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.     

We need more than just male bodies in classrooms: Recruiting and retaining culturally relevant Black male teachers in early childhood education

Nationwide, school districts struggle to recruit and retain Black males to the teaching profession. As a result, the presence of Black male teachers is lacking in public schools, which impacts the overall student outcomes for all children, particularly Black boys. Such recruitment and retention becomes even worse at the early childhood level, which explains the paucity of Black male teachers who are early childhood educators. To date, only 2% of teachers are Black males, of which most are middle and high school teachers. Furthermore, the clarion call for Black males to become teachers does not often prioritize Black male teachers who are culturally relevant. In this article, we focus on educational research regarding the lack of diversity of teachers and among males in K-12 to provide recommendations to the field of early childhood education, with specific attention to recruiting and retaining culturally relevant Black male teachers in early childhood education.     

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.     

International Development Models for Educational Reform1

Over the last half century of educational development, six distinctive administrative models have emerged: (1) neo-colonial, (2) centralised, (3) decentralised-regionalisation, (4) decentralised-sectoral, (5) community-based, and (6) transformative models. The origins, strengths, and weaknesses of the respective models are reviewed and compared.