A hybrid neural network model for consciousness

INTRODUCTION The goal of understanding the brain and making artificial minds has propelled many scientificfields greatly. In a sense it may be the final goal othe whole science. It is impossible that one unifiedtheory will be sufficient for explaining the brain’functionality because of its unimaginable complexity. Multi-discipline combinations havebrought about so many achievements towards thigoal. Taylor (1994) introduced the “relationamind” approach in …     

Mobile and Information Literacy Perceptions and Skills of Library and Information Sciences and Humanities Students From Bulgaria and Turkey

Welcome to the Global Postcards column! We are so excited to bring you news and projects from around the world. In this column, our contributors from Bulgaria and Turkey bring us a comparative study of mobile and information literacy among students at two universities in those countries. In addition to presenting perceptions of students around these literacy concepts, their research project also highlights growing trends in the use of distance education and web-based study materials. Thanks to the contributors for this issue, and please keep the submissions coming!

If you would like to send a submission, please contact either of the column's co-editors: Jacqueline Solis, jsolis@email.unc.edu, and Robin Kear, rlk25@pitt.edu.     

The role of index compression in score-at-a-time query evaluation

This paper explores the performance of top k document retrieval with score-at-a-time query evaluation on impact-ordered indexes in main memory. To better understand execution efficiency in the context of modern processor architectures, we examine the role of index compression on query evaluation latency. Experiments include compressing postings with variable byte encoding, Simple-8b, variants of the QMX compression scheme, as well as a condition that is less often considered—no compression. Across four web test collections, we find that the highest query evaluation speed is achieved by simply leaving the postings lists uncompressed, although the performance advantage over a state-of-the-art compression scheme is relatively small and the index is considerably larger. We explain this finding in terms of the design of modern processor architectures: Index segments with high impact scores are usually short and inherently benefit from cache locality. Index segments with lower impact scores may be quite long, but modern architectures have sufficient memory bandwidth (coupled with prefetching) to “keep up” with the processor. Our results highlight the importance of “architecture affinity” when designing high-performance search engines.     

Identifying top relevant dates for implicit time sensitive queries

Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.     

Statistical biases in Information Retrieval metrics for recommender systems

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes.     

A Bibliometric Analysis of High Impact Research in the Middle East Using Champion Works

Increasing political and financial support for scientific research in the Middle East requires academic and research communities in the region to demonstrate the visibility and impact of their scientific output. However, for countries with smaller scientific communities or lack of detailed information on their scientific production, the use of common metrics of scientific impact (e.g., number of papers, impact factor, h-index, etc.) may fail to reveal their true ability to produce high quality research, and thus guarantee the wanted societal support. In such cases, identifying and highlighting outstanding papers produced by national institutions or scientists may be another way to demonstrate scientific capacity and impact. In this context, this work aims to provide an overview of champion works (papers that have received over 1,000 citations) produced by Middle East countries. This analysis focuses on science, medicine, and technology papers featured in the Science Citation Index Expanded of Web of Science. The authors identified 213 champion works authored by Middle East scientists published since the 1970s. Israel is currently the leading nation in the Middle East in terms of published champion works, but at least one such work was identified for the majority of countries in the region. Middle East champion works were published on a diverse range of subject categories and often featured in the top journals worldwide (e.g., Science, Nature, etc.). The top institutions in the Middle East authoring champion works and their leading collaborating countries worldwide are listed, and the role of international scientific collaborations in achieving these highly cited papers is highlighted.     

多线程在大幅图像分块预处理中的应用

在分析大规模二值图像特点和预处理原理的基础上,提出一种采用多线程实现分块预处理大幅图像的方法.该方法有效地解决了大幅图像预处理的时空及长时间等待与人机交互的矛盾.