In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

[EX Q]: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification. The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written. - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough. - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.
[EX A]: en

[EX Q]: The authors propose an approach to learning optimization algorithms by framing the problem as a policy search task, then using the guided policy search algorithm. The method is a nice contribution to the "learning to learn" framework, and actually was developed simultaneously to a few papers that have since already been published. It's definitely a useful addition to this space. The biggest issue with this paper is that the results simply aren't that compelling. The methodology and proposed approach is nice, and the text is improved upon a previous version posted to Arxiv, but it seems that for most problems the results aren't that much better than some of the more common off-the-shelf optimization approaches that _don't_ require learning anything. Furthermore, in the one domain where the method does seem to (marginally) outperform the other methods, the neural net domain, it's unclear why we'd want to use the proposed approaches instead of SGD-based methods and their like (which of course everyone actually does for optimization). Pros: + Nice contribution to the learning to learn framework + Takes a different approach from past (concurrent) work, namely one based upon policy search Cons: - Experiments are not particularly compelling Overall, this work is a little borderline. Still, the PCs have determined that it was deserving of appearing at the conference. We hope the authors can strengthen the empirical validation for the camera ready version.
[EX A]: en

[EX Q]: Resumen: El trabajo muestra un modelo para medir el valor y buscar su trazabilidad. Se propone la co-creacion de valor con el cliente como metodo de crear valor. Con el fin de analizar el aporte de la co-creacion se introduce el concepto de trazabilidad. Luego explica una serie de variables propuestas para medir el valor co-creado bajo un modelo conceptual junto con otro modelo que permite la trazabilidad de los distintos aportes a traves de enlaces. Finalmente se expone brevemente los resultados del modelo en un caso. Evaluacion: Implementar tipos de co-creacion es muy importante para las organizaciones actualmente. Es interesante como el articulo mezcla la co-creacion con la trazabilidad y propone maneras de medirla, presentando nuevas herramientas para la organizacion. Lamentablemente el articulo posee grandes debilidades: 1) El alcance puede que no sea apropiado para un congreso como INFONOR. 2) Los resultados son breves y debilmente ejemplificados, lo que dificulta la comprension de uso del modelo. 3) Podrian usarse mas ejemplos con fines de facilitar la lectura. 4) El formato del paper tiene problemas. Otros comentarios: Se encuentran problemas de redaccion: a)"Las organizaciones se comprometen con los consumidores en producir unos conceptos o unas ideas iniciales y utilizan los consumidores" Abuso en la palabra consumidor b)"se valido la el modelo de Arquetipos Sistemicos" pequeno error, claramente "la" esta demas
[EX A]:
es