Reviewerly/exHarmony huggingface dataset:
Comparative Analysis between the Conditions Associated to Traditional teaching and those Associated to Online teaching

Throughout history, society has faced different kinds of problems in all ways; from wars, poverty, climate change, natural phenomena up to mortal diseases. These afflictions have managed to get in people&#x2019;s lives in a devastating way causing a great number of deaths due to pandemics such as the COVID-19, which has been, until the present days, an overwhelming enemy for almost all the countries in the world. This pandemic has forced society to adapt to a different lifestyle in order to face the virus and be alert and protected in any environment. In the education system, teachers were forced to move from traditional education to online education which involves the use of technologies at all times. This action has contributed to teachers and students&#x2019; physical and mental burnout causing mental or physical problems. This article will present a comparative analysis of the physical and psychosocial conditions that appeared during the traditional teaching and the online teaching in a group of teachers at the Universidad tecnol&#x00F3;gica de Torre&#x00F3;n, as well as the repercussions in their lives and possible solution alternatives for their conditions.
Analysis and Comparison of the Structure and Performance of Local Neural Networks

The paper synthesizes the local neural networks. Network structures and their activation functions of three local networks CMAC, B-spline, RBF that are often used to approach functions are analyzed and compared in detail. The network structure of ART-2 is also discussed. Based on the fuzzy system of these local networks, the paper depicts their fuzzy structures and performances. The study and analysis in the paper are useful to instruct to select and design the local neural networks.
“DADI-DADI DOGEN”AS IDEOLOGY IN THE DEVELOPMENT OF ELEMENTARY SCHOOL LIBRARY IN THE CITY OF SINGARAJA, BULELENG, BALI

The Law of the Republic of Indonesia Number 43 Year 2007 concerning Libraries states that schools are required to have school libraries based on the National Standards of Library (SNP). This condition forces every school to develop a library, even though the conditions are still far from the standard set. To realize the existence of library, each school uses various resources in its school to establish a school library. This study aims to explore the efforts of primary schools in Singaraja City to fulfill their obligations in providing library space. This study uses a qualitative approach based on critical social theory commonly used in the Cultural Studies approach. The data collection was carried out by in-depth interviews, involved observation and document study. Data analysis in this study follows Peter Berger's model with the following steps: conceptualization, conceptualization results, verification, and objectivation. The results show that the obligation to have a school library at the primary school level is not easy due to various obstacles. This gave rise to the ideology of permissivism or in Balinese it is called dadi dadi dogen. This ideology allows schools to use various types of spaces to become libraries, for example classrooms, teacher rooms, warehouses, UKS, and so on.&#x0D; Keywords: elementary school libraries, dadi dadi dogen, permissivism, ideology
Pengembangan Sistem Career Development Center

Alumnuses have a role in the college progress. one of the roles of alumnuses is that alumnuses provide input to college for mapping job market. Alumnuses inputs are obtained by college through tracer study at college career center. The Career Development Center (CDC) of the Cilacap State Polytechnic (PNC) conducts a tracer study process by sending a form link via short message to the group of alumnuses. The problem with this method is that the tracer study form can be filled out by everyone who has the link. On the other hand, the officer must re-enter the same list of questions for alumnuses in different years. This study aims to create a Career Development Center application that can help PNCs to monitor the distribution of alumnuses and help CDC disseminate job vacancy information. The method used in software development uses the waterfall method. At the software testing stage, the method used is blackbox testing. Based on the results of customer feedback, 30 respondents produced a very good predicate. The conclusion of this study, the development of the Career Development Center application can help spread alumni and help CDC disseminate job vacancies information to alumni.
Hand Gesture Video Identification using Spatial and Temporal Convolutional

Hand gestures can be used for indirect interaction. The hand movement in the video causes the hand position of each frame to move. Position shift can be information in the identification of hand movements. However, video identification is not easy; it requires comprehensive feature detection in each part of the frame and identifies connectivity patterns between each frame. The use of architecture in recognizing each characteristic pattern in each frame can affect the identification results due to the number and arrangement of layers in extracting features in each frame. Previous research has identified video hand movements used Convolutional Neural Networks (CNN) with the Single-Stream Spatial CNN method to identifying hand movement patterns but ignoring the relationship between frames. In other research, the Single-Stream Temporal CNN was used to identify hand movements used two frames to connected the relationship between frames at a specific time. This research proposed the Two-Stream CNN method, namely spatial and temporal. Spatial to get the pattern on the frame as a whole. Temporal to obtain information on the relationship between the frame and Optical Flow used the Gunnar Farneback method by looking for light transfer points in pixels in all parts of the frame between two interconnected frames. Two different architectures and optimizers were used, namely VGG16 and Xception, for the architecture, while the optimizer were SGD and AdaDelta. The proposed method used two architectures, namely VGG16 and Xception. Besides, the optimizer model used SGD and AdaDelta. As a result, the Xception architecture with the SGD optimizer model provided a higher accuracy of 98.68%.
A survey of unsupervised learning in medical image registration

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.
FPGA Realization of Image Encryption System with Chaotic Attractors: Two-Wings and Jerk

This article carries out the FPGA implementation of an information encryption system using chaos. First, the discrete numerical methods of Improved Euler and Runge-Kutta of the fourth order are used to solve the differential equations that describe two chaotic oscillator models: Two-Wings and Jerk. Subsequently, the performance and use of logical resources are compared in order to find the numerical method that is most efficient due to the limited amount of resources that an FPGA has. The contributions of this work are a simulator and a modular image encryption system with encrypted-decrypted correlation coefficients of less than 1 and a low consumption of logical resources.
Model Variasi Kalender pada Regresi Runtun Waktu untuk Peramalan Jumlah Pengunjung Grojogan Sewu

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Grojogan Sewu visitors experience a significant increase during school holidays, year-end holidays, and also Eid al-Fitr holidays. The determination of Eid Al-Fitr uses the Hijriyah calendar so that the occurrence of Eid al-Fitr will progress 10 days when viewed from the Gregorian calendar, this causes calendar variations. The objective of this paper is to apply a calendar variation model based on time series regression and SARIMA models for forecasting the number of visitors in Grojogan Sewu. The data are Grojogan Sewu visitors from January 2009 until December 2019. The results show that time series regression with calendar variation yields a better forecast compared to the SARIMA model. It can be seen from the value of root mean square error (&lt;em&gt;RMSE&lt;/em&gt;) out-sample of time series regression with calendar variation is less than of SARIMA model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;Calendar variation, time series regression, SARIMA, Grojogan Sewu&lt;/p&gt;
A Framework for Automated Detection of Ground-Motion Signals on Seismic and Infrasound Arrays using Computer Vision, Machine Learning, and Sensor Fusion

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Seismological Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]A Framework for Automated Detection of Ground-Motion Signals on Seismic and Infrasound Arrays using Computer Vision, Machine Learning, and Sensor FusionAuthorsXinxiangZhangiDStephenArrowsmithSotiriosTsongasChrisHaywardSee all authors Xinxiang ZhangiDCorresponding Author• Submitting AuthorSouthern Methodist UniversityiDhttps://orcid.org/0000-0002-9749-3336view email addressThe email was not providedcopy email addressStephen ArrowsmithSouthern Methodist Universityview email addressThe email was not providedcopy email addressSotirios TsongasSouthern Methodist Universityview email addressThe email was not providedcopy email addressChris HaywardSouthern Methodist Universityview email addressThe email was not providedcopy email address
IMPROVING STUDENTS’ WRITING SKILL OF RECOUNT TEXT THROUGH DIARY WRITING AT EIGHTH GRADE STUDENTS OF MTS NEGERI 2 PALU

This research with a quantitative approach aims to determine whether the use of diary writing strategy is effective to improve writing skill in recount text of eighth grade students at MTS Negeri 2 Palu. The population in this research were students at MTS Negeri 2 Palu and the sample was students in grades VIII A and B with a total sample of 70 students. However, due to the pandemic ccovid-19with all the limitation, the researcher only obtained 24 students as the samples who took the test in this research. In this research the researcher used a purposive sampling technique. The data collection technique using test, namely pre-test and post- test. The technique of data analys is that the researcher used is the t-test. The results of the data analysis showed that t-count is and t-table (t-count10,632 &gt; t-table1,796). It means the results of the research showed that there is improvement in the eighth grade students’ writing skill in recount text after the implementation of Diary writing strategy at MTS Negeri 2 Palu.
Online Course as a Means of Mastering Professional Communicative Skills of Future Engineers

The urgent need for e-learning is on the increase nowadays. It has been caused by rapid digital economy and digital education development, COVID-19 pandemic challenges, and requirements of the Master&#x0027;s students, which were revealed in a preliminary study among 282 learners majoring in engineering. However, the academic curricular of most university educational programmes are not flexible enough to provide students with completely online university education. To balance the situation, the authors designed the online course for Masters of Engineering Science as a part of blended learning method to improve communicative competence and skills in their professional field, which is available on the educational platform of South Ural State University. The article analyzes the results of the implementation of career guidance content&#x0027;s course and a set of tasks to train theoretical and practical aspects of the major using the features of English/German for specific purposes discipline and e-learning.
A genetic blockchain approach for securing smart vehicles in quantum era

Connecting the sensible vehicles in a blockchain framework is sufficient to secularize the conveyance location and commuter-related information until the arrival of quantum computers. Almost all of the modernized vehicles have onboard processors and inbuilt Internet of Things devices that can be used to execute the blockchain architecture for the intellectualization of the conveyances without the need of any auxiliary equipment. Furthermore, the execution of blockchain courses of action is entirely wise and does not require extra hardware. Securitization of smart vehicles is done by incorporating the rental cars in the blockchain framework and performing a genetic approach on the same. Blockchain connectivity would intellectualize the vehicles, by sharing the road images and location of the vehicles on the blockchain, hence reducing accidents. Transport industry intellectualization calls for real-time traffic and travel data, and constant data contributes altogether to the development of populace portability.
Environmental Roles and Benefits in Indonesian Language Learning

Environmental problems are very diverse and complex so that many environmental problems have failed to be resolved, this is not a new problem.
Using Mapper Graphs to Reveal Morphological Relationships in Passiflora Leaves

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing an older version [v1]Go to new versionUsing Mapper Graphs to Reveal Morphological Relationships in Passiflora LeavesAuthorsSarahPercivaliDBerondaMontgomery L.iDDaniel H.ChitwoodArjunKrishnaniDErik J.AmézquitaSourabhPalandeElizabethMunchiDAmanHusbandsSee all authors Sarah PercivaliDCorresponding Author• Submitting AuthorMichigan State UniversityiDhttps://orcid.org/0000-0003-1024-4618view email addressThe email was not providedcopy email addressBeronda Montgomery L.iDMichigan State UniversityiDhttps://orcid.org/0000-0003-2953-0956view email addressThe email was not providedcopy email addressDaniel H. ChitwoodMichigan State Universityview email addressThe email was not providedcopy email addressArjun KrishnaniDMichigan State UniversityiDhttps://orcid.org/0000-0002-7980-4110view email addressThe email was not providedcopy email addressErik J. AmézquitaMichigan State Universityview email addressThe email was not providedcopy email addressSourabh PalandeMichigan State Universityview email addressThe email was not providedcopy email addressElizabeth MunchiDMichigan State UniversityiDhttps://orcid.org/0000-0002-9459-9493view email addressThe email was not providedcopy email addressAman HusbandsThe Ohio State Universityview email addressThe email was not providedcopy email address
Fifth Third Bank launches community revitalization program

Nonprofit Business AdvisorVolume 2021, Issue 387 p. 12-12 News Fifth Third Bank launches community revitalization program First published: 10 November 2021 https://doi.org/10.1002/nba.31136Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2021, Issue387December 2021Pages 12-12 RelatedInformation
Prior latent distribution comparison for the RNN Variational Autoencoder in low-resource language modeling

Probabilistic Bayesian methods are widely used in the machine learning domain. Variational Autoencoder (VAE) is a common architecture for solving the Language Modeling task in a self-supervised way. VAE consists of a concept of latent variables inside the model. Latent variables are described as a random variable that is fit by the data. Up to now, in the majority of cases, latent variables are considered normally distributed. The normal distribution is a well-known distribution that can be easily included in any pipeline. Moreover, the normal distribution is a good choice when the Central Limit Theorem (CLT) holds. It makes it effective when one is working with i.i.d. (independent and identically distributed) random variables. However, the conditions of CLT in Natural Language Processing are not easy to check. So, the choice of distribution family is unclear in the domain. This paper studies the priors selection impact of continuous distributions in the Low-Resource Language Modeling task with VAE. The experiment shows that there is a statistical difference between the different priors in the encoder-decoder architecture. We showed that family distribution hyperparameter is important in the Low-Resource Language Modeling task and should be considered for the model training.
Sentiment analysis on covid-19 vaccine using Twitter data: A NLP approach

Sentiment analysis is the process of mining the perception of people towards a service, product, policy or imminent issue from textual data. In this project, tweets relevant to Covid-19 Vaccine are extracted utilizing the Tweepy library. Next, tweet texts are converted into usable form in order to do sentiment analysis. After this, SentiWordNet lexicon is used to label the sentiment of the tweets. Stop words removal, Lemmatizing, stemming operations are also applied on the COVID-19 Vaccine tweets text data. Count Vectorizer and Tfidf Vectorizer are applied for mathematical conversion of the preprocessed text. Then, nine classification techniques namely - Multinomial-NB, Bernoulli-NB, Logistic-Regression, Ridge Classifier, Passive-Aggressive-Classifier, Perceptron, Random Forest classifier, AdaBoostClassifier and Linear SVM are applied on the dataset obtained for sentiment classification and results are obtained in terms of accuracy. The best cross validation test score obtained is 0.785 with Logistic Regression Classifier and TfidfVectorizer.
REVEALING PAST IDENTITY OF UPCYCLED PRODUCTS: HOW SIMPLE NARRATIVE IMPROVE PRODUCT PERCEPTION

Manuscript type: Research Article&nbsp; Research Aims: Telling a story about the past identity of a product is believed to help improve the positive perception towards the product. Certain product has gone through transformation from its past identity to new and more valuable product, known as upcycled product. This study aimed to investigate whether a simple narrative about repurposed products can generate higher perception of product biography, product appeal, felt specialness, and purchase intention of consumers.&nbsp;&nbsp; Design/methodology/approach: To test the hypotheses, a two-group between subject experiment involving 103 university students was conducted . Two upcycled products (pouch and bag) with simple narrative were used as stimulus.&nbsp; Research Findings: The result revealed that people who are aware of the past identity of the product perceived that the product has a story and feel more special, unique, and recognized if they use the product. Contrary to the previous research, this study found no difference in perception about product appeal and purchase intention between two groups.&nbsp;&nbsp; Theoretical Contribution/Originality: This research contribute in filling the gap of the impact of storytelling with simple narrative toward upcycled products especialy in Indonesia. Practitioner/Policy Implication: This study could provide some insights to business owners and marketers that building a story about the product or around the product can help build positive attitude toward the product and a simple or minimal narrative can function well. Research limitation/Implications: This research however has some limitations such as the variety of participants which limited to university students. Furthermore, this research did not take into account the prior knowledge and awareness of participants about repurposed products.
Assessing Optimal Forests of Decision Trees

The interest in algorithms for learning optimal decision trees (ODTs) has increased significantly in recent years. These algorithms use combinatorial search to find a predictive machine learning model in the form of a tree. It was shown that ODTs can obtain better predictive performance than trees found using traditional, heuristic algorithms. In many applications where accuracy is important, however, in practice machine learning developers use forests of decision trees instead of single trees. The most popular approaches for learning forests, such as Adaboost, are heuristic in nature, and it is not clear where their good performance comes from. In an attempt to study this, a number of earlier papers developed approaches that rely on the definition of a global optimization criterion for learning forests; however, unfortunately, these papers did not present algorithms for optimizing these criteria exactly, hence leaving the value of the optimization criteria unclear. In this work, we show that by using recent techniques for learning exact ODTs, we can now also optimize forests exactly. We show the optimization gap with existing approaches and evaluate the performance of optimal decision forests using different optimization criteria compared to Adaboost. This reveals that optimal decision forests can be a good approach depending on the optimization criterion and in some cases can outperform Adaboost.
Computer intelligent dynamic simulation and optimization for structural platform of water treatment subsystem on the spacecraft

Abstract In order to reduce the load response condition of space-borne equipment, an optimized design scheme of carbon fiber composites sandwich panel was proposed in this paper. The state before and after optimization was analyzed and compared trough simulation calculation. The comparison shows that after the honeycomb sandwich structure was optimized, its fundamental frequency increased by 75%, the drawing force of embedded parts decreased by 50%, and the maximum acceleration response also decreased. Finally, the response under sinusoidal vibration was evaluated by test, and the results show that the optimization of the structure is reasonable and feasible, it can be the reference of other similar products.
Game Controllers

To say the least, Apple has had a long and complicated history with gaming on its platforms. When the iPhone first began to gain traction and the world was handed the first native SDK, gaming was clearly not a priority. Since that time and demonstrated by the technologies this very book is written about, Apple has come a long way. iOS Game Controllers were first announced at WWDC 2013 as part of iOS 7. Focusing on the screen while not being able to feel where gaming controls were proved difficult. Beginning with iOS 15, Apple would introduce new SDKs to handle on-screen Game Controllers.