niryuu/sni-each-converted huggingface dataset:
This paper develops Submodular Sum Product Networks (SSPNs) and an efficient inference algorithm for approximately computing the most probable labeling of variables in the model. The main application in the paper is on scene parsing. In this context, SSPNs define an energy function with a grammar component for representing a hierarchy of labels and an MRF for encoding smoothness of labels over space. To perform inference, the authors develop a move-making algorithm, somewhat in the spirit of fusion moves (Lempitsky et al., 2010) that repeatedly improves a solution by considering a large neighborhood of alternative segmentations and solving an optimization problem to choose the best neighbor. Empirical results show that the proposed algorithm achieves better energy that belief propagation of alpha expansion and is much faster.  This is generally a well-executed paper. The model is interesting and clearly defined, the algorithm is well presented with proper analysis of the relevant runtimes and guarantees on the behavior. Overall, the algorithm seems effective at minimizing the energy of SSPN models.  Having said that, I don't think this paper is a great fit for ICLR. The model is even somewhat to the antithesis of the idea of learning representations, in that a highly structured form of energy function is asserted by the human modeller, and then inference is performed. I don't see the connection to learning representations. One additional issue is that while the proposed algorithm is faster than alternatives, the times are still on the order of 1-287 seconds per image, which means that the applicability of this method (as is) to something like training ConvNets is limited.  Finally, there is no attempt to argue that the model produces better segmentations than alternative models. The only evaluations in the paper are on energy values achieved and on training data.  So overall I think this is a good paper that should be published at a good machine learning conference, but I don't think ICLR is the right fit.
Output: en
Conclusiones: "... cambiar el paradigma del actual desarrollo de aplicaciones enfocadas en esta área, la pregunta a realizar ya no debe ser ¿Cómo se realizará el plegado de la proteína?, sino el cómo se puede mejorar la forma de aplicar las reglas de Meckler. ..."  LAS CONCLUSIONES NO SON CLARAS. HAY MUCHO TRABAJO A FUTURO. POR EJEMPLO: "...queda abierta la interrogante acerca de cómo se puede mejorar la simulación de tal manera que la secuencia de aminoácidos..."  DEBEN REDACTAR LAS CONCLUSIONES BASADAS EN LOS RESULTADOS EXPERIMENTALES Y LA TEORÍA USADA.
Output: es
Muestra una mezcla de tecnologías web para su desarrollo e implementa una solución nativa.  No muestra cuanto ancho de banda se consume en las troncales por la aplicación cuando están las cámaras funcionando todas juntas. Necesita mostrar más resultados.
Output: es
This paper presents a method to learn both a model and inference procedure at the same time with recurrent neural networks in the context of inverse problems. The proposed method is interesting and results are quite good. The paper is also nicely presented.   I would be happy to see some discussion about what the network learns in practice about natural images in the case of denoising. What are the filters like? Is it particularly sensitive to different structures in images? edges? Also, what is the state in the recurrent unit used for? when are the gates open etc.  Nevertheless, I think this is nice work which should be accepted.
Output: en
This paper proposed a dynamic coattention network for the question answering task with long contextual documents.  The model is able to encode co-dependent representations of the question and the document, and a dynamic decoder iteratively pointing the potential answer spans to locate the final answer.   Overall, this is a well-written paper.  Although the model is a bit complicated (coattention encoder, iterative dynamic pointering decoder and highway maxout network), the intuitions behind and the details of the model are clearly presented.  Also the performance on the SQuAD dataset is good.  I would recommend this paper to be accepted.
Output: en
El paper presenta una investigación enfocada a evaluar el impacto de los sistemas instrumentados de seguridad en los procesos a los cuales se aplican.  La cantidad de referencias es pobre, considerando la naturaleza de la investigación. Es especialmente escaso el uso de referencias en la sección introductoria. También, la introducción no deja muy claro el aporte de la investigación ni la metodología utilizada, ambas cosas deberían ser parte de esta sección de manera concisa.  Se debe evitar el uso de primera persona plural en la redacción (ej., 3er párrafo de la sección "análisis teórico").  Colocar las referencias dentro del párrafo, no fuera (ej., párrafo "Medidas de seguridad", "... iniciado. [7]", debería ser "... iniciado [7].". Esto se repite en otras partes del documento.  Error de escritura en primer párrafo de 3era página.  Formato de referencias inconsistente (segundo párrafo en "Riesgo remanente aceptado"). También mal uso de puntuación (Primer párrafo de 4ta página).  Sobre los resultados, estos no son claros. La pregunta de investigación está mal o ambiguamente planteada. En base a esto, es imposible entender el aporte o el enfoque planteado. Esto sumado al escaso marco teórico entregado, no se justifica la relevancia de la investigación presentada.  Las conclusiones no hacen referencia a ningún aspecto de investigación ni trabajo futuro.  Se debe revisar el formato de las referencias.  Por todos los motivos expuestos anteriormente, el trabajo no tiene la calidad suficiente para ser aceptado.
Output: es
El tema es de vital actualidad, factible de realizar (se dan evidencias de aquello) y se recrea al mismo tiempo la ejecución de la "tercera misión" a través de la propuesta. Me parece meritoria e interesante el efecto multiplicador que esto ha tenido, al incorporar los actores a la propuesta.  En específico me refiero a las empresas o pymes, las universidades e institutos (alumnos), INDAP, en abordar problemas reales, con "personas" reales.  La propuesta logra presentar una dinámica, en este caso, de la sociedad agrícola, que apalanque la gestión del conocimiento. Una derivada interesante de la propuesta es que ha servido para identificar las oportunidades que existen en la Región y su entorno, a la luz de las acciones que se están ejerciendo con el fin de obtener aún más de ellas, en lo específico me refiero a la Universidad, entidades gubernamentales y la sociedad.  Carencia de referencias internacionales de los estándares (si es que existen?) que se desean alcanzar en éste ámbito. Se compromete a mejorar la "rentabilidad de los pequeños productores", pero no se dice cómo, ya que con sólo la incorporación de la tecnología no es suficiente.
Output: es
El trabajo presentado a Infonor - Chile, cumple con las expectativas siguientes: 1. Realiza una acertada discusión bibliográfica 2. Incorpora un algoritmo híbrido de búsqueda novedosa 3. Los resultados obtenidos con scheduling con restricciones usando el algoritmo (recursos limitados) muestra resultados alentadores.
Output: es
Most dialog systems are based on chit-chat models. This paper explores goal-directed conversations, such as those that arise in booking a restaurant. While the methodology is rather thin, this is not the main focus of the paper. The authors provide creative evaluation protocols, and datasets. The reviewers liked the paper, and so does the AC. The paper will make a nice oral, on a topic that is largely explored but opening a direction that is quite novel.
Output: en
El artículo presenta un sistema detección y seguimiento de ojos para ayuda al análisis clínico psicológico.  Comentarios generales.  En el tema que aborda el artículo se ha publicado bastante. Sin embargo, no se citan otros trabajos relevantes directamente relacionados, que nos ayuden a ubicar la propuesta dentro de la literatura. Un buen punto de inicio puede ser el número especial de la revista: Computer Vision and Image Understanding Volume 98 ,  Issue 1  (April 2005) Special issue on eye detection and tracking  La parte experimental del trabajo es muy breve. Es importante describir detalladamente los datos empleados en la experimentación, cuáles son las condiciones de captura, e incluso hacerlos públicos para que otros investigadores puedan comparar con ellos. En cuanto a los resultados, no basta con dar un porcentaje de aciertos. Hay que analizar los falsos positivos y negativos y relacionarlos con la técnica utilizada y, si es posible, con los futuros temas a investigar.  Otros comentarios:  En el artículo se menciona que se ha considerado la velocidad de respuesta, pero no se proporcionan cifras.  En la introducción se cita la referencia [3] en la revisión relacionada sobre los aspectos psicológicos del artículo, cuando esta referencia parece ser de morfología matemática.  Algunas referencias están incompletas: + En la 1 y 2 falta la editorial + En la 3 la conferencia/revista. + En la 5 el volumen, número y páginas  El artículo aborda un tema de gran interés técnico y práctico.  Se describe un sistema que parece no estar completamente desarrollado.
Output: es
El artículo trata sobre la colaboración en la creación de contenido a través de redes sociales. El articulo está bien estructurado y con algunos detalles de redacción. La bibliografía no cumple el formato exigido por el congreso.  Con respecto al tema del artículo, inicialmente se tuvo la duda sobre si era pertinente el artículo para el congreso, ya que era un experimento sobre el comportamiento social de unos individuos a través de redes sociales. Posteriormente al revisar el experimento, se puede apreciar que la pertinencia  está en la perspectiva de educación en ingeniería informática. Por lo anterior recomiendo que en el artículo se modifique el resumen mencionando el alcance del experimento para que el lector, desde un principio, tenga mayor conocimiento sobre el artículo. Además, para mayor pertinencia con el congreso, se recomienda dar mayor énfasis en la colaboración para el desarrollo de algunas tareas propias de la disciplina, tales como el análisis y/o diseño de sistemas. Por último el artículo es novedoso en la manera de cómo realizar algunas tareas a través de redes sociales.
Output: es
This work combines a LDA-type topic model with a RNN and models this by having an additive effect on the predictive distribution via the topic parameters. A variational auto-encoder is used to infer the topic distribution for a given piece of text and the RNN is trained as a RNNLM. The last hidden state of the RNNLM and the topic parameters are then concatenated to use as a feature representation.  The paper is well written and easy to understand. Using the topic as an additive effect on the vocabulary allows for easy inference but intuitively I would expect the topic to affect the dynamics too, e.g. the state of the RNN. The results on using this model as a feature extractor for IMDB are quite strong. Is the RNN fine-tuned on the labelled IMDB data? However, the results for PTB are weaker. From the original paper, an ensemble of 2 LSTMs is able to match the topicRNN score. This method of jointly modelling topics and a language model seems effective and relatively easy to implement.  Finally, the IMDB result is no longer state of the art since this result appeared in May (Miyato et al., Adversarial Training Methods for Semi-Supervised Text Classification).  Some questions: How important is the stop word modelling? What do the results look like if l_t = 0.5 for all t?  It seems surprising that the RNN was more effective than the LSTM. Was gradient clipping tried in the topicLSTM case? Do GRUs also fail to work?  It is also unfortunate that the model requires a stop-word list. Is the link in footnote 4 the one that is used in the experiments?  Does factoring out the topics in this way allow the RNN to scale better with more neurons? How reasonable does the topic distribution look for individual documents? How peaked do they tend to be? Can you show some examples of the inferred distribution? The topics look odd for IMDB with the top word of two of the topics being the same: 'campbell'. It would be interesting to compare these topics with those inferred by LDA on the same datasets.  Minor comments: Below figure 2: GHz -> GB \Gamma is not defined.
Output: en
La novedad del tema a tratar hace del artículo una investigación interesante. Tiene una muy buena presentación y es fácil de leer, lo cual da un aspecto llamativo al tema. A pesar de ser un tema que incluye dos disciplinas muy distintas, han logrado sincronizar adecuadamente los conceptos de tal forma que la investigación es importante y aporta conocimiento desde la parte inicial del trabajo realizado, hasta las propias conclusiones Se recomienda a los autores seguir con el trabajo a fin de acabar otros aspectos relacionados con la salud pública.    Recomendaciones: Es indispensable mejorar la calidad de las figuras, existen algunas que es imposible visualizar, como es el caso de Figura 4, Figura 6, Figura 7 y Figura 8.
Output: es
The paper presents a new environment, called Retro Learning Environment (RLE), for reinforcement learning. The authors focus on Super Nintendo but claim that the interface supports many others (including ALE). Benchmark results are given for standard algorithms in 5 new Super Nintendo games, and some results using a new "rivalry metric".  These environments (or, more generally, standardized evaluation methods like public data sets, competitions, etc.) have a long history of improving the quality of AI and machine learning research. One example in the past few years was the Atari Learning Environment (ALE) which has now turned into a standard benchmark for comparison of algorithms and results. In this sense, the RLE could be a worthy contribution to the field by encouraging new challenging domains for research.  That said, the main focus of this paper is presenting this new framework and showcasing the importance of new challenging domains. The results of experiments themselves are for existing algorithms. There are some new results that show reward shaping and policy shaping (having a bias toward going right in Super Mario) help during learning. And, yes, domain knowledge helps, but this is obvious. The rivalry training is an interesting idea, when training against a different opponent, the learner overfits to that opponent and forgets to play against the in-game AI; but then oddly, it gets evaluated on how well it does against the in-game AI!   Also the part of the paper that describes the scientific results (especially the rivalry training) is less polished, so this is disappointing. In the end, I'm not very excited about this paper.  I was hoping for a more significant scientific contribution to accompany in this new environment. It's not clear if this is necessary for publication, but also it's not clear that ICLR is the right venue for this work due to the contribution being mainly about the new code (for example, mloss.org could be a better 'venue', JMLR has an associated journal track for accompanying papers:
Output: en
- Strengths:  Detailed guidelines and explicit illustrations.  - Weaknesses:  The document-independent crowdsourcing annotation is unreliable.   - General Discussion:  This work creates a new benchmark corpus for concept-map-based MDS. It is well organized and written clearly. The supplement materials are sufficient. I have two questions here. 1)              Is it necessary to treat concept map extraction as a separate task? On the one hand, many generic summarization systems build a similar knowledge graph and then generate summaries accordingly. On the other hand, with the increase of the node number, the concept map becomes growing hard to distinguish. Thus, the general summaries should be more readable. 2)              How can you determine the importance of a concept independent of the documents? The definition of summarization is to reserve the main concepts of documents. Therefore, the importance of a concept highly depends on the documents. For example, in the given topic of coal mining accidents, assume there are two concepts: A) an instance of coal mining accidents and B) a cause of coal mining accidents. Then, if the document describes a series of coal mining accidents, A is more important than B. In comparison, if the document explores why coal mining accidents happen, B is more significant than A. Therefore, just given the topic and two concepts A&B, it is impossible to judge their relative importance.  I appreciate the great effort spent by authors to build this dataset. However, this dataset is more like a knowledge graph based on common sense rather than summary.
Output: en
This paper presents a dialogue agent where the belief tracker and the dialogue manager are jointly optimised using the reinforce algorithm. It learns from interaction with a user simulator. There are two training phases. The first is an imitation learning phase where the system is initialised using supervising learning from a rule-based model. Then there is a reinforcement learning phase where the system has jointly been optimised using the RL objective.  - Strengths: This paper presents a framework where a differentiable access to the KB is integrated in the joint optimisation. This is the biggest contribution of the paper.   - Weaknesses: Firstly, this is not a truly end-to-end system considering the response generation was handcrafted rather than learnt. Also, their E2E model actually overfits to the simulator and performs poorly in human evaluation. This begs the question whether the authors are actually selling the idea of E2E learning or the soft-KB access. The soft-KB access actually brings consistent improvement, however the idea of end-to-end learning not so much. The authors tried to explain the merits of E2E in Figure 5 but I also fail to see the difference. In addition, the authors didn't motivate the reason for using the reinforce algorithm which is known to suffer from high variance problem. They didn't attempt to improve it by using a baseline or perhaps considering the natural actor-critic algorithm which is known to perform better.  - General Discussion: Apart from the mentioned weaknesses, I think the experiments are solid and this is generally an acceptable paper. However, if they crystallised the paper around the idea which actually improves the performance (the soft KB access) but not the idea of E2E learning the paper would be better.
Output: en
The paper describes an MT training data selection approach that scores and ranks general-domain sentences using a CNN classifier. Comparison to prior work using continuous or n-gram based language models is well done, even though  it is not clear of the paper also compared against bilingual data selection (e.g. sum of difference of cross-entropies). The motivation to use a CNN instead of an RNN/LSTM was first unclear to me, but it is a strength of the paper to argue that certain sections of a text/sentence are more important than others and this is achieved by a CNN. However, the paper does not experimentally show whether a BOW or SEQ (or the combination of both( representation is more important and why. The textual description of the CNN (one-hot or semi-supervised using pre-trained embeddings)  is clear, detailed, and points out the important aspects. However, a picture of the layers showing how inputs are combined would be worth a thousand words.  The paper is overall well written, but some parentheses for citations are not necessary (\citet vs. \citep) (e.g line 385).  Experiments and evaluation support the claims of the paper, but I am a little bit concerned about the method of determining the number of selected in-domain sentences (line 443) based on a separate validation set: - What validation data is used here? It is also not clear on what data hyperparameters of the CNN models are chosen. How sensitive are the models to this? - Table 2 should really compare scores of different approaches with the same number of sentences selected. As Figure 1 shows, the approach of the paper still seems to outperform the baselines in this case.   Other comments: - I would be interested in an experiment that compares the technique of the paper against baselines when more in-domain data is available, not just the development set. - The results or discussion section could feature some example sentences selected by the different methods to support the claims made in section 5.4. - In regards to the argument of abstracting away from surface forms in 5.4: Another baseline to compare against could have been the work of Axelrod, 2015, who replace some words with POS tags to reduce LM data sparsity to see whether the word2vec embeddings provide an additional advantage over this. - Using the sum of source and target classification scores is very similar to source & target Lewis-Moore LM data selection: sum of difference of cross-entropies. A reference to this work around line 435 would be reasonable.  Finally, I wonder if you could learn weights for the sum of both source & target classification scores by extending the CNN model to the bilingual/parallel setting.
Output: en
El artículo propone, según el artículo, una plataforma computacional relacionada con información académica del Propedeútico de la USACH. Posteriormente, en "III. Desarrollo de la Propuesta" se plantea una metodología. No queda claro si es plataforma computacional, si es una metodología, si es una sistematización de información...  El Abstract debe ser mejorado, al igual que las Conclusiones.  El artículo es breve, podría haber presentado mejor la propuesta si se hubiese extendido un poco más (la propuesta se presenta en menos de 2 hojas).  Otras observaciones: - Cambiar "Palabras Reservadas" por "Palabras Clave" - En Introducción, las páginas web mencionadas deben ser declaradas en Referencias. - Se confunde las Referencias con la identificación de los títulos de sub-secciones. Por ejemplo: "[1] Decisión Multicriterio" (no es la Referencia 1). - En página 3, cambiar "organziados" por "organizados". - En página 5, dice "Tabla 2", debe decir "Tabla 3".
Output: es
This paper considers the energy-based model interpretation of GAN, where the discriminator is an unnormalized model for the likelihood of a generative model p(x|theta) and the generator is a directed model that approximates this distribution. The generator is used to draw approximate negative phase samples that are used in stochastic maximum likelihood / contrastive divergence learning of the EBM / discriminator.  The main idea in the paper is to fit the generator by following the Stein variational gradient. In practice this gradient consists of the usual gradient provided by the discriminator with an added term that provides a repulsive force between the sampled data points to increase sample diversity.  The idea of using a kernel to push apart the sampled points is interesting, and will work in low dimensions, but it is hard to see how it can work in full scale images. For high dimensional samples x, the proposed kernel is unlikely to provide a useful distance measure between points. There are no convincing experiments in the paper that show otherwise. Specifically:  - There is no experiment that compares between standard GAN and GAN + repulsion, using the same architecture. (please address this in the rebuttal) - If the Stein variational idea is taken literally, the right thing to do would be to fully optimize the generator at every step, and then taking a single optimization step on the discriminator. Instead, each is updated in turn, and the learning rates of both steps are adjusted to keep the two "in line". - The kernel used to fit the generator is defined in the auto-encoder space of the discriminator, and thus depends on the discriminator parameters. The objective that is used to fit the generator thus changes at every step, and the procedure can no longer be interpreted as stochastic gradient descent with respect to any single well defined objective.  The authors obtain good results: The generated images clearly look better than those generated by DCGAN. However, their approach has a number of changes compared to DCGAN, so it is not clear where the improvement comes from. In addition, by now the DCGAN is no longer a very strong baseline, as various other techniques have been proposed.  Note: The use of phi for both the "particle gradient direction" and energy function is confusing
Output: en
El paper muestra una aplicación práctica desarrollada con realidad aumentada aplicada a entregar información turística en espacios abiertos El trabajo permitirá dar a conocer la tecnología a nivel nacional y sus potencialidades, por lo que se considera un aporte. Sin embargo, el paper carece de un estudio comparativo con otros trabajos similares aplicando esta tecnología a este mismo objetivo, por lo que no se logra obtener una idea del grado de originalidad del mismo, ni da la posibilidad de contrastar algunas de las limitaciones del mismo que el autor menciona al final del paper con trabajos ya desarrollados anteriormente.
Output: es
This paper presents a new dataset with annotations of products coming from online cybercrime forums. The paper is clear and well-written and the experiments are good. Every hypothesis is tested and compared to each other.  However, I do have some concerns about the paper:  1. The authors took the liberty to change the font size and the line spacing of the abstract, enabling them to have a longer abstract and to fit the content into the 8 pages requirement.  2. I don't think this paper fits the tagging, chunking, parsing area, as it is more an information extraction problem.  3. I have difficulties to see why some annotations such as sombody in Fig. 1 are related to a product.  4. The basic results are very basic indeed and - with all the tools available nowadays in NLP -, I am sure that it would have been possible to have more elaborate baselines without too much extra work.  5. Domain adaptation experiments corroborate what we already know about user-generated data where two forums on video games, e.g., may have different types of users (age, gender, etc.) leading to very different texts. So this does not give new highlights on this specific problem.
Output: en
This paper proposes an approach for multi-lingual named entity recognition using features from Wikipedia. By relying on a cross-lingual Wikifier, it identifies English Wikipedia articles for phrases in a target language and uses features based on the wikipedia entry. Experiments show that this new feature helps not only in the monolingual case, but also in the more interesting direct transfer setting, where the English model is tested on a target language.  I liked this paper. It proposes a new feature for named entity recognition and conducts a fairly thorough set of experiments to show the utility of the feature. The analysis on low resource and the non-latin languages are particularly interesting.  But what about named entities that are not on Wikipedia? In addition to the results in the paper, it would be interesting to see results on how these entities are affected by the proposed method.   The proposed method is strongly dependent on the success of the cross-lingual wikifier. With this additional step in the pipeline, how often do we get errors in the prediction because of errors in the wikifier?  Given the poor performance of direct transfer on Tamil and Bengali when lexical features are added, I wonder if it is possible to regularize the various feature classes differently, so that the model does not become over-reliant on the lexical features.
Output: en
- Strengths: Nicely written and understandable. Clearly organized. Targeted answering of research questions, based on  different experiments.  - Weaknesses: Minimal novelty. The "first sentence" heuristic has been in the summarization literature for many years. This work essentially applies this heuristic (evolved) in the keyword extraction setting. This is NOT to say that the work is trivial: it is just not really novel.  Lack of state-of-the-art/very recent methods. The experiment on the system evaluation vs state-of-the-art systems simply uses strong baselines. Even though the experiment answers the question "does it perform better than baselines?", I am not confident it illustrates that the system performs better than the current state-of-the-art. This somewhat reduces the value of the paper.  - General Discussion: Overall the paper is good and I propose that it be published and presented.   On the other hand, I would propose that the authors position themselves (and the system performance) with respect to: Martinez‐Romo, Juan, Lourdes Araujo, and Andres Duque Fernandez. "SemGraph: Extracting keyphrases following a novel semantic graph‐based approach." Journal of the Association for Information Science and Technology 67.1 (2016): 71-82. (with which the work holds remarkable resemblance in some points)  Le, Tho Thi Ngoc, Minh Le Nguyen, and Akira Shimazu. "Unsupervised Keyphrase Extraction: Introducing New Kinds of Words to Keyphrases." Australasian Joint Conference on Artificial Intelligence. Springer International Publishing, 2016.
Output: en
En este trabajo se realiza una propuesta exploratoria sobre un perfil de adecuación de técnicas de educción de requisitos de software. El trabajo es una extensión de un trabajo previo.  Se recomienda mejorar algunos aspectos de este manuscrito como son: -Describir a más detalle el trabajo previo en el que se basa este escrito y mencionar las diferencias y contenido nuevo. -Consistencia entre los términos requisitos y requerimientos, en la pág. 3 se emplea el término requerimientos. -En la medida de lo posible mantener términos en castellano. -Añadir acento en "solo" siempre y cuando se refiera a "únicamente". -Corregir "Lo" por "Los" final de pág. 4, después de ref. 15., añadir acento a "comparara", en misma pág., corregir "cuatros" final pág. 6. -Corregir alineación de texto en primeros párrafos de ambas columnas en pág. 10, el primer párrafo de la segunda columna debería estar en la primera columna.
Output: es
En este trabajo se realiza una propuesta para la identificación de riesgos relativos al proceso de outsourcing de software, para lo cual se diseña una taxonomía que es aplicada preliminarmente en una organización como forma de validación, aunque sólo en la forma de una encuesta de opinión.  Para robustecer el aporte científico sería ideal contar con una aplicación real que acompañe el proceso de outsourcing de comienzo a fin de modo de poder dimensionar de mejor manera el real aporte y efectividad de la taxonomía propuesta
Output: es
Very interesting paper. Maybe you could read paper by Gleizes about cooperative agent, self-organization and resolution through emergence … This paper could interest you : Self-adaptive complex systems, Marie-Pierre Gleizes, 2011, EUMAS.
Output: en
The paper presents a method to learn a low-dimensional state representations from raw obervation for multi-task setting. In contrast to classic multi-task learning setting where a joint representation is usually learned by exploring the transferable information among different tasks, the method aims to identify individual task and solve them separately. To this end, the authors extend the learning with robotic priors approach by extending the loss function with additional term for task coherence, i.e., a task only changes representation between training episodes. The method has been evaluated on two tasks, multi-task slot-car racing and mobile navigation to prove its efficacy.  there were several unclear issues:  1. The first question is that if the method is only appealing on the scenario like the slot-car racing, otherwise it should be benchmarked with mutli-task learning. While the author made the argument in the related work, the proposed method is orthogonal to multi-task learning they did admit both explore shared knowledge between tasks. What's the advantage and disadvantage for the proposed method for general mutiple task setting, in particular over the multi-task learning? The reply of the authors was not fully satisfactory. The argument did not support the lack of comparison to multi-task joint-learning. It seems they don't plan to include any comparison neither. I think it's important for the fundamental motivation for the work, without such comparison, the method seems to be purely an alternative to multi-task joint-learning without any(or much) practical advantage.  2.Following up to the previous question, please clarify the results on the mobile navigation scenario. It's not clear how the plot on the right indicates MT-LRP identifies all tasks as the author claimed and and seems very weak to support the method, in particular compared to the multi-slot car-racing driving experiment, there is too little results to make sound argument (almost no comparison to alternative methods, i.e. no baseline method, is that true for the problem). The explanation of the authors did provide more details and more explicit information.   3. The proposed gated neural network architecture seems to be a soft gated structure(correct me if I am wrong), a possible baseline would be a hard gated unit, how would this affect the conclusion. This is particularly interesting as the authors reflect on the constraint that the representation should stay consistent during the training. The author simply stated again what they did for the modeling without counter the comparison to hard-gating, but it's probably less an issue compared to Question 1.  In summary, while there are remaining concerns about lacking comparisons, the is a weak tendency towards accepting the submission.
Output: en
This paper proposes a novel approach ParMAC, a parallel and distributed framework of MAC (the Method of Auxiliary Coordinates) to learn nested and non-convex models which is based on the composition of multiple processing layers (i.e., deep nets). The basic idea of MAC to optimise the nested objective function, which is traditionally learned using methods based on the chain-rule gradients but inconvenient and is hard to parallelise, is to break nested functional relationships judiciously by introducing new variables ( the auxiliary coordinates) as equality constraints, and then to optimise a penalised function using alternating optimisation over the original parameters (W step) and over the coordinates (Z step).  The minimisation (W step) updates the parameters by splitting the nested model into independent submodels and training them using existing algorithms, and the coordination (Z step) ensures that corresponding inputs and outputs of submodels eventually match.  In this paper, the basic assumptions of ParMAC are that with large datasets in distributed systems, it is imperative to minimise data movement over the network because of the communication time generally far exceeds the computation time in modern architectures. Thus, the authors propose the ParMAC to translate the parallelism inherent in MAC into a distributed system by data parallelism and model parallelism. They also analyse its parallel speedup and convergence, and demonstrated it with MPI-based implementation to optimise binary autoencoders. The proposed ParMAC is tested on 3 colour image retrieval datasets.   The organization of the paper is well written, and the presentation is clear. My questions are included in the following: - The MAC framework solves the original problem approximately. If people use the sigmoid function to smooth the stepwise function, the naive optimization methods can be easier applied. What is the difference between these two? Or why do we want to use a new approach to solve it? - The authors do not compare their ParMAC model with other distributed approaches for the same nested function optimization problem.
Output: en
El campo de utilización de la aplicación es muy buena y necesaria, por lo que contribuye a la robótica. En la utilización de visión, falta incorporar tecnologías de visión computacional más actuales que la detección de objetos por flujo óptico, tal vez en este caso sea más factible utilizar tecnología de dos vistas con imágenes sucesivas. Su contenido es bastante claro.  Debe mejorar los siguiente:   Página 1	Resumen y título : Si bien está en inglés, falta escribir el resumen y el título en español o portugués  Página 2	No presenta trabajos relacionados, Si bien presenta referencias, no hace una presentación de trabajos relacionados  Página 3	No indica fielmente como detecta las botellas plásticas u obstáculos 	En vez de colocar a la popular Lena, seria más interesante colocar el cómo detecta las botellas plásticas u otros objetos con el detector de borde que utiliza.  Página 3        La referencia [10]	Existen trabajos de flujo óptico más actuales  al indicado  Página 6	En Resultados	Falta medir el grado efectividad de la recolección de objetos, se debería presentar medidas al respecto.  A que velocidad funciona el limpiador .. Falta mas trabajo en este ítem. Página 6	Robustez estructural alcanzada 	No presenta datos que permitan aseverar esto. Página 7	Ídem párrafo 2	No presentan la efectividad de la tecnología de visión computacional ni como  contribuye a evitar obstáculos, detección y recolección.. Es decir, se entiende que lo presentado se puede lograr, pero no presenta datos para concluir aquello.
Output: es
1.	Lo que se propone es interesante como una aplicación pero no es un trabajo científico. Además ya no es interesante la aplicación de un Datamart desde el punto de vista técnico como es presentado por el autor pues existe mucha documentación y trabajos al respecto. Por este motivo ahora hay que buscar "otros aspectos (diseño, metodología, etc.)" que hagan interesante un artículo del área Datamart.  2. Hay errores de diseño, por lo cual lo más probable es que los resultados que se muestran estén erróneos.  3. Debe tener cuidado con la sumarización.
Output: es
El artículo describe muy bien los SGBD para móviles, así como también algunos motores de BD con tecnología no-relacional conocidas como NoSQL, en un contexto de almacenamiento de datos en la nube o cloud-computing. Realiza una  buena comparación de las características técnicas de algunos gestores de BD en este ámbito (tabla 3). Plantea una metodología, al parecer para definir y realizar un experimento que permita evaluar y comparar BD de este tipo, según lo definido en el título del artículo. Sin embargo,  sólo llega a establecer una comparación simple y cualitativa de componentes y otros aspectos como servicios (tabla 4). No hay evidencias de una comparación cuantitativa y objetiva, a nivel de desempeño y con datos, transacciones y tiempos de procesamiento que permitan realmente sostener, como lo hace sin estas variables, cual BD es más eficiente respecto a otra. El resumen no se condice ni identifica el trabajo completo. Las conclusiones se plantean en base a las características y no a pruebas comparativas de desempeño. En síntesis, es un trabajo desde el punto de vista que sería un aporte si formalizara la metodología y describiera la aplicación cuantitativa de una experimentación que realmente permita comparar los SGBD en un contexto de cloud-computing. Se sugiere complementar con un experimento controlado con datos y registro de transacciones de prueba y medir variables, obtener estadísticas objetivas que permitan evaluar los desempeños de los sistemas de BD móviles y NoSQL, bajo condiciones y parámetros claramente definidos y en las mismas condiciones medio-ambientales (ancho de banda, tas de transacciones, volumen de datos, equipo, etc).
Output: es
El artículo cumple con el formato establecido y posee referencias bibliográficas actualizadas.  No se explica claramente qué es JPI, no queda claro el objetivo del artículo. El artículo es una descripción de la aplicación del concepto de JPI, el cual no está bien desarrollado, no queda claro si los autores son quienes proponen esta nueva estrategia o están aplicando una propuesta de otros investigadores. La redacción puede ser mejorada. Se sugiere extender el artículo para que expliquen claramente todos los conceptos, alcances y objetivos de la investigación.
Output: es
Se ajusta al formato solicitado y se puede leer sin dificultad.  El artículo básicamente es un revisión de las herramientas Xpath, Xquery y SQL/XML en Oracle 10g, ilustrando su uso con varios ejemplos.  No hay un aporte, en ya sea nuevo conocimiento o aplicación de la tecnología para resolver un problema real.  No hay resultados de investigación que mostrar ni discutir.
Output: es
EDIT: Updated score. See additional comment.  I quite like the main idea of the paper, which is based on the observation in Sec. 3.0 - that the authors find many predictable patterns in the independent evolution of weights during neural network training. It is very encouraging that a simple neural network can be used to speed up training by directly predicting weights.  However the technical quality of the current paper leaves much to be desired, and I encourage the authors to do more rigorous analysis of the approach. Here are some concrete suggestions:  - The findings in Section 3.0 which motivate the approach, should be clearly presented in the paper. Presently they are stated as anecdotes.  - A central issue with the paper is that the training of the Introspection network I is completely glossed over. How well did the training work, in terms of training, validation/test losses? How well does it need to work in order to be useful for speeding up training? These are important questions for anyone interested in this approach.  - An additional important issue is that of baselines. Would a simple linear/quadratic model also work instead of a neural network? What about a simple heuristic rule to increase/decrease weights? I think it's important to compare to such baselines to understand the complexity of the weight evolution learned by the neural network.  - I do not think that default tensorflow example hyperparameters should be used, as mentioned by authors on OpenReview. There is no scientific basis for using them. Instead, first hyperparameters which produce good results in a reasonable time should be selected as the baseline, and then added the benefit of the introspection network to speed up training (and reaching a similar result) should be shown.  - The authors state in the discussion on OpenReview that they also tried RNNs as the introspection network but it didn't work with small state size. What does "didn't work" mean in this context? Did it underfit? I find it hard to imagine that a large state size would be required for this task. Even if it is, that doesn't rule out evaluation due to memory issues because the RNN can be run on the weights in 'mini-batch' mode. In general, I think other baselines are more important than RNN.  - A question about jump points:  The I is trained on SGD trajectories. While using I to speed up training at several jump points, if the input weights cross previous jump points, then I gets input data from a weight evolution which is not from SGD (it has been altered by I). This seems problematic but doesn't seem to affect your experiments. I feel that this again highlights the importance of the baselines. Perhaps I is doing something extremely simple that is not affected by this issue.  Since the main idea is very interesting, I will be happy to update my score if the above concerns are addressed.
Output: en
A lo largo del artículo se exagera el rol de la automatización de las pruebas dentro de las pruebas de software y en forma más general en el marco de aseguramiento. Un modelo de madurez para la prueba de software necesariamente incluye más aspectos que la automatización. Los casos automatizados siempre van coexistir con casos manuales y evaluaciones realizadas por personas. Como está tratado en el artículo, automatización es un concepto demasiado general, puede abarcar la generación, ejecución, evaluación (por ejemplo: mutación, criterios de cobertura) y validación (problema del oráculo).  Aunque la motivación está claramente expresada en la introducción, hacen falta referencias para fundamentar las afirmaciones realizadas sobre el rol de la prueba y el impacto de la automatización en la misma. Decir que la automatización permite realizar las pruebas más rápido o a menor costo es simplista: esto depende del contexto. Los costos de generar casos de prueba automáticos, la estimación sobre la evaluación futura del software y la caducidad de los casos de prueba, junto con otros factores, deben ser tenidos en cuenta en el modelo de costos para determinar la conveniencia de automatizar cierta parte de la prueba.  La sección dedicada a la prueba de software realiza definiciones generales y recorre la historia de la prueba desde los mainframes. Para un artículo de investigación, sugiero realizar definiciones más específicas relacionadas con el foco del estudio (estudios experimentales sobre técnicas de prueba, automatización, costos de las pruebas, modelos de madurez).  Sería adecuado poner el desarrollo del modelo de madurez luego de presentar el trabajo relacionado. No se describe el proceso para obtener la propuesta de un modelo de madurez para testing.  Se debe vincular el desarrollo del modelo a otros modelos de madurez existentes (por ejemplo el Testing Maturity Model) establecer conexiones con la literatura aceptada de testing o normas (por ejemplo ISO 29119) y en general  con modelos de procesos de software.  No se explicita el string de búsqueda utilizado. No todos los modelos encontrados deben catalogarse como ‘modelos de madurez’. Algunos de los modelos incluidos son claramente modelos más generales, que no se enfocan en el problema de la automatización de las pruebas. Se debería al menos discutir estas diferencias.  No se explican los atributos utilizados en la tabla III, ni la forma de obtener los resultados de nivel de madurez. ¿Qué significa ‘nivel de las opiniones publicadas’? Este concepto resulta ambiguo. Se debe reforzar la descripción de este proceso de investigación ya que resulta clave para obtener el resultado de nivel ‘adolescente’.
Output: es
The paper explores a VAE architecture and training procedure that allows to generate new samples of a concept based on several exemplars that are shown to the model. The proposed architecture processes the set of exemplars with a recurrent neural network and aggregation procedure similar to the one used in Matching Networks. The resulting "summary" is used to condition a generative model (a VAE) that produces new samples of the same kind as the exemplars shown. The proposed aggregation and conditioning procedure are better suited to sets of exemplars that come from several classes than simple averaging. Perhaps surprisingly the model generalizes from generation conditioned on samples from 2 classes to generation conditioned on samples from 4 classes. The experiments are conducted on the OMNIGLOT dataset and are quite convincing. An explicit comparison to previous works is lacking, but this is explained in the appendices, and a comparison to architectures similar to previous work is presented.
Output: en
Es innegable el interés que ocasiona la problemática de la navegación de robots, por lo que el trabajo presentado constituye una alternativa para la elección de la arquitectura de control, dado que nos plantea mediante la comparación de métricas una posible solución al problema, sin embargo debe tenerse presente que ha sido realizado en un ambiente de simulación, bajo ciertas condiciones que no necesariamente se replicarán en la práctica lo cual a mi parecer restringe el ámbito de aplicación de los resultados obtenidos. Por otra parte las referencias y desarrollo de cálculos estadísticos dan validez a los resultados obtenidos con la consideración del ambiente en que fue realizada la prueba.  Trabajo bien desarrollado, aunque se plantea la necesidad de realizar pruebas con el móvil real, bajo diversas condiciones de entorno del robot, para validar los resultados obtenidos en simulador.
Output: es
Aunque el paper está bien escrito y estructurado, y la experimentación parece extensa, no se percibe un grado de originalidad en la versión del algoritmo presentado en el paper respecto de las anteriores versiones del mismo existentes en la literatura y que han sido ya aplicadas a la misma problemática.
Output: es
Trabajo muy interesante pues incorpora una evaluación con usuarios del prototipo propuesto. Está bien redactado y los resultados son claros.
Output: es