User simulations for evaluating answers to question series

Recently, question series have become one focus of research in question answering. These series are comprised of individual factoid, list, and “other” questions organized around a central topic, and represent abstractions of user–system dialogs. Existing evaluation methodologies have yet to catch up with this richer task model, as they fail to take into account contextual dependencies and different user behaviors. This paper presents a novel simulation-based methodology for evaluating answers to question series that addresses some of these shortcomings. Using this methodology, we examine two different behavior models: a “QA-styled” user and an “IR-styled” user. Results suggest that an off-the-shelf document retrieval system is competitive with state-of-the-art QA systems in this task. Advantages and limitations of evaluations based on user simulations are also discussed.     

Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

To better understand the statistical properties of the deterministic inputs, noisy “and” gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the prior distribution matches the latent class structure. However, when the latent classes are of indefinite structure, the empirical Bayes method in conjunction with an unstructured prior distribution provides much better estimates and classification accuracy. Moreover, using empirical Bayes with an unstructured prior does not lead to extremely poor results as other prior-estimation method combinations do. The simulation results also show that increasing the sample size reduces the variability, and to some extent the bias, of item parameter estimates, whereas lower level of guessing and slip parameter is associated with higher quality item parameter estimation and classification accuracy.     

Exploring the everyday realities of grass-roots football coaching: towards a relational perspective

While scholars have increasingly engaged with the (micro)political and emotional experiences of coaches in professional and semi-professional football, little attention has been given to grass-roots coaches’ understandings of these issues. The aim of this paper is to outline one possible research agenda that could contribute to the development of a rich and increasingly nuanced understanding of the everyday realities of being a grass-roots football coach. In particular, we consider (volunteer) coaches’ participation in grass-roots football to be an inherently relational endeavour. Following the presentation of a creative fiction that is based upon our shared experiences of being grass-roots football coaches, we then illustrate how relational thinking might be productively applied to exploring the social, (micro)political and emotional features of grass-roots football coaching.     

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.     

From Loving the Hero to Despising the Villain: Sports Fans,Facebook, and Social Identity Threats

This research explored how University of Cincinnati football fans used Facebook to manage a social identity threat arising from head football coach Brian Kelly leaving the school to become the head coach at the University of Notre Dame. A thematic analysis of 717 wall postings in the “Get Out of Our City Brian Kelly” Facebook group was conducted. Results revealed that fans responded to this social identity threat in the following ways: (a) rallying, (b) stigmatizing, (c) victimization, (d) intimidation, and (e) degradation. The results suggest that social media sites are prime vehicles for sports fans to collectively manage social identity crises. Social media enables fans to perpetuate messages that elevate group distinctiveness, minimize in-group issues, and derogate out-group members.     

Multi-candidate reduction: Sentence compression as a tool for document summarization tasks

This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization—a “parse-and-trim” approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.     

Photocatalytic Degradation of Persistent and Toxic Organic Pollutants and its Mechanism

Persistent and toxic organic pollutants are serious environmental concerns in many parts of the world. These pollutants are often difficult to deal with using conventional treatment processes. Photocatalysis is an emerging technology which uses environmentally-friendly oxidants (oxygen, hydrogen peroxide, ozone),photocatalysts (titanium dioxide, ferrous ions or its complexes) and ultraviolet (UV) radiation to degrade and mineralize the toxic organic pollutants. The major drawback is that photocatalytic processes need to be activated by ultraviolet light, which accounts for only about 4% of the incoming solar energy; the overall reaction efficiency is still very low.     

Combining integrated sampling with SVM ensembles for learning from imbalanced datasets

Learning from imbalanced datasets is difficult. The insufficient information that is associated with the minority class impedes making a clear understanding of the inherent structure of the dataset. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced, because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs may suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique, which incorporates both over-sampling and under-sampling, with an ensemble of SVMs to improve the prediction performance. Extensive experiments show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers.