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]: Los autores hacen un análisis comparativo   entre diversas tecnologias para automación industrial. El análisis me pareció razonablemente profundo y amplio, pero lo califique como "weak accept" por no tener contribuciones científicas fuertes.  El texto necesita ser revisado, hay frases largas (por ejemplo la segunda del segundo parágrafo de la introducción tiene 11 líneas) y puntuaciones faltando o colocadas incorrectamente. La primera frase del segundo parágrafo de la introducción.  Incluir también el significado de las siglas PLC y PC.
[EX A]: es

[EX Q]: Es un trabajo bien hecho sin lugar a dudas, el autor dice que hace una contrastación en varias organizaciones sobre el tema.  Faltan antecedentes en relación a los ERP investigados. El modelo que él plantea no es novedoso, es más bien lógico y práctico. Es una investigación normal y su aporte es regular. Creo que un análisis cómo este requiere un análisis más profundo en aspectos económicos y financieros, porque las decisiones para la adquisición de este tipo de herramientas son: operacionales, económicas y financieras.
[EX A]: es

[EX Q]: Overall I think this is an interesting paper which shows empirical performance improvement over baselines. However, my main concern with the paper is regarding its technical depth, as the gist of the paper can be summarized as the following: instead of keeping the batch norm mean and bias estimation over the whole model, estimate them on a per-domain basis. I am not sure if this is novel, as this is a natural extension of the original batch normalization paper. Overall I think this paper is more fit as a short workshop presentation rather than a full conference paper.  Detailed comments:  Section 3.1: I respectfully disagree that the core idea of BN is to align the distribution of training data. It does this as a side effect, but the major purpose of BN is to properly control the scale of the gradient so we can train very deep models without the problem of vanishing gradients. It is plausible that intermediate features from different datasets naturally show as different groups in a t-SNE embedding. This is not the particular feature of batch normalization: visualizing a set of intermediate features with AlexNet and one gets the same results. So the premise in section 3.1 is not accurate.  Section 3.3: I have the same concern as the other reviewer. It seems to be quite detatched from the general idea of AdaBN. Equation 2 presents an obvious argument that the combined BN-fully_connected layer forms a linear transform, which is true in the original BN case and in this case as well. I do not think it adds much theoretical depth to the paper. (In general the novelty of this paper seems low)  Experiments:  - section 4.3.1 is not an accurate measure of the "effectiveness" of the proposed method, but a verification of a simple fact: previously, we normalize the source domain features into a Gaussian distribution. the proposed method is explicitly normalizing the target domain features into the same Gaussian distribution as well. So, it is obvious that the KL divergence between these two distributions are closer - in fact, one is *explicitly* making them close. However, this does not directly correlate to the effectiveness of the final classification performance.  - section 4.3.2: the sensitivity analysis is a very interesting read, as it suggests that only a very few number of images are needed to account for the domain shift in the AdaBN parameter estimation. This seems to suggest that a single "whitening" operation is already good enough to offset the domain bias (in both cases shown, a single batch is sufficient to recover about 80% of the performance gain, although I cannot get data for even smaller number of examples from the figure). It would thus be useful to have a comparison between these approaches, and also a detailed analysis of the effect from each layer of the model - the current analysis seems a bit thin.
[EX A]:
en