varunr14/newsummary huggingface dataset:
Identical twins pose a great challenge to face recognition due to high similarities in their appearances . Motivated by the psychological findings that facial motion contains identity signatures and the observation that twins may look alike but behave differently we develop a talking profile to use the identity signatures in the facial motion to distinguish between identical twins . The talking profile for a subject is defined as a collection of multiple types of usual face motions from the video . Given two talking profiles we compute the similarities of the same type of face motion in both profiles and then perform the classification based on those similarities . To compute the similarity of each type of face motion we give higher weights to more abnormal motions which are assumed to carry more identity signature information . Our approach named Exceptional Motion Reporting Model is unrelated with appearance and can handle realistic facial motion in human subjects with no restrictions of speed of motion or video frame rate . We first conduct our experiments on a video database containing 39 pairs of twins . The experimental results demonstrate that identical twins can be distinguished better by the talking profiles over the traditional appearance based approach . Moreover we collected a non twin YouTube dataset with 99 subjects . The results on this dataset verified that the talking profile can be the potential biometric . We further conducted an experiment to test the robustness of talking profile to the time . Videos from 10 subjects which span across years or even decades in their lives are collected . The results indicated the robustness of talking profile to the aging process . 


Summary: We prove that twins look alike but they behave differently. A talking profile consisting of usual facial motions is proposed as a biometric. Our model counts more on abnormal action and gains the superior performance. Our experiments show that the talking profile is robust to some aging effect.
This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized tomography images . Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition storage and mainly processing and analysis of the biomedical data . Also this work identifies the progress made so far evaluates the challenges to be overcome and provides an analysis of future prospects . As far as the authors know this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images which makes this work of noteworthy value . The research covered the published works in the Web of Science PubMed Science Direct and IEEEXplore up to December 2014 . Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective methodology and results . Based on the analysis of the selected works several studies were seen to be useful for the construction of medical diagnostic aid tools . However there are certain aspects that still require attention such as increasing algorithm sensitivity reducing the number of false positives improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and finally the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems . Based on this analysis we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks . 


Summary: A review about 3D automatic detection of pulmonary nodules in CT images is presented. Tasks tools public image databases and strategies are introduced. The integration with related data systems is taking into account. The techniques found are discussed and possible advances are identified. This review is interesting both for researchers and health professionals.
Recently there has been a growing interest in the field of metabolomics materialized by a remarkable growth in experimental techniques available data and related biological applications . Indeed techniques as nuclear magnetic resonance gas or liquid chromatography mass spectrometry infrared and UV visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery biotechnology and drug development . However as it happens with other omics data the analysis of metabolomics datasets provides multiple challenges both in terms of methodologies and in the development of appropriate computational tools . Indeed from the available software tools none addresses the multiplicity of existing techniques and data analysis tasks . In this work we make available a novel R package named specmine which provides a set of methods for metabolomics data analysis including data loading in different formats pre processing metabolite identification univariate and multivariate data analysis machine learning and feature selection . Importantly the implemented methods provide adequate support for the analysis of data from diverse experimental techniques integrating a large set of functions from several R packages in a powerful yet simple to use environment . The package already available in CRAN is accompanied by a web site where users can deposit datasets scripts and analysis reports to be shared with the community promoting the efficient sharing of metabolomics data analysis pipelines . 


Summary: The paper firstly reviews a number of tools for metabolomics data analysis identifying some of their limitations. The work proposes a novel R package that provides a set of methods for metabolomics and spectral data analysis. The provided functions include data loading pre processing metabolite identification univariate multivariate data analysis machine learning and feature selection. The implemented methods provide support for the analysis of data from experimental techniques including NMR GC and LC MS UV vis and IR. The paper provides a number of case studies illustrating the use of the package s functions covering different types of data and analyses types.
Background and objective Lung sound auscultation is one of the most commonly used methods to evaluate respiratory diseases . However the effectiveness of this method depends on the physician s training . If the physician does not have the proper training he she will be unable to distinguish between normal and abnormal sounds generated by the human body . Thus the aim of this study was to implement a pattern recognition system to classify lung sounds . Methods We used a dataset composed of five types of lung sounds normal coarse crackle fine crackle monophonic and polyphonic wheezes . We used higher order statistics to extract features Genetic Algorithms and Fisher s Discriminant Ratio to reduce dimensionality and k Nearest Neighbors and Naive Bayes classifiers to recognize the lung sound events in a tree based system . We used the cross validation procedure to analyze the classifiers performance and the Tukey s Honestly Significant Difference criterion to compare the results . Results Our results showed that the Genetic Algorithms outperformed the Fisher s Discriminant Ratio for feature selection . Moreover each lung class had a different signature pattern according to their cumulants showing that HOS is a promising feature extraction tool for lung sounds . Besides the proposed divide and conquer approach can accurately classify different types of lung sounds . The classification accuracy obtained by the best tree based classifier was 98.1 for classification accuracy on training and 94.6 for validation data . Conclusions The proposed approach achieved good results even using only one feature extraction tool . Additionally the implementation of the proposed classifier in an embedded system is feasible . 


Summary: A pattern recognition system to classify five lung sounds is proposed. The system is based on HOS and on a divide and conquer approach. The proposed approach uses Genetic Algorithms to dimensionality reduction. K Nearest Neighbor and Naive Bayes classifiers are used to recognize the signals. The system achieved a high classification accuracy and can be implemented in an embedded system.