TASK DEFINITION: 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].
PROBLEM: This paper makes a valuable contribution to provide a more clear understanding of generative adversarial network (GAN) training procedure.   With the new insight of the training dynamics of GAN, as well as its variant, the authors reveal the reason that why the gradient is either vanishing in original GAN or unstable in its variant. More importantly, they also provide a way to avoid such difficulties by introducing perturbation. I believe this paper will inspire more principled research in this direction.   I am very interested in the perturbation trick to avoid the gradient instability and vanishment. In fact, this is quite related to dropout trick in where the perturbation can be viewed as Bernoulli distribution. It will be great if the connection can be discussed.  Besides the theoretical analysis, is there any empirical study to justify this trick? Could you please add some experiments like Fig 2 and 3 for the perturbated GAN for comparison?

SOLUTION: en

PROBLEM: 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?

SOLUTION: en

PROBLEM: El trabajo muestra el trabajo desarrollado para la construcción de una aplicación Web orientada a una interfaz móvil (tablet). La descripción del trabajo desarrollado no es detallada y deja muchas dudas sobre su implementación.   Problemas de fondo encontrados:  - Se describe la metodología RUP y BPM, sin embargo, no se detalle la razón de ello y el aporte asociado al trabajo más allá de las 2 figuras mostradas. - Se menciona la utilización de GPS para registro/tracking de mascotas, pero no se entregan detalles técnicos de la solución propuesta. Esto hace dudar de la propuesta realizada. - No se realiza el análisis de la calidad de interface humano-computador, por lo menos debería incluir una encuesta de usabilidad. - Se menciona la utilización del lenguaje PHP y MySQL como base de datos, sin embargo, no se dan detalles de la implementación utilizada (Patrones, SQL, ORM, etc). - No muestra evidencias de la implementación de la aplicación: diagrama de clases, diagrama de base de datos, contratos, patrones utilizados (DAO, Active Record, etc.).   Problemas de forma identificados en el trabajo:  - Las capturas de pantalla no son claros y pierden mucho espacio al utilizar como marco la interface de un tablet. - La figura 7 se encuentra sobrepuesta sobre texto, el cual es ilegible. - La figura 11 hace referencia a la aplicación Google Maps y no se ve clara la asociación con la aplicación.

SOLUTION:
es