Although the analysis of data is a task that has gained the interest of the statistical community in recent years and whose familiarity with the statistical computing environment, they encourage the current statistical community (to students and teachers of the area) to complete statistical analysis reproducible by means of the tool R. However for years there has been a gap between the calculation of matrices on a large scale and the term "big data", in this work the Normalized Cut algorithm for images is applied. Despite the expected, the R environment to do image analysis is poorly, in comparison with other computing platforms such as the Python language or with specialized software such as OpenCV.
Being well known the absence of such function, in this work we share an implementation of the Normalized Cut algorithm in the R environment with extensions to programs and processes performed in C ++, to provide the user with a friendly interface in R to segment images. The article concludes by evaluating the current implementation and looking for ways to generalize the implementation for a large scale context and reuse the developed code.
Key words: Normaliced Cut, image segmentation, Lanczos algorithm, eigenvalues and eigenvectors, graphs, similarity matrix, R (the statistical computing environment), open source, large scale and big data.
O conceito de automação residencial é definido como o conjunto de serviços proporcionados por sistemas tecnológicos
integrados, sendo a melhor maneira de satisfazer as necessidades básicas de segurança, comunicação, gestão energética
e conforto de uma habitação. Seguindo essa concepção, surgiu-se a ideia da criação de um Kit automatizado para
janelas utilizando a plataforma Arduíno, visando a solução de problemas do dia a dia como o transtorno causado pela
chuva e principalmente, gerando praticidade e comodidade para os cidadãos.
In this paper, we evaluate a baseline word embedding model for a set of clinical notes derived from patient records. For our baseline, we extract features for this embedding using the Word2Vec module from the gensim package. We also build two models, a word2vec skipgram model with negative sampling and a positive point-wise mutual information (PPMI) model by training on the processed clinical notes. Our evaluation shows that both the PPMI and the skipgram models show improved results for medically-related terms when compared with the baseline model. PPMI shows the best result out of all three models.
This paper provides a sample of a LATEX document for the NIME conference series. It conforms, somewhat loosely, to the formatting guidelines for ACM SIG Proceedings. It is an alternate style which produces a tighter-looking paper and was designed in response to concerns expressed, by authors, over page-budgets. It complements the document Author’s (Alternate) Guide to Preparing ACM SIG Proceedings Using LATEX2ε and BibTEX. This source file has been written with the intention of being compiled under LATEX2ε and BibTeX.
We all have a good reason to learn a new language; discovering our roots, passion for travel, academic purposes, pure interest etc. However most of us find it hard to become conversationally fluent in a new language while we use traditional resources for learning like textbooks and tutorials on the internet. In this paper we propose a novel approach to learn a new language. We aim to develop an intelligent browser extension, LanGauger, that will help users learn foreign languages. This application will allow users to look up words while they are browsing, by highlighting the text to be learned. The application will then provide a translation of the word, its pronunciation and its usage context in sentences. In addition, this intelligent tutor will also remember what words have been seen by the user, and quiz them on these words at appropriate times. While testing the recall of the user, this feature will also allow users to frequently think about the language and use it.
This is a skeleton file demonstrating the use of IEEEtran.cls (requires IEEEtran.cls version 1.8b or later) with an IEEE Computer
Society conference paper.
For other IEEE conferences, please see the IEEE conference paper template, and to find additional IEEE templates please use the tags below.
IEEEtran.cls version: 1.8b
Instructions for preparing papers for the CMBEC are presented. They are intended to guide the authors in preparing the electronic version of their paper. Only papers prepared according to these instructions will be published in the online version of proceedings.