Distributed Information Retrieval using Keyword Auctions

This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Scientific Output from Latin America and the Caribbean – Identification of the Main Institutions for Regional Open Access Integration Strategies

Latin America is a region in which two thirds of the investment in research and development are funded by State resources. It can be foreseen that in the near future governments in the region will encourage and promote, or require by law or mandates, that scientific output from the region become visible and accessible in open access repositories and portals. This paper presents the results of a survey to identify the institutions of the region with the largest volume of scientific output and most exposure of their output on the Web, in order to help make those institutions visible to national, regional and international organizations involved in open access strategies and programs in Latin America and the Caribbean. The results show a leading position by universities from Brazil; a strong presence of universities from Mexico, Colombia, Argentina, Chile and Venezuela, and some presence of universities from Ecuador, Peru, Costa Rica, Cuba, Puerto Rico and Urugua

Improving Information Retrieval Effectiveness in Peer-to-Peer Networks through Query Piggybacking

Περιέχει το πλήρες κείμενοThis work describes an algorithm which aims at increasing the quantity of relevant documents retrieved from a Peer-To-Peer (P2P) network. The algorithm is based on a statistical model used for ranking documents, peers and ultra-peers, and on a “piggybacking” technique performed when the query is routed across the network. The algorithm “amplifies” the statistical information about the neighborhood stored in each ultra-peer. The preliminary experiments provided encouraging results as the quantity of relevant documents retrieved through the network almost doubles once query piggybacking is exploited

A Meta-Analysis of Evolution of Deep Learning Research in Medical Image Analysis

With a text mining and bibliometrics approach, we review the literature on the evolution of deep learning in medical image literature from 2012 – 2020 in order to understand the current state of the research and to identify the major research themes in image analysis to answer our research questions: RQ1: What are the learning modes that are evident in the literature? RQ2: What are the emerging learning modes in the literature? RQ3: What are the major themes in medical imaging literature? The analysis of 8704 resulting from a data collection process from peer-reviewed databases, our analysis discovered the six major themes of image segmentation studies, studies with image classification, evaluation procedures such as sensitivity and specificity, optical coherence tomography studies, MRI imaging studies, and Chest imaging studies. Additionally, we assessed the number of articles published each year, the frequent keywords, the author networks, the trending topics, and connections to other topics. We discovered that segmenting and classifying the images are the most common tasks. Transfer learning is the most researched area and cancer is the highly targeted disease and Covid-19 is the most recent research tren

Applying Hierarchical Task Analysis Method to Discovery Layer Evaluation

While usability tests have been helpful in evaluating the success or failure of implementing discovery layers in the library context, the focus of usability tests has remained on the search interface rather than the discovery process for users. The informal site-­‐ and context specific usability tests have offered little to test the rigor of the discovery layers against the user goals, motivations and workflow they have been designed to support. This study proposes hierarchical task analysis (HTA) as an important complementary evaluation method to usability testing of discovery layers. Relevant literature is reviewed for the discovery layers and the HTA method. As no previous application of HTA to the evaluation of discovery layers was found, this paper presents the application of HTA as an expert based and workflow centered (e.g., retrieving a relevant book or a journal article) method to evaluating discovery layers. Purdue University’s Primo by Ex Libris was used to map eleven use cases as HTA charts. Nielsen’s Goal Composition theory was used as an analytical framework to evaluate the goal charts from two perspectives: a) users’ physical interactions (i.e., clicks), and b) user’s cognitive steps (i.e., decision points for what to do next). A brief comparison of HTA and usability test findings is offered as a way of conclusion

CyberCraft: Protecting Electronic Systems with Lightweight Agents

The United States military is seeking new and innovative methods for securing and maintaining its computing and network resources locally and world-wide. This document presents a work-in-progress research thrust toward building a system capable of meeting many of the US military’s network security and sustainment requirements. The system is based on a Distributed Multi-Agent System (DMAS), that is secure, small, and scalable to the large networks found in the military. It relies on a staged agent architecture capable of dynamic configuration to support changing mission environments. These agents are combined into Hierarchical Peer-to-Peer (HP2P) networks to provide scalable solutions. They employ Public Key Infrastructure (PKI) communications (with digital signatures), and support trust chain management concepts. This document, a work-in-progress, presents the motivation and current challenges in choosing a network communications architecture capable of supporting one million or more agents in a DMAS