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Smart health systems improve the efficiency of healthcare infrastructures and biomedical systems by integrating information and technology into health and medical practices. However, reliability, scalabilty and latency are among the many challenges hindering the realization of next-generation healthcare. In fact, with the exponential increases in the volume of patient data being produced and processed, many healthcare system' are being overwhelmed with the deluge of data they are facing. Many systems have been proposed to improve the system latency and scalability. However, there are concerns related to some of theses systems regarding the increasing levels of required human interaction which impact their efficiency. Recently, machine learning techniques are gaining a lot of interest in health applications as they exhibit fast processing with realtime predictions. In this paper, we propose a new healthcare system to reduce the waiting time in emergency department and improve the network scalability in any distributed system. The proposed model integrates the power of edge computing with machine learning techniques for providing a good quality of healthcare services. The machine learning algorithm is used to generate a classifier that can predict with high levels of accuracy the likelyhood of a patient to have a heart attack using his physiological signals ECG. The proposed system stores the patient data in a centralized database and generates a unique index using a new data-dependent Indexing algorithm that transforms the patient data into unique code to be sent for any medical data exchange. Multiple machine learning algorithms are studied and the best algorithm will be selected based on efficient performance result for the prediction of heart attack problem. simulation results show that the proposed model can effectively detect the abnormal heart beats with 91% using SVM algorithm. We show also that the proposed system outperforms conventional indexing algorithm systems in terms of collisions rate. 2020 IEEE. Qatar National Research Fund Scopus

In this paper we propose a novel action tube extractor for RGB-D action recognition in trimmed videos. The action tube extractor takes as input a video and outputs an action tube. The method consists of two parts: spatial tube extraction and temporal sampling. The first part is built upon MobileNet-SSD and its role is to define the spatial region where the action takes place. The second part is based on the structural similarity index (SSIM) and is designed to remove frames without obvious motion from the primary action tube. The final extracted action tube has two benefits: 1) a higher ratio of ROI (subjects of action) to background; 2) most frames contain obvious motion change. We propose to use a two-stream (RGB and Depth) I3D architecture as our 3D-CNN model. Our approach outperforms the state-of-the-art methods on the OA and NTU RGB-D datasets. © 2018 IEEE.

Conference of 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 ; Conference Date: 22 October 2017 Through 29 October 2017; Conference Code:134301; International audience; When textual annotations of Web and social media images are poor or missing, content-based image retrieval is an interesting way to access them. Finding an optimal trade-off between accuracy and scalability for CBIR is challenging in practice. We propose a retrieval method whose complexity is nearly independent of the collection scale and does not degrade results quality. Images are represented with sparse semantic features that can be stored as an inverted index. Search complexity is drastically reduced by (1) considering the query feature dimensions independently and thus turning search into a concatenation operation and (2) pruning the index in function of a retrieval objective. To improve precision, the inverted index look-up is complemented with an exhaustive search over a fixed size list of intermediary results. We run experiments with three public collections and results show that our much faster method slightly outperforms an exhaustive search done with two competitive baselines.

Conference of 13th International Workshop on Content-Based Multimedia Indexing, CBMI 2015 ; Conference Date: 10 June 2015 Through 12 June 2015; Conference Code:113803; International audience; We consider the problem of indexing and searching image duplicates in streaming visual data. This task requires a fast image descriptor, a small memory footprint for each signature and a quick search algorithm. To this end, we propose a new descriptor satisfying the aforementioned requirements. We evaluate our method on two different datasets with the use of different sets of distractor images, leading to large-scale image collections (up to 85 million images). We compare our method to the state of the art and show it exhibits among the best detection performances but is much faster (one to two orders of magnitude).

Expressive facial animation synthesis of human like characters has had many approaches with good results. MPEG-4 standard has functioned as the basis of many of those approaches. In this paper we would like to lay out the knowledge of some of those approaches inside an ontology in order to support the modeling of emotional facial animation in virtual humans (VH). Inside this ontology we will present MPEG-4 facial animation concepts and its relationship with emotion through expression profiles that utilize psychological models of emotions. The ontology allows storing, indexing and retrieving prerecorded synthetic facial animations that can express a given emotion. Also this ontology can be used a refined knowledge base in regards to the emotional facial animation creation. This ontology is made using Web Ontology Language and the results are presented as answered queries. Copyright © 2006 John Wiley and Sons, Ltd.

Recent research has studied how to measure the size of a search engine, in terms of the number of pages indexed. In this paper, we consider a different measure for search engines, namely the quality of the pages in a search engine index. We provide a simple, effective algorithm for approximating the quality of an index by performing a random walk on the Web, and we use this methodology to compare the index quality of several major search engines.

Recently, there has been an increasing use of pre-recorded motion capture data, making motion indexing and classification essential for animating virtual characters and synthesising different actions. In this paper, we use a variety of features that encode characteristics of motion using the Body, Effort, Shape and Space components of Laban Movement Analysis (LMA), to explore the motion quality from acted dance performances. Using Principal Component Analysis (PCA), we evaluate the importance of the proposed features - with regards to their ability to separate the performer's emotional state - indicating the weight of each feature in motion classification. PCA has been also used for dimensionality reduction, laying the foundation for the qualitative and quantitative classification of movements based on their LMA characteristics. Early results show that the proposed features provide a representative space for indexing and classification of dance movements with regards to the emotion, which can be used for synthesis and composition purposes. Copyright © ACM. Sponsors: ACM Special Interest Group on Computer-Human Interaction (SIGCHI) ACM Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) Conference code: 101663 Cited By :4

This paper studies the problem of imposing an existing hierarchical semantic structure onto a corresponding spoken document in which the structures are embedded, with the goal of indexing such documents for easier access. We propose a graph-partitioning framework to solve a semantic tree-to-string alignment problem through optimizing a normalized-cut criterion. We present models with different modeling capabilities and time complexities in this framework and provide experimental evidence of their performance. We relate graph partitioning to conventional dynamic time warping (DTW) as it applies to this problem, and show that the proposed framework can naturally include topic segmentation to accommodate cohesion constraints. © 2006-2012 IEEE.

Date of Conference: 23-26 June, 2008 Conference name: IEEE International Conference on Multimedia and Expo, 2008 We describe a method to automatically extract important video objects for object-based indexing. Most of the existing salient object detection approaches detect visually conspicuous structures in images, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of regional and inter-regional color, shape, texture and motion features for all regions, which are classified as being important or not using SVMs trained on a few hundreds of example regions. Finally, each important region is tracked within each shot for trajectory generation and consistency check. Experimental results from news video sequences show that the proposed approach is effective. © 2008 IEEE.

Date of Conference: 25-27 June 2007 Conference name: International Workshop on Content-Based Multimedia Indexing, 2007 The management and exchange of multimedia data is challenging due to the variety of formats, standards and intended applications. In addition, production of multimedia data is rapidly increasing due to the availability of off-the-shelf, modern digital devices that can be used by even inexperienced users. It is likely that this volume of information will only increase in the future. A key goal of the MUSCLE (Multimedia Understanding through Semantics, Computation and Learning) network is to develop tools, technologies and standards to facilitate the interoperability of multimedia content and support the exchange of such data. One approach for achieving this was the creation of a specific "E-Team", composed of the authors, to discuss core questions and practical issues based on the participant's individual work. In this paper, we present the relevant points of view with regards to sharing experiences and to extracting and integrating multimedia data and metadata from different modes (text, images, video). © 2007 IEEE.

Present day information retrieval systems largely ignore the issues of lexical and compositional semantics, and rely mainly on some statistical measures for choosing or evolving an indexing scheme. This has been the reason for the decreasing precision in their responses, given an exponentially increasing number of Web pages. The work reported in this paper addresses this issue from a linguistic point of view. We show that the detection of domain-specific phrases can capture the task-specific semantics of documents. We introduce the notion of n*-gram formalism to characterize the domain-specific phrases and their variants, taking a few sample domains. A method to construct a phrase grammar from a small set of documents is proposed. A method of conceptual indexing based on the phrase grammar has also been proposed. In order to demonstrate the effectiveness of the proposed method, we have designed a versatile system that can perform concept-based retrieval, in addition to several document-processing tasks, such as text classification, extraction-based summarization, context tracking, and semantic tagging. Collectively, the system can address the semantic content of documents. Considering the fact that an average user prefers highly relevant results in the top-ranked subset to an exhaustively retrieved set, it is shown that the proposed system performs better in that it retrieves documents that are more conceptually relevant than those retrieved by Google, and at 95% confidence level. Copyright ? 2005 Taylor & Francis Inc.

Cataloged from PDF version of article. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. In this study we introduce automatic Web search engine evaluation method as an efficient and effective assessment tool of such systems. The experiments based on eight Web search engines, 25 queries, and binary user relevance judgments show that our method provides results consistent with human-based evaluations. It is shown that the observed consistencies are statistically significant. This indicates that the new method can be successfully used in the evaluation of Web search engines. (C) 2003 Elsevier Ltd. All rights reserved.