Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) TEXTURE BASED CROP CLASSIFICATION USING LOW ALTITUDE REMOTE SENSING PLATFORMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: the paper needs a grammar polishing. Please have it proofread carefully again.
Experimental design: The experiment needs to be more comprehensive, I would suggest to add deep learning algorithms such as CNN and LSTM. In addition, the paper should include comparision with state-of-the-art approaches
Validity of the findings: The dataset collection is very interesting and it can be helpful for future research direction
Additional comments: Gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied.
How is the data annotation? Is the data labelled by someone who expert in agriculture?

I would recommend to use deep learning algorithms in this paper you can find information in the following paper:
Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B. and Hussain, A., 2020. A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, pp.1-10.
I would recommend to use graph neural network:
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M. and Monfardini, G., 2008. The graph neural network model. IEEE Transactions on Neural Networks, 20(1), pp.61-80.
I would suggest to compare your results with state-of-the-art approaches such as:
Zahid, A., Abbas, H.T., Ren, A., Zoha, A., Heidari, H., Shah, S.A., Imran, M.A., Alomainy, A. and Abbasi, Q.H., 2019. Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves. Plant Methods, 15(1), p.138.
Sun, J., Abdulghani, A.M., Imran, M.A. and Abbasi, Q.H., 2020, April. IoT Enabled Smart Fertilization and Irrigation Aid for Agricultural Purposes. In Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things (pp. 71-75).


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) TEXTURE BASED CROP CLASSIFICATION USING LOW ALTITUDE REMOTE SENSING PLATFORMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this paper, the optical images were used to classify different crops using UAV. The topic was interesting. The used calibration methods were normally used methods, the experiment design was not quite practical in real applications.
Experimental design: From Table 1 and Figure 1, the classified crops (wheat, rice, soybean, maiz) were not planted at the same time. So the results of this classifiation was not a practical production scenario. We could classify the crops based on the planting time.
The detals of experiment should be supplied, such as planting time or imaging acquisition time, the flight information of UAV, the calibration board of UAV imaging and so on.
Validity of the findings: I think the methods used for crop classification is helpful, eventhough these methods were normally used methods.The authors just used for comparision, not any improvement for related methods.
Additional comments: I suggest to make a crop or surface features discrimination at the same time in practical agricultual productions, not just for the purpose of classification.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) TEXTURE BASED CROP CLASSIFICATION USING LOW ALTITUDE REMOTE SENSING PLATFORMS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The conclusion can't represent the whole results. What reviewer can see is that the GLCM based classification outperform the grayscale based in some cases. And it's hard to get how the 13.5% is reached.
Experimental design: The experiment was conducted in seperated fields at different time. It can be more conclusive and usable if this classifier be used in a large orthoimage which contains different types of ground cover.
Validity of the findings: More detailed discussion and conclusion are needed since the result is realy ambiguous.
Additional comments: The topic of this paper is interesting but needs improvement. The conclusion in abstract can't find proof in results section.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) TEXTURE BASED CROP CLASSIFICATION USING LOW ALTITUDE REMOTE SENSING PLATFORMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have made extensive revision and the article meet my requirement of publishing. The article use clear, unambiguous, technically correct text. The structure, figures. tables meet the journal requirement
Experimental design: The experiment design is kind of regular, perhapes lack of novelty. The method is described in detail
Validity of the findings: The acceptance is based on the new application of texture analysis in drone based remote sensing. So in term of validity of the findings, i recommend this article published.
Additional comments: PeerJ is a new thing to me. If in other computer science related journals, this article can't meet my requirement due to the lack of novelty. However, the PeerJ encourage replication. And in terms of novelty in applications, I recommend it accepted for publication.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A DEEP DESCRIPTOR FOR CROSS-TASKING EEG-BASED RECOGNITION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The topic of this paper is interesting and can be accepted in the PeerJ after do the following corrections:
1- What are the most challenging tasks to apply deep learning for EEG-based recognition?
  this must be well addressed and mentioned in the Introduction section.
2- Section "PROPOSED APPROACH AND EVALUATION SCENARIOS" not explain well and needs to rewrite in a more scientific way. Also, there are some don't explain for example in Figure 3 "feature extraction" step but there aren't any details about it.
3- The authors used the EER measure to evaluate the proposed method. Did you try other measures?
4- What are the limitations of the proposed method?
Experimental design: 1- More experiments are required.
2- The authors should use the newest EEG datasets.
Validity of the findings: 1- More experiments with different validation measures are required.
2- The proposed method must be compared with the state of art to prove that its has a good performance.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A DEEP DESCRIPTOR FOR CROSS-TASKING EEG-BASED RECOGNITION
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1. Most contexts are overclaiming. For example, "We show that the proposed method is robust, even when trained and evaluated on different motor tasks
and fewer electrodes (nine channels). Due to deep learning hardware accelerators, we claim that our proposal is suitable to be embedded in a real-world application.".
Subjects performed different actions, but they are 1) motor-related tasks, 2) one single day with a one-time recording setup. I would recommend the author avoiding overclaiming.

2. This recent review article would help the author improve the Introduction on either general EEG applications or EEG-based biometrics https://ieeexplore.ieee.org/document/8945233.

3. One important work in the literature is missing. This is very recent work from late 2020. There are over 30 citations so far. https://ieeexplore.ieee.org/abstract/document/8745473. It is a deep learning approach for EEG-based biometrics.

4. Make the contribution list concise and address my comment in 1.

5. Figure 2 is not informative. The author can remove It.

6. Related works should be in a table form. The current form is complicated to read and to follow.
Experimental design: 1. Remove "source: the author" from Figure 3, 5, ....

2. I am not agreed that data segmentation with overlapping is equal to data augmentation.

3. Validation is not explained clearly. Such as k fold cross-validation.

4. The author must compare their own proposed model to the previous works by reusing their code on the same data processing or by reproducing the code from their journal papers. It is not a fair comparison at this moment.
Validity of the findings: 1. There is no statistical testing.

2. There is a slight novelty in using EEG from the different motor-related actions, which the author called multi-tasks.But, I would suggest the author make the deep learning model more novel. Or the author may include some more research questions to make this work solid. The novelty of the current form might not be enough.
Additional comments: Good luck with your chances on a revision.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A DEEP DESCRIPTOR FOR CROSS-TASKING EEG-BASED RECOGNITION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The authors propose a biometric recognition system relying on EEG data, specifically addressing the issue of cross-task recognition.

This topic is indeed relevant yet not novel. The authors do not cite several recent relevant studies, such as
- Del Pozo-Banos et al, Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram, 2018;
- Vinothkumar et al, Task-Independent EEG based Subject Identification using Auditory Stimulus, 2018
- Kong et al, Task-Independent EEG Identification via Low-rank matrix decomposition, 2018;
- Fraschini et al, Robustness of functional connectivity metrics for EEG-based personal identification over task-induced intra-class and inter-class variations, 2019;-
- Kumar et al, Subspace techniques for task independent EEG person identification, 2019;

The novelty of the performed study with respect to the aforementioned works should have been deeply detailed. In more detail, several of the aforementioned studies use CNN to extract features from EEG, further affecting the contribution of the present manuscript.
Experimental design: There is a severe falw in the employed experimental design. The authors use CNNs to extract discriminative features from EEG data. The employed CNNs are trained over the same subjects then used for testing. Such conditions are impossible to be replicated in real life: they would imply any potential malicious subject to be available during the enrolment of a user. The obtained equal error rates are therefore achieved in unproper conditions. A proper verification test should be carried out over subjects which have not been considered during the training of the employed network,
Validity of the findings: A major issue of the paper regards the employed database. It is widely known that the PhysioNet database has been acquired performing a single recording session for each subject. Although several tasks are performed by each subject, they are carried out during the same session. The acquired data are extremely dependent on the session-specific conditions (for instance, the EEG recording device is never took off by the subjects during the whole session). the obtained fundings are therefore not reliable. It is recommended to perform tests on database collected, for each subject, during different days.
Additional comments: Given that CNNs have been already used for EEG-based biometric recognition (also considering cross-task conditions), a discussion about the novelty of the proposedapproach has to be given. Moreover, a performance comparison between the effectiveness of the proposed approach and the literature ones should be included.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A DEEP DESCRIPTOR FOR CROSS-TASKING EEG-BASED RECOGNITION
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Thank you for addressing all comments.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ESTIMATION OF GRANGER CAUSALITY THROUGH ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO PHYSIOLOGICAL SYSTEMS AND CHAOTIC ELECTRONIC OSCILLATORS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Work written carefully. I have no objections to the editing side of the work.
Experimental design: Well presented results.
Validity of the findings: The article is clearly written and transparent so I have no critical comments.
Additional comments: Write what are other possible practical applications of the methods presented in the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ESTIMATION OF GRANGER CAUSALITY THROUGH ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO PHYSIOLOGICAL SYSTEMS AND CHAOTIC ELECTRONIC OSCILLATORS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper is very well written throughout. The theory and methods are both clearly explained and articulated. I enjoyed reading the paper, but I have two comments to make.

My first comment relates relates to the high level description of the method. The paper presents the method as being ANN based, but it only uses a single layer. As I understand it, this would be somewhat equivalent to an ordinary least squares, and indeed the authors seem to be getting at this point when they say:

"Thus, the described ANN is completely equivalent to a VAR model, except for the fact that the training process induces sparsity into the weight matrix W."

The sparsity constraints seem to be the most important feature here. As such, I found it a bit strange to call it an ANN since it seem to be doing linear regression with sparsity constraints. I also think that would be a bit easier to explain. I do want to emphasis, however, that this comment is only about the description. The method proposed in the paper is actually quite a nice approach.

My second comment is that description of the physiological case study could be improved. As I understood it, the entire experiment was captured in one time series whilst the subject was working through a set of different tasks. I assume that the authors have then split the time series into separate experiments, which is fine. However, if the authors have estimated the Granger Causality across the entire experiment, then the time series would hardly be stationary, which would be problematic. I don't think there is any issue here, rather I just thing the description could be improved.
Experimental design: I believe that the people who would be most interested in applying the findings from this paper will be applied scientist in biology, economics and neuroscience. Nevertheless, in my opinion, the paper is definitely within the scope of the journal since it focuses on the theoretical and technical aspects of the implementation. The work is of a high standard and is suitable for publication in this journal.
Validity of the findings: Everything looked fine to me.
Additional comments: Just so the authors can contextualise my review, my background is in the information theory and the estimation of the information-theoretic measures, i.e. I am not an expert in neural networks, so if I have misunderstood something and it is entirely different to ordinary least squares, then the authors may disregard the first of my above comments. That being said, some clarification around this point would improve the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ESTIMATION OF GRANGER CAUSALITY THROUGH ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO PHYSIOLOGICAL SYSTEMS AND CHAOTIC ELECTRONIC OSCILLATORS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The manuscript is well written, in a clear and adequate form.
The essential literature and background are properly described in the Introduction. Methods and results are clearly explained, and the terminology follows scientific standards.
The shared code is clean, sufficiently commented, and well organized, with sporadic comments in Italian that require an update.
Experimental design: The work fits within the aims and scopes of the journal.
The authors developed an Artificial Neural Network (ANN)-based method for estimating AR processes in the context of Granger Causality (GC) through State-Space modeling (SS). The performance and optimal settings of the ANN were investigated in a first study and compared against the classical Ordinary Least Squares (OLS) approach in a second simulation study. In two additional investigations, the authors compared ANN and OLS performance using physiological time-series and data generated by electronic oscillators.
The main goal was to propose a method for SS-GC that promotes sparsity and robustness against data paucity. Overall, most of the metrics of comparison are adequate and informative, revealing how ANNs present a valid and less time-consuming alternative to OLS in reconstructing the topology of GC interactions in simulated and real networks.
The simulations used, however, represent a very restricted scenario in which the ground truth is topologically sparse (e.g., networks with a density of 15%) and the AR process is reduced (e.g., lags with non-zero coefficients are randomly assigned to a subset of the possible 1-16 lags, if my reading is correct). This creates conditions, in simulated data, that favor sparser solutions (both in the topology and lag space). In more realistic conditions, such as in functional brain networks, the topology of network interactions often presents denser structures and full AR processes (e.g., AR coefficients that are non-zero at each considered lag, but fade out exponentially as the lag increases). It is therefore important, for the sake of application, to discuss or even test the performance of the proposed method under such circumstances.
Similarly, simulations are performed with a fixed level of SNR, but SNR is a crucial factor to assess the performance of different methods in GC, particularly when l1 regularized solutions are at stake. For this reason, the robustness to SNR variations should be evaluated as an additional factor and compared across methods.
It seems that the main advantages of ANN over OLS rely on the intrinsic l1 regularization. A more stringent comparison, to evaluate the advantage of an ANN against the pure regularization, would be to use a LASSO-penalized LS as the comparison (e.g., Pagnotta et al., 2019, Antonacci et al., 2020). The authors should at least mention this possibility.
Validity of the findings: The reported findings are useful and informative for future methodological developments and applications of GC metrics. Some limitations should be discussed. For instance, how does the sparsity regularization affect the structure of estimated AR coefficients over lags? In many applications of VAR-GC models in neuroscience, a  frequency or time-frequency representation of the AR process is often preferred for interpretation (e.g., Partial Directed Coherence). Such metrics are obtained from a z-transform of the AR coefficients over lags. Imposing sparsity (both at the level of topology and lags) might introduce uneven shrinking of AR coefficients over lags, which eventually causes undesired alterations in their frequency content.
Also, using comparison metrics based on binary classification tasks hides potential differences between the two methods in their ability to estimate the (relative) weights of connections in a weighted network, an aspect that is typically relevant in many investigations. Using quantile-based thresholding criteria and AUC could be an alternative method that the authors may consider (Pascucci et al., 2020).
Additional comments: The paper introduces a very promising alternative to standard OLS. I believe that a few additional tests or discussions, which should be considered as midway between minor and major revisions, will improve the manuscript and characterize better the performance of the proposed ANN.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ESTIMATION OF GRANGER CAUSALITY THROUGH ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO PHYSIOLOGICAL SYSTEMS AND CHAOTIC ELECTRONIC OSCILLATORS
Review round: 2
Reviewer: 1

Output:
Basic reporting: OK
Experimental design: OK
Validity of the findings: OK
Additional comments: An interesting and well-written article I have no comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ESTIMATION OF GRANGER CAUSALITY THROUGH ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO PHYSIOLOGICAL SYSTEMS AND CHAOTIC ELECTRONIC OSCILLATORS
Review round: 2
Reviewer: 2

Output:
Basic reporting: No new comment.
Experimental design: No new comment.
Validity of the findings: No new comment.
Additional comments: Thank you for your efforts in integrating the suggestions. I think the paper is ready for publication.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ESTIMATION OF GRANGER CAUSALITY THROUGH ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO PHYSIOLOGICAL SYSTEMS AND CHAOTIC ELECTRONIC OSCILLATORS
Review round: 2
Reviewer: 3

Output:
Basic reporting: In line with my previous review, the manuscript is well written and clear, in standard scientific format and notation. The background literature is exhaustively covered and methods well described. I have no further concerns on basic reporting.
Experimental design: The Authors have successfully addressed my previous concerns with additional simulations and tests.
Validity of the findings: The additional discussion and the revised conclusion and limitation sections provide a final exhausitve content. I have no further concerns.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LANDMARK-FREE, PARAMETRIC HYPOTHESIS TESTS REGARDING TWO-DIMENSIONAL CONTOUR SHAPES USING COHERENT POINT DRIFT REGISTRATION AND STATISTICAL PARAMETRIC MAPPING
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript is written in clear, unambiguous, and professional English. The overall structure of the manuscript meets PeerJ standards, although some minor deviations might have been missed by this reviewer. The figures chosen are relevant, high quality, and well labelled. However, there are a couple additions that could be made in order to increase the readers ability to interpret the results and various claims of methodological value made by the authors. While the introductory and background sections do provide important context for this research, some references are missing, and a variety of similar methods have been totally missed by the authors.

First, the authors should add the missing references to lines 70 and 93.

Second, the authors explicitly focus on the weaknesses of Procrustes based GM analyses and the strengths of their contour-based MV methods (with some statements of contour method weakness in the discussion that are largely brushed aside). This is nowhere near a fair and balanced comparison for readers who may be unfamiliar with both methods. Of course, as stated by the authors, it is not appropriate to review all the detailed literature of GM here in this research article. But, the authors should take at least a few sentences to describe the scenarios in which GM methods are valuable, including those where biological homology (either morphological or developmental) is critical for interpretation of results. These are situations for which randomly identified points on a contour cannot provide the information necessary to interpret results effectively. Some relevant examples are likely found in the already cited Zelditch, et al., 2012 textbook. Other arguments for the value of explicit homology for interpretations of biological shape space can be found in recent reviews of GM within Biology such as Hallgrimsson, et al, 2015 (https://doi.org/10.1016/bs.ctdb.2015.09.003). Explicit recent reports on the strength of Procrustes ANOVA and related analyses within the study of multivariate shape data have been written by Adams and Coyler, among others (e.g., https://doi.org/10.1038/hdy.2014.75). The authors should (at minimum) acknowledge and cite previous papers that describe the value of these methods and the contexts within which they are most useful.

Third, there is no introduction of other non-Procrustes Distance based contour morphometric comparative methods (e.g., elliptic fourier analysis described in Bonhomme, et al., 2014 (https://hal.inrae.fr/hal-02635896) and Claude, 2013, Hystrix 24(1):94-102) or automated landmark correspondence quantification methods that have been previously published (e.g., Peter Claes’s 3D facial surface automated methods (https://www.esat.kuleuven.be/psi/members/00041773). While the MV method described in this manuscript is interesting and potentially useful as an alternative method to sparse landmark GM, it is not the first method that has been suggested as an alternative.  Related and relevant previously published methods should be acknowledged. A literature dive to identify the range of these previously published methods and research is strongly suggested so that the authors can provide a broader theoretical and methodological context for the currently proposed approach. This will also allow the authors to compare their 2D contour method (and proposed 3D extension) with other contour and surface-based alternatives to GM (such as the Claes et al., methods) within their discussion.

Fourth, the fact that homologous landmarks have been collected manually (as stated on line 50) does not mean that automated methods are impossible. The issue until recently has been that automated methods are simply bad at successfully identifying biological relevant homologous points across a broad range of morphology. However, there are recent pushes to use modern nonlinear registration and machine learning to improve the quality of automated homologous landmark identification. This recent work should be acknowledged (e.g.,  https://doi.org/10.1007/s11692-020-09508-8 and https://doi.org/10.1186/s12983-015-0127-8).

Finally, a raw dataset of contours and landmark points is not provided. It is true that the authors used contours from a previously published dataset which is (presumably) publicly available. However, in order to replicate their results, a complete list of the 10 (out of 20) chosen shapes for each shape category is required. It would also be necessary to report which of those 10 shapes were placed in group A and placed in group B for each category. The best way to allow for replication would be to include a raw dataset that contains only the contours they analyzed, the group identities of each contour, and the manually identified landmarks of each contour.
Experimental design: The research within this manuscript appears to fit within the scope of the journal, although it largely represents a combination of previously published methods into a novel analysis pipeline. The authors do a good job identifying the purpose of their new pipeline and their comparison of relevant statistical methods. It appears that the authors have completed the work at a high technical and ethical standard. In most cases, the authors have done a great job describing the methodological details. However, there are a few places where further detail will be helpful to readers.

Line 117 – Since there are 20 shapes within each category that were presumably produced using explicit randomization parameters, why not have two random groups of 10 shapes. In other words, for each category, why were two groups of 5 chosen and explicitly how were they chosen? Were they chosen randomly or were they subjectively chosen in an effort to produce two groups that would probably display a significant difference in shape?

Line 148 – It would be useful to indicate what data is being used as the input for the T2 statistic. Two dimensional coordinate data is implied later in the section, but the input data should be made explicit. Also, it is not immediately clear what is meant by T2max. Is this the maximum T2 value between group A and B across all landmarks (which the results imply) or is this the maximum difference between specimens as calculated with a summary value of all landmarks within a specimen?

Line 165 – Certainly, a dataset of unordered points is possible within an automated phenotyping framework. However, it seems unlikely that 2D contour points generated within a real world scientific framework would be unordered, because there is clearly an order in position along the contour (i.e., it is a fairly simple ~1.5 dimension object). Are there examples of common real-world research datasets where the contour points are usually (i.e., generally) unordered at the beginning of the statistical analysis?

Line 175 – In order to complete a valid Procrustes ANOVA of automatically identified contour points, it would be necessary for all contours to be represented by the same number of points. At what stage in the Contours UV analysis is the number of points within all shapes equalized? Is there an interpolation to increase the number of points in all shapes to match the contour with the largest number of points (as implied in this passage)? If not, then which points are chosen to be included in the Procrustes analysis and how?
Validity of the findings: The underlying data and results appear robust, statistically sound, and controlled. The results are presented plainly and clearly, which is appreciated. There are a couple minor issue in the results that can be quickly fixed.

Table 2 – Face UV Contours p-value of 0.052 is not actually lower than the 0.050 cutoff. This should be corrected in the table and associated text

Line 215 – Table number is missing

Moving into the Discussion, most statements are reasonable suppositions from the presented results. The author’s attempt to present the data at face value for the judgement of readers is somewhat successful. In addition to highlighting some strengths of their proposed pipeline, the authors do mention a few weaknesses. This is also appreciated. However, in some parts of the discussion, the strength of the proposed pipeline and the weakness of Procrustes ANOVA analysis are overexaggerated to the point of being misleading. These statements should be amended to more accurately reflect the general value of these methods.

Line 226-229 – It is shown that the parametric T2 method and the nonparametric method identify some of the same significant differences. This is true. However, this does not show that parametric hypothesis testing is valid for these types of datasets. The author’s statement that their use is “possible” implies that it is statistically valid and appropriate to use for these datasets. If the authors want to say that parametric approaches are statistically valid and appropriate, they need to show that the input data for the T2 test actually meets the assumptions of a T2 distribution. If not, then they need to be clear that this may not be a statistically valid approach even if it does identify some of the same significant differences in shape as Procrustes ANOVA.

Line 232 (point 1) – It is true that manual landmark placement requires subjective judgement on the placement of landmarks on a surface or contour. In this way, it is more subjective than the placement of landmarks using the automated method proposed here. However, the choice of parameters and procedures for the automated landmark placement algorithm are also subjective. In addition, the fact that a given version of the algorithm produces 100% consistent landmark placement (i.e., high repeatability) does not necessarily mean that the automatically identified landmarks provide accurate, useful, or interpretable results. The authors should acknowledge that this method (as described here) might be more useful for some datasets and research questions than others. In other cases, care should be taken by researchers to validate that this “objective” method quantifies shape in a useful and interpretable way.

Line 232 (point 2) – The results do not been show that this method generally provides “direct morphological meaning in hypothesis testing results.” Yes, the T2 method does produce a p-value for all points along a contour, because a test is completed for each contour point. This is true. However, because those points have been randomly placed (rather than being morphologically defined homologous points of biological meaning), the points have no inherent meaning. It is absolutely still necessary to produce a visualization of significant landmark location along the contour (as done in Figure 8) in order to begin interpreting the meaning of those significant landmark coordinate differences. Both the Procrustes ANOVA and the proposed method require additional steps of visualization and follow up tests to generate valuable interpretations of results. Furthermore, randomly identified points along a contour have no inherent biological, morphological, developmental, or other meaning while well-defined homologous (and “subjective” manually placed) landmarks or semi-landmarks often do.

Line 255 – The authors correctly state that manual landmark placement can be very time consuming. However, they also imply that this contour based method requires much less researcher time to complete. This is not true in this reviewer’s experience with landmarking, segmentation, and contour definition. The authors have completely ignored the amount of time that it takes to produce the contours upon which automated landmarks are placed in a real world research scenario. On a 2D image, manually identifying landmarks might take many minutes to identify carefully. However, identifying a sparse set of landmarks on an image is much less time consuming than drawing an accurate contour on a 2D image. This is a major reason why landmark based methods, rather than contour-based methods have been preferred in the past by researchers. Even if a semi-automated method is used to identify a contour (such as with edge detection), a researcher usually needs to open up every image in order to verify that the automated contours are accurate and often needs to make at least a few corrections to each contour. So, this reviewer agrees that the automated placement of points along an already defined contour using the described method is faster than manually identifying landmarks on an already defined contour. But, this will not translate into real world time savings for researchers in the way that the authors imply. The time and effort required to produce accurate contours in real world research situations needs to be acknowledged.

Line 277 & Figure 8 – The authors correctly point out that the proposed multivariate method fails to identify differences in group A and group B shape across many of the shape categories, including around the horseshoe, the belly of the fish, differences in the head of key, and the top of the bell. As mentioned, this is likely because the drawn mean shapes in Figure 8 do not represent the full scope of the shape variance within group A and group B of each category. It is highly likely that high contour shape variance explains 1) the failure of the multivariate method to identify these horseshoe differences AND 2) the failure of the UV method to identify significant shape differences for several of the shape categories. However, in order for the reader to easily judge this possibility, they need to have a visualization of the shape variance. To clarify this issue, the authors should include the outlines of group A mean, group B mean (perhaps as thick colored lines) and all individual shape contours (perhaps as thin dotted colored lines) within Figure 8’s panels. This will improve reader understanding of the dataset and improve the strength of the discussion about why each method failed to identify significant shape differences in some cases.

Line 295 – As mentioned in my comments about the introduction, previous methods of 3D automated landmarking and analysis like the Claes methods should be cited here.
Line 297-305 – Given the described sensitivity to algorithmic specifics, would the authors recommend that multiple separate MV tests be run with random point location seeds? This might allow researchers to identify whether or not their shape is at ~0.05 p-value and to determine the typical level of significance for the shape. Computationally, this should be possible given the small amount of time required for each run.
Additional comments: Generally speaking, this reviewer was glad to read this paper, found the quality of the results presentation to be high, and will genuinely consider the automated contour point identification method for future use.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LANDMARK-FREE, PARAMETRIC HYPOTHESIS TESTS REGARDING TWO-DIMENSIONAL CONTOUR SHAPES USING COHERENT POINT DRIFT REGISTRATION AND STATISTICAL PARAMETRIC MAPPING
Review round: 1
Reviewer: 2

Output:
Basic reporting: Line 79:  It is absolutely incorrect to claim that geometric morphometric analyses are generally univariate, relying on analysis of PCs.  There are innumerable examples that show this is not correct and as such this statement suggests that the authors do not know the GM literature. It is also not true that a univariate Procrustes ANOVA is generally used to test for shape differences.  The criticism of Procrustes ANOVA and RRPP is also unnecessary for this paper.  Validating a robust landmark-free method would be tremendously useful and entering into what appears to be an insufficiently well informed (or at least insufficiently supported) critique of the statistical hypothesis testing in GM is an irrelevant distraction for this paper.

A major issue is that it is not clear how one might use this form of morphometric qualification beyond the simple task of statistical comparison of mean shape and the visualization of differences in shape between groups.  This is only the most basic of morphometric tasks. A key requirement for morphometric methods is the ability to extract measures of shape variation that can be quantitatively compared to covariates.  For example, the regression of shape on size to quantify allometry is a basic task in most morphometric analyses. Similarly, regression on covariates that one wishes to control for or remove from an analysis is a basic requirement. Finally, much of GM focuses on analysis of covariance structure with methods such as PLS used to quantify covariation in shape between parts of an organism. There are existing landmark-free methods that enable comparison of means and visualization of mean differences.  Most fail to be adopted by the morphometric community because they fail to allow for analyses of integration, covariance structure or analyses involving multiple covariates. The claim is made in the paper that the method is amenable to standard (including multivariate statistical) techniques such as regression. This is a critical point and needs to be developed further. How might one do a regression of shape on size, visualize the resulting variation and then remove the covariance of shape on size prior to further analyses?  For this method to be viewed as promising by the morphometric community, the pathway to such analyses must be clear.
Experimental design: I have no concerns in this area.
Validity of the findings: The validation is robust as far as it goes, although see more general concerns as outlined above.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LANDMARK-FREE, PARAMETRIC HYPOTHESIS TESTS REGARDING TWO-DIMENSIONAL CONTOUR SHAPES USING COHERENT POINT DRIFT REGISTRATION AND STATISTICAL PARAMETRIC MAPPING
Review round: 2
Reviewer: 1

Output:
Basic reporting: 113-131: More details on group definition and shape choice are appreciated. However, as written, information in these three paragraphs is presented in a confusing order. Please reorder and edit to improve reader understanding. For example, at one point, it is claimed that shapes are assigned to groups in a pseudo-random manner. Presumably, this is after 10 shapes were chosen randomly for each object type (as described in the next paragraph). Then, in the last paragraph is described explicitly or is this something else?

Figure 8: Based on other tables, it appears that the parametric p-values are found in dark grey shapes and the nonparametric p-values are found in light grey shapes. However, this should probably be indicated explicitly within the Figure caption.

280: imply instead of apply?

Discussion: Perhaps subsection headings of the discussion would be useful. For example (~Line 374), to separate more general discussion of the SPM method from discussion of specific results presented in the paper?

433: ad hoc instead of add hoc?
Experimental design: 201: The clarification of contour points is helpful. However, the statement that the shape with the maximum number of points is used as a template for shape registration is still potentially confusing within the context of a standard Procrustes ANOVA analysis. Within GM, Procrustes superimposition of shapes would be based on an equal number of 1:1 homologous points per shape. The number of points is one of several factors that will lead to different Procrustes distance estimations. So, if a standard Procrustes ANOVA is being conducted, the reader assumption is likely to be that Procrustes distances were estimated between shapes represented by equal numbers of points. The author response to previous comment 1.11 is comforting to the reviewer, but will not be seen by other readers. The statement that the shape with maximum points (even if this means maximum number of points before parameterization) will be off-putting to GM readers. Can this implication of shapes with different numbers of points within Procrustes ANOVA be clarified?
Validity of the findings: 114-116 & other places: The clarification that one major goal is to show statistical power to identify differences using parametric methods is helpful here and elsewhere. As someone who applies existing methods more often than he develops statistical methods, this reviewer previously assumed that phrases like “suitability” and “possible” did imply empirical appropriateness of methods rather than being specifically related to statistical power. The explicit clarification of meaning that have been made throughout the paper are much appreciated.

However, in this reviewer’s experience/interpretations of various readings, the main concern about parametric testing methods for multivariate morphometric data from small datasets has been that the assumptions of parametric methods (including distribution assumptions) may not be met. This leads to a concern that statistically significant signals generated with a parametric test are invalid and may not reflect a “real” difference between groups under study. Of course, “real” means different things in different contexts. But, the idea of statistical power seems secondary to the idea of empirical validity to this reviewer (who is acknowledges a potential blindspot on this subject). That being stated, the explicit statements of word meaning and research goals added by the authors in the revised manuscript are certainly adequate to prevent reader confusion. This potential philosophical difference is no reason to delay publication and requires no practical changes to the manuscript.
Additional comments: The authors have made major revisions and welcome additions to the Introduction and Discussion session that address most previous reviewer comments. These changes will make this work more accessible and increase its impact among practicing morphometricians. The in-depth comments, clarifications, and illustrations of statistical concepts within the author response are helpful and much appreciated. A few potential issues remain, but they are minor in nature.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LANDMARK-FREE, PARAMETRIC HYPOTHESIS TESTS REGARDING TWO-DIMENSIONAL CONTOUR SHAPES USING COHERENT POINT DRIFT REGISTRATION AND STATISTICAL PARAMETRIC MAPPING
Review round: 3
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: I have no further concerns regarding the publication of this manuscript.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LANDMARK-FREE, PARAMETRIC HYPOTHESIS TESTS REGARDING TWO-DIMENSIONAL CONTOUR SHAPES USING COHERENT POINT DRIFT REGISTRATION AND STATISTICAL PARAMETRIC MAPPING
Review round: 3
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: This is an interesting addition to the literature. In this revision, the authors have done a much better job of contextualizing this new method and exploring its limitations (which are really just areas for further development) and advantages.  One thing I would like to see in the future is validation by comparison to GM approaches (sparse and dense landmark) on real-world datasets that have been used for hypothesis testing. However, the work as presented here stands on its own and so should get out there for people to consider and digest.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LOW-DIAMETER TOPIC-BASED PUB/SUB OVERLAY NETWORK CONSTRUCTION WITH MINIMUM–MAXIMUM NODE DEGREE
Review round: 1
Reviewer: 1

Output:
Basic reporting: Overall, your paper is written in a clear and understandable fashion.
However, there are multiple instances of broken math notation (e.g., lines 157, 164, 260, 261, and table 3).

The introduction could benefit from example usecases of publish/subscribe networks.
In lines 45-54, you should add a concrete explanation on how messages actually travel from publisher to subscribers using the sub graph.
It is not clear to me why these particular systems form lines 79 to 99 where chosen as background on publish/subscribe systems.
Lines 113 to 120 use notation that is only introduced later.

Your pseudocode and algorithm descriptions are clear and understandable.
Your figures would benefit from more information, both as part of the figure and in the caption.
As is, the steps explained in figures 2-7 are only understandable with parallel examination of the corresponding sections of your text.
To improve this, add a short description of the executed step to each caption and augment the figures themselves with information such as the current list of topics T.
Further, Figure 1 contains an error:
Node u's topic list is missing topic 60, which u has subscribed to according to line 194.
While your examples are helpful in understanding how your algorithms work, the wording of the example's titles makes them sound like contradicting statements.

As required, your paper is self-contained:
You state your research question, present your proposed algorithms and evaluate them to show that they meet their expectations.

The preliminary section mostly defines the required terms in an understandable way.
It might not hurt to state that |x| denotes the number of elements in x.
Further, there is no definition of the diameter of an overlay network, despite it being a central term of your paper.
Experimental design: Lines 55 to 71 clearly define the relevance of your research question and how your proposed algorithms will improve the current state of the art.

Related work (Chockler 2007a, Section 8), seems to use a much greater number of nodes (up to 10,000 compared to your 100) in their evaluation.
Could you please explain why you you have not evaluated your results using a greater number of nodes.

You might be able to improve your evaluation by proving bounds of your algorithms, similar to lemmas 6.5 and 6.6. in Chockler 2007a.
Validity of the findings: As noted previously, your pseudocode and description of the algorithm is clear and understandable.
However, your included source code lacks any documentation.
The output of the program does not indicate which of your experiments is run.

Line 350 could have clearer formulation, since Tables 11 and 12 do not reveal on first glance that CD-MAX is faster.
You should provide more context at which point it becomes infeasible to use CD-MAXref over CD-MAX, seeing that is has a much greater running time.
Overall, I can see the links between your conclusions and the results of your evaluation.
Additional comments: It's a bit of a stretch to call a paper form 2007 "recent" (line 54).


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LOW-DIAMETER TOPIC-BASED PUB/SUB OVERLAY NETWORK CONSTRUCTION WITH MINIMUM–MAXIMUM NODE DEGREE
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper is well written and does a good job of motivating the problem. The authors furthermore present a clear comparison to the related work and put their contributions into context. Raw data is provided, figures are appropriate and easy to understand.

The paper is mostly well structured, though I propose to spend less time on examples and put more emphasis on proofing that (steps of) the algorithms achieve the desired properties.

The main drawback of the paper is the lack of clarity in both the algorithm description and the proof. A more minor issues are some aspects in the notation.

=====Proof for Algorithm 2======
There are two issues in the proof of Algorithm 2:
i) The algorithm eliminates all edges of a node associated with a topic, however, what happens if the edge is associated with more than one topic? it seems to me like there might be removals that lead to nodes not receiving all topic information. If I misunderstand, you have to explain in the proof why that is not the case.
ii) The pseudocode searches for a node q with d_q + d_t <d_u − d_t whereas the text (line 179, page 5) states that the algorithm finds a node with  d_q + d_t <d_u...Please either clarify or fix this seeming inconsistency

=====Proof for Algorithm 1======
There is no proof for Algorithm 1 but I think it is non-obvious that the algorithm does indeed include edges for all topics and users and does not accidentally remove some in Line 18.

=====Pseudocode=====
The pseudocode is unclear on multiple occasions, all of which can probably be clarified. For Algorithm 1:
-Line 4-7: I assume you'd calculated the maximum after calculating each individual value. Maybe you mean that you compare the old maximum with the value for node u but the code seems like you compare all the values again.
-Line 8: The term 'max_n is not a single' is unclear, I assume you mean |Max_n|>1, however, it isn't even clear that Max_n is a set up to that point, so it's a bit sloppy
-Line 9: What happens if there is more than one element in Max_n with maximal degree?
For Algorithm 2:
- My interpretation of all the —s is minus but that does not make sense as the algorithm would never enter the while loop (because -\infty < -FindMaxNodeDegree()), so maybe it means something else or they should just not be there.
- what happens if there are several such nodes q (Line 10)
- I think you never really defined centre node

======Notation=====
There are some small unclarities in the mathematical notation that make reading harder and require some guessing as to what is meant:
- p.3: In Line 114-115, you define Int(x,m) as a binary function and then use it as a set in Line 119
- p.3: Line 113-114: 	“There are m ∈ M|n : Int(n,m) = 1| individual”  This sounds like m ∈ M|n : Int(n,m) = 1| is a number but it’s not (do you mean the sum over all m?)
- p.4: Eq. 2, the function e(u,v) is not defined, it could be e=(u,v)?
- p.4: Eq. 4, can you give an intuition of what the equation conveys? You also state it’s normalized but it seems like the denominator can easily be bigger than the nominator
- p.5, Line 164, du -> d_u
- p. 11, Line 260: there seem to be some \in missing
Experimental design: The experimental design considers all relevant parameters and explores meaningful scenarios. A meaningful comparison to other algorithms is conducted. It is mainly well explained but for minor two aspects:
i) the number of runs is not given
ii) the part on the probability pi is not clear: first it is set to 1, then it is said that it follows a zipf distribution…please clarify
Validity of the findings: The conclusions presented by the authors are well justified and provide an in-depth comparison of multiple algorithms. Overall, the validation is one of the strength of the paper. The only aspect that needs to be improved is that the authors do not present statistical tests, standard deviations, or confidence intervals to show that the results are indeed statistical significant. Such an analysis has to be added to the revised version.
Additional comments: I liked the idea of the algorithms but I was wondering if there are not other criteria that play a similar important role as diameter and degree, such as i) resilience to node failures, ii) load balancing. Your algorithm might result in edges that distribute content for many topics. The limited bandwidth of these edges might delay dissemination.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LOW-DIAMETER TOPIC-BASED PUB/SUB OVERLAY NETWORK CONSTRUCTION WITH MINIMUM–MAXIMUM NODE DEGREE
Review round: 1
Reviewer: 3

Output:
Basic reporting: The english is ok, literature references are ok as well. The article structure is mainly ok, but the authors also explain in length five different examples on topologies generated by their algorithm, which is too much. As a result the paper looks a bit unbalanced.
The experimental results are displayed in tables only. Graphs would probably be simpler to read.
Experimental design: See general comments.
Validity of the findings: See general comments.
Additional comments: The paper proposes a novel centralized algorithm to construct low-diameter topologies intended for publish-subscribe content distribution. This is definitely an interesting topic.

However, I am not really confident about this paper. First of all, the proposed algorithm is not presented in sufficient detail. The authors put the actual algorithm, but the algorithm itself is not explained in the text. As a result, the algorithm as main contribution is introduced on a bit more than one page. Instead the authors give in total 5 examples on topologies produced by their algorithm when some nodes and their subscriptions are given as input. That is for sure too much. When the algorithm is explained properly, one or two examples should actually be enough and the authors would also save a lot of space.
Furthermore, the paper could benefit from a bit more theoretical foundation. A complexity analysis would be interesting here. Moreover, the whole problem could be formalized as optimization problem, which probably someone in related work already did. However, this optimization problem could also serve as a benchmark on how close the proposed algorithm actually can get to the optimum (for small node numbers at least).

On the positive side, it has to be mentioned that the authors compare their approach to several approaches from related work. Nevertheless, none of these approaches seems to be contained in the related work section of the paper. I would recommend to introduce and explain them shortly.

Regarding the experimental results I am not sure if the metrics used by the authors (average and maximum node degree) give the complete picture here. What about the number of edges? What about additional metrics that quantify the load on single nodes, e.g., centrality?

Minor comments:
---------------
* page 4: you can refer to lines in the algorithm from the text to explain what it actually does
* page 5: "with a lower node degree du" -> du? is that notation or do you mean "do"?
* page 10: "all three existing CD algorithms (CD-ODA, CD-ODA, CD-ODA, and 2d-ODA)" -> that is three times the same algorithm and in total 4 instead of 3.
* Page 10,¸ Figure 10: "To illustrate, node u that is ..." -> where is node u in Figure 10?
* Page 11: what does a probability pi of 1 mean? 100% probability??? probably not. Please explain.
* Page 11: "Each node n N" -> do you mean "n \in N" (n element of N)?, same for "t T"
* Page 11: All subsection heading in the result section start with the misleading term "Fixed Average and Maximum Node Degree". I understood that your approach generates low-diameter topologies and tries to minimize the maximum node degree, but how can the maximum degree then be static? In your results you again refer to results as CD-MAX and this values differ depending on the node size.
* Page 11: What is the diameter that your algorithm tries to reach here? 2? Not mentioned.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LOW-DIAMETER TOPIC-BASED PUB/SUB OVERLAY NETWORK CONSTRUCTION WITH MINIMUM–MAXIMUM NODE DEGREE
Review round: 2
Reviewer: 1

Output:
Basic reporting: The updates section require more proof-reading, e.g.:

- L45: Pub/sub system *have* a variety of use cases.
- L65: *A* constructed..
- L73:
    1. What exactly do you mean by "decoupling"
    2. I'm sure one can find examples where decoupling is not advantageous
    3. Either "systems with decoupling mechanism*s*" or "systems with *a* decoupling mechanism"
- L152: Why not use $(u,v)\in E$ instead of introducing $e(u,v)$?
- L182: *The* selected node...
- L184: ..., *the* pub/sub overlay network
- L303: ...which *are* listed in...
- L329:
    - Tables 5-12 *have* confidence values...
    - ...results are calculated as**:**
Experimental design: Please give some more intuition on why CD-MAX-Ref behaves so different from anything else in Figures 14 and 15
Validity of the findings: no comment
Additional comments: Thank you for your updated manuscript and taking the time to implement my previous comments.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: LOW-DIAMETER TOPIC-BASED PUB/SUB OVERLAY NETWORK CONSTRUCTION WITH MINIMUM–MAXIMUM NODE DEGREE
Review round: 2
Reviewer: 2

Output:
Basic reporting: The new version is greatly improved.

However, I still believe that the correctness (in the sense of all nodes receiving messages for their topics) of Algorithm 1 is not addressed properly. The main reason stated for correctness is "Unless there is a topic which is subscribed by a single node, pub/sub overlay network is connected." but you don't i) prove that it is connected, and ii) explain exactly why connected guarantees that nodes receive all messages. I don't even understand why the statement is correct, as the PubSub overlay should not be connected if e.g., there are two topics each with three distinct parties interested.
I think your algorithm achieves what you want, but I think you are not using the proper argument here.

Similarly, you start the complexity proof in Line 216 with 'Finding a node with minimum node degree takes O(|V |2 ∗ |T |)' without giving an explanation on why that is the case.

Only remaining minor issues are:
- p.2 can you add a references that PubSub is heavily used in cloud computing systems/that the named systems use PubSub
- Line 77: Not only do β -> does
- Algorithm 1: you are still using Max_n as both a set and a number...it can only be one, you probably need two variables here
- Line 10 of Algorithm 1:  u⇐w∈Maxn AND w has the largest du. -> it's not clear from which set of nodes w has the largest d_u (and shouldn't it be d_w?), is it all nodes or only those in Max_n]
Experimental design: Concerns appropriately addressed.
Validity of the findings: Concerns appropriately addressed.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COLLECTIVE VIEW: MAPPING SARGASSUM DISTRIBUTION ALONG BEACHES
Review round: 1
Reviewer: 1

Output:
Basic reporting: "Diadema antillarum" on line 48 should be italicized because it is a scientific name.

The reference to "Rodriguez-Martinez et al." on line 52 should be cited to a specific source; there are a number of citations matching that description. Also, consistent use of í should be used.

"open data" should not be hyphenated when used as a noun as it is on line 62

Line 68, "However, the large presence of clouds in the region is an issue that causes false positives" should probably be rewritten as, "However, the frequent presence of clouds in the region can cause false positives." The largeness of the presence (assuming that makes sense) probably isn't the problem so much as the temporal frequency of said clouds. If size of clouds is an issue, perhaps, "However, the frequency and spatial extent of clouds in the region can cause false positives"

The authors have not consistently capitalized several terms, including "citizen science," "crowdsourcing," and "crowd-mapping" A single approach should be chosen and followed.

Line 91: the "content areas" do not need to be capitalized as they are not proper nouns or specialized terms.

Line 98: since the authors mentioned the managing institution for Epicollect, they should do the same for iNaturalist, which is managed by the California Academy of Sciences and the National Geographic Society.

Line 101: the fact that iNaturalist is "open-sourced," i.e. that the source code for its software is publicly accessible and licensed for re-use at no cost, is not much of an advantage to scientists. Perhaps it would be more accurate to say that iNaturalist data is openly accessible on the Web and that much of it is licensed for re-use or free of intellectual property restrictions.

Line 106: "Using this approach can become complicated unless a way to process the images automatically is found." This is vague and the paragraph it leads does not resolve that vagueness. What is it about crowdsourcing image collection or classification that is complicated, and/or how do the unique properties of this process complicate the use of its outputs? What kind of processing are the authors referring to?

Lines 152-156: references to specific projects on Epicollect, iNaturalist, and Naturalista should be cited or at least linked to in footnotes

Lines 158-159: what is image "diversity" and why is it beneficial to Sargassum monitoring? Similarly, why is a "constant flow of photographs" important for monitoring, how constant does the flow need to be to be useful for monitoring, and how does the flow of images on crowdsourcing platforms compare to constancy of flow that would be useful?

Line 183: if this paper purports to describe the entire Collective View platform as a methodology for generating real-time Sargassum maps, the Data Collection section needs to go into more detail about the design of the mobile app, the training users received in how and what to collect, information about the abilities and motivations of the people who were involved in outreach, etc. Even if the photographers didn't provide any classification data (e.g. marking photos as containing Sargassum or not), they might still have been biased by being asked to look for Sargassum, as opposed to being asked to photograph any marine algae, or indeed any beach under any conditions to provide meaningful absence data. If outreach was focused on tourist hotels and their clients, then the training data will have a spatial bias toward those places and any resulting models might fail to detect Sargassum in less popular areas or beaches predominantly used by locals. Without this kind of information it seems impossible to assess the quality of the training data.

Line 225: Google Images (https://images.google.com/) is not a primary source of photos. While it's useful to know it was used to find images, it says nothing about the provenance of those images or the reliability of any metadata data contained in those images. The authors should probably cite the actual sources of the images, perhaps by URL in the supplementary materials, or better yet, include the images themselves in the supplementary materials.

Line 231: how was the presence or absence of Sargassum determined in these images? Manually or automatically? By the authors, graduate students, Mechanical Turk workers? If photographers weren't contributing to image classification, why did the authors build a separate mobile application when there are any number of existing applications they could have used to collect geotagged images?

Line 240: these images should be included in the supplementary materials and referenced here.
Experimental design: Since this is more of a methods paper there isn't really an experiment being conducted. I lack the expertise to comment on the machine learning methodology, but any description of the data generation process seems completely absent aside from the the fact that the authors built an Android app and asked some people to use it. If this paper was intended to address only the ML aspects of the project, I think that's appropriate, but the scope of the paper should be narrowed to reflect that.

The tools used to build the platform also go without mention, even though it seems like the authors used ESRI products for data storage and analysis, given the inclusion of https://arcg.is/1STq0C in a footnote. Replication of the full platform would be impossible without these kinds of details.

The supplemental materials do not contain enough data to replicate the results included in the manuscript. One reason is that there is no description of the required software dependencies and their versions. The authors could address this by including a requirements.txt file exported from pip and including the versions (see https://pip.pypa.io/en/stable/user_guide/#repeatability). The Python notebook in the supplemental materials also references a file ("/content/drive/MyDrive/datasets/Sargazo/sargassum_dataset.zip") that is not included with the materials, making replication impossible. Furthermore, there are references to other resources that are not included in the archive, including "/content/drive/My Drive/GIS LATAM 2020/vgg16_cm.eps" and "https://www.elpuntosobrelai.com/wp-content/uploads/2018/10/sargazo-majahual.jpeg". Resources on the authors' computers are not accessible to readers, and even web resources are not guaranteed to exist in perpetuity.
Validity of the findings: I have no reason to believe the model performance attributes reported are inaccurate. However, regarding the ultimate utility of the proposed solution to the problem of real-time Sargassum monitoring, I think the authors will have to go to greater lengths to describe their process of data generation, its biases, and how data quality was ensured and assessed. This information has serious bearing on the utility and even the performance of any machine learning models derived from such data.
Additional comments: I think you've described an interesting application of machine learning technology in natural resource management, but given that this paper focuses almost exclusively on the ML part of that process I think you should narrow the scope of the work to only that aspect and defer most of the project description and background to a more comprehensive future methodological description.

Aside from the issues of training data quality and bias I've already mentioned, it seems to me that the success of such a platform depends on keeping volunteers motivated over the time span required, and/or how to maintain recruitment of new volunteers to compensate for the loss of existing ones. These are sociological questions more than computer science ones, but they are just as important to the success of this project as the technical feasibility of ML-based maps.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COLLECTIVE VIEW: MAPPING SARGASSUM DISTRIBUTION ALONG BEACHES
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: I think the experimental design was sufficient.
Validity of the findings: I believe the authors convinced me that AlexNet was indeed the best neural network to train the sargassum images.
Additional comments: It was a pleasure to read the manuscript “Collective View: Mapping Sargassum distribution along beaches”. This was an interesting manuscript aimed at environmental monitoring of sargassum on beaches. The authors used three different neural networks to classify images with and without sargassum and found that AlexNet was the most productive in terms of accuracy and loss. The results are convincing. And I believe that crowdsourcing/citizen science will undoubtedly form an important role in future environmental monitoring, which the authors help to demonstrate here. I believe this Collective View program is in its infancy. Is it still running? Are the number of submissions still increasing? Or was this a one off? Some more information about the crowdsourcing part of this manuscript will help. For example, I interpreted Figure 1 and parts of the introduction that this Collective View program was continuously updated as this is where the maximal benefit in environmental monitoring will be. But I’m not 100% convinced this is the case, or that it is possible. If the authors can make this clear and fully highlight that Collective View will be a dynamic resource for local policy makers/tourists etc. then I think this can be an important paper! This is highlighted below regarding a major limitation of the current study which is that the authors train all images from August to December – but sargassum presence is dynamic in space and time. Currently, however, the paper is largely about the use of neural networks to train sargassum images. I hope the authors can use these suggestions and improve their manuscript. I provide some further comments below.

Lines 30-59: I actually would recommend switching the order of the first two paragraphs to start broadly about sargassum as an ‘issue’ and the economic damage it can cause etc. Then switch to a more specific case about sargassum relevant to the Mexican coast etc. I guess I just recommend starting a bit broader and then narrowing in.

Lines 81-82: What is the resolution of these sensors? I’d list it here and then add a sentence demonstrating why we need more fine-scale resolution – which is what you are attempting here.

Lines 106-115: I’d consider cutting this paragraph. Or at least trim it and integrate it with the paragraph below on ‘maps’. You repeat a fair bit of this below in the paragraph starting on line 122.

Line 163: So here it becomes clear that your major advancement is the ability to make maps and monitor Sargassum at the ‘beach scale’. I think this is a good goal! But, I think a couple sentences here highlighting why at the beach scale is important would help to highlight why your work is indeed an advancement.

Line 164: I’d suggest a paragraph break at “This study proposes…”

Lines 181-182: When the authors say that each of the components were used in different stages it implies to me that they are not continuously being updated. But my interpretation is that users can continue to upload photographs and then this leads to map generation in a dynamic and updated fashion in near real-time. Unless I am misinterpreting something? Assuming I am right, maybe just a bit different wording here to highlight this. Also, could somehow switch Figure 1 to a circle or have a ‘feedback’ arrow from map generation to data collection to highlight the updated fashion of your project!

Line 205: Used by citizens and tourists for what purpose? This is relevant to a previous comment above and this should be just a little bit more explicit in the introduction.

Line 206: It would be interesting to see a graph showing the accumulation of images over time. Is it still increasing? Was there a big spike in the beginning and then quickly levelled off? Maybe as a supplementary figure or something?

Line 222: I’m a little confused as to why there were so many images not geotagged? If they were taken with a smartphone shouldn’t they have been automatically geotagged?

Line 231: This is the result of someone manually looking at them all?

Line 246: This whole section could be improved if some references are added throughout here and there.

Line 402-403: Is there a reference for this?

Line 422: This is probably my most substantial comment. How does the presence of sargassum change through time? Presumably the quantity of sargassum can wax and wane through time, especially over a long period as that from August to December? This is pointed out by the authors here. Can your collective view program account for this? How so? I think more details are needed here. This is made clear in Figure 15 where you have present and absent photos pretty much ‘on top’ of one another. Presumably these photos were taken at different times? Can the authors attempt to use smaller subsets of their data and fit through AlexNet, say for each month? Would this show the presence/absence of sargrassum through time?

Line 438: I’m curious how many photos there are now!

I really liked all the figures and thought they did a great job of conveying the relevant information.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COLLECTIVE VIEW: MAPPING SARGASSUM DISTRIBUTION ALONG BEACHES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Line 50: "Rodríguez-Martínez et al., 2020" does not need parentheses when directly referenced in the sentence.

Lines 66-67: "Outlook of 2020" should be cited

Lines 119-121: "The main advantages of this platform are the data is openly accessible on the Web and that much of it is licensed for re-use or free of intellectual property restrictions." This is missing the word "that" between "are" and "the."

Lines 134-138: While the revision emphasizes the subjective nature of the authors' choice not to use existing crowdsourcing tools, it does not adequately justify that choice, in my opinion. What about these existing platforms makes it "difficult to automate the acquisition, storage, and processing of images"? Do they lack application programming interfaces that would enable data extraction? Are they not translated into the languages that your expected data contributors speak? Do they make data available in a format that was unfamiliar to the authors or whoever built the custom tools for this project?

Lines 186-191: Again, terms like "diversity" and "attributes" are not defined in context to the point that a scientist in another field would understand what they mean. I assume that the authors mean "diversity" as in the diversity of visual attributes like angle, background elements, lighting, lens distortion, etc. that would make an ML model robust enough to classify input images with a high degree of variance in these attributes, but that needs to be explained.
Experimental design: The requirements.txt that the authors included (cs-55549-requirements.txt in the supplementary materials) is not usable to install the dependencies required to run their code because it still contains numerous references to files on the authors' computers as opposed to simply naming packages and versions. For example, the first line is `anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1610315727443/work/dist`. In addition to referencing a directory hierarchy that is specific to the authors' computers, it doesn't reference a version. These should be re-generated so a line like this would appear as `anyio==2.1.0`.

Since the authors mentioned they had intended their notebook to be run in Google Colab, I tried uploading the .ipynb file there to see if it would run, but it errored out on this line:

zip_ref = zipfile.ZipFile("./data/sargassum_dataset.zip", 'r')

The file "./data/sargassum_dataset.zip" was not included with the supplementary materials so this code also will not run. I see the URL to this zip on figshare was included in the caption to Figure 3, and using that file in Google Colab I can get the notebook to run, but that is not where I would have expected to find the data required to run the code. In my opinion, it should all be included with the supplementary materials.

I would highly recommend that the authors try to run their own code on a fresh machine using only the materials provided with the paper to ensure all required software and data are accessible and referenced correctly.
Validity of the findings: no comment
Additional comments: I am still not seeing an explanation of how participants were acquired or trained and how that acquisition and other aspects of participant behavior may have biased the training data. You did a good job of explaining the limitations of a crowdsourcing approach due to these kinds of bias in your rebuttal, but I wish you had included that in the paper as well.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COLLECTIVE VIEW: MAPPING SARGASSUM DISTRIBUTION ALONG BEACHES
Review round: 2
Reviewer: 2

Output:
Basic reporting: N/A
Experimental design: N/A
Validity of the findings: N/A
Additional comments: I think the authors have done a good job in strengthening their manuscript and addressing comments from both Reviewers. It sounds like a cool project and a good example of interdisplinary research to help a fundamental problem in many coastal parts of the world. Good luck in growing the project and keeping it afloat.

All the best.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PROFILING SLAS FOR CLOUD SYSTEM INFRASTRUCTURES AND USER INTERACTIONS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The writing and structure must be improved
Experimental design: Introduction seems too long , it must be shortened and concised with relevant studies only.
It is very hard to find the novelty or any scientific contribution in the paper, please justify it and provide a separate section.
Validity of the findings: Which testbed you have used for cloud configuration?
It would be nice if you can provide validation and state of the art comparison
Please provide the link of your tool
Cloud configuration performance and analysis is more theoretical and boring
It is suggested to add more case studies to justify the study
There are many cited online references, I suggest to reduce the citation of online references, if unavoidable then please give access time/data with them. E.g. Last access on 07-December-2020.
Figures 13 , 14 are not readable, please provide clear figures.
Additional comments: please revise carefully according to my comments above


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PROFILING SLAS FOR CLOUD SYSTEM INFRASTRUCTURES AND USER INTERACTIONS
Review round: 1
Reviewer: 2

Output:
Basic reporting: This study attempts to investigate the role of SLAs on IaaS. In addition, this study also attempts to understand the benefits afforded to SLA and clouds, provide conceptual insights into it, and test the proposed model. In general, the paper was even well written and interesting, but the logic does not flow well and needs to supplement or enrich.
Experimental design: The results part needs to provide more information to audiences, not just pronounce the significant coefficients in tables. Besides, the simulation results need to show in some other figures, and the causal relationship need to explain systematically and under your inferences and hypotheses. Authors should explain it with a standalone paragraph and tell us how to test the mediation effect.
Validity of the findings: It seems valid.
Additional comments: *       With respect to writing style, the entire paper strongly requires to be reviewed by a native English speaker. There many grammatical issues, such as missing articles, missing verbs, and mixed-up singulars/plurals.
*       The motivation of the work is not clearly presented especially in the abstract. They should better motivate their work so that they could be able to position the work to the concurrent solutions.
*There are some style and spelling mistakes. Some sentences are repeated with almost the same words.
* Font sizes need to be consistent.
* There is no clear indication of what the model is.
* The implications of the study should be rearranged according to the implication of theory, methods, and practice. Thus, it would be easy for the readers to see the volume of the contribution of the paper. It should explain how this study departs from previous research particularly in terms of new knowledge and theory.
*The explanation of the data set is superficial and needs to put more explanation.
* The methodology is simplistic and poorly documented.
* The Abstract is absolutely not informative.
* The literature is not critically written and more towards a positive tone. Contradiction stands need to be synthesized as well. Or else the literature review is just merely reporting, It needs to be more analytical, decisive, and avoid a positive tone all the way. Tabulate the literature to enable the readers to see the volume of works done in this area to date.
* Also the motivational approach of cloud and SLA would be important to integrate shortly into the introduction and related works. Just a few studies to mention:
Cloud service composition using an inverted ant colony optimisation algorithm. 13(4), 257-268.
A new genus of oryzomyine rodents (Cricetidae, Sigmodontinae) with three new species from montane cloud forests, western Andean cordillera of Colombia and Ecuador. 8, e10247.
Human resources ranking in a cloud-based knowledge sharing framework using the quality control criteria.
Epiphytic bryophyte biomass estimation on tree trunks and upscaling in tropical montane cloud forests. 8, e9351.
SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. 7, 9490-9500.
A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. 10(5), 1851-1864.
SCelVis: exploratory single cell data analysis on the desktop and in the cloud. 8, e8607.
Collaborative SLA and reputation-based trust management in cloud federations. 100, 498-512.
A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm. 33(5), e4259.
Enforcing trustworthy cloud SLA with witnesses: A game theory–based model using smart contracts. e5511.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PROFILING SLAS FOR CLOUD SYSTEM INFRASTRUCTURES AND USER INTERACTIONS
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comments
Experimental design: no comments
Validity of the findings: no comments
Additional comments: Good luck


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A NOVEL NO-SENSORS 3D MODEL RECONSTRUCTION FROM MONOCULAR VIDEO FRAMES FOR A DYNAMIC ENVIRONMENT
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors need to clearly state their motivation, goal and contributions at least in the abstract and the introduction section of the paper.

Literature review must be improved as it lacks details and in-depth comparison to other state of the art techniques and applications,e.g.: https://www.mdpi.com/1424-8220/19/7/1553
https://doi.org/10.2991/ijcis.d.190617.001
https://doi.org/10.1109/IECON43393.2020.9255086
https://doi.org/10.1109/JTEHM.2019.2946802
https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-018-0253-2

Rewrite adding a section on each item, how it works, how your solutions differs, what has been achieved, end with how your goals correlate.

Novelty should be better explained as similar solutions do exist:
https://openaccess.thecvf.com/content_CVPR_2019/html/Ku_Monocular_3D_Object_Detection_Leveraging_Accurate_Proposals_and_Shape_Reconstruction_CVPR_2019_paper.html
https://doi.org/10.1109/TVCG.2020.3023634
https://dl.acm.org/doi/10.1145/3414685.3417877
https://link.springer.com/article/10.1007/s00371-020-01908-3

Figure 9 is very low res and hard to see.

Figure 3 should be replaced with activity diagram. Add all configuration parameters.

Get rid of trivial text on the methods, a link to original source is enough. Your reader will be familiar as the approach is not unheard.
Experimental design: The experiments should be contextualized better (the reader should not be left to assume that they will get their own conclusions). The experiments should be described more clearly (e.g. set up and carry out process, results in raw format, etc.). How was the accuracy of the model evaluated? By what metrics? Expert knowledge? Add full and rigorous statistical reliability analysis proving that recreated model is accurate. Add full performance metrics.
Validity of the findings: Article contains no direct comparison to other works with KITTI dataset (and there has been quite a few)

Code attached does not work.

Conclusions are quite generic. Focus on the results only. Move the rest to discussions.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A NOVEL NO-SENSORS 3D MODEL RECONSTRUCTION FROM MONOCULAR VIDEO FRAMES FOR A DYNAMIC ENVIRONMENT
Review round: 1
Reviewer: 2

Output:
Basic reporting: Article proposes a two-tier depth estimation and pointcloud reconstruction capable of rebuilding occluded data from a complex scene. Where depth, camera pose and egomotion is estimated using monocular RGB camera videos, with the resulting depth recreating the entire scene, including occlusions, using the imperfect depth prediction.
Experimental design: Experiments are valid.
Validity of the findings: Results seem to be valid. The use of additional metrics, addition of confidence would improve evaluation of findings.
Additional comments: 1. Improper formatting of graphs 4, 6, 7, 8; unlabeled axes; units; error bars.
    2. Line 349 “Thus” → “This”
    3. Dataset split is not defined, what percentage of the dataset is used as training, testing, validation.
    4. Because live recording of a  camera feed is used. What are real-world applications of the framework, how viable in terms of performance (FPS) is it?
    5. In experimental results you specify input frames as 416x128, which corresponds to 50000 points in pointcloud. Assumptions are made that this is the reconstructed pointcloud density. However, this is the first time it is mentioned. Specify it earlier as it is important to understand this information when the method is being described.
    6. Euler distance as pointcloud estimation is used. While it is valid estimation metric in the specific application case that the article uses, Chamfer Distance and Earth Movers Distance are more widely used metrics when dealing with pointclouds. Adding comparisons using those metrics would allow researches working in the field more easily evaluate your results.
    7. Hard to tell what the reader is looking at in figure 5, some visual aids would be helpful to parse the visuals.
    8. Substantiate your conclusion assertions using experimental results.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A NOVEL NO-SENSORS 3D MODEL RECONSTRUCTION FROM MONOCULAR VIDEO FRAMES FOR A DYNAMIC ENVIRONMENT
Review round: 2
Reviewer: 1

Output:
Basic reporting: -
Experimental design: -
Validity of the findings: -
Additional comments: Most of my remarks have not been taken very seriously and the article was changed only slightly, thus the original negative aspects still remain and I cannot recommend processing it further.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A NOVEL NO-SENSORS 3D MODEL RECONSTRUCTION FROM MONOCULAR VIDEO FRAMES FOR A DYNAMIC ENVIRONMENT
Review round: 2
Reviewer: 2

Output:
Basic reporting: Article proposes a two-tier depth estimation and pointcloud reconstruction capable of rebuilding occluded data from a complex scene. Where depth, camera pose and egomotion is estimated using monocular RGB camera videos, with the resulting depth recreating the entire scene, including occlusions, using the imperfect depth prediction.
Experimental design: Experiments are valid.
Validity of the findings: Findings are valid and apt for publication.
Additional comments: Authors seem to have addressed all issues raised in previous review. No new issues have been found. Article is ready to be published.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A NOVEL NO-SENSORS 3D MODEL RECONSTRUCTION FROM MONOCULAR VIDEO FRAMES FOR A DYNAMIC ENVIRONMENT
Review round: 3
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: My review remarks have been addressed, therefor I can recommend to accept this paper.
Some English grammar and style issues remain so I would recommend getting a native speaker to proofread the paper (I've marked the review as minor - no need to send me for review)


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: FUSION OF TEXT AND GRAPH INFORMATION FOR MACHINE LEARNING PROBLEMS ON NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. The writing should be improved.  I suggest the authors edit the incomplete sentence at lines 313-314 "One can that the results are quite similar to ...". Meanwhile, there are many complex sentences where adding punctuations can make it clear. For example, the sentence at lines 254-256 "After that in order to obtain embeddings for sentences ...", and the sentence at lines 268-269 "After embeddings for all of the nodes are learned ...".  There are still some minor issues, e.g., the first word of a sentence should be capitalized, as shown at lines 259 and 264.

2. The paper is not well-organized and the presentation should be improved. The authors use a very long section with lots of tables to explain the results of node classification and link classification in different settings. Readers may easily be overwhelmed by those tables and text. It would be better to provide some figures to make the comparison result clearer and digestible.

3. Insufficient field background. The literature introduction in Section 2 seems to be weak. For instance, the authors only introduce the algorithms being evaluated in their study and ignore some of the more recent and popular graph neural network models, such as GraphSage [1], GAT [2]. The authors are expected to provide detailed background knowledge about the network and text embedding methods and provide reasons for the model chosen in the experiment. Meanwhile, some studies are focusing on fusing network and text information, e.g., ERNIE[3], Glean[4]. It would be beneficial if the author could include some advanced fusion models when introducing the background.

[1] Hamilton, Will, Zhitao Ying, and Jure Leskovec. "Inductive representation learning on large graphs." Advances in neural information processing systems. 2017.
[2] Veličković, Petar, et al. "Graph attention networks." arXiv preprint arXiv:1710.10903 (2017).
[3] Zhang, Zhengyan, et al. "ERNIE: Enhanced language representation with informative entities." arXiv preprint arXiv:1905.07129 (2019).
[4] Deng, Songgaojun, Huzefa Rangwala, and Yue Ning. "Dynamic Knowledge Graph based Multi-Event Forecasting." Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This work is an interesting empirical study that compares different fusion methods of network and text embeddings for machine learning tasks on graphs. The experimental settings are clearly provided, and the results seem to be detailed.