The ability to solve physics problems in symbolic and numeric representations

The aim of this study is to analyze the ability of students to solve the problems of linear motion kinematics expressed in symbolic and numeric representation. Research was survey with cross-sectional design. Research subjects included 26 first year undergraduate students in physics at one of the State Universities in Malang which was consisted of 10 men and 16 women. The research instrument was open-ended test of linear motion kinematics problems expressed in symbolic and numeric representations with a reability of 0,807 The research data were analyzed using descriptive and non-parametric inferential statistics. The results showed that the ability of students to solve linear motion kinematics problems in both symbolic and numeric representation was medium. Students had difficulty solving physical problems in both symbolic and numeric representations. It was also found that the problems of linear motion kinematics in symbolic representations were more difficult for students to solve than numeric representations. The study suggested further research to explore the causes of student difficulties more authentically, e.g. by interviewing or thinking aloud.

Analyzing mixed-type data by using word embedding for handling categorical features

Most of real-world datasets are of mixed type including both numeric and categorical attributes. Unlike numbers, operations on categorical values are limited, and the degree of similarity between distinct values cannot be measured directly. In order to properly analyze mixed-type data, dedicated methods to handle categorical values in the datasets are needed. The limitation of most existing methods is lack of appropriate numeric representations of categorical values. Consequently, some of analysis algorithms cannot be applied. In this paper, we address this deficiency by transforming categorical values to their numeric representation so as to facilitate various analyses of mixed-type data. In particular, the proposed transformation method preserves semantics of categorical values with respect to the other values in the dataset, resulting in better performance on data analyses including classification and clustering. The proposed method is verified and compared with other methods on extensive real-world datasets.

ZIP Code Versus Georeference

When dealing with predictive modeling of credit-granting, different types of attributes are used: Cadastral, Behavioral, Business / Proposal, Credit Bureaux, in addition to Public, Private or Subsidiaries Sources. The Postal Address Code (Código de Endereçamento Postal CEP in Portuguese) in Brazil, in particular, has a unique contribution capacity (uncorrelated with most other attributes in general) and reasonably good predictive power. CEP is frequently used by truncating its numeric representation, considering the first d digits, for example. In this report, a preliminary methodology is proposed, aiming to elaborate clustering sets of CEPs by considering the information of clients' defaults over a period of time. Additionally, we tested the number of clusters obtained using the Information Value criterion. Promising solutions are obtained using statistical and optimizing approaches. Other methodologies are suggested and could be complementary with the principal methodology proposed.

Women Representation in Nepalese Local Election 2017: Issues and Challenges

Democracy is for power and justice. Women's participation in politics is for achieving these twin goals. The main objective of this study is to reviews the women representation at Nepalese local in political domain. The essential data and information are collected from secondary sources. Reports, information, facts, figures, policies, acts and program published by Nepal Election Commission-2017 and other related sources reports are basically used in this paper. One of the key factors to ensure higher and meaningful participation of women in politics is these favorable (reservation) electoral provisions. The 2017 local elections were significant in advancing female political representation in Nepal. However, what has been achieved so far is not enough and continued concentrated action is essential. There are more issues and challenges to be resolve for fair and meaningful political participation of women. The women's representation in politics should not be just a numeric representation. It should rise in the societal awareness for women and build capacity of female leaders and ultimately helps in the overall development of the country.

Embedded linear model predictive control in field-programmable gate array using register-transfer level implementation and floating-point representation applied to a quadrotor system model

Model predictive control is increasingly becoming a popular control strategy for a wide range of applications in both industry and academia, mainly motivated by its ability to systematically handle constraints imposed on a system, regardless of its nature. However, this generates high computational demands, limiting the applicability of model predictive control. Field-programmable gate arrays are reconfigurable hardware platforms that allow the parallel implementation of model predictive control, accelerating such algorithms, but most works found in the literature opt to use high-level synthesis tools and fixed-point numeric representation to generate embedded controllers, resulting in faster-designed solutions but not exactly efficient and flexible ones, that can be applied to different scenarios. Regarding such matter, this work proposes the manual implementation (register-transfer level implementation) of linear model predictive control and the usage of floating-point numeric representation applied to a quadrotor system. The initial results obtained using the proposed controller are presented in this article, achieving 29.34 ms of calculation time at 50 MHz for the attitude control of a quadrotor model containing twelve states and four control outputs.

Fast and efficient Rmap assembly using the Bi-labelled de Bruijn graph

Genome wide optical maps are high resolution restriction maps that give a unique numeric representation to a genome. They are produced by assembling hundreds of thousands of single molecule optical maps, which are called Rmaps. Unfortunately, there are very few choices for assembling Rmap data. There exists only one publicly-available non-proprietary method for assembly and one proprietary software that is available via an executable. Furthermore, the publicly-available method, by Valouev et al. (Proc Natl Acad Sci USA 103(43):15770–15775, 2006), follows the overlap-layout-consensus (OLC) paradigm, and therefore, is unable to scale for relatively large genomes. The algorithm behind the proprietary method, Bionano Genomics’ Solve, is largely unknown. In this paper, we extend the definition of bi-labels in the paired de Bruijn graph to the context of optical mapping data, and present the first de Bruijn graph based method for Rmap assembly. We implement our approach, which we refer to as rmapper, and compare its performance against the assembler of Valouev et al. (Proc Natl Acad Sci USA 103(43):15770–15775, 2006) and Solve by Bionano Genomics on data from three genomes: E. coli, human, and climbing perch fish (Anabas Testudineus). Our method was able to successfully run on all three genomes. The method of Valouev et al. (Proc Natl Acad Sci USA 103(43):15770–15775, 2006) only successfully ran on E. coli. Moreover, on the human genome rmapper was at least 130 times faster than Bionano Solve, used five times less memory and produced the highest genome fraction with zero mis-assemblies. Our software, rmapper is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/Rmapper.

Novel PSSM-Based Approaches for Gene Identification Using Support Vector Machine

By understanding the function of each protein encoded in genome, the molecular mechanism of the cell can be recognized. In genome annotation field, several methods or techniques have been developed to locate or predict the patterns of genes in genome sequence. However, recognizing corresponding gene of a given protein sequence using conventional tools is inherently complicated and error prone. This paper first focuses on the issue of gene prediction and its challenges. The authors then present a novel method for identifying genes that involves a two-step process. First the research presents new features extracted from protein sequences using a position specific scoring matrix (PSSM). The PSSM profiles are converted into uniform numeric representation. Then, a new structured approach has been applied on PSSM vector which uses a decision tree-based technique for obtaining rules. Finally, the rules of single class are joined together to form a matrix which is then given as an input to SVM for classification purpose. The rules derived from algorithm correspond to genes. The authors also introduce another approach for predicting genes based on PSSM using SVM. Both the methods have been implemented on genome DNAset dataset. Empirical evaluation shows that PSSM based SAFARI approach produces better results.

Fast and Efficient Rmap Assembly using the Bi-labelled de Bruijn Graph

Genome wide optical maps are high resolution restriction maps that give a unique numeric representation to a genome. They are produced by assembling hundreds of thousands of single molecule optical maps, which are called Rmaps. Unfortunately, there exists very few choices for assembling Rmap data. There exists only one publicly-available non-proprietary method for assembly and one proprietary method that is available via an executable. Furthermore, the publicly-available method, by Valouev et al. (2006), follows the overlap-layout-consensus (OLC) paradigm, and therefore, is unable to scale for relatively large genomes. The algorithm behind the proprietary method, Bionano Genomics' Solve, is largely unknown. In this paper, we extend the definition of bi-labels in the paired de Bruijn graph to the context of optical mapping data, and present the first de Bruijn graph based method for Rmap assembly. We implement our approach, which we refer to as Rmapper, and compare its performance against the assembler of Valouev et al. (2006) and Solve by Bionano Genomics on data from three genomes - E. coli, human, and climbing perch fish (Anabas Testudineus). Our method was able to successfully run on all three genomes. The method of Valouev et al.(2006) only successfully ran on E. coli. Moreover, on the human genome Rmapper was at least 130 times faster than Bionano Solve, used five times less memory and produced the highest genome fraction with zero mis-assemblies. Our software, RMAPPER is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/Rmapper.

Homeomorphic architecture: Radial prisons and contracted graphs

This paper introduces a type of graph called ‘homeomorphically irreducible tree’ (HIT) and explores its analytical and computational aspects in the architecture of radial prison plans. As a theoretical introduction, HITs are first diagrammatically presented using a taxonomy of 20 different radial prisons. Using this analysis, a generative algorithm that transforms plan connectivity to a simple sequential numeric representation is developed. This method is applied as an architectural plan generator that is parametrically explored using graphs as building skeletons with configurable wing typologies. The aim of the paper is to lay the foundation of a new graph-based approach for the morphogenetic study of symmetry in architectural plans.

CODE2VEC Based Cognitive Agent System to Retrieve Relevant Code Component from Repository

The cognitive agent system helps to retrieve most relevant code component by introducing latest techniques. In this paper the authors used latest approach of code embedding which undergoes code2vec tokenization model by tokenizing and converting the code components present in the dataset into a numeric representation to create a input for neural network environment and also implemented cosine similarity matching technique to acquire the relevancy and perform retrieval of code component.