On the relationship between society and higher education: what path should we take?

Abstract

The current network society, with its primacy of information and communication technologies, is challenging the higher education model. The needs and expectations of today’s students differ from those of students in the past, and educational practices should adapt to modern times. But what changes will we see in the relationship between societal transformation and the higher education system? In this research paper, we set out a quantum-based approach in order to analyze this relationship and to advance the understanding about the role that distance education will play in the future. Under this perspective, we provide an illustration that allows for the representation and evaluation of future scenarios.     

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.     

The Visual Holdings of the Slavonic Library,Prague

This study focuses on the visual collections in the holdings of the Slavonic Library (a section of the National Library of the Czech Republic). The library holdings also include unique collections relevant to the topic of Russian and Ukrainian emigration: collections of visual art, ex libris, posters and flyers by Ukrainian émigrés (1918–1945), and a collection of invitations and flyers by Russian émigrés (1918–1945). Rudolf H?lka’s collection of photographs, dating back to the 1920s, captures a series of ethnographic, folkloric, and architectonic phenomena which no longer exist. Moreover, Russian posters from the period of WWI, from the revolutionary year of 1917, and the subsequent civil war are equally fascinating as are banknotes from the same period and region.     

Long-term effects of a mediated reading programme on the development of emotional competencies / Efectos a largo plazo de un programa de lectura mediada para el desarrollo de competencias emocionales

Emotional competencies are considered protective factors against behaviour problems and have been associated with adequate social and academic development. Early intervention means a greater chance of stability and development of these skills; however, few programmes address these aspects and evaluate their effectiveness in the short, middle or long term. Our research goal was to implement an early intervention programme for the development of emotional competencies based on the mediated reading of children’s literature and to evaluate its long-term effectiveness. Participants were 115 children between the ages of five and eight who were divided into two groups. To assess the impact of the programme, a factorial design was implemented with four independent variables. The ‘Mediated reading’ and ‘Traditional reading’ groups were compared on measures of facial emotion recognition, empathy and emotional regulation. Results indicate that the mediated reading group showed the best performance on all the dimensions evaluated, with significant differences from the traditional reading group. The study concludes by analysing the outcomes and limitations of the programme implemented.     

“Salúd,Amigos”: Negotiating Border History in Rural and Farm Radio of the Rio Grande Valley

The southernmost area of Texas, known as the Rio Grande Valley, is largely absent from histories of early radio. The social, political, and economic history of the Valley from 1848 to the 1950s prompts the authors to approach the region’s broadcast history from the context of rural and farm radio. The papers of broadcaster Bradford Smith, housed at the Margaret H. McAllen Memorial Archives at the Museum of South Texas History, provide an entryway for Valley radio to become part of the larger body of rural and farm radio scholarship. Smith’s radio reports emphasize the centrality of localism for remote communities, and they provide competing narratives about the region’s modern identity and its Mexican roots.     

The strange case of reproducibility versus representativeness in contextual suggestion test collections

The most common approach to measuring the effectiveness of Information Retrieval systems is by using test collections. The Contextual Suggestion (CS) TREC track provides an evaluation framework for systems that recommend items to users given their geographical context. The specific nature of this track allows the participating teams to identify candidate documents either from the Open Web or from the ClueWeb12 collection, a static version of the web. In the judging pool, the documents from the Open Web and ClueWeb12 collection are distinguished. Hence, each system submission should be based only on one resource, either Open Web (identified by URLs) or ClueWeb12 (identified by ids). To achieve reproducibility, ranking web pages from ClueWeb12 should be the preferred method for scientific evaluation of CS systems, but it has been found that the systems that build their suggestion algorithms on top of input taken from the Open Web achieve consistently a higher effectiveness. Because most of the systems take a rather similar approach to making CSs, this raises the question whether systems built by researchers on top of ClueWeb12 are still representative of those that would work directly on industry-strength web search engines. Do we need to sacrifice reproducibility for the sake of representativeness? We study the difference in effectiveness between Open Web systems and ClueWeb12 systems through analyzing the relevance assessments of documents identified from both the Open Web and ClueWeb12. Then, we identify documents that overlap between the relevance assessments of the Open Web and ClueWeb12, observing a dependency between relevance assessments and the source of the document being taken from the Open Web or from ClueWeb12. After that, we identify documents from the relevance assessments of the Open Web which exist in the ClueWeb12 collection but do not exist in the ClueWeb12 relevance assessments. We use these documents to expand the ClueWeb12 relevance assessments. Our main findings are twofold. First, our empirical analysis of the relevance assessments of 2  years of CS track shows that Open Web documents receive better ratings than ClueWeb12 documents, especially if we look at the documents in the overlap. Second, our approach for selecting candidate documents from ClueWeb12 collection based on information obtained from the Open Web makes an improvement step towards partially bridging the gap in effectiveness between Open Web and ClueWeb12 systems, while at the same time we achieve reproducible results on well-known representative sample of the web.