Operationalising and measuring language dominance

The paper offers a new way to measure language ability in bilinguals, based on measures of lexical richness. The validity of proposed approach is tested in a variety of ways

Epistemic and social scripts in computer-supported collaborative learning

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. One possibility to improve collaborative learning environments is to conceptualize epistemic scripts, which specify how learners work on a given task, and social scripts, which structure how learners interact with each other. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors ‘epistemic script’ and ‘social script’ have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not to lead to the expected effects

Consequences of various landscape-scale ecosystem management strategies and fire cycles on age-class structure and harvest in boreal forests

At the landscape scale, one of the key indicators of sustainable forest management is the age-class distribution of stands, since it provides a coarse synopsis of habitat potential, structural complexity, and stand volume, and it is directly modified by timber extraction and wildfire. To explore the consequences of several landscape-scale boreal forest management strategies on age-class structure in the Mauricie region of Quebec, we used spatially explicit simulation modelling. Our study investigated three different harvesting strategies (the one currently practiced and two different strategies to maintain late seral stands) and interactions between fire and harvesting on stand age-class distribution. We found that the legacy of initial forested age structure and its spatial configuration can pose short- (<50 years) to medium-term (150-300 years) challenges to balancing wood supply and ecological objectives. Also, ongoing disturbance by fire, even at relatively long cycles in relation to historic levels, can further constrain the achievement of both timber and biodiversity goals. For example, when fire was combined with management, harvest shortfalls occurred in all scenarios with a fire cycle of 100 years and most scenarios with a fire cycle of 150 years. Even a fire cycle of 500 years led to a reduction in older forest when its maintenance was not a primary constraint. Our results highlight the need to consider the broad-scale effects of natural disturbance when developing ecosystem management policies and the importance of prioritizing objectives when planning for multiple resource use

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

ASTEC -- the Aarhus STellar Evolution Code

The Aarhus code is the result of a long development, starting in 1974, and still ongoing. A novel feature is the integration of the computation of adiabatic oscillations for specified models as part of the code. It offers substantial flexibility in terms of microphysics and has been carefully tested for the computation of solar models. However, considerable development is still required in the treatment of nuclear reactions, diffusion and convective mixing.Comment: Astrophys. Space Sci, in the pres

The use of orthogonal similarity relations in the prediction of authorship

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-37256-8_38Recent work on Authorship Attribution (AA) proposes the use of meta characteristics to train author models. The meta characteristics are orthogonal sets of similarity relations between the features from the different candidate authors. In that approach, the features are grouped and processed separately according to the type of information they encode, the so called linguistic modalities. For instance, the syntactic, stylistic and semantic features are each considered different modalities as they represent different aspects of the texts. The assumption is that the independent extraction of meta characteristics results in more informative feature vectors, that in turn result in higher accuracies. In this paper we set out to the task of studying the empirical value of this modality specific process. We experimented with different ways of generating the meta characteristics on different data sets with different numbers of authors and genres. Our results show that by extracting the meta characteristics from splitting features by their linguistic dimension we achieve consistent improvement of prediction accuracy.This research was partially supported by ONR grant N00014-12-1-0217 and by NSF award 1254108. It was also supported in part by the CONACYT grant 134186 and by the European Commission as part of the WIQ-EI project (project no. 269180) within the FP7 People Programme.Sapkota, U.; Solorio, T.; Montes Gómez, M.; Rosso, P. (2013). The use of orthogonal similarity relations in the prediction of authorship. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 463-475. https://doi.org/10.1007/978-3-642-37256-8_38S463475Baker, L.D., McCallum, A.: Distributional clustering of words for text classification. 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In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 288–298. Association for Computational Linguistics, Portland (2011)Hayes, J.H.: Authorship attribution: A principal component and linear discriminant analysis of the consistent programmer hypothesis. I. J. Comput. Appl., 79–99 (2008)Houvardas, J., Stamatatos, E.: N-gram feature selection for authorship identification. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS (LNAI), vol. 4183, pp. 77–86. Springer, Heidelberg (2006)Karypis, G.: CLUTO - a clustering toolkit. Tech. Rep. #02-017 (November 2003)Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram based author profiles for authorship attribution. In: Proceedings of the Pacific Association for Computational Linguistics, pp. 255–264 (2003)Koppel, M., Schler, J., Argamon, S.: Authorship attribution in the wild. Language Resources and Evaluation 45, 83–94 (2011)Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, pp. 513–520 (August 2008)Luyckx, K., Daelemans, W.: The effect of author set size and data size in authorship attribution. In: Literary and Linguistic Computing, pp. 1–21 (August 2010)Marneffe, M.D., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: LREC 2006 (2006)Plakias, S., Stamatatos, E.: Tensor space models for authorship identification. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 239–249. Springer, Heidelberg (2008)Raghavan, S., Kovashka, A., Mooney, R.: Authorship attribution using probabilistic context-free grammars. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 38–42. Association for Computational Linguistics, Uppsala (2010)Slonim, N., Tishby, N.: The power of word clusters for text classification. In: 23rd European Colloquium on Information Retrieval Research, ECIR (2001)Solorio, T., Pillay, S., Raghavan, S., Montes-y-Gómez: Generating metafeatures for authorship attribution on web forum posts. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, IJCNLP 2011, pp. 156–164. AFNLP, Chiang Mai (2011)Stamatatos, E.: Author identification using imbalanced and limited training texts. In: 18th International Workshop on Database and Expert Systems Applications, DEXA 2007, pp. 237–241 (September 2007)Stamatatos, E.: Author identification: Using text sampling to handle the class imbalance problem. 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In: Proceedings of the Workshop on Data Mining for Security Applications, 8th ACM Conference on Computer Security (2001)Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann (2005

Genetic Relationships of Crown Rust Resistance, Grain Yield, Test Weight, and Seed Weight in Oat

Integrating selection for agronomic performance and quantitative resistance to crown rust, caused by Puccinia coronata Corda var. avenae W.P. Fraser & Ledingham, in oat (Avena sativa L.) requires an understanding of their genetic relationships. This study was conducted to investigate the genetic relationships of crown rust resistance, grain yield, test weight, and seed weight under both inoculated and fungicide-treated conditions. A Design II mating was performed between 10 oat lines with putative partial resistance to crown rust and nine lines with superior grain yield and grain quality potential. Progenies from this mating were evaluated in both crown rust-inoculated and fungicide-treated plots in four Iowa environments to estimate genetic effects and phenotypic correlations between crown rust resistance and grain yield, seed weight, and test weight under either infection or fungicide-treated conditions. Lines from a random-mated population derived from the same parents were evaluated in three Iowa environments to estimate heritabilities of, and genetic correlations between, these traits. Resistance to crown rust, as measured by area under the disease progress curve (AUDPC), was highly heritable (H = 0.89 on an entry-mean basis), and was favorably correlated with grain yield, seed weight, and test weight measured in crown rust-inoculated plots. AUDPC was unfavorably correlated or uncorrelated with grain yield, test weight, and seed weight measured in fungicide-treated plots. To improve simultaneously crown rust resistance, grain yield, and seed weight under both lower and higher levels of crown rust infection, an optimum selection index can be developed with the genetic parameters estimated in this stud