Reviewerly/exHarmony huggingface dataset:
Implementasi Algoritma Klasifikasi C4.5 Untuk Memprediksi Kelayakan Pembelian Kendaraan

To choose a vehicle that suits your needs and the funds needed by consumers, you need careful and accurate analysis. The criteria used as a benchmark in choosing a vehicle among other prices, maintenance costs, number of doors, room capacity, baggage area, and vehicle safety level. To process all of these questions is needed a way that can help consumers in choosing the vehicle that best suits their needs. C4.5 algorithm is an algorithm used to compile a decision tree. C4.5 algorithm is used to determine the car to be taken by the buyer according to the background of the purchase criteria (purchase price), maintenance (maintenance costs), doors (number of doors), people (capacity for passenger announcements), lug_boot (delivery area) , safety (vehicle safety level) The results of this study are car purchasing assessment systems that are in line with expectations for potential car buyers in decision making considerations.
Guided Random Forest and its application to data approximation

We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with localized partitioning to obtain a global partitioning. We show that global partitioning bridges the gap between decision trees and boosting algorithms. We empirically demonstrate that global partitioning reduces the generalization error bound. Results on 115 benchmark datasets show that GRAF yields comparable or better results on a majority of datasets. We also present a new way of approximating the datasets in the framework of random forests.
Interaction between Big-Pharma’s and Small Enterprises: an Analyses Focused on the Incorporation of Inventors

This article focuses on the incorporation of knowledge by the Big-Pharma as it acquire small enterprises.The objective is to identify in which degree the incorporation of the knowledge, related to the inventors, is effected in this process.Through a qualitative methods, based on a sample of Big-Pharmas and small companies acquired by them from 2005 to 2012 we compile the patents granted to small companies within the USPTO, through the base PatFT.The analysis develops two central measures.The first is the use of inventors defined as small enterprises' inventors that were incorporated and start to develop patents for the large enterprise company.The other is the incorporation of research trajectories meaning inventors who cite their past work as they are incorporated into the larger enterprise.It is concluded that large companies incorporate inventors of at least one of the acquired companies, but the number of incorporated inventors is relatively low.More rare is the incorporation of research trajectories that, therefore, demonstrate their relevance.
MySQL Shell Tutorial

Now that we know what the MySQL Shell is and where it fits in the suite of MySQL products, it is time to learn what the shell can do for us. Specifically, what commands does it support, how does it connect to the server, and what features does it support?
Kemampuan Berpikir Logis dan Penguasaan Kosa kata Bahasa Jerman

There are many factors that can influence students' vocabulary mastery in foreign language learning. This study aims to determine the relationship between logical thinking skills and students' mastery of German vocabulary. Research is an experimental study involving only the experimental class without the control class. Data collection techniques are done through writing test and logical thinking test in the form of Intelligent Structure Test (IST). The sample in this study were 21 high school students selected through random sampling techniques. The results of the analysis of this study indicate that there is a significant relationship between the ability to think logically with students' mastery of German vocabulary. Students who have good critical thinking skills, tend to more easily remember and understand new vocabulary in learning foreign languages.
Soil Quality Analysis using Data Mining (Comparison of Two Algorithm ID3 and Naive Bayes)

Farmers must be aware of macronutrients and micronutrients present in soil to gain maximum yield of particular crop and to know which fertilizer to use. This system will help farmers in yield prediction. Yield prediction is very popular among farmers these days, which particularly contributes to the proper selection of crops for sowing. With the help of this system type of soil will be displayed. Also it will predict the crops suitable for the particular type of soil. In addition to this, it will improve the soil if the farmer wants to yield particular crop in the same soil by suggesting the requirements of the nutrients for the same soil. It is useful to farmers to get right insight to perform their activities with less cost and to improve the crop yields, such as by measuring soil properties the farmers can decide what kind of crops to be adopted and use of fertilizers etc. The soil analysis may use in many dimensions such as to protect the environment, diagnosis of crop culture troubles, to identify nutrient deficiencies, energy conversation, and so onI.
KAPASITAS PEMERINTAH PROVINSI JAMBI DALAM MENGHADAPI BENCANA BANJIR DAN ASAP AKIBAT KEBAKARAN HUTAN DAN LAHAN

Preventing and reducing disaster victims is crucial in the disaster risk reduction context. The government, particularly at the local level, plays a vital role in reducing such risk. Therefore, strengthening the capacity of local governments is needed to reach the goal. This necessity is clearly stated in the Sendai Framework for Disaster Risk Reduction (SFDDR) 2015-2030. The capacity of local government in disaster risk management is essential in preventing and minimizing the number of victims. Floods and forest and land fire haze are the most common disasters in Jambi Province. Understanding the local government capacity is imperative in the context of disaster risk reduction as such. This paper aims to explain the current capacity of the Jambi local government in managing flood and forest and land fire haze. Data were collected qualitatively from government officials through focus group discussions (FGDs) and interviews. The findings showed that the capacity of Jambiâ€™s local government needs to be improved, and there are issues to be solved. Jambi Province policy capacity tended to focus on forest fires and lacked attention on flood-related policies. On implementation capacity, there exist some issues to be overcome, such as the local government officialsâ€™ knowledge and skills related to disaster risk management, redundant disaster preparedness programs at the village level, and the need for more detailed hazard maps.Â
ANALISIS PERBEDAAN GENRE GAME PADA GOOGLE PLAY MENGGUNAKAN FRAMEWORK OCTALYSIS

Android-based games are divided into various genres so that there are many variations of the game that can be played but many game developers don't know how to develop games based on the player's perspective that is closely related to human computer interaction.&#x0D; The purpose of this study was to find out the strongest characteristics of player motivation in each genre available on Google Play using Octalysis framework based on design and gameplay so that differences in player motivation were found. The Octalysis Framework has been used by many companies to make gamification products, because in gamification there are game elements so that they can be used to analyze game products.&#x0D; With this research, it is expected that each genre has the strength of characteristics and weaknesses as a benchmark of data for developers and game designers to create more varied games.
High-Tech Elements in Minerals of Massive Sulfide Deposits: LA-ICP-MS Data

LA-ICP-MS data on trace element zonation reflecting a local variation of physicochemical conditions of mineralization in black, grey and clear smokers from the Pacific and Atlantic oceans are used for comparison with the ancient chimneys of the Urals, Rudniy Altai, Pontides and Hokuroko massive sulfide deposits. Host rocks also influence on high-tech trace element assemblages in chalcopyrite: ultramafic (high Se, Sn, Co, Ni, Ag and Au) → mafic (high Co, Se, Mo and low Bi, Au and Pb) → bimodal mafic (high Te, Au, Ag, Bi, Pb, Co, moderate Se, and variable As and Sb) → bimodal felsic (high As, Sb, Mo, Pb, moderate Bi, and low Co, Te and Se). In sphalerite of the same range, the contents of Bi, Ga, Pb, In, Ag, Au and Sb increase versus Fe, Se, Sn and Co. The variations in pyrite coincide with these changes. Diagenetic evolution of high-tech elements is recognized in sulfide nodules.
One Model to Learn Both: Zero Pronoun Prediction and Translation

Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should be recalled in non-pro-drop languages. This discourse phenomenon poses a significant challenge for machine translation (MT) when translating texts from pro-drop to non-pro-drop languages. In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models. Specifically, we jointly learn to predict and translate ZPs in an end-to-end manner, allowing both components to interact with each other. In addition, we employ hierarchical neural networks to exploit discourse-level context, which is beneficial for ZP prediction and thus translation. Experimental results on both Chinese-English and Japanese-English data show that our approach significantly and accumulatively improves both translation performance and ZP prediction accuracy over not only baseline but also previous works using external ZP prediction models. Extensive analyses confirm that the performance improvement comes from the alleviation of different kinds of errors especially caused by subjective ZPs.
Adoption of Mobile Technologies in Learning: An Agribusiness Case

The purpose of this study is to identify the impact of mobile learning technologies in agribusiness education by examining the effectiveness of a DISCOVERe mobile technology classroom program on students' participation in class activities, their use of instructional materials, their interaction with peers, and their overall learning experience. Online survey data collected from agribusiness students at a California State University during the spring 2018 semester were analysed, hypothesis tests were conducted and a three-stage learning adoption framework was estimated. The results demonstrate that taking the DISCOVERe course significantly improves students' participation in class activities, enhances their access of course materials, makes it easier for them to interact with fellow students, and improves students' overall learning experience. The conclusion is that as students become more familiar with new technologies, they will be more confident in their ability to learn and will be more efficient in acquiring knowledge and exchanging ideas.
Electric Technology in Wind Turbines from a Dialectic Perspective

Wind turbines have been used by many groups of humans for many centuries. Wind turbines have allowed groups of humans to perform many different tasks in the past (grinding grain, pumping water, etc.). However, only a century and a half ago, they began to be used to convert the energy captured from wind into electric energy. Moreover, only approximately twenty-five years ago, we started to introduce on a massive scale the energy generated from wind turbines into the electric networks of most developed countries in the world for regular consumption. According to 2017 statistics, approximately 12 percent of the electric energy consumed in the EU is produced by wind turbines. Despite the fact that wind turbines generally appear quite similar externally—i.e., a three-blade structure, a nacelle, a tower, etc.—if we carefully examine the electric technology used within them, we find quite a wide range of technologies for energy conversion, which is a key issue in wind turbine technology. Hence, this paper adopts a dialectic perspective towards analyzing and understanding why several electric technologies coexist in wind turbine technology. We explain the specific factors that have influenced different wind turbine manufacturers to adopt different electric technologies across the last twenty-five years. We show how their actions and the technological directions that have followed have been mutually codetermined, resulting in a technological evolution that has produced today’s wind turbine variety.
A Study on the Assessment in Art Education for Developing the Next National Art Curriculum

본 연구의 목적은 교육과정에서 제시하는 미술과의 평가를 종합적으로 점검하고 이를 토대로 차기 교육과정의 핵심 역량에 기초한 미술과 내용 체계 및 교수·학습 방법과 연계한 미술과 평가를 제안하는 것이다. 이 시대의 교과 평가는 학생들의 학습 능력을 점검하고 이를 다시 교사의 교수·학습 계획에 환류할 수 있는 실질적인 기능을 해야 한다는 데 본 연구의 핵심이 있으며, 이를 위하여 교육과정 내에서 연계적 특성이 고려되어야 한다는 생각에서 본 연구가 출발하게 되었다. 이를 위하여 첫째, 2015 개정 교육과정의 각 교과별로 평가 및 평가도구가 교과 특성에 따라 어떻게 제시되었는가를 알아보았으며, 제1 차부터 2015 개정 교육과정까지 일반 교육과정에서 미술과의 평가가 어떻게 변화되었는지를 살펴보았다. 둘째, 2015 개정 교육과정의 초·중학교 미술과 성취기준에 따른 평가기준을 점검함으로써 차기 교육과정에서 성취기준 및 평가기준의 개발 시 고려해야 할 사항을 제안하고자 하였다. 셋째, 선행 연구를 통해 제시한 바 있는 핵심 역량에 기초하여 내용 체계 및 교수·학습 방법과의 연계를 고려한 미술과 평가도구의 실제를 제안하였다. 본 연구는 미술과 평가에 대한 전반적이고 포괄적 인 연구로서, 차기 교육과정 개발을 위한 의미 있는 기초 연구로 활용되기를 기대한다.The purpose of this study was to examine the system of the assessment in national curriculums including art education, and to propose the assessment for the next National Art Curriculum in relation to the core competencies and instructional methods in art education based on previous studies of co-workers. The assessment in the curriculum should be focused on examining students learning abilities and being a practical standard for teachers’ instructional guidance as feedback. For this purpose, the following were planned. First, each subject’s assessment in 2015 revised National Curriculum and historical change of the assessment in art education from the 1st to 2015 revised National Curriculum were examined. Second, the achievement and assessment standards of 2015 revised National Art Curriculum were analyzed in order to draw better implications. Third, the assessment methods in art education were proposed, which can be linked with the core competencies, the content system and instructional methods for the next National Curriculum. It was expected that this study would be used as a meaningful basic research for the development of the next curriculum.
On perfectness in Gaussian graphical models

Knowing when a graphical model is perfect to a distribution is essential in order to relate separation in the graph to conditional independence in the distribution, and this is particularly important when performing inference from data. When the model is perfect, there is a one-to-one correspondence between conditional independence statements in the distribution and separation statements in the graph. Previous work has shown that almost all models based on linear directed acyclic graphs as well as Gaussian chain graphs are perfect, the latter of which subsumes Gaussian graphical models (i.e., the undirected Gaussian models) as a special case. However, the complexity of chain graph models leads to a proof of this result which is indirect and mired by the complications of parameterizing this general class. In this paper, we directly approach the problem of perfectness for the Gaussian graphical models, and provide a new proof, via a more transparent parametrization, that almost all such models are perfect. Our approach is based on, and substantially extends, a construction of Lněnička and Matúš showing the existence of a perfect Gaussian distribution for any graph.
Biofabrication AR, a Microsoft Hololens experience

Biofabrication AR is an exploration of biofabrication technologies and the hospital of the future in augmented reality for use on the Microsoft Hololens. The digital experience can be set up anywhere and features a guided, step-by-step journey for the user to use the biofabrication platform technologies of 3D scanning, 3D modelling and 3D printing to repair a defect in a digital patient's skull. The work is a collaboration between medical researchers and mechatronics students. The experience was presented at the inaugural Herston Biofabrication Institute conference (22/03/2019) and at Robotronica (18/08/2019) for a total of 70 members of the public and healthcare professionals to discover augmented reality and become familiar with biofabrication technologies.
Prospects for promoting the online model of Russian education exports in Africa

Problem and goal. The task of increasing the number of foreign students twice by 2024, set before the Russian universities by the country’s leadership in the framework of the national project “Education”, will require a revision of the traditional approach to the recruitment of foreign students. It is obvious that in conditions of limited organizational, human and financial resources of the country’s universities will require fundamentally new forms and technologies of recruiting, and in the future and new forms of training of students. The article deals with the actual problem of implementation and realization of the online model of export of Russian education in the conditions of expansion of export activity of Russian universities for the recruitment of foreign students in Africa. The purpose of the described study was to determine the basic conditions for the implementation of the online model of export of Russian education in Africa, identifying the target countries of Africa, promising for recruiting students. Methodology. The definition of the features and conditions of the online model of education export was carried out on the basis of the analysis of the best practices of foreign universities (TOP-500) and Russian universities (participants of Project 5-100). The main conditions for the implementation of this online model of education exports, the availability of appropriate infrastructure, the potential effectiveness of the export model in Africa (target countries) are analyzed. Results. The study revealed that the use of the online model of export of education by Russian universities in Africa, based on the use of telecommunication technologies, can affect the increase in the number of Foreign students and have a positive impact on the effectiveness of recruiting activities of the Russian University in modern conditions through the introduction of online recruiting methods of recruitment of foreign students, attracting a new audience of foreign students, the implementation of online training in Africa, which, ultimately, greetings to the increase in university income from educational activities received from foreign sources. Conclusion. It is shown that the use of the online model of education exports can have a significant impact on the competitiveness of Russian universities in Africa and the increase in the number of foreign students enrolled in online programs of Russian universities in their target countries.
Implementasi Kombinasi Metode AFF dan FBET Untuk Peningkatan Kualitas Citra

Image enhancement is one of the most popular research topics currently in the field of image processing. Often image has poor quality caused by various factors such as lighting factor, enviromental factor or low quality of camera, and others. A number of these disturbances often become a barrier in improving the image quality where handling is the main objective of this research is done in the form of methodology base on the combination of AFF dan FBET algorithm. The result of the test on the proposed methodology show that in the process of noise loss continued with the image sharpening process obtained the value of PSNR = 18.56 dB is more optimal than the first done image sharpening process with the value of PSNR = 18.10 dB.
Distributionally Robust Language Modeling

Language models are generally trained on data spanning a wide range of topics (e.g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e.g., restaurant reviews). In this paper, we first show that training on text outside the test distribution can degrade test performance when using standard maximum likelihood (MLE) training. To remedy this without the knowledge of the test distribution, we propose an approach which trains a model that performs well over a wide range of potential test distributions. In particular, we derive a new distributionally robust optimization (DRO) procedure which minimizes the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution. Our approach, called topic conditional value at risk (topic CVaR), obtains a 5.5 point perplexity reduction over MLE when the language models are trained on a mixture of Yelp reviews and news and tested only on reviews.
Profil Efikasi Diri Peserta Didik Berdasarkan Hasil Belajar Biologi Kelas X MIA MAN 1 Makassar

Abstract.This research is a descriptive research that aims to know the self-efficacy profile of students based on biology learning result. The population in this study is all students of class X MIA MAN 1 Makassar, sample of research is 68 people. Data collection was conducted in even semester of academic year 2017/2018. Data of self-efficacy obtained by questionnaire and biology learning result obtained from biology learning result test. Data of self-efficacy and biology learning result analyzed by using descriptive statistics. The results showed that: (1) for students with very high, high and medium of biology learning result have not self-efficacy (2) for students with low and very low of biology learning result have self-efficacy.Keywords:descriptive research, self-efficacy, biology learning result.
LibGuides: UG CamGuides: How do I find books and articles from a reading list?: Understanding references

Break down a reading list into different types of resource and know how to recognise them from their reference.
Question-type Driven Question Generation

Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type $how$ while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.
HishabNet: Detection, Localization and Calculation of Handwritten Bengali Mathematical Expressions

Recently, recognition of handwritten Bengali letters and digits have captured a lot of attention among the researchers of the AI community. In this work, we propose a Convolutional Neural Network (CNN) based object detection model which can recognize and evaluate handwritten Bengali mathematical expressions. This method is able to detect multiple Bengali digits and operators and locate their positions in the image. With that information, it is able to construct numbers from series of digits and perform mathematical operations on them. For the object detection task, the state-of-the-art YOLOv3 algorithm was utilized. For training and evaluating the model, we have engineered a new dataset 'Hishab' which is the first Bengali handwritten digits dataset intended for object detection. The model achieved an overall validation mean average precision (mAP) of 98.6%. Also, the classification accuracy of the feature extractor backbone CNN used in our model was tested on two publicly available Bengali handwritten digits datasets: NumtaDB and CMATERdb. The backbone CNN achieved a test set accuracy of 99.6252% on NumtaDB and 99.0833% on CMATERdb.