Reviewerly/exHarmony huggingface dataset:
Identifying of the relationship between lineament density and vegetation index at Tumpangpitu Mining Area, East Java, Indonesia

Tumpangpitu Mining Area was in Banyuwangi Regency, East Java, Indonesia. This area has potential of mineral resources, forests, plantations and agriculture. The potential for mineral resources was developed into the Tumpangpitu mining area. Geological structures as a controller of mineral resource potential can be observed through satellite imagery using lineament analysis. Furthermore, the lineament can be tested for its effect on land cover diversity through analysis of the vegetation index. Utilization of satellite imagery such as Landsat-8 and SRTM DEM can be used to determine the lineament and vegetation index. In this case, Landsat-8 is useful for knowing the vegetation index, while SRTM DEM can be used to determine lineament of the morphology and geological structures. The spatial characterization of lineament was obtained by measuring lineament density (intersection, number, and length) using modified Segmen Tracing Algorithm (mSTA). The measurement of vegetation index was processed using NDVI (Normalized Difference Vegetation Index). In this case, the vegetation index has a range of -0.48 to 0.87, with a mean of 0.48. This indicates that the study area was dominated by dense vegetation, especially on the northern part in the study area. Lineament density which includes intersection, number, and length of lineament have a positive correlation with vegetation index value. Thus, it could be concluded that the greater lineament density in a zone, the greater of the vegetation index.
Indonesia Democracy Index (IDI) Forecasting in 2019 using Moving Average and Correlation Between IDI's Aspect Using Pearson Correlation Coefficient

&lt;p class="Abstract"&gt;&lt;em&gt;Abstract&lt;/em&gt;—This experiment aims to analyze the forecasting of the Indonesian Democracy Index (IDI) in 2019, which uses each province data by the Moving Average method. The parameters used in this experiment refer to data obtained from the Central Statistics Agency (BPS) in 2009-2018. The level of achievement of IDI is measured based on the development and implementation of 3 aspects, 11 variables, and 28 indicators. Experiment purposes to find the average percentage of absolute error MAPE (Mean Absolute Percentage Error) for each province and looks for correlations between the three main aspects of forming IDI namely civil liberties, political rights, and democratic institutions. IDI Indonesia's forecasting results in 2019 the IDI has an average value of 68.28 with a MAPE of 4.78%. The results of the correlation between the three aspects of forming the IDI using the Pearson correlation coefficient resulted in the aspect of civil liberties having no correlation with aspects of political rights or aspects of democratic institutions with Pearson values of -0.05 and -0.19. Whereas aspects of political rights correlate with democratic institutions with Pearson's value of 0.48.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;Keywords—Forecasting, Indonesian Democracy Index, Moving Average. Pearson Correlation Coefficient
A Novel Approach to Transform Bitmap to Vector Image for Data Reliable Visualization considering the Image Triangulation and Color Selection

Vector image is a type of image composed of many geometric primitives. Compared with bitmaps, vector images have the ability to save memory as well as to enlarge without distortion. Meanwhile, it has been commonly adopted in data visualization (image data) because it can be scaled to multiple sizes to fit different scenes. For instance, it can be applied for the illustrations in newspapers and magazines, the logo on the web, the background for poster, the design of text, and traffic signs. However, transforming a bitmap to vector image is still a challenging problem because of the complicated content of a bitmap, which tends to consist of more than just simple geometry. Aiming at this issue, there is a new approach proposed to transform from bitmaps to vector images, which is based on triangle units and consists of three steps. In detail, firstly, there is an initial mesh constructed for one image in pixel level after detecting features. Then, the initial mesh will be simplified by collapsing two vertices as the initial mesh is too dense to represent one image. Specifically, there are two main parts in this step, which are collapse conditions and collapse influences. In the step of collapsing, issues such as overlap and sharp triangles can be conquered by a sort-edge method (which will be illustrated specifically later). The final step is to select one color for each triangle, since it is helpful to save the memory and speed up the process of this method. In addition, one color will represent one triangle; hence, in the final step, the four-triangle sample method will be applied in order to prevent a vector image from generating too large color discontinuity. Once the pretest proceeds without mistake, the method above is able to work for the general bitmaps. Note that our method can be applied to information security and privacy, since one image can be encoded to some triangles and colors.
Teaching Methods Of Economics Contributing To Economic Competence Of Bachelors Of Engineering

Nowadays, bachelors should be good production engineers, and they should also understand economic, environmental, social and other problems.When appointing bachelor's graduates as lowand middlelevel managers and preparing them for a professional career of directors of private industrial companies, it was revealed and proved that it was necessary to apply didactic methods and methods of responding to economic challenges as fundamental for teaching economics to bachelor's students.The study is aimed at revealing, providing a theoretical and methodological rationale for methods contributing to a growth of efficiency of shaping economic competence among bachelor's students in technical majors, when teaching economics.In view of the above objective and subject of the research, a key task is to develop economics teaching methods contributing to shaping economic competence among bachelor's students.In the paper the following research methods were used: analysis, synthesis, statistical methods of processing educational research result.Following the conducted research, the authors suggest that economics teaching methods should include a set of didactic methods and methods of responding to economic challenges, equally ensuring an appointment of bachelor's graduates as managers and contributing to their orientation to a professional career of a private industrial company director.
Multi-Modal Interactive Task Learning from Demonstrations and Natural Language Instructions

Interactive task learning (ITL) allows end users to 'teach' an intelligent agent new tasks, the corresponding task conditions,and the relevant concepts. This paper presents my research on expanding the applicability, generalizability, robustness, expressiveness, and script sharability of ITL systems usinga multi-modal approach. My research demonstrates that a multi-modal ITL approach that combines programming by demonstration and natural language instructions can empower users without significant programming expertise to extend intelligent agents for their own app-based computing tasks.
Penggunaan Telegram Sebagai Media Komunikasi Dalam Pembelajaran Online

Communication media are all means used to produce, reproduce, distribute or disseminate and convey information. Communication media plays an important role in people's lives. One of the communication media that is often used in learning is online media, such as whats app, email, classroom and telegram. The purpose of this research is to get a picture of the communication process in learning by utilizing telegraph communication media. The method used in this research is descriptive qualitative method. Online learning is a teaching and learning process that utilizes the internet and digital media in the delivery of material. As a means of communication media that support the learning process, the use of telegrams is a source of communication that makes it easy for students to obtain information through groups . Like face-to-face classes, in virtual classes (e-learning) must also be managed well in order to create a conducive class with the activeness and creativity of lecturers as well as students in them. Keywords: Communication Media, Telegram, Online Learning
Information and Analytical Support for Biomedical Research in the Field of the Cardiovascular Disease Risk Prediction

The aim of the article is to study Data mining methods to predict the cardiovascular disease risks' level, based on data from medical information systems.We propose a new approach to the development of information and analytical support for biomedical research.Based on the proposed approach, we have developed the special methods, technologies and services to extract and anonymize valid problem-oriented information from unstructured electronic medical records.Created data store contains more than 70,000 records of electronic medical records and provides the researcher anonymized "smart" information in accordance with the possible scenario of its use.The developed services for visualizing the values of objective indicators allow us to determine the optimal data structure for the diagnosis of a specific group of diseases.This is shown by the example of risk testers of cardiovascular diseases.Based on the selected indicators, models for personified prediction of the cardiovascular disease risks' level were developed and tested)/
SynET: Synonym Expansion using Transitivity

In this paper, we study a new task of synonym expansion using transitivity, and propose a novel approach named SYNET, which considers both the contexts of two given synonym pairs.It introduces an auxiliary task to reduce the impact of noisy sentences, and proposes a Multi-Perspective Entity Matching Network to match entities from multiple perspectives.Extensive experiments on a realworld dataset show the effectiveness of our approach.
Continuous Learning in a Single-Incremental-Task Scenario with Spike Features

Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is trained on a first task and the same DNN is trained on the next task it forgets the first task. This phenomenon of forgetting previous tasks is also referred to as catastrophic forgetting. On the other hand a mammalian brain outperforms DNNs in terms of energy efficiency and the ability to learn sequentially without catastrophically forgetting. Here, we use bio-inspired Spike Timing Dependent Plasticity (STDP) in the feature extraction layers of the network with instantaneous neurons to extract meaningful features. In the classification sections of the network we use a modified synaptic intelligence that we refer to as cost per synapse metric as a regularizer to immunize the network against catastrophic forgetting in a Single-Incremental-Task scenario (SIT). In this study, we use MNIST handwritten digits dataset that was divided into five sub-tasks.
Moderating Our (Dis)Content: Renewing the Regulatory Approach

As online platforms become central to our democracies, the problem of toxic content threatens the free flow of information and the enjoyment of fundamental rights. But effective policy response to toxic content must grasp the idiosyncrasies and interconnectedness of content moderation across a fragmented online landscape. This report urges regulators and legislators to consider a range of platforms and moderation approaches in the regulation. In particular, it calls for a holistic, process-oriented regulatory approach that accounts for actors beyond the handful of dominant platforms that currently shape public debate.
Intelligent Analyses on Storytelling for Impact Measurement

This paper explores how Dutch diary fragments, written by family coaches in the social sector, can be analysed automatically using machine learning techniques to quantitatively measure the impact of social coaching.The focus lays on two tasks: determining which sentiment a fragment contains (sentiment analysis) and investigating which fundamental social rights (education, employment, legal aid, etc.) are addressed in the fragment.To train and test the new algorithms, a dataset consisting of 1715 Dutch diary fragments is used.These fragments are manually labelled on sentiment and on the applicable fundamental social rights.The sentiment analysis models were trained to classify the fragments into three classes: negative, neutral or positive.Fine-tuning the Dutch pre-trained Bidirectional Encoder Representations from Transformers (BERTje) (de Vries et al., 2019) language model surpassed the more classic algorithms by correctly classifying 79.6% of the fragments on the sentiment analysis, which is considered as a good result.This technique also achieved the best results in the identification of the fundamental rights, where for every fragment the three most likely fundamental rights were given as output.In this way, 93% of the present fundamental rights were correctly recognised.To our knowledge, we are the first to try to extract social rights from written text with the help of Natural Language Processing techniques.
Detection of Weak Relevant Variables using Random Forests

In general, the purpose of a machine learning technique is to obtain an approximation of a function based on a set of training data. To obtain a good approximation of the function, the input variables irrelevant to the output should be removed in advance. The process used to remove the irrelevant input variables is known as feature selection. The existing feature selection methods generally select input variables that make it possible to approximate the function more accurately. As a consequence, these methods often fail to detect input variables that weakly affect the output, especially when the training data given are insufficient. In some practical applications of machine learning techniques, however, all of the input variables that actually affect the output must be detected. In this study, we propose a new feature selection method to overcome this drawback of the existing methods. Our method evaluates the relevance of a certain input variable by comparing it with a random variable. We show, through numerical experiments, that the proposed method is capable of detecting even input variables that weakly affect the output.
ASCII Art Classification Model by Transfer Learning and Data Augmentation

In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also used VGG16, ResNet-50, Inception v3, and Xception’s pre-trained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy rate of 80% or more was obtained in the “human” category.
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval

The limited availability of ground truth relevance labels has been a major impediment to the application of supervised methods to ad-hoc retrieval. As a result, unsupervised scoring methods, such as BM25, remain strong competitors to deep learning techniques which have brought on dramatic improvements in other domains, such as computer vision and natural language processing. Recent works have shown that it is possible to take advantage of the performance of these unsupervised methods to generate training data for learning-to-rank models. The key limitation to this line of work is the size of the training set required to surpass the performance of the original unsupervised method, which can be as large as 1013 training examples. Building on these insights, we propose two methods to reduce the amount of training data required. The first method takes inspiration from crowdsourcing, and leverages multiple unsupervised rankers to generate soft, or noise-aware, training labels. The second identifies harmful, or mislabeled, training examples and removes them from the training set. We show that our methods allow us to surpass the performance of the unsupervised baseline with far fewer training examples than previous works.
Research on the Curriculum Development of Education for Sustainable Development(ESD) from the Perspective of VR / AR assisted instruction——Take Guangdong Business and Technology University as an Example

Abstract On September 25, 2015, the United Nations Sustainable Development Summit adopted the 2030 Agenda for sustainable development. Education, as one of these goals, is an important way to achieve sustainable development, the function and responsibility of Universities to foster sustainable development should be emphasized. About the curriculum development of Education of Sustainable Development(ESD), along with VR/AR virtual reality equipment technology getting gradually mature, it is a trend that VR/AR equipment used in the class of universities. Based on this background, we proposed two questions: how to integrate VR/AR equipment into ESD teaching and learning? Does the VR/AR device hybrid model have a significant impact on the effects of the ESD course? A total of 46 sophomore students from Guangdong Business and Technology University were selected to participate in the quasi-course experiment. We conducted a questionnaire survey of students, used statistics analysis, compared the pre-test data with the post-test ones and interviewed a teacher. We concluded that VR/AR virtual reality equipment can be integrated into the curriculum of sustainable development. This teaching and learning model has a significant impact on students’ grasp of the three dimensions: The cognitive, social emotional and the behavioral dimension of the curriculum of sustainable development. By integrating VR/AR into traditional classes, we can realize the virtualization of teaching environment and the liberalization of teaching space, further stimulate students’ learning motivation and practical interest, and realize situational and immersive teaching and learning.
Penerapan visual basic for application spreadsheet excel untuk simulasi konsep motor listrik tiga fasa dan aplikasinya sebagai media pembelajaran

This research is a study that aims to: (1) simulate a three-phase electric motor using an Excel spreadsheet; (2) knowing the Excel Spreadsheet application on learning media.This study uses the VBA Excel 2013 Spreadsheet found on a laptop.All stages of research are carried out entirely in the Physics Education Computer Laboratory.The result data, several things are important notes: (1) simulations that have been made using the Visual Basic for Application Excel Spreadsheet can adjust to the real situation that occurs in the simulation of a three-phase electric motor by changing the parameter data and assumed functions according to the circumstances actually, as well as Excel VBA Macros are very useful for giving command buttons to cells whose data we want to change, so that it can give a visual impression to the graph that has been created; (2) based on the simulation test results it can be said that the manufacture of a three-phase electric motor simulation as a learning medium using the spreadsheets and macro VBA features in Ms. Excel can be used to simulate a case related to a three-phase electric motor that is given a current, a magnetic field will arise that can rotate the rotor and can analyze changes in the position of each phase in a three-phase electric motor.
Conceptual Planning and Design of Sponge Campus--Taking the South Campus of Puwan Campus of Dalian Vocational and Technical College as An Example

Abstract This article fully understands the goals and requirements of the national guidance to promote the construction of sponge cities. The author fully understands the current situation of research and construction of rainwater management, sponge cities and sponge campuses in domestic and foreign cities. Based on the natural and artificial environment characteristics of the South Campus of the Puwan Campus of Dalian Vocational and Technical College (hereinafter referred to as the campus), this article focuses on the prevention of waterlogging in the rainy season and the collection and utilization of rainwater in the campus, and follows the concept of LID, and proposes a sponge campus “2 principles→4 blocks” →8 sub-items” conceptual planning and design plan. Through the conceptual design of small, source and decentralized control facilities such as permeable pavement, sunken green space, ecological grass ditch, rainwater garden, and underground storage structure, the author constructs a simulation of nature through in-situ collection, natural purification, and replenishment of groundwater.
Effect on Self-Directed Learning Abilities, Interpersonal Understanding, and Satisfaction with the Class among University Students based in a Team-Project Lesson

The purpose of this study is to establish the relationship between self-directed learning abilities, interpersonal understanding, and satisfaction of participants in team-based, focused classes. To achieve the purpose of this research, 409 university students who participate in team-based classes at a university located in South Korea were selected as candidates for the study. For data processing methods, we did frequency analysis, reliability analysis, and correlation analysis using SPSS 21.0 for Window, and used a structural equation model to verify the analysis of confirmatory factors and the theory of research using AMOS 18.0. Our findings were as follows. First, the self-directed learning ability of the team-based class participants affected interpersonal understanding. Second, the self-directed learning ability of team-based learning class participants affected satisfaction with the class. Third, the understanding of interpersonal relationships between team-based class participants affected the level of satisfaction with the class.
A development of computer vision elements for processing the results of tensile tests of specimens and building diagrams of ultimate plasticity

Abstract The use of specimens with a round notch allows expanding the application of tensile tests in order to study the ultimate plasticity of materials. However the main difficulty in processing the results of experiments is the need to measure the radius of curvature of the neck and the diameter in the minimum cross-section of the specimen. These dimensions determine a stress triaxiality value of the material in the center of the neck according to the Bridgman model or the other ones. This article is devoted to the development of elements of a computer vision system designed to measure the neck, which is formed both on cylindrical specimens and on specimens with a notch. To extract the contour of the specimen and to measure the forming neck, the video recording of the test process on a digital single-lens reflex (DSLR) camera is used. The paper proposes a method for recording the test process using the background, which allows to increase the accuracy of determining the contour of the specimen during subsequent image processing. The Sobel filter is used to extract the contour of the specimen, and a special neck profile equation allows to determine the neck dimensions.