TASK DEFINITION: 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].
PROBLEM: Este trabajo ofrece un análisis comparativo del uso de tecnología cableada y tecnología programada.  El ámbito de "trabajo relacionado" presentado en el documento es inexistente. En este contexto, no es posible determinar si los criterios de comparación sugeridos en el trabajo son, efectivamente, los pertinentes para realizar una evaluación.  Los criterios de comparación de tecnologías son en algunos casos objetivos y en otros casos subjetivos. En este sentido, el trabajo no sugiere cómo enfrentar la valoración de cada criterio para finalmente efectuar una comparación (por ejemplo, en el ítem "seguridad" o en el ítem "programación").  Existen problemas gramaticales (por ejemplo, "alto tiempo). Adicionalmente, el trabajo está pobremente ordenado, dificultando la comprensión por el bajo esfuerzo en localizar el contenido de las tablas de una manera legible.  No se visualiza un aporte real de este trabajo. En numerosas oportunidades se cita a "Rexroth Bosch Group", sin agregar mucho más en cada punto. El impacto y relevancia  de este trabajo, en cuanto a la novedad, no es observable.

SOLUTION: es

PROBLEM: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification.  The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written.  - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough.  - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.

SOLUTION: en

PROBLEM: This paper addresses the problem of decoding barcode-like markers depicted in an image.  The main insight is to train a CNN from generated data produced from a GAN.  The GAN is trained using unlabeled images, and leverages a "3D model" that undergoes learnt image transformations (e.g., blur, lighting, background).  The parameters for the image transformations are trained such that it confuses a GAN discriminator.  A CNN is trained using images generated from the GAN and compared with hand-crafted features and from training with real images.  The proposed method out-performs both baselines on decoding the barcode markers.  The proposed GAN architecture could potentially be interesting.  However, I won’t champion the paper as the evaluation could be improved.  A critical missing baseline is a comparison against a generic GAN.  Without this it’s hard to judge the benefit of the more structured GAN.  Also, it would be worth seeing the result when one combines generated and real images for the final task.   A couple of references that are relevant to this work (for object detection using rendered views of 3D shapes):  [A] Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko, Learning Deep Object Detectors from 3D Models; ICCV, 2015.  [B] Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views. Francisco Massa, Bryan C. Russell, Mathieu Aubry. CVPR 2016.  The problem domain (decoding barcode markers on bees) is limited.  It would be great to see this applied to another problem domain, e.g., object detection from 3D models as shown in paper reference [A], where direct comparison against prior work could be performed.    I found the writing to be somewhat vague throughout.  For instance, on first reading of the introduction it is not clear what exactly is the contribution of the paper.    Minor comments:  Fig 3 - Are these really renders from a 3D model?  The images look like 2D images, perhaps spatially warped via a homography.    Page 3: "chapter" => "section".  In Table 2, what is the loss used for the DCNN?  Fig 9 (a) - The last four images look like they have strange artifacts. Can you explain these?

SOLUTION:
en