niryuu/sni-each-converted huggingface dataset:
This paper combines variational RNN (VRNN) and domain adversarial networks (DANN) for domain adaptation in the sequence modelling domain.  The VRNN is used to learn representations for sequential data, which is the hidden states of the last time step.  The DANN is used to make the representations domain invariant, therefore achieving cross domain adaptation.  Experiments are done on a number of data sets, and the proposed method (VRADA) outperforms baselines including DANN, VFAE and R-DANN on almost all of them.  I don't have questions about the proposed model, the model is quite clear and seems to be a simple combination of VRNN and DANN.  But a few questions came up during the pre-review question phase:  - As the authors have mentioned, DANN in general outperforms MMD based methods, however, the VFAE method which is based on MMD regularization on the representations seems to outperform DANN across the board.  That seems to indicate VRNN + MMD should also be a good combination.  - One baseline the authors showed in the experiments is R-DANN, which is an RNN version of DANN.  There are two differences between R-DANN and VRADA: (1) R-DANN uses deterministic RNN for representation learning, while VRADA uses variational RNN; (2) on target domain R-DANN only optimizes adversarial loss, while VRADA optimizes both adversarial loss and reconstruction loss for feature learning.  It would be good to analyze further where the performance gain comes from.
Output: en
Este artículo tiene como objetivo identificar la incidencia de las competencias de negocio TI en la percepción del éxito profesional. Se presenta el problema, el modelo de análisis, la hipótesis y la metodología de investigación.  Es un tema interesante, pero la autora indica que la investigación se  encuentra en la fase de recolección de datos y que faltan unos 7 meses para  obtener los resultados finales.  Sin embargo, el análisis teórico realizado es  bastante importante.  La Figura 3 es muy simple. Sugiero que el modelo propuesto aporte mayor información. Para ello, puede unir en una sola figura las Figuras 3,  4 y 2.  Utiliza referencias numéricas y referencias en formato APA (debe usar solamente referencias numéricas y de manera correcta).  Hay 2 páginas con número de página 3.  En página 5 aparece la Figura 4, en página 6 aparece la Figura 2 ¿? ¿Existe la Figura 3?  En Referencia [11] no indica el nombre de la conferencia, Referencia [15] no tiene fecha, la Referencia [30] ¿es un libro?, la Referencia [34] tiene su título escrito totalmente en mayúsculas, Referencia [45] dice solamente “Quinta”, Referencia [46] dice “Segunda”.  En Conclusiones, cambiar “tesis q del” por “tesis del”. Cambiar “se mostrara” por “se mostrará”.  Las Referencias [8] y [46] no son utilizadas en el texto.  El nombre del autor e institución debería ser anónimo (según normas del congreso).  En página 5, debe revisar que el texto esté justificado a la derecha.  En página 8, cambiar “se capaces” por “ser capaces”.
Output: es
Evaluación general  El trabajo es interesante pero está mal planteado, faltan referencias importantes. Se sugiere revisar las referencias que se indican y extender el artículo mejorando su calidad en cuanto a la “forma” de plantear la comparativa, además de mostrar más ejemplos de los casos de estudio que le den mayor peso al artículo, con esto también podrá incorporar los trabajos futuros que son muchos y actualmente no aparece nada.  Correcciones de Formato  Las referencias no están en el formato que pide la revista Ingeniare, revisar formato y modificar.  La figura 1 es una ecuación y está fuera del formato que pide la revista Ingeniare.  Correcciones de Contenido  En la página 3 se hace referencia a [8] que es una tesis de grado, ¿qué tipo de grado?. Sería mejor referenciar a una metodología de artículo científico y eso daría  más "peso" al artículo. Cuando se habla de HL7/CDA se debería indicar una referencia para mayor información. No queda claro el uso de PGFOUNDRY  que es una extensión de postgreSql, ¿ se usó con Mysql?. Se debería haber sacado más provecho del uso de PGFOUNDRY. En el diseño del caso de estudio en la figura 5 hay errores graves en las relaciones entre las tablas Consulta y Paciente, entre las tablas Medicamento_Sintoma y Medicamento. No se indica en el artículo que el atributo duración es de tipo PERIOD, eso es importante. En los ejemplos de consultas hay un error en la consulta SGBDT, figura 7, ya que se indica el atributo periodo y el atributo es duración. Las conclusiones son muy evidentes y no plantean trabajos futuros que puedan ser interesantes de realizar. (Existe potencial y bastante que hacer) Faltan referencias importantes como por ejemplo: 1.	A review of temporal database research and its use in GIS applications. International Journal of Geographical Information Systems. Volume 3, Issue 3, 1989. 2.	Temporal Data and the Relational Model: A Detailed Investigation Into the ... Escrito por C. J. Date,Hugh Darwen,Nikos A. Lorentzos. 3.	A generalized model for a relational temporal database  Proceeding SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data… 4.	A glossary of temporal database concepts…
Output: es
Desarrollar investigación asociada al tema de visión artificial es interesante de conocer, aún bajo condiciones que no son las óptimas, particularmente en los recursos de hardware.  En la sección de pruebas de luminosidad hay cierto desorden en la presentación de los resultados (por ejemplo, la figura 10. debería titularse Prueba Botella Cloro y no Prueba Caja). Lo que hace difícil su seguimiento. Las referencias bibliográficas podrían ser más actuales y de mejor nivel.
Output: es
This paper presents a clear hierarchical taxonomy of transfer learning methods as applicable to sequence tagging problems. This contextualizes and unifies previous work on specific instances of this taxonomy. Moreover, the paper shows that previously unexplored places in this taxonomy are competitive with or superior to the state of the art in key benchmark problems.  It'd be nice to see this explored further, such as highlighting what is the loss as you move from the more restrictive to the less restrictive transfer learning approaches, but I believe this paper is interesting and acceptable as-is.
Output: en
This paper proposes a new kind of expert model where a sparse subset of most reliable experts is chosen instead of the usual logarithmic opinion pool of a PoE. I find the paper very unclear. I tried to find a proper definition of the joint model p(x,z) but could not extract this from the text. The proposed “EM-like” algorithm should then also follow directly from this definition. At this point I do not see if such as definition even exists. In other words, is there is an objective function on which the iterates of the proposed algorithm are guaranteed to improve on the train data? We also note that the “product of unifac models” from Hinton tries to do something very similar where only a subset of the experts will get activated to generate the input:
Output: en
Este artículo presenta un  Sistema de Reservas de Productos Turísticos mediante Servicios WEB y Dispositivos Móviles. Son utilizadas herramientas de dominio público y el desempeño es verificado  usando simulación. Considerando que los modelos no son perfectos, sobre todo  por la dificultad de modelar el comportamiento  humano, la realización de un estudio de caso (un piloto en escala reducida), donde se pueda medir el beneficio real de la herramienta propuesta.
Output: es
This paper proposes to initialize the weights of a deep neural network layer-wise with a marginal Fisher analysis model, making use of potentially the similarity metric.   Pros:  There are a lot of experiments, albeit small datasets, that the authors tested their proposed method on.  Cons: lacking baseline such as discriminatively trained convolutional network on standard dataset such as CIFAR-10. It is also unclear how costly in computation to compute the association matrix A in equation 4.  This is an OK paper, where a new idea is proposed, and combined with other existing ideas such as greedy-layerwise stacking, dropout, and denoising auto-encoders. However, there have been many papers with similar ideas perhaps 3-5 years ago, e.g. SPCANet.   Therefore, the main novelty is the use of marginal Fisher Analysis as a new layer. This would be ok, but the baselines to demonstrate that this approach works better is missing. In particular, I'd like to see a conv net or fully connected net trained from scratch with good initialization would do at these problems.  To improve the paper, the authors should try to demonstrate without doubt that initializing layers with MFA is better than just random weight matrices.
Output: en
This paper proposes an alternative to Conditional Variational Auto-Encoders and Conditional MultiModal Auto-Encoders to perform inference of missing modalities in dataset with multiple modalities. The proposed approach is a Variational Auto-Encoder jointly on all the modalities  with additional KL divergence penalties between the approximate posterior given all the modalities and the approximate posterior given a subset of the modalities. The approach is named Joint Multimodal Variational Auto-Encoder. The authors make a connection between this approach and the Variation of Information. It is unclear why the authors chose the JMVAE approach instead of a more elegant Variation of Information approach. Another unaddressed issue is the scalability of the method. As far as I can tell (given that no code is provided and the specification of the encoder is missing), this approach requires a new encoder per subset of missing modalities. Right now this approach seems to scale since there are only two modalities. The fact that the estimating the log-likelihood log(p(x)) using multiple modalities provide a lower value than with just one in Table 1 is a bit odd. Could you explain that ? The comparison with between the representation learned by JMVAE and CVAE might be unfair given that the representation of CVAE is learned conditionally, on the label in the case of MNIST, and should therefore not consider the label in this representation. Intuitively, this representation could represent "style" as shown in (Kingma et al., 2014) in their conditional generation figure. For CelebA, comparing log-likelihood on models that use GANs is probably not significant since GAN does not optimizes log-likelihood.  Overall this is an interesting problem and there are also interesting ideas worth exploring further, but the execution of the paper requires more work.
Output: en
This paper proposes a method for evaluating topic quality based on using word embeddings to calculate similarity (either directly or indirectly via matrix factorisation), achieving impressive results over standard datasets.  The proposed method represents a natural but important next step in the evolutionary path of research on topic evaluation. The thing that troubled me most with the results was that, while you achieve state-of-the-art results for all three datasets, there are large inconsistencies in which methods perform and which methods perform less well (below the state of the art). In practice, none of the proposed methods consistently beats the state of the art, and the SVD-based methods perform notably badly over the genomics dataset. For someone who wants to take your method off the shelf and use it over any arbitrary dataset, this is a considerable worry. I suspect that the lower results for SVD over genomics relate to the proportion of OOV terms (see comment below), and that it may be possible to automatically predict which method will perform best based on vocab match with GloVe etc., but there is no such discussion in the paper.  Other issues:  - the proposed method has strong similarities with methods proposed in the   lexical chaining literature, which I would encourage the authors to read up   on and include in any future version of the paper  - you emphasis that your method has no parameters, but the word embedding   methods have a large number of parameters, which are implicit in your   method. Not a huge deal, but worth acknowledging  - how does your method deal with OOV terms, e.g. in the genomics dataset   (i.e. terms not present in the pretrained GloVe embeddings)? Are they simply   ignored? What impact does this have on the method?  Low-level issues:  - in your description of word embeddings in Section 2.1, you implicitly assume   that the length of the vector is unimportant (in saying that cosine   similarity can be used to measure the similarity between two vectors). If   the vectors are unit length, this is unproblematic, but word2vec actually   doesn't return unit-length vectors (the pre-trained vectors have been   normalised post hoc, and if you run word2vec yourself, the vector length is   certainly not uniform). A small detail, but important.  - the graphs in Figure 1 are too small to be readable
Output: en
Interesante propuesta en un ambiente de desarrollo de software que es bastante pobre en aspectos metodológicos de desarrollo.  Falta un poco de trabajo relacionado o quizás discutirlo mejor en la introducción con referencias para fundamentar la interesante propuesta. Revisar redacción. No está en formato pedido por Infonor. Se sugiere no agregar links de páginas web en el texto del artículo.
Output: es
El artículo presenta el desarrollo de una aplicación móvil para realizar denuncias, avisar de algún acto criminal y crear un mapa del delito propio. El artículo requiere de una mejor redacción y mejorar su estructura técnica como artículo de investigación. Por ejemplo, no presenta el resumen en español.  Al ser una aplicación software, su contribución a la investigación es limitada. Sugiero postular a track o workshop de aplicaciones empresariales.
Output: es
Variational auto-encoders, adversarial networks, and kernel scoring rules like MMD have recently gained popularity as methods for learning directed generative models and for other applications like domain adaptation. This paper gives an additional method along the scoring rules direction that uses the matching of central moments to match two probability distributions. The technique is simple, and in the case of domain adaptation, highly effective.  CMD seems like a very nice and straightforward solution to the domain adaptation problem. The method is computationally straightforward to implement, and seems quite stable with respect to the tuning parameters when compared to MMD. I was skeptical reading through this, especially given the fact that you only use K=5 in your experiments, but the results seem quite good. The natural question that I have now is: how will this method do in training generative models? This is beyond the scope of this paper, but it’s the lowest hanging fruit.  Below I give more detailed feedback.  One way to speed up MMD is to use a random Fourier basis as was done in “Fastmmd: Ensemble of circular discrepancy for efficient two-sample test” by Zhao and Meng, 2015. There are also linear time estimators, e.g., in “A Kernel Two-Sample Test“ by Gretton et al., 2012. I don’t think you need to compare against these approaches since you compare to the full MMD, but they should be cited.  The paper “Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy” by Sutherland et al. submitted to ICLR 2017 as well, discusses techniques for optimizing the kernel used in MMD and is worth citing in section 3.  How limiting is the assumption that the distribution has independent marginals?  The sample complexity of MMD depends heavily on the dimensionality of the input space - do you have any intuitions about the sample complexity of CMD? It seems like it's relatively insensitive based on the results in Figure 4, but I would be surprised if this were the case with 10,000 hidden units. I mainly ask this because with generative models, the output space can be quite high-dimensional.  I’m concerned that the central moments won’t be numerically stable at higher orders when backpropagating. This doesn’t seem to be a problem in the experimental results, but perhaps the authors could comment a bit on this? I’m referring to the fact that ck(X) can be very large for k >= 3. Proposition 1 alleviates my concerns that the overall objective is unstable, I’m referring specifically to the individual terms within.  Figure 3 is rather cluttered, and aside from the mouse class it’s not clear to me from the visualization that the CMD regularizer is actually helping. It would be useful to remove some of the classes for the purpose of visualization.  I would like some clarification about the natural geometric interpretations of K=5. Do you mean that the moments up to K=5 have been well-studied? Do you have any references for this? Why does K >= 6 not have a natural geometric interpretation?  Figure 4 should have a legend
Output: en
The paper provides a first study of customized precision hardware for large convolutional networks, namely alexnet, vgg and googlenet. It shows that it is possible to achieve larger speed-ups using floating-point precision (up to 7x) when using fewer bits, and better than using fixed-point representations.   The paper also explores predicting custom floating-point precision parameters directly from the neural network activations, avoiding exhaustive search, but i could not follow this part. Only the activations of the last layer are evaluated, but on what data ? On all the validation set ? Why would this be faster than computing the classification accuracy ?  The results should be useful for hardware manufacturers, but with a catch. All popular convolutional networks now use batch normalization, while none of the evaluated ones do. It may well be that the conclusions of this study will be completely different on batch normalization networks, and fixed-point representations are best there, but that remains to be seen. It seems like something worth exploring.  Overall there is not a great deal of novelty other than being a useful study on numerical precision trade-offs at neural network test time. Training time is also something of interest. There are a lot more researchers trying to train new networks fast than trying to evaluate old ones fast.   I am also no expert in digital logic design, but my educated guess is that this paper is marginally below the acceptance threshold.
Output: en
This paper presents a Stack LSTM parser based on the work of Henderson et al. (2008, 2013) on joint syntactic/semantic transition-based parsing and Dyer et al. (2015) on stack LSTM syntactic parsing. The use of the transition system from the former and the stack LSTM from the latter shows interesting results compared to the joint systems on the CoNLL 2008 and 2009 shared tasks.  I like this paper a lot because it is well-written, well-explained, the related work is good and the results are very interesting. The methodology is sound (with a minor concern regarding the Chinese embeddings, leading me to believe than very good embeddings can be more informative than a very clever model...).  Moreover, the description of the system is clear, the hyperparameters are justified and the discussion is interesting.  The only thing I would say is that the proposed system lacks originality in the sense that the work of Henderson et al. puts the basis of semi-synchronised joint syntax-semantic transition-based parsing several years ago and Dyer et al. came up with the stack LSTM last year, so it is not a new method, per say. But in my opinion, we were waiting for such a parser to be designed and so I'm glad it was done here.
Output: en
This paper proposed to use RL and RNN to design the architecture of networks for specific tasks. The idea of the paper is quite promising and the experimental results on two datasets show that method is solid. The pros of the paper are: 1. The idea of using RNN to produce the description of the network and using RL to train the RNN is interesting and promising. 2. The generated architecture looks similar to what human designed, which shows that the human expertise and the generated network architectures are compatible.  The cons of the paper are: 1. The training time of the network is long, even with a lot of computing resources.  2. The experiments did not provide the generality of the generated architectures. It would be nice to see the performances of the generated architecture on other similar but different datasets, especially the generated sequential models.  Overall, I believe this is a nice paper. But it need more experiments to show its potential advantage over the human designed models.
Output: en
Este trabajo muestra un enfoque de segmentación de imágenes de tejido mamario para la clasificación binaria de pixels de una imagen mediante SVM, para determinar la presencia o ausencia de la tinción de las membranas de las células cancerígenas Este trabajo es interesante y valorable, debido a la dificultad de la obtención de este tipo de imágenes y por el enfoque interdisciplinario, presentando además un interesante trabajo futuro. Se encuentra bien escrito y los resultados mostrados aunque parciales parecen correctos y justificados.  Verificar redacción y tipografía de la página 4 en sus párrafos primero y penúltimo. La principal debilidad de este trabajo puede ser la baja cantidad de imágenes de prueba y la falta de comparación con otros métodos. También sería deseable mostrar resultados generales de la clasificación y no sólo del proceso de validación. Además, las propiedades explicadas en  la tabla de puntaje (completitud e intensidad) no son utilizadas directamente en el proceso de clasificación.
Output: es
- Strengths: i. Well organized and easy to understand ii. Provides detailed comparisons under various experimental settings and shows the state-of-the-art performances  - Weaknesses: i. In experiments, this paper compares previous supervised approaches, but the proposed method is the semi-supervised approach even if the training data is enough to train.  - General Discussion: This paper adopts a pre-training approach to improve Chinese word segmentation. Based on the transition-based neural word segmentation, this paper aims to pre-train incoming characters with external resources (punctuation, soft segmentation, POS, and heterogeneous training data) through multi-task learning. That is, this paper casts each external source as an auxiliary classification task. The experimental results show that the proposed method achieves the state-of-the-art performances in six out of seven datasets.   This paper is well-written and easy to understand. A number of experiments prove the effectiveness of the proposed method. However, there exist an issue in this paper. The proposed method is a semi-supervised learning that uses external resources to pre-train the characters. Furthermore, this paper uses another heterogeneous training datasets even if it uses the datasets only for pre-training. Nevertheless, the baselines in the experiments are based on supervised learning. In general, the performance of semi-supervised learning is better than that of supervised learning because semi-supervised learning makes use of plentiful auxiliary information. In the experiments, this paper should have compared the proposed method with semi-supervised approaches.  POST AUTHOR RESPONSE  What the reviewer concerned is that this paper used additional “gold-labeled” dataset to pretrain the character embeddings. Some baselines in the experiments used label information, where the labels are predicted automatically by their base models as the authors pointed out. When insisting superiority of a method, all circumstances should be same. Thus, even if the gold dataset isn’t used to train the segmentation model directly, it seems to me that it is an unfair comparison because the proposed method used another “gold” dataset to train the character embeddings.
Output: en
- Strengths: The paper broadens the applicability of readability scores to an additional language, and produces a well-validated applicability score for Vietnamese.   - Weaknesses: The greatest weaknesses, with respect to ACL are that 1) readability scores are of limited interest within the field of computational linguistics. While they are somewhat useful in educational and public communication fields, their impact on the progress of computational linguistics is limited.  A minor weakness is in the writing: the paper has numerous minor grammatical errors. Although the discussion compares the performance of the PDS1 and PDW1 features from the previous work, it is unclear how poorly the previous readability measures perform, relevant to the one developed here, for practical purposes.  - General Discussion: This paper would be a stronger candidate for inclusion if the corpus (and importantly, labels developed) were released. It could be used more widely than the development of scalar readability metrics, and would enable (e.g.) investigation of application of more powerful feature-selection methods.
Output: en
CONTRIBUTIONS When training LSTMs, many of the intermediate gradients are close to zero due to the flat shape of the tanh and sigmoid nonlinearities far from the origin. This paper shows that rounding these small gradients to zero results in matrices with up to 80% sparsity during training, and that training character-level LSTM language models with this sparsification does not significantly change the final performance of the model. The authors argue that this sparsity could be exploited with specialized hardware to improve the energy efficiency and speed of recurrent network training.  NOVELTY Thresholding gradients to induce sparsity and improve efficiency in RNN training is a novel result to my knowledge.  MISSING CITATIONS Prior work has explored low-precision arithmetic for recurrent neural network language models:  Hubara et al, “Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations”,
Output: en
El artículo cumple con el requisito de presentar una experiencia empresarial en aplicaciones de TIC’s con énfasis en la minería, por lo tanto, la evaluación no se hará teniendo en consideración alguna propuesta innovativa en un artículo de carácter científico.  Este artículo describe una experiencia exitosa en el uso de BPMN para modelar el proceso de negocio del área de Ingeniería y Proyectos de una empresa minera peruana. Se usó un pequeño y simple conjunto de elementos de BPMN para modelar el proceso de negocio. Esta experiencia es válida, pero es una solución ad hoc para el proceso de negocio escogido. La solución tendría mayor valoración si se hubiese presentado un modelo pequeño que pudiese ser validado y que pudiese solucionar un conjunto determinado de procesos de negocio asociados a la minería.  Algunas observaciones y/o comentarios menores: - No cumple con el requisito de presentar los artículos como anónimos de autores e institución. - En Resumen, cambiar “control sus procesos” por “control de sus procesos”. - En Introducción, cambiar “beneficios BPMNs” por “beneficios de BPMN”. - En página 3 y otras, utiliza las palabras “figura” y “Figura” en el texto. - En página 5, cambiar “La figura 2 se ha incluido” por “En la Figura 2 se han incluido”. - La referencia a algunas figuras 3 y 4 (mostradas en página 6) solamente se mencionan al final de la página 7. Lo ideal es referenciarla primero y después mostrar la figura. - En página 7, cambiar “el agrupa las tareas” por “agrupa las tareas”. - En Referencia [5] cambiar “BPMNModeling” por “BPMN Modeling”. - La Referencia [7] debe ser completada, la Referencia [12] debe incluir el año.
Output: es
This paper introduces the concept of fuzzy paraphrases to aid in the learning of distributed word representations from a corpus augmented by a lexicon or ontology. Sometimes polysemy is context-dependent, but prior approaches have neglected this fact when incorporating external paraphrase information during learning. The main idea is to introduce a function that essentially judges the context-sensitivity of paraphrase candidates, down-weighting those candidates that depend strongly on context. This function is inferred from bilingual translation agreement.  The main argumentation leading to the model selection is intuitive, and I believe that the inclusion of good paraphrases and the elimination of bad paraphrases during training should in principle improve word representation quality. However, the main questions are how well the proposed method achieves this goal, and, even if it achieves it well, whether it makes much difference in practical terms.  Regarding the first question, I am not entirely convinced that the parameterization of the control function f(x_ij) is optimal. It would have been nice to see some experiments investigating different choices, in particular some baselines where the effect of f is diminished (so that it reduces to f=1 in the limit) would have been interesting. I also feel like there would be a lot to gain from having f be a function of the nearby word embeddings, though this would obvious incur a significant slowdown. (See for example 'Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space' by Neelakantan et al, which should probably be cited.) As it stands, the experimental results do not clearly distinguish the fuzzy paraphrase approach from prior work, i.e. tables 3 and 4 do not show major trends one way or the other.  Regarding the second question, it is hard to draw many conclusions from analogy tasks alone, especially when effects unrelated to good/bad paraphrasing such as corpus size/content, window size, vocabulary size, etc., can have an outsize effect on performance.   Overall, I think this is a good paper presenting a sensible idea, but I am not convinced by the experiments that the specific approach is achieving its goal. With some improved experiments and analysis, I would wholeheartedly recommend this paper for acceptance; as it stands, I am on the fence.
Output: en
The responses to the pre-review questions are not strong; especially w.r.t. the question about dataset density and why the dataset had to be subsampled, the authors responded that subsampling is common in recommender systems work, including the papers cited. This isn't a particularly strong justification of why subsampling is a good idea, and in particular doesn't answer the question of "how would the results look without subsampling," which I think is a question that could easily have been answered directly.  Especially given that the goal of dealing with the cold-start issue is so heavily emphasized in the paper, in seems odd to sample the data to reduce sparsity.  Other than that, the pre-review questions seem to have been answered satisfactorily.  The contribution of the paper is to propose user and item embedding methods, as a means of learning complex non-linear interactions between users and items. This is fairly similar to recent work on deep RS, though the network formulation has some differences.  Overall this is an reasonably put together paper that makes a contribution in an important area, though there are still some shortcomings that should be addressed, namely: 1) The evaluation is unusual. Recall@M is the only result reported, though this is not usually an evaluation seen in recommender systems research. At the very least other performance measures (rmse or AUC) should be reported for completeness, even if the results are not strong 2) Given that the contribution is fairly simple (i.e., the "standard" recommender systems task, but with a new model) it's a shame that unusual data samples have to be taken. This should be a case where it's possible to report results against competing methods using *exactly* the same data they used, and exactly the same error measure, for the fairest comparison possible.  Without the above it's hard to tell how much the performance improvements are really due to the method being better, versus the choice of datasets and the choice of loss functions.
Output: en
Este artículo presenta los resultados de un experimento donde se evidencia que las técnicas de  co-creación aplicadas a través de las redes sociales y haciendo uso de dispositivos móviles pueden potenciar la creatividad de los participantes. Es un tema interesante y que considero que puede ser aporte al congreso.  Se debe mejorar la forma de presentación del artículo.  Algunas observaciones y/o comentarios: - Los dos primeros párrafos de la Introducción es casi la totalidad del Resumen. - En página 4, cambiar “cada que” por “cada vez”. - En página 5, Paso 3, no cierra paréntesis en dos ocasiones. - Sugiero agrandar la Figura 2. - Considero excesivo utilizar una página para una sola figura. - En la Referencia [11] , corregir “Conference on the, 2009” - El número de  página 3 se repite tres veces. - Después de la página 7, sigue con la página 3. - Referencia [4] dice “2004b”
Output: es
The authors propose to combine a CCA objective with a downstream loss.  This is a really nice and natural idea.  However, both the execution and presentation leave a lot to be desired in the current version of the paper.  It is not clear what the overall objective is.  This was asked in a pre-review question but the answer did not fully clarify it for me.  Is it the sum of the CCA objective and the final (top-layer) objective, including the CCA constraints?  Is there some interpolation of the two objectives?    By saying that the top-layer objective is "cosine distance" or "squared cosine distance", do you really mean you are just minimizing this distance between the matched pairs in the two views?  If so, then of course that does not work out of the box without the intervening CCA layer:  You could minimize it by setting all of the projections to a single point.  A better comparison would be against a contrastive loss like the Hermann & Blunsom one mentioned in the reviewer question, which aims to both minimize the distance for matched pairs and separate mismatched ones (where "mismatched" ones can be uniformly drawn, or picked in some cleverer way).  But other discriminative top-layer objectives that are tailored to a downstream task could make sense.  There is some loose terminology in the paper.  The authors refer to the "correlation" and "cross-correlation" between two vectors.  "Correlation" normally applies to scalars, so you need to define what you mean here.  "Cross-correlation" typically refers to time series.  In eq. (2) you are taking the max of a matrix.  Finally I am not too sure in what way this approach is "fully differentiable" while regular CCA is not -- perhaps it is worth revisiting this term as well.  Also just a small note about the relationship between cosine distance and correlation:  they are related when we view the dimensions of each of the two vectors as samples of a single random variable.  In that case the cosine distance of the (mean-normalized) vectors is the same as the correlation between the two corresponding random variables.  In CCA we are viewing each dimension of the vectors as its own random variable.  So I fear the claim about cosine distance and correlation is a bit of a red herring here.  A couple of typos:  "prosed" --> "proposed" "allong" --> "along"
Output: en
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.
Output: en
This paper presents a method for embedding data instances into a low-dimensional space that preserves some form of similarity.  Although the paper presents this notion as new, basically every pre-trained embedding (be it auto-encoders or word2vec) has been doing the same: representing items in a low-dimensional space that inherently encodes their similarities. Even when looking at the specific case of word/context embeddings, the method is not novel either: this method is almost identical to one of the similarity functions presented in "A Simple Word Embedding Model for Lexical Substitution" (Melamud et al., 2015). The novelty claim must be more accurate and position itself with respect to existing work.  In addition, I think the evaluation could be done better. There are plenty of benchmarks for word embeddings in context, for example:  *
Output: en
Vanishing and exploding gradients makes the optimization of RNNs very challenging. The issue becomes worse on tasks with long term dependencies that requires longer RNNs. One of the suggested approaches to improve the optimization is to optimize in a way that the transfer matrix is almost orthogonal. This paper investigate the role of orthogonality on the optimization and learning which is very important. The writing is sound and clear and arguments are easy to follow. The suggested optimization method is very interesting. The main shortcoming of this paper is the experiments which I find very important and I hope authors can update the experiment section significantly. Below I mention some comments on the experiment section:  1- I think the experiments are not enough. At the very least, report the result on the adding problem and language modeling task on Penn Treebank.  2- I understand that the copying task becomes difficult with non-lineary. However, removing non-linearity makes the optimization very different and therefore, it is very hard to conclude anything from the results on the copying task.  3- I was not able to find the number of hidden units used for RNNs in different tasks.  4- Please report the running time of your method in the paper for different numbers of hidden units, compare it with the SGD and mention the NN package you have used.  5- The results on Table 1 and Table 2 might also suggest that the orthogonality is not really helpful since even without a margin, the numbers are very close compare to the case when you find the optimal margin. Am I right?  6- What do we learn from Figure 2? It is left without any discussion.
Output: en
En mi opinión, siendo la propuesta de muy alto nivel, no se puede apreciar su grado de relevancia y utilidad.  En algunos pasajes la redacción hace difícil la lectura. Esto ocurre a lo largo de todo el documento.  Presenta una revisión de los conceptos de data warehouse y data mining muy básica, que no está basada en una revisión de literatura relevante.  Carece de elementos concretos que puedan utilizar para desarrollar una solución a un problema concreto.  La bibliografía, tal cual se presenta, está fuera de toda norma utilizada en artículos científicos.  Se sugiere ya sea reenfocar el estudio hacia algo más concreto, o bien proveer evidencias que permitan evaluar de mejor forma la propuesta.
Output: es
Es una aplicación interesante, sólo que hay medidas como promedios que no son aditivas, por lo que el diseño debe ser corregido, pues los resultados del sistema pueden ser erróneos (no usar para tomar decisiones hasta eliminar los promedios, estos deben construirse en cada nivel de rol up).
Output: es
This paper proposed a very interesting idea of using cognitive features for sentiment analysis and sarcasm detection. More specifically, the eye-movement patterns of human annotators are recorded to derive a new set of features. The authors claim that this is the first work to include cognitive features into the NLP community.   Strength:  1. The paper is generally well written and easy to follow 2. Very interesting idea which may inspire research in other NLP tasks.  Weakness: 1. The motivation of using cognitive features for sentiment analysis is not very well justified. I can imagine these features may help reflect the reading ease, but I don't see why they are helpful in detecting sentiment polarities. 2. The improvement is marginal after considering cognitive features by comparing Sn+Sr+Gz with Sn+Sr. 3. Although the authors discussed about the feasibility of the approach in Section 7, but I'm not convinced, especially about the example given in section 7.2, I don't see why this technique is helpful in such a scenario.
Output: en
Trata de aplicar una nueva estrategia a la gestión de requerimientos  No se respalda ni considera la aplicabilidad de técnicas propias de procesos de manufactura a procesos de software. Si bien es cierto hay autores como Demming que plantean que es posible hacerlo, existen otros autores como Sommerville que plantean que por las características propias del desarrollo de software, las técnicas de procesos de manufactura no son aplicables al software. Los autores de este artículo no entregan respaldo en relación a la validez de este aspecto. El artículo comienza planteando que la propuesta es para mejorar la educción de requerimientos, sin embargo, luego se deriva en que lo que se mejora es la generación de la SRS. La educción y la generación del documento de requerimientos son aspectos distintos. En el caso de experimentación que se presenta, no se ve por ninguna parte que se solucionen problemas ni en la educción, ni en la generación de la SRS, son más bien problemas de la implementación. Los errores detectados son aspectos básicos que cualquier programador con un mínimo de experiencia sabe cómo solucionarlos, sin necesidad que en la SRS se explicite el tipo de validaciones mínimas que se debe realizar como parte de las pruebas del sistema. La propuesta carece de guías prácticas que ayuden en su aplicación ¿cómo se deben realizar estas revisiones POKA-YOKE? ¿En qué aspectos hay que fijarse? ¿Existen artefactos que guíen la revisión? Hay errores de edición en el texto (palabras repetidas) Existen problemas de ortografía (acentuación de palabras)
Output: es
Sin duda en este campo se pueden realizar muchos aportes de tipo innovativos (y este trabajo lo es).  En él artículo se debió destacar más fuertemente los aportes informáticos y no tan sólo destacarlos débilmente. Tampoco se ve con claridad el "cómo se va a ahondar" en las reglas del negocio y se menciona sólo genéricamente esbozos metodológicos.
Output: es
RESUMEN: El trabajo relata los pasos seguidos para usar el software ERP SAP en Embonor, una productora y  distribuidora de productos Coca-cola.  Antes de reportar esta experiencia, esta empresa ya contaba con algunos módulos SAP implementados (Financieros, FI-CO, Logistica, Recursos Humanos).  Evaluación General. Aunque el trabajo no presenta una estructura científica de un artículo, es sumamente interesante (en mi opinión) escuchar en más detalle de esta experiencia en infonor 2013. Tal vez, recomendaría agregar/presentar un poco más acerca de las evaluaciones esta implantación (en su conclusión, mencionan esto como un trabajo futuro).  Comentarios menores: - Principalmente cuidar los espaciados entre palabras y puntos. - Figura 1 es muy poco nítida. - Figura 4 dice: "Menús de la tabla xx (estándar en cada pantalla SAP)" xx?
Output: es
This paper proposes SEM, a simple large-size multilabel learning algorithm which models the probability of each label as softmax(sigmoid(W^T X) + b), so a one-layer hidden network. This in and of itself is not novel, nor is the idea of optimizing this by adagrad. Though it's weird that the paper explicitly derives the gradient and suggests doing alternating adagrad steps instead of the more standard adagrad steps; it's unclear whether this matters at all for performance. The main trick responsible for increasing the efficiency of this model is the candidate label sampling, which is done in a relatively standard way by sampling labels proportionally to their frequency in the dataset.  Given that neither the model nor the training strategy is novel, it's surprising that the results are better than the state-of-the-art in quality and efficiency (though non-asymptotic efficiency claims are always questionable since implementation effort trades off fairly well against performance). I feel like this paper doesn't quite meet the bar.
Output: en
Hay importantes aportes, la combinación de procesamiento Digital de imágenes en geología y específicamente en la identificación de de Fracturas en Imágenes de Pozos es un aporte novedoso (en mi opinión).  Un aporte fundamental al trabajo tiene que ver con el la propuesta del método automático de identificación de líneas base y parametrización de fracturas, basado en técnicas de procesamiento de imágenes y un proceso de votación implementado por medio de la transformada de Hough.  en el archivo adjunto se incluyen más observaciones.
Output: es
No hay contribución científica interesante ya que es la aplicación de modelamiento a un caso de estudio. A pesar de que el trabajo está bien escrito técnicamente y es fácil de seguir no veo cómo es la misma notación la que permite detectar los cuellos de botella. Es el análisis del BPMN por parte del autor quien los detecta.  Otras correcciones: pag 1	título	Creo que puede ser mejorado: “Uso de BPMN para detectar cuellos de botella: un caso de estudio” pag 1	Abstract línea 2	gestión y control de sus procesos  pag 2	Titulo BPMN Creo que es mejor nombrar la sección como background o marco teórico pag 3	Figura 1	La cita de la figura 1 debería aparecer antes que ella (revisar todas las figuras)
Output: es
El artículo trata sobre la aplicación de BPMN para la comprensión de un proceso de negocio en una empresa minera de Perú.  El documento se encuentra bien estructurado y su lectura es fácil. Se observan pocos errores de redacción. Además su formato es el exigido por el congreso.  En relación al tema del artículo, al ser un aplicación de BPMN, no es novedoso. A partir del caso, creo que era posible realizar un mayor aporte, quizás proponiendo una metodología para abordar la revisión de procesos de negocios, considerando que los actores internos y externos de la empresa, generalmente priorizan la optimización de sus procesos productivos más que la revisión de otros procesos. Por último y no menos importante, el énfasis del artículo es la revisión (auditoría) del proceso con BPMN, alejándose de las temáticas del congreso. Se podría dar mayor énfasis aplicaciones asociados al congreso, tales como análisis de requerimientos a partir de BPMN, implementación de SOA desde BPMN, monitoreo de procesos con BAM y BPMN, etc.  En algunas figuras, a pesar que se mencionan a los contratistas como una entidad externa, no se utiliza un “lane” para representarlos y así observar la interacción con el proceso en revisión.  El trabajo futuro planteado está relacionado con los resultados obtenidos de las mejoras implementadas a partir del análisis realizado en este trabajo. Creo que más que seguir las mejoras implementadas es conveniente hacer un seguimiento a la forma en que se aborda la revisión de un procesos en un ambiente que no prioriza este tipo de tareas o es complejo de realizarlas.
Output: es