Reviewerly/exHarmony huggingface dataset:
Prediction Model of Grain Mold Probability Based on Naive Bayes Algorithm

Grain is by far the largest amount of food stored in the world.The early prediction of grain mold (GM) is mainly to be able to detect it in time before its significant growth activity to avoid causing loss of stored grain.Microorganisms are tiny and not visible to the naked eye in the early stage, if microbial colonies can be observed in the grain pile, these parts of the grain often already have serious deterioration, mold metabolism is more hazardous to produce mycotoxins, which has a more adverse impact on the safe storage of grain, in fact, as long as there is the growth of microorganisms such as mold in the grain, even if the colonies of mold are not visible to the naked eye, the grain quality will have significant changes.In order to ensure the quality of grain output, the best way is to prevent GM.In this regard, this paper suggests a model for predicting the probability of GM based on the Naive Bayes(NB) algorithm, cultivating GM samples, and comparing the prediction errors of these three models with the PSO-LSSVM model and ARIMA model to predict the probability of mold in samples, we know that the prediction error(PE) of the NB model is the smallest, which means that the difference between the predicted value of GM and the real value of the model is small, and the accuracy of using this model for prediction is more reliable.The accuracy of the model is more reliable.
The Role of Learning Climate on Self-Efficacy Beliefs and Self- Perceived Communication Competences of EFL Learners in Higher Education

This study explored the role of learning climate on self-efficacy beliefs and self-perceived communication competences of EFL learners in higher education. The learning climate perceptions, self-efficacy beliefs, and self-perceived communication competence levels of EFL students were found to be moderate. There was not a significant difference among students' learning climate, self-efficacy, and self-perceived communication points in terms of their sex. There was a difference between 1st Grade and 2nd Grade students for learning climate, and there was not a significant difference among students' self-efficacy and self-perceived communication points in terms of their grade. A significant relationship among students' learning climate and self-efficacy and self-perceived communication competence in a positive way was found. While learning climate was found to be a lower predictor of self-efficacy and self-perceived communication competence, self-efficacy, and self-perceived communication competence were found to be stronger predictors of each other.
A publicação independente brasileira no Século XXI: O caso da Editora Elefante

Considering the Brazilian independent market, this paper investigates Elefante, a publishing house founded in 2011 by Tadeu Breda and Bianca Oliveira, with a production comprising emergent socio-political subject matters.Focusing on the time frame of 2011-2021, we characterize Elefante and its publications, highlighting symbolic values linked to them, underlining the context it passes through as well as exploring relations between founding partners and the scene, their intentions, and the field they aim to operate in.The research method includes literature on book matters, independent publishing, and a approach in Microhistory; data gathering via semi-structured interview with Breda and Oliveira, emblematic examples of Elefante's trajectory; and data analysis that conclude on the influence of graphical language and editorial line to the estimation of Elefante's publications, form and content on the book's signification process, particularities of the publisher, and the perspective of its agents on the definition of an independent publisher.
IDENTIFIKASI CENDAWAN PADA BENIH JAGUNG (Zea mays L.) DAN BIJI KAKAO (Theobroma cacao) DENGAN METODE BLOTTER TEST

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A Convergence Study on the Influence Factors of Virtual Tourism on Users" Travel Intention in the Field

At the height of COVID-19, the movement of people was severely restricted, and field travel activities were greatly affected. During this period, cloud tourism and virtual tourism have developed rapidly. Whether virtual tourism can facilitate or even replace the development of onsite tourism in the post-COIVD-19 era has become a popular research topic. This study takes virtual tourism users as the research object, adopts structural equation modeling based on the theory of planned behavior, and introduces two variables, virtual reality tourism experience and the Effect of COVID-19, to investigate the factors influencing the transformation of virtual reality tourism experience to field tourism behavior. The factors influencing the transformation of the virtual reality tourism experience to field travel behavior are studied. This study collected data from 373 valid respondents through a questionnaire. SmartPLS 4.0.0 validated the causal model, and most of the study hypotheses were supported. The results of this study show that: first, virtual reality tourism experience, perceptual behavioral control, and subjective norm can significantly affect the attitude of virtual tourism users, but only virtual reality tourism experience and virtual tourism users" attitude can directly and positively affect field travel intention in the field. First, virtual reality tourism experience, perceptual behavioral control, and subjective norm can significantly affect the attitude of virtual tourism users experience, and virtual tourism users" attitudes can directly and positively influence field travel intention in the field. Second, the virtual reality tourism experience is the most important factor facilitating the transformation of travel intention in the field. Third, the virtual reality tourist attitude has almost no mediating effect in converting the virtual reality tourism experience into travel intention in the field. Third, the Effect of COVID-19 can significantly affect virtual reality tourist attitudes but cannot promote the conversion of virtual tourism users to field travel behavioral intention. The study results help government departments and tourism companies use virtual tourism to enhance tourists" travel intention, thus recovering tourism in the post-COVID-19 era and developing related policies and services.
Automatic Head Count Based on Machine Learning in Intelligent Video Surveillance

With the wide application of video surveillance system, visual information has become the key research element of modern security technology.Computer vision related technology can be applied to the field of intelligent surveillance, so that computers can process video.People can use computers to understand video surveillance, directly get the number of people in an area, or get the distribution of people.This paper first analyzes the existing two types of target detection algorithms, and chooses Fast R-CNN algorithm as the research object of this paper.This paper combines the research method of background modeling with the research method of deep convolutional neural network based on statistical learning to fuse all the calibration boxes of pedestrian detection results.A pedestrian count evaluation method is proposed, and the pedestrian count results are smoothed and fused.
Teachers’ Strategies in Teaching English to Students With Special Needs

This research aims to know the teachers’ strategy in teaching English to students with special needs according to their type. The research was designed as a systematic literature review study. The researcher used PRISMA Model for collecting the data. There were 16 research articles that matched the criteria. The data were analyzed through a narrative method. The results of the analysis uncovered various strategies used by teachers. Those are paper braille for reading paper strategies, JAWS App for reading e-text, preview strategy and small group for strategy to obtain the content of instruction, TPR, realia and modeled talk for strategy to obtain the language development, communication games and dictogloss for strategy to encourage active involvement, teacher-mediated, routine-based and specific naturalistic strategies, explained the subject matter, flashcards, pictures, visuals, modifying classroom environment, requested students to always carry the dictionaries, alphabetic signs, patted the students' back to get their attention, repetition strategy, gave examples, song, gave reward, two languages, co-teaching strategy, IEP, differentiated instruction, created oral language comprehension, echolalia, the PECS method to improve communication abilities, direct method, lexical method, SIOP Model, instructional scaffolding, comprehensive support input, posters, real objects, modeling strategies, innovative teaching methods.
PERBANDINGAN KOMBINASI FUNGSI PELATIHAN JARINGAN SYARAF TIRUAN BACKPROPAGATION PADA PERAMALAN BEBAN

Electricity system planning is very important for electricity providers (PLN). One of them is electricity load forecasting. Backpropagation artificial neural network is one of the best methods used in electricity load forecasting because it can give high accuracy values. In application, backpropagation neural networks often provide poor convergence speed values during the training process. Therefore, it is necessary to do various combinations of training functions to accelerate the convergence of network training. In this study, a backpropagation neural network model was developed with a combination of gradient descent training functions (traingdm, traingda, traingdx). The architecture of this network model uses 24 inputs, 1 hidden layer consisting of 16 neurons and 1 output. This model uses peak load data from Pemecutan Kelod Substation and the number of kWh sold in the South Bali area as an input variable. The results show that the best model of the neural network is using the traingdx training function. In this model, the MSE training is 1.03x10-8 and with a training convergence speed is 4 seconds and MAPE testing is 6.24% with a network accuracy is 93.75%.
Explainability for Natural Language Processing

Natural language processing tasks such as text classification and sentiment analysis can be explained using explainable AI libraries such as SHAP and ELI5. The objective of explaining the text classification tasks or sentiment analysis tasks is to let the user know how a decision was made. The predictions are generated using a supervised learning model for unstructured text data. The input is a text sentence or many sentences or phrases, and we train a machine learning model to perform text classification such as customer review classification, feedback classification, newsgroup classification, etc. In this chapter, we will be using explainable libraries to explain the predictions or classifications.
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models.

A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories (SDMs), and more recently the modern continuous Hopfield networks (MCHNs), which possess close links with self-attention in machine learning. In this paper, we propose a general framework for understanding the operation of such memory networks as a sequence of three operations: similarity, separation, and projection. We derive all these memory models as instances of our general framework with differing similarity and separation functions. We extend the mathematical framework of Krotov & Hopfield (2020) to express general associative memory models using neural network dynamics with local computation, and derive a general energy function that is a Lyapunov function of the dynamics. Finally, using our framework, we empirically investigate the capacity of using different similarity functions for these associative memory models, beyond the dot product similarity measure, and demonstrate empirically that Euclidean or Manhattan distance similarity metrics perform substantially better in practice on many tasks, enabling a more robust retrieval and higher memory capacity than existing models.
Using evidence from systematic studies to guide a PhD research in Requirements Engineering: an experience report

Conducting systematic studies during a postgraduate program, such as systematic review, systematic mapping, and tertiary review, can benefit the project’s success. They provide an overview of the literature considering currently available research findings, establish baselines for other research activities, and support decisions made throughout the research project. However, there is a shortage of research that presents systematic studies experiences in supporting academic projects. This paper’s main contribution is reporting our experience on how the evidence found in tertiary and secondary studies positively influenced a PhD project’s decisions. Initially, a tertiary study was conducted, followed by a systematic mapping. The evidence returned by the tertiary study led to the definition of the PhD research proposal in the Requirement Engineering field. Moreover, a systematic mapping contributed to the definition of the PhD research problem. From this experience in undertaking systematic studies to support a PhD project, the paper also presents lessons learned and recommendations to guide PhD students’ decisions.
PENGEMBANGAN PENGINTEGRASIAN PENDIDIKAN LALU LINTAS DALAM MATA PELAJARAN PKN MELALUI MEDIA PEMBELAJARAN BERBASIS WEB UNTUK MENINGKATKAN KESADARAN HUKUM PESERTA DIDIK DI SMPN 5 BANDUNG

Teenage drivers are famous for unsafe and disorderly driving behavior, and a minimal level of awareness in traffic. In thiscase the researcher wants; 1) knowing the learning conditions of traffic education integration materials in Civic Educationsubjects at SMP Negeri 5 Bandung, 2) knowing how to develop web-based learning media for conceptual education of traffic education materials in Civics subjects, 3) knowing how to implement learning-based media web in conceptual integration of traffic education material in Civics subjects, 4) knowing the increase of students' legal awareness after theapplication of web-based learning model in the material of integrating traffic education in Civics subjects. Somantri (Wuryan and Syaifullah, 2009.76) interpreted Civics as a selection, adaptation from cross-disciplines of social sciences, citizenship, humanities, technology, religion, basic human activities organized and presented psychologically andscientifically. to participate in achieving one of the objectives of social science education and national education goals. The approach used by researchers is the Research and Development (R &amp; D) approach. By using data collectiontechniques, including observation, interviews, questionnaires, literature studies, documentation studies. The population used is the population of SMPN 5 Bandung. The research samples were: class VII-A, class VII-B, class VII-C, class VII-D, and class VII-E. Analysis of his research uses qualitative analysis and quantitative analysis. The results showed that; 1) the condition of learning the integration of traffic education in Civics subjects is still conservative, especially in theuse and use of learning media, resulting in students being less interested in learning material for integrating traffic education in Civics subjects, 2) producing web-based learning media that in accordance with the purpose of its manufacture, the stages that must be carried out by the media developer are starting from the preproduction, productionand post-production stages. To be able to run this web-based learning media does not require special skills in learning, only basic computer skills are needed for teachers and students because this web-based learning media usescomputer media and internet connection, 3)web-based learning media contributes significantly to increasing legal awareness student traffic in learning material integrates traffic education in Civics subjects and 4)contributes significantly to improving student learning outcomes.
401. Using a U-net architecture to develop an automated ultrasound images analysis method in a rabbit breeding program

Since 2014, HYCOLE has been using in vivo saddle by ultrasound measurements to estimate the animals’ carcass yield. The saddle’s area measurement was done manually by specialist operators and was time consuming. HYCOLE therefore searched a method to automatically determine the saddle area and its measurement. Deep learning, using a U-net architecture model, was the appropriate answer to this issue. The model was trained in two phases with 4,124 images over 4 years with an associated mask and then, a correlation validation phase was made with a set of 1,410 which were not used in the training phase and also measured manually by a specialist operator. Correlations between the automatically measured criterion (S23auto) and the manually measured criterion (S23ref) were calculated: the rank correlations with the selection criterion are between 0.80 and 0.94 and between 0.86 and 0.96 for the rank correlations between the areas. A genetic evaluation was carried out to validate the trait and automatic measure gave more accurate estimation with higher heritability. S23auto is validated to use in the genetic program and will be add as a data in the « operator » variable from the carcass yield genetic evaluation.
THE FEATURES OF TEACHER PROFESSIONAL TRAINING IN FINLAND

An overview of the distinctive features of teacher training in Finland, which has had high international student assessment scores for more than 20 years, is presented. The authors adapted the interdisciplinary method of PEST analysis to identify the political, economic, and social aspects of teacher training that enable students of Finnish schools to get high scores in PISA. Several generalized categories of the national education system in Finland are highlighted for analysis in the research. They are the main stages or levels of education, the features of educational policy and standardization of education, the methods of teaching, the content of education, the goals and values of teacher training and the development of a teacher’s personality. The following features of teacher training at Finnish universities have been identified: orientation towards alternative educational policy in contrast to unifying global education reforms; decentralization of planning, content and focus of the curricula in teacher training as a reflection of university autonomy; the significant prevalence of practical and research components over theoretical training of pre-service teachers. The main goal of teacher training is stated by universities and the professional expert community in general; it is supported by the parliament and is defined as training a reflective responsible teacher-researcher capable of making independent educational decisions within the framework of different models and principles of teaching.
Proof of identity - a blockchain consensus algorithm to create a dynamically permissioned blockchain

Blockchains can be either permissioned or permissionless. Existing blockchain consensus algorithms used to create permissioned blockchains to not allow new validators to be dynamically added to the blockchain during runtime. This can be a problem when blockchain is used in swarm robotics, where redundancy is required. Besides, popular algorithms such as proof of work (PoW) that offer redundancy consume a lot of resources, making them unsuitable to run on the powerless robots used in swarm robotics. In addition, such algorithms are also vulnerable to threats such as the 51% attack. This research proposes a novel proof-of-identity (PoI) blockchain consensus algorithm to create a dynamically permissioned blockchain to be used in swarm robotics. This algorithm uses a swarm controller to generate a private-public key pair that is used to authorise validators. Tests carried out showed that the PoI algorithm is immune to the 51% attack threat and can run on low-powered devices such as Raspberry Pi 3. Moreover, the consensus time was also shown to be shorter in comparison to PoW.
Analysis of instantaneous, daily and monthly statistics 2017-2021 of rooftop solar energy

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.Cover :Leatherback (a), green (b - adult &amp; f - hatchling), hawksbill (c) and olive ridley (d) turtles who nest in Sri Lankan beaches, green turtle crawl marks (e) and by-catch of sea turtles in fisheries (c &amp; d)Photo credits: SJ Perera (a &amp; c), WPN Perera (b &amp; e), K Ekaratne (d &amp; f)
Reimagining Section 230 and Content Moderation: Regulating Incivility on Anonymous Digital Platforms

This article draws from surveys about the experiences of users with Yik Yak and other anonymous social media platforms to consider how digital intermediaries encourage civility and minimize hateful speech in their communities—given the protections from liability that Section 230 of the Communications Decency Act (CDA) affords them. It then turns to debates over Section 230 of the Communications Decency Act by congressional and Federal Communications Commission (FCC) leadership from 2017 and 2021. In so doing, it considers how hoped-for changes from both the Republican and Democratic parties may conflict with the First Amendment in their proposals to reform Section 230. The article argues that human content moderation is particularly important to maintain as part of the regulatory process on digital platforms given (1) the disconnected discourse emanating from the left and the right about what kind of online expression is problematic, and (2) the parallel disconnected discourse among users of anonymous platforms where online expression wields the least amount of individual accountability.