BioMeRSA: The Biology media repository with semantic augmentation

With computers now capable of easily handling all kinds of multimedia files in vast quantity, and with the Internet now well-suited to exchange these files, we are faced with the challenge of organizing this data in such a way so as to make the information most useful and accessible. This holds true as well for media pertaining to the field of biology, where multimedia is particularly useful in education, as well as in research. To help address this, a software system with a Web-based interface has been developed for improving the accuracy and specificity of multimedia searching and browsing by integrating semantic data pertaining to the field of biology from the Unified Medical Language System (UMLS). Using the Biology Media Repository with Semantic Augmentation (BioMeRSA) system, users who are considered to be `experts\u27 can associate concepts from UMLS with multimedia files submitted by other users to provide semantic context for the files. These annotations are used to retrieve relevant files in the searching and browsing interfaces. A wide variety of image files are currently supported, with some limited support for video and audio files

Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques

Web-based Tools -— NED VO Services

The NASA/IPAC Extragalactic Database (NED) is a thematic, web-based research facility in widespread use by scientists, educators, space missions, and observatory operations for observation planning, data analysis, discovery, and publication of research about objects beyond our Milky Way galaxy. NED is a portal into a systematic fusion of data from hundreds of sky surveys and tens of thousands of research publications. The contents and services span the entire electromagnetic spectrum from gamma rays through radio frequencies, and are continuously updated to reflect the current literature and releases of large-scale sky survey catalogs. NED has been on the Internet since 1990, growing in content, automation and services with the evolution of information technology. NED is the world‛s largest database of crossidentified extragalactic objects. As of December 2006, the system contains approximately 10 million objects and 15 million multi-wavelength cross-IDs. Over 4 thousand catalogs and published lists covering the entire electromagnetic spectrum have had their objects cross-identified or associated, with fundamental data parameters federated for convenient queries and retrieval. This chapter describes the interoperability of NED services with other components of the Virtual Observatory (VO). Section 1 is a brief overview of the primary NED web services. Section 2 provides a tutorial for using NED services currently available through the NVO Registry. The “name resolver” provides VO portals and related internet services with celestial coordinates for objects specified by catalog identifier (name); any alias can be queried because this service is based on the source cross-IDs established by NED. All major services have been updated to provide output in VOTable (XML) format that can be accessed directly from the NED web interface or using the NVO registry. These include access to images via SIAP, Cone- Search queries, and services providing fundamental, multi-wavelength extragalactic data such as positions, redshifts, photometry and spectral energy distributions (SEDs), and sizes (all with references and uncertainties when available). Section 3 summarizes the advantages of accessing the NED “name resolver” and other NED services via the web to replace the legacy “server mode” custom data structure previously available through a function library provided only in the C programming language. Section 4 illustrates visualization via VOPlot of an SED and the spatial distribution of sources from a NED All-Sky (By Parameters) query. Section 5 describes the new NED Spectral Archive, illustrating how VOTables are being used to standardize the data and metadata as well as the physical units of spectra made available by authors of journal articles and producers of major survey archives; quick-look spectral analysis through convenient interoperability with the SpecView (STScI) Java applet is also shown. Section 6 closes with a summary of the capabilities described herein, which greatly simplify interoperability of NED with other components of the VO, enabling new opportunities for discovery, visualization, and analysis of multiwavelength data

Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits

Windows Memory Forensic Data Visualization

Modern criminal investigators face an increasing number of computer-related crimes that require the application of digital forensic science. The major challenge facing digital forensics practitioners is the complicated task of acquiring an understanding of the digital data residing in electronic devices. Currently, this task requires significant experience and background to correctly aggregate the data their tools provide from the digital artifacts. Most of the tools available present their results in text files or tree lists. It is up to the practitioner to mentally capture a global understanding of the state of the device at the time of seizure and find the items of evidentiary interest. This research focuses on the application of Information Visualization techniques to improve the analysis of digital forensic evidence from Microsoft Windows memory captures. The visualization tool developed in this work presents both global and local views of the evidence based on user interactions with the graphics. The resulting visualizations provide the necessary details for verifying digital artifacts and assists in locating additional items of relevance. This proof-of-concept model can be modified to support various digital forensic target platforms including Mac OS X, Linux, and Android

Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

Improving Recall of Browsing Sets in Image Retrieval from a Semiotics Perspective

The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR)

Text mining with the WEBSOM

The emerging field of text mining applies methods from data mining and exploratory data analysis to analyzing text collections and to conveying information to the user in an intuitive manner. Visual, map-like displays provide a powerful and fast medium for portraying information about large collections of text. Relationships between text items and collections, such as similarity, clusters, gaps and outliers can be communicated naturally using spatial relationships, shading, and colors. In the WEBSOM method the self-organizing map (SOM) algorithm is used to automatically organize very large and high-dimensional collections of text documents onto two-dimensional map displays. The map forms a document landscape where similar documents appear close to each other at points of the regular map grid. The landscape can be labeled with automatically identified descriptive words that convey properties of each area and also act as landmarks during exploration. With the help of an HTML-based interactive tool the ordered landscape can be used in browsing the document collection and in performing searches on the map. An organized map offers an overview of an unknown document collection helping the user in familiarizing herself with the domain. Map displays that are already familiar can be used as visual frames of reference for conveying properties of unknown text items. Static, thematically arranged document landscapes provide meaningful backgrounds for dynamic visualizations of for example time-related properties of the data. Search results can be visualized in the context of related documents. Experiments on document collections of various sizes, text types, and languages show that the WEBSOM method is scalable and generally applicable. Preliminary results in a text retrieval experiment indicate that even when the additional value provided by the visualization is disregarded the document maps perform at least comparably with more conventional retrieval methods.reviewe

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

FISH_ROCK : a tool for identifying and counting benthic organisms in bottom photographs

Recent advances in underwater robotics and imaging technology now enable the rapid acquisition of large datasets of near-bottom high-resolution digital imagery. These images provide the potential for developing a non-invasive technique for fisheries data acquisition that reveals the organisms in their natural habitat and can be used to identify important habitat characteristics. Using these large datasets effectively, however, requires the development of computer-based techniques that increase the efficiency of data analysis. This document describes one such tool, FISH_ROCK, which was developed for a group of fisheries researchers using the SeaBED AUV during a research cruise in October 2005. FISH_ROCK is a graphical user interface (GUI) that is executed within Matlab, and allows users digitally generate a database that includes organism identification, quantity, size and distribution as well as details about their habitat. Further development of this GUI will enable its use in different oceanographic environments including the deep sea, and will include modules that perform data analysis.Funding was provided by the National Oceanic and Atmospheric Administration under Grant No. AB133F05SE5828