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]: I reviewed the manuscript as of December 6th. Summary: The authors build upon generative adversarial networks for the purpose of steganalysis -- i.e. detecting hidden messages in a payload. The authors describe a new model architecture in which a new element, a 'steganalyser' is added a training objective to the GAN model. Major Comments: The authors introduce an interesting new direction for applying generative networks. That said, I think the premise of the paper could stand some additional exposition. How exactly would a SGAN method be employed? This is not clear from the paper. Why does the model require a generative model? Steganalysis by itself seems like a classification problem (i.e. a binary decision if there a hidden message?) Would you envision that a user has a message to send and does not care about the image (container) that it is being sent with? Or does the user have an image and the network generates a synthetic version of the image as a container and then hide the message in the container? Or is the SGAN somehow trained as a method for detecting hidden codes performed by any algorithm in an image? Explicitly describing the use-case would help with interpreting the results in the paper. Additionally, the experiments and analysis in this paper is quite light as the authors only report a few steganalysis performance numbers in the tables (Table 1,2,3). A more extensive analysis seems warranted to explore the parameter space and provide a quantitative comparison with other methods discussed (e.g. HUGO, WOW, LSB, etc.) When is it appropriate to use this method over the others? Why does the seed effect the quality of results? Does a fixed seed correspond realistic scenario for employing this method? Minor comments: - Is Figure 1 necessary? - Why does the seed value effect the quality of the predictive performance of the model?
[EX A]: en

[EX Q]: The starting point of this work is the understanding that by having decorrelated neurons (e.g. neurons that only fire on background, or only on foreground regions) one provides independent pieces of information to the subsequent decisions. As such one gives "complementary viewpoints" of the input to the subsequent layers, which can be thought of as performing ensembling/expert combination within the model, rather than using an ensemble of networks. For this, the authors propose a sensible method to decorrelate the activations of intermediate neurons, with the aim of delivering complementary inputs to the final classification layers: they split intermediate neurons to a "foreground" and a "background" subset, and append side-losses that force them to be zero on background and foreground pixels respectively. They demonstrate that this can improve classification on a mid-scale classification example (a fraction of imagenet, and a ResNet with 18, rather than 150 layers), when compared to a "vanilla" baseline that does not use these losses. I enjoyed reading the paper because the idea is simple, smart, and seems to be effective. But there are a few concerns; -firstly, the way of doing this seems very particular to vision. In vision one knows that masking the features (during both training and testing) helps, e.g.
[EX A]: en

[EX Q]: Trabajo muy mal presentado, con severos errores de ortografia y redaccion. Pese al tema, interesante, no es posible encontrar un hilo adecuado en el desarrollo del trabajo. Desde el inicio surgen los problemas, en el resumen se afirma que "el objetivo de este articulo es la realizacion de un analisis a un conjunto de registros de diagnosticos medicos, puestos a manera de investigacion, en busca de una mejor perspectiva y visualizacion de los datos para responder a los indicadores que se plantearon" Primero, el objetivo del articulo no es la realizacion del analisis, sino mostrar el resultado de algun proceso (el proceso puede ser el analisis).La frase "puestos a manera de investigacion" es del todo inadecuada. No se responde a los indicadores..., que sentido tiene eso? Las citas en italico incluyen a los autores; falto apoyo de alguien mas experimentado en la escritura del articulo. Lamentablemente, el trabajo esta plagado de errores inaceptables. No solo de escritura. La pagina 3 indica que uno de los problemas encontrados dice relacion con la "Estructuracion de datos". Cual es el problema? Por que se produce? Como lo enfrenta el autor? Es satisfactorio su enfoque? Creo que esos antecedentes (que se repiten de manera similar en toda la extension del texto), justifican mi rechazo. Resumiendo, es muy pobre el trabajo.
[EX A]:
es