Abstract

Vertical video editing is the process of digitally editing the image within the frame as opposed to horizontal video editing, which arranges the shots along a timeline. Vertical editing can be a time-consuming and error-prone process when using manual key-framing and simple interpolation. In this paper, we present a general framework for automatically computing a variety of cinematically plausible shots from a single input video suitable to the special case of live performances. Drawing on working practices in traditional cinematography, the system acts as a virtual camera assistant to the film editor, who can call novel shots in the edit room with a combination of high-level instructions and manually selected keyframes.

Abstract

We present an algorithm for automatic blur detection of document images using a novel approach based on edge intensity profiles. Our main insight is that the edge profiles are a strong indicator of the blur present in the image, with steep profiles implying sharper regions and gradual profiles implying blurred regions. Our approach first retrieves the profiles for each point of intensity transition (each edge point) along the gradient and then uses them to output a quantitative measure indicating the extent of blur in the input image. The real time performance of the proposed approach makes it suitable for most applications. Additionally, our method works for both hand written and digital documents and is agnostic to the font types and sizes, which gives it a major advantage over the currently prevalent learning based approaches. Extensive quantitative and qualitative experiments over two different datasets show that our method outperforms almost all algorithms in current state of the art by a significant margin, especially in cross dataset experiments.

Abstract

The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.

Abstract

The task of automatic image annotation involves assigning relevant multiple labels/tags to query images based on their visual content. One of the key challenge in multi-label image annotation task is the class imbalance problem where frequently occurring labels suppress the participation of rarely occurring labels. In this paper, we propose to exploit the multi-scale behavior in hypergraph heat diffusion framework for the automatic image annotation task. The proposed novel technique enables to model the higher order relationship among images in the feature space and provides a multi-scale label diffusion mechanism to address the class imbalance problem in the data.

Abstract

Embodiments of a method are disclosed for computing trust index among multiple entities associated with a resource marketplace. The method includes receiving multiple inputs including interaction attributes, attribute importance factors, references to databases, and multiple entities associated with the resource marketplace. The method also includes creating a weighted-interaction graph based on the received inputs. The weighted-interaction graph includes multiple vertices representing the entities. The method further includes performing a topology-specific analysis of the weighted-interaction graph. The method furthermore includes computing Euclidean distances for each pair of vertices in the weighted-interaction graph based on the performed analysis. The method also includes determining a trust index for a first entity in the received multiple entities based on the computed Euclidean distances. The trust index includes ranking of at least one of the multiple entities with respect to the first entity. The ranking is inversely proportional to the computed Euclidean distances.

Abstract

Face frontalization is the process of synthesizing a frontal view of a face, given its non-frontal view. Frontalization is used in intelligent photo editing tools and also aids in improving the accuracy of face recognition systems. For example, in the case of photo editing, faces of persons in a group photo can be corrected to look into the camera, if they are looking elsewhere. Similarly, even though recent methods in face recognition claim accuracy which surpasses that of humans in some cases, perfor- mance of recognition systems degrade when profile view of faces are given as input. One way to address this issue is to synthesize frontal views of faces before recognition. We propose a simple and efficient method to address the face frontalization problem. Our method leverages the fact that faces in general have a definite structure and can be represented in a low dimensional subspace. We employ an exemplar based approach to find the transformation that relates the profile view to the frontal view, and use it to generate realistic frontalizations. Our method does not involve estimating 3 D model of the face, which is a common approach in previous work in this area. This leads to an efficient solution, since we avoid the complexity of adding one more dimension to the problem. Our method also retains the structural information of the individual as compared to that of a recent method [4], which assumes a generic 3 D model for synthesis. We show impressive qualitative and quantitative results in comparison to the state-of-the-art in this field.

Abstract

Automating the process of indexing journal abstracts has been a topic of research for several years. Biomedical Semantic Indexing aims to assign correct MeSH terms to the PubMed documents. In this paper we report our participation in the Task 3a of BioASQ challenge 2015. The participating teams were provided with PubMed articles and asked to return relevant MeSH terms. We tried three different approaches: Nearest Neighbours, IDF-Ratio based indexing and multi-label classification. The official challenge results demonstrate that we consistently performed better than the baseline approaches for Task 3a.

Abstract

In this paper, we describe our participation in the 2015 BioASQ challenge on Bio-Medical Question Answering. For Question Answering task (Task 3b), teams were provided with natural language questions and asked to retrieve responses from PubMed corpus in the form of documents, snippets, concepts and RDF triplets (Phase A) and direct answers (Phase B). For Phase A, we took the support of PubMed search engine and our snippet extraction technique. In our QA system, apart from the standard techniques discussed in literature, we tried the following novel techniques to - a) leverage web search results for improving question processing and b) identify domain words and define a new answer ranking function based on number of common domain words. We scored an F-measure of 0.193 for document extraction and F-measure of 0.0717 in snippet generation

Abstract

The memory and core requirements of a virtual machine depend on the performance requirements of the applications hosted on it. In this paper, we propose algorithms for dynamic memory and core scaling using a combination of machine learning and feedback control techniques. These algorithms work for sequential and parallel applications such as scientific computations where speedup is the primary performance metric. Then we use these algorithms to address the simultaneous memory and core allocation problem, which is more complex due to possible correlation between these resource requirements. All these algorithms can be applied in a black box fashion without instrumenting the source code of applications.

Abstract

The telecare medicine information system (TMIS) helps the patients to gain the health monitoring facility at home and access medical services over the Internet of mobile networks. Recently, Amin and Biswas presented a smart card based user authentication and key agreement security protocol usable for TMIS system using the cryptographic one-way hash function and biohashing function, and claimed that their scheme is secure against all possible attacks. Though their scheme is efficient due to usage of one-way hash function, we show that their scheme has several security pitfalls and design flaws, such as (1) it fails to protect privileged-insider attack, (2) it fails to protect strong replay attack, (3) it fails to protect strong man-in-the-middle attack, (4) it has design flaw in user registration phase, (5) it has design flaw in login phase, (6) it has design flaw in password change phase, (7) it lacks of supporting biometric update phase, and (8) it has flaws in formal security analysis. In order to withstand these security pitfalls and design flaws, we aim to propose a secure and robust user authenticated key agreement scheme for the hierarchical multi-server environment suitable in TMIS using the cryptographic one-way hash function and fuzzy extractor. Through the rigorous security analysis including the formal security analysis using the widely-accepted Burrows-Abadi-Needham (BAN) logic, the formal security analysis under the random oracle model and the informal security analysis, we show that our scheme is secure against possible known attacks. Furthermore, we simulate our scheme using the most-widely accepted and used Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The simulation results show that our scheme is also secure. Our scheme is more efficient in computation and communication as compared to Amin-Biswas’s scheme and other related schemes. In addition, our scheme supports extra functionality features as compared to other related schemes. As a result, our scheme is very appropriate for practical applications in TMIS.