Dynamic recomposition of documents from distributed data sources

Dynamic recomposition of documents refers to the process of on-the-fly creation of documents. A document can be generated from several documents that are stored at distributed data sites. The source can be queried and results obtained in the form of XML. These XML documents can be combined after a series of transformation operations to obtain the target document. The resultant document can be stored statically or in the form of a command, which can be invoked later to recompose this document dynamically. Also, in case a change is made to a document, then only the change can be stored, instead of storing the modified document in its entirety. The purpose of this research was to provide a way to recompose dynamic documents. A solution is proposed at the level of algebra for update and recomposition of documents stored at distributed data sources. The issue of representation of a document by a command, i.e., a composition operator and/or an editing command along with one or more path expressions has also been researched. The construction of a dynamic document has three phases to it. The first one is the information retrieval. Phase two deals with building of real document: this includes the filtering of retrieved data by selecting relevant subset of a document and then applying update operations, and finally the ordering and assembling of the document. The final phase consists of displaying or storing or exchanging it over the web through a convenient means

Dmenisions of Human Behavior in The Novel “The Foreigner” by Arun Joshi

In the modern age of globalization and modernization, people have become selfish and self-centered.  Feeling of sympathy and kindness towards poor people have almost bolted from the hearts of those who have richly available resources.  They leave needy people running behind their luxurious chauffer-driven cars.  Poor and marginalized people keep shouting for help for their dear ones but upper class people trying to show as if they did not hear any long distant sound crept into their eardrums.  This trauma, agony, pain and sufferings is explored in the novel, The Foreigner

Investigation on advanced image search techniques

Content-based image search for retrieval of images based on the similarity in their visual contents, such as color, texture, and shape, to a query image is an active research area due to its broad applications. Color, for example, provides powerful information for image search and classification. This dissertation investigates advanced image search techniques and presents new color descriptors for image search and classification and robust image enhancement and segmentation methods for iris recognition. First, several new color descriptors have been developed for color image search. Specifically, a new oRGB-SIFT descriptor, which integrates the oRGB color space and the Scale-Invariant Feature Transform (SIFT), is proposed for image search and classification. The oRGB-SIFT descriptor is further integrated with other color SIFT features to produce the novel Color SIFT Fusion (CSF), the Color Grayscale SIFT Fusion (CGSF), and the CGSF+PHOG descriptors for image category search with applications to biometrics. Image classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. Experimental results on four large scale, grand challenge datasets have shown that the proposed oRGB-SIFT descriptor improves recognition performance upon other color SIFT descriptors, and the CSF, the CGSF, and the CGSF+PHOG descriptors perform better than the other color SIFT descriptors. The fusion of both Color SIFT descriptors (CSF) and Color Grayscale SIFT descriptor (CGSF) shows significant improvement in the classification performance, which indicates that various color-SIFT descriptors and grayscale-SIFT descriptor are not redundant for image search. Second, four novel color Local Binary Pattern (LBP) descriptors are presented for scene image and image texture classification. Specifically, the oRGB-LBP descriptor is derived in the oRGB color space. The other three color LBP descriptors, namely, the Color LBP Fusion (CLF), the Color Grayscale LBP Fusion (CGLF), and the CGLF+PHOG descriptors, are obtained by integrating the oRGB-LBP descriptor with some additional image features. Experimental results on three large scale, grand challenge datasets have shown that the proposed descriptors can improve scene image and image texture classification performance. Finally, a new iris recognition method based on a robust iris segmentation approach is presented for improving iris recognition performance. The proposed robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. As the limbic circle, which has a center within a close range of the pupil center, is selectively detected, the eyelid detection approach leads to improved iris recognition performance. Experiments using the Iris Challenge Evaluation (ICE) database show the effectiveness of the proposed method