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 Input:
Quality, Clarity: There is no consensus on this, with the readers having varying backgrounds, and one reviewer commenting that they found it to be unreadable. Originality, Significance: The reviews are mixed on this, with the high score (7) acknowledging a lack of expertise on program induction. The paper is based on the published TerpreT system, and some think that it marginal and contradictory with respect to the TerpreT paper. In the rebuttal, point (3) from the authors points to the need to better understand gradient-based program search, even if it is not always better. This leaves me torn about a decision on this paper, although currently it does not have strong support from the most knowledgeable reviewers. That said, due to the originality of this work, the PCs are inclined to invite this work to be presented as a workshop contribution.

Ex Output:
en

Ex Input:
Abstract This paper presents a study of the security of software products, life cycle, risks and costs associated --> This paper presents a study of the security of software products, the life cycle, the risks and the associated costs This article is part of a research postgraduate --> This article is a part of a postgraduate research in turn to the people --> an at the same time its affects the people. The fundamental purpose of this article rests on whether --> The fundamental purpose of this paper rests on determining whether Left open the possibility of an empirical study --> Left open the possibility of performing an empirical study Introduccion "Los errores en un software pueden provocar danos irreversibles como la perdida de vidas humanas" --> si bien esto es cierto depende del tipo de software y el entorno en el cual este funcionando, por lo que recomiendo especificar en que entornos y/o situaciones el software puede causar la perdida de vidas humanas Desarrollo de Software Seguro Mejorar la calidad de la Figura 1 Recomiendo modificar la Figura 2, ya que su unico objetivo es mostrar el ciclo de vida, por lo que es suficiente incluir las fases, ya las actividades de cada fase de describen en la Tabla 1 Riesgos y costos asociados a la ausencia de seguridad [29]--> la referencia se lista por orden de aparicion en este caso le corresponde el numero 10 Definir: Cm, Cs y Cn Analisis de la tecnologia y su influencia en la seguridad Revisar la Figura 3: (1) sera que estan intercambiadas los titulos de las columnas de los anos?; (2) recomiendo dar una explicacion a los elementos de la tabla; (3) a que te refieres con change y los simbolos de la columna Recomiendo agregar en la seccion de sistemas operativos y tecnologia de autenticacion algo sobre las vulnerabilidades encontradas en esta tecnologia. Seguridad y tecnologia Revisar la tabla 3: (1) titulo de columna: posibilidad de incorporacion --> posibilidad de incorporacion al ciclo de seguridad En la tabla 2 mencionas tecnologia, momento de decision y etapa de uso, que luego no concuerda con lo la Tabla 3 o al menos eso entiendo, por lo que recomiendo hacer una descripcion justificando los valores que asignaste en la tabla, para resaltar la aportacion Referencia No date = (nd) Revisar referencia 16 -->"la Casa"? Referencia 20 y 22 esta repetida

Ex Output:
es

Ex Input:
This paper proposes new initialization for particular architectures and a correction trick to batch normalization to correct variance introduced by dropout. While authors state interesting observations, the claims are not supported with convincing results. I guess Figure 1 is only for mnist and for only two values of p with one particular network architecture, the dataset and empirical setup is not clear. The convergence is demonstrated only for three dropout values in Figure 2 which may cause an unfair comparison. For instance how does the convergence compare for the best dropout rate after cross-validation (three figures each figure has three results for one method with different dropouts [bests cv result for each one])? Also how is the corresponding validation error and test iterations? Also only mnist does not have to generalize to other benchmarks. Figure 3 gives closer results for Adam optimizer, learning rate is not selected with random search or bayesian optimization, learning decay iterations fixed and regularization coefficient is set to a small value without tuning. A slightly better tuning of parameters may close the current gap. Also Nesterov based competitor gives unreasonably worse accuracy compared to recent results which may indicate that this experiment should not be taken into account. In Table 2, there is no significant improvement on CIFAR10. The CIFAR100 difference is not significant without including batch normalization variance re-estimation. However there is no result for 'original with BN update' therefore it is not clear whether the BN update helps in general or not. SVHN also does not have result for original with BN update. There should be baselines with batch normalizations for Figure 1,2 3 to support the claims convincingly. The main criticism about batch normalization is additional computational cost by giving (Mishkin et al, 2016 ) as reference however this should not be a reason to not to compare the initialization to batch-normalization. In fact, (Mishkin et al, 2016) performs comparison to batch normalization and also with and without data augmentation with recent state of art architectures. None of the empirical results have data augmentation. It is not clear if the initialization or batch normalization update will help or make it worse for that case. Recent state of art methods methods like Res Net variant and Dense Net scale to many depths and report result for ImageNet. Although the authors claim that this can be extended to residual network variants, it is not clear if there is going to be any empirical gain for that architectures. This work requires a comprehensive and fair comparison. Otherwise the contribution is not significant.

Ex Output:
en