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]: This paper is motivated by the ability that human's visual system can recognize contents of environment by from critical features, and tried to investigate whether neural networks can also have this kind of ability.  Specifically, the paper proposed to use Auto-Encoder (AE) as the network to reconstruct the low fidelity of visual input. Moreover, similar to Mnih et al. (2014),  the paper also proposed to use a recurrent fashion to mimic the sequential behavior the  human visual system.   I think the paper is well motivated. However, there are several concerns: 1. The baselines of the paper are too weak. Nearest neighbor, bilinear, bicubic and cubic interpolations without any learning procedure are of course performed worse than AE based models. The author should compare with the STOA methods such as
[EX A]: en

[EX Q]: This work address the problem of supervised learning from strongly labeled data with label noise. This is a very practical and relevant problem in applied machine learning.  The authors note that using sampling approaches such as EM isn't effective, too slow and cannot be integrated into end-to-end training. Thus, they propose to simulate the effects of EM by a noisy adaptation layer, effectively a softmax, that is added to the architecture during training, and is omitted at inference time. The proposed algorithm is evaluated on MNIST and shows improvements over existing approaches that deal with noisy labeled data.  A few comments. 1. There is no discussion in the work about the increased complexity of training for the model with two softmaxes.   2. What is the rationale for having consecutive (serialized) softmaxes, instead of having a compound objective with two losses, or a network with parallel losses and two sets of gradients?  3. The proposed architecture with only two hidden layers isn't not representative of larger and deeper models that are practically used, and it is not clear that shown results will scale to bigger networks.   4. Why is the approach only evaluated on MNIST, a dataset that is unrealistically simple.
[EX A]: en

[EX Q]: El artículo promete presentar un método para medir la calidad del desarrollo de software en SCRUM.  Considero la propuesta interesante y relevante, sin embargo el trabajo tiene debilidades en su construcción.  La promesa del título se diluye en eL abstract y la introducción, con la “introducción” de SPEM como herramienta. Luego en el cuerpo de artículo no queda clara la vinculación entre SPEM y el método de medición de calidad propuesto.  Tampoco queda claro el caso de estudio sobre el que están basando su trabajo. El modelado en SPEM es sobre SCRUM en forma teórica en sobre el caso de estudio. La evaluación del método no parece medir la calidad del software, sino los criterios de los involucrados con respecto al apego al proceso modelado. Y en todo caso, siempre mide documentos intermedios del proceso y no defectos o atributos primeros de la calidad del software producido.  Comentarios menores: - Revisar la construcción de las referencias - El modelado de SCRUM no incluye la reunión de retrospectiva. Si se está modelando una implementación particular de SCRUM aclararlo en el cuerpo del paper. De lo contrario es un error importante de modelado. - Por que utilizan un proceso tradicional para modelar un proceso ágil? No podría haberse usado uno de estos últimos? - En la primera hoja, al final de la primera columna, cambia el tipo de letra.
[EX A]:
es