Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

Rating scale development: a multistage exploratory sequential design

The project chosen to showcase the application of the exploratory sequential design in second/ foreign (L2) language assessment comes from the context of rating scale development and focuses on the development of a set of scales for a suite of high-stakes L2 speaking tests. The assessment of speaking requires assigning scores to a speech sample in a systematic fashion by focusing on explicitly defined criteria which describe different levels of performance (Ginther 2013). Rating scales are the instruments used in this evaluation process, and they can be either holistic (i.e. providing a global overall assessment) or analytic (i.e. providing an independent evaluations for a number of assessment criteria, e.g. Grammar, Vocabulary, Organisation, etc.). The discussion in this chapter is framed within the context of rating scales in speaking assessment. However, it is worth noting that the principles espoused, stages employed and decisions taken during the development process have wider applicability to performance assessment in general

Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201

Effective skill refinement: Focusing on process to ensure outcome

In contrast to the abundance of motor skill acquisition and performance research, there is a paucity of work which addresses how athletes with an already learnt and well-established skill may go about making a subtle change, or refinement, to that skill. Accordingly, the purpose of this review paper is to provide a comprehensive overview of current understanding pertaining to such practice. Specifically, this review addresses deliberately initiated refinements to closed and self-paced skills (e.g., javelin throwing, golf swing and horizontal jumps). In doing so, focus is directed to three fundamental considerations within applied coaching practice and future research endeavours; the intended outcomes, process and evaluative measures of skill refinement. Conclusions suggest that skill refinement is not the same as skill acquisition or performing already learnt skills with high-levels of automaticity. Due to the complexity of challenge faced, refinements are best addressed as an interdisciplinary solution, with objective measures informing coach decision making

Linking objective and subjective modeling in engineering design through arc-elastic dominance

Engineering design in mechanics is a complex activity taking into account both objective modeling processes derived from physical analysis and designers’ subjective reasoning. This paper introduces arc-elastic dominance as a suitable concept for ranking design solutions according to a combination of objective and subjective models. Objective models lead to the aggregation of information derived from physics, economics or eco-environmental analysis into a performance indicator. Subjective models result in a confidence indicator for the solutions’ feasibility. Arc-elastic dominant design solutions achieve an optimal compromise between gain in performance and degradation in confidence. Due to the definition of arc-elasticity, this compromise value is expressive and easy for designers to interpret despite the difference in the nature of the objective and subjective models. From the investigation of arc-elasticity mathematical properties, a filtering algorithm of Pareto-efficient solutions is proposed and illustrated through a design knowledge modeling framework. This framework notably takes into account Harrington’s desirability functions and Derringer’s aggregation method. It is carried out through the re-design of a geothermal air conditioning system