Query Load Balancing by Caching Search Results in Peer-to-Peer Information Retrieval Networks

For peer-to-peer web search engines it is important to keep the delay between receiving a query and providing search results within an acceptable range for the end user. How to achieve this remains an open challenge. One way to reduce delays is by caching search results for queries and allowing peers to access each others cache. In this paper we explore the limitations of search result caching in large-scale peer-to-peer information retrieval networks by simulating such networks with increasing levels of realism. We find that cache hit ratios of at least thirty-three percent are attainable

Query-Based Sampling using Only Snippets

Query-based sampling is a popular approach to model the content of an uncooperative server. It works by sending queries to the server and downloading the returned documents in the search results in full. This sample of documents then represents the server’s content. We present an approach that uses the document snippets as samples instead of downloading entire documents. This yields more stable results at the same amount of bandwidth usage as the full document approach. Additionally, we show that using snippets does not necessarily incur more latency, but can actually save time

Query-Based Sampling using Snippets

Query-based sampling is a commonly used approach to model the content of servers. Conventionally, queries are sent to a server and the documents in the search results returned are downloaded in full as representation of the server’s content. We present an approach that uses the document snippets in the search results as samples instead of downloading the entire documents. We show this yields equal or better modeling performance for the same bandwidth consumption depending on collection characteristics, like document length distribution and homogeneity. Query-based sampling using snippets is a useful approach for real-world systems, since it requires no extra operations beyond exchanging queries and search results

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

Sound ranking algorithms for XML search

Ranking algorithms for XML should reflect the actual combined content and structure constraints of queries, while at the same time producing equal rankings for queries that are semantically equal. Ranking algorithms that produce different rankings for queries that are semantically equal are easily detected by tests on large databases: We call such algorithms not sound. We report the behavior of different approaches to ranking content-and-structure queries on pairs of queries for which we expect equal ranking results from the query semantics. We show that most of these approaches are not sound. Of the remaining approaches, only 3 adhere to the W3C XQuery Full-Text standard

Evaluating Relevance Feedback: An Image Retrieval Interface for Children

Studies on information retrieval for children are not yet\ud common. As young children possess a limited vocabulary\ud and limited intellectual power, they may experience more\ud difficulty in fulfilling their information need than adults.\ud This paper presents an image retrieval user interface that\ud is specifically designed for children. The interface uses relevance feedback and has been evaluated by letting children\ud perform different search tasks. The tasks were performed\ud using two interfaces; a more traditional interface - acting as a control interface - and the relevance feedback interface. \ud One of the remarkable results of this study is that children\ud did not favor relevance feedback controls over traditional\ud navigational controls

Can They Teach? A Look at How Professors Learn To Educate

The purpose of this research project was to examine ways in which higher education professors are trained to teach. Eight professors from a small, liberal arts college in the northeast, who were also recipients of the college’s annual Teacher of the Year award, were observed in the classroom and interviewed about their educational training and background. By following the strategies of inductive reasoning and synthesizing these professors’ experiences and reflections, The author determined that many professors do not receive formal teaching training. The majority of the professors claimed to have learned to teach by trial and error and by emulating their favorite teachers’ approaches and tactics. Even so, it took years of trial and error for many to learn the logistics of teaching at the college level. Given the characteristics of the current millennial college student and the increasing cost of higher education, it is more necessary than ever for professors to engage in some form of educational training to increase the students’ value of education. By examining the ethics of these professors and their classroom approaches, the author recommends that the most effective way to train college professors how to teach is to design a training program or orientation experience grounded in adult education theory. Because research suggests most college students are developmentally adults and the fact that the successful professors observed in this study already use some adult education theory in their classrooms, it seems only logical to incorporate adult education theory into a training program for future professors

Obstetrical ultrasound training of and practise by general practitioners in the private sector, Free State

Background: The aim of the study was to determine the level of obstetrical ultrasound training and practice of general practitioners in the Free State private sector. Methods: In this descriptive study, questionnaires were mailed to all general practitioners in the Free State private sector. The questionnaire included demographic information about the practitioner, the ultrasound profile of the practice, and the type of machine used. Results: Four hundred and eighty-one questionnaires were sent to general practitioners and 229 (47.6%) were returned. Of the 176 practising respondents, 47 (26.8%) used ultrasound. The majority of ultrasound examinations done per month were obstetrical. Eight practitioners had relevant qualifications for using ultrasound and more than a third (18, 38.3%) had no training in ultrasound use. Less than half (19, 40.4%) of the practitioners that use ultrasound were aware of the South African Association of Ultrasound in Obstetrics and Gynaecology (SASUOG). Conclusions: The response to the questionnaire was low and may have influenced the results. The study indicates that there are general practitioners who perform ultrasound examinations without training. As general practitioners mainly do obstetrical ultrasound, it is recommended that the SASUOG play a bigger role in their training. A diploma course in ultrasound and support from medical aid organisations to only pay full fees to doctors who can prove that they have sufficient ultrasound training and competence will be ideal. For full text, click here:SA Family Pract 2004,46(6): 25-2

Predicting semantic labels of text regions in heterogeneous document images

Contains fulltext : 214639.pdf (publisher's version ) (Open Access)KONVENS 2019: 15th Conference on Natural Language Processing, Erlangen, Germany, October 9-11, 201