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].
--------
Question: El artículo es especialmente válido como una guía de implementación de un data center. Su nivel teórico no es relevante, sin embargo su aplicabilidad le hace de interés.  El artículo está bien escrito y documentado.  Se debe insistir que se trata de un caso y este caso está desarrollado completo siguiendo las normas TIA propuestas como referencia.

Answer: es


Question: Trata de aplicar una nueva estrategia a la gestión de requerimientos  No se respalda ni considera la aplicabilidad de técnicas propias de procesos de manufactura a procesos de software. Si bien es cierto hay autores como Demming que plantean que es posible hacerlo, existen otros autores como Sommerville que plantean que por las características propias del desarrollo de software, las técnicas de procesos de manufactura no son aplicables al software. Los autores de este artículo no entregan respaldo en relación a la validez de este aspecto. El artículo comienza planteando que la propuesta es para mejorar la educción de requerimientos, sin embargo, luego se deriva en que lo que se mejora es la generación de la SRS. La educción y la generación del documento de requerimientos son aspectos distintos. En el caso de experimentación que se presenta, no se ve por ninguna parte que se solucionen problemas ni en la educción, ni en la generación de la SRS, son más bien problemas de la implementación. Los errores detectados son aspectos básicos que cualquier programador con un mínimo de experiencia sabe cómo solucionarlos, sin necesidad que en la SRS se explicite el tipo de validaciones mínimas que se debe realizar como parte de las pruebas del sistema. La propuesta carece de guías prácticas que ayuden en su aplicación ¿cómo se deben realizar estas revisiones POKA-YOKE? ¿En qué aspectos hay que fijarse? ¿Existen artefactos que guíen la revisión? Hay errores de edición en el texto (palabras repetidas) Existen problemas de ortografía (acentuación de palabras)

Answer: es


Question: 1) Summary  This paper investigates the usefulness of decoupling appearance and motion information for the problem of future frame prediction in natural videos. The method introduces a novel two-stream encoder-decoder architecture, MCNet, consisting of two separate encoders -- a convnet on single frames and a convnet+LSTM on sequences of temporal differences -- followed by combination layers (stacking + convolutions) and a deconvolutional network decoder leveraging also residual connections from the two encoders. The architecture is trained end-to-end using the objective and adversarial training strategy of Mathieu et al.  2) Contributions  + The architecture seems novel and is well motivated. It is also somewhat related to the two-stream networks of Simonyan & Zisserman, which are very effective for real-world action recognition. + The qualitative results are numerous, insightful, and very convincing (including quantitatively) on KTH & Weizmann, showing the benefits of decoupling content and motion for simple scenes with periodic motions, as well as the need for residual connections.  3) Suggestions for improvement  Static dataset bias: In response to the pre-review concerns about the observed static nature of the qualitative results, the authors added a simple baseline consisting in copying the pixels of the last observed frame. On the one hand, the updated experiments on KTH confirm the good results of the method in these conditions. On the other hand, the fact that this baseline is better than all other methods (not just the authors's) on UCF101 casts some doubts on whether reporting average statistics on UCF101 is insightful enough. Although the authors provide some qualitative analysis pertaining to the quantity of motion, further quantitative analysis seems necessary to validate the performance of this and other methods on future frame prediction. At least, the results on UCF101 should be disambiguated with respect to the type of scene, for instance by measuring the overall quantity of motion (e.g., l2 norm of time differences) and reporting PSNR and SSIM per quartile / decile. Ideally, other realistic datasets than UCF101 should be considered in complement. For instance, the Hollywood 2 dataset of Marszalek et al would be a good candidate, as it focuses on movies and often contains complex actor, camera, and background motions that would make the "pixel-copying" baseline very poor. Experiments on video datasets beyond actions, like the KITTI tracking benchmark, would also greatly improve the paper.  Additional recognition experiments: As mentioned in pre-review questions, further UCF-101 experiments on action recognition tasks by fine-tuning would also greatly improve the paper. Classifying videos indeed requires learning both appearance and motion features, and the two-stream encoder + combination layers of the MCNet+Res architecture seem particularly adapted, if they indeed allowed for unsupervised pre-trainining of content and motion representations, as postulated by the authors. These experiments would also contribute to dispelling the aforementioned concerns about the static nature of the learned representations.  4) Conclusion  Overall, this paper proposes an interesting architecture for an important problem, but requires additional experiments to substantiate the claims made by the authors. If the authors make the aforementioned additional experiments and the results are convincing, then this paper would be clearly relevant for ICLR.  5) Post-rebuttal final decision  The authors did a significant amount of additional work, following the suggestions made by the reviewers, and providing additional compelling experimental evidence. This makes this one of the most experimentally thorough ones for this problem. I, therefore, increase my rating, and suggest to accept this paper. Good job!

Answer:
en