Learning Stochastic Tree Edit Distance

pages 42-53International audienceTrees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance for which improvements in terms of complexity have been achieved during the last decade. However, this classic edit distance usually uses a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree edit distance. We use an adaptation of the expectation-maximization algorithm for learning the primitive edit costs. We carried out several series of experiments that confirm the interest to learn a tree edit distance rather than a priori imposing edit costs

Melody recognition with learned edit distances

In a music recognition task, the classification of a new melody is often achieved by looking for the closest piece in a set of already known prototypes. The definition of a relevant similarity measure becomes then a crucial point. So far, the edit distance approach with a-priori fixed operation costs has been one of the most used to accomplish the task. In this paper, the application of a probabilistic learning model to both string and tree edit distances is proposed and is compared to a genetic algorithm cost fitting approach. The results show that both learning models outperform fixed-costs systems, and that the probabilistic approach is able to describe consistently the underlying melodic similarity model.This work was funded by the French ANR Marmota project, the Spanish PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018), and the Pascal Network of Excellence

Cardiovascular Health in Anxiety or Mood Problems Study (CHAMPS): study protocol for a randomized controlled trial

Background: Previous psychological and pharmacological interventions have primarily focused on depression disorders in populations with cardiovascular diseases (CVDs) and the efficacy of anxiety disorder interventions is only more recently being explored. Transdiagnostic interventions address common emotional processes and the full range of anxiety and depression disorders often observed in populations with CVDs. The aim of CHAMPS is to evaluate the feasibility of a unified protocol (UP) for the transdiagnostic treatment of emotional disorders intervention in patients recently hospitalized for CVDs. The current study reports the protocol of a feasibility randomized controlled trial to inform a future trial. Methods/Design: This is a feasibility randomized, controlled trial with a single-center design. A total of 50 participants will be block-randomized to either a UP intervention or enhanced usual care. Both groups will receive standard CVD care. The UP intervention consists of 1) enhancing motivation, readiness for change, and treatment engagement; (2) psychoeducation about emotions; (3) increasing present focused emotion awareness; (4) increasing cognitive flexibility; (5) identifying and preventing patterns of emotion avoidance and maladaptive emotion-driven behaviors (EDBs, including tobacco smoking, and alcohol use); (6) increasing tolerance of emotion-related physical sensations; (7) interoceptive and situation-based emotion-focused exposure; and (8) relapse prevention strategies. Treatment duration is 12 to 18 weeks. Relevant outcomes include the standard deviation of self-rated anxiety, depression and quality of life symptoms. Other outcomes include intervention acceptability, satisfaction with care, rates of EDBs, patient adherence, physical activity, cardiac and psychiatric readmissions. Parallel to the main trial, a nonrandomized comparator cohort will be recruited comprising 150 persons scoring below the predetermined depression and anxiety severity thresholds. Discussion: CHAMPS is designed to evaluate the UP for the transdiagnostic treatment of emotional disorders targeting emotional disorder processes in a CVD population. The design will provide preliminary evidence of feasibility, attrition, and satisfaction with treatment to design a definitive trial. If the trial is feasible, it opens up the possibility for interventions to target broader emotional processes in the precarious population with CVD and emotional distress.Phillip J. Tully, Deborah A. Turnbull, John D. Horowitz, John F. Beltrame, Terina Selkow, Bernhard T. Baune, Elizabeth Markwick, Shannon Sauer-Zavala, Harald Baumeister, Suzanne Cosh and Gary A. Witter

Reading the texts of assessment task sheets in two Year 8 English classrooms

Task sheets are written texts generated specifically for assessment purposes in high schools in Queensland, Australia. They attempt to make assessment transparent by explaining the criteria used to assess performance. The criteria and statements of standards describe how a grade is awarded and provide evidence of teacher accountability for assessment. In this paper, task sheets from two Year 8 English classes in two such schools are described and analysed to investigate three questions: (1) What do task sheets reveal about practices and relationships operating in classroom-based literacy assessment?(2)How do task sheets document what counts as literacy and performance in literacy at this level of schooling? (3) Can any task sheet be ‘transparent’ and how much interpretation is required to make sense of it? The analysis was guided by a framework of methods drawn from Fairclough (1992), Gee (1990),and critical linguistic approaches. It found that task sheets are complex texts which communicate dimensions of assessment relating to teachers’ and students’ roles, the purposes of assessment, and criteria for what counts as performance. It is argued that assessment task sheets are themselves documents of the assessment culture, part of the socialisation of students into assessment practices in the classroom

XSLTGen: A system for automatically generating XML transformations via semantic mappings

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