Reviewerly/exHarmony huggingface dataset:
Achieving More Page Views Through Search Engine Optimization

A user who wants to search for any data online uses a search engine which displays a list of relevant websites based on their ranking in the search engine result pages. In general, the user will go through only the sites which are displayed in the top positions of the results page. So, the owners of the websites should see that their site is listed in the initial positions to get more views. Especially a commercial or business website should get more viewers to their website compared to that of their competitors. Search engine optimization is an art of designing a website by using different techniques to get a good ranking by the search engine. This paper proposes some new SEO techniques. The experimentation is done with Google search engine, and the results proved that the new techniques, if followed along with the existing techniques, can result more traffic to a website.
First data on the geochemistry of rare earth elements and platinoids in the rocks of the gold mining deposit Ulyuk-Bar (the Southern Urals)

Research subject . The object of the study is the Ulyuk-Bar gold deposit ore-bearing rocks, located in the Bashkir meganticlinorium (the Southern Urals). Methods . The article presents the first data on the distribution of rare earth elements (REE), platinum group elements (PGE) and Au, obtained by inductively coupled plasma mass spectrometry (ICP-MS) on “ELAN- 6100 DRC” and “Agilent 7700” with the use of the computer data processing program “TOTALQUANT”, which includes automatic accounting of isotopic and molecular overlays on the mass spectral analytical lines of the determined elements. The chemical composition of ore minerals (native gold, pyrite, pyrrhotite, gersdorfite, chalcopyrite, arsenopyrite, and galena) was determined using an REMMA-202M scanning electron microscope with an LZ-5 X-ray energy-dispersive spectrometer and detectors for secondary and reflected electrons. Results. As a result of the studies, the enrichment of heavy lanthanides from ore-bearing rocks of the Ulyuk-Bar deposit in comparison with the sandstones and shales of the Bolshoi Inzer Formation outside of the ore field was established. It is shown that the noble-metal geochemical specialization of ore-bearing sediments of the Bolshoi Inzer Formation of the Ulyuk-Bar deposit is similar in a number of parameters to rocks of the Mashak Formation of the Shatak complex, formed under the influence of Middle Riphean magmatism on sedimentary rocks of the upper layers of the earth’s crust. Based on the calculation of the age of the minerals thorium and uranium, according to the isotopic studies of Rb-Sr (996 ± 26 Ma) and K-Ar (676–706 Ma) methods and galena by the Pb-Pb method (950 Ma), it was established that mineralization was formed in two stages. The first of which is associated with tectono-thermal activation at the border of the Middle and Late Riphean, and the second was realized about 600 Ma ago. Conclusions. It is concluded that the REE-EPG-Au taxonomy of ore-bearing deposits of the Ulyuk-Bar deposit is due to the polychrony and polygenicity of ore formation processes associated with the Middle Riphean plume magmatism, manifested over a vast territory, and the Late Vendian dynamothermal collisional metamorphism.
Graph technologies in an intelligent system of complex analysis of data from Internet sources

The purpose of the work outlined in the article is to review and demonstrate the use of graph technologies for deep data analysis. The first part of the article discusses the Intelligent System for the Comprehensive Analysis of Internet Sources Data and its possible directions for its further development. This system is a multi-purpose cluster using technologies for constructing a knowledge graph, methods and models of machine learning for in-depth analysis of data from Internet sources (for example, scientific publications, social networks, media). The purpose of the analysis is to identify the most important publications in a certain area (for example, in robotics, space research, healthcare, in the social sphere), thematic analysis of these publications, to identify the leader of a scientific direction and to predict trends in the development of directions and interaction of groups of people. When developing this system, we utilized probabilistic machine learning algorithms and methods for constructing and maintaining a graph model of the social network of authors and their publications, determining the rating of a particular author, determining the topics of publications and classifying them by areas of knowledge. The basis for the creation of intelligent applications is graph technology, which allows you to make predictions that are more accurate. The combined application of methods and algorithms of machine learning with graph technologies allows you to get hidden dependencies and perform predictive analysis of information, get answers in real time, and implement artificial intelligence algorithms. Methods of collaboration with graph technologies and a learning machine (for example, using neural networks) are based on graph embedding. This technology allows you to perform a comprehensive, deep and intelligent analysis of information. At the end of the article, there are analytical reports obtained using graph technologies in the Intelligent System for Complex Analysis of Internet Sources Data.
APLIKASI METODE FUZZY TSUKAMOTO DALAM SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN JUMLAH PERSEDIAAN BERAS PADA PERUM BULOG DIVRE SUMUT

Indonesia's food security development, which guarantees the availability and consumption of adequate food, safe, quality,and nutritious balance at the household, regional, and national levels throughout the time and evenly. The food supply thatcan be accessed by any household at affordable prices is very important for the creation of food security, and the economicdevelopment of Indonesia. Many methods are used to deal with uncertainty in determining the rice supply. One is to usefuzzy logic. The fuzzy logic is a logic that confronts the concept of a partial truth, where the classical logic states thateverything can be expressed in binary terms (0 or 1).
Mining Local Discourse Annotation for Features of Global Discourse Structure

Descriptive approaches to discourse (text) structure and coherence typically proceed either in a bottom-up or a top-down analytic way. The former ones analyze how the smallest discourse units (clauses, sentences) are connected in their closest neighbourhood, locally, in a linear way. The latter ones postulate a hierarchical organization of smaller and larger units, sometimes also represent the whole text as a tree-like graph. In the present study, we mine a Czech corpus of 50k sentences annotated in the local coherence fashion (Penn Discourse Treebank style) for indices signalling higher discourse structure. We analyze patterns of overlapping discourse relations and look into hierarchies they form. The types and distributions of the detected patterns correspond to the results for English local annotation, with patterns not complying with the tree-like interpretation at very low numbers. We also detect hierarchical organization of local discourse relations of up to 5 levels in the Czech data.
A Comparative Analysis of R and Weka for Market Basket Analysis Using Apriori Algorithm

Now, in this competitive era, it has become very essential for retail organizations to retain old customers and simultaneously attract new customers. To fulfill this need, business organizations are taking up many approaches to smooth the progress of their customers in various ways. To bound customers purchasing from them market basket analysis technique using association rule mining technique creates the associations among the items purchased collectively by using market basket analysis. This association rule analysis technique vastly applied for various benefits like identifying the majority of sold products and identifying buyers on the basis of their purchasing pattern. Several researchers have implemented various algorithms for association rule mining. This paper comprises results of association rule mining using Apriori algorithm in Weka and R programming. On the basis of results, a comparative analysis is made between R and Weka. This paper uses the transaction data of sales (a grocery store) for experiment.
Say it with Emojis : Co-designing relevance cues for searching in the classroom

Search Engine Result Pages (SERP) include snippets of retrieved resources as a means to help searchers select the ones that satisfy their information needs. This way, result relevance can be determined by scanning through snippets, an exercise that requires experience with reading, understanding, and assessing the value of a document. These are skills that primary school children are still developing and thus are not yet proficient with. As web search tools are essential to support children learning at school and home, we explore how to help young searchers in making informed relevance assessments while conducting searches in a classroom. In this paper, we describe a collaborative design exercise involving primary school children as co-designers: we asked them to examine interfaces with combinations of different emojis to help them assess the usefulness of results in SERP-a crucial factor to determine relevance for the classroom. This activity made our child experts engage with the design exercise while enabling us to collect their judgments so as to get a better sense of the user requirements for this age group. Here we discuss the main design issues emerging from the analysis of children's preferences, the rationale behind them, comments and concerns raised, and alternative proposals children sketched.
The Role of Discretization of Continuous Variables in Socioeconomic Classification Models on the Example of Logistic Regression Models and Artificial Neural Networks

Logistic regression models and artificial neural networks require the use of appropriate quality data. One of the methods of improving the quality of raw data is the discretization of continuous variables. It can be a way to deal with outliers and influential observations and can be helpful when the assumptions of some of the models are not met. This paper shows that despite the fact that the discretization of continuous variables means that reduced information is used for the modeling, it can improve the classification accuracy of machine learning models. This is particularly important when searching for the best predictive model when a limited set of explanatory variables is available, as well as when analyzing large data sets. In addition, by selecting the methods used to discretize continuous variables we decide about the number and type of variables that are included in the model and, as a result, are subject to interpretation. The selection of cut-off points matching the purpose of the research can be made using supervised discretization methods. In this study, the data from the Generations and Gender Survey (GGS) for Poland was used. The status of respondents on the labor market was considered. For the considered data, the advantages of using supervised discretization of continuous variables based on the entropy criterion and the Gini criterion were pointed out. Importantly, discretization based on these methods provided predictive models of better classification accuracy than the models based on discretization procedure frequently applied in socioeconomic studies.
NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature.

We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classification, and 5. text classification. In this article, we describe the outcomes of this pilot annotation phase. Through the exercise we have obtained an annotation methodology; and found ten core information units that reflect the contribution of the NLP-ML scholarly investigations. The resulting annotation scheme we developed based on these information units is called NLPContributions. The overarching goal of our endeavor is four-fold: 1) to find a systematic set of patterns of subject-predicate-object statements for the semantic structuring of scholarly contributions that are more or less generically applicable for NLP-ML research articles; 2) to apply the discovered patterns in the creation of a larger annotated dataset for training machine readers of research contributions; 3) to ingest the dataset into the Open Research Knowledge Graph (ORKG) infrastructure as a showcase for creating user-friendly state-of-the-art overviews; 4) to integrate the machine readers into the ORKG to assist users in the manual curation of their respective article contributions. We envision that the NLPContributions methodology engenders a wider discussion on the topic toward its further refinement and development. Our pilot annotated dataset of 50 NLP-ML scholarly articles according to the NLPContributions scheme is openly available to the research community at this https URL.
Miniaturized and Highly Randomized True Random Bit Generator

This paper introduces true random bit generator (TRBG) with a higher quality of randomness based on chaos circuit and unpredictable linear feedback shift register (LFSR). This miniaturized true random bit generator made of two stages of randomness. The first stage produces the continuous random variable, and the second stage provides the Metastability to the circuit. The chaos circuit gives immunity from the side channel attack, and the circuit has Metastability for every events of bit generation and also ensures a 1 Mbps bit rate data output. This generator can be used as the seed generator for any encryption design (like image encryption), the true random generator is designed for the cheaper devices like complex programmable logic devices (CPLDs) and field programmable gate arrays (FPGAs) for any lightweight encryption design. The National Institute of Standard and Technology (NIST) tests of randomness are applied for testing the randomness of TRBG.
Relationship among Achievement goal, Perception of errors and Grit of Taekwondo Athletes in Middle and High Schools

In order to achieve this, a sample of 410 middle and high school Taekwondo players was by using purposive sampling method selected from the seoul gyeonggi and inchen area. data acquired from 11 middle and high school taekwondo players were excluded due to inconsistency in their responses. Therefore, the data of 394 people was used for a final analysis. For data processing, frequency analysis, correlation analysis, confirmatory factor analysis, and structural equation modeling were performed using SPSS 23.0 and Amos 23.0 programs. First, it was found that the achievement goal had a partial effect on the realization. Specifically, the task goal, which is a sub-factor of the achievement goal, was found to have a positive effect on the realization. Second, achievement goals were found to have a partial effect on grit. Specifically, the task goal, a sub-factor of the achievement goal, was found to have a positive effect on the grit. Third, it was found that real recognition has a positive effect on grit.
iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool

In recent years, deep neural networks have reached state of the art performance across many different domains. Computer vision in particular has benefited immensely from deep learning. Despite their high performance, deep neural networks often lack interpretability and are mostly regarded as a black box. Therefore, the availability of tools capable to provide insights into the models and identify potential errors is crucial. Such tools need to seamlessly integrate within the workflow of data scientists and ML researchers. In this paper we propose iNNvestigate-GUI, an open-source graphical toolbox which offers an extensive set of functionalities for users to compare different networks behavior and give an explanation to their outputs.
Even Better Fixed-Parameter Algorithms for Bicluster Editing

Given a bipartite graph G, the BICLUSTER EDITING problem asks for the minimum number of edges to insert or delete in G so that every connected component is a bicluster, i.e. a complete bipartite graph. This has applications in various areas such as social network analysis and bioinformatics. We study the parameterized complexity of the problem, the best published algorithm so far attaining a time of $$O^*(3.24^k)$$ , with k the number of edges to edit. Using novel but intuitive ideas, we significantly improve this to an $$O^*(2.695^k)$$ time complexity. Our algorithm has the advantage of being conceptually simple and does not require tedious case handling. Previous approaches were based on finding a forbidden induced subgraph (e.g. a $$P_4$$ ) and branching into several ways of eliminating such a subgraph. We take a departure from this local viewpoint, and instead solve conflicts globally. That is, we take two vertices that prevent the graph from containing only biclusters, and branch into the ways of resolving all the conflicts they are part of, at once. We hope that these ideas will allow simpler algorithms for other forbidden induced subgraph problems. As a complementary result, we also show that BICLUSTER EDITING admits a problem kernel with 5k vertices.
Aprendizaje Basado en la Indagación en el Contexto Educativo Español

Inquiry based learning is constituted as an opportunity for the development of the student body through techniques arranged from the pedagogy focused on the use of the method and design of scientific studies. In this research, 302 surveys are applied to instructors belonging to Spanish institutions in order to examine the strategies, phases and actions of inquiry-based learning. The results show that the strategies focus on the optimization of informational literacy; on the other hand, the phase called orientation is configured towards the responsibility of stimulating the interest of the student body and as for the actions, it is evident the adaptation of the scientific method both in the critical development and the obtaining of evidence from the empirical plane. Hence, the need to evaluate media supports in the teaching-learning process based on inquiry and to complement the impact with future regional analysis.
Noise-robust MRI upsampling using adaptive local steering kernel

Upsampling and denoising of magnetic resonance images are conventionally performed separately, which would introduce unwanted artifacts such as blurring. To address this problem, we propose an innovative adaptive interpolation framework to achieve simultaneous image upsampling, denoising, and detail enhancement. First, local steering kernel (LSK) function is leveraged to adapt the interpolation weights according to geometric structures in the magnetic resonance (MR) images. An adaptive sharpening of the LSK weight matrix and a Rician bias correction are then adopted to remove Rician noise and enhance fine details. In this regard, the adaptive LSK extends the zero-order point estimation framework to higher orders of regression, and therefore facilitates edge preservation and detail reconstruction. The post-processing Rician correction avoids the bias caused by the asymmetry of Rician noise distributions. Experimental results using both real and synthetic clinical MR cranial images (with and without noise) demonstrated that our algorithm produced better reconstruction results than several traditional interpolation-based upsampling methods, including nearest neighbor (NN), non-local means (NLM), self-learning super resolution (SLSR), Gaussian process regression (GPR), and even comparable to four deep-learning-based methods but with less data requirements and computational complexity. The proposed technique resulted in PSNR and SSIM values were ~3%–16% higher than any of the other traditional algorithms tested, and our method recovered more clear textures from noisy images compared with deep-learning-based methods. As such, the presented technique is a viable new approach to MR upsampling, particularly for noisy images.
Design and Development of self-Adaptive Learning System Based on Data Analysis

In the era of rapid development of artificial intelligence and big data technology, the education departments are also experiencing the transformation from the traditional classroom to the blending learning mode mixed with online learning and offline classroom. In order to promote the modernization of education and realize the inclusiveness and individuation of future education, the self-adaptive learning platform is rising gradually, which emphasizes service orientation and provides personalized learning contents to students based on data collections and analyses, enhancing the pertinence and efficiency of students' learning, and improving students' learning experience. In this paper, the writer applies to big data and artificial intelligence technology into the educational environment and attempts to build an intelligent self-adaptive learning system to realize personalized teaching model for the purpose of serving for teachers and students. Besides, the writer also carries out some innovative explorations on personalized teaching based on learner-centered and improves teaching qualities with remindings and early warnings.
Development of a Semaphore of Anemia: Screening Method Based on Photographic Images of the Ungueal Bed Using a Digital Camera

Anemia is a disease present worldwide. High prevalence of anemia (43%) is found in the child population and its main long-term effect (slow cognitive development) can remain even if the disease has disappeared. One of the main reasons for the high prevalence of anemia in Peru is the poor screening coverage during the growth of the child due to the parents' fear of infringing pain on their children. We take advantage that anemia produces pallor in the hands, fingers and ungueal bed to develop a semaphore for this disease. This screening tool uses photographic images of the patient's ungueal bed to determine if they have a high, medium or low possibility of having anemia. Sixty people participated in the study and 6 photographic images for each participant's right hand were captured. The images were processed to extract regions of interest from each of the fingernails. Datasets were generated and a neural network was used to predict the risk of anemia. Initial results show that the proposed semaphore of anemia reaches a sensitivity of 0.79 and specificity of 0.91. These results indicate that the semaphore of anemia may be used as a screening method to reduce the number of blood tests and the time of evaluation from 15 minutes (rapid test with portable hemoglobinometer) to 1 minute.
We’ve Only Just Begun: Children Searching in the Classroom

In this extended abstract, we present an overview of our ongoing project. Specifically, we briefly discuss the motivation for our research agenda, research goals in the short and long term, and the body of work we have published thus far that serves as the foundation upon which we build the next steps related to Information Retrieval and Children in the Classroom Setting.