Cookies and log in-based authentication often provide incomplete data for stitching website visitors across multiple sources, necessitating probabilistic deduplication. We address this challenge by formulating the problem as a binary classification task for pairs of anonymous visitors. We compute visitor proximity vectors by converting categorical variables like IP addresses, product search key...

This paper presents a comparison between two image segmentation approaches based on background subtraction and supervised learning. Real images from two important issues, which have been studied by several computer vision research groups, were used in our experiments: namely, sign language interpretation and mouse behavior classification. According to performance measures, such as accurate rate...

We developed an R toolkit to manage data described by attributes, able to make clusters with a support vector clustering method (SVC). We have implemented an original 2D-grid labeling approach to extract clusters to optimize time processing. In this sense, svc can be seen as an efficient cluster extraction if clusters are separable in a 2-D map. Secondly we showed that this SVC approach using a...

We present a case-based approach to character identification in natural language text in the context of our Voz system. Voz first extracts entities from the text, and for each one of them, computes a feature-vector using both linguistic information and external knowledge. We propose a new similarity measure called Continuous Jaccard that exploits those feature-vectors to compute the similarity ...

In case of salient subject recognition, computer algorithms have been heavily relied on scanning of images from top-left to bottom-right systematically and apply brute-force when attempting to locate objects of interest. Thus, the process turns out to be quite time consuming. Here a novel approach and a simple solution to the above problem is discussed. In this paper, we implement an approach t...

This paper presents an end-to-end solution for MRI thigh quadriceps segmentation. This is the first attempt that deep learning methods are used for the MRI thigh segmentation task. We use the state-of-the-art Fully Convolutional Networks with transfer learning approach for the semantic segmentation of regions of interest in MRI thigh scans. To further improve the performance of the segmentation...

Document sketching using Jaccard similarity has been a workable effective technique in reducing nearduplicates in Web page and image search results, and has also proven useful in file system synchronization, compression and learning applications [6, 4, 5]. Min-wise sampling can be used to derive an unbiased estimator for Jaccard similarity and taking a few hundred independent consistent samples...

The Exact Set Similarity Join problem aims to find all similar sets between two collections of sets, with respect to a threshold and a similarity function such as overlap, Jaccard, dice or cosine. The näıve approach verifies all pairs of sets and it is often considered impractical due the high number of combinations. So, Exact Set Similarity Join algorithms are usually based on the Filter-Verif...

Our system to KBA Track at TREC2012 is described in this paper, which includes preprocessing, index building, relevance feedback and similarity calculation. In particular, the Jaccard coefficient was applied to calculate the similarities between documents. We also show the evaluation results for our team and the comparison with the best and median evaluations.

PURPOSE Precise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducib...