays life in the Campania province of Caserta with a style that is reminiscent of gangsta rap reworked to be both ironic and irreverent. This article analyzes a corpus of six songs released by the campanian artist on YouTube between 2017 and 2019. The description of a harsh reality, implemented through a particular verbal violence, is the main feature of his rap music, interesting both on a textual and musical level.
Sentiment Analysis of Social and Topic Context UsingMachine Learning Techniques

Sentiment Analysis could be a new area in research and is beneficial in many other fields. In thepresent time, a large amount of textual data is collected using surveys, comments, and reviews online. Allthe collected data are employed to enhance the items and services provided by both public and privateorganizations around the world. This Paper introduces a sentiment analysis of social and context reviews usingfeature-based opinion mining and supervised machine learning. The Sentiment Analysis techniques are to functionon a series of expressions for a given item that supported the product quality, and item features. Sentiment analysisis additionally called Opinion mining because of the significant volume of opinion. Analyzing customer opinionis extremely important to rate the items. To automate rate the opinions within the type of unstructured data is beena challenging problem today. Social context and topic context are combined by the Laplacian matrix of the graphbuilt by these contexts and Laplacian regularization is added into the microblog sentiment analysis model.Experimental results on two real Twitter data sets demonstrate that our proposed model can outperform baselinemethods consistently and significantly. Various topics beyond item reviews like online shopping, stock markets,elections, disasters, medicine, software engineering, and Cyberbullying extend the utilization of sentimentanalysis.
Pengembangan Perangkat Lunak Payment Remainder di Universitas Bina Darma

The development of technology has a very important role in all types of human activities such as work, play, and search for information that is currently up to date, like Telegram is a software that uses computer assistance and utilizes cellular technology that is integrated to distribute messages that are integrated through information systems through the media. Bina Darma University has a payment system of tuition in installments, but there are some students who forget to pay off the remaining payments. therefore, an application is needed that can remind students of their payments. To be able to run this software, a Telegram API is needed that has been synchronized with the Telegram Bot service, therefore a software that can send broadcase messages to all students of Bina Darma University through the Telegram application
SMIE: Weakness is Power!

Automatic indentation of source code is fundamentally a simple matter of parsing the code and then applying language- and style-specific rules about relative indentation of the various constructs. Yet, in practice, full parsing is not always an option, either because of quirks of the language, or because the code is temporarily syntactically incorrect, or because of an incomplete or broken grammar. I present the design of Emacs's Simple-Minded Indentation Engine (SMIE), which gets its power from the weakness of the underlying parsing technique. It makes it possible to perform local parsing, which is hence unaffected by irrelevant surrounding code. This provides a form of graceful degradation in the face of incomplete, erroneous, or just plain problematic information.