Reviewerly/exHarmony huggingface dataset:
유아 교사의 자아탄력성과 자기조절력이 유아 문제행동지도 전략에 미치는 영향

This study sought to determine how ego-resilience and self-regulation affect the problem behavior guidance strategy of early childhood teachers. A sample of 217 early childhood teachers participated in this study. We analyzed the data using SPSS 24.0 and SPSS Macro, and the results were as follows: First, both ego-resilience and self-regulation were positively related to a positive proactive strategy and a positive reactive strategy but were not associated with a negative reactive strategy. Second, ego-resilience and self-regulation had significant influences on the positive proactive strategy. Third, ego-resilience and self-regulation also had substantial impacts on the positive reactive strategy. Based on these results, we have discussed how early childhood teachers could utilize the behavior guidance strategies more promptly and positively.
An Unsupervised Bayesian Neural Network for Truth Discovery

The problem of estimating event truths from conflicting agent opinions is investigated. An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations. A Bayesian network model is proposed to guide the learning of the autoencoder by modeling the dependence of agent reliabilities corresponding to different data samples. At the same time, it also models the social relationships between agents in the network. The proposed approach is unsupervised and is applicable when ground truth labels of events are unavailable. A variational inference method is used to jointly estimate the hidden variables in the Bayesian network and the parameters in the autoencoder. Simulations and experiments on real data suggest that the proposed method performs better than several other inference methods, including majority voting, the Bayesian Classifier Combination (BCC) method, the Community BCC method, and the recently proposed VISIT method.
Spatial Variation for Iron and Manganese Forms by Using Time Series Analysis Under Different Environments

Six regions in Anbar governorate were selected to study spatial variation of iron and manganese forms using time series analysis under different environments. Al-Baghdadi, Heet, Abu Teban, Ramadi, Khalidiya and Falluja were selected. two pedons was selected under two environments: desert and alluvial soil, toward vertical in to Euphrates River. Total, available, Crystalline free and Amorphous free, for each iron and manganese was measurement as well as Crystalline form for two elements. statistical analysis by using time series was calculated. The results of the research showed the following:1.Differentiation of models that describe forms of iron and manganese according for different sedimentation environment as well as heterogeneity of spatial reliability and sampling methods.2.The method of Semi Variance showed its preference in obtaining the samples and describing the variation of iron and manganese forms compared
BENTUK DAN JENIS KALIMAT BAHASA INDONESIA ANAK USIA 3-4 TAHUN DI PLAY GROUP LERENG HIJAU KABUPATEN JENEPONTO

This study aims to (1) describe the forms of the basic Indonesian sentences basic children aged 3-4 years playinggreen slope district of jeneponto and (2) describe the types of single sentences of children aged 3-4 years in the district green slope play group jeneponto. This research is a descriptive research. The approach used in this study is a syntactic approach. This research data is a sigle sentence spoken by children aged 3-4 years in the green slope play group jeneponto district. The method of reffering and proficient. The technique used in the refer and refer method is the recording technique and recording technique. The results of this study indicate that (1) forms of basic sentences based on single sentences of children aged 3-4 year in the district green slope play group jeneponto consists of  KB + KB, KB + KK, KB + KS, KB + KDep, KB + KBil, and FB + KDep and (2) types of child single sentences which consist of question words, who, when, and where the presence is at the beginning of the sentence, in the middle, and at the end sentence. As for, the question asked how and why the researchers did not find. Furthermore, the imperative sentences or command sentence consists of the commad sentence verily, prohibited sentences, and sentences invite.
Минералогия, геохимия и возраст метакарбонатно-силикатных пород ильменогорского комплекса

Research subject. This paper presents the results of a series of mineralogical, petrological, geochemical and isotope­chronological studies carried out to investigate metacarbonate­silicate rocks in the area of Ishkul Lake, the Ilmenogorsky complex. Materials and methods. The microprobe analysis of the composition of minerals was performed using a REMMA­202M scanning microscope equipped with a microanalyzer. The content of major, trace and rare­earth elements (REE) was determined using a mass spectrometer ICP­MS. The U­Pb age of zircons was obtained by a microprobe SHRIMP II. The content of REE in zircons was determined by an ion probe CAMECA IMS­4F. Results. The studied metacarbonate­silicate rocks are shown to be represented by spinel­forsterite­calcite, diopside­scapolite­calcite and clinopyroxenite varieties with a di verse range of minerals, including diopside, calcite, forsterite, spinel, scapolite, anorthite, enstatite, alumoenstatite, augite, fassaite, tschermakite, pargasite, hornblend, tremolite, barium­containing feldspar, celsian, phlogopite, graphite, titanite, fluorapatite, picroilmenite, pyrrhotite, pentlandite, sphalerite, violarite, gersdorffite, maucherite. In terms of petrochemical properties, the metacarbonate­silicate rocks under study are characterized by significant variations in the content of SiO2, CaO, MgO at a Ca/Mg ratio of 1.4–8.2, as well as by increased Ni and Cr content, low ratios of Sr/Ba, Th/U, Zr/Hf and Nb/Ta. The small ΣREE amounts of 6–25 ppm (rarely up to 70–72 ppm) correspond to sedimentary formations with a significant amount of ultrabasic material. Conclusions. The specific features of the composition of olivine, spinel, ilmenite, as well as the titanium content in the early generation zircon indicate the formation (transformation) of spinel­forsterite­calcite rocks at t = 830–850°C. According to the established specifics of REE distribution and the Th/U ratio, the early generation zircons refer to the granulite type zircons, while the late generation zircons correspond to those of transformed syenites­miaskites and various metasomatites. The formation (transformation) of the rocks is found to correspond to the following age stages: PR1 (1720–1780 Ma) ­ “granulite” metamorphism; D1–C (345–399 Ma) – metasomatic transformations caused by the formation of alkaline rocks associated with rifting processes; P1 (282 Ma) – tectonic­metasomatic transformations caused by shear processes.
MAPS Mineralogy Brochure

Automated Spatial Mineralogy SolutionsLarge sample, fast-track spatial mineralogy and elemental analysis based on 40 years of technology innovation
PREPARING OF PRE-SERVICE TEACHERS FOR CREATING DIGITAL DIDACTIC VISUAL MEANS

Visualization of educational material is one of the recognized strategies for the successful work of a modern teacher, the implementation of which requires him to be able to independently create pedagogically effective digital didactic visual means. The complexity of such facilities as design objects necessitates the introduction into the system of educational process of pre-service teachers of the training course, which ensures that they acquire a complex of diverse knowledge and skills necessary for the creation of such means. The purpose of the article is to reveal the content, structure, and peculiarities of the implementation of the course «Creating Digital Didactic Visual Tools» for pre-service teachers. Research methods used: analysis of scientific, psychological and pedagogical, educational, methodological and special literature, information sources on the research problem; synthesis and generalization of theoretical positions, discovered in the scientific and educational-methodical literature; generalization of own pedagogical experience of preparing pre-service teachers for creating visual content, as well as practical experience of such activity. The article deals with the issues of preparation of pre-service teachers for the creation of qualitative digital didactic visual means. The relevance of introducing the course «Creating Digital Didactic Visual Tools» in the process of professional training of future teachers is substantiated. The place of the given course in the system of the specified preparation, its purpose, the task is determined. Structurally, the course consists of modules: «Theoretical bases for using visualization», «Fundamentals of visual objects design», «Modern tools for creating a didactic visual means», «Visualization technologies in educational practice». The target orientation and content lines of modules, peculiarities of the organization of student activity are revealed. Shown that the logic of the course's construction is oriented on the gradual mastery of future teachers by a complex of pedagogical, psychological, design, technological, ergonomic knowledge and skills that determine its ability to independently create pedagogically effective digital didactic visual means.
Automated Question Extraction and Tagging for Cloud based Online Communities

Crowd-based question answering forums and cloud-based community question answering platforms provide us with the dais to post questions and answers online. This helps the users to get desired answers from expert users. It is a challenge for a person with mobility needs to go out and explore. The 'wheelchair accessible' information provided by Google Maps is useful to explore before going out. Local guides share this knowledge on Google Maps by answering quick questionnaire. Automated question generation is a key challenge that we face with regard to natural languages in the context of users visited locations, already reviewed places, likes, interests and user experience. In this paper, we have implemented an automatic question generation system that comprises of part-of-speech (POS) tagger, text-to-question generation task using syntactic analysis and a named entity extraction. The proposed system is tested with human effort and is generating valid questionnaire.
The Forecasting of Foreign Tourists Arrival in Indonesia based on the Supply Chain Management: an Application of Artificial Neural Network and Holt winters Approaches

Tourism plays crucial role for improving the social and economic development of a country through open employment opportunities in surrounding tourism area. The supply chain mangement strategy can be effective in developing the turisim industry.The number of foreign tourists who come to Indonesia continues to increase from time to time. Therefore, tourist arrivals need to be forecasted to assist the government in providing optimal infrastructure and accommodation for tourists to avoid an imbalance between the number of tourists with the infrastructure and accommodation provided. The purpose of this study is to forecast the arrival of foreign tourists in Indonesia by using Artificial Neural Network and Holt-Winters approach esutilising the historical data from January 2011 to December 2017. From the calculation process, we found that MAPE (Mean Absolute Percentage Error) value of Artificial Neural Network and Holt-Winters approaches are 5.60% and 5.43%, respectively. So it can be concluded that the Holt-Winters approach is better than the Artificial Neural Network approaches in forecasting the foreign tourists arrival in Indonesia.
The development of gaming application to facilitate resource center

The activity of promoting resource center service is a crucial activity that the resource center held up to provide knowledge to users. It facilitates self-research as well as encourage users more into readings. The practices that have been pursued from the past need to be updated to keep pace with the world. The researchers have developed a gaming application to promote resource center mainly to keep the accessibility modernized and interesting to end users. The result in this research reveals that users level of satisfactions towards the application to promote resource center of Suan Sunandha Rajabhat university is high.
UPAYA UNTUK MENINGKATKAN HASIL BELAJAR IPS DENGAN MENGGUNAKAN MEDIA VIDEO PADA SISWA KELAS VI SDN 004 TERATAK BULUH

This research is motivated by low student social studies learning outcomes. This study aims to improve social studies learning outcomes by using video media in class VI SDN 004 Teratak Buluh. This research is a classroom action research that includes activities with stages of planning, action, observation and reflection. The results showed that the increase in social studies learning outcomes of class VI students, the average classical completeness before class action research was 75% in the first cycle of 82.14% and in the second cycle it was 92.85%. Scientific performance learning outcomes before class action research is 65.36%, and scientific performance learning outcomes after class action research cycle I is 72.76% increased by 7.4%, and scientific performance learning outcomes after class II cycle action research is 73.17% also increased by 0.41% from cycle I. The occurrence of increased learning outcomes of scientific performance also affects student learning outcomes. Based on the results of classroom action research, it can be concluded that using video media can improve social studies learning outcomes. Grade VI students of SDN 004 Teratak Buluh.
LibGuides: Library Support for Research Students: Who to Contact

A Guide to library services for Research staff and students at the University of the Highlands and Islands
Classifying Snapshots of the Doped Hubbard Model with Machine Learning

Quantum gas microscopes for ultracold atoms can provide high-resolution real-space snapshots of complex many-body systems. We implement machine learning to analyse and classify such snapshots of ultracold atoms. Specifically, we compare the data from an experimental realization of the two-dimensional Fermi–Hubbard model to two theoretical approaches: a doped quantum spin liquid state of resonating valence bond type1,2, and the geometric string theory3,4, describing a state with hidden spin order. This technique considers all available information without a potential bias towards one particular theory by the choice of an observable and can therefore select the theory that is more predictive in general. Up to intermediate doping values, our algorithm tends to classify experimental snapshots as geometric-string-like, as compared to the doped spin liquid. Our results demonstrate the potential for machine learning in processing the wealth of data obtained through quantum gas microscopy for new physical insights. Quantum gas microscopes provide high-resolution real-space snapshots of quantum many-body systems. Now machine-learning techniques are used in choosing theoretical descriptions according to the consistency of their predictions with these snapshots.
Fully Convolutional Networks for Mammogram Segmentation

Segmentation of mammograms pertains to assigning a meaningful label to each pixel found in the image. The segmented mammogram facilitates both the function of Computer Aided Diagnosis Systems and t ...
Statistical GGP Game Decomposition

This paper presents a statistical approach for the decomposition of games in the General Game Playing framework. General game players can drastically decrease game search cost if they hold a decomposed version of the game. Previous works on decomposition rely on syntactical structures, which can be missing from the game description, or on the disjunctive normal form of the rules, which is very costly to compute. We offer an approach to decompose single or multi-player games which can handle the different classes of compound games described in Game Description Language (parallel games, serial games, multiple games). Our method is based on a statistical analysis of relations between actions and fluents. We tested our program on 597 games. Given a timeout of 1 h and few playouts (1k), our method successfully provides an expert-like decomposition for 521 of them. With a 1 min timeout and 5k playouts, it provides a decomposition for 434 of them.
Thompson Sampling Based Active Learning in Probabilistic Programs with Application to Travel Time Estimation

The pertinent problem of Traveling Time Estimation (TTE) is to estimate the travel time, given a start location and a destination, solely based on the coordinates of the points under consideration. This is typically solved by fitting a function based on a sequence of observations. However, it can be expensive or slow to obtain labeled data or measurements to calibrate the estimation function. Active Learning tries to alleviate this problem by actively selecting samples that minimize the total number of samples needed to do accurate inference. Probabilistic Programming Languages (PPL) give us the opportunities to apply powerful Bayesian inference to model problems that involve uncertainties. In this paper we combine Thompson Sampling with Probabilistic Programming to perform Active Learning in the Travel Time Estimation setting, outperforming traditional active learning methods.
How to Compose Product Pages to Enhance the New Users’ Interest in the Item Catalog?

Converting first-time users into recurring ones is key to the success of Web-based applications. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) to provide useful recommendations to users without historical data. Traditionally, RSs assume that non-personalized recommendation can mitigate this problem. However, several users are not interested in consuming just biased-items, such as popular or best-rated items. Then, we introduce two new approaches inspired by user coverage maximization to deal with this problem. These coverage-based RSs reached a high number of distinct first-time users. Thus, we proposed to compose the product’s page by mixing complementary non-personalized RSs. An online study, conducted with 204 real users confirmed that we should diversify the RSs used to conquer first-time users.
Intelligent modeling using a novel feature extraction based multiple activation functions extreme learning machine

For industry 4.0, intelligent modeling is very important. Modelling plays a very important role in making control strategies and production plans. Nevertheless, establishing an accurate and robust model becomes more difficult because of the increasing complexity of modelling data. To solve this problem, a novel feature extraction based multiple activation functions extreme learning machine (LV-MAFELM) is presented. The LV-MAFELM model is easy to construct: firstly, generate the input weights at random; secondly, select several different nonlinear activation functions and compute the hidden layer outputs; thirdly, extract principal components from the hidden layer outputs; finally, compute the output weights analytically. For verifying the model performance, the LV-MAFELM model is applied in one petrochemical industry process -the Purified Terephthalic Acid (PTA) process. Simulation results demonstrate that the presented LV-MAFELM achieves good performance, which indicates that accuracy and stability of energy prediction models can be improved.
Clark Family Library: PrezTech Challenge: What is the PrezTech Challenge?

The Committee on Libraries and Educational Technology (COLET) is pleased to announce PrezTech Challenge. PrezTech Challenge encourages W&J faculty to enhance student learning by using library and educational technology.
Arsip Dinamis Mendukung Whole of Government di Perguruan Tinggi

One of the Agenda 3 subject material, for participant in basic training of civil servant candidates (CPNS) Group III is Whole of Government (WoG). These CPNS are prospective young lecturers at various universities in Indonesia. The presence of WoG training subject, it is hoped that the prospective young lecturers can quickly actualize the concepts and implementation of WoG concepts in higher education in a policy perspective in Indonesia. Problems faced by Indonesia universities is the website ranking is still low based on Webometrics. The development of a website must have sufficient insight into the concept of Dynamic Archives, the concept of e-Government and the concept of WoG that are interrelated and have an impact. It is because of universities have vision and mission to become World Class University (WCU), and one of efforts to reach that by increasing the international ranking in Webometrics.
Os repositórios de dados de pesquisa na perspectiva dos atributos de encontrabilidade da informação

The exponential growth of the research data demands the implementation of digital repositories that can gather, store and make accessible these digital objects, taking into account their different formats and typologies. These environments, called Research Data Repositories, cover much more than the sharing of traditional scientific productions and are part of the discussions in the context of Open Science that are aimed at enhancing the process of sharing, using and reusing research data. In this sense, for the projection, implementation and evaluation of these repositories, we rely on Information Findability (EI) and its theoretical-practical approach, which is inserted in the Information Architecture (AI) studies. It is believed that AI is the solution to EI problems, thus composing the systems of organization, navigation, representation and search of AI in a data repository, from the attributes and concepts of EI, can enhance the findability, access to and use of the data made available. The objective of this study is to present the importance of IE attributes to the research data repositories in the context of AI. This is an exploratory qualitative approach. It uses the bibliographical survey in national and international bases for the construction of the theoretical reference regarding the themes that permeate the study, which are: Open Science and Research Data; Research Data Repositories; and Information Findability in the context of Information Architecture. It was possible to present the results in a framework formed by the systems that compose the AI ​​anatomy, highlighting where each EI attribute could be inserted, besides those that converge with one or more systems. Once this step is done, we can define the concepts of each attribute, and what they affect in the functionalities of the research data repositories. Subsequently, in the next phase of the research, it is intended to identify and exemplify guidelines, recommendations and technologies that could be applied in the data repositories in each of these attributes and of these systems, affecting the functionalities of these environments in a positive way, that is, and the success of these environments in the context of Open Science.
High-Resolution High-Sensitivity Human-Computer Algorithm and Gesture Control SOC Chip for Artificial Intelligence Applications

This paper presents a human-computer algorithm and gesture control SOC chip. Based on single CIS and active infrared method, the gesture control SOC chip realizes high-sensitive and high-precision discrimination of various human-computer interaction gestures by utilizing two main algorithms: target pattern extraction and specific gesture recognition. The SOC chip integrates all functional modules is implemented in 55 nm low power process with chip area of 4148*5148 um <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and power consumption of 88 mW, providing advantages such as wide field of view of 600*600*600 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , high precision of 1 mm and high resolution 1 mm/pixel, which indicate that the presented highly integrated SOC chip has a great potential for artificial intelligence (AI) human-computer applications.