varunr14/newsummary huggingface dataset:
Increasingly realistic virtual three dimensional 3D models have been created that demonstrate a variety of landscape designs. They have supported a more collaborative and participative approach in planning and design. However these 3D landscape models are often developed for use in bespoke virtual reality labs that tie the models to expensive graphics hardware or complex arrays of screens with the viewer spatially detached from the actual site. Given the increase in prevalence of advanced smartphone and tablet technology with GPS and compass functionality this paper demonstrates two methods for on demand dissemination of existing virtual 3D landscape models using 1 a touch based interface with integrated mapping 2 a standard web browser interface on mobile phones. The latter method demonstrates the potential to reduce the complexity of accessing an existing 3D landscape model on site to simply pointing a smartphone in a particular direction loading a web page and seeing the relevant view of the model as an image. A prototype system was developed to demonstrate both methods successfully but it was also ascertained that the accuracy of GPS positional data can have a negative effect on the browser based method. Finally potential developments are presented exploring the future of the technology underpinning the method and possible extensions to the prototype as a technique for increasing public participation in planning and design. 


Summary: A theoretical discussion on the capabilities of differing types of landscape presentation methods Proposed and implemented landscape visualisation on demand and on site from existing virtual 3D landscape models Use of smartphones and standard web browsing technologies to access the landscape visualisations.
Social media data are increasingly perceived as alternative sources to public attitude surveys because of the volume of available data that are time stamped and precisely located . Such data can be mined to provide planners marketers and researchers with useful information about activities and opinions across time and space . However in their raw form textual data are still difficult to analyse coherently and Twitter streams pose particular interpretive challenges because they are restricted to just 140 characters . This paper explores the use of an unsupervised learning algorithm to classify geo tagged Tweets from Inner London recorded during typical weekdays throughout 2013 into a small number of groups following extensive text cleaning techniques . Our classification identifies 20 distinctive and interpretive topic groupings which represent key types of Tweets from describing activities or informal conversations between users to the use of check in applets . Our motivation is to use the classification to demonstrate how the nature of the content posted on Twitter varies according to the characteristics of places and users . Topics and attitudes expressed through Tweets are found to vary substantially across Inner London and by time of day . Some observed variations in behaviour on Twitter can be attributed to the inferred demographic and socio economic characteristics of users but place and local activities can also exert a considerable influence . Overall the classification was found to provide a valuable framework for investigating the content and coverage of Twitter usage across Inner London . 


Summary: links social media usage with different urban land uses and locations. provides geo temporal Twitter profiles of behaviour and social attitudes. infers demographic and socio economic characteristics of Twitter users. profiles Twitter messages with respect to inferred age and gender.
This paper proposes a method for keyword spotting in off line Chinese handwritten documents using a contextual word model which measures the similarity between the query word and every candidate word in the document by combining a character classifier and the geometric context as well as linguistic context . The geometric context model characterizes the single character likeliness and between character relationship . The linguistic model utilizes the dependency of the word with the external adjacent characters . The combining weights are optimized on training documents . Experiments on a large handwriting database CASIA HWDB demonstrate the effectiveness of the proposed method and justify the benefits of geometric and linguistic contexts . Compared to transcription based text search the proposed method can provide higher recall rate and for spotting words of four characters the proposed method provides both higher precision and recall rate . 


Summary: We propose a contextual word model for keyword spotting from handwritten Chinese documents. The contextual word model combines character classifier geometric and linguistic contexts. Promising results were obtained on a large handwriting database CASIA HWDB. The geometric and linguistic contexts improve the spotting performance significantly.
Background and objectives Mammography analysis is an effective technology for early detection of breast cancer . Micro calcification clusters are a vital indicator of breast cancer so detection of MCs plays an important role in computer aided detection system this paper proposes a new hybrid method to improve MCs detection rate in mammograms . Methods The proposed method comprises three main steps firstly remove label and pectoral muscle adopting the largest connected region marking and region growing method and enhance MCs using the combination of double top hat transform and grayscale adjustment function secondly remove noise and other interference information and retain the significant information by modifying the contourlet coefficients using nonlinear function thirdly we use the non linking simplified pulse coupled neural network to detect MCs . Results In our work we choose 118 mammograms including 38 mammograms with micro calcification clusters and 80 mammograms without micro calcification to demonstrate our algorithm separately from two open and common database including the MIAS and JSMIT and we achieve the higher specificity of 94.7 sensitivity of 96.3 AUC of 97.0 accuracy of 95.8 MCC of 90.4 MCC PS of 61.3 and CEI of 53.5 these promising results clearly demonstrate that the proposed approach outperforms the current state of the art algorithms . In addition this method is verified on the 20 mammograms from the People s Hospital of Gansu Province the detection results reveal that our method can accurately detect the calcifications in clinical application . Conclusions This proposed method is simple and fast furthermore it can achieve high detection rate it could be considered used in CAD systems to assist the physicians for breast cancer diagnosis in the future . 


Summary: We propose a new MCs detection method using contourlet transform and non linking simplified PCNN in mammograms. We first introduce the non linking simplified PCNN to detect MCs. We first come up with the evaluate indicators MCC PS CEI which take the samples proportion into account. The results tested on two open and common databases including the MIAS and the database from JSMIT. This method is verified on the mammograms from the People s Hospital of Gansu Province to show our method can be used in clinical application.
This paper presents a novel skeleton pruning approach based on a 2D empirical mode like decomposition . The EMD algorithm can decompose any nonlinear and non stationary data into a number of intrinsic mode functions . When the object contour is decomposed by empirical mode like decomposition the IMFs of the object provide a workspace with very good properties for obtaining the object s skeleton . The theoretical properties and the performed experiments demonstrate that the obtained skeletons match to hand labeled skeletons provided by human subjects . Even in the presence of significant noise and shape variations cuts and tears the resulted skeletons have the same topology as the original skeletons . In particular the proposed approach produces no spurious branches as many existing skeleton pruning methods and moreover does not displace the skeleton points which are all centers of maximal disks . 


Summary: Usage of Ensemble Empirical Mode Decomposition EEMD on object skeletonization Modeling of object contour with a 2D EEMD like procedure The produced IMFs provide a very good and novel workspace for image skeletonization. The proposed method is fully automated and unsupervised.
Human faces encode plenty of useful information . Recent studies in psychology and human perception have found that facial features have relations to human weight or body mass index . These studies focus on finding the correlations between facial features and the BMI . Motivated by the recent psychology studies we develop a computational method to predict the BMI from face images automatically . We formulate the BMI prediction from facial features as a machine vision problem and evaluate our approach on a large database with more than 14 500 face images . A promising result has been obtained which demonstrates the feasibility of developing a computational system for BMI prediction from face images at a large scale . 


Summary: A computational approach is developed for BMI prediction in face images for the first time. Our work can validate the psychology study results on a large scale database. Our computational approach can be useful for smart health.
Using image hierarchies for visual categorization has been shown to have a number of important benefits. Doing so enables a significant gain in efficiency e.g. logarithmic with the number of categories 16 12 or the construction of a more meaningful distance metric for image classification 17 . A critical question however still remains controversial would structuring data in a hierarchical sense also help classification accuracy In this paper we address this question and show that the hierarchical structure of a database can be indeed successfully used to enhance classification accuracy using a sparse approximation framework. We propose a new formulation for sparse approximation where the goal is to discover the sparsest path within the hierarchical data structure that best represents the query object. Extensive quantitative and qualitative experimental evaluation on a number of branches of the Imagenet database 7 as well as on the Caltech 256 12 demonstrate our theoretical claims and show that our approach produces better hierarchical categorization results than competing techniques. 


Summary: A new hierarchical classification scheme by sparse approximation is proposed. Leverage large scale structured data for the accurate hierarchical classification. Distance function taking into account the hierarchical structure is introduced. Defined two images to be similar if they shared a similar path in the hierarchy Achieved better performances than flat 1 vs N classification methods
This paper presents a tool for automatic assessment of skeletal bone age according to a modified version of the Tanner and Whitehouse clinical method . The tool is able to provide an accurate bone age assessment in the range 0 6 years by processing epiphysial metaphysial ROIs with image processing techniques and assigning TW2 stage to each ROI by means of hidden Markov models . The system was evaluated on a set of 360 X rays achieving a high success rate in bone age evaluation as well as outperforming other effective methods . The paper also describes the graphical user interface of the tool which is also released thus to support and speed up clinicians practices when dealing with bone age assessment . 


Summary: Automated segmentation of X ray images for skeletal bone age assessment. Hidden Markov models for bone development modelling. A software tool for supporting clinicians in the X Ray investigation according to the TW2 clinical method.
This article discusses the motion analysis based on dense optical flow fields and for a new generation of robotic moving systems with real time constraints. It focuses on a surveillance scenario where an especially designed autonomous mobile robot uses a monocular camera for perceiving motion in the environment. The computational resources and the processing time are two of the most critical aspects in robotics and therefore two non parametric techniques are proposed namely the Hybrid Hierarchical Optical Flow Segmentation and the Hybrid Density Based Optical Flow Segmentation. Both methods are able to extract the moving objects by performing two consecutive operations refining and collecting. During the refining phase the flow field is decomposed in a set of clusters and based on descriptive motion properties. These properties are used in the collecting stage by a hierarchical or density based scheme to merge the set of clusters that represent different motion models. In addition a model selection method is introduced. This novel method analyzes the flow field and estimates the number of distinct moving objects using a Bayesian formulation. The research evaluates the performance achieved by the methods in a realistic surveillance situation. The experiments conducted proved that the proposed methods extract reliable motion information in real time and without using specialized computers. Moreover the resulting segmentation is less computationally demanding compared to other recent methods and therefore they are suitable for most of the robotic or surveillance applications. 


Summary: This paper focuses on segmenting the motion from dense optical flow fields. Two unsupervised clustering methods are presented and a model selection is proposed. A comparison between the proposed techniques with the K means and EM is made. Experiments are conducted in a surveillance scenario with an autonomous mobile. The proposed techniques are superior in terms of robustness and computational demands
Background Structural changes of the brain s third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases . To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures shape analysis is a promising approach . However there are hurdles in modeling the third ventricle shape . First it has topological variations across individuals due to the inter thalamic adhesion . In addition as an interhemispheric structure it needs to be aligned to the midsagittal plane to assess its asymmetric and regional deformation . Method To address these issues we propose a model based shape assessment . Our template model of the third ventricle consists of a midplane and a symmetric mesh of generic shape . By mapping the template s midplane to the individuals brain midsagittal plane we align the symmetric mesh on the midline of the brain before quantifying the third ventricle shape . To build the vertex wise correspondence between the individual third ventricle and the template mesh we employ a minimal distortion surface deformation framework . In addition to account for topological variations we implement geometric constraints guiding the template mesh to have zero width where the inter thalamic adhesion passes through preventing vertices crossing between left and right walls of the third ventricle . The individual shapes are compared using a vertex wise deformity from the symmetric template . Results Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume gender and the fluid intelligence at age 72 . It also revealed that the proposed method can detect the regional and asymmetrical deformation unlike the conventional measures volume and width of the third ventricle . Similarity measures between binary masks and the shape model showed that the latter reconstructed shape details with high accuracy . Conclusions We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle providing high accuracy and inter subject consistency in the shape quantification . This shape modeling method with geometric constraints based on anatomical landmarks could be extended to other brain structures which require a consistent measurement basis in the morphometry . 


Summary: Present a model based approach to investigate the morphology of the third ventricle. Assess the regional deformations in relation to the atrophy of surrounding structures. Use a symmetric template model with the midplane definition for unbiased analysis. Achieve a robust surface modeling using a progressive surface deformation. Validate the method on a healthy aging sample with different clinical variables.
A major difficulty with chest radiographic analysis is the invisibility of abnormalities caused by the superimposition of normal anatomical structures such as ribs over the main tissue to be examined . Suppressing the ribs with no information loss about the original tissue would therefore be helpful during manual identification or computer aided detection of nodules on a chest radiographic image . In this study we introduce a two step algorithm for eliminating rib shadows in chest radiographic images . The algorithm first delineates the ribs using a novel hybrid self template approach and then suppresses these delineated ribs using an unsupervised regression model that takes into account the change in proximal thickness of bone in the vertical axis . The performance of the system is evaluated using a benchmark set of real chest radiographic images . The experimental results determine that proposed method for rib delineation can provide higher accuracy than existing methods . The knowledge of rib delineation can remarkably improve the nodule detection performance of a current computer aided diagnosis system . It is also shown that the rib suppression algorithm can increase the nodule visibility by eliminating rib shadows while mostly preserving the nodule intensity . 


Summary: We introduce a two step algorithm for eliminating rib shadows in chest radiographic images. The algorithm delineates the ribs using a novel hybrid self template approach. It suppresses the delineated ribs using an unsupervised regression model. The rib delineations can improve the performance of CAD systems.
Background In the last few years the use of social media in medicine has grown exponentially providing a new area of research based on the analysis and use of Web 2.0 capabilities . In addition the use of social media in medical education is a subject of particular interest which has been addressed in several studies . One example of this application is the medical quizzes of The New England Journal of Medicine that regularly publishes a set of questions through their Facebook timeline . Objective We present an approach for the automatic extraction of medical quizzes and their associated answers on a Facebook platform by means of a set of computer based methods and algorithms . Methods We have developed a tool for the extraction and analysis of medical quizzes stored on Facebook timeline at the NEJM Facebook page based on a set of computer based methods and algorithms using Java . The system is divided into two main modules Crawler and Data retrieval . Results The system was launched on December 31 2014 and crawled through a total of 3004 valid posts and 200 081 valid comments . The first post was dated on July 23 2009 and the last one on December 30 2014 . 285 quizzes were analyzed with 32 780 different users providing answers to the aforementioned quizzes . Of the 285 quizzes patterns were found in 261 . From these 261 quizzes where trends were found we saw that users follow trends of incorrect answers in 13 quizzes and trends of correct answers in 248 . Conclusions This tool is capable of automatically identifying the correct and wrong answers to a quiz provided on Facebook posts in a text format to a quiz with a small rate of false negative cases and this approach could be applicable to the extraction and analysis of other sources after including some adaptations of the information on the Internet . 


Summary: New England Journal of Medicine NEJM is a very prestigious medical journal. NEJM Facebook page currently has more than 1.25 million of users. Medical quizzes are one of the methods to test the knowledge of future physicians. Our approach allows extracting medical quizzes published in NEJM Facebook page. This is the first study done about the content of medical quizzes in social networks.
Background and objective Probabilistic topic models provide an unsupervised method for analyzing unstructured text . These models discover semantically coherent combinations of words that could be integrated in a clinical automatic summarization system for primary care physicians performing chart review . However the human interpretability of topics discovered from clinical reports is unknown . Our objective is to assess the coherence of topics and their ability to represent the contents of clinical reports from a primary care physician s point of view . Methods Three latent Dirichlet allocation models were fit to a large collection of clinical reports . Topics were manually evaluated by primary care physicians and graduate students . Wilcoxon Signed Rank Tests for Paired Samples were used to evaluate differences between different topic models while differences in performance between students and primary care physicians were tested using Mann Whitney U tests for each of the tasks . Results While the 150 topic model produced the best log likelihood participants were most accurate at identifying words that did not belong in topics learned by the 100 topic model suggesting that 100 topics provides better relative granularity of discovered semantic themes for the data set used in this study . Models were comparable in their ability to represent the contents of documents . Primary care physicians significantly outperformed students in both tasks . Conclusion This work establishes a baseline of interpretability for topic models trained with clinical reports and provides insights on the appropriateness of using topic models for informatics applications . Our results indicate that PCPs find discovered topics more coherent and representative of clinical reports relative to students warranting further research into their use for automatic summarization . 


Summary: A topic model with three different parameter settings is fit to a large collection of clinical reports. The interpretability of discovered topics is evaluated by clinicians and laypersons. Clinicians are significantly more capable of interpreting topics than laypersons. Topics hold potential for applications in automatic summarization.