Reviewerly/exHarmony huggingface dataset:
Emotion extraction from text: Analysis of four different algorithms

Emotions play an important role in human life. In today’s internet world textual data has been proven as a main medium of communication in human-human and human-machine interaction. This increase in use of text for communication makes it a need to find the emotion present in textual data. This makes it important to provide a framework that can recognize the emotions present in the communication or the emotions of the users. Emotion recognition or sentiment analysis is a natural language processing task that mine information from textual data like twitter tweets, blogs, movie reviews and reviews from online websites and classify on the basis of polarity. Emotion Recognition Model extracts emotion from text at the sentence level. Our method uses 4 different deep learning algorithm and detects emotion from a text-input. To make the recognition more accurate, emotion-affect-bearing words and phrases were also analyzed. This experiment shows that the method could generate a good result for emotion detection from text input.
How students write: A linguistic analysis. LauraLouise AullNew York, Modern Language Association of America, 2020. 206 pp. US$ 34.00, ISBN: 9781603294522

International Journal of Applied LinguisticsEarly View BOOK REVIEW How students write: A linguistic analysis. Laura Louise Aull New York, Modern Language Association of America, 2020. 206 pp. US$ 34.00, ISBN: 9781603294522 Nergis Danis, Corresponding Author ndanis@iastate.edu orcid.org/0000-0002-2746-1283 Iowa State University, Ames, Iowa, USA ndanis@iastate.eduSearch for more papers by this author Nergis Danis, Corresponding Author ndanis@iastate.edu orcid.org/0000-0002-2746-1283 Iowa State University, Ames, Iowa, USA ndanis@iastate.eduSearch for more papers by this author First published: 04 May 2021 https://doi.org/10.1111/ijal.12351Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
PENGARUH MODEL PEMBELAJARAN SUGESTI IMAJINASI TERHADAP KEMAMPUAN MENULIS PUISI SISWA KELAS X SMA NEGERI 48 JAKARTA

This study aims to determine the effect of learning outcomes in writing poetry using imagination suggestion learning models in class X students of SMA 48 Jakarta in the second semester of the academic year 2018-2019. The research method used is a quantitative research method with a research design Pretest-Posttest Control Group Design. The sample used is Simple Random Sampling. In the experimental class obtained Lcount = 0.13 1.67 = Ttable at a significant level of 0.05 turns out Tcount> Ttable then H0 is rejected and H1 is accepted. That is, there is a significant influence by using the imagination suggestion learning model on the ability to write poetry of class X students of SMA 48 Jakarta
Spectral Quantum Tomography

We introduce spectral quantum tomography, a simple method to extract the eigenvalues of a noisy few-qubit gate, represented by a trace-preserving superoperator, in a SPAM-resistant fashion, using low resources in terms of gate sequence length. The eigenvalues provide detailed gate information, supplementary to known gate-quality measures such as the gate fidelity, and can be used as a gate diagnostic tool. We apply our method to one- and two-qubit gates on two different superconducting systems available in the cloud, namely the QuTech Quantum Infinity and the IBM Quantum Experience. We discuss how cross-talk, leakage and non-Markovian errors affect the eigenvalue data.
4차 산업혁명시대에 대응하는 친환경디자인 STEAM 교육프로그램 시행연구

The purpose of this study is to enhance the minds of youths to protect the global environment through the use of digital technology and UCC campaigns and contribute to the development of human resources. Therefore, the lessons of lecture learning were reduced, the use of digital tools, the production of UCC and the campaign activities were carried out and the results is as follows. First, by making a UCC that explains the project execution process and application techniques, it has increased the experience of digital technology, and led to the creation and sharing of contents. Second, by promoting campaign activities and UCC, it contributed to raising environmental awareness among local residents and human worldwide as well as program participants.
Application of Google Analytics Model for Evaluating the Visibility of Library Web Portals of the Uva Wellassa University, Sri Lanka

Google Analytics is the most prevalent page tagging technique to evaluate the visibility of web portals. The study was designed with the purpose of applying google analytics model on the library Web Portals (WP) of the Uva Wellassa University (UWU) which includes, Home Page (HP), Online Public Access Catalogue (OPAC) and Institutional Repository (IR). It was observed that the web resources of UWU have been viewed by ~366756 local &amp; global users during the study period. The USA is the predominant country visited the UWU e. Repository with the count of 53,078 (15.82%) followed by Netherlands with 14,044 (4.78%) and France 15,775 (4.70%).
建置整合管理系統一以認證機構印尼工業認證協會 (BSI) 為例

This study integrated four management systems in the body certification, such as Product certification, Quality Management System certification, Environmental Management System certification, and Food Safety Management System certification to be one conceptual model (Integrated Management System).We implemented it in one of the certification bodies in Indonesia, the Institute for Industrial Certification, Ministry of Industry, and created a new model implementation and business process model using BPMN.The conceptual model and the implementation model are well verified and validated where the results are better than the separate management system and current management system
Deep Contrastive Explanation

Author(s): Feghahati, Seyedamirhossein | Advisor(s): Shelton, Christian | Abstract: I propose a method which can visually explain the classification decision of deep neural networks(DNNs). Many methods have been proposed in machine learning and computer vision seeking to clarify the decision of machine learning black boxes, specifically DNNs. All of these methods try to gain insight into why the network “chose class A” as an answer. Humans search for explanations by asking two types of questions. The first question is, “Why did you choose this answer?” The second question asks, “Why did you not choose answer B over A?” The previously proposed methods are not able to provide the latter directly or efficiently. I introduce a method capable of answering the second question both directly and efficiently. In general, the proposed method generates explanations in the input space of any model capable of efficient evaluation and gradient evaluation. It neither requires any knowledge of the underlying classifier nor uses heuristics in its explanation generation, and it is computationally fast to evaluate. I provide extensive experimental results on three different datasets, showing the robustness of my approach and its superiority for gaining insight into the inner representations of machine learning models. As an example, I demonstrate my method can detect and explain how a network trained to recognize hair color actually detects eye color, whereas other methods cannot find this bias in the trained classifier. I provide the details on how this framework can be applied to discrete data as well. I proposed a SoftEmbedding function to be employed in conjunction with the discrete embedding function. I show results on textual reviews demonstrating my method’s ability to find bias in learned classifiers. Finally, I provide the results of a user study, measuring how much this feedback helps users to improve their understanding of the network’s learned function in comparison with other possible method
DIGITAL IMAGE ENHANCEMENT USING MULTIPLE MOTION ANALYSIS

Objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a multitude of choices for improving the visual quality of images. Appropriate choice of such techniques is greatly influenced by the imaging modality, task at hand and viewing conditions. This project will provide an overview of underlying concepts, along with algorithms commonly used for image enhancement. The paper focuses on spatial domain techniques for image enhancement, with particular reference to point processing methods and histogram processing.
TO STUDY COMPUTATIONAL PSYCHOMETRICS ANALYSIS OF EDUCATIONAL BEHAVIOUR IN LEARNERS

Learning is a many-faceted process or phenomenon. Learning processes or phenomenon include theacquisition of new calculative or declarative knowledge, the development of motor and cognitiveskills through instruction or practice, the organization of new knowledgeinto general, effective representations, and the discovery of new facts andtheories through observation and experimentation. The Elo system helpful in educational systems when we interpretlearner's response. The success of current technology enhanced learning canbe increased by tailoring the content and the learning resourcesfor every learner; thus, optimizing the learning process. The calculated ratings are used further in the teachingprocess as a recommendation of coding exercises that try tomatch the user’s current knowledge.From a psychometric point of view, assessment means to infer what alearner knows and can do in the real world from limited evidence observed in a standardizedtesting situation. From a learning analytics perspective assessment means toobserve real behavior in digital learning environments to conclude the learner statuswith the intent to positively influence the learning process. Although psychometricsand learning analytics share similar goals, for instance, formative assessment,while applying different methods and theories, the two disciplines are so far highlyseparated
IMPLEMENTATION OF WEB-BASED ARDUINO MULTIFUNCTION STUDY CARD SYSTEM IN SMK KARE

This study conducted data collection activities by directly observing Kare State Vocational School and conducting interviews with related staff in terms of student attendance and violation points aimed at observing the old system that was already running. The results of the study provide information that designing an automatic attendance application using RFID is a school requirement, where the application can do attendance recap and student offense points automatically. In making this application programming is done by using the programming languages HTML, PHP, Javascript, laravel framework and js node framework. By using this application it is hoped that later it will simplify the attendance system and student violation points at school.
Sensibilidad del docente como pedagogo: caso de Héctor Abad Gómez

Pedagogy is understood as a field of knowledge that guides the study processes on educational media, models and practices. In this sense, the pedagogue is recognized as the subject that beyond accrediting an institutional degree in pedagogy, generates social reflections about teaching and learning practices, with which knowledge is socialized and dynamizes for social benefit. Methodologically the present paper acquired a heterodox position on traditional pedagogical discourses; The research is qualitative taking Hector Abad Gomez as a case study, to demonstrate the sensitivity on educational and social issues that the pedagogue affects when his concerns transcended his specific field of study, and considers the context, the territory and its singularities in his reflective process and generator of alternative forms of teaching and learning. The document’s findings, discussions, and contributions aim to advance and strengthen critical lines of research that show pedagogical work as a socially sensitive act that goes beyond the institutional structure of the educational establishment.
The Effect of Hands-On Minds-On Learning on Reducing Kindergarteners' Foreign Language Learning Anxiety

AbstractWhile research has mainly focused on foreign language learning anxiety, its causes and effects on the learning process and the ways to alleviate it among learners, it has rarely focused on foreign language learning anxiety among kindergarteners. Thus, this study aims at qualitatively investigating the effect of hands-on minds-on learning on reducing kindergarteners’ foreign language learning anxiety. The sample group consisted of 21 experts and teachers of English as a foreign language in kindergarten. Interviews were conducted to collect qualitative data. Results revealed that hands-on minds-on learning can reduce foreign language learning anxiety among kindergarteners, and that much more research attention should be given to foreign language learning anxiety and to the use of hands-on minds-on learning in classes of teaching English as a foreign language in kindergarten.
Cybertime

Adversaries are advantaged in many ways when conducting cyberattacks. These advantages are often the result of the posture of the digital health defenders, in particular, the ability, or lack of the ability, of defenders to meet attacks in the time frame in which they evolve. In this chapter, the components of a cyberattack are explored, as are organizational approaches to reduce the response time to attacks through risk management. Defensive and resilience strategies that negate threat speed and optimize time are described. Organizations are directed toward engaging in organizational risk management; in part, they are directed to prioritize protection of critical data. Additionally, IT best practices and workforce development are promoted as part of the health clinic of the future.
하이는 어떻게 한글을 배웠나 -재외동포 아동의 한글교실 경험에 관한 이해-

The purpose of this study is to analyze the experience of overseas Korean preschoolers in a Korean language class and to understand their experiences through qualitative research methods. The researcher analyzed the specific process and experiences of Hai by participating in this Korean language class in other countries. Based on this, the researcher was able to examine the difficulties of the Korean language learning class and suggests solutions on how to overcome them. This study also provides basic materials of curriculum design for Korean language classrooms. To carry out this study, the researcher collected relevant basic data from March, and observed and recorded the Korean classroom classes from May to November 2019. The results of analyzing the experience of an overseas Korean child Hai are as follows: First, Hai showed a tendency to distinguish between “being” and “missing” in the process of Korean learning while using a method of learning in his native language scheme, and showed conflict with aspects such as “accepting” or “revealing” the Korean language located between the second language and the heritage language. Although he had a strange view and attitude with the “unfamiliar” way of learning Korean as a foreign language, he gradually showed a tendency to overcome it from “inherent intimacy”. Based on these findings, a vivid understanding and ongoing exploration of the Korean learning experience of overseas Korean children should be made in the future, and practical implications should be obtained in the design of the related curriculum. (Sollute Corp.)
Sistem Informasi Pengolahan Data Penjualan Dan Peramalan Pembelian Setiap Pelanggan Dengan Metode Trend Projection Berbasis Webs

PT. Aldora Sukses Perkasa is an ice cream distributor located on Jalan Martadinata Tanjungpinang. PT. Aldora Sukses Perkasa usually only makes sales by salesmen by visiting shops that are already customers. The problems faced by the company are the absence of an online sales system so that it can make it easier for customers to order the items needed and the customer purchase forecasting system that can predict customer purchases in the next period. With the development of “SISTEM INFORMASI PENGOLAHAN DATA PENJUALAN DAN PERAMALAN PEMBELIAN SETIAP PELANGGAN DENGAN METODE TREND PROJECTION BERBASIS WEBSITE PADA PT. ALDORA SUKSES PERKASA TANJUNGPINANG“ this very helpful in processing data, information on inventory, sales reporting and reporting of inventory data
Biophysical Modeling of the Relative Relationships between the Brain and the “Hypothetical Secondary Brains”

When we place humans at the peak of the phylogeny and evolution of the living world, we primarily rely on the extraordinary development of their nervous system and brain. The development of the human brain is far from complete, and its immense capacity is not yet exhausted. In particular, new, more modern learning methods are needed to make brain performance more effective by learning about and taking greater account of age-specific characteristics. Learning methods can only be used in certain cases. They cannot replace, but merely complement the acquisition of knowledge, practice, problem and task solving based on experience or observation. Quantitative growth alone is mainly characteristic of those parts of the brain that are called called the hypothetical secondary brain. These consist of constant (stable) topograms and functiograms born with us. During a person’s life, they barely change, these provide the centers of the reflexes we are born with and the neural activities associated with them. Memorization is closely related to the formation of unstable topograms and their persistence over time. The significant difference occurs in qualitative growth, which is not characteristic of hypothetical secondary brains, while in the central nervous system it plays a decisive role. Qualitative growth creates the elements and conditions of psychic regulation in humans.
Fault Detection of Induction Motors Using Just In Time Classifiers

Induction motors (IMs) are efficient and reliable but may fail like any other machinery. Failure to detect IM faults in a timely manner will result in irreparable damage. In the real world, data collected from IMs is due to a change in status (load change, speed change, etc.), which can lead to concept drift (CD). In this work, stator data is collected under dynamic conditions from a setup IM in the laboratory. We use Just In Time (JIT) classifiers to detect stator fault in the non-stationary environments, which can be adapted to CD and increase classification accuracy, and the Intersection of Confidence Interval-based change detection tests is used to detect the CD. This mechanism is extended using a preprocessing method for multi-dimensional data. To evaluate this method, several scenarios have been used to simulate CD in the forms of abrupt, gradual, recurrent concepts and the appearance of a new class.
Penerapan Sistem Kearsipan Dalam Meningkatkan Kelancaran Proses Administrasi Kerja Pegawai Pada Bagian SDM Dan Administrasi Di PT PLN (Persero) UP3 Samarinda

Archiving activities are important for a company or agency, because archiving itself has a function as a source of memory, a source of information, and material for making decisions, for that, archiving needs to be managed and handled properly and appropriately. The focus of the problem in this research is how the application of filing systems in the human resources and administration at PT. PLN (Persero) UP3 Samarinda. The purpose of this study was to determine the Application of Filing Systems in the HR and Administration section of PT. PLN (Persero) UP3 Samarinda. The data collection techniques used by the writer are observation and literature study. While the analysis used is descriptive analysis, which describes an object at a time. The results of this study indicate that the archival management system in the HR and Administration section at PT. PLN (Persero) UP3 Samarinda uses a subject system and a chronological system. However, in terms of management and storage, it is still lacking, because when looking for an archive it must take quite a long time. In addition, the problem that the author experienced was that the information in the ordner was not written in the year corresponding to the year in the letter. The ordner storage is also not neatly ordered. It is recommended that the company provide special training for employees regarding archiving in order to pay more attention to the existing archives at PT. PLN (Persero) UP3 Samarinda, starting from its creation to its destruction.
A Study of the Mathematics of Deep Learning

Deep Learning/Deep Neural Nets is a technological marvel that is now increasingly deployed at the cutting-edge of artificial intelligence tasks. This dramatic success of deep learning in the last few years has been hinged on an enormous amount of heuristics and it has turned out to be a serious mathematical challenge to be able to rigorously explain them. In this thesis, submitted to the Department of Applied Mathematics and Statistics, Johns Hopkins University we take several steps towards building strong theoretical foundations for these new paradigms of deep-learning. In chapter 2 we show new circuit complexity theorems for deep neural functions and prove classification theorems about these function spaces which in turn lead to exact algorithms for empirical risk minimization for depth 2 ReLU nets. We also motivate a measure of complexity of neural functions to constructively establish the existence of high-complexity neural functions. In chapter 3 we give the first algorithm which can train a ReLU gate in the realizable setting in linear time in an almost distribution free set up. In chapter 4 we give rigorous proofs towards explaining the phenomenon of autoencoders being able to do sparse-coding. In chapter 5 we give the first-of-its-kind proofs of convergence for stochastic and deterministic versions of the widely used adaptive gradient deep-learning algorithms, RMSProp and ADAM. This chapter also includes a detailed empirical study on autoencoders of the hyper-parameter values at which modern algorithms have a significant advantage over classical acceleration based methods. In the last chapter 6 we give new and improved PAC-Bayesian bounds for the risk of stochastic neural nets. This chapter also includes an experimental investigation revealing new geometric properties of the paths in weight space that are traced out by the net during the training.