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]: El artículo esta adecuadamente escrito, excepto por algunos problemas de acentos (implementara, utilizaran, ...) Las herramientas utilizadas, así como los algoritmos implementados se encuentran adecuadamente descritos. La única crítica esta en la implementación de los mismos, realizada en una única máquina (seguramente por un problema de recursos), algo que quita méritos a lo realizado, considerando que se esta hablando de técnicas pensadas para ser usadas en forma distribuida (donde debería estar su máximo aprovechamiento).  Pese a lo anterior, pienso que es un trabajo bien presentado, valioso en la discusión de las limitaciones de Hadoop vs Giraph, y cuyos resultados son interesantes de conocer y debatir en el ámbito del congreso.
[EX A]: es

[EX Q]: The authors extend GANs by an inference path from the data space to the latent space and a discriminator that operates on the joint latend/data space. They show that the theoretical properties of GANs still hold for BiGAN and evaluate the features learned unsupervised in the inference path with respect to performance on supervised tasks after retraining deeper layers.  I see one structural issue with this paper: Given that, as stated in the abstract, the main purpose of the paper is to learn unsupervised features (and not to improve GANs), the paper might spent too much space on detailing the relationship to GANs and all the theoretical properties. It is not clear whether they actually would help with the goal of learning good features. While reading the paper, I actually totally forgot about the unsupervised features until they reappeared on page 6. I think it would be helpful if the text of the paper would be more aligned with this main story.  Still, the BiGAN framework is an elegant and compelling extension to GANs. However, it is not obvious how much the theoretical properties help us as the model is clearly not fully converged. To me, especially Figure 4 seems to suggest that G(E(x)) might be doing not much more than some kind of nearest neighbour retrival (and indeed one criticism for GANs has always been that they might just memorize some samples). By the way, it would be very interesting to know how well the discriminator actually performs after training.  Coming back to the goal of learning powerful features: The method does not reach state-of-the-art performance on most evaluated tasks (Table 2 and 3) but performs competitive and it would be interesting to see how much this improves if the BiGAN training (and the convolutional architecture used) would be improved.  The paper is very well written and provides most necessary details, although some more details on the training (learning rates, initialization) would be helpful for reproducing the results.  Overall I think the paper provides a very interesting framework for further research, even though the results presented here are not too impressive both with respect to the feature evaluation (and the GAN learning).  Minor: It might be helpful to highlight the best performance numbers in Tables 2 and 3.
[EX A]: en

[EX Q]: El artículo describe una interesante aplicación de control de un dispositivo físico (para el caso en estudio, un robot móvil de Lego Mindstorms), mediante el procesamiento de las señales derivadas de cambios de índole facial y/o mental, provenientes de un casco  Emotiv EEG, el cual realiza un electroencefalograma de alta resolución. Sin embargo a pesar de lo interesante del trabajo presentado, el artículo carece del rigor que caracteriza al proceso metodológico de investigación, no se observa una discusión bibliográfica critica, no hay una definición clara del problema a investigar, presenta una pobre discusión  de resultados y abundantes errores de forma (Ej. la página 3  presenta el siguiente texto “¡Error! No se encuentra el origen de la referencia”, no existe página 2, etc.), que denotan que el autor no fue riguroso en su revisión antes del envío. Finalmente en mi opinión el trabajo es un buen aporte como un excelente trabajo de proyecto o taller dentro la malla curricular de alguna carrera de Informática o Electrónica.
[EX A]:
es