Invisible computer for collaborative design: evaluation of a multimodal sketch-based environment

In the areas of design, especially in architectural design, collaboration has become an important challenge. The specialization of skills increase, work teams are more and more extensive and the geographic distance between them increases too. The economic and ecological stakes related to remote collaboration are an evidence. This context involves the need to support most efficiently possible remote working meetings. We present the Distributed Collaborative Digital Studio (DSDC), a tool designed to recreate, in distant situations, the context of copresence meetings. This shared environment is created in the "invisible computer" approach [11]. The idea is that the tool should disappear from user's consciousness. Indeed, creative design activities require some fluidity in their process. Therefore, any involuntary interruption created by the system can potentially brake creativity. In this perspective, we investigate specifically the "invisibility" of our environment. To do this, we propose a framework for the operationalization of the concept and a methodology to test the system invisibility. This methodology was applied through a case study consisting of a corpus of 12 hours of remote collaborative design sessions with the DSDC. We highlight the learning effects while using our system, conclude on its effectiveness and discuss our methodology.     

Nonparametric density estimation from data with a mixture of Berkson and classical errors

The author considers density estimation from contaminated data where the measurement errors come from two very different sources. A first error, of Berkson type, is incurred before the experiment: the variable X of interest is unobservable and only a surrogate can be measured. A second error, of classical type, is incurred after the experiment: the surrogate can only be observed with measurement error. The author develops two nonparametric estimators of the density of X, valid whenever Berkson, classical or a mixture of both errors are present. Rates of convergence of the estimators are derived and a fully data‐driven procedure is proposed. Finite sample performance is investigated via simulations and on a real data example.     

Nonparametric Kernel Methods with Errors‐in‐Variables: Constructing Estimators,Computing them,and Avoiding Common Mistakes

Estimating a curve nonparametrically from data measured with error is a difficult problem that has been studied by many authors. Constructing a consistent estimator in this context can sometimes be quite challenging, and in this paper we review some of the tools that have been developed in the literature for kernel‐based approaches, founded on the Fourier transform and a more general unbiased score technique. We use those tools to rederive some of the existing nonparametric density and regression estimators for data contaminated by classical or Berkson errors, and discuss how to compute these estimators in practice. We also review some mistakes made by those working in the area, and highlight a number of problems with an existing R package decon .     

Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error

In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.     

Local bandwidth selectors for deconvolution kernel density estimation

We consider kernel density estimation when the observations are contaminated by measurement errors. It is well-known that the success of kernel estimators depends heavily on the choice of a smoothing parameter called the bandwidth. A number of data-driven bandwidth selectors exist, but they are all global. Such techniques are appropriate when the density is relatively simple, but local bandwidth selectors can be more attractive in more complex settings. We suggest several data-driven local bandwidth selectors and illustrate via simulations the significant improvement they can bring over a global bandwidth.     

Development and Validation of an Environmental Quality of Life Scale: Study of a French Sample

The objective of this research is to develop a scale of perceived environmental quality of life and test its factorial structure and validity. The constitutive items of the scale were generated using semi-directive interviews. Four principal quality of life factors were isolated in an exploratory study carried out with 760 participants. This factorial structure was then established in a cross-validation of the scale during a confirmatory study applied to a second sample of 740 participants. The internal consistency of the environmental quality of life dimensions, the adequacy of the measurement model to the data, and the stability of the factorial structure are all proof of the good psychometric qualities of this scale.     

The role of mutuals and community-based insurance in social health protection systems: International experience on delegated functions

The institutional architecture for the provision of social health protection varies across countries, as do the actors and organizations involved. In some countries, mutual benefit societies and community-based health insurance organizations (CBHI) play a role in this area. In the 1990s, these were promoted particularly as a means of extending social security coverage, especially in sub-Saharan Africa. In the current context, the adoption of the 2030 Agenda for sustainable development, as well as renewed political will to realize universal coverage, has led to a questioning of the role of mutuals/CBHI. However, the literature on the roles they play in national social security systems remains limited. For this scoping review, 49 documents were analysed, covering 18 countries worldwide, focused on the delegation of functions to mutuals/CBHI in national social health protection systems. The results reveal the dynamics of the delegation of functions within social protection systems over time and their implementation processes. These provide areas for reflection that can inform policy processes.     

Anxiety,Depression and Fraternal Experiences on the Birth Child in a Foster Home

If foster care primarily concerns foster children displaced from a family to another, it also constitutes a singular event for the entire family, including the children of the foster parents. The aim of this study was to examine anxiety and depressive symptoms in foster families’ birth children and to explore the statistical associations between the different types of horizontal relations (with a sibling or a foster child) and the psycho-affective equilibrium of the birth children. Self-evaluation scales on the quality of sibling relations, anxiety and depressive symptoms were used. Forty five birth adolescents, aged from 11 to 18 years old, took part in the study. These participants showed more balanced scores of anxiety and depression than the population of the same age. Post-hoc tests additionally suggest that these ones vary depending on the gender, possibly due to specific role expectations. The relations between siblings or with the foster child present differentiated outcomes on anxiety and depression scores. This study confirms that the quality of horizontal relations is important for the psycho-affective equilibrium of the birth child. The relationships between birth and foster children need to be more systematically considered in order to promote successful fostering.     

Rate-optimal nonparametric estimation in classical and Berkson errors-in-variables problems

We consider nonparametric estimation of a regression curve when the data are observed with Berkson errors or with a mixture of classical and Berkson errors. In this context, other existing nonparametric procedures can either estimate the regression curve consistently on a very small interval or require complicated inversion of an estimator of the Fourier transform of a nonparametric regression estimator. We introduce a new estimation procedure which is simpler to implement, and study its asymptotic properties. We derive convergence rates which are faster than those previously obtained in the literature, and we prove that these rates are optimal. We suggest a data-driven bandwidth selector and apply our method to some simulated examples.     

Measuring Quality of Life in the Neighborhood: The Cases of Air-Polluted Cities in Tunisia

The objective of this paper is to study the quality of life in the neighborhood by developing a scale to measure the perceived environmental quality of life. Items of the scale were produced using semi-structured interviews. Three main dimensions of environmental quality of life (EQL) were recognized by an exploratory factor analysis applied on 732 participants. This factor structure was then subjected to a confirmatory factor analysis performed on a second sample of 791 people. The satisfactory internal consistency of the three EQL dimensions, the good fit of the measurement model to the data and the stability of the factorial structure all confirm the excellent psychometric qualities of the designed scale.