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]: Este es un trabajo interesante, donde el autor quiere mostrar a las Honeynets como medida de seguridad alternativa, para aprendizaje del  modo de operar de intrusos y así crear nuevas formas de protección.  Observaciones:  - El nombre del único autor debe estar centralizado en el artículo. - Palabras y figuras en inglés. El artículo está en español. - Las referencias están inadecuadas: hay  un único artículo citado y con referencia incompleta. Mejorar la revisión bibliográfica, citando artículos y libros  actuales.
[EX A]: es

[EX Q]: This paper is a well written paper. This paper can be divided into 2 parts: 1.Adversary training on ImageNet  2.Empirical study of label leak, single/multiple step attack, transferability and importance of model capacity  For part [1], I don’t think training without clean example will not make reasonable ImageNet level model. Ian’s experiment in “Explaining and Harnessing Adversarial Examples” didn't use BatchNorm, which may be important for training large scale model. This part looks like an extension to Ian’s work with Inception-V3 model. I suggest to add an experiment of training without clean samples.  For part [2], The experiments cover most variables in adversary training, yet lack technical depth.  The depth, model capacity experiments can be explained by regularizer effect of adv training;  Label leaking is novel; In transferability experiment with FGSM, if we do careful observe on some special MNIST FGSM example, we can find augmentation effect on numbers, which makes grey part on image to make the number look more like the other numbers. Although this effect is hard to be observed with complex data such as CIFAR-10 or ImageNet, they may be related to the authors' observation "FGSM examples are most transferable".    In this part the authors raise many interesting problems or guess, but lack theoretical explanations.   Overall I think these empirical observations are useful for future work.
[EX A]: en

[EX Q]: This paper introduces a mechanism for active learning with convolutional neural networks (CNNs). I would not go as far as the authors in calling these "deep", seeing that they seem to have only 2 hidden layers with only 20 filters each. The active learning criterion is a greedy selection scheme based on variational free energy and a series of approximations.  The paper is sometimes hard to read, due to (a) many grammatical errors and (b) sloppy notation in some places (e.g., on page 5, line 1, f is used but never introduced before). Overall, I give an accepting score, but a weak one because of the grammatical errors. If the paper is accepted, these should be fixed for the final version, optimally by a native speaker.   The paper's topic is interesting, and the paper appears to succeed in its goal of showing a proof of concept for active learning in CNNs (if only on toy datasets). I'm surprised by the new results on uncertainty sampling and curriculum learning the authors added: why do these methods both break for USPS? In particular, uncertainty sampling did very well (in fact, better than the authors' new method) on MNIST, but apparently horribly on USPS; some explanation for this would be useful.  I have one more question: why is it necessary to first sample a larger subset D \subset U, from which we select using active learning? Is this merely done for reasons of computational efficiency, or can it actually somehow improve results? (If so, it would be instrumental to see the worse results when this is not done.)
[EX A]:
en