POCASUM : Policy Categorizer and Summarizer Based on Text Mining and Machine Learning.

Having control over your data is a right and a duty that every citizen has in our digital society. It is often that users skip entire policies of applications or websites to save time and energy without realizing the potential sticky points in these policies. Due to obscure language and verbose explanations majority of users of hypermedia do not bother to read them. Further, sometimes digital media companies do not spend enough effort in stating their policies clearly which often time can also be incomplete. A summarized version of these privacy policies that can be categorized into the useful information can help the users. To solve this problem, in this work we propose to use machine learning based models for policy categorizer that classifies the policy paragraphs under the attributes proposed like security, contact etc. By benchmarking different machine learning based classifier models, we show that artificial neural network model performs with higher accuracy on a challenging dataset of textual privacy policies. We thus show that machine learning can help summarize the relevant paragraphs under the various attributes so that the user can get the gist of that topic within a few lines.

Automatic body mass index detection using correlation of face visual cues.

Body mass index (BMI) is used widely as an indicator in general health. Determination of BMI using non-intrusive measurements are of interest and recent advancements in the availability of digital imaging sensors have paved the way for performing quick and automatic measurements. In this work, we consider automatic computation of BMI using correlation features from face images. We show that using face detection based facial fiducial points analysis provides good BMI prediction. Experimental results on comparing the correlation coefficients of facial ratios along with the colour feature has higher significance in BMI of a person.

Quantification of corneal transparency in post-mortem human corneas using laser scatter image analysis.

BACKGROUND: The cornea is a highly transparent structure covering the anterior one-fifth of the eyeball. The suitability of post-mortem donor corneas for keratoplasty is currently qualitatively assessed. This makes inferences prone to bias and subjective variability. This study aimed to develop a simple, feasible and cost-effective method to quantify corneal transparency. METHODS: An artificial anterior chamber was modified to provide a central transparent passage and a standardised pressure segment. All corneas graded 'fair' were included in this study. The corneoscleral buttons were mounted on the modified artificial anterior chamber. The mounted chamber was held in a horizontal position at a fixed distance from a white projection screen. The laser source was placed in alignment with an artificial anterior chamber so that it passed through the centre of the cornea. A camera mounted on a tripod stand was placed at a prefixed distance. An image of the scattered laser spot that formed after the laser passed through the mounted cornea on the screen was captured with a single digital camera and standardised settings. Image analysis was performed using ImageJ, an open platform for scientific image analysis. The average red pixel intensity, max intensity, and full-width half max were calculated. RESULTS: The average red intensity was 132.45 ± 6.65 SD. The mean for max intensity was 51.1 ± 3.78 SD and the full-width half max 787.7 ± 84.7 SD. CONCLUSION: Laser quantification is a simple and cost-effective method of quantifying corneal transparency. The study lends proof to the principle involved.