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: COMPARATIVE INVESTIGATION OF PARALLEL SPATIAL INTERPOLATION ALGORITHMS FOR BUILDING LARGE-SCALE DIGITAL ELEVATION MODELS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Some English syntax and grammar problems remain in this version, but many fewer than in the first version. Literature references are much more consistently formatted. Reference and citation list appear to have been edited to correct earlier inconsistencies, and to cite correct original authors, effectively reducing their self-citation problems from the first version. The literature review has also been expanded to provide more informative details about relevant literature. Some tables and figures are not called tables /figures correctly, and one section of pseudocode is called a figure (Figure 1 on page 6).
Experimental design: I suggest  justifying the choice of the three tested algorithsms in the Introductory section – why these three and not others? I suggest also spelling out the algorithm names fully in the main text, not just in the abstract, for a smoother flow of text for readers. Additionally, begin each algorithm description in the Background section with a 1-2 sentence description in lay terms of how it works.  For example, the Radial Basis Functions operate as a spline, essentially fitting a series of piecewise surfaces to approximate a complex terrain. And so forth. Diving straight into the mathematics might seem impressive, but it’s more helpful to briefly explain in lay terms to those readers trying to understand concepts as well as mechanics.

Thanks for additional information on the source of the DEM data. But the legends in Figure 3 are still confusing in that the same color ramp is applied to 48 m range for one DEM and the 5800 m range in the second DEM. This implies that a very flat landscape is somehow comparable to a very mountainous terrain. And the caption is incorrect, in that both figures are 2.5D (not 3D). I think what the authors are trying to show is one DEM with uniformly high frequency detail, and a second DEM with a mix of low frequency and medium frequency detail (a river network). The inclusion of the hillshade still seems to invert the terrain, giving a visual impression that the river channels are the highest elevations.  Or is the legend still inverted? One way to avoid this would be to use a less saturated color ramp, or monochrome ramp.
Validity of the findings: Thanks for giving more detail on the NRMSE metric. And on the regular and irregular sampling in flat and rugged terrain.  But while these differences could be readily quantified, the authors have not yet taken that simple step, which would really strengthen the paper.

Line 281 on page 10 should refer to Table 4, not Figure 4.  And a question, how do the authors establish that point density is uniformly or non-uniformly distributed?  Was a Nearest Neighbor analysis run, or Moran’s I, and if so could those results be described briefly?  It’s interesting that interpolation accuracies differ depending on the density, and I expect that terrain uniformity also plays a role , based on my own research, but this isn’t mentioned, even though the two DEMS differ most significantly in uniformity and frequency of detail and the results shown seem to highlight regular and irregular point distribution. So quantitatively speaking, how regular or irregular are the two distributions?

Table 9 compares NRMSE values but here again I don’t see units.  And 10 to minus 5th meters (sorry, could not format  as an equation on the journal website) would be hundredths of a millimeter, which seems mighty small magnitudes for such large DEMs. The findings need to be clarified as to whether the NRMSEs summarize entire DEMs, or some focal window that is averaged, or… ?
Additional comments: You have responded to many of my comments, and the paper is improved.  My major concern with your methods at  this point is that you never quantitatively determine regular and irregular point sampling, but could do so easily.  Given that this has become a primary highlight of your revised analysis, that quantification becomes warranted.


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: COMPARATIVE INVESTIGATION OF PARALLEL SPATIAL INTERPOLATION ALGORITHMS FOR BUILDING LARGE-SCALE DIGITAL ELEVATION MODELS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The English grammar needs some improvements. Improper sentence structure is used in several places. Multiple statements are included in single sentences and these need to be divided into additional sentences.

Definitions are presented well, but need a few suggested adjustments.

Some methods are presented in the discussion section. This should be revised.

Much detail is presented in the results, but too much information is provided. Results need to be more concisely presented. Ranges in y-axis of several side-by-side charts are different and it is not mentioned in the text. It may be best to show these charts with the same y-axis range.
Experimental design: no comment
Validity of the findings: Some refinements are needed in results statements and conclusions.
Additional comments: This paper presents some interesting results, but there is much redundancy in the presentation of the results. Therefore, it is very difficult to read this paper. The introduction should be revised to be more concise. The methods section is fairly well presented. The results section must be reduced to just the most important results. Accuracy should be presented in tables and charts but then only summarized in the text once. The discussion section presents the most important results, but it also describes some methods.  I suggest this paper be rewritten to make the result section much more concise. Describe the best solution for each condition once, and indicate how much better the solution is than the other methods. Present processing time of the methods and indicate the speed up of each of the parallel implementations. Overall, the audience will want to know what is the best interpolation method for each terrain type and each point distribution. Processing speed of the interpolation methods is not as important as accuracy estimates when large variations in accuracy are found. It is good to provide an estimate of the needed time for processing, along with how parallel implementations can improve processing speed. However, it seems too much space is used to evaluate speed-up times when the ranges of improved speeds could be easily summarized in a single table or chart. Some suggestions for needed improvements are listed below.

The paragraphs in the introduction are not well organized.

First paragraph redundantly uses ‘fine resolution’. Content in this paragraph could be condensed.

Paragraph 2 indicates a variety of spatial interpolation algorithms exist that behave differently for different data configurations and landscape conditions. Consequently, the accuracy of a DEM is sensitive to the interpolation technique, and it is important to understand how the various algorithms affect a DEM. Therefore, this study is being conducted.

The authors mention two types of interpolation methods: exact and approximate. It may be good to coordinate the introduction to describe and compare the exact methods, and then describe and compare the approximate methods. Subsequently, the test algorithms could be described as exact and approximate. It is unclear if the tested methods are exact or approximate. If exact and approximate distinctions are not important, then they should not be included in the introduction.

Need an ‘and’ at the end of line 111.

Line 184, kNN should be defined in this line, which is the first occurrence of kNN.  That is, k-Nearest Neighbor (kNN), and then kNN can just be used in line 195 and subsequent places.

Line 217: need period (.) after were selected. Start new sentence with ‘The topography…’.

Line 226: It is not clear that importing the sample points into the DEM provides the z values that are used for interpolation. I suggest changing line 226 to ‘algorithm in the square region S, and then accessing the corresponding z coordinates from the DEM.’

Likewise, to improve clarity, line 239 should be changed to ‘point is obtained by accessing the z value of the DEM at the associated x and y coordinates.’  I expect the DEM z-values at the mesh points are used as control for testing the accuracy of the interpolated z values. If this is true, then it should be clearly stated as the last sentence of the paragraph.

In lines 240-243, why are the coordinate values too large and transformed into a local coordinate system?

Results lines 270-293: The range of the y-axis for the regularly distributed interpolation points in figures 4 and 5 is less than 1/3 of the range of the y-axis for the irregularly distributed interpolation points. Thus, the regular distribution provides a more accurate solution for both the rugged and the flat areas. This is not pointed out in the results discussion. This needs to be pointed out. Possibly, the charts could use the same range on the y-axis to better accentuate this fact.

Line 302: should be ‘computational time from slowest to fastest is: …’

It would be easier to interpret the speedups if Tables 6, 7, and 8 were presented in charts.

Line 310: need a period (.) after (lower speedup).

Sentence starting in line 342 is not relevant. Original MLS has best accuracy in the rugged zone with irregularly distributed interpolation points.

Line 400, should start ‘A small number of larger’. Remove “It that”. Also put period after ‘interpolation accuracy,” and start new sentence.

Lines 414-417. Statemen is true, except for the kNNRBF, which seems to take the same amount of time for regular and irregular distributed points.

Processing time is important. General estimate of speedup time is important, but would not be major concern in deciding processing method. So figures 9, 10, and 11 are not important.


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: RECORD LINKAGE OF BANKS AND MUNICIPALITIES THROUGH MULTIPLE CRITERIA AND NEURAL NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper analyzes several strategies for linking records in different dataset. The paper is well presented, the experimental methodology is appropriate, and the obtained results are interesting.
Experimental design: The experimental design is appropiate and the obtained results are interesting and significant.
Validity of the findings: From my point of view the main contributions are:
- Good state of the art
- Interesting analysis of several strategies comparing between them
- Good results with the architecture proposed.
Additional comments: This paper analyzes several strategies for linking records in different dataset. The paper is well presented, the experimental methodology is appropriate, and the obtained results are interesting.
My comments to improve the paper are:
-	The background section is very interesting and complete. Regarding the related work, my suggestion would be to include more references from the last 2-3 years. Some of them are very all.
-	About the data description, I’d like to ask if this data is available and where?
-	Line 167: All figure of merits -> All figures of merit??
-	When describing the MLP, what initialization have you used?
-	For the experiments you have used precision and recall. I’d suggest using (f_measure as in the annex) that combines both and the comparison is easier.
-	I’d like more explanation about table 3. What is column #? How did you compute the confidence intervals? You said these number are variability but how is it computed?


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: RECORD LINKAGE OF BANKS AND MUNICIPALITIES THROUGH MULTIPLE CRITERIA AND NEURAL NETWORKS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Generally good English, with good citations to relevant sources.

Here are minor updates that should be made or considered:

26, 28 – The word “quest” seems awkward. Consider using “process of identifying” instead.

35 – “multi-nationality grow” => “number of nationalities grows”

35 – Consider replacing the semicolon (;) with a period, and starting a new sentence.

46 – Instead of “inhomogeneous”, you could use “heterogeneous”

52 – add commas: “approach, on the contrary, relies…”

55 – By “threshold-based methods”, do you mean “probabilistic methods?” Because a neural network still uses thresholds on its final classification output—it just uses trained weights instead of observed statistics to come up with the value.

85 – I don’t agree that record linkage is solely needed because of “a poor data quality process.” In many cases, different sources gathered different data according to their own needs, and may have done so with very high quality; and yet someone else later decided to combine data from these sources together and try to decide which entities are the same. A health care system will gather more health data; and a banking system will gather more financial data; and a travel system may include passport data. Even if these are all done with high quality, the failure to have the same fields in each system is not necessarily a “quality” problem as much as a natural difference in original intent. Similarly, a female changing her name when she gets married doesn’t constitute a quality problem, but, rather, a natural name variation that occurs over time. I do agree that quality is one factor, but natural differences in the original intent of the data capture is another.

90 “Some of the main issues of administrative data linkage are privacy…” (Add “Some of the” and get rid of “due to”, as that is awkward).

95 – “hold” => “held”

101 – “among which” => “including”

111 – The precision and recall numbers mentioned there are somewhat meaningless without listing the P/R of the probabilistic methods they are being compared to. Finish the sentence with “…compared to x% precision and y% recall of the probabilistic methods.”

139 – “Memorynetworks” => “Memory networks”
141 – classics => classical
143 – uses also => also uses
145 – use => used
146 – which is => which was
147 – show => showed
152 – “each corresponding attributes” => “each corresponding attribute”

191: Figure 2 is never referenced in the text.

200: “as loss function” => “loss function” (don’t need “as” because you have “a” earlier)

202: “the experimental tests” => “experimental tests”

217: Seems like you need spaces between SMOTE and the authors’ names. (“SMOTENCBowyer et al (2011)” => “SMOTE-NC (Bowyer et al., 2011)”)

229: I don’t understand. Do you mean “due to the moderate imbalance, as explained by Christen and Goiser (2007)”?

244: Need a space between your text and the reference (“distanceLevenshtein”)
Experimental design: The experiments seemed reasonably well designed.  The following are simply suggestions that the authors may want to consider either commenting on in the paper, or perhaps updating their experiments (for this paper or in future work).

153: In addition to comparing corresponding attributes with multiple similarity functions, have you considered higher-level combinations of attributes? This may not always make sense, but with some data sources, you can have higher-level features beyond direct attribute comparisons, like when one person has a birth place available in the data and the other doesn’t; but the other person has a residence place but the first doesn’t. Then a feature that says “Do any of the places overlap?” would not use corresponding attributes, but can still be helpful. Just a thought to consider.

158: Is the final goal of the classifier to be used only on pairs within the same geographical area? If so, then this is fine. If not, then there may well be a “hole” in the classifier: People can move from one area to another, and if the classifier is meant to discover those pairs as well, then the training data may fail to cause these cases to work well, and the evaluation data may fail to alert you to this gap.

168: It would be good to look at all cases in the gold standard data where the fiscal code mismatched but the classifier says it’s a match and correct any that are auto-labeled incorrectly. (And vice-versa: Cases where the fiscal code says it’s a match but the classifier disagrees strongly). Correcting your training data takes time, but is usually needed for world-class accuracy.

193: Using string-based similarity metrics on dates is likely not the best thing to do. You should really parse the dates to get year, month and day; and then compare those values. Being a couple ‘characters’ off on the day is less severe than being off on the year, for example. You might consider looking at your data to analyze how often matches and non-matches disagree on dates, and by how much. Are matches sometimes off by a few days? Or are typos the largest source of differences? You can sometimes use a higher-level feature like “Date is within 1 day; within 1 week; within 1 year; greater than 1 year difference” if date differences are possible on matching records, and have each of these features be a neural network weight, rather than having just one feature per attribute.

Similarly, place standardization should tell you that “IT” and “ITALY” are the same place, whereas “AL” and “ITALY” are not, even though both have only a two character overlap.
Validity of the findings: The conclusions were not surprising, but were well stated. I was a little confused at first by the table of counts and results, but I think I eventually realized that the counts and accuracies for each cell in the tables in Figure 4 and beyond were those pairs that were found in the original blocking stage. You might want to state that.
Additional comments: Overall, this seemed like a fairly straightforward application of neural networks to a record linkage problem using traditional multi-pass blocking and simple field comparison features with a list of string comparison functions for field comparison. I would guess that doing actual date and place standardization would improve results, rather than doing generic string-matching on these kinds of fields.


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: RECORD LINKAGE OF BANKS AND MUNICIPALITIES THROUGH MULTIPLE CRITERIA AND NEURAL NETWORKS
Review round: 1
Reviewer: 3

Output:
Basic reporting: Clear, unambiguous, professional English language used throughout.
Experimental design: In lines 23-24, you say your method scored remarkable results compared to threshold-based method
Qs: How does your method perform against the other methods, i.e. the rule-based approach?

In lines 157-158, You say, " databases have been gathered, chosen in the same
geographical area to increase the probability "

Qs: Please can you elaborate more on the challenges arise when using databases from different geographical areas?

In lines 188- 189 you say "These blocking criteria were determined through experimental test and chosen to maximize the pair completeness as much as possible by keeping the number of candidate record pairs generated low, to reduce the execution time in view of the numerous tests to be performed."

Qs: Can you please give more details about how you conducted this experimental test?
Validity of the findings: If there is a weakness in the manuscript, it's because the findings are based on using data from the same geographical area and relatively small compared to other studies mentioned in lines 100 - 121.
Additional comments: 1) Figure3 and the description of the ANN in lines 200 - 207 is too generic. I suggest you provide more details on the input size, structure of the hidden layers, etc.


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: RECORD LINKAGE OF BANKS AND MUNICIPALITIES THROUGH MULTIPLE CRITERIA AND NEURAL NETWORKS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have addressed propoerly my comments so I think the paper is ready for publication.
Experimental design: The authors have addressed propoerly my comments so I think the paper is ready for publication.
Validity of the findings: The authors have addressed propoerly my comments so I think the paper is ready for publication.
Additional comments: The authors have addressed propoerly my comments so 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: RECORD LINKAGE OF BANKS AND MUNICIPALITIES THROUGH MULTIPLE CRITERIA AND NEURAL NETWORKS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The updated paper fixed all of the minor English errors I observed. The paper is well written with clear references to figures and tables.
Experimental design: The paper presents a somewhat straightforward application of machine learning to a record linkage problem. The experimental design is solid, and the paper can serve as a decent example of approaching such a problem.
Validity of the findings: The findings seem valid. The explanation by the author of the choices they made in terms of features, etc., satisfy my 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: RECORD LINKAGE OF BANKS AND MUNICIPALITIES THROUGH MULTIPLE CRITERIA AND NEURAL NETWORKS
Review round: 2
Reviewer: 3

Output:
Basic reporting: Clear and unambiguous, professional English used throughout.
Literature references, sufficient field background/context provided.
Experimental design: Original primary research within Aims and Scope of the journal.
Validity of the findings: Conclusions are well stated, linked to original research question & limited to supporting results.
Additional comments: I am satisfied with the provided answers. Please incorporate them within the 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: ATTRIBUTE BASED HONEY ENCRYPTION ALGORITHM FOR SECURING BIG DATA: HADOOP DISTRIBUTED FILE SYSTEM PERSPECTIVE
Review round: 1
Reviewer: 1

Output:
Basic reporting: - I would suggest sending the article to English-native person. There are many parts of the paper that are hard to understand and many sentences that do not make sense.
For example (examples only): 2 first sentences in Introduction are not clear and there are some "s"-s missing and Hadoop Security first paragraph is not clear at all.

- It would be better to concentrate on the most relevant topics of the paper. For instance, sections 2.3 and 2.4 are not really relevant to the paper and can be shortened considerably.

- Relevant work: I liked the summary of the related work, this is a great idea. The format is not correct, no need to make each paper into a separate sub-section, also some sub-sections should be shortened. In the summary fo the related work: point 2 is not convincing as even though the encryption/decryption take time due to its distributed nature Hadoop (HDFS) will suffer from smaller penalty.
Point #4 - not sure how this is a conclusion from the related works.
Point #5 ends abruptly and not clear what was the purpose of it.

- Section 3: should explain more about Honey encryption and also clearly define the addition made in the paper.

- HE is weaker than AES and was originally targeted to support authentications and not data at rest. It should be mentioned, as the speed up in encryption and improvement in the data size comes with a price.

- Section 3.2: the algorithm is not very clear and there are some "bugs" in definitions. For example (one out of ...): P is defined as private key in step 01.b and as policy in step 02.
Experimental design: It is not clear if the reported results are from a single run or not.
If not, then STD should be reported as well.

In context of HDFS, it is important to run on more than one node. Both due to data replication and etc.
Validity of the findings: As mentioned above HE is weaker than AES and was originally targeted to support authentications and not data at rest. It should be mentioned, as the speed up in encryption and improvement in the data size comes with a price.

The paper does not mention how the data is shared between different clients. This might be a problem with password and private key.
Another issue that is not mentioned is the data replication of HDFS, how are the keys managed and synchronized.

Another not clearly explained issue is how map-reduce paradigm (among others) are supported with the encryption and password scheme.
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: ATTRIBUTE BASED HONEY ENCRYPTION ALGORITHM FOR SECURING BIG DATA: HADOOP DISTRIBUTED FILE SYSTEM PERSPECTIVE
Review round: 1
Reviewer: 2

Output:
Basic reporting: Not clear
Ambiguous
Non-professional article structure
No formal results
Experimental design: No rigorous investigation
Validity of the findings: Conclusions are not well stated
Additional comments: The paper provides a secure job execution approach for MapReduce based on public/private key and passwords. However, the approach is not practical as well as secure. For example, the proposed approach suggests that mappers/reducers at the cloud will decrypt the file and then execute the computation. At this time, the adversarial cloud can observe the computation and data. Decryption operation at a mapper/reducer cannot secure the data/computation. The authors should have to check Intel SGX and see: VC3: Trustworthy Data Analytics in the Cloud using SGX. Another problem is that the paper is not able to motivate the problem and providing any new contribution.


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: ATTRIBUTE BASED HONEY ENCRYPTION ALGORITHM FOR SECURING BIG DATA: HADOOP DISTRIBUTED FILE SYSTEM PERSPECTIVE
Review round: 2
Reviewer: 1

Output:
Basic reporting: The language of the paper could be improved. There are not many typos but there are unclear sentences.
Despite that, the paper is clear structured well.

Examples (not all of them) of misuse:
Line 71 - typo
line 80 - small letter
line 101 - "atmosphere"?

line 106 - B includes C (115) and D (125)

line 120 - "instrument" ?
Kerberos description is not clear and very high-level

line 138 - It is hard to say that Hadoop security is at early stage, as the first paper cited was in 2010 and since 2009 Yahoo is working on addition security mechanisms.
It could be a case that the existing protection is unsatisfactory, but then the reason for this should be listed.

line 394: "the technique" - what technique?
Also, the separation is not really of cryptography keys but rather of encryption methods.
Experimental design: The experiment results are somewhat unclear to me.
Specifically the source of the performance improvement. The authors do not discuss it apart from pointing it out.

Given that the proposed algorithm adds a password for the encryption, it seems that the gain in performance is in using a faster, standard encryption algorithm.
If this is the case, then the comparison is not really clear and valid.
Validity of the findings: I am not entirely convinced why the proposed encryption scheme is more secure than existing ones.
The schema is based on password protection first, which is not really an encryption scheme.
The second stage is to use an existing encryption algorithm to encrypt the data.
Adding another password before encryption is hardly making a more secure encryption schema.
Additional comments: In my opinion, the paper lacks a more rigorous explanation of the proposed improvement and a proof why it is better than the existing ones.


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: ATTRIBUTE BASED HONEY ENCRYPTION ALGORITHM FOR SECURING BIG DATA: HADOOP DISTRIBUTED FILE SYSTEM PERSPECTIVE
Review round: 2
Reviewer: 2

Output:
Basic reporting: Some important references and discussion are missing.

Can we use the proposed approach in YARN?

What is the difference from the following work? why the following work cannot be used in Hadoop
1. Overencryption: Management of Access Control Evolution on Outsourced Data
2.GuardMR: fine-grained security policy enforcement for MapReduce systems
3. Vigiles: Fine-grained access control for mapreduce systems
4. AccountableMR: Toward accountable MapReduce systems
5. TrustMR: Computation integrity assurance system for MapReduce

Why hybrid cloud-based MR system cannot solve the issue you have, if we do not outsource sensitive data?
1. Towards Privacy for MapReduce on Hybrid Clouds Using Information Dispersal Algorithm
2. SEMROD: Secure and Efficient MapReduce Over HybriD Clouds
3. Hybrid storage architecture and efficient MapReduce processing for unstructured data

It will be good to write difference from the following work
1. MtMR: Ensuring MapReduce Computation Integrity with Merkle Tree-based Verifications
2. SEINA: A Stealthy and Effective Internal Attack in Hadoop Systems
3. Security Threats to Hadoop: Data Leakage Attacks and Investigation
4. Privacy-Preserving Secret Shared Computations using MapReduce

Why SGX-based MR system cannot solve the issue that you are trying to deal with?
1. VC3: Trustworthy Data Analytics in the Cloud using SGX
2. M2R: Enabling Stronger Privacy in MapReduce Computation
Experimental design: please include comments given above
Validity of the findings: please include comments given above
Additional comments: please include comments given 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: ATTRIBUTE BASED HONEY ENCRYPTION ALGORITHM FOR SECURING BIG DATA: HADOOP DISTRIBUTED FILE SYSTEM PERSPECTIVE
Review round: 3
Reviewer: 1

Output:
Basic reporting: The paper is written clearly and improved very much since the last revisions.
Experimental design: The experiments are satisfactory.
Validity of the findings: no comment
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: ATTRIBUTE BASED HONEY ENCRYPTION ALGORITHM FOR SECURING BIG DATA: HADOOP DISTRIBUTED FILE SYSTEM PERSPECTIVE
Review round: 3
Reviewer: 2

Output:
Basic reporting: Accept
Experimental design: Accept
Validity of the findings: Accept
Additional comments: Accept


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: BUILDING BLOCKS FOR COMMODITY AUGMENTED REALITY-BASED MOLECULAR VISUALIZATION AND MODELING IN WEB BROWSERS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The structure of the article is overall clearly structured and easy to follow. There are a few phrases that are hard follow or understand, see below for examples. Some additional references as noted below would improve the article.

- line 28: The sentence starting in this line is hard to comprehend.
- line 92: VMD needs a brief description and a reference.
- line 93: "WebGL primitives (like spheres, cylinders, etc.)" spheres and cylinders are not WebGL primitives but rather primitives of a framework like three.js on top of WebGL.
- line 151: "I have developed on the impact this power has had in scientific computing in the last decade, especially regarding the development of tools for online analysis and visualization of data." This sentence does not make sense to me.
- line 157: "If achieved fast enough, these possibilities enable applications where the user gets real-time numerical response from the web app as (s)he handles the molecular system." This sentence is a bit unclear to me. Does "achieved fast enough" refer to development speed or to execution speed?
- line 176: A Reference and an examples would be helpful.
- line 356: "Anyway, as always with computer-based methods for simulations, the conclusions obtained from 357 a given model must be consistent with its detail." It unclear to me what this sentences means, in particular the last part.
- line 358: In contrast to the previous sections the example is not linked in the text (although it is in the figure caption).
- line 405: The first part of the sentence is hard to comprehend.

A number of references do not include enough information to identify them, including in lines 454, 481, 485.
Experimental design: The author describes the current state-of-art to create AR/VR applications inside of the browser using mainly HTML markup with limited use of JavaScript. The necessary parts for creating an AR/VR application including hand/object tracking and gesture/speech recognition are introduced with a description of software and hardware options. Working examples and code are provided.
Validity of the findings: The examples use very approachable web technology and libraries, are keept relatively simple and include fairly detailed technical descriptions. This makes the examples section a great reference document for anyone who wants to get started creating their own AR/VR web apps. The use of a phone's vibration capability is a nice and surely memorable effect. I find the example involving stereochemistry a particularly good showcase for the use of AR/VR. The examples generally work as advertised although the marker recognition fails often which makes the AR objects disappear and hard to work with. It would be good if the marker recognition could be improved and/or if a document could be added/linked that describes how to create better conditions for marker recognition (e.g. good lighting, non-reflective paper, ...).

Notes on the apps
- the link to get the markers did not work (https://lucianoabriata.altervista.org/jsinscience/arjs/armodeling/markers.docx) but they where included in the supplemental files so I could test the site.
- some examples, like the one on residue-specific paramagnetic relaxation enhancements and the one including ubiquitin, contain a lot of z-fighting artifacts. Maybe the two aligned representations can be displayed with slightly different radii to avoid those artifacts. (Or maybe it is a driver issue on my machine but it was quite distracting.)
- please change the near/far planes of the camera to a smaller range to get rid of the very distracting amount of z-fighting
Additional comments: It would be interesting if the author could discuss the option of making the existing web molecular graphics tools ready for AR/VR.


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: BUILDING BLOCKS FOR COMMODITY AUGMENTED REALITY-BASED MOLECULAR VISUALIZATION AND MODELING IN WEB BROWSERS
Review round: 1
Reviewer: 2

Output:
Basic reporting: First of all, I would like to say that it is obvious that the author implemented some interesting prototypes and did some useful experiments. Especially as he is the only author of this article a decent amount of work was here invested. The article can be fluently read although the quality of the English language is not sufficient for a scientific publication. But with some effort this problem can be solved.

The larger problem is that this manuscript as well as the software is in a premature state. Let us first talk about the software: it is obvious that the author introduces here prototypes and from my computer science perspective I do not expect that this tools out-of-the-box.

However, based on the article's text a reader can expect that the software will work, as it clearly says that it runs in web browsers. And it does not. I did some tests with my Android smartphone - I never reached the state showing a molecule, so I was not able to experience VR or AR. When I tried to switch to VR mode, a black screen appeared. In the standard mode which should be AR the camera shows an inverted image of the environment which will prevent to show a molecule in a real environment mapped to an intutive location. Having a computer science background I am not surprised, as I would expect that this prototype will only work with certain browsers with maybe special drivers for the browser. E.g., the WebGL switch in Chrome might have to be switched on, or Google VR services have to be installed. However, I am not willing to be beta tester here, especially as the website as well as the supplementary material does not provide useful information concerning which browser to use and what to install. Also, the link to some of the markers did not work.

So, for a publication I expect at least that there is a documentation and if I follow these steps that the software will do what was mentioned in the paper. I do NOT expect that it works with all browsers and all phones on all OS. But this has to be clearly stated in the paper, otherwise the author just disappoints potential future users of VR/AR. So here a decent amount of work should be invested prior a publication.

Now the manuscript: this is in my opinion a draft with potential, but it is not ready for publication. It is clear that the author is self-confident regarding his results and it might be sufficient to convince readers who have no idea about MolVis and AR/VR, but not people who are working in this field. Throughout the article, some approaches are mentioned, e.g. "After around two decades of works slowly introducing AR and VR for chemistry and structural biology, the last two years saw four very inspiring pieces of literature showing the real potential of modern ..." Then, four papers are listed, but there is no direct comparison provided to the tool presented here. The text mentions a number of related works without making clear what is really new here - it might be even that all parts have been part of other software packages before. Therefore, an overview table with the most similar tools would be useful, showing clearly the new properties the presented approaches in this paper can offer. Then, I know that a number of approaches which are providing basic AR and/or VR features in the web are not even mentioned. So please urgently check the WebGL-based tools running on PDBj and RCSB PDB (and maybe ePDB). These are the standard-defining websites and some of the tools there having features which are interesting for this article. Also look at Mol* (MolStar) and Aquaria with the Leap Motion extension. What e.g. about UnityMol (yes, more Unity and not web based, but maybe there is a Web export function?) and https://doi.org/10.1515/jib-2018-0043. These papers could at least give some hints what is possible.

Then from the paper: "There is no need for plugins and thus no installations, no manual updates, and no costs associated to these web apps." -> If this is the case, why does the presented software not run out of the box?

Another huge problem of the manuscript is the motivation. To show an example from the Introduction: "However, actual reach, user acceptance and educational effects of
AR- and VR-based technologies still require deep evaluation". After reading this sentence, I expected that this paper will change something here. But it does not, it just provides some building blocks without a) concretely comparing it to previous work (as mentioned earlier) and b) not giving evidence that the presented software will be accepted by users. Here, it is asked for an evaluation, but the author does not provide an evaluation. Basically I am just convinced that he likes his software, which is not sufficient for a paper. So in any case a comparison to previous approaches is required, but moreover I would strongly suggest to make at least a user evaluation with a number of people (no, the evaluation from my point of view must not be CHI-compatible, but should provide some convincing feedback, e.g. as an expert study with Chemists/Biologists or related researchers - just as an example).

Then: the paper's title clearly says that AR/VR is here the leading topic. But throughout the paper, I often have the feeling that the features provided would work in 3D without VR/AR properties as well. E.g.: "Another interesting tool to integrate with AR/VR is a physics engine, to add realistic mechanics to the simulation." Why do I need AR/VR in the context of a physics engine. I do not see that the presented software give evidence for this purpose. I could as well say: a 3D representation is crucial to illustrate the physics properties." Yes, but why AR/VR.

The, the previous sentence also shows that the author is a little bit lax with the language: what are realistic mechanics? Do you really want to claim that a simple simulation with Cannon.js can compete with a complex Gromacs MD simulation running on a cluster? And then, is a Gromacs MD simulation "realistic"? Or should we be more careful with the terms?

Yes, I clearly have to say that I like the idea with using Cannon.js to give the impression of molecular dynamics, but it has to be make clear that this is just for illustrative purposes, e.g. interesting for education.

Then, from the abstract "As an alternative of much wider reach, I explore here state-of-the-art technologies for building immersive human-computer interfaces through augmented and virtual reality in web browsers." If I look now to the examples from the paper, I have to say these are the smallest examples of molecules which I have seen in a paper since a long time. Speaking of state-of-the-art, in times where already WebGL-based tools can visualize molecules with thousands to millions of atoms, this is not state of the art. Therefore, from a visualization point of view, this is not state-of-the-art and not even publishable. So if this manuscript should be published, the clear focus to VR/AR has to be worked out and clearly stated.

The images show interesting use cases, but everything is AR and only Fig. 1D could be VR. However, this is not a proof, I can create all these images by using Photoshop without writing a line of code. Why not provide a video for the readers in addition to a clear HOWTO explaining how to use the software?

"The latter example can be slightly modified to better fit in smartphone screens for
visualization with commodity goggles (Figure 1D) by flipping the video horizontally and
splitting the view in 2 images slightly differently oriented to render a 3D experience (which is handled by the A-Frame library itself)." -> How do you address the problem that the object will be in 3D and the background coming from a single smartphone camera will be just 2D? The term is by the way 3D-Stereoscopy what you are explaining here and gives me a hint that some understanding of the basic principles of VR might be missing here.

"The product formed in this reaction is by itself an interesting molecule to visualize and move around in 3D through AR, because it contains two fused six-membered rings which are often hard to understand in 2D." Good, more of these arguments! But again: this is an argument for 3D, why do I need AR here?

If I look now to Fig. 6a - this is something I am expecting based on the title, and if you focus at these applications, showing that the user can use two hands to move the molecule including some simple dynamics, provide a nice video and collecting feedback - great contribution and I want to use it! And then think about focusing more at education than research as the level of realism is for sure too low or clearly narrow the field down to applications where it still might make sense for research topics. Do not promise too much and convince the reader that you can provide strong evidence for your special focus. Maybe even remove VR from the title, I do not see much VR at the moment here. You can mention VR in the paper but say that your focus is AR. Otherwise provide example applications in VR and discuss them here in detail.
Experimental design: see 1
Validity of the findings: see 1
Additional comments: The structure of the text should be improved. Make sure to explain aspects like "dynamics" not somewhere in the paper after you have already talked about it. Provide in the beginning a section discussing the background of special terms used throughout the manuscript. Keep your audience of the journal in mind - it will usually not have much background in MD simulation/molecular visualization etc.

While reading, I would have expected the related work earlier (again, the discussion is not sufficient by now) but it might make sense to maintain it in the Discussion, extend it. You might want to detach the Outlook and extend the text towards a broader vision for AR/VR.

For the motivation, especially in scientific context, please check the term "Immersive Analytics", there are a number of publications in this field available by now.

I could not download the document containing the markers in most of the demos.

Please also see the attached 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: INTRINSIC RGB AND MULTISPECTRAL IMAGES RECOVERY BY INDEPENDENT QUADRATIC PROGRAMMING
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is generally well written and clear, however the emphasis on the novelty using compared to the authors' previous work is missing in some parts including the abstract.
Experimental design: The importance of the work can be better shown if  the authors explain in more details the advantages of having the objective function in the logarithmic domain which seems to be the main contribution of this work.
Validity of the findings: The results presented are promising, however they lack more discussions. For example, the Specularity can be seen on the apple and pepper reflectance images of the authors' proposed method and previous method, and this point is not discussed.

Moreover, the computation time of the proposed method compared to state of the art approaches is not specified.
Additional comments: The paper is clear with interesting results. However, I suggest to give more emphasis on the originality of
 the paper compared to your previous work regarding the logarithmic objective function. Moreover, more details and discussions on the results especially on the errors in estimation is essential in my point of view for a better understanding and impact of this work.


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: INTRINSIC RGB AND MULTISPECTRAL IMAGES RECOVERY BY INDEPENDENT QUADRATIC PROGRAMMING
Review round: 1
Reviewer: 2

Output:
Basic reporting: A few typos are still present. I notice numerous errors in section 4.1. “Fig. 1 presents shading images g resulting from all methods on five examples”: where the fixe examples are in figure 1 ? “The next rows are respectively the results  given by LS, KL, Barron, Yang…” it’s certainly columns ?
Experimental design: The authors cite a previous work. To my opinion, more information of this previous method must be given to understand this part.
Validity of the findings: It would be advisable to add a table with the computing time.
Additional comments: The paper deals with reflectance, shading and specularity estimation form multispectral image. The process has been evaluated in MIT and CAVE datasets. The authors provide an interesting method and seems to be very efficient. Unfortunately, a few typos are still present.
Major issues (not in priority order):
1°) “3 Method”: this part starts with a citation of a previous work on this topic. To my opinion, more information of this previous method must be given to understand this part.
2°) “4.1 Qualitative results”: I notice numerous errors in this section. “Fig. 1 presents shading images g resulting from all methods on five examples”: where the fixe examples are in figure 1 ? “The next rows are respectively the results  given by LS, KL, Barron, Yang…” it’s certainly columns ?
3°) “4.2 Quantitative results”: it would be advisable to add a table with the computing time.

Minor issues:
1°) Equation 1 is the Lambertian BDRF and the reference of 2012 is not appropriate.
2°) Page 2, line 28: “… a special case of the Bidirectional Reflectance Distribution Function (BRDF)…” the BRDF do not include the cos(theta).
3°) page 8, line 216: why “now” ? Only one method is proposed 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: INTRINSIC RGB AND MULTISPECTRAL IMAGES RECOVERY BY INDEPENDENT QUADRATIC PROGRAMMING
Review round: 2
Reviewer: 1

Output:
Basic reporting: Most of the issues raised in the first review phase have been address. The authors have improved the manuscript to a level that could be useful for readers of PeerJ Computer Science.
Experimental design: no comment
Validity of the findings: no comment
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: IMAGE-BASED EFFECTIVE FEATURE GENERATION FOR PROTEIN STRUCTURAL CLASS AND LIGAND BINDING PREDICTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The topic of the paper is interesting and the problems are significant, although I am not too familiar with the relevant bioinformatics field. One of the key references (Karim et al) use a similar technique for retrieving similar proteins as opposed to classification, so it would be good if the authors can clarify their motivation and whether classification is a significant topic. Following the paper was difficult in places because the sequence of topics is not always logical, e.g. SIFT features are suddenly mentioned at the end of the paper after not being mentioned at all before. The paper does need a more logical organisation.

Overall I found the quality of the paper is at a draft stage and needs more work before publication. There are many, many improvements still to be made. Some of them, but not all:
-- novel contributions should be made clear, and distinguished from already existing contributions (currently they are all over the place)
-- Referencing needs to be fixed, e.g. Mohri et al did not invent all of those ML algorithms, please put the proper sources down
-- for the ligand binding problem no example images are given so it's not clear how they look compared to the others
-- many things need to be explained better, e.g. how does the Karim et al method actually generate images? how are points arranged along the x/y axis and how does this affect the accuracy? what was the distribution of original image scales before scaling and is scaling significant?
-- Gabor filter is not fully explained (at least, only one of the wikipedia formulas are given in the paper)
-- Fig 4 seems to be wrong, i.e. the numbers going from (b) to (c) are divided by 10, not 100 which the text states
-- motivation for the new LBP features should be given. Why did the authors think these modifications are good?
Experimental design: In terms of experiments, I am not convinced that the right methodology was followed. SMOTE was used to artificially balance the dataset for the 5 class problem. However this appears to have been done *before* splitting the data for cross validation. Therefore the training data contains artificial examples derived the test data which is not fair. For the ligand binding dataset, one class or outlier detection methods could be used since there is no true negative training data. And the high sensitivity in Table 5 is probably offset by low specificities. The authors should provide both results as well as F1.
Validity of the findings: See answer above. Experimental design impacts validity.
Additional comments: Overall the paper is interesting but I think in it's current form it is not clearly described enough to be reproduced, the evaluation needs improvement, and the paper needs more logical organisation and improvements in presentation.


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: IMAGE-BASED EFFECTIVE FEATURE GENERATION FOR PROTEIN STRUCTURAL CLASS AND LIGAND BINDING PREDICTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: See general comments
Experimental design: See general comments
Validity of the findings: See general comments
Additional comments: Unfortunately, this study makes no sense. Protein structures are converted into distance matrices, which are used to “predict” the structural class. You know the structural class directly from the structure, so why converting into images and then predicting the class? Furthermore, what is the point of assigning known structures to 7 major SCOP classes (all alpha, all beta, etc)? SCOP, CATH, ECOD and other classification systems are much more sophisticated and include evolutionary relationships. These resources are widely to study various aspects of protein structure and function. The proposed method does not offer any unique capabilities and I just cannot see how it could be useful to anyone.


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: IMAGE-BASED EFFECTIVE FEATURE GENERATION FOR PROTEIN STRUCTURAL CLASS AND LIGAND BINDING PREDICTION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The authors had taken an effort to classify and predict protein structures and  ligand binding.

The article should include sufficient introduction and relevant prior literature should be appropriately referenced.

All appropriate raw data have been made available.
Experimental design: Validation of the algorithm is not clear. Sensitivity and specificity values and correlation for ML should have been furnished.
Validity of the findings: No comment
Additional comments: The introduction part needs to be improved. Ligand definitions are not clear.

Comparison with the existing methods thereby the novelty be established.

Validation of the algorithm is not clear. Sensitivity and specificity values and correlation for ML should have been furnished.

Important Features identified and their influence to the prediction and the significance needs to be discussed.

Case studies with a protein for class and ligand binding may be given.


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: IMAGE-BASED EFFECTIVE FEATURE GENERATION FOR PROTEIN STRUCTURAL CLASS AND LIGAND BINDING PREDICTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: Tittle: Image-based effective feature generation for Protein Structural Class and Ligand Binding prediction
Authors: Nafees Sadique et al.

In general the authors have made a tremendous effort to improve the article.
Data analysis and prediction are included.
However, if the tool developed is live then it will be of great help to the users.
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: RIDEOGRAM: DRAWING SVG GRAPHICS TO VISUALIZE AND MAP GENOME-WIDE DATA ON THE IDIOGRAMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Line 30-31: "As boundaries between model and non-model species are shifting" -> Model organisms are well defined, and have particular characteristics that make them be considered that way, not just their sequenced genome. The message is correct, but the manner is not appropriate.

Line 43-45: "many sequenced species have their genomes updated to chromosome level and more and more non-model species have their genomes sequenced" -> Redundant.

Line 49: "A ideogram" -> An idiogram.

Line 50: "a eukaryotic" -> an eukaryotic.

Idiogram and Ideogram is not the same, watch out!

There are more tools that offer the features described in this package and are not mentioned. Some examples are: KaryoPlotR, GenomeGraphs, RCircos, DRAWID, etc...

Need more References (look PDF)

To Include: I suggest including a comparison of this package with its main alternatives (look PDF)

If the strength of the package and the need it solves is to generate karyotypes of any species, it would be better to include one main-figure of a non-model species, not only human ideograms.
Experimental design: Line 56-58: "We still lack the choice of available drawing tools for plotting idiograms conveniently and effectively with a wide range species, despite the widespread need to visualize features along entire chromosomes" -> One of the packages mentioned in this article does the same and exactly the same way. Other packages such as KaryoPlotR also generate these types of figures and with many more features.
Validity of the findings: Despite specifying the need to generate ideograms due to the rise of new sequencing technologies, there is not direct connection between the results of these technologies and the package. It would be necessary to extract information from these results using programming tools or third parties. Although the flexibility provided by this tool makes it very useful and versatile, it is necessary to establish a connection between typical genomics files (Fasta, GTF/GFF, etc.) with the package.

At the current state of the package does not offer new options or greater utility than current alternatives. That is why I suggest the implementation of new functions for the package similars to  present alternatives, providing the generation of SVG graphics and a different style as a differential element. For example, this package generates the same as ChromoMap, differentiating it only in the generation of the image and the style. But it is far from being a serious competitor of Idiogram.js or KaryoPlotR.
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: RIDEOGRAM: DRAWING SVG GRAPHICS TO VISUALIZE AND MAP GENOME-WIDE DATA ON THE IDIOGRAMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Citation is missing for chromoMap (biorxiv), and I think karyoploteR, another non-overlapping but extensive package for plotting above ideograms, should be mentioned and added to the comparisons. Otherwise, the manuscript appears to meet all the standards of the journal.
Experimental design: Rideogram is different enough from other packages that draw ideograms, and is likely to be useful for exploratory and comparative genome-wide trait visualization.
Validity of the findings: The code is available on CRAN and github and the package includes clear documentation and reproducible examples.
Additional comments: This is a straightforward and user-friendly R package for drawing ideograms. I support the authors suggestion to add future extensions to the package and was satisfied to see responses to questions and issues raised on github. My one suggestion for an extension, while not at all necessary, is to add a function that aggregates raw data, by averaging or counting over a custom window-size, to produce the final “overlaid“ data. I believe this will increase usability among researchers without extensive computer programming expertise.


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: RIDEOGRAM: DRAWING SVG GRAPHICS TO VISUALIZE AND MAP GENOME-WIDE DATA ON THE IDIOGRAMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: - Good use of the figures. They show the versatility of the package.
Experimental design: - It is easier to identify the strengths of the library by defining well the existing competitors. Good job.
Validity of the findings: - Good implementation of genomic files to the tool. I hope it is the first implementation of many more.
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: RIDEOGRAM: DRAWING SVG GRAPHICS TO VISUALIZE AND MAP GENOME-WIDE DATA ON THE IDIOGRAMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The 2nd introduction paragraph (line 51-68) is not clear. I think it would be more compelling if it was separated into these two paragraphs: 1. Feature comparison between Rideogram and other idiogram packages and 2. Novel use of Bezier curves in idiograms to express the relationship between genome-wide features and synteny.

The new option for plotting syntenic Bezier curves is a great addition and I expect many will find it useful. However, figure 2 does not include an idiogram although I expected it to show both, as an R package that plots idiograms with Bezier curves. I could also imagine that a dual-genome comparison figure of closely related species would be more useful as a comparative genomics example.

Syntenic Bezier curves are not a standard datatype. It would be helpful to add at least one citation of the software you used to generate the data (MCScan?), some documentation in the R package about the data structure, and maybe a wrapper function to handle the data.

The highlighted/colored Bezier curves in figure 2 extend beyond the range of the chromosome and would not align correctly with features in an idiogram. The plotting parameters should be fixed to accurately match the chromosomal coordinates.

Chromosome 19 in Fig S6 is missing a line graph.
Experimental design: The added feature of Syntenic Bezier curves in combination with idiograms is a novel feature and it would be worth expanding on why such a feature is needed/useful. For instance, there is some evidence that syntenic genes are more highly expressed and conserved. Rideogram could be a great tool to visualize this and also further explore potential relations between syntenic regions and other genomic features.
Validity of the findings: The implementation of line and bar charts makes Rideogram more comparable to existing tools. It remains to be a user-friendly and easily customizable R package for drawing high quality idiograms, which makes it a good alternative to existing packages. The addition of syntenic Bezier curves on top of the idiograms is a novel addition that could distinguish Rideagram from other idiogram-plotting packages.
Additional comments: Thank you for addressing the reviewer comments and for the additions you have made to the package. I think the addition of syntenic Bezier curves is a very useful addition that would fill a gap between idiogram and Circos plots. For this reason, I think it deserves to be described in more detail.


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: RIDEOGRAM: DRAWING SVG GRAPHICS TO VISUALIZE AND MAP GENOME-WIDE DATA ON THE IDIOGRAMS
Review round: 3
Reviewer: 1

Output:
Basic reporting: Text and figures are clear, and the relevant background and citations are included.
Experimental design: The purpose of the package is well defined and does well in explaining the gap it aims to fill.
Validity of the findings: Rideogram has a comparable and useful set of features to existing tools, with the novel feature of syntenic bezier curves. It stands out as an easy to use and customize R package for genome-wide feature comparisons.
Additional comments: Thank you for addressing the comments. The article is in good shape and I am looking forward to future additions to Rideagram, especially those related to genomic-feature comparisons alongside synteny data.


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-BASED HOMOLOGOUS MULTI-POINT WARPING APPROACH TO 3D FACIAL RECOGNITION USING MULTIPLE DATASETS
Review round: 1
Reviewer: 1

Output:
Basic reporting: There are problems with the language and with the level at which details are reported. Please, see comments to the Authors.
Experimental design: There are many ambiguities on what exactly has been done. Please, see comments to the Authors
Validity of the findings: Presently, the validity of the findings is, at best, hard to evaluate.
Additional comments: The manuscript “Automatic homologous multi-points warping for 3D facial landmark” by Agbolade and colleagues reports on a new method to automatically place points on 3D surfaces – and more specifically on digital surfaces of human faces.

The manuscript is potentially interesting for this journal and for the community, but its current state should strongly discourage its acceptance. There are three main problematic areas: 1. the English language is often poor, 2. what has been done is not described well enough to gauge the novelty of the method and whether the results are valid, 3. existing methods, particularly geometric morphometric methods, are at best mischaracterized (if not entirely misunderstood). I will comment on the specific aspects below.

The Authors insist in the Abstract (lines 21-23) and elsewhere (e.g., lines 48-51, 100) that manual annotation is more error-prone than automated methods. In the Abstract they also mention a series of other limitations. However, I do not think they provide substantial evidence for their claims. As far as I am concerned, I consider manual annotation like the current “gold standard” relative to which automated methods are usually tested. Typically, automated methods are considered in research workflows simply because they are less time-consuming than manual methods, not because they are better.

L58-69: The definitions of landmark types provided by the Authors contain many imprecisions and mischaracterizations and do not correctly reflect the original subdivision by Bookstein. For instance, the definition at lines 59-61 of type I landmarks is entirely not understandable. I suggest the Authors to re-read the relevant classical papers by Bookstein. In addition, they may want to consider reading the book “Geometric Morphometrics for Biologists” by Zelditch and collagues and perhaps also a recent discussion of various landmark classification by Wärmländer, et al.
The Authors’ misunderstanding/mischaracterization of current state-of-the-art methods with regards to landmark/semilandmark definition and placement resurfaces around lines 212-220, when invoking “100% homology” for semilandmarks, claiming that after sliding semilandmarks become homologous points (this is also claimed at lines 253-254), and that semi-landmarks are constructed by randomly spreading points across a surface based on landmark points and that they contain “progressively less biological information”. In addition to consulting again the relevant literature, the Authors should also consider that in current practice and parlance in geometric morphometrics semilandmarks are never homologous (they do retain positional correspondence, though) neither before nor after sliding (as sliding cannot make them homologous). Simply, after sliding they are treated the same as the homologous landmarks with respect to downstream analyses. Further, semi-landmarks are not typically constructed “randomly spreading points”. Rather, they can be, for instance, manually placed, or placed based on some criterion (e.g., equally distant from each other). However, there is no strict one-on-one correspondence between how these points are taken and the fact of being semi-landmarks.

The Authors should also be clear about what is novel in their method and what is the same as previous methods. This is far from obvious to me as various formulas and steps refer to existing practice and methods developed, for instance, by Bookstein and collaborators. I guess that perhaps the novelty is that semilandmarks are used to deform the actual surface (step 4 of the algorithm at lines 311-327), but, even if this is the case, it should be made explicit to the reader.

L289-291: the Authors claim that there is “no consensus” about how to quantify geometric morphometric data and cite a study which presents the difficulty of identifying landmark-specific error (i.e., some specific problem) to paint a grim picture of the current state of the field. They should perhaps consult the relatively recent review by Fruciano (2016), which discusses some of these methods and how there is some substantial consensus on them. Briefly, the methods which are practically speaking more useful are the ones which quantify globally the error (rather than at individual landmarks) and relate it to biological variation (rather than quantifying absolute error).

L294-300: the whole definition/explanation of Procrustes distance is problematic, for instance with respect to the reference the Authors make to removing “the effect of size” (what about rotation and scale?) and to “the first PC” (what about the others?). I encourage the Authors to consult carefully the literature on the subject. A somewhat similar problem surfaces again at line 327 where “average landmark error is computed using Procrustes distance”. But, if Procrustes distance is a distance between two shapes, how exactly is it used to compute “average landmark error”?

It is not entirely clear what the Authors have done in their study. For instance, the empirical datasets are not clearly described for the general public and it is not clear how one quantifies error with them. Is there a “true” shape that can be used to see how the new algorithm performs? From the way the manuscript is phrased it (for instance in the Results) seems like the Authors have repeated multiple times the landmarking with their method and found that running their algorithm multiple times on the same data gave similar results. If this is the case, obviously this is far from being a proper quantification of error. First, one would expect that two runs of the same algorithm on the same data should give the same results (or very similar ones). Second, both runs might be wrong with respect to the true shape that one wants to estimate. The Authors claim (lines 379-381) that manual landmarking is impractical when dealing with many points. This is true. But, then, with respect to what did they compute their error? Further, it seems that the sliding has been only used to place the semilandmarks on the surface but then it is not clear how shapes were compared. In current practice, one first places landmarks and semilandmarks on a surface (whether using automatic or manual methods, or a mixture of the two), then landmarks and semilandmarks are subjected to a generalized Procrustes analysis with sliding of the semilandmarks, finally the configurations of points thus aligned are subjected to downstream analyses. If the Authors have departed from this typical workflow, extensive explanations and clarifications should be provided.

Similarly, it is not clear how the “significance” of individual landmarks (discussed starting at line 339) was obtained nor what does it mean for landmarks to be “significant”. I should perhaps also note that after generalized Procrustes analysis and/or sliding of semilandmarks the points are not independent from each other, so it is often impossible/impractical/inappropriate to compute landmark-specific error.

L372-374: I am not clear why the Authors report the definition of centroid size here.

L396-397: the Authors seem to refer to different datasets used here as different species (which seems to be echoed in the table presenting the Procrustes ANOVA results). However, I had understood that all these datasets are data from human faces, are not they?

L413-416: the point that combining different datasets/devices could introduce error has been made in earlier studies (e.g., Fruciano 2016; Fruciano, et al. 2017) , which should be cited here.

L417-421: t tests and “feature” appear in this part, but it is not clear what they refer to.

Table 6: the term “Repeatability” has a precise use in current geometric morphometrics (see Fruciano 2016) and should be avoided here (unless the Authors actually compute repeatability, that is).

English problems
The manuscript suffers from many problems with the English language. I will present a few examples below but, please, do not consider this an exhaustive list as there are many more. The Authors should go through the manuscript and make sure they fix the English before resubmission, enlisting the help of a native speaker if necessary (as it seems the case).
L42: “is a three-dimensional object”
L58: “into three types: “
L70-71: the whole “anchor points on a face graph” thing is unclear/unknown to me
L101: “landmarking”
L115: “landmarks that fulfil”
L120: I suggest “further” instead of “furthermore”
L150: I suggest to remove “homologous” (as I doubt anyone can find 500 homologous points on a face, if we use biological/operational definitions of homology normally used in geometric morphometrics).
L167,172 “the Stirling”, “the Bosphorus”
L185 “description”
L198: maybe the Authors mean “overlapping” instead of “overlapped”?
L330-337: the whole section is not understandable

Minor problems:
L30-32: these results can be omitted from here as these datasets and their analyses are not presented in the abstract.
L71-73: rather than stating the obvious that no human face is ever two-dimensional, the Authors could perhaps refer to Cardini’s work (Cardini 2014) about the potential problems arising from a 2D projection of a 3D shape.
L92: do the Authors actually mean that this other method requires little investment of time (which would be a good thing) or do they mean that it requires substantial time (as the phrasing seems to imply)?
L200: the use of the term “morphological integration” should be avoided because in biology it has a very different meaning (i.e., non-independence between parts)
Not all symbols of formulas are explained (for instance the formulas at lines 232-234).
L329 a space is missing

References cited in the review
Cardini A 2014. Missing the third dimension in geometric morphometrics: how to assess if 2D images really are a good proxy for 3D structures? Hystrix, the Italian Journal of Mammalogy 25: 73-81.
Fruciano C 2016. Measurement error in geometric morphometrics. Development Genes and Evolution 226: 139-158. doi: 10.1007/s00427-016-0537-4
Fruciano C, et al. 2017. Sharing is caring? Measurement error and the issues arising from combining 3D morphometric datasets. Ecology and Evolution 7: 7034-7046. doi: doi:10.1002/ece3.3256
Wärmländer SKTS, Garvin H, Guyomarc'h P, Petaros A, Sholts SB Landmark Typology in Applied Morphometrics Studies: What's the Point? The Anatomical Record 0. doi: doi:10.1002/ar.24005


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-BASED HOMOLOGOUS MULTI-POINT WARPING APPROACH TO 3D FACIAL RECOGNITION USING MULTIPLE DATASETS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The quality of language should be improved. Sometimes, the text is hard to follow and
 it contains a lot of typos or grammatical errors which are making the understanding
even worse.
The intro section would benefit of further structuring. For example, the related work
is tossed in the introduction section and starts without further warning. The methods
mentioned in the related work "section"  lack logical structuring. One method even
describes a video-based approach, unlike the others, so its hard to understand why it
should be relevant, etc.
Figures numbering is shifted starting after the Figure 3 (probably because Figure 3 is
used twice and then the counting continues).
Raw data are provided, however, the comprehensive description is missing.
The authors are criticizing manual annotation and later, they admit they are also using
it for annotation of the most important landmarks.
Experimental design: There is a typo in Eq. (1) the second order partial derivatives of h should be over x, y
and z, not only over x. The Thin-plate Spline acronym TPS is used without first being defined. There are missing spaces when inline mathematics is used, which makes it harder
to read. I am quite puzzled of measurements in "mm", firstly because this would make the
measurements incomparable, e.g., adults and children and secondly because it would require some aditional callibration which is not mentioned anywhere in the text.
The section describing the landmark error measurement contains a lot of stuff which fits into the related work, it is hard for the reader to realize what is the metric used in the author's experiments.
From the description, as it is now, it is not possible to replicate the author's results.
Validity of the findings: The novelty of the method is not specified. The reported results look suspiciously good in
 comparison to the state of the art. Some discussion why is that would be more than welcome. The tables 2, 3, and 4 could be presented within one table for easier comparison.
It is not clear, why the authors propose to introduce so much semi-landmarks when only 16 of them carry some important information, moreover, only these 16 landmarks are annotated manually.
Additional comments: The paper topic looks interesting. However, there are several issues, which prevents me from accepting the paper in its current form. The quality of English and paper structuring should be improved. Some parts are hard to follow because of grammatical errors and typos, some would benefit from better structuring.
The reported results look suspiciously good in comparison to the state of the art methods. The discussion of why this happens would be welcome.


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-BASED HOMOLOGOUS MULTI-POINT WARPING APPROACH TO 3D FACIAL RECOGNITION USING MULTIPLE DATASETS
Review round: 1
Reviewer: 3

Output:
Basic reporting: This paper proposed an Automatic Homologous Multi-Points Warping (AHMW) for 3D facial landmarking.

The writing is not readable.
Even for abstract, there are some typos.

'wether' face recognition --> whether
'insuffient' landmarks --> insufficient

Please polish up the writing for the whole paper.
Experimental design: There are some experiments on three datasets using 500 landmarks.
Validity of the findings: The experimental results show that the method is robust when compared with the state-of-the-art methods with minimum localisation error (Stirling/ ESRC:0.077; Bosphorus:0.088; and FRGC v2: 0.083).
Additional comments: Please polish up writing to get the paper accepted.


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-BASED HOMOLOGOUS MULTI-POINT WARPING APPROACH TO 3D FACIAL RECOGNITION USING MULTIPLE DATASETS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Serious problems with the English and it is not entirely clear what the Authors have done. Please, see section "General comments for the author" for examples and in-depth discussion.
Experimental design: There are (still) many methodological problems, including that what the Authors have done remains elusive. Please, see section "General comments for the author" for examples and in-depth discussion.
Validity of the findings: Because it is not clear what has been done exactly, findings and claims are hard to evaluate. For my current interpretation of the results, as they are provided, the claim of small error with this new method does not seem to be supported. Please, see section "General comments for the author" for examples and in-depth discussion.
Additional comments: I have reviewed the revised version of the manuscript “Automatic Homologous Multi-Points Warping for 3D Facial Landmark” by Agbolade and colleagues.

I have to commend the Authors for making an effort to accommodate my comments in the revised version of the manuscript, and for politely addressing my concerns. The text has also overall been improved.
Having said that, I think that the manuscript is still far from being publishable and a series of problems remain. Line numbers below refer to the PDF version of the manuscript

L23: Again, I appreciate that we might not have the same opinions when it comes to manual landmarking, but the claims that “current facial landmark methods in 3D […] contain insufficient landmarks” and “lack homology” are not supported by any evidence. Current approaches, when digitizing only a few landmarks such as in many geometric morphometric studies, often insist on homology. Other approaches relax the assumption of homology (for instance by using semilandmarks) and allow to place very many landmarks. This statement should be qualified more precisely and made more fitting to the manuscript and the novelty of the method.

The whole section at lines 71-88 is hard to understand and the concepts are not clear. For instance, at line 83 a reference to a “shape parameter” is made, as if this is some sort of scalar or something that is totally independent from the thin-plate spline and that is “estimated” through Procrustes superimposition (line 84). However, the Procrustes superimposition is simply a transformation which removes location, orientation and scale and allows one to analyze shape (which are the coordinates after the superimposition). Please, rephrase this section.

While I do appreciate that the Authors have consulted the relevant literature on landmark types, the explanation (L100-120) is still extremely difficult to follow. It should be rephrased. This problem is somewhat in-between a problem with English (see below) and a problem with conceptual representation of these landmark types. The Authors are very welcome to find their favourite way of rephrasing the text, but I would suggest that perhaps simplifying and clarifying the definitions (one sentence per type) and forwarding the reader to the relevant literature might be the easiest choice to implement.

L227: The sentence “the method was fully implemented in ViewBox 4.0” is not clear. Have the Authors implemented their method for automatic point placement in ViewBox or have they used ViewBox for the geometric morphometric analyses described? Please, clarify.

I thank the Authors for clarifying their methods and what the novelty relative to work by Bookstein and colleagues is (L26-29; 168-172). Nonetheless, it is still not clear what is novel in this manuscript. For instance, methods to project a set of points from a template to all surfaces in a sample, as well as for sliding of semilandmarks, relaxation on surfaces and mesh deformation already exist, are at least in part implemented in the literature, and are regularly used. For instance, see functions placePatch and relaxLM (and other related functions) in the R package Morpho. I encourage the Authors to stress even more what the actual elements of novelty of this are, and clearly distinguish these from existing/implemented methods.

L304-312: The explanation of principal component analysis is probably unnecessary – or at least it is unnecessary in biology (I am not sure about computer science). If the Authors decide to keep it in, they should make it correct and include more details. For instance, was the eigenvalue decomposition performed on the matrix M or, as I assume, the matrix K? The description also sounds like “eigenvalue decomposition […] produce highest ranking eigenvectors […] with the help of […] eigenvalues”. However, the eigenvalue decomposition produces a set of eigenvectors and eigenvalues, it is not that one is obtained with the help of the other. Perhaps the Authors refer to the ranking of principal components here. Further, it is not clear how many principal components were retained (here x) and using which criterion.

L314-336: The title should be “Measurement error”. Also, there are a series of imprecisions and lack of clarity which should be improved. For instance, it is not clear why it is more difficult in geometric morphometrics compared to traditional multivariate morphometrics. I do agree that non-independence of landmarks create issues for certain analyses (i.e., landmark-specific computations of error, as presented in the previous version of the manuscript), but, if data is treated as multidimensional, I do not see very large differences between traditional multivariate morphometrics and geometric morphometrics in terms of analysis of measurement error. Further, Procrustes ANOVA is not based on generalized Procrustes analysis. It uses data which has been subjected to generalized Procrustes analysis/Procrustes fitting and exploits the fact that these are mean-centered, but Procrustes ANOVA itself is not based on generalized Procrustes analysis. Please, see Fruciano 2016 or the original sources for details on this and rephrase these parts accordingly. Also, it is not clear why dimensionality reduction (L334-336) was performed here, as Procrustes ANOVA can be performed using the Procrustes-aligned coordinates.

As before, it is not clear how the measurement error analysis was performed. From the way this is phrased at lines 340-356, it seems like a single mesh was used as template and here 16 fixed points were digitized. Then the method described above was used to automatically place and slide points/semilandmarks. Then the Procrustes distances were computed between target and reference for Procrustes ANOVA. However, it is not clear when/how manual annotation of five specimens by the same person (L320) comes into place. Also, no Procrustes superimposition is ever mentioned (however, if this has been carried out in MorphoJ, it has most likely been performed as it is an essential step after importing data). For the reasons above, it is not clear what the repetitions represent here. And what the “error” represents or whether this “error” is actually variation due to rotation, translation and scale (which would be present if Procrustes fitting were not performed). To be clear, if the Authors performed the analysis to test how the choice of a manually-annotated template affects variation in shape in target shapes they should do the following:
1.	Chose 5 (or whatever other number) surfaces as references
2.	Use a sample (for instance 20 or 30, or entire datasets) of other surfaces (distinct from the 5 references, each one belonging to a different individual)
3.	Perform their method of automatic landmarking using the same sample from point 2 but each time using a different reference from point 1
4.	Superimpose all the configurations of points obtained at point 3 (and not the references) in the same Procrustes analysis
5.	Perform Procrustes ANOVA on the superimposed configurations from point 4 using Individual as factor (and possibly other factors, if they wish/are available)
The analysis described above would give information not about “error” in general but about the sensitivity of the method developed here to reference choice. This should be stressed very clearly all along the manuscript, as different sources of error exist and any claim about "error" should qualify what kind of error refers to. Importantly, as mentioned in my earlier review, the fact that this method produces consistent estimates of shape is a good thing but they may be “consistently wrong”. So, if there is no “ground truth” (e.g., manual annotation of the same surfaces) to which this can be compared, this should be made extremely clear to the reader.
To be clear, what I detailed above is just an example of what could be done in terms of studying measurement error, but I am not sure what the Authors have actually done because the way the manuscript is written does not allow me to figure it out.

L359-361: Please, document in more detail what was done with which software (also, this should be part of the Materials and Methods, not the results).

While the manuscript and the analyses presented are certainly much improved relative to the first version, I am not quite convinced by some of the figures and related analyses. For instance, I am not sure why the asymmetric component is presented in Fig. 5 A-D. This is not clearly described in the text and I am not sure which function does it have. Most importantly, if the PCAs have been performed only on the symmetric component – which would be reasonable – this should be mentioned in the text. Interestingly, typical analyses which include asymmetry in MorphoJ will then include a Side and an Individual x Side term in the Procrustes ANOVA, but these do not appear in Table 3. This is not to say the analysis is not correct, as it is really hard to evaluate at this stage. But the Authors should more clearly specify how exactly they have carried out the analysis. The issue with asymmetry appears again at lines 422-426, where differences between populations are discussed in terms of asymmetries and not in terms of their mean symmetric shape (although Table 4 does not mention asymmetry).
Further, I am not quite clear why centroid size in Fig. 6 B takes negative values and it is not clear how the Authors have used their data (supposedly replicates) for the comparisons of group means (tests based on Procrustes and Mahalanobis distances).

More importantly, the results in Table 3, as they stand now, show a very large error. It is not clear what exactly the Authors have done (see above the issue with Procrustes ANOVA and what the Residual term stand for), so I will have to suspend the judgement for now. But if the “Residual” term were variation between replicates of the same individual (as it is normally the case in this kind of analysis of measurement error), this accounts (based on the numbers provided) to 46% of total variation and accounts for more variance than any other supposedly biological source of variation (Population and Individual). The mean squares for the Residual term are also very similar to the mean squares for the Individual term. In other words, this means that (supposed) replicates of the same individuals vary more than the variation between individuals. If the final aim of this method were facial recognition in humans (supposedly within populations), this would mean that the error in landmark placement is so large to make the method unsuitable for this goal (but perhaps still useful when the biological signal is large, for instance when comparing different species). Again, I will suspend the judgement for now as it is not clear what exactly it is shown here.

Because of the lack of clarity in many parts and the possibility I just mentioned that measurement error is actually quite high, at this stage I will not evaluate the claims in the Discussion (e.g., the claim of small error, at lines 439-440).

The manuscript has still enormous problems with the quality of the English language. Even in the abstract, there are mistakes or the text is not clear. I will make here just a few examples of the problems. The Authors, however, are strongly encouraged to consult with a native speaker. To be clear, what follows are just examples, and only on the first page or so of manuscript, and much more should be done in the text. I do realize this might be annoying, but unfortunately, even if/when the other problems in the manuscript were to be fixed, I would not be able to suggest acceptance if the English is in this state.
Title: perhaps “landmarking” instead “landmark” would be more appropriate (same at L24).
L19-21: Apart from being unclear what the Authors mean, it is not appropriate to say that “scientists […] have been digging deep into research to know whether face recognition is performed holistically […]”. I mean, scientists are the ones using face recognition and developing methods for it. So it is not that they have to dig so deep in research. Perhaps the Authors refer to the fact that scientists have been asking whether one approach is better than the other?
L27: I am not clear what “concept” refers here to. Maybe “approach”?
L35: “Landmark-based” (without “The”)
L38: what does “listed” refer to? Also what about “collaborative behavior”? It is not very clear what the Authors mean here.
L42: “facing 2D face” does not sound quite right, maybe “affecting”?
L45: maybe “a drop”?
L52-53: have these methods received sporadic (i.e., rare) interest or is this one of the most vigorous research areas in biometrics? Cannot be both…

Minor issues:
L66-67: “geometric morphometrics” is not necessarily “the latest approach”, particularly if one includes in this field methods not based on landmarks but on function-fitting (e.g., elliptic Fourier analysis), which are used in biology since the early 80s (whereas landmark-based approaches, while older, had a strong impulse in the early 90s, so it is hard to frame them as particularly new). Most importantly, the description which follows refers only to landmark-based geometric morphometrics. I suggest avoiding reference to “the latest approach” (it is not that recent) and mentioning how popular they are, further mentioning directly “landmark-based geometric morphometrics”.

L364-366: It is perhaps worth to notice that the number of PCs found is due to the sample size.

L391-403: the description of methods belongs to the methods section (where certain analyses like the Kruskal-Wallis test are not even mentioned), not the 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: LANDMARK-BASED HOMOLOGOUS MULTI-POINT WARPING APPROACH TO 3D FACIAL RECOGNITION USING MULTIPLE DATASETS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors significantly improved the quality of the writing and addressed all reviewers comments in the updated manuscript.
Experimental design: The experimental evaluation was improved by using different metric, supported by a clearer description.
Validity of the findings: All underlying data have been provided.
Additional comments: The overall quality of the manuscript is significantly improved, thanks to addressing all reviewers comments. Now, I do not see any issue preventing the publication of the 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-BASED HOMOLOGOUS MULTI-POINT WARPING APPROACH TO 3D FACIAL RECOGNITION USING MULTIPLE DATASETS
Review round: 2
Reviewer: 3

Output:
Basic reporting: The revised version has solved the problem I mentioned before.
Experimental design: The experiment is extensive.
Validity of the findings: The proposed method is novel and practical.
Additional comments: Please have a further improvement on the writing.