Information-Centric Content Retrieval for Delay-Tolerant Networks

The shift from host-centric to information-centric networking (ICN) promises seamless communication in mobile networks. However, most existing works either consider well-connected networks with high node density or introduce modifications to {ICN} message processing for delay-tolerant Networking (DTN). In this work, we present agent-based content retrieval, which provides information-centric {DTN} support as an application module without modifications to {ICN} message processing. This enables flexible interoperability in changing environments. If no content source can be found via wireless multi-hop routing, requesters may exploit the mobility of neighbor nodes (called agents) by delegating content retrieval to them. Agents that receive a delegation and move closer to content sources can retrieve data and return it back to requesters. We show that agent-based content retrieval may be even more efficient in scenarios where multi-hop communication is possible. Furthermore, we show that broadcast communication may not be necessarily the best option since dynamic unicast requests have little overhead and can better exploit short contact times between nodes (no broadcast delays required for duplicate suppression).

Functional activation of the human ventrolateral frontal cortex during mnemonic retrieval of verbal information.

Regional cerebral blood flow was measured with positron emission tomography during the performance of a verbal free recall task, a verbal paired associate task, and tasks that required the production of verbal responses either by speaking or writing. Examination of the differences in regional cerebral blood flow between these conditions demonstrated that the left ventrolateral frontal cortical area 45 is involved in the recall of verbal information from long-term memory, in addition to its contribution to speech. The act of writing activated a network of areas involving posterior parietal cortex and sensorimotor areas but not ventrolateral frontal cortex.

CETIS : Complex Effluents Toxicity Information System : CETIS retrieval system user's manual /

Mode of access: Internet.

The DCS information storage and retrieval system for graduate applicants and students /

Mode of access: Internet.

Storage and retrieval of technical information; interim technical report.

"Prepared under contract NONR551(40), 1 September 1963."

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

Biological Principles of Information Storage and Retrieval

Published in Electronic Handling of Information: Testing and Evaluation, Kent, Taubee, Beltzer, and Goldstein (ed.), Academic Press, London(1967), pp. 123–147.

The notion of structure and the information records of the documentary systems

Assuming as a starting point the acknowledge that the principles and methods used to build and manage the documentary systems are disperse and lack systematization, this study hypothesizes that the notion of structure, when assuming mutual relationships among its elements, promotes more organical systems and assures better quality and consistency in the retrieval of information concerning users` matters. Accordingly, it aims to explore the fundamentals about the records of information and documentary systems, starting from the notion of structure. In order to achieve that, it presents basic concepts and relative matters to documentary systems and information records. Next to this, it lists the theoretical subsides over the notion of structure, studied by Benveniste, Ferrater Mora, Levi-Strauss, Lopes, Penalver Simo, Saussure, apart from Ducrot, Favero and Koch. Appropriations that have already been done by Paul Otlet, Garcia Gutierrez and Moreiro Gonzalez. In Documentation come as a further topic. It concludes that the adopted notion of structure to make explicit a hypothesis of real systematization achieves more organical systems, as well as it grants pedagogical reference to the documentary tasks.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.