The Design and Implementation of Bloqqi - A Feature-Based Diagram Programming Language

This dissertation presents the design and implementation of a new block diagram programming language, Bloqqi, for building control systems with focus on variability. The language has been developed in collaboration with industry with the goal of reducing engineering time and improving reuse of functionality.When building a control system for a plant, there are typically different variants of the same base functionality. A plant may have several variants of a tank, for example, one variant with heating and another one without. This dissertation presents novel language mechanisms for describing this kind of variability, based on diagram inheritance. For instance, Bloqqi supports specifying what features, like heating, the base functionality can have. These specifications are then used to automatically derive smart-editing support in the form of a feature-based wizard. In this wizard, the user can select what features the base functionality should have, and code is generated corresponding to these features. The new language mechanisms allow feature-based libraries to be created and extended in a modular way.This dissertation presents techniques for implementing rich graphical editors with smart editing support based on semantic analysis. A prototype compiler and graphical editor have been implemented for the language, using the semantic formalism reference attribute grammars (RAGs). RAGs allow tools to share the semantic specifications, which makes it possible to modularly extend the compiler with support for advanced semantic feedback to the user of the graphical editor

Developing Second Language Writing through Scaffolding in the ZPD: A Magazine Project for an Authentic Audience

In the present study, Vygotsky’s (1978, 1986) sociocultural framework of the zone of proximal development (ZPD) and scaffolding writing (Bodrova & Leong, 1995, 1996; Ross, 1976) are used as the theoretical basis to study the development of second language writing. A course project is presented in which advanced English language learners of Spanish acted as authors and editors to create their own professional magazines for an authentic audience. In the project, each student authored four essays which went through four peer- and instructor-edited stages of scaffolding writing techniques. After each stage, ratings were given by the editors who also facilitated feedback debriefing sessions (Lidz, 1991). Statistical analyses revealed significant improvement within the four essays demonstrating writing development of subsequent revisions of a single essay. There was also significant improvement between the four essays revealing a linear, continuous writing development. In all, these results support a notion that scaffolding writing techniques and feedback debriefing sessions within the ZPD effectively develops writing skills in second language learning when contextualized through a writing workshop involving the creation of a professional magazine designed for an authentic audience

Constraint specification by example in a Meta-CASE tool

CASE tools are very helpful to software engineers in different ways and in different phases of software development. However, they are not easy to specialise to meet the needs of particular application domains or particular software modelling requirements. Meta-CASE tools offer a way of providing such specialisation by enabling a designer to specify a tool which is then generated automatically. Constraints are often used in such meta-CASE tools as a technique for governing the syntax and semantics of model elements and the values of their attributes. However, although constraint definition is a difficult process it has attracted relatively little research attention. The PhD research described here presents an approach for improving the process of CASE tool constraint specification based on the notion of programming by example (or demonstration). The feasibility of the approach will be demonstrated via experiments with a prototype using the meta-CASE tool Diagram Editor Constraints System (DECS) as context

A meta-analytic review of stand-alone interventions to improve body image

Objective Numerous stand-alone interventions to improve body image have been developed. The present review used meta-analysis to estimate the effectiveness of such interventions, and to identify the specific change techniques that lead to improvement in body image. Methods The inclusion criteria were that (a) the intervention was stand-alone (i.e., solely focused on improving body image), (b) a control group was used, (c) participants were randomly assigned to conditions, and (d) at least one pretest and one posttest measure of body image was taken. Effect sizes were meta-analysed and moderator analyses were conducted. A taxonomy of 48 change techniques used in interventions targeted at body image was developed; all interventions were coded using this taxonomy. Results The literature search identified 62 tests of interventions (N = 3,846). Interventions produced a small-to-medium improvement in body image (d+ = 0.38), a small-to-medium reduction in beauty ideal internalisation (d+ = -0.37), and a large reduction in social comparison tendencies (d+ = -0.72). However, the effect size for body image was inflated by bias both within and across studies, and was reliable but of small magnitude once corrections for bias were applied. Effect sizes for the other outcomes were no longer reliable once corrections for bias were applied. Several features of the sample, intervention, and methodology moderated intervention effects. Twelve change techniques were associated with improvements in body image, and three techniques were contra-indicated. Conclusions The findings show that interventions engender only small improvements in body image, and underline the need for large-scale, high-quality trials in this area. The review identifies effective techniques that could be deployed in future interventions

Improving the post-editing experience using translation recommendation: a user study

We report findings from a user study with professional post-editors using a translation recommendation framework (He et al., 2010) to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We analyze the effectiveness of the model as well as the reaction of potential users. Based on the performance statistics and the users’comments, we find that translation recommendation can reduce the workload of professional post-editors and improve the acceptance of MT in the localization industry

Automatic differentiation in machine learning: a survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure

Automatic case acquisition from texts for process-oriented case-based reasoning

This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.Comment: Sous presse, publication pr\'evue en 201

Query Expansion with Locally-Trained Word Embeddings

Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships. We study the use of term relatedness in the context of query expansion for ad hoc information retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when trained globally, underperform corpus and query specific embeddings for retrieval tasks. These results suggest that other tasks benefiting from global embeddings may also benefit from local embeddings

Statistical Literacy Among Applied Linguists and Second Language Acquisition Researchers

The importance of statistical knowledge in applied linguistics and second language acquisition (SLA) research has been emphasized in recent publications. However, the last investigation of the statistical literacy of applied linguists occurred more than 25 years ago (Lazaraton, Riggenbach, & Ediger, 1987). The current study undertook a partial replication of this older work by investigating (a) applied linguists’ general experiences with statistics, (b) underlying factors that constitute applied linguists’ knowledge about and attitudes toward statistics, and (c) variables that predict attitudes toward statistics and statistical self-efficacy. Three hundred thirty-one scholars of applied linguistics and SLA completed a questionnaire. Eighty percent had taken a statistics class; however, only 14% of doctoral students and 30% of professors felt that their statistical training was adequate. A factor analysis of participants’ knowledge of statistical terms revealed three factors: common inferential statistics knowledge, advanced statistics knowledge, and basic descriptive statistics knowledge. An analysis of participants’ attitudes toward statistics revealed two factors: statistics are important and lack of statistical confidence. Regression analyses found that a quantitative research orientation was the strongest predictor of positive attitudes toward statistics; nevertheless, participants also expressed support for qualitative research. Recommendations for improving quantitative methods in our field are made based on our findings

A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey