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

Input:
Title: A SURVEY OF THE STATE OF THE PRACTICE FOR RESEARCH SOFTWARE IN THE UNITED STATES
Review round: 1
Reviewer: 4

Output:
Basic reporting: The text follows established, scientific form and quality. I would even go as far and say that especially the text up until the "Analysis" section is written well above average quality.

However, starting with the "Analysis" section, it feels like the publication was written by entirely different authors. This (long) section contains mostly tables, figures, and just a little text to describe the former.
The tables and figures could be improved in several places (see detailed notes). Especially for the figures I would suggest to rethink again what they are supposed to show and whether their respective current format is really the best way to display that. Detailed comments can be found later in the review.

I am not sure if this is an artifact of the review-pdf compilation, but almost all citations miss their dois and some citations are not meaningful without either a doi or an URL. Details can be found later in the review.

Up until line 247 (~1/3 of the paper), the text is written in a country-neutral way (including title and abstract), leaving the reader under the impression that the whole RSE landscape is under review. However, as the authors mention themselves: "results differ across the world". Thus, I strongly suggest to mention either in the title or at at least in the abstract the limitation that this survey primarily covers only the USA.
Experimental design: The raw answer data is provided, along with a (pdf) description of the questions. Something I miss is documentation of the mentioned partial dependencies of questions on answers of previous questions. This unnecessarily complicates a repetition or comparison with other surveys. In addition to providing these inter-dependencies in the supplementary material, it would be beneficial to the reader of the pdf-part of the publication if those could be highlighted or marked in some way.

What I miss most is the source code of the scripts used to generate the results from the raw data, including the same for generating the figures. While it is not difficult to do this with the provided csv data from scratch, the vast number of reported numbers and plots would make scripts to generate all of those valuable, and in case of discrepancies between own and reported results the reader would be left without clear way to see where the differences are, in short: they would be very valuable to replicate the results from the data. This is especially disappointing given the connection of this issue with the topic of the paper and the background of the authors. It is entirely possible that I missed those scripts, but I could not find them either in the files submitted to the journal, nor in the data published on Zenodo.
Validity of the findings: All raw data used in the report has been provided. Repetition would be hindered by the lack of dependency information and scripts for the analysis of the raw data, as mentioned in review parts 1 and 2.

Conclusions are overall well stated, with only few exceptions that are mentioned in the detailed review.
Additional comments: While reading the article, I collected notes of everything I spotted, even small issues like typos and the like. In the following, those notes are given in roughly the order of appearance in the text. This also means that rather more important issues are mixed with rather trivial ones . I hope this order helps to speed up the improvement of this article, because I believe it to be valuable to the scientific community.

- line 80: "hey often focus on small groups or in laboratory settings" → something is off here. Maybe remove the "in"
- line 84: "as briefly described next in the next subsection" → drop the first "next"
- line 145: "Software Saved International Survey" is not the proper name of the survey, but rather generated from the github project name. It is commonly known as and should be better named "SSI international RSE survey".
- line 169: missing parentheses around Philippe et al. (2019)
- line 169: double-parentheses around Pinto et al., 2018; Hannay et al., 2009
- line 178: there is a space missing in between "integration(Shahin"
- line 199: "(Nangia and Katz, 2017)": the same publication was already cited within the same sentence context. I suggest to drop the second reference.
- line 201: The sentence "The AlNoamany and Borghi (2018) survey reported similar results" seems to first suggest it reports similar results as the previous sentence, which mentions a gender gap. However, reading further, the reference "similar results" seems to refer to the gender-neutral percentage of training which was discussed further up. While not technically wrong, I suggest to reword this a little to make it easier to understand the reference.
- line 207: This sentence contradicts the sentence before directly, in which it is stated that only a minority of researchers (~20%) found "formal training to be important or very important". If the authors intended the meaning that this result may be caused by a lack of said training ("They don't put importance into something because they don't know it"), this should be made clearer.
- line 276: "Therefore" seems to suggest to refer to the previous sentence: "The results showed that...", but this does not make sense here. It would make more sense to refer to the much previously mentioned "most of the previous surveys did not address the topic of credit": please rephrase to make this clearer.
- line 279: "asking individuals who, ..., directly have important information" → "asking individuals who, ..., directly have the relevant information"
- line 290: "conferences papers" → "conference papers"
- line 292: "(e.g. hiring" → "(e.g., hiring" (comma)
- line 306: "as is occurs" → "as occurs"
- line 330: To the reader, it is not immediately clear that the statement about politeness is also a result of the earlier-quoted work by Ortu et al. (which it is). A solution could be "However, they also show that this demographic ..."
- line 366: "topics" → "topic"
- line 401: given that developers are also focus of later plots it would be good to also mention their number here, even if it can be calculated at this point.
- table 2: numbers in "Administrative & business studies" seem to be mixed-up ("10 0" in one cell, nothing in the last cell, and indeed there seems to be an "&" missing in the source file)
- table 2: minor: right-aligning the numbers would look a little nicer
- figure 1: It is not clear which group is which in each sub-plot (left/right vs. combination/developers?). It is also not mentioned in the lines that describe the plot: lines 435-438. In general, the figure also suffers from the small numbers displayed in each already small sub-plot. I wonder whether it would be better to switch axes and put all 10 sub-plots as rows into one plot which can then run the entire text width. Also (but hard to see and judge as it is right now), it might be worth noting that at least the left group (combination?) wishes for more training time than they actually use.
- caption figure 1: Why capitalize each word?
- line 442: "The only one that is technical is testing." It might be worth noting that this is only a particular high result for the 'combination' group, but not the 'developers' group. On the other hand, for those, "requirements engineering" is comparatively high, while for the 'combination' group it is not.
- lines 444-446: if possible during type-setting the paper for paper-format, it would be nice to use the space of these two lines for the figures and put the content of the lines on the next page.
- figure 2: It would be better to plot the two differently-colored bars side-by-side (like in figure 3). This way, the two groups can be more easily compared between bars of the same color. If, on the other hand, the differently-colored bars are meant to be "behind each other", with the red always being "in front of" the yellow bar, this should be indicated. Even better would be a percentage-based graph, which would also make it possible to compare the two respondent groups with each other, given their different size.
  Also, with ~50 being the highest number seen here for groups of a size a factor of 10 larger, I wonder whether this was a multiple-choice-question or a single choice question. The difference matters, as with a multiple-choice question a vast majority effectively answered for every aspect that it is not more difficult than it needs to be, while for a single-choice question only the "most annoying" aspect could be selected, effectively producing a potentially drastically different plot than when asking the multiple-choice version.
- line 444: "Focusing on the one technical aspect that respondents perceived to be more difficult than it should be": this would imply that "testing" would be the only technical aspect where respondents answered that they would be more difficult than they would be. However, figure 2 shows a lot of higher-than-zero answers for also other technical topics. "Testing" is the one technical aspect that was selected most often when it comes to being more difficult than it should be, but not the only one.
- figure 3: It is not clear which group forms the basis for the plot (combination or developer or the sum of both). The numbers in the text give indication that it is likely either combination or combination + developer, but it is not explicitly mentioned.
- line 450: Within the first sentence, it is again not clear which group was takes as basis for those numbers. Given that this was already an issue for more than one question, it might be a good idea to add a general indicator in the paper for each question that marks which of the groups received that particular question and that numbers without group statement always mean "everyone who received that particular question".
- line 467: "we see a different story": not necessarily. The given list of topics to document might not contain, or appear not to contain, the type of documentation applied when documenting code in-line. For instance, a comment on a particular loop structure might not appear to be within "software design" it respondents correspond that answer with the general design of the overall software package.
- figure 4: The text about the figure concentrates around percentages (e.g.: "Less than 30% of the respondents reported"). Therefore, changing the figure to use percentages would help the reader. Although the figure itself will show large differences, the y-axis will. Also, since the text combines 'extremely supported" and "very supported", it would be helpful to visualize those separately from the others, either by using similar colors (and dissimilar to the others), or to add a small black bar between these two and the rest. In addition, if the current order of the topics does not have special meaning, it might be good to sort them by, e.g., the percentage of combined "extremely supported" and "very supported".
- line 484: From the text it is not clear which group received that question, i.e., which concrete dependency was connected to this question.
- line 488: The "However" might not be warranted. It does imply that an overlap of respondents who answered "use git most of the time" and "use copy/zip most of the time" would be noteworthy and possibly troublesome. An alternative explanation might be that those with overlap indeed use both, to achieve an even higher level of backup than one single practice alone.
- line 491: "The lack of use of standard version control methods": The authors state earlier, that "83/87 of respondents answered to use version control. I cannot see a large lack of use of standard version control methods given those numbers.
- line 500: It would be interesting to look at the reason for the (small) difference. I could imaging that different gender ratios in specific disciplines and different availability of training in those disciplines could have an effect of comparable size. While the text does not explicitly state a causation, it might be good to explicitly state that this is indeed by itself no indication for causation.
- line 510: double "half half": the first likely should be a "than"
- lines 511/514: like at line 500 it would be interesting (and should be within the data collected) to see whether this could be a correlation with something else, like differences in availability of training and gender ratio differences in different disciplines.
- figure 5: plotting percentages would allow comparisons between the different groups, while plotting absolute numbers do not really add benefit.
- figure 8: percentages would be a better choice to show the data.
- figure 9: same as for figure 8, but this is even more important here, where the current plot does not really enable a good comparison between the two groups shown here.
- figure 10: also here, a percentage plot would be more useful than absolute numbers
- line 563: The text states "Figure 10 shows, the respondents saw little chance for career advancement for those whose primary job is software development". However, when looking at figure 10, the group of "developers" show a far better opportunity for advancement (50/50 no/yes) when compared to the other two groups (far lower numbers of "yes" compared to "no"). Also, it is interesting to see for the group of researchers only, the group of "don't know" is the largest overall.
- line 565: with so big differences between groups in figure 10, I wonder how relevant the statement concerning gender is really for the gender topic and how much could result from different gender ratios in the groups plotted here.
- figure 11: percentages would be a little more useful. Luckily, the total numbers are somewhat similar here.
- figures 12&13: percentages would be really important here, as the absolute numbers for developers and the combination group are so different that gets really hard to see the composition of the numbers for developers.
- caption of figure 12: "sfaff" → "staff"
- line 597: I would argue that availability of resources is about as important then the other three mentioned here, for the combination group reaching 30 for 'extremely' and 'very' important combined, and even if hard to see, this seems to be even more important for the group of developers. Thus, I feel it should be mentioned as important as well.
- caption of figure 15: "current" → "currently"
- caption of figure 16: "Does" → "do", "casess" → "cases"
- figure 18: percentages would help comparing the different groups with each other.
- line 634: "This answer again seems at odds"...: Not necessarily. Assuming most of these projects are within scientific institutions, those more likely do have one of both of these, so while the project might not have any, it would be covered by the institutional one (but the question only asked about the project specifically). Also, there are different views of the effectiveness of these measures, which might lead projects to adopt different practices to further inclusion.
- figures 19, 20 and 21 are for some reason displayed differently than most of the others before: here, the groups use different colors and the answers are displayed as different bars, while it is the other way around for the figures beforehand. It would be easier for the reader if this would be done more consistently. Besides consistency, using percentages instead of absolute numbers (and swapping variables as mentioned above) would the reader judge differences between different groups a lot easier.
- line 653: I would argue that any time spent on debugging is too much, as ideally there would not be any need for debugging. In this sense, it is not surprising that a lot of people responded that they spend more time for debugging than they ideally would (because that would be no time at all).
- line 661: I suggest to add an "of" inside of "the development *of* research software"
- line 721 should likely be cited as specified on F1000: https://f1000research.com/articles/9-295
- line 724: Is there a DOI or an URL available for reference?
- line 725: There seem to be two (different) dashes in between the page numbers.
- line 726: Likely, at least the word "interdependencies" should start with a capital "I"
- line 728: URSSI should be all capital; also a link to the data or a doi would be nice.
- line 744: UK should be all capital
- line 793: "investigating" likely should start capitalized
- line 797-799: The doi to the data is missing: 10.5281/zenodo.2585783. Without the doi, the citation is hard to use: a reader would not even know that the name refers to a github organization without utilizing a search engine. Also, a link to an analysis of the relevant data would be interesting to readers: https://www.software.ac.uk/blog/2018-03-12-what-do-we-know-about-rses-results-our-international-surveys
- line 800: "an" should likely start capitalized
- line 811: That citation should at least include the link to the blog entry. As it is, one would have to rely on a search engine to try to find what is meant.
- line 819: There seem to be two dashes: "CIDCR1––CIDCR7"
- line 820: An URL would be useful.
- line 828: There are two dashes in between the page numbers.
- This may be an issue with the reviewer version of the PDF, but clickable links, both internal (footnotes, citations), as well as external (dois/URLs) should work.
- p.2, footnote 6: URL uses different font than other URLs (and probably should be an ssl-URL too)
- p.6, footnotes 7011: URLs use a different font than URLs in footnotes 3-5. Given the length and subsequent unreadable line-break in the URL of footnote 11, I suggest to try the (smaller) font of the URLs 3-5 here.
- Aside from line 766, all references seem to be missing their dois.
- All figures seem to have been provided in png format. However, being a raster-format, this might produce low-quality results for various zoom-levels. Please consider using scalable formats instead for plots like the ones in this publication.


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

Input:
Title: A SURVEY OF THE STATE OF THE PRACTICE FOR RESEARCH SOFTWARE IN THE UNITED STATES
Review round: 2
Reviewer: 1

Output:
Basic reporting: The edits to the basic reporting show attention to detail and respect for the reviewer comments.  The highlighted concerns seem to have been addressed.  However, some of the edits could be improved as follows:

1. Figure 1 is difficult to read.  The font size is too small for the titles of the subplots.

2. Figures 1 and 7 show dots that are confusing.  The text does not say what the dots represent.  They could be data points for individual responses, but then they don't show the multiplicity when more than one respondent has the same answer.  Also, there are cases (like NIH for Developers and DoE for Developers in Figure 7) where there are no "data points" below the mean.  This makes me think that I am misinterpreting the dots.  They should either be explained, or, if they don't convey any useful information, removed.

3. Although the response to reviewers stated that a Conclusions section was added, there still isn't a Conclusions section.  The authors should add a Conclusions section.
Experimental design: The edits to the experimental design address the reviewer comments.
Validity of the findings: The edits to this section more accurately reflect the story that is told by the data, and the limitations of the data.
Additional comments: no comment


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

Input:
Title: A SURVEY OF THE STATE OF THE PRACTICE FOR RESEARCH SOFTWARE IN THE UNITED STATES
Review round: 3
Reviewer: 1

Output:
Basic reporting: My concerns have all been addressed.
Experimental design: My concerns have all been addressed.
Validity of the findings: My concerns have all been addressed.
Additional comments: My concerns have all been addressed.


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: MULTI-MODAL AFFINE FUSION NETWORK FOR SOCIAL MEDIA RUMOR DETECTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: Summary

 - In this study, the authors proposed the multimodal affine fusion network (MAFN) combined with entity recognition, a new end-to-end framework that fuses multimodal features to detect rumors effectively. The MAFN mainly consists of four parts: the entity recognition enhanced textual feature extractor, the visual feature extractor, the multimodal affine fuser, and the rumor detector. The entity recognition enhanced textual feature extractor is responsible for extracting textual features that enhance semantics with entity recognition from posts. The visual feature extractor extracts visual features. The multimodal affine fuser extracts the three types of modal features and fuses them by the affine method, and it cooperates with the rumor detector to learn the representations for rumor detection to produce reliable fusion detection. Extensive experiments were conducted on the MAFN based on real Weibo and Twitter multimodal datasets, which verified the effectiveness of the proposed multimodal fusion neural network in rumor detection.
2. Strength

 - this paper firstly introduced the four modules of the proposed MAFN model, i.e., the entity recognition enhanced textual feature extractor, the visual feature extractor, the multi-modal affine fuser, and the rumor classifier. Secondly, this paper describes how to integrate the four modules to represent and detect rumors.
3. Weakness
 - Lack of novelty of research. textual feature extractor -based problem solving is a very common approach in the recent deep learning field, and post-processing is also difficult to consider as a new algorithm.
   - The part about the learning scenario is confusing. A more understandable explanation is needed for training and testing.
- The entity resolution feature is quite confusing, and difficult to understand.
4. Minor comments
  - There is an error in the reference. I haven't looked at all of them in detail.
- The manuscript is not well organized. The introduction section must introduce the status and motivation of this work and summarize with a paragraph about this paper.
- What are the limitations of the related works
-Are there any limitations of this carried out study?
-How to select and optimize the user-defined parameters in the proposed model?
Experimental design: please see above
Validity of the findings: please see above
Additional comments: please see 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: MULTI-MODAL AFFINE FUSION NETWORK FOR SOCIAL MEDIA RUMOR DETECTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors proposed the multimodal affine fusion network (MAFN) combined with entity recognition, a new end-to-end framework that fuses multimodal features to detect rumors effectively.
Experimental design: The MAFN mainly consists of four parts: the entity recognition enhanced textual feature extractor, the visual feature extractor, the multimodal affine fuser, and the rumor detector. The entity recognition enhanced textual feature extractor is responsible for extracting textual features that enhance semantics with entity recognition from posts. The visual feature extractor extracts visual features. The multimodal affine fuser extracts the three types of modal features and fuses them by the affine method, and it cooperates with the rumor detector to learn the representations for rumor detection to produce reliable fusion detection.
Validity of the findings: The manuscript sounds technically good, I have the following concerns that should be addressed before any decision.

Grammatical mistakes
1.	However, without supervision, the authenticity of published information cannot be detected--> should be ... "However, the authenticity of published information cannot be detected without supervision".
2.	With the rapid development of the Internet, people obtain abundant information from social media such as Twitter and Weibo every day. However, due to the complex structure of social media, many rumors with corresponding images are mixed in real information to be widely spread, which misleads readers and exerts negative effects on society --> should be---> "With the rapid development of the Internet, people obtain much information from social media such as Twitter and Weibo every day. However, due to the complex structure of social media, many rumors with corresponding images are mixed in real information to be widely spread, which misleads readers and exerts adverse effects on society."
3.	Established based on the multi-modal affine fuser, the rumor detector sent the finally obtained multi-modal feature--> "Based on the multi-modal affine fuser, the rumor detector sent the finally obtained multi-modal feature."
Minor Changes:
1.	The author should provide only relevant information related to this paper and reserve more space for the proposed framework.
2.	The theoretical perceptive of all the models used for comparison must be included in the literature.
3.	What are the real-life use cases of the proposed model? The authors can add a theoretical discussion on the real-life usage of the proposed model.
4.	However, the author should compare the proposed algorithm with other recent works or provide a discussion. Otherwise, it's hard for the reader to identify the novelty and contribution of this work.
5.	The descriptions given in this proposed scheme are not sufficient that this manuscript only adopted a variety of existing methods to complete the experiment where there are no strong hypotheses and methodical theoretical arguments. Therefore, the reviewer considers that this paper needs more works.
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: MULTI-MODAL AFFINE FUSION NETWORK FOR SOCIAL MEDIA RUMOR DETECTION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The abstract should be reformulated. The abstract is an extremely important and powerful representation of the article. The authors have to clarify what is the novelty of this paper in abstract.
•	Reduce challenges list as much as you can.
•	Provide the related works clearly highlight the main gap.
•	Authors have proposed three algorithms, but I do not understand which one is used in the comparison with state-of-the-art works.
•	Figures 5abcd, have same label which quite confusing what is the point from each figure.
•	Figures looks very fuzzy, and resolution of image is poor.
•	Replace Case Study Performance Visualization with the discussion section as it is very poor and more deep discussion is needed for findings of the study.
•	Conclusion needs to be improved. The most important obtained results should be briefly and clearly mentioned through the support of numerical data in the conclusion.
•            The details in this manuscript are vague, especially in the depth feature extraction, which is the major defect of this paper. In addition,   there is a gap between the experimental condition and the real scene. So this method can not be applied to the real scene effectively.
•      There are many words and figures about the background/architectures of the proposed networks used in this paper that can be omitted. These proposed networks are in the field for a while and they are known by most likely every researcher in the field.
•      There is a lack of comparison with other studies in the discussion. I do know that from the “related work” introduced in this paper that most previous study provides a very high accuracy/statistic in a much smaller dataset. The quantitative results are lower if just compare to the numeric values. However, the model in this study could be more robust than other previously published models applying your dataset using other models.
Experimental design: see above
Validity of the findings: see above
Additional comments: see 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: MULTI-MODAL AFFINE FUSION NETWORK FOR SOCIAL MEDIA RUMOR DETECTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: I'm satisfied with the current version.
Experimental design: I'm satisfied with the current version.
Validity of the findings: I'm satisfied with the current version.
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: MULTI-MODAL AFFINE FUSION NETWORK FOR SOCIAL MEDIA RUMOR DETECTION
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper seems improved as compared to previous versions. Hence, it is acceptable for publication.
Experimental design: The paper seems improved as compared to previous versions. Hence, it is acceptable for publication.
Validity of the findings: The paper seems improved as compared to previous versions. Hence, it is acceptable for publication.
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: MULTI-MODAL AFFINE FUSION NETWORK FOR SOCIAL MEDIA RUMOR DETECTION
Review round: 2
Reviewer: 3

Output:
Basic reporting: Thanks, although the manuscript is improved, however, there are some minor things that should be considered before publication.

- For example, the organization of the paper. All tables and figures should be cited in order from low to high.
Experimental design: Looks good to me
Validity of the findings: Looks good to me
Additional comments: - Some grammatical and punctuation issues exist in the paper. It should be rectified.
- The figure quality still needs enhacments.


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: DEEP LEARNING METHODS FOR INVERSE PROBLEMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: I suggest you clarify the innovation of this paper in the introduction, that is, the contribution made by this article.
Secondly, this article contains a large number of formulas. I hope you can explain the meaning of each symbol in the formula in detail, so as not to cause trouble to readers.
In addition, the figures in this paper need to be explained in more detail or clearer images are selected, as shown in FIG. 11
Formal results should include clear definitions of all terms and theorems, and detailed proofs.
Experimental design: This article explores deep learning methods for inverse problems. In order to facilitate readers to implement the methods used in this article, I suggest that you need to clearly list the hyperparameters required for deep learning training in the article.
The data set used in this study are available publically which is completely limited and imbalance.
In this article, you use various quantitative indicators to measure the advantages and disadvantages of various methods. I hope you can describe in detail the principle of these indicators and their relationship with the quality of the model.
For the overall beauty of the article, I suggest you use a unified style for the drawings of this article.
Validity of the findings: The methods used in this article are the results of early work. I suggest you use the latest achievements to enrich the work of this article, such as the work of the last year.
Additional comments: No comment. See the comments in the above three parts for details


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: DEEP LEARNING METHODS FOR INVERSE PROBLEMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The main innovation of this manuscript is to compare the robustness of the three categories, and report a statistical analysis of their differences, which is not novel enough. The method of this paper is not innovative enough. In fact, most of the work is done by combining other people’s methods. Authors need to highlight their innovative contributions.
Although the article is clear on the whole, I still hope you pay attention to the clarity of the article. There are numerous places in the article that say the same thing over and over again. I hope you can check and delete the redundant parts.
Pay attention to consistency to avoid confusion. Another obvious problem with this paper is the lack of sufficient experimentation to demonstrate the validity and applicability of the three methods.
Experimental design: I recommend you to read this manuscript: " Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs"Perhaps the author can find inspiration by reading more literature in this field to further optimize this paper.
Please check the normalization and accuracy of the figure in the paper carefully. Many figures are far away from the analysis paragraphs. I hope they can be adjusted.
The literature on deep learning methods for solving inverse problems was classified into three categories, of which was evaluated on sample inverse problems of different types. Why not try more categories? You can make some experiments on more categories.
Validity of the findings: One obvious problem with this paper is the lack of enough experimentation to demonstrate the validity and applicability of the proposed method. The author needs to do more experiments with more angles and show them in this paper.
Additional comments: No comments


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

Input:
Title: DEEP LEARNING METHODS FOR INVERSE PROBLEMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: According to the author's response, the author solved related problems, revised the grammatical errors in the revised paper, and clearly expounded the contribution of this paper.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: According to the author's response, the author solved related problems, revised the grammatical errors in the revised paper, and clearly expounded the contribution of this 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: DEEP LEARNING METHODS FOR INVERSE PROBLEMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The article meet our standards. The article include sufficient introduction and background to demonstrate how the work fits into the broader field of knowledge. Most of the problems have been solved.
Experimental design: In last version the article is the lack of sufficient experimentation to demonstrate the validity and applicability of the three methods. The author repeated the training procedures 5-10 times and reported the resulting statistics and aggregated the overall results in a separate “Discussion” section to provide a
insights regarding issues facing all of the types of inverse problems in their experiments.
Validity of the findings: The author maintain that the relative merits and the relative applicability of the various methods is not particularly understood, and it is precisely this relative performance which is the purpose and contribution of this paper.
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: BUILDING A KOREAN MORPHOLOGICAL ANALYZER USING TWO KOREAN BERT MODELS
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, a Korean morphological analyzer (KMA) is designed to output a sequence of a single morpheme and its Part-of-speech (POS) that follows the morpheme. As the lengths of the input and output are different, Korean morphological analysis can be defined as an encoder-decoder problem.In this work,the author adopt the Transformer architecture to implement a KMA.
The author's contributions are as follows:
1.	In this work, the author utilized two kinds of BERT models to initialize Transformer. The wBERT is pre-trained with Korean raw sentences and the mBERT is pre-trained with morphologically analyzed sentences consisting of morphemes and POS tags.And the author initialized an encoder of Transformer with wBERT and a decoder of Transformer with mBERT.
2.	The author found the optimal number of layers for the encoder and decoder to achieve the best performance of KMA.
3.	The author summarized the experimental results that evaluate the impact of initializing wBERT and mBERT.
4.	The author measured changes in accuracy in the models as the size of training dataset decreased from 100% to 10%.
Experimental design: Strengths and Weaknesses
1.	In the experiment of finding the optimal number of layers for the decoder,the encoder is initialized with wBERT while the decoder is randomly initialized.But there seems to be no reason to do so, so I think the author should explain the specific reasons for it.
2.	In the experiment of finding the optimal number of layers for the encoder, the author fixed the number of layers in the decoder at 4. So the conclusion of this experiment is based on the premise that decoder has 4 layers, and it doesn't mean that the optimal number of layers is that(maybe when the decoder has other layers).
3.	In table 7, when wBERT and mBERT are used to initialize the model and the data set is 10%, the data in this cell should be 95.23% instead of 98.23%.
4.	In table 8, the comparation of F1 values in different models is meaningless, because they used different test datasets.
Validity of the findings: Innovation
This paper is the first attempt to initialize both an encoder and decoder for Transformer with two kinds of Korean BERT.
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: BUILDING A KOREAN MORPHOLOGICAL ANALYZER USING TWO KOREAN BERT MODELS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The structure of the overall article is very logical, but there are some problems in the details.
（1）In the relevant work, the analysis of the relevant research on Korean at this stage is not very clear, and there is no clear analysis of the research deficiencies at this stage and the innovation of this research. It is necessary to explain the enlightenment or reference of the past literature research to this research.
（2）The paper does not explain how many words the longest text and the shortest text of the experimental data are respectively. It is necessary to further explain whether the algorithm is suitable for long text or short text. According to the experimental data set in the paper, it seems that it is only suitable for the training of short text, so we need to focus on this aspect again.
Experimental design: （1）The specific process description of the experiment is a little simple, such as pretreatment, model experiment process, etc., which should be connected with the subsequent evaluation.
（2） In the introduction section, in the second paragraph, in order to verify the problem of "the output sequence of a KMA differs from a raw input sequence in both length and surface form.", I think that not all sentences are the same problem, please add which one kind of sentence has this problem.
（3） In the section of SURVEY ON KOREAN MORPHOLOGICAL ANALYZER, it only proves the rationality of the transform structure, but does not prove the rationality of "initialize both an encoder and decoder for Transformer with two kinds of Korean BERT.", please add relevant explanations.
Validity of the findings: （1） The main contribution of this article is "train the morphological analyzer faster and with less training data".In Table 7, the size of the data set is divided into 10%, 30%, 50%, 70%, 100%. The F1 score first drops and then rises, but the F1 value of the data set at 100% is significantly better than the F1 value of 10%, please Explain why the model proposed in this paper requires a smaller amount of data set.
（2）For the main contribution 2 "find the most appropriate number of layers in the BERT models for a Korean morphological analyzer"&" we observed that the accuracy of the Korean morphological analyzer is highest when it has four layers in the decoder and 12 layers in the encoder."Compare the experiment, whether the data set in other fields is applicable to the results of this article.
Additional comments: No comment.


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 A KOREAN MORPHOLOGICAL ANALYZER USING TWO KOREAN BERT MODELS
Review round: 1
Reviewer: 3

Output:
Basic reporting: some details are missing in this version.
the reviewer believes that the introduction is overlength.
Experimental design: some mismatched data in the experiment section.
Validity of the findings: the proposed model is worked in a certain dataset.
more experiments are required to validate the proposed model.
Additional comments: 1 the author should clarify how the encoder and decoder are initialized with the pre-trained BERT model.
2 it is really necessary to define the classification between the word separator token and morpheme tokens as a multi-task learning? The authors should provide sufficient rationale.
3 more experiments should be conducted to validate the multi-task learning in this work.
4 In section experiment, the data (line 180) is mismatched with that of in Tab.7.
5 more details should be provided to improve the readability of this work, such as the initialization.


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 A KOREAN MORPHOLOGICAL ANALYZER USING TWO KOREAN BERT MODELS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Compared with the previous version, the author has made some supplements and modifications to the experiment, but there are still some problems in the details.

(1)When exploring the influence of training set size on experimental results, there should be experiments with full-layers for both encoder and decoder, as in the previous experiments.

From my point of view,  I suggest a few modifications are still needed before publication.
Experimental design: (1)In the experiment of finding the optimal number of encoder and decoder layers , I think the two experiments made by the author can be combined into one, that is, the optimal combination of layers for encoder and decoder can be determined by the first experiment alone.
Validity of the findings: Compared with the previous version, the author has made some supplements and modifications to the experiment, but there are still some problems in the details.
Additional comments: (2)In table 6, the fourth column heading should be '> 100 tokens' not '< 100 tokens'.


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: PREDICTING DEFECTS IN IMBALANCED DATA USING RESAMPLING METHODS: AN EMPIRICAL INVESTIGATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The structure or the organization of the paper is not very clear to me. I understand that the authors of the paper are trying to investigate the impact of class reblancers on defect prediction models. However, I am not sure why the authors investigate the features that are selected consistently by   a CFS method. They do not use this finding anywhere else. Furthermore, they assert that using all the features might lead to overfitting of the models. However, the studied defect datasets do not have more than 30 features! So I would recommend that the authors explain why they study a feature selection method along with class rebalancing if there is no connection between the findings.

I recommend the authors to replace table 6, 7, 8,9 ,10, 11 with side-by-side boxplots for better readability
Experimental design: several prior studies (e.g., https://arxiv.org/abs/1609.01759) find that tuning the machine learning classifiers is extremely important for the defect prediction models to perform well. In this case, it could particularly be an important confounder that affects the results. I would recommend the authors to tune the models that they use in their study

I am not clear about how the statistical tests are used. For instance, the authors say they used a Friedman test followed by a Wilcoxon test to rank the performance of the classifiers. However, a friedman test to my knowledge doesn’t generate ranking and a Wilcoxon test is only a parwise comparison. So I am not sure how the authors arrive at the ranking. I would urge the authors to explain the statistical tests that they used to arrive at their results a little clearly

Similarly, to rank the results of Friedman test, a post-hoc nemenyi test is the typical suggestion, rather than the Wilcoxon. Hence I would suggest the authors to use that (e.g., you can follow this paper: https://arxiv.org/abs/1707.09281, https://joss.theoj.org/papers/10.21105/joss.02173)

If Wilcoxon is used to compare multiple pairs, you need to use a p-value correction like a Bonferroni correction for the results to be reliable.
Validity of the findings: Major concern: Some of the key related works are missing. For instance, a recent TSE (https://arxiv.org/abs/1801.10269) and an ICSE (https://arxiv.org/pdf/1705.03697.pdf) study through a comprehensive study finds that SMOTE is indeed better than other class rebalancing methods. These studies are not discussed. I would recommend the authors to consider these studies and position their findings in the context of these studies.

More along my previous point,  the aforementioned papers assert that tuning the SMOTE is important for the benefits of SMOTE to shine through. Tantithamthavorn et. al. (https://arxiv.org/abs/1801.10269) in particular find that tuned SMOTE helps defect prediction models perform better than unbalanced classifier. They also compare several class rebalancing methods (though not as comprehensive). So I would like the authors to position their findings in the context of this paper. Therefore, I would suggest the authors to include the datasets used by Tantithamthavorn et. al. also in their studies to see if their findings agree with Tantithamthavorn et. al. or if they could refute them.

Because the statistical issues outlined earlier I am not sure if the results are reliable.
Additional comments: 1.	The paper compares an impressive array of class rebalancing methods and defection prediction classifiers.
2.	The paper comprehensively tries to estimate the impact of the class rebalancing method by experimenting with multiple configurations.


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: PREDICTING DEFECTS IN IMBALANCED DATA USING RESAMPLING METHODS: AN EMPIRICAL INVESTIGATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: - In general, the manuscript is clear and well-written, however it lacks a Discussion section which would be useful to present a general overview of the findings and discuss the results on a higher level.

- The study can also benefit from a small description of the research questions under study to make it more clear to the reader.

- While Figure 1 might be comprehensive, it would be better to include a small description which might make it easier for the reader to understand the full experimental process of the study.

- The paper contains grammatical/spelling errors that can be fixed with a detailed proofread, some of them are listed below:
        Line 25: “…softwares…” --> “…software…”
        Line 37: “…with uncovering the probable…” --> “…with uncovering probable…”
        Line 44: “…becomes a difficult task.” --> “…become difficult tasks.”
        Line 52: “…trained on the similar…” -->  “…trained on similar…”
        Line 65: “…it is widely…” --> “…it is a widely…”
        Line 66: “…and emerged…” --> “…and it has emerged…”
        Line 114: “…for existing ones.” --> “…from existing ones.”
        Line 303: “…by Wilcoxon signed-rank test…” --> “…by the Wilcoxon signed-rank test…”
        Line 341: “…IQA is scribed…” --> “…IQA is described…”
        Line 350: “…66.67%%...” --> “…66.67%...”
Experimental design: The paper includes a good experimental design of a large empirical study by including a set of widely used ML techniques and datasets, a set of non-biased evaluation measures and statistical tests. However, statistical significance cannot be assessed alone without analysing the effect size (Arcuri and Briand, 2004 https://dl.acm.org/doi/10.1002/stvr.1486). The effect size is a quantitative measure of the magnitude of the experimental effect and it would give the reader a better understanding of the impact of the difference in results.

Some of the datasets used in this study describe systems that consist of multiple versions, for example Log4j1.0 and Log4j1.1. Is there any reason 10-fold cross validation was favoured and applied over the more realistic cross-version defect prediction scenario whereby the model is trained on an older version and tested on the most recent one?
Validity of the findings: The study includes a well-developed related work section but fails to position the work with respect to existing ones, for example, the work of Wang and Yao, 2013. This undermines the contribution and novelty of the study presented which should be better highlighted in the manuscript with respect to the large number of existing studies tackling the data imbalance problem and comparing over- and under-sampling techniques.
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: PREDICTING DEFECTS IN IMBALANCED DATA USING RESAMPLING METHODS: AN EMPIRICAL INVESTIGATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have addressed the previously raised comments
Experimental design: The authors have addressed the previously raised comments
Validity of the findings: The authors have mainly addressed the previously raised comments, thanks. I only still have a concern regarding the position of the paper with respect to related work. I believe
it would be easier for the reader to understand the contribution of the work if the differences between the proposed study and previous work are highlighted in a better and clearer manner. I also believe that the newly added paragraph about previous similar work would be a better fit in Section 2 (Related Work) rather than in Section 5 (Discussion).
Additional comments: no comment


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: NOVEL HYBRID FIREFLY ALGORITHM: AN APPLICATION TO ENHANCE XGBOOST TUNING FOR INTRUSION DETECTION CLASSIFICATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. In the abstract of this paper, the authors consider that “One of the greatest issues from the domain of network intrusion detection systems are relatively high true positives and false negatives rates.” Nevertheless, a high true-positive rate is precisely what researchers in the IDS need and pursue. So why is it an issue to address? Please clarify whether the authors are a clerical error. If not, please fully explain. If it is indeed a clerical error, please check the article carefully to avoid ambiguity and misunderstanding caused by similar problems.

2. In the introduction of this paper, the authors need to verify their understanding of the basic concepts. In particular, the significance of false-positive rate and false-negative rate in IDS. If these two definitions are not proper, are subsequent performance metrics based on these defined correctly? Are subsequent experimental designs and data reliable?
Experimental design: 1. The authors have carried on the sufficient experiment, the multi-level multi-stage comparison, and submitted the source code. However, could the authors consider presenting experimental data more diversely? Large and unlabeled forms can make reading difficult. In addition, the dataset is somewhat monolithic so that authors can try multiple datasets rather than just NSL-KDD.
Validity of the findings: 1. Reducing false positive and false negative rates is an urgent problem in IDS. However, the authors need to explain more fully (1) why XGBoost is used as a classifier for IDS to solve this problem (especially when the model has been plagued by a high false-positive rate and false-negative rate) and (2) why the hyperparameters of XGBoost are optimized by a heuristic algorithm rather than other methods. It requires not only theoretical analysis but also experiments.

2. Please further describe the challenges met and problems solved in the research. And match them to the contribution of this work.

3. In the contribution of the paper, the authors state that “A novel enhanced FA metaheuristics has been developed by specifically targeting the well-known deficiencies of the original FA implementation;” Please specify the “well-known weaknesses” and how to overcome them? Given such shortcomings, why is it still necessary to base FA metaheuristics design solution? Why can’t other similar schemes replace FA metaheuristics?

4. The authors provide a clear and sufficient description of improvements of the firefly algorithm but lack the content of the overall scheme. Please describe the complete process of this scheme in an appropriate way.
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: NOVEL HYBRID FIREFLY ALGORITHM: AN APPLICATION TO ENHANCE XGBOOST TUNING FOR INTRUSION DETECTION CLASSIFICATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: Basic reporting is average.  Language and presentation should be improved.  There are several typos that should be addressed.  Authors need to make a careful revision of the document in this regard.
Further IDS using ML and DL models is used widely discussed in the literature, including XGBoost algorithm and also with other ML based techniques. Further, there are techniques based on XGBoost to take care of encrypted environment.   For eg.
Intrusion detection model using fusion of chi-square feature selection and multi class SVM, 2017.
Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models, 2021.
Under this backdrop authors need to provide a justification for the new proposal.
Is the focus is to improve FA or improve IDS accuracy? The statements in second page are confusing.
Contributions listed do not indicate the IDS aspect.  So the understanding goes is that the authors have worked on the optimization algorithm and in order to validate it they have used IDS datasets.  In such case, the title of the paper is not justifiable.
There are several computational aspects in the proposed algorithm which do not have specific way of handling.  For e.g Perform search operations.  What is the complexity?
What is K in the algorithm?  First of all,, what are the inputs to the algorithm and what are the output?
It is better if the extensive literature reported are summarized in the background section.
Experimental design: Results are fine.  However there are other datasets such as UNSW, VIT Sparc, etc.
Validity of the findings: Results are validated using the standard dataset.  Hence they are validated.
Additional comments: NIL


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: NOVEL HYBRID FIREFLY ALGORITHM: AN APPLICATION TO ENHANCE XGBOOST TUNING FOR INTRUSION DETECTION CLASSIFICATION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The English is clear.
Experimental design: A design setup of the proposed architecture is required for analyzing the whole approach.
Validity of the findings: What is the motivation for this work as there are many other models developed for IDS integrated with meta heuristic algorithms?

The proposed model is tested on NSL-KDD dataset which is very old. The authors should select recent public benchmark intrusion detection datasets and show the superiority of the proposed model.

Though ensemble models result in better accuracy, their execution time is greater in comparison to base classifiers. The authors should analyze the computational complexity of the proposed work.

A comparative analysis of the proposed model with state-of-the-art intrusion detection models should be performed.

What is the effect of optimizing the XGBoost parameters in the proposed approach. The authors should analyze in detail how these parameters had a positive impact in performance in discussion section.
Additional comments: Certain intermediate results of CEC2013 can be separately added in a appendix section at the end of 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: NOVEL HYBRID FIREFLY ALGORITHM: AN APPLICATION TO ENHANCE XGBOOST TUNING FOR INTRUSION DETECTION CLASSIFICATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: It is improved from the previous version.
Experimental design: While the design looks good, experimental results are not presented clearly.  Some of the tables are not visible.
Validity of the findings: Yes.
Additional comments: While answering the questions/suggestions some of the responses are overlapping towards the queries.  The same answer is provided for different queries.  This confusion should be clarified.


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: NOVEL HYBRID FIREFLY ALGORITHM: AN APPLICATION TO ENHANCE XGBOOST TUNING FOR INTRUSION DETECTION CLASSIFICATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: Clear
Experimental design: All comments are addressed by the authors
Validity of the findings: All comments are addressed by the authors
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: NOVEL HYBRID FIREFLY ALGORITHM: AN APPLICATION TO ENHANCE XGBOOST TUNING FOR INTRUSION DETECTION CLASSIFICATION
Review round: 3
Reviewer: 1

Output:
Basic reporting: Basic reporting is fine.
Experimental design: Experimental design is explained fine.
Validity of the findings: It is experimentally validated by the authors.
Additional comments: Article is improved from the current version.


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: CGCNIMP: A CAUSAL GRAPH CONVOLUTIONAL NETWORK FOR MULTIVARIATE TIME SERIES IMPUTATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: Thanks for the submission and for sending me the manuscript (MS) by Liu et al. on Imputations of Geese titled “Bird-CGCNImp: a causal graph convolutional network for multivariate time series imputation”
I find the approach is experimental and creative. I am not much against it, but the current MS falls short on a few relevant science points:
-Whatever authors propose and defend, unless we see the data and the code with ISO compliant metadata, this work is not repeatable or transparent. It currently does not meet latest principles of science, or best professional practices. It can easily be fixed though as all data can now fully be made open access and with open source code models with ISO standard documentation (metadata), online, in GBIF and in Movebank even. I stress that specifically because work on these two species and the flyway is already full open access and available with 24 years of remote field work data. See GBIF and then Solovyeva et al. 2021. So why not here ? Not sharing data is not defendable whatsoever and it violates any best professional practices and collegiality. Before that is not clearly done I do not approve of the review or publication.
-Imputation is just one of many methods. Personally, I would use the method of predicted ecological niches instead, or in parallel and to compare. Authors brought it down to a very narrow and self-fulfilling prophecy, just as the endless data filtering to get rid of so-called outliers (as often done in such telemetry works). From the MS and figures, authors seem to believe birds fly on a straight line connecting dots, which is highly dubious. How about krigging, Least Cost paths, or Circuitscape or Marxan approaches instead? One certainly needs a CERTAINTY and CONFIDENCE in the approach, outcome and maps; done how ? Anyways, if authors stick with imputations, please state citations on the issue, namely Jerome Friedman, classification trees and boosting, and many forest inventory and remote sensing references. Much literature and expertise sit there that was not used here. It should.
-The way how to best approach the gap filling should be like a scenario, an hypothesis, or a re-analysis of the existing data. That should be made clear and pursued that way.
-I like Figures 5, 6 and 7 for the concept but those are currently  way too small and too selective to be useful or convincing. We need it for all the data, not just some examples.
-let’s agree that the biggest topics - research design, representative sampling of the tagged species, technical fault patterns in the transmission, and impacts of anesthesia - are not mentioned. They must, as those do overrule the data and policy question one wants to purse, e.g. where are the ‘true’ dots, where are the flyways, habitats and do we have broadfront migration or any mixing ? Getting stuck with a line is just not appropriate for a cohort or population inference. The latter is presumably our all policy goal.
-the title and concept of “Bird-CGCNImp” reads odd, and is odd. It’s not needed that way and should be improved. Same is true for ‘causal graph convolutional network’. That’s hardly English, not a term, and nobody speaks that way (in England, or the English speaking/reading world).
 -re ‘Identifying bird migration trajectories and discovering habitats is very important for conserving species diversity.’: That’s 100% untrue. Can you show me an example in China, or Malta or anywhere else ? We see flyway declines all over and for many years, see Jiao et al. 2016. The protected areas are way too tiny, paper parks, and do not help. So, this phrase above sets up a strawman; not needed but better to be honest.
-the Literature references are poorly formatted, e.g. first names, abbreviations, and widely incomplete for topics
-Finally, the problem we have in flyways and in China is a ruthless habitat conversion and loss scheme; as it is found globally now. Connecting the bird dots on a line for policy helps little. Happy to be shown any other.

If authors can fix those items, specifically data shared and all data they have at hand, as well as conservation progress, then I would be happy to re-read their update perhaps.
Thanks, best regards
    Falk Huettmann
Experimental design: The article has no typical research design
Validity of the findings: As stated in section 1, authors assume linear movements, which is not justified.
Additional comments: Thanks.

I am happy to support creative and experimental work, but the MS - as presented - fails on some basic
science issues, namely transparency and repeatability.


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: CGCNIMP: A CAUSAL GRAPH CONVOLUTIONAL NETWORK FOR MULTIVARIATE TIME SERIES IMPUTATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this paper, the authors propose a causal GCN for bird migration trajectories motivated by the existence of both the attribute correlation and the temporal auto-correlation dependencies. The proposed method is novel and the application of bird migration trajectories shows its effectiveness.
Experimental design: In the proposed Bird-CGCNImp method, the authors establish an end-to-end multi-task model to capture both attribute correlation and temporal auto-correlation dependencies. Besides, the authors also notice the noise in the actual sampling process and design a total variation reconstruction regularization term to improve the imputation accuracy. The authors clearly present the proposed method's motivation and technology details.
Validity of the findings: In the experiment, the authors conduct the experiment on one public time-series imputation benchmark and two real-world bird migration trajectory datasets. The experimental results are promising. The authors also demonstrate the effectiveness of each component in Bird-CGCNImp by ablation study. My suggestion of the experiment is that the authors should repeat the experiment several times and report the mean and standard deviation of the metric so that the experimental results would be more convincing.
Additional comments: Missing citations:

Suo Q, Yao L, Xun G, Sun J, Zhang A. Recurrent imputation for multivariate time series with missing values. In2019 IEEE International Conference on Healthcare Informatics (ICHI) 2019 Jun 10 (pp. 1-3). IEEE.

minor:

Some of the citations are incomplete. For example, the following citations miss the journal name:

Nazabal, A., Olmos, P. M., Ghahramani, Z., and Valera, I. (2020). Handling incomplete heterogeneous
481 data using vaes.

Yoon, J., Zame, W., and Schaar, M. (2017). Multi-directional recurrent neural networks : a novel method
498 for estimating missing 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: FEATURE SELECTION BY INTEGRATING DOCUMENT FREQUENCY WITH GENETIC ALGORITHM FOR AMHARIC NEWS DOCUMENT CLASSIFICATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: This work focuses on the application of Feature Selection by Integrating Document Frequency with a Genetic Algorithm for Amharic News Document Classification.  A hybrid of document frequency (DF) and genetic algorithm (GA)-based feature selection method is suitable for use in a variety of applications requiring Amharic document classification. Datasets from different domains with different categories are considered.

First, the writing should be improved a lot. A lot of grammar issues, unsound statements,
and incomplete sentences phrases are very common. there are also very very long sentences, please make it readable, write short and concise sentences.

There are some issues to be fixed.
Introduction: " Examine the performance of the proposed feature selection method in
terms of accuracy, precision, recall, and F-measure."  it should be not your main contribution.
What was your starting Research Question or the gap you want to fill? Clearly define your research questions.
Introduction: "2007 census [1]" This is a very old reference, do you have a near time reference for this?

On related work: revise it again even you have not cited the latest paper that was done in
Amharic news document classification like this https://doi.org/10.1371/journal.pone.0251902
Data processing -Normalization: Which is "canonical" normalization or standardization?
What was your base to normalize homophone characters into a single representation?
It would be also nice if your approach is compared with homophone normalization and without homophone normalization approaches.

Results and discussion section (table 6):
Is it the different hybrid results are from the previously conducted paper or from your data?
if it is from the previous paper, the data you have used is different, set clearly your experiment results.
Experimental design: - Even the paper has minor, easily fixable, technical flaws that do not impact the validity of the main results, the paper contributes some new ideas and applicable resources.
- Extensive experiments with multiple feature selection techniques.

- Set clearly the gap that you have filled at the end of your experiments.
- Link your results to the research questions that should be provided.
Validity of the findings: The paper contributes some new findings regarding feature selection for Amharic news document classification.
The dataset that has been provided is also valid and highly applicable for the next research.
Additional comments: Go ahead through the suggestions that are mentioned in the above sections.
Indicate your significant contribution very briefly.


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: FEATURE SELECTION BY INTEGRATING DOCUMENT FREQUENCY WITH GENETIC ALGORITHM FOR AMHARIC NEWS DOCUMENT CLASSIFICATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: no comment
Validity of the findings: There is no comparative study of results with other authors [10] on the same dataset. The dataset used in this study does not match the dataset referenced [10].
Additional comments: Line 124, It looks like a regular pre-processing step for any other language. How is the pre-processing different in context to Amharic?

Line 144, Gasser's HornMorpho stemmer: what is the accuracy of the stemmer used? How is it evaluated? is it a Rule-Based or Statistical??

Table 2, what basis are the documents classified into major categories? Who did this classification? Can a document not belong to multiple types, e.g. education and health?

Table 2, what is the length of the documents in each category?
Line 248, Any observations on varying the splitting ratio versus the proposed model's performance?

Can the proposed methodology, etc. used be adapted/used for other similar languages? Conclusion of future work may discuss this.

The dataset used here is small in size, and I recommend that the authors test your proposed algorithm on a larger dataset in a future studies.


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: FEATURE SELECTION BY INTEGRATING DOCUMENT FREQUENCY WITH GENETIC ALGORITHM FOR AMHARIC NEWS DOCUMENT CLASSIFICATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment.
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: FEATURE SELECTION BY INTEGRATING DOCUMENT FREQUENCY WITH GENETIC ALGORITHM FOR AMHARIC NEWS DOCUMENT CLASSIFICATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: No Comment
Experimental design: No Comment
Validity of the findings: No Comment
Additional comments: Kindly cite the following papers.

https://doi.org/10.4218/etrij.2019-0458

doi: 10.1109/ICDIM.2018.8847044


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: MULTI-LABEL EMOTION CLASSIFICATION OF URDU TWEETS
Review round: 1
Reviewer: 1

Output:
Basic reporting: please see my review below
Experimental design: please see my review below
Validity of the findings: please see my review below
Additional comments: The manuscript is centered on a very interesting and timely topic, which is also quite relevant to the themes of PeerJ Computer Science. Organization of the paper is good and the proposed method is quite novel. The length of the manuscript is about right but keyword list is missing. The paper, moreover, does not link well with recent literature on sentiment analysis appeared in relevant top-tier journals, e.g., the IEEE Intelligent Systems department on "Affective Computing and Sentiment Analysis". Also, latest trends in multilingual sentiment analysis are missing, e.g., see Lo et al.’s recent survey on multilingual sentiment analysis (from formal to informal and scarce resource languages). Finally, check recent resources for multilingual sentiment analysis, e.g., BabelSenticNet.

Authors seem to handle sentiment analysis simply as a binary classification problem (positive versus negative). What about the issue of neutrality or ambivalence? Check relevant literature on detecting and filtering neutrality in sentiment analysis and recent works on sentiment sensing with ambivalence handling.

Finally, the manuscript only cites a few papers from 2020 and 2021: check latest works on attention-based deep models for sentiment analysis and recent efforts on predicting sentiment intensity using stacked ensemble.

Some parts of the manuscript may result unclear for some readers of this journal. A short excursus on emotion categorization models and algorithms could resolve this lack of clarity (as the journal does not really feature many papers on this topic) and improve the overall readability of the paper. On a related note, the manuscript presents some bad English constructions, grammar mistakes, and misuse of articles: a professional language editing service is strongly recommended (e.g., the ones offered by IEEE, Elsevier, and Springer) to sufficiently improve the paper's presentation quality for meeting the high standards of PeerJ Computer Science.

Finally, double-check both definition and usage of acronyms: every acronym should be defined only once (at the first occurrence) and always used afterwards (except for abstract and section titles). Also, it is not recommendable to generate acronyms for multiword expressions that are shorter than 3 words (unless they are universally recognized, e.g., AI).


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: MULTI-LABEL EMOTION CLASSIFICATION OF URDU TWEETS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Well written, but needs revisions.
Experimental design: NA
Validity of the findings: NA
Additional comments: Abstract and Introduction should be revised.
Problem statement must be clearly defined in the Introduction.
Use simple present tense throughout the paper.
Related work should have one paragraph of motivation due to limitations of existing approaches. Also, it should have references to the recent similar works.
Authors can consider referring the following articles:
A Consolidated Decision Tree-based Intrusion Detection System for binary and multiclass imbalanced datasets
Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research
Applications in Security and Evasions in Machine Learning: A Survey
Comparison of the work with an recent existing approach is necessary to show the performance improvement.
Result analysis must be thorough.
Conclusion should include limitations of the existing 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: MULTI-LABEL EMOTION CLASSIFICATION OF URDU TWEETS
Review round: 1
Reviewer: 3

Output:
Basic reporting: - The use of the verb "talks" refers to Section 2 in line 64 of page 2 is not suitable.

- Spelling errors and punctuation errors are found in the second paragraph of page 9 (line 286-295).

- Section 4.3: I suggest use variables represent "number of c(e)" or "number of p(e)" in equations on page 10. The variable c(e) should be written in mathematical form (italic). This change should apply to all variables in this article.

- In Section 4.2, page 9: The use of capital letters in "We used the following BERT
301 parameters: MAXSEQLENGTH= 64, BATCH SIZE = 32, LEARNING RATE =2e5,
and NUM TRAIN EPOCHS= 2.0.
Experimental design: - The paper should explain the importance of all evaluation metrics in the context of this task. Explain how the metrics can describe the performance of the algorithms.

- Coding for the project is publicly available.

- In paragraphs 3 and 4 of section 5: The deep learning algorithms performed poorly in this study, why do authors claim that the algorithms perform well and the results are promising?

- In section 4.2, why the values of parameters were selected in this project.
Validity of the findings: -  The description of the challenges faced in collecting information for this dataset and the annotation process should not be considered a contribution.

- No prior work on multilabel emotion classification exists for the Urdu language, and the generation of the dataset can be considered a contribution of this research.

- However, the performance of deep learning and machine learning algorithms for this task are very poor with low accuracy. Would you please explain why this is the case?

- I would suggest more evaluation be conducted to identify algorithms with better accuracy.  Please justify the relevance of your findings even though the accuracy results are mostly low for all algorithms in this study.
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: MULTI-LABEL EMOTION CLASSIFICATION OF URDU TWEETS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Many of the claims made by the authors are not backed by the revision, e.g., author claim to have added literature related to neutrality and ambivalence for sentiment analysis but there is no mention whatsoever of neither of them. Also, authors claim to have fixed acronyms but that is simply not true. Finally, presentation is still not up to PeerJ standards and important relevant literature on multilingual sentiment analysis is still missing.
Experimental design: Please refer to my previous review
Validity of the findings: Please refer to my previous review
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: MULTI-LABEL EMOTION CLASSIFICATION OF URDU TWEETS
Review round: 3
Reviewer: 1

Output:
Basic reporting: The authors have addressed all of my concerns and their revisions have substantially improved the manuscript.
Experimental design: Good.
Validity of the findings: Good.
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: BMDD: A NOVEL APPROACH FOR IOT PLATFORM (BROKER-LESS AND MICROSERVICE ARCHITECTURE, DECENTRALIZED IDENTITY, AND DYNAMIC TRANSMISSION MESSAGES)
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. Abstract has not describe completely the outstanding results of this research.
2. Why used Raspberry pi, not the other microcontroller, need to justified in this paper?
Experimental design: The area of work seems to be interested. However, authors just made an experiment in lab (Amazon EC2 virtual machines (VMs) or prototype)), has no real experiment yet. We suggest authors to do more.
Validity of the findings: The results in tables 4,5,6 and 7 can be simplified again, making it easier for readers to see the comparison results.
Additional comments: no comment


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: BMDD: A NOVEL APPROACH FOR IOT PLATFORM (BROKER-LESS AND MICROSERVICE ARCHITECTURE, DECENTRALIZED IDENTITY, AND DYNAMIC TRANSMISSION MESSAGES)
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors propose an IoT platform called BMDD (Broker-less and Microservice architecture, Decentralized identity, and Dynamic transmission messages) built on broker-less and microservice architecture using gRPC protocol as the primary protocol for data collection and device control. Besides, a decentralized authentication model based on blockchain technology to enhance the security of the IoT Platform will be introduced. Furthermore, BMDD also provides the function to manage user devices and channels that reduce security issues from user behavior. In addition, providing dynamic message exchange, as the purpose of this proposal, creates uniformity in IoT architecture and eliminates the distinction between different applications, data types, and IoT device manufacturers. The final recommendation is to add a message queue system to enhance platform reliability.
The paper is well structured and readable.
The paper has a good potential for being appreciated and cited, but it requires some improvements and also extension.
The section Introduction should clarify better and provide concise information with regard to the problem definition and scope of the paper.
About the related work section, each paper should clearly specify what is the proposed methodology, novelty, and results from experimentation. At the end of related works, highlight better in some lines what overall technical gaps are observed in existing works, that led to the design of the proposed approach. Innovative and self-organizing methodologies, as https://ieeexplore.ieee.org/abstract/document/9409962 should be reported.
Experimental design: What is the time complexity for the proposed algorithm?
The authors should highlight in what %age and in what parameters, the proposed methodology was found better as compared to existing ones
Analysis about scalability features of the approach could be added to further improve the strength of the paper.
Validity of the findings: The future scope of the methodology should be extended/highlighted.
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: WRITER VERIFICATION OF PARTIALLY DAMAGED HANDWRITTEN ARABIC DOCUMENTS BASED ON INDIVIDUAL CHARACTER SHAPES
Review round: 1
Reviewer: 1

Output:
Basic reporting: [ABSTRACT] The abstract should reflect the contributions of the manuscript. I suggest rewriting it.
[INTRODUCTION] The authors should provide a clear problem definition and contributions in the introduction section.
[RELATED WORK] Where are the related studies? They should be declared in a separate section.
[RELATED WORK] A table of comparisons should be added at the end of the related studies section to praise the pros. and cons. of them. The year column should be added and they should be ordered by it.
[EQUATIONS] The authors should follow the journal authors’ guidance in writing the equations, symbols, and variables. Please, refer to the authors guidelines on the journal official website.
[EQUATIONS] Where are the equations of the used metrics?
[DATASETS] Samples from the used dataset should be added and annotated.
[METHODOLOGY] The suggested approach is not clearly discussed. More scientific details should be added.
[METHODOLOGY] What are the used equations in the suggested approach? In other words, how the suggested approach is derived?
[METHODOLOGY] Where is the overall pseudocode? Flowchart? of the suggested approach?
[ABBREVIATIONS] The authors should add a table of abbreviations in the revised manuscript.
[SYMBOLS] The authors should add a table of symbols in the revised manuscript.
[REFERENCES] There are no citations for many sentences in the manuscript. Why? Please check.
[REFERENCES] The references should be written in the same style following the journal authors’ guidance.
[REFERENCES] Recent citations from 2021 should be added to the manuscript. Only one is found.
[PROOFING] The authors should get editing help from someone with full professional proficiency in English.
[PROOFING] The manuscript should be checked again to fix any typos such as missing spaces and commas.
[CONSISTENCY] The manuscript structure is too short. It must be elaborated in their applied technology as should support more rigorous technical aspects.
[CONSISTENCY] Some paragraphs are wrapped in more than 10 lines. They should be split concisely.
Experimental design: [EXPERIMENTS] The experimental configurations (i.e., settings) should be declared and added to a table.
[EXPERIMENTS] The working environment (i.e., software and hardware) should be declared and added to a table.
[EXPERIMENTS] What are the criteria for selecting the experimental configurations?
[EXPERIMENTS] More experiments should be conducted using different configurations.
[EXPERIMENTS] Where is the tabular representation of the reported results?
[EXPERIMENTS] The figures in the experiments section should be gridded. For example, Figure 8.
[EXPERIMENTS] Why did not the authors compare their approach with others in a table?
[EXPERIMENTS] Why did not the authors compare their approach with another approach to compare the suggested approach efficiency and applicability?
[EXPERIMENTS] Can the authors draw the area under the curve (AUC)?
[EXPERIMENTS] Why did not the authors calculate other performance metrics such as specificity and f1-score?
[EXPERIMENTS] Where is the detailed and statistical discussion of the reported results?
[EXPERIMENTS] More experiments should be conducted using a different dataset to prove the generalization.
Validity of the findings: [NOVELTY] What is the novelty of the suggested approach?
[CONCLUSIONS] The conclusions in this manuscript are primitive. Please, write your conclusions.
Why did not the authors use transfer learning? More experiments should be conducted using it.
Why the authors added Figure 8?
In Table 1: Why the authors selected that model? What is the used criteria?
[LIMITATIONS] What are the limitations of the current study? It should be added in a separate section.
Additional comments: The manuscript presented “a verification of partially damaged handwritten Arabic documents approach”. However, the major and critical weak points are that:
(1)	Their proposed work discussion is weak distributed to be described or analyzed.
(2)	The novelty is not guaranteed.
(3)	Their work is not compared with state-of-the-art approaches nor related studies.
(4)	Their experiments leak from the descriptive and statistical analysis.


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: WRITER VERIFICATION OF PARTIALLY DAMAGED HANDWRITTEN ARABIC DOCUMENTS BASED ON INDIVIDUAL CHARACTER SHAPES
Review round: 1
Reviewer: 2

Output:
Basic reporting: The article  proposes an approach for offline text independent writer verification of partially damaged handwritten Arabic documents, based on individual characters. The extraction of "alphabets" from handwritten text is a manual process. The idea is excellent, however, my main reservation is on the procedure, dataset used and results reached. The dataset collection itself is excellent, however, I suggest to expand it and make it based on all Arabic Character Shapes.

Although the write up is professional there are some inconsistencies in some terms. Example of "handwritten" is spelled in 3 different ways: Two words (hand written) like in the abstract and line 88, (hand-written) as in line 254 and one word (handwritten) as in line 252 etc.. The authors use the term alphabets, letters, characters for Arabic. The most accepted term in the literature is character shape. Arabic characters take different shapes depending on their position in a word.

Better to write the Arabic character shape in Arabic as in line 47 it helps the reader to see the character shape in question. The name of the character shape is not enough.
Experimental design: The authors claim they are using all Arabic Alphabets. Their Extracted Alphabets Dataset (EAD) is collected from users using text in Figure 5. The text uses all Arabic Alphabet, however, it does not use all Arabic Character Shapes. Only about a quarter of the shapes are present in the text used. I suggest to expand the collection for other shapes as some missing shapes have very discriminatory features that could improve the writer verification and will be useful for an extension to writer identification. In this way I claim your reduced model will perform even better.

I don't see the advantage of having IAD and EAD separate. IAD is only one character shape among the 4 possible shapes that an Arabic alphabet can take. The difference is only in the segmentation of Words into characters. If the extraction is manual where is the advantage, please clarify.

Not clear:
* our domain of work .. does not address the problem of writer identification from the Arabic letters. Our main focus is on how to identify the writer based on the "given" handwritten Arabic letters.

* Comparison of isolated and extracted Arabic alphabet based approaches for writer verification.
* Isolated Alphabet:
Samples of user written isolated alphabets in Figure 4, is the middle column as I understand it. What is isolated alphabets in Figure 3, these are not the isolated shapes. Both columns are for middle shape or begin shape characters.
Validity of the findings: I have a serious reservation about the findings.
Why the same character body shape is giving different average error or average accuracy like in Figure 9 or Figure 11.
Examples:
Yaa-begin, Beh-begin, Noon-begin
Teh, Theh, Haa, Khaa
Feh-begin, Qaf-begin
Feh-middle, Qaf-middle
etc..
Additional comments: I encourage you to address the comments and reservation above and resubmit for eventual publication of your 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: WRITER VERIFICATION OF PARTIALLY DAMAGED HANDWRITTEN ARABIC DOCUMENTS BASED ON INDIVIDUAL CHARACTER SHAPES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can declare that they have made a suitable major revision.
Experimental design: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can declare that they have made a suitable major revision.
Validity of the findings: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can declare that they have made a suitable major revision.
Additional comments: Journal: PeerJ Computer Science
Manuscript Title: Writer Verification of Partially Damaged Handwritten Arabic Documents based on Individual Character Shapes
Manuscript ID: PeerJ 67980 R1
Reviewer Number: 1
Submission Date: Wednesday, March 16, 2022
Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can declare that they have made a suitable major revision.
However, I have two minor comments:
(1)	Increase the size (i.e., width) of Figure 7 and Figure 9.
(2)	The authors should get another editing help from someone with full professional proficiency in English. For example, Table 2 caption “List of Symbols used” should be “List of Symbols”.

For the authors in case of the authors got a chance to review the manuscript and submit the revised one after the editor’s decision, please, provide a table in the revised manuscript mentioning (1) the comment, (2) the authors’ response, and (3) the authors’ change (if applicable). Please, consider all of the comments and don’t ignore any of them.
Please, refer to the attached file "PeerJ 67980 R1 Reviewer.pdf" for the same comments in an organized format.


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: WRITER VERIFICATION OF PARTIALLY DAMAGED HANDWRITTEN ARABIC DOCUMENTS BASED ON INDIVIDUAL CHARACTER SHAPES
Review round: 3
Reviewer: 1

Output:
Basic reporting: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can recommend the acceptance of the manuscript in its current version.
Experimental design: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can recommend the acceptance of the manuscript in its current version.
Validity of the findings: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can recommend the acceptance of the manuscript in its current version.
Additional comments: Thanks to the authors for updating the manuscript. After checking their responses to my comments, I can recommend the acceptance of the manuscript in its current version.


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: ADJUSTING EYE ASPECT RATIO FOR STRONG EYE BLINK DETECTION BASED ON FACIAL LANDMARKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: interesting topic and the presented writing is adequate. However, majority of the literature references are too old (more than 5 years). The gap of knowledge of the proposed topic is unclear. Too short prior arts have been discussed, thus, not much of gap of knowledge can be identified from the present works. The landmark used in the study are rely on prior reported works from Dlib, and dataset utilized the publicly available resources from eyeblink8. Suggest the author to test on their own dataset.
Experimental design: the proposed methods are inline with their problem statement. Again, I don't see much of novelty of their proposed works here although the methodology are explained sufficiently.
Validity of the findings: Both qualitative and quantitative analyses are adequate.
Additional comments: NA