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Hiram Calvo

Bio: Hiram Calvo is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: WordNet & Thesaurus (information retrieval). The author has an hindex of 12, co-authored 113 publications receiving 565 citations. Previous affiliations of Hiram Calvo include Nara Institute of Science and Technology.


Papers
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Journal ArticleDOI
TL;DR: In this article, a set of avocados (10 samples) were used to follow the changes in image features during ripening by applying a computer vision system, extracting color and textural parameters.
Abstract: This work was undertaken to analyze the ripening process of avocados variety Hass (Persea americana Mill.) by image processing (IP) methodology. A set of avocados (10 samples) was used to follow the changes in image features during ripening by applying a computer vision system, extracting color and textural parameters. Other 16 avocados were used to evaluate the firmness and mass loss. Three maturity stages of avocados were established, and a classification was obtained by applying principal component analysis and k-nearest neighbor algorithm. During the ripening process (12 days), avocado firmness decreased from 75.43 to 2.63 N, while skin color values kept invariable during 6 days; after that, a decrement in the peel green color (a*) was observed (−9.68 to 2.32). Image features showed that during ripening the color parameters (L*, a*, and b*), entropy (4.29 to 4.00), angular second moment (0.287 to 0.360), and fractal dimension (2.58 to 2.44) had a similar path as compared to mass loss, a*, and firmness ripening parameters, respectively. Relationships between image features and ripening parameters were obtained. The parameter a* was the most useful digital feature to establish an acceptable percentage of avocado classification (>80%) in three different maturity stages found. Results obtained by means of IP could be useful to evaluate, at laboratory level, the ripening process of the avocados.

63 citations

Journal ArticleDOI
TL;DR: A novel Recurrent Neural Network Soft Sensor designed to estimate and predict the contact area that tires of a car are making with the ground by using a modified differential evolution algorithm.

41 citations

Journal ArticleDOI
01 Feb 2017
TL;DR: The motivation is to assess the effectiveness of support vector networks (SVN) on the task of detecting deception in texts, as well as to investigate to which degree it is possible to build a domain-independent detector of deception in text using SVN.
Abstract: Our motivation is to assess the effectiveness of support vector networks (SVN) on the task of detecting deception in texts, as well as to investigate to which degree it is possible to build a domain-independent detector of deception in text using SVN. We experimented with different feature sets for training the SVN: a continuous semantic space model source represented by the latent Dirichlet allocation topics, a word-space model, and dictionary-based features. In this way, a comparison of performance between semantic information and behavioral information is made. We tested several combinations of these features on different datasets designed to identify deception. The datasets used include the DeRev dataset (a corpus of deceptive and truthful opinions about books obtained from Amazon), OpSpam (a corpus of fake and truthful opinions about hotels), and three corpora on controversial topics (abortion, death penalty, and a best friend) on which the subjects were asked to write an idea contrary to what they really believed. We experimented with one-domain setting by training and testing our models separately on each dataset (with fivefold cross-validation), with mixed-domain setting by merging all datasets into one large corpus (again, with fivefold cross-validation), and with cross-domain setting: using one dataset for testing and a concatenation of all other datasets for training. We obtained an average accuracy of 86% in one-domain setting, 75% in mixed-domain setting, and 52 to 64% in cross-domain setting.

29 citations

Journal Article
TL;DR: A heuristic technique for converting a constituency treebank into a de- pendency treebank, which has 99% precision and 80% recall in identifying the head in the rules, which gives 92% accuracy in identifying dependencies between words.
Abstract: We present a heuristic technique for converting a constituency treebank into a de- pendency treebank. In particular, we comment on our experience in converting the Spanish treebank Cast3LB. We extract a context-free grammar from the treebank, automatically identify the head in each rule, and use this information for constructing the dependency tree. Our heuris- tics have 99% precision and 80% recall in identifying the head in the rules, which gives 92% accuracy in identifying dependencies between words.

29 citations

Book ChapterDOI
17 Feb 2009
TL;DR: This work experiments by using natural language techniques such as lemmatizing and using manual and automatic thesauri for improving question based document retrieval by comparing the answers given by a traditional information retrieval system and the answers to 21 questions given manually by the general lawyer of the National Polytechnic Institute.
Abstract: Previous work has shown that modeling relationships between articles of a regulation as vertices of a graph network works twice as better than traditional information retrieval systems for returning articles relevant to the question. In this work we experiment by using natural language techniques such as lemmatizing and using manual and automatic thesauri for improving question based document retrieval. For the construction of the graph, we follow the approach of representing the set of all the articles as a graph; the question is split in two parts, and each of them is added as part of the graph. Then several paths are constructed from part A of the question to part B, so that the shortest path contains the relevant articles to the question. We evaluate our method comparing the answers given by a traditional information retrieval system--vector space model adjusted for article retrieval, instead of document retrieval--and the answers to 21 questions given manually by the general lawyer of the National Polytechnic Institute, based on 25 different regulations (academy regulation, scholarships regulation, postgraduate studies regulation, etc.); with the answer of our system based on the same set of regulations. We found that lemmatizing increases performance in around 10%, while the use of thesaurus has a low impact.

27 citations


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Journal ArticleDOI

6,278 citations

Journal ArticleDOI
TL;DR: This review discusses the techniques and procedures for the measurement and analysis of colour in food and other biomaterial materials, focusing on the instrumental and visual measurements for quantifying colour attributes and highlights the range of primary and derived objective colour indices used to characterise the maturity and quality of a wide range of food products and beverages.
Abstract: Colour is an important quality attribute in the food and bioprocess industries, and it influences consumer’s choice and preferences. Food colour is governed by the chemical, biochemical, microbial and physical changes which occur during growth, maturation, postharvest handling and processing. Colour measurement of food products has been used as an indirect measure of other quality attributes such as flavour and contents of pigments because it is simpler, faster and correlates well with other physicochemical properties. This review discusses the techniques and procedures for the measurement and analysis of colour in food and other biomaterial materials. It focuses on the instrumental (objective) and visual (subjective) measurements for quantifying colour attributes and highlights the range of primary and derived objective colour indices used to characterise the maturity and quality of a wide range of food products and beverages. Different approaches applied to model food colour are described, including reaction mechanisms, response surface methodology and others based on probabilistic and non-isothermal kinetics. Colour is one of the most widely measured product quality attributes in postharvest handling and in the food processing research and industry. Apart from differences in instrumentation, colour measurements are often reported based on different colour indices even for the same product, making it difficult to compare results in the literature. There is a need for standardisation to improve the traceability and transferability of measurements. The correlation between colour and other sensory quality attributes is well established, but future prospects exist in the application of objective non-destructive colour measurement in predictive modelling of the nutritional quality of fresh and processed food products.

1,232 citations

Proceedings ArticleDOI
01 Jun 2014
TL;DR: The CoNLL-2014 shared task was devoted to grammatical error correction of all error types as discussed by the authors, where a participating system is expected to detect and correct grammatical errors of all types.
Abstract: The CoNLL-2014 shared task was devoted to grammatical error correction of all error types. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results. Compared to the CoNLL2013 shared task, we have introduced the following changes in CoNLL-2014: (1) A participating system is expected to detect and correct grammatical errors of all types, instead of just the five error types in CoNLL-2013; (2) The evaluation metric was changed from F1 to F0.5, to emphasize precision over recall; and (3) We have two human annotators who independently annotated the test essays, compared to just one human annotator in CoNLL-2013.

484 citations