instruction:
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:
Este trabajo propone un nuevo enfoque para el apoyo de la priorización de requisitos a través de un proceso de transformación de los Casos de Uso en un grafo de dependencia a través de la aplicación de técnicas de IR, redes bayesianas y evaluación del experto en el dominio. El enfoque se presenta como un interesante aporte para la priorización de requisitos y muestra su aplicación a un caso real. El documento está bien escrito y muy bien soportado por referencias. Los resultados son robustos según el enfoque utilizado y se presentan trabajos futuros bien direccionados.  Algunos errores tipográficos o de redacción pueden ser encontrados en la página 5 (párrafo 1 y 2). Confusión por el uso de k y K en la página 5 (o error tipográfico). Puede que no quede claro la utilización del conocimiento de la priorización realizada en el caso base de prueba con respecto a la realizada semi-automáticamente por su enfoque, salvo que se utiliza el conocimiento del experto. Quizás una comparación objetiva entre las dos priorizaciones sería adecuado.
answer:
es


question:
This is an interesting paper about quantized networks that work on temporal difference inputs.  The basic idea is that when a network has only to process differences then this is computational much more efficient specifically with natural video data since large parts of an image would be fairly constant so that the network only has to process the informative sections of the image (video stream). This is of course how the human visual system works, and it is hence of interest even beyond the core machine learning community.   As an aside, there is a strong community interested in event-based vision such as the group of Tobi Delbrück, and it might be interesting to connect to this community. This might even provide a reference for your comments on page 1.  I guess the biggest novel contribution is that a rounding network can be replaced by a sigma-delta network, but that the order of discretization and summation doe make some difference in the actual processing load. I think I followed the steps and  Most of my questions have already been answers in the pre-review period. My only question remaining is on page 3, “It should be noted that when we refer to “temporal differences”, we refer not to the change in the signal over time, but in the change between two inputs presented sequentially. The output of our network only depends on the value and order of inputs, not on the temporal spacing between them.”  This does not make sense to me. As I understand you just take the difference between two frames regardless if you call this temporal or not it is a change in one frame. So this statement rather confuses me and maybe should be dropped unless I do miss something here, in which case some more explanation would be necessary.  Figure 1 should be made bigger.  An improvement of the paper that I could think about is a better discussion of the relevance of the findings. Yes, you do show that your sigma-delta network save some operation compared to threshold, but is this difference essential for a specific task, or does your solution has relevance for neuroscience?
answer:
en


question:
This paper proposes a recurrent architecture for simultaneously predicting motion and action states of agents. The paper is well written, clear in its presentation and backed up by good experiments. They demonstrate that by forcing the network to predict motion has beneficial consequences on the classification of actions states, allowing more accurate classification with less training data. They also show how the information learned by the network is interpretable and organised in a hierarchy.  Weaknesses: - a critical discussion on the interplay between motion an behaviour that is needed to experience the benefits of their proposed model is missing from the paper. - moreover, a discussion on how this approach could scale to more challenging scenarios "involving animals" and visual input for instance and more general "behaviours" is also missing; The criticism here is pointed at the fact that the title/abstract claim general behaviour modelling, whilst the experiments are focused on two very specific and relatively simple scenarios, making the original claim a little bit far fetched unless its backed up by additional evidence. Using "Insects", or "fruit flies" would be more appropriate than "animals".
answer:

en