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 paper presenta problemas importantes en su formulación.  Los papers de experiencias prácticas deben entregar algo más que una simple descripción del sistema desarrollado: deben indicar claramente qué nuevo conocimiento, técnica o solución se obtuvo de dicha experiencia. Consecuentemente, un paper de esta naturaleza debería incluir, al menos, los siguientes elementos esenciales:  - Una introducción que explique claramente el propósito del trabajo: (a) el problema que fue resuelto directa o indirectamente con la experiencia práctica, (b) por qué el problema es importante y no trivial y (c) por qué que esta experiencia entregó información nueva dentro de dicho contexto (no obtenida con experiencias similares). - Marco teórico - La descripción del sistema: no debe ser una mera enumeración de sus componentes y funcionalidades, sino indicar claramente aquellos elementos que, durante su desarrollo, aportaron información relevante al problema del estudio. - Conclusiones: deben estar alineadas con los elementos de la introducción y deben estar soportados por la evidencia entregada en la descripción del sistema.  Analizando el paper bajo esta perspectiva, encontramos problemas importantes:  Primero, la introducción no es clara respecto al propósito del trabajo y. La única parte donde se mencionan los objetivos es en el 5o párrado, donde se indica que el caso de estudio es "para demostrar empíricamente los costos de desarrollo" asociados a las tecnologías mostradas en el paper. Esto, junto con "destacar las principales características y potenciales virtudes de CakePHP y MVC.." parecen ser los elementos centrales del paper.  Segundo, la descripción del sistema no difiere mucho de un manual de sistema; falta evidencia esencial para soportar los elementos de la introducción: costos de desarrollo asociados (tiempo, personas, recursos) que, se supone, deberían ser menores que con otra metodología. Si bien las secciones que describen CakePHP y MVC tienen información relevante para el problema, no aportan valor al trabajo si no están respaldadas por evidencia obtenida directamente de la experiencia de desarrollo con esta tecnología. Por sobre todo, eché de menos una comparación con alguna otra experiencia similar, indicando, claramente qué mejoras al desarrollo introduce CakePHP/MVC.  Por último, las conclusiones del paper no están alineadas con los elementos indicados en la introducción; algunas de ellas entregan información fuera del contexto (conclusiones #5 y #6) o son irrelevantes (conclusión #2); otras carecen de evidencias que las soporten (conclusión #1) o no son explicitas en asociar la evidencia con lo concluido (conclusiones #3 y #4).  Al margen de las observaciones anteriores, el paper también requiere mejoras en la presentación de la información. Por ejemplo, no es explícito el valor que el código SQL de las figuras 10 y 11 entregan al paper. Las Figuras 12-15 no están adecuadamente descritas. Una persona que no comprenda bien el trabajo con línea de comando puede terminar totalmente desorientada y no entenderá los argumentos esgrimidos.  Por todos esos motivos el paper no está en condiciones de ser aceptado para publicación.
[EX A]: es

[EX Q]: This work gives expected results and creates a structure for further research. I would suggest in addition of compass the use of radio waves for improving localization (WiFi for indoor and GPS for outdoor). In addition, there are some low cost platforms in the market that can be used and study, as for example Ardupilot or other similar systems. I was unable to see clearly enough, in the paper, the use of the camera; algorithms, environment settings, etc.; used for following robot. In Figure 7, it seems that camera trajectory is worse than that calculated with odometry, perhaps due to orientation error, but in my opinion it is not very clear during reading. Finally, I would suggest think if is it there some possible feedback of EKF results to odometry in order to reduce its error.
[EX A]: en

[EX Q]: This paper discusses Hyperband, an extension of successive halving by Jamieson & Talwalkar (AISTATS 2016). Successive halving is a very nice algorithm that starts evaluating many configurations and repeatedly cuts off the current worst half to explore many configuration for a limited budget.  Having read the paper for the question period and just rereading it again, I am now not entirely sure what its contribution is meant to be: the only improvement of Hyperband vs. successive halving is in the theoretical worst case bounds (not more than 5x worse than random search), but you can (a) trivially obtain that bound by using a fifth of your time for running random configurations to completion and (b) the theoretical analysis to show this is said to be beyond the scope of the paper. That makes me wonder whether the theoretical results are the contribution of this paper, or whether they are the subject of a different paper and the current paper is mostly an empirical study of the method? I hope to get a response by the authors and see this made clearer in an updated version of the paper.  In terms of experiments, the paper fails to show a case where Hyperband actually performs better than the authors' previous algorithm successive halving with its most agressive setting of bracket b=4. Literally, in every figure, bracket b=4 is at least as good (and sometimes substantially better) than Hyperband. That makes me think that in practice I would prefer successive halving with b=4 over Hyperband. (And if I really want Hyperband's guarantee of not being more than 5x worse than random search I can run random search on a fifth of my machines.)  The experiments also compare to some Bayesian optimization methods, but not to the most relevant very closely related Multi-Task Bayesian Optimization methods that have been dominating effective methods for deep learning in that area in the last 3 years: "Multi-Task Bayesian Optimization" by Swersky, Snoek, and Adams (2013) already showed 5x speedups for deep learning by starting with smaller datasets, and there have been several follow-up papers showing even larger speedups.   Given that this prominent work on multitask Bayesian optimization exists, I also think the introduction, which sells Hyperband as a very new approach to hyperparameter optimization is misleading. I would've much preferred a more down-to-earth pitch that says "configuration evaluation" has been becoming a very important feature in hyperparameter optimization, including Bayesian optimization, that sometimes yields very large speedups (this can be quantified by examples from existing papers) and this paper adds some much-needed theoretical understanding to this and demonstrates how important configuration evaluation is even in the simplest case of being used with random search. I think this could be done easily and locally by adding a paragraph to the intro.  As another point regarding novelty, I think the authors should make clear that approaches for adaptively deciding how many resources to use for which evaluation have been studied for (at least) 23 years in the ML community -- see Maron & Moore, NIPS 1993: "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation" (
[EX A]:
en

