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

Input:
Title: COMPARATIVE ANALYSIS OF MAGNETIC INDUCTION BASED COMMUNICATION TECHNIQUES FOR WIRELESS UNDERGROUND SENSOR NETWORKS
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1. In line 474 and 476, two references are same.
2. Figure 7 should show the 3-D structure more clearly.
3. Literature research is not sufficient.
Experimental design: As a literature review paper, most references are too early, more latest should be listed. For example, from line 397 to 417, the reference of advanced 3D-MI technique are published in 2016.
Validity of the findings: Lack of quantitative analysis for each technology. For example, more quantitative data should be presented in Table 1. More figures of analysis or comparasion of the results shoud be presented.
Additional comments: Authors should strengthen literature review in related fields. There should be more academic content, such as proof of the novelty of the method and quantitative analysis, rather than vague written statements.


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

Input:
Title: COMPARATIVE ANALYSIS OF MAGNETIC INDUCTION BASED COMMUNICATION TECHNIQUES FOR WIRELESS UNDERGROUND SENSOR NETWORKS
Review round: 2
Reviewer: 1

Output:
Basic reporting: 1. The literature was not well modified. Only a few new literatures were added without detailed interpretation.
2. Some pictures are not clear enough. It is better to use vector pictures.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: 1. Please add the latest references, and they must be interpreted in detail in the text, not simply cited.
2. Please increase the clarity of the picture.
3. The references on which key technologies are described are still too old.


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

Input:
Title: COMPARATIVE ANALYSIS OF MAGNETIC INDUCTION BASED COMMUNICATION TECHNIQUES FOR WIRELESS UNDERGROUND SENSOR NETWORKS
Review round: 4
Reviewer: 1

Output:
Basic reporting: 1. Authors did not follow the previous comments and the references are still very old. This is not a qualified review paper.
2. The overviews of Figure 5 and Figure 7 are better put together.
3. In page 14, authors only added two technologies in the last 2 rows of the table without describing them.
Above all, authors should focus on reviewing newer technologies.
Experimental design: no comment
Validity of the findings: no comment
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: COMPARATIVE ANALYSIS OF MAGNETIC INDUCTION BASED COMMUNICATION TECHNIQUES FOR WIRELESS UNDERGROUND SENSOR NETWORKS
Review round: 5
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
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: THE ROLE OF SOFTWARE IN SCIENCE: A KNOWLEDGE GRAPH-BASED ANALYSIS OF SOFTWARE MENTIONS IN PUBMED CENTRAL
Review round: 1
Reviewer: 1

Output:
Basic reporting: This is an excellent paper. The scope and purpose of this paper was well documented. The literature review is thorough and reflective. The selected data was expansive and appropriate for the purpose of the proposed research. Methods and data materials were made available to the general public.
Experimental design: Software recognition is a complex task. The reviewer worked on several projects that intended to extract software entities in his past research and understand the challenges involved. Thus, I am very appreciative of the the method proposed and the large-scale data analytics reported in this research.

The method is capable of not only identifying software entities but also several associated metadata which is quite valuable. The interpretation of results were insightful as they included discipline-, journal-, and timeline-based analysis. The improved methods were compared with baseline methods and it was clear a better performance was attained.

A few minor comments:
1. It may be helpful to define what software is. Based on the results, OS, environments, plugins, and applications were all included as software. It is useful to draw theorization from STS/science of science to operationalize software for purpose of gaining meaningful results.
2. It is unclear what negative sentences meant in Table 2.
3. It is unclear what is n is Tables 7 and 8.
Validity of the findings: The figures reported in the results section were informative. Journal- and discipline-level differences can be readily discerned. I wonder if there is any artifact in Table 8 in which late 1990s and early 2000s seemed to have more software mentions with developers than any other periods.
Additional comments: The softwareKG is very valuable and it is likely to garner attention by fellow researchers to plan further research in this area.


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: THE ROLE OF SOFTWARE IN SCIENCE: A KNOWLEDGE GRAPH-BASED ANALYSIS OF SOFTWARE MENTIONS IN PUBMED CENTRAL
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this paper, a data extraction process is given to extract data about scientific research which employs software. The paper is well-written and does not require any modification in terms of language use. An up-to-date background and literature review/related work are provided. The paper is well-organized and supported with appropriate figures and tables.
Experimental design: The research question is defined and how this research fills the identified gap is clearly explained. Three specific contributions (which implicitly define the RQs) are formulated as (1) A large-scale analysis of software usage, (2) A comprehensive knowledge graph of software citations, and (3) Robust supervisor information extracting models for disambiguating software mentions and related knowledge.

Methods to implement the aforementioned questions/contributions are properly explained and the relevant data sets were defined and cited. The data model for the Knowledge Graph was put forward and the information extraction process using the machine learning algorithms was investigated in detail. Performance of the BERT and ML,sw,opt models are compared in terms of Precision, Recall, and FScroe parameters.
Validity of the findings: The results for the three contributions were discussed along with their limitations.  All underlying data have been provided and appear to be robust and controlled. Conclusions are well-stated and the future work is included.
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: THE ROLE OF SOFTWARE IN SCIENCE: A KNOWLEDGE GRAPH-BASED ANALYSIS OF SOFTWARE MENTIONS IN PUBMED CENTRAL
Review round: 1
Reviewer: 3

Output:
Basic reporting: This work describes an approach for detecting software mentions in literature by combining named entity recognition, relation extraction and entity disambiguation techniques. As a result, the authors create a dataset of thousands of software mentions in papers, represented as a knowledge graph, and using reification to indicate the confidence of the results. The authors then proceed to analyze the role of scientific software in different domains; sharing insights on how software is becoming a first class citizen in most disciplines. Many software creators would be happy to see how thanks to this work they can assess how influential their software is.

The paper is well written and easy to follow. I think this work is timely and highly relevant for PeerJ and the Open Science community. The paper is thorough, and does not omit important implementation details such as the hyperparameters used or the pointers to the tools used for the conversion into annotations for the models, which always take significant time to piece together. Therefore, I think the paper would be a nice addition to the journal, and I look forward to reusing some of the outcomes made available by the authors.

I have some comments and questions that would be great to address/clarify in the final version of the paper (my "minor revision" is really minor). I add them in the "additional comments" field
Experimental design: Robust and thorough experiments. Nice discussion of the results, highly related to the journal. Some questions to be addressed in the "Additional comments" field below
Validity of the findings: The findings and analysis are highly interesting, build on prior work and improve the state of the art. I have found a few errors regarding the vocabularies used, please see "Additional comments" below
Additional comments: - I found that the skg namespace (http://data.gesis.org/softwarekg2/) returns a 404, which will harm the reusability of the endpoint (I see examples online, but the data model documentation is what is critical for performing queries). I also see that some properties use http://data.gesis.org/softwarekg/software, which also returns a 404.

- The authors present baseline results in the same table as the results of the paper. However, in the text, they insist that direct comparison is not appropriate. Then why adding tables comparing both approaches? I think it misleads the reader.

- Having the ability to find whether software is created or repurposed in a publication is great. Are there areas where this happens more than others? I think I have not seen this part in the discussion.

- I am surprised not to see Zenodo as a common archive for depositing software. Are authors (in general) not depositing their releases there, or it something out of the scope for this paper? I know that it's a common practice for researchers to point to their Zenodo releases through the GitHub release integration.

- The relationship extraction between software is deemed as challenging in the paper. However, I have not found why.  Is there an explanation for this?

- I find curious some of the choices by the authors. For example, programming languages are marked as Software, and packages in those programming languages are plugins. Is this representation easier for the training part?
  - Also, I wonder if for the clustering/disambiguation, the plugins could play a part in the similarity metric. At least in that case the python/Python separation would be fixed.

- I imagine the authors have tweaked the BERT models used in their evaluation. However, I have not found any of the tweaked models in any of the links provided by the authors. Where can the models be found?

Minor:

- The github repositories provided with the resources are not tagged (do not have releases), and therefore may be difficult to rerun the experiments if one changes.

- Typo: Page 6: Objective of this information extraction step -> The objective. Similar thing in line 354.
  - Missing "." in line 802.


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: THE ROLE OF SOFTWARE IN SCIENCE: A KNOWLEDGE GRAPH-BASED ANALYSIS OF SOFTWARE MENTIONS IN PUBMED CENTRAL
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors addressed all my concerns.
Experimental design: The authors addressed all my concerns.
Validity of the findings: The authors addressed all my concerns.
Additional comments: No additional comments


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

Input:
Title: THE ROLE OF SOFTWARE IN SCIENCE: A KNOWLEDGE GRAPH-BASED ANALYSIS OF SOFTWARE MENTIONS IN PUBMED CENTRAL
Review round: 2
Reviewer: 2

Output:
Basic reporting: All corrections suggested by the other two reviewers are handled with care. No additional 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: THE ROLE OF SOFTWARE IN SCIENCE: A KNOWLEDGE GRAPH-BASED ANALYSIS OF SOFTWARE MENTIONS IN PUBMED CENTRAL
Review round: 2
Reviewer: 3

Output:
Basic reporting: The authors have answered all my questions (reflecting them in the paper when appropriate) and addressed all my minor concerns about the paper. I think this work will be a nice contribution to this journal.

A few typos:
- line 552 has a restricted overleaf link which I think it's not intended there.
- Github should be GitHub
Experimental design: n/a
Validity of the findings: n/a
Additional comments: n/a


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: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 1
Reviewer: 1

Output:
Basic reporting: The language of the paper is clear. I spotted a few mistakes that could be removed with an additional edit, but they did not detract much fromt he paper: e.g. line 126 "that that".

The freferences to the literature were appropriate.

The tables and data were all clear.

For me, a major failing of the paper was a failure to examine, even superficially, the concept of "trust", a central concept of the paper. "Do you trust this robot?" but to do what, exactly? In th eonly concrete example given int he paper, it is stated that, "[P]articipants  were  presented  with  mathematical 375problems  which  would  be  too  complex  for  human  calculation  (i.e.  887x974  &  997x1066). Participants  would  then  be  asked  to  identify  which  Canbot  (A-H)  was  displaying  the  correct solution upon their chest screen. This question required participants to determine the answer they deemed  correct  based  solely  on  trusting  the  robot’s  physical  appearance". The logic of this appears to be that, if the calculation is too complex for a person to complete, a decision could only be based on the appearance of the robot. Even allowing that the calcualtion was "too complex for human calucaltion", a human shoul dbe able to decide whther the corect answer was odd or even, and perhaps have a fair guess at whethe rit was larger or smaller than a million, or even calculate the final digit. So the idea that it could only be the appearance  of the robot that determined the outcome seems to me to be deeply flawed.
Experimental design: I have already mentioned some of the shortcomings of the design, in terms of exactly what "trust" was taken to mean.

However, in this section I find the question of sampling and data collection to be of more concern. Respondents to the questionnaire numbered only 74, from all over the world and with a range of different levels of experience of robots. there were obvioulsy too few participants to be able to analyse the differences between groups, but 74 seems much too few to be able to generalise the results to the whole of humanity. So this raises questions about how the sample was selected, what part self-selection palayed, and any resulting biases.

I can see no details in the paper as to how the sample was selected, so it is impossible to be clear about how effectively the process was conducted.
Validity of the findings: In each of the above sections I have commented on weaknesses that I think have a serious impact on the robustness of the findings. I do not feel confident tha tthe evidence provided supports the conlcusions.
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: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 1
Reviewer: 2

Output:
Basic reporting: This is an interesting and timely paper. It is written in clear, unambiguous language.
Literature references are sufficient to provide background and context  for research results. The paper includes sufficient introduction and background to demonstrate how the work fits into the broader field of knowledge. Relevant prior literature should be appropriately referenced. The structure of the article conforms to an acceptable format of ‘standard sections’. The submission is ‘self-contained,’ representing an appropriate ‘unit of publication’, including results relevant to the hypothesis.
Experimental design: The authors provide the reader with original primary research inline with the within aims and scope of the journal. Research question is well defined, relevant & meaningful. It is stated how research fills an identified knowledge gap. The submission clearly defines the research question, which are relevant and meaningful. The knowledge gap investigated is identified, and statements are made explaining how the study contributes to filling that gap.
Validity of the findings: All underlying data have been provided and they are robust and credible. Conclusions are well stated, linked to original research question & limited to supporting results.
Additional comments: This is a timely piece that looks at a novel and interesting research area. Should the authors consider it appropriate, references to complementary disciplines, such as psychology or copyright might be included. Overall this is a good piece of academic writing, ready to be published.


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

Input:
Title: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 1
Reviewer: 3

Output:
Basic reporting: In general, it is a well-written, easy-to read report with clear and sufficient theory and method.
I found a few very small typographical errors (e.g. missing blank space),which I marked in the attached pdf file.
However, I have two larger suggestion for revisions, both concerning the researcher reflection on what they research and in what context.

1. You seem to take for granted that users always should trust robots, and that when users don't trust robots, something is wrong. However, looking at the problem from a human-centered design perspective (or, a critical design perspective as well), we should acknowledge that sometimes technology should not be trusted. So, just an acknowledgement about this, and a reflection on why you take for granted that trust is good, might be good.

2. You have chosen to frame the discussion as a discussion about aesthetics. However, very much of what you have investigated could also be framed as design. Yet, you mention very little about design. Not necessarily an error in the research, but I think it would be good to briefly explain why you have chosen to talk about aesthetics instead of design, and how you see the relation between those two terms (for example, is aesthetics a sub-set of design, or is design a means to reach aesthetics, or even the other way around. While bringing up this, it could also be good to mention that there are very much work done within design when it comes to emotional effects such as trust (trust for brands, trust for other mechanical objects such as cars etc). There is also existing design patterns and design frameworks, for example the gestalt laws, that have been exploring similar questions for many many decades. So an awareness of this might support your results.
Experimental design: In my opinion, a survey is highly limited as a research tool to make statements about trust users would feel towards a robot. The respondents will answer what they THINK they would feel in a real-life situation, or what they THINK you as researchers want them to feel. (Some of the survey questions are quite revealing, it is quite easy for a respondent to second-guess what you as researcher anticipate the respondent to answer.)

If users actually interacted with real-life robots in field studies (real-life situations), then it is possible that their reactions would be quite different from what they state in this survey (as you hint at when you mention further research at the end).

Of course you can't change that now, you have the data you have, and even if I question how much conclusions we can draw, I still think the result you have is worth reporting. But, I strongly suggest that you yourself bring up a discussion about these limitations of your study. Also, when you describe the results, it might be good to choose wording that emphasize that the results are limited, such as "this suggests", "this indicates", andso on.
Validity of the findings: It is difficult to get a clear view of how the survey was designed and how the exact questions where phrased. Some of the Figures seem to show questions, but it is not clear what part of the question is and what is your explanation for me the reader.
I have some issues with the wordings of the survey questions in Figure 4 (If this is the actual survey question, which is as noted not clear). The word “new” is used to describe the right hand image of the robot. Did the respondents see the word “new”. In that case it can have created a bias since some people probably interpret new as better.
Besides this and my comment on 2. Experimental design, I have no further critique or suggestions for revision concerning validity.
Additional comments: I would suggest a change of the title. It is difficult to understand the title before reading the article, and after reading the article, the title doesn't seem to match the actual content of the article (which is probably why it was hard to understand from the start). The article is not about visualization at all, so I strongly suggest taking away that word. Uncertainty is just one of the aspects of trust in robots that are discussed, so I find it strange to include that in the title. I would suggest something like: "Users attitudes concerning trust in robots - towards an understanding of how robot aesthetics impact anticipated trust".

On line 88 you write "between man and machines is in how well they understand each other". It is a philosophical/ontological question maybe, but I object strongly towards the notion that machines "understand" anything. There is no proof as far as I know that any robot or AI actually understands anything, and it will take decades until general intelligence have made this possible. Could this be re-formulated some how (I don't know how it was formulated in Barnes and Jentsch (2010) ) ? Something like "....is how well they adapt to each others behavior", or so. That phrasing doesn't assume any actual understanding.

I don't understand the heatmaps in Figure 6 and 7. Heat maps are used to visualize values distributed in two dimensions. Which value (data) does the heat map represent? Did respondents click on the image, and the heat map shows frequency of clicks? It doesn't make sense, but I can't come up with any other explanation. Please consider if heatmap is correctly used, and either take away or if it is kept explain what it visualize and how.


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: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 2
Reviewer: 1

Output:
Basic reporting: I think that the paper could still benefit from another review of the text to remove typographical errors, some of which have been introduced in the course of the modification. For example:
Line 111: There is an unnecessary apostrophe after the full stop.
Line 132: Guthrie should be cited rather than citied.
Line 148: ‘designs’ should have an apostrophe after the ‘n’.
Line 326: Is the question not whether the robot is trustworthy, rather than trusting?
Line 326: ‘robots’ needs an apostrophe after the ‘t’.
Experimental design: The authors have responded to the comments of the reviewers in regard to failures in the descrioption of the experiemntal design, rather than falinings in the desing itself, and this is now much better.
Validity of the findings: In responding to the comments of the reviewers, the authors have now added some clarifications and caveats that strengthen the paper in this regard.
Additional comments: I have to say that I am impressed by the way the authors have responded to the points of criticism raised by all the  reviewers, and I would now say that the paper could be published.

I still think there is something lacking in the analysis of trust, and of whether it is the robot that deserves or does not deserve trust. I trust a calculator to produce an answer that accurately reflects the buttons that have been pressed according to its own algorithm. I might doubt my competence in button pressing (fat fingers) or the ease of intuiting the order in which buttons have to be pressed, and therefore have doubts about the answer, but my trust in the calculator would not be diminished. In this study, participants were confronted by robots that had been deliberately programmed to deceive; was it the robots or the programmers who were being assessed as trustworthy? But perhaps this is work for future study.


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: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 2
Reviewer: 2

Output:
Basic reporting: The amendments introduced in the paper fully reflect my original comments, thank you for incorporating them.
Experimental design: The paper, as amended, meets the PeerJ standards. No specific comment to be added.
Validity of the findings: No further comment.
Additional comments: Thank you for amending the paper, hope the comments have been useful.


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: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 2
Reviewer: 3

Output:
Basic reporting: The revisions that have been made is a bit shallow and a bit "quick fixes", but they are extensive enough and good enough to make the publication acceptable in my opinion. The authors seems to have understood the feedback, and made appropriate changes.
Experimental design: no comment
Validity of the findings: The limitations and arguments around the findings is more clear now.
Additional comments: No further comments, I will suggest an accept.


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

Input:
Title: HUMAN-ROBOT INTERACTION: THE IMPACT OF ROBOTIC AESTHETICS ON ANTICIPATED HUMAN TRUST
Review round: 2
Reviewer: 4

Output:
Basic reporting: Summary: This paper titled “Human-robot interaction: The impact of robotic aesthetics on anticipated human trust” examined people’s preference on some robot faces in terms of facial expression mood, blurriness, and color, as well as conflicting information on the chest display. The authors’ writing is great and clear. However, from a scientific research perspective, the rationale and clarity of the concepts, the experiment design, and the findings all have flaws that make the paper limited contribution to the trust-in-robot literature. Not any of the findings particularly adds new knowledge. Sorry, but I would not recommend publishing this paper.

Background:
1.	In the authors’ own words in the introduction, “As Barnes and Jentsch (2010) identified, the key to a successful relationship between man and machines is in how well they can work and adapt to each other,” esthetics of a facial expression is not part of how well they can work and adapt to each other. Trust is a process-based decision-making phenomenon. If the results that the robot gave are wrong, no matter how pretty the face looks, the robot is still untrustworthy.

2.	The authors stated, “As Gurthrie citied in Daminao and Dumouchel (2018) points out, the tendency to see human faces in ambiguous shapes provides an important advantage to humans, helping them to distinguish between friend or enemies and establish an alliance.” This is not true. The same person can be a friend at one time and be an enemy at another. It is not the look of the face that changed, but the identity and mechanism changed.

3.	The contents of trust have many dimensions, such as trusting the purpose/intention, trusting the process, and trusting the performance. Trust also has different phases, such as initial trust, trained/informed trust, and situational trust. The study is not clean on the direction: Are people willing to trust the robot (Canbot) to do what? If this question is not clear, how does esthetics matter?
Experimental design: Experiment design:
4.	The authors stated that “It is the ‘effective’ interaction which is of interest to the authors of this paper (i.e., the ability to build a trusting relationship through effective human-robot interaction).” By definition, interactions mean “mutual or reciprocal action or influence” (Marriam Webster Dictionary). However, the facial expression is just showing the options, and there is no interaction involved. The robots’ answer correctness is not the independent variable for people’s trust in the robot, but the robot’s appearance is the independent variable.  The experiment design is not based on interactions.

5.	The questions about trust should be more specific. The original question was, “Would you trust Canbot by the way they look?” However, what does it really mean? Trust Canbo on what? Trust Canbo is a robot? Trust Canbo can do all math problems correctly? Trust Canbo provides correct information on all questions? Trust Canbo can take care of your children? Trust Canbo has human conscience? Trust Canbo have the good intention for you?
Validity of the findings: Results and findings:
6.	The authors only reported the percentage of participants’ response category, without any statistical significance report at all. More advanced statistics (e.g., non-parametric statistics such as Chi-square in this case) are strongly encouraged.

7.	The results that people preferred the good proportioned positive faces (or the visualization with no modifications) better than a weird/angry/blurred/mentally retarded looking is not surprising at all. Is it really trusting in a robot or the one chosen is just least displeasing?

8.	The authors stated, “In situations such as trusting robots where a person [should this be a robot?]’s past behaviours and reputations are unknown, we acquire other sources of information to determine a person’s motivations (DeSteno et al., 2012).”, based on this logic, this study is only talking about *the first initial impression of the robots’ trustworthiness*. Once the robot presents one result to a question, that information itself is showing whether the robot is trustworthy. The so-called trust shall not go past the initial impression. The paper should not overgeneralize the findings on the impact of esthetics on trust in a robot.
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: NETWORK INTRUSION DETECTION USING OVERSAMPLING TECHNIQUE AND MACHINE LEARNING ALGORITHMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.The author claims in lines 70-71 that the proposed model performs better than previous related research, but there is no specific explanation and comparison in the paper, which is hard to convince. I suggest that the experiment is not just to compare the performance differences of existing Methodologies, but to compare the superiority of the detection framework over the previous research when facing the same dataset.
2.Lines 72-73 indicate that the data preprocessing technology mentioned in this paper is novel and has not been employed. However, there are abundant researches on data standardization and normalization technology and SMOTE technology for dealing with imbalances. So this statement is debatable. Readers are more concerned about the innovation of this work. Therefore, the author should not only declare the novelty of the work but also discuss and prove it in detail. This will help readers grasp the value of the work.
Experimental design: 3.There are some errors in the presentation of data, tables and conclusions:
1）Since the IG method is used, the description from 89.5% to 95.1% in RF in line 358 should be modified from 89.5% to 95% in RF.
2）The decrease in ANN accuracy is incorrectly stated in lines 370 to 371 because 76.8% is the accuracy of KNN. The ANN accuracy decreases from 80.3% to 71.7%.
3）In lines 377 to 378 the conclusion is wrong, the accuracy of KNN increases from 84.0% to 84.7% after using PCA, which does not reflect the decrease in accuracy with PCA, and we note that the accuracy of ANN drops exactly 85.2% to 77.6% again after using PCA.
In line 385 the conclusion is wrong, class balancing does not affect Logistic Regression and Artificial Neural Networks, not Decision Tree and Artificial Neural Networks as described in the paper.
Validity of the findings: no comment
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: NETWORK INTRUSION DETECTION USING OVERSAMPLING TECHNIQUE AND MACHINE LEARNING ALGORITHMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: •	In general, the abstract should be about 240/260 words in length and up to the 500 words limit. It can contain short statements summarising the project scope, research significance/motivation/problem statement, aim, objectives, the method and techniques used to work towards these objectives, and the results achieved and conclusions made. It should give a reader sufficient information to decide whether or not to read the rest of your paper. Make sure to revise your abstract to address most of the above requirements, especially your motivation and aims.
•	The abstract keywords are not presented.
•	The uncommon abbreviations should be spelt out at first use only. EM algorithm in line 107 is not abbreviated, and SMOTE is abbreviated twice in lines 87 & 199. Therefore, the English language should be improved, I suggest you have a colleague who is fluent in English and familiar with the subject matter review your manuscript.
•	Make sure all the equations are in the middle of the line.
•	In line 318 the table name error.
•	In line 155 it’s F1-score, and not f1-score.
•	Line 101 – 103, the reference style in table 1 is not as the main text, also, there is no reference to what you cited in this table. Also, you should add more research from 2020 and 2021 in this table.
Experimental design: •	In lines 66-67, you need to clarify why you used only the UNSW-NB15 dataset and update this section as in 2021 more than 24 open access papers mention the UNSW-NB15 dataset. https://paperswithcode.com/dataset/unsw-nb15
•	In line 71 you mentioned the previous research, and you claim that your result achieves better accuracy than the previous research. You should cite them here.
•	In lines 79-79 you should provide detailed information about your experimental setup.
•	A methodology diagram in the methodology section will benefit this section.
•	In line 316, the label has been extracted as a feature, can you clarify why?
•	In lines 184-188 More details and discussion are required in this section.
•	The percentage in table 3 is not accurate if you consider all datasets and not just the training datasets. You can refer to Table VIII in the reference given below for more details.  N. Moustafa and J. Slay, "UNSW-NB15: comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," 2015 Military Communications and Information Systems Conference (MilCIS), 2015, pp. 1-6, doi: 10.1109/MilCIS.2015.7348942.
Validity of the findings: •	In this paper, the authors used the SMOTE technique and feature selection techniques to address the class imbalance issue for some attack types that have very few instances on the UNSW-NB15 Dataset. They managed to achieve a better result when applying both techniques. However, the authors haven’t validated their results with the current research results. A Validation section should be included in this paper.
Additional comments: •	Only one dataset has been tested with the proposal framework, I recommend adding more than one dataset to evaluate their framework.
•	I noticed that the raw data and the code are not submitted
•	However, I do think the authors could further improve the papers by addressing the above comments, and adding a discussion section at the end of this paper will make it even more readable and comprehensive.


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: NETWORK INTRUSION DETECTION USING OVERSAMPLING TECHNIQUE AND MACHINE LEARNING ALGORITHMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The author has answered my concerns and I think it can be published.
Experimental design: The author has answered my concerns and I think it can be published.
Validity of the findings: The author has answered my concerns and I think it can be published.
Additional comments: The author has answered my concerns and I think it can be published.


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

Input:
Title: NETWORK INTRUSION DETECTION USING OVERSAMPLING TECHNIQUE AND MACHINE LEARNING ALGORITHMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: I recommend accepting this paper; it is much improved from the previous version. It's now relatively easy to read from beginning to end, and the technical content of the paper has been settled, so I have no additional technical comments on this one
Experimental design: .
Validity of the findings: .
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: MANIFOLD-ADAPTIVE DIMENSION ESTIMATION REVISITED
Review round: 1
Reviewer: 1

Output:
Basic reporting: The language is generally clear, but it can be improved in certain sections.  The literature is well referenced. The structure is not conforming to PeerJ Computer Science standard (namely, the Methods section should be placed right after the Introduction, not after the Discussion). The figures are high quality, but adding more details in the corresponding captions would facilitate their comprehension.
Experimental design: The article tackles a critical problem in the computer science field - dimensionality estimation. The research question, however, should be better stated in the introduction. Specifically, authors should clearly state the rationale and the aim of their study. The methods should be moved after the introduction and before the results so as to facilitate the reader following the flow of the article.
Validity of the findings: The proposed approach solves certain limitations of the algorithm that is based on. The authors performed an extensive comparison of their approach with other state-of-the art algorithms on various synthetic datasets. Perhaps, an additional comparison of the proposed approach with such algorithms on neural data might be ideal. Conclusions are missing.
Additional comments: The paper sets to revisit a dimensionality estimation algorithm, the manifold adaptive Farahmand-Szepesva’ri-Audibert (or FSA). The authors first computed the local probability density function following the original FSA pipeline and then e the median of such pdf to obtain the global estimate of the dimensionality. They further corrected for finite sample effect implementing a correction formula and finally compared the performances of their algorithm with those of the original FSA as well as other state-of-the-art techniques. The proposed approach outperforms the original FSA and perform similarly to other methods when applied to synthetic datasets. When applied to neural signals recorded during epileptic seizures, the authors hypothesize that low-dimensional brain regions might be potential sources for the seizure onset.

The overall structure of the paper is ok to follow. However, certain parts of the manuscript can be improved and some details need to be added. Following are some specific comments.

1. Line 96. I suggest you provide more justification for your study. What is the rationale of your approach? How do you expect your result to differ from the original FSA algorithm? Also, could you be clearer when you say 'we correct the underestimation effect by an exponential formula'?
2. Line 98, end of introduction. Could you please add some description of your following section in a clearer way?
3. Authors should also compare their approach to the original FSA (or to some other methods) on the neural dataset and not the synthetic ones only.
4. The method section should be moved right after the introduction. This would allow to describe the proposed approach before showing the corresponding results. Moreover, some results are already described in this section (e.g., lines 252, 256, 274). Those sentences should be removed and included in the results section only. Also, please put the figures closer to the corresponding location in the main text where they are referred to.
5. Authors should add a conclusion section.
6. Line 167, cmFSA acronym should be defined before use.
7. Line 244, authors should justify why they chose to test those three specific values of k.
8. There are some inconsistencies related to the use of the notation for the true and the predicted dimensionality. According to line 255, D indicates the true dimensionality and d the predicted dimensionality. However, in line 270 you use d to indicate the true dimensionality and d ̂ for the predicted one. Choose one notation and stick with it.
9. The captions of each figure should be more detailed. Specifically, they should briefly describe the take-home message of the figure (one or two sentences are enough).
10. Figure 1: it is not clear to the reviewer how the histogram was obtained. Didn’t you test only one realization in this case (line 242)?
11. Table I: what are cmFSA_fr and M_DANCO_fr?


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: MANIFOLD-ADAPTIVE DIMENSION ESTIMATION REVISITED
Review round: 1
Reviewer: 2

Output:
Basic reporting: The manuscript is sound and well written. References are generally exhaus-
tive. The methodology is clearly explained.
Experimental design: The propose method seems to be competitive with state-of-art methods, and I believe it
offers some advantages with respect to some of issued of ID estimators, in particular
boundary effects, and variations of the density of points in the data.
I think the manuscript is a fair contribution to the field of ID estimation, and it can be of interest to researchers in this area, and more in general to researchers needing accurate ID estimation as part of their data analysis pipelines.
Validity of the findings: Major comments:

1) In my opinion, the main problem with the boundary-effect correction is that it is o
ptimized for uniformly-sampled hypercubes, and may lead to overestimation of the ID in cases when the data are not uniforly sampled. This is clearly visible form table I: while the estimation is nearly perfect for uniformly sampled data on linear subspaces [M2,M9,M10a-c], or gener-
ally uniformly sampled data on locally at spaces [M5,M7,M13], it yields an overestimation in the case of non-uniformities, such as he Gaussian case [M12], the non-linear manifold case [M6], or the sphere [M1]. The overstimation may be even more sever for non-uniform samplings with heavy-tailed distributions, such as the Cauchy distribution used in Facco
et al. 2015. The authors should extensively comment on this point.

2) Since this is a methodological work, I would recommend that the authors
make publicly available the code implementing cmFSA.

3) It is not clear how the different sample sizes were included in the calibration of the correction term. It seems that the calibration term used to infer the ID of the datasets M1-M13 was inferred from the n = 2500 hypercubes. Is one going to use the same term with datasets of different n ? It seems that one should rather use a term calibrated on that specific n . The authors should comment on this point. Furthermore, why was
k = 5 used for calibration, instead of k = 1 used in subsequent analyses?

Minor comments in attached PDF.
Additional comments: Comments in attached PDF.


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: MANIFOLD-ADAPTIVE DIMENSION ESTIMATION REVISITED
Review round: 2
Reviewer: 1

Output:
Basic reporting: In their revised manuscript, the Authors have satisfactorily addressed my main criticism and suggestions. The revised manuscript is clear and well-written, and its overall structure has improved. Many minor confusing points have been clarified.
Experimental design: The methodology proposed is scientifically sound.
Validity of the findings: The Authors have been very careful in delimiting the scope of validity of their method, its advantages and shortcomings.
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: MICROSERVICE SECURITY: A SYSTEMATIC LITERATURE REVIEW
Review round: 1
Reviewer: 1

Output:
Basic reporting: Microservices is an emerging paradigm for developing distributed systems. With their widespread adoption, more and more work investigated the relation between microservices and security. In this work, the authors conduct a systematic review of the field, gathering 290 relevant publications.  The paper is well written  and organized. However, it needs significant updations before it can b e accepted
Experimental design: Good
Validity of the findings: Satisfactory.
Additional comments: 1. In the introduction, the authors can draw a table showing the difference between the current survey and existing surveys.
2. Some of the recent papers related to security such as the following can be discussed in the paper: "Iwendi, C., Jalil, Z., Javed, A. R., Reddy, T., Kaluri, R., Srivastava, G., & Jo, O. (2020). Keysplitwatermark: Zero watermarking algorithm for software protection against cyber-attacks. IEEE Access, 8, 72650-72660., Bhardwaj, A., Shah, S. B. H., Shankar, A., Alazab, M., Kumar, M., & Gadekallu, T. R. (2020). Penetration testing framework for smart contract Blockchain. Peer-to-Peer Networking and Applications, 1-16."
3. List out the main contributions of the survey.
4. The authors can add a section, lessons learnt and future directions to pave the way for researchers interested to work on this topic.


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: MICROSERVICE SECURITY: A SYSTEMATIC LITERATURE REVIEW
Review round: 1
Reviewer: 2

Output:
Basic reporting: Well written, well structured.

One important issue with the use of references. (see later)
Experimental design: According to the standards of a literature survey.

However, I would like to see an explicit motivation on the exclusion of the grey literature.
Validity of the findings: The authors conclude with a convincing call for action to help the community make steps forward.
Additional comments: Summary.

This paper reports on a systematic literature survey on microservice security. They analysed 290 relevant publications (published since 2014) in a quantitative and qualitative way. They complement this analysis by a thorough knowledge about the field and as such identify open areas where future research is welcome. They conclude with a convincing call for action to help the community make steps forward.

Points in favour

* The authors conducted the literature survey according to the guidelines for doing so. The data is publicly available as I would expect from a publication appearing in PeerJ On top of that they conducted a meta analysis with clusters of authors and a word-net of abstracts which was a cherry on the pie as far as quantitative analysis goes.

* I was suspicious with the qualitative analysis driven by 20 (twenty!) research questions. However, as later explained what is actually happening is that these 20 questions serve as markers to classify whether these papers cover a certain topic or not. This allows for the correlation matrix (Table 2) which was again a nice complement.

* The field is quite diverse which is a good sign for a literature survey. The publication outlets and the research communities illustrate quite well that a systematic literature survey like this one is due.

* Throughout the paper I feel a sense of credibility: the authors know what they are talking about and are able to link and integrate findings from a very diverse field. The discussion and conclusion section formulated a s a series of open challenges is therefore very convincing. I especially appreciated the "React & recover Techniques" and the "DevSecOps"

Suggestions for improvement
(All below are considered minor. I trust that the authors will do their best to accommodate these suggestions and if they do not I can live with the paper)

* They way to authors list papers in the bibliography is very annoying to look up references. In the end I resorted to a search on the digital PDF document while reading the article in paper form.

Citations start with the family name of the first author followed by the year (e.g. Casale et al.[2016]) However in the bibliography the first name comes first, family name comes second followed by all other author names; title, misc and the year at the very end of the entry. (i.e. Giuliano Casale, Cristina Chesta, ... Cloud Forward, pages 34–42, 2016). The alphabetical sorting of the references is thus hard to infer and scanning the list is counterintuitive. Note that some authors have multiple first names (e.f. Mohammad Bany Taha) which makes it even more difficult to discern the alphabetical sorting of the references.
The authors also split the reference section in two subsection (References + Publications from the Dataset). So scanning from the end of the bibliography was awkward. (Looking for the entry "Wuyts 538 et al. [2018]" I started from the back and did not find it on the first attempt. Only the PDF search revealed what was happening)

* I would like to authors to explicitly motivate why they did not include grey literature in their survey. Especially in a field like microservices which is driven by practitioners this may provide complementary perspective. Perhaps there would be blog posts on "React & recover Techniques" and the "DevSecOps" which makes the call for action even more worthwhile.

* "Methodology." Don't use the term "methodology" for what is a method, it is inflation of words. The postfix -OLOGY stands for "study of". (i.e. biology = the study of the living organisms; psychology = is the study of the human mind; geology = is the study of the earth). Thus methodology is "the study of the methods".

* lines 101-102 "We analysed these publications to collect statistical and objective answers". There is no such thing as an "objective" answer. The very fact that you classified papers already implies a subjective interpretation. I would suggest to replace "objective" by this by "transparant".

* Line 360-361 "This is reflected by the sharp increase in the number of publications since 2014." I am not convinced by this argument. The overall number of publications in computers science is steadily increasing, so the absolute number of papers does not say much.


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: MICROSERVICE SECURITY: A SYSTEMATIC LITERATURE REVIEW
Review round: 1
Reviewer: 3

Output:
Basic reporting: The authors present a systematic literature review on microservices’ security, in a well-written manuscript. In addition, the topic is certainly timely and relevant, and one such review can provide various benefits to both researchers and practitioners working on the topic. At the same time, the submitted manuscript must be revised to address various limitations it is currently suffering from, before it can be considered for publication in an international journal. I am listing such limitations in the following sections, together with suggestions hopefully helping the authors in improving their manuscript.
Experimental design: As for the search for studies, some concerns should be clarified by the authors. Firstly, why did they choose to restrict their focus to “white literature” only? It is known that industry is heavily investing on microservices, with quite many solutions (also for security) being proposed and posted in industry-driven outlets (whitepapers, blog posts, YouTube channels, etc.). I am not saying that the authors should include “grey literature” as well, but they should at least clarify why they decided to keep it out from their study. (The authors actually notice this in their conclusions, hence raising the point of why they did not consider grey literature from the beginning)

In addition, I am not sure on the “repeatability” of one of the exclusion criteria, viz., “We also excluded cases in which the work tangentially mentioned the satisfaction of some security aspects, without detailing the design/development of the security technologies to accomplish them”. Whilst all other exclusion criteria are objective, this criteria seem to potentially be threaten by observer bias (as an observer may a manuscript to satisfy this conditions and hence get excluded, while another may consider the treating of security “not so tangent”, so to say). The authors should clarify how they limited/avoided possible observer biases when evaluating this criteria, e.g., in a “Threats to Validity” section.

Finally, the authors should conclude this section by listing all selected studies (e.g., in a table). This would help keeping the authors review self-contained, hence helping readers in better understanding their results.

As for the research questions, the authors state that they adopted 20 dichotomous questions with the goal of favoring precision and objectiveness. It is however not clear whether/how they ensured such precision and objectiveness. The marking of a publication as “yes” or “no” for a research question seems indeed to be subject to observer biases and errors. How did the authors avoid/limit this? This should be discussed in a “Threats to Validity” section.
Validity of the findings: The presentation of results can and must be improved, by also expanding their discussion. For instance, the authors should consider presenting the publication outlets (viz., conferences and journals) as plots, so that readers can visually observe them (rather than finding a flat list of names). Much better it would be to cluster venues according to some criteria and to show aggregated results. For instance, the authors could expand their discussion on the communities where microservices’ security is most discussed (with some graphical support).

Also, I am not sure on whether what the authors call “qualitative results” actually provide “qualitative” information. For instance, the distribution of types of publication is still a “quantitative” information, with which the authors partition the different contributions in the field (here the authors use the word “survey” to denote “reviews”). This type of information is typically present in systematic literature reviews, and it is usually classified as a “quantitative result”.

The same holds for the other aspects discussed in Sects. 5.2.2-5. The authors state “how many” publications were marked as “yes” for the research questions pertaining to each aspect, hence providing “quantitative information” on the distribution for such aspects. To make the things more “qualitative” the authors should better enter into the details of "what" is discussed in each publication and "how". Whilst the “what” can be easily shown with tables showing the authors’ classification of considered works (e.g., with rows associated with works, columns with research questions, and checkmarks placed in cells to denote that a research question is treated in a research work), the “how” requires the authors to expand their discussion by discussing how (clusters of) works discuss/tackle the research questions.

As a minor comment, in Section 5.2.6 the authors state that they “were surprised to find many citations to blockchain technologies (as reported above) as well as the lack of more and more mainstream technologies like service mesh and serverless”. I am not sure on whether “service mesh” are more mainstream than “blockchains”. The authors should consider rewording this, or at least cite a reference showing the higher recognition/usage of “service mesh” if compared with “blockchains”.

In their concluding remarks (Sect. 6), the authors draw some concluding remarks on the open challenges that emerged from their study. A reader however misses the links between the results presented in Sect. 5 and the open challenges/research directions listed in Sect. 6. The authors should try to make such links more explicit. For instance, they could introduce open challenges (e.g., in a “highlighting box”) in Sect. 5, immediately after the discussion highlighting the need/openness of such challenge. They could then retake/recap the open challenges (as they currently do) in Sect. 6.

Last, but not least, systematic literature reviews, and systematic studies in general, are known to be prone to possible threats to their validity (like those I tried to highlight in my former comments above). The authors should discuss how they mitigated/avoided possible threats to the validity of their study in a devoted section.
Additional comments: Other comments follow:

[ “survey” vs. “review” ]
I am not sure on whether the word “survey” used by the authors in the manuscript’s title and throughout the text is correct. Snyder’s guidelines, used by the authors to design their research, speak about “systematic literature reviews”. I would hence recommend the authors to revise their wording in “systematic literature review”, both in the title and all over the manuscript. Such wording is that most commonly associated with the type of studies like that presented by the authors in this manuscript, hence helping potential readers to better grab the type of content they would find in this manuscript.

[ Related Work ]
The related work discussion should be expanded to include other relevant related reviews on  “microservices & security”, e.g., that on “microservices security smells” by Ponce et al. (https://arxiv.org/abs/2104.13303) .

[ References ]
The authors should cite all papers they considered in their literature review, to give credit to the authors who published such papers.

[ Appendix ]
The authors present selected studies and their classification in a supplemental appendix. As noticed in my above comments, such information is not supplemental, but crucial for readers to understand which papers were considered and how they were classified to answer to the authors’ research questions. The information in the appendix should hence be included in the main text of the manuscript.

*** other comments ***
- “Alas” -> “Unfortunately” or “At the same time”?
- p5/28: “within the” -> “up to the”


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: MICROSERVICE SECURITY: A SYSTEMATIC LITERATURE REVIEW
Review round: 2
Reviewer: 1

Output:
Basic reporting: No remarks here, 2nd review
Experimental design: No remarks here, 2nd review
Validity of the findings: No remarks here, 2nd review
Additional comments: The authors took into account all suggestions from the reviewers and complied as best as they could


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: MICROSERVICE SECURITY: A SYSTEMATIC LITERATURE REVIEW
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have thoroughly addressed all my former comments, significantly improving their paper. I would hence recommend accepting the paper in its current form.
Experimental design: no comment
Validity of the findings: no comment
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: MODEL DESIGN AND PARAMETER OPTIMIZATION OF CNN FOR SIDE-CHANNEL CRYPTANALYSIS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The overall passage is well structured with adequate deep learning background information, which I think provides basic intuitions for researchers who want to study deep learning based side channel attack. It should be better if more background information about side channel attack is included, e.g. the meaning of the rank, how it is calculated, the relationship between rank and classification results, and so on.

The language is good, easy to understand. Although there are some typos, which I point below. No problem to use the public database ASCAD, I am not sure it is necessary to publish the author’s training and testing codes, but they share the model structure, which I think is enough.

As for the figures, please rename the figures to make the indexes consistent with the figure titles. Now they are confusing.

Typos:
In line 53, secret.key. ->secret key.
In line 101, deep network The problem-> deep network. The problem
In line 108, Parameter -> parameter
In line 122, please rephrase your sentence
In lines 282-283, x_j is not the inputs of all neurons but one neuron
In line 395, please rephrase your sentence
Please check the name ‘Conv’, there are many ‘Cnov’ in the passage
Experimental design: I like the way the authors design the experiment, the whole experiments are designed in sequential way, and the CNNSCAnew is updated after every experiment. They analyze the model parameters first and then switch to the global parameters, and finally determine the optimal model structure and parameters.

However, I think the authors miss one important thing. They should explain why the whole experiments flow is designed like this. For example, why they choose to determine the model parameters first instead of the global training parameters? and in section 2.2, why they choose to determine the number of Conv layers first instead of kernel size or the number of filters first?

In addition, I know there are lots of parameters to be optimized and it is tough to go through all the combinations. So I am thinking that whether the impacts of different parameters on the model performance are independent, if yes, there is no problem to optimize the model parameter by parameter, if no, then the whole experiment should be carefully addressed in order to find the optimal model. I will appreciate it if the authors can give more details about this.

Finally, please explain why choose the epoch number 75 for most of the experiments ? Have you tried other epoch numbers? And I think the authors should also add comparison results between models with SEnet and without SEnet if they want to show SEnet can reduce the gradient dispersion.
Validity of the findings: Based on the conclusions, the authors provide us with a new CNNSCA structure that has better classification results and lower computational time than previous model structures (Benadj, Alex VGG). This part is strongly related to the experiment design part, other than the updated model structure and short training time, I expect more conclusions here.
Additional comments: Please gives more background information about why it is important to use deep learning in side channel attacks and comparison between deep learning performance and traditional side channel attack analysis methods.


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: MODEL DESIGN AND PARAMETER OPTIMIZATION OF CNN FOR SIDE-CHANNEL CRYPTANALYSIS
Review round: 1
Reviewer: 2

Output:
Basic reporting: - This paper proposes a convolutional neural network-based side-channel attack model design and hyperparameter optimization to improve side-channel attack performance. The paper is mostly clear but there are numerous grammatical errors. For example, the sentences in lines 90, 124, 319-323, 339, and 639 need revision.
- The paper contains sufficient references. I can only recommend the authors add a reference for the MNIST dataset in line 306 of the paper.
- Table 1 is not clearly explained in the text. The authors can mention one or two sentences about remarks in table 1.
- Figure 1 is not explained in detail.
- For figure 2, the authors can use labels to show which data is input, kernel, and output.
- In line 135, "BP algorithm" is mentioned but readers may not know the BP. What does BP stand for?
Experimental design: - The performance of convolutional-based side-channel attacks has dramatically improved by researchers over the last 5 years. However, there is still room to develop it.
- In this paper, there are valuable experimental results. The authors compare the performance of the proposed model with other models in the literature. The proposed method is superior to the other methods.
Validity of the findings: - Although the authors did not propose a completely new method/model, they adapted existed methods well to generate a new model. This combined model is not proposed previously in the literature. The experimental results show that it is better than the other methods.
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: MODEL DESIGN AND PARAMETER OPTIMIZATION OF CNN FOR SIDE-CHANNEL CRYPTANALYSIS
Review round: 1
Reviewer: 3

Output:
Basic reporting: This paper aims to improve the state of art of deep learning based side channel-attacks by proposing a new model, named CNNSCAnew. The architecture of this model relies on a new kind of block of layers, the SE module, whose effectiveness has already been established in the domain of computer vision. A fine tunning of the hyperparameters is performed by studying the impact of each parameters independently in several experiences. The performance of the final model is compared with the state of the art models on the open dataset ASCAD.

From the editorial side, this paper is not well written. There is a lot of approximations, confusions and contradictions, mostly due to the fact that some english technical terms are not correctly used. Some sentences are not grammatically correct. The overall meaning of the text can be understood (or somehow guessed), but it is very tedious and some typos or formulations can be easily corrected with a more careful review of the paper.

Here are some examples found in the abstract and introduction (the list is not exhaustive)
l12. decryption -> cryptanalysis
l13. decryption -> cryptanalysis
l13. Alex -> AlexNet
l14. there is Model training -> the trained model
l23. the SEnet module : the "SEnet" abbreviation is not yet defined
l24. unnecessary parameters are maximized: do you mean "optimized" instead of maximized?
l28. Optimize ... CNSSCA : the meaning of this sentence is not clear, do you mean : "We optimized the various ... CNNSCA"?
l26. first-order mask data set of -> first-order masked dataset from
l34. guess entropy -> guessing entropy
l35. successful attack key -> successful recovery of the key
l36. the performance was better -> we obtained better performance
l42. and using cryptographic algorithms to leak ... harware encryption: the formulation is not correct, I would suggest something like : "by using the leakage of cryptographic algorithms during the computation of data (...) on hardware devices"
l43. radiation Etc -> radiation, etc.
l44. to crack : the word is a little bit slang
l50: the weak -> weak
l53: the secret. key: typo
l54. the best decryption effect -> the best cryptanalysis attack
l58. energy traces -> power consumption traces
l69. At present, at home and abroad : ?
l79. the best perfomance of breaking secrets -> the best cryptanalysis performance
l123. declassification -> classification
Experimental design: The study of new attention mechanisms for side-channel attacks is in line with the current trends in deep learning research and the efforts to use open dataset and reproductible results are appreciated. The results are interesting and the overall methodology for fine tunning the deeplearning model sounds correct. All the model parameter choices are published and the code is available, which is very valuable to the side channel community. The experiments rely on an open dataset and they are reproductible, which strenghten the confidence in the results. The hyperparameters are carefully selected and their impact are thorougly studied. The figures depicting the results are clear. The final model, named CNNSCAnew, contributes to improve the state of the art on SCA attacks based on deeplearning.
Validity of the findings: Nevertheless, due to the poor level of english and typos, it is difficult to undestand the description of the experiments and sometimes the results are not clear. By example, in the section "Structural parameter optimization", experiment 1, the authors have tested three deeplearning architectures with different number of convolutional layers for each block (namely 1, 2 and 2/3). At line 416, they claim that "if [the number of layers] exceeds 2 or more, ..., the 8G[bytes] GPU memory ... will be exhausted, unable to run code". But they computed the guessing entropy for some of their architecture where the number of layer was 2 (cnov2 and cnov3), which is in contradiction with the fact that they were unable to run the code.

Moreover some claims are vague or not correct. By example l62. "but it also loses effectiveness when attacking encryption with protection." : this statement is not true : in the ASCAD paper, a MLP is successfuly applied to a protected AES encrytion. Another example at l539, the authors claim that if the number of channels of the dense layer is low, then the computational complexity increase. However if n is the input dimension and c the number of channel, then the number of computation is equal to c*n and it increases with the number of channel.

Finally, the comparaison with the state of the art models lacks some robustness. I recommand to perform a 10-fold crossvalidation or a validation method with a high number of different training/testing steps: the results obtained with these well-known methods will be more significant than a single training/testing evaluation and will reduce bias.
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: ATTENTION ENHANCED CAPSULE NETWORK FOR TEXT CLASSIFICATION BY ENCODING SYNTACTIC DEPENDENCY TREES WITH GRAPH CONVOLUTIONAL NEURAL NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper makes a focused study on the text classification problem from the text syntactic relationship, sequence structure, and semantics. Specifically, the authors first comprehensively summarize the shortcomings of existing models and conclude the challenges for text classification. Later, the author introduced GCN to extract syntactic information for dependencies relationship representation on the one hand and built multi-head attention to encoding the different influence of words to enhance the effect of capsule networks on text classification on the other hand. In the end, the author proved that CapsNet, GCN, and multi-head attention have an integration effect for text classification through experiments.

In general, the author states the research problems very clearly, and presents a very interesting and effective model named Syntax-AT-CapsNet. The paper is organized logically. Thus, I strongly suggest this work should be accepted.
Experimental design: The experiment designed by the author is very sufficient, and the effectiveness of the model (Syntax-AT-CapsNet) proposed by the author can be well proved through the experiment. However,  the evaluation metrics could be further enhanced. More diverse evaluation indicators could be added to evaluate the effectiveness of the model in a single-label classification task.
Validity of the findings: The author verified the validity of the conclusion through theoretical description and experimental verification, and it has strong persuasiveness and credibility.
Additional comments: Firstly, several typos should be corrected, such as:
1. At lines 160-161, “... (BERT)also also takes multi-head attention as its basic component.” -> (also) “... (BERT) also takes multi-head attention as its basic component.”
2. At lines 281, “... denotes that Nword vectors in the sentence are connected in series...”-> (N-word) “... denotes that N-word vectors in the sentence are connected in series...”.

In addition, it is suggested that Fig.4 and Fig.6 should be more clear. Maybe it would be better to increase the resolution of the picture.

Finally, the format of the formula in the paper should be unified.


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: ATTENTION ENHANCED CAPSULE NETWORK FOR TEXT CLASSIFICATION BY ENCODING SYNTACTIC DEPENDENCY TREES WITH GRAPH CONVOLUTIONAL NEURAL NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper is logically organized, the research content is systematic and the structure is reasonable. The paper considers that the text syntactic relationship and word sequence are important and useful for text classification. So it analyzes the sequence structure and syntactic relations of text, combines the syntactic relationship, sequence structure, and semantics for text representation. And proposes a novel model that utilizes GCN for syntactic relationship, multi-head attention for words and corporate them in capsule network for text classification. The experimental results prove the effectiveness of the proposed method.
Experimental design: The problem researched in this paper is clearly defined. The experimental part is related to the research problem, which is designed reasonably. Experiments on single classification and multi-classification tasks, Syntax module and module ablation experiment were carried out in the experimental part. Experiments prove that graph convolutional neural networks, capsule networks, and multi-head attention have an integrated effect on text classification tasks. And the module ablation experiment shows that each module has a certain role in improving the text classification effect. Methods described with sufficient detail and analyzed the results accordingly.
Validity of the findings: The paper analyzes the background and research status of the problem, proposes an attention enhanced capsule network-based text classification model, which is quite innovative. The technical introduction of the model is clear. The analysis of the experimental results is reasonable, and the data charts are clear. The results of the experiment also verify the effectiveness of the proposed model.
Additional comments: 1. It is better to briefly introduce the formulas in section 3 and separately add a new section to describe the details of the algorithm by pseudo-code overall.
2. It's better to give some insight into each module. Not just simply put the conclusions of others’ papers in “related work”. For example, what is the characteristics of CapsNet? Why it is better? I really want to see your own understanding with the formulas. The same as to GCN and multi-head attention. The introduction of those modules is not deep enough.
3. Elaborate on the principle of Syntactic Dependency Tree.
4. There are several mistakes about the format. Such as the label “Figure” in section 3.3 paragraph 2, misses number 3; Section 4.1 paragraph 1, the label “Table” misses number 1, section 4.2 (1), the label “Table 33” repeats number 3, etc. 6. In section 3, the authors should add citations of the formulas.
The description of Equation 17 needs to be clearer.
( Alaparthi, and Mishra 2020) proposed a pre-trained model of language representation (BERT)also also takes multi-head attention as its basic component.
"n-grams" should be "N-grams".
5.  Why Table 6 can show that the motivation of using graph neural network to encode syntactic information and other models is correct?


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: ATTENTION ENHANCED CAPSULE NETWORK FOR TEXT CLASSIFICATION BY ENCODING SYNTACTIC DEPENDENCY TREES WITH GRAPH CONVOLUTIONAL NEURAL NETWORK
Review round: 1
Reviewer: 3

Output:
Basic reporting: This article proposes a new network architecture for the text classification task. In the feature extraction phase, it uses multi-head attention to encode words' embeddings to extract long-range dependency information, uses GCN to encode Syntactic Dependency Tree to extract syntactic info, and subsequently concatenate them by add operation. Then pass it through a CapsNet for classification.
Experimental design: Generally, the motivation is clear and the experimental work is solid and comprehensive.
Validity of the findings: It's a combination of existed works but results in better performance than some of the previous works.
Additional comments: Follows are some comments to further improve the paper:
1. (1) The English writing of the thesis needs to be further improved and the tenses be unified. Some contents are in the past tense and some are in the present tense. (2) There are a lot of errors in the cross-reference of tables and charts in the paper. There are no corresponding labels behind many tables and figures. (3) The writing of formulas also needs to be more standardized. The corresponding "," needs to be added after most formulas, and where should be lowercase without uppercase. (4) The data set used needs to be added to the corresponding reference instead of a footnote.
2. There are many kinds of methods that can extract syntactic information. Why do the authors choose the GCN instead of other models? I mean what is the benefit of GCN?
3. In addition to the experimental comparison with several baselines, it is also necessary to add the experimental results compared with some other existing algorithms.


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: ATTENTION ENHANCED CAPSULE NETWORK FOR TEXT CLASSIFICATION BY ENCODING SYNTACTIC DEPENDENCY TREES WITH GRAPH CONVOLUTIONAL NEURAL NETWORK
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have addressed all my concerns and thus I think it can be accepted.
Experimental design: The authors have addressed all my concerns and thus I think it can be accepted.
Validity of the findings: The authors have addressed all my concerns and thus I think it can be accepted.
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: DATA PRIVACY DURING PANDEMICS: A SYSTEMATIC LITERATURE REVIEW OF COVID-19 SMARTPHONE APPLICATIONS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This literature review has followed a comprehensive methodology to study different digital technologies such as CTAs which have been used to control the COVID-19 spread. The manuscript has been written clearly using professional English which is easy to understand.
Experimental design: The article content has fallen in the Aims and Scope of PeerJ Computer Science.

The study has done a thorough investigation with a high ethical standard on all kinds of contact tracing applications (CTAs) and reviewed 800+ papers published in some prestigious journals such as Science Direct, IEEE, Scopus, etc.
Validity of the findings: Conclusions are well stated, and valid. The result and recommendations made are beneficial to governments and countries all over the world.
Additional comments: Figure 1 is a bit ambiguous. I would suggest changing it as in the attachment.


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: DATA PRIVACY DURING PANDEMICS: A SYSTEMATIC LITERATURE REVIEW OF COVID-19 SMARTPHONE APPLICATIONS
Review round: 1
Reviewer: 2

Output:
Basic reporting: - The paper is very well written and in simple words. It is thus easy to understand which is a very important characteristic of any document.

- The sections are titled as questions. While this is innovative and gets the point across, I prefer a more conventional approach to naming sections

- Each section and each point has to be discussed in much more detail. The authors superficially discuss the  ideas of various works and do so in a rather heterogeneous manner with separate paragraphs dedicated to separate papers. I would look forward to a more homogeneous discussion of ideas with disparate work smoothly falling into the discussion.
Experimental design: - Very little discussion is dedicated to the users’ privacy and its breach. The authors discuss laws that are prevalent around the world, “ethical” issues, and “values”. I would prefer a much more detailed analysis of the issues around privacy and its breach first in a general context and subsequently specifically with respect to the pandemic and the tracing apps.

- The authors discuss the attempts made by health authorities to protect and preserve privacy. I feel this discussion is limited. The authors should also include the efforts made by governments, health authorities, world bodies, and also those made at individual levels.
Validity of the findings: - Conclusions on various aspects of the study are not drawn. The authors talk about the issues based on the contributions of various endeavours but do not draw appropriate conclusions based on these.
Additional comments: - A few figures, block diagrams etc. would help the reader more effectively grasp the ideas being discussed.

- The authors may want to justify the content and both ends for a better appearance.


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: DATA PRIVACY DURING PANDEMICS: A SYSTEMATIC LITERATURE REVIEW OF COVID-19 SMARTPHONE APPLICATIONS
Review round: 2
Reviewer: 1

Output:
Basic reporting: No coment
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: DATA PRIVACY DURING PANDEMICS: A SYSTEMATIC LITERATURE REVIEW OF COVID-19 SMARTPHONE APPLICATIONS
Review round: 2
Reviewer: 2

Output:
Basic reporting: My concerns on basic reporting have been addressed.
Experimental design: My concerns regarding the study design have been addressed.
Validity of the findings: My concerns regarding the validity of the findings have been addressed.
Additional comments: All my concerns have been addressed. I am happy to recommend that the paper be accepted.


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

Input:
Title: PARTICIPATION IN WIKI COMMUNITIES: RECONSIDERING THEIR STATISTICAL CHARACTERIZATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript uses appropriate language, references relevant research, and follows a standard article structure.
Experimental design: The data collection and analysis strategy is well-motivated, clearly described, builds on prior work, extends into a new domain, and is rigorously applied.
Validity of the findings: The data was appropriately retrieved and analyzed using constructs/methods, the findings replicate other empirical findings about power laws in social systems, discussion emphasizes truncated power law as appropriate fit and the corresponding mechanisms and interpretations that generate them.
Additional comments: I would add the following references into the manuscript:

Mitzenmacher, M. (2004). A Brief History of Generative Models for Power Law and Lognormal Distributions. Internet Mathematics, 1(2), 226–251. https://doi.org/10.1080/15427951.2004.10129088

Andriani, P., & McKelvey, B. (2009). Perspective—From Gaussian to Paretian Thinking: Causes and Implications of Power Laws in Organizations. Organization Science, 20(6), 1053–1071. https://doi.org/10.1287/orsc.1090.0481


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: PARTICIPATION IN WIKI COMMUNITIES: RECONSIDERING THEIR STATISTICAL CHARACTERIZATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: I am happy to review this article, which examines the distribution of participation in online wiki communities.

Overall, the language of this manuscript is adequate. There are a number of places where the language feels awkward to a native English speaker, but only rarely do these weaknesses hinder comprehension. I have marked some of the most egregious examples in the attached PDF. There were a few places, most notably at the end of the introduction, where \ref's were broken. It looks like this may have been because the style of the manuscript does not include section numbers?

The paper identifies the most important relevant literature and is well-situated from a methodological perspective. I think that the authors could have done more to explain the practical importance of the research and to identify more concretely the practical implications of identifying a context as having distributions which are fit better by one function versus another. In other words, what is the scientific and theoretical benefit of applying Broido and Clauset's approach to all of these new contexts?

In general, the structure of the article was effective. The figures were well done and persuasive. I found the multiple colors of Figure 1 a bit confusing. Does the cutoff point represent the mean, for example? This should be clarified, and referenced in the legend. I found Figure 2 engaging, but I wished that the edges were colored or weighted based on the percentage, to make it easier to distinguish the "winners" visually. At least in my copy, Figure 3 appears to have some compression artifacts and should be produced as a vector image (also Figures 5 and 6). Finally, I thought that Figure 4 might be more persuasive if it also included empirical data points (although it's possible that this might make the figures too noisy).

Regarding the data, the data and code include appears to be adequate for running the statistical analyses. I was unable to find the code used to actually gather the edits per person, as described in lines 195-204. Nor was this raw data made available. I did not run any of the code to test it.
Experimental design: The overall design of this paper is appropriate for a computer science journal, well defined, and well executed. The methods are well situated in previous work and well described.

As explained above, I do think that the authors could and should do more to identify the knowledge that this approach gives us that we didn't have before, especially in the Introduction / Background section.

I have only two, fairly minor suggestions for the methods section. First, the authors claim to consider only communities with 100 contributors at times, and at other times those with 100 registered users. These are different measures, and it should be clear which cutoff is the actual cutoff. If it is registered users, does that mean that unregistered users (i.e., "IP users") are also removed? If so, this should be made clear and justified. Second, the authors should explain why 8K wiki endpoints were not available. I would guess that this is because the wikis no longer existed, but that should be made clear.
Validity of the findings: The findings are well-supported and the analysis of this paper appears both reasonable and statistically sound. Overall, the authors make a convincing case that wikis are typically well-described by a truncated power law.

As mentioned above, I would have liked to have a more substantive understanding of what the conclusions infer for our understanding of how these communities operate, as well as what else we might be able to do with this approach. When these sorts of explanations occurred, I was often unconvinced. For example, the manuscript seems to argue that the findings show that high-volume contributors differ from low-volume contributors, but this seems like it would already be very likely. Indeed, if anything a power-law distribution would suggest that they are more different from each other (in the sense that the discrepancy in the number of edits is larger).

I believe that the concluding remarks section should be restructured from bullet points to paragraphs. I found the description of generalizability at ~398-401 confusing.
Additional comments: Overall, I found this paper to be convincing in showing that a truncated power law is a reasonable distribution for characterizing wikis. The analysis is narrow but well-executed and while I provided a number of suggestions for improvements to the presentation and discussion of the results, I think that the paper shows a number of strengths.


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: TOWARDS UBIQUITOUS REQUIREMENTS ENGINEERING THROUGH RECOMMENDATIONS BASED ON CONTEXT HISTORIES
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. the work is prepared well and having good background of the issues considered in the proposed model
2. Nhatos architecture using the Technical Architecture Module (TAM) modeling specification has been considered, which is right move
Experimental design: 1. the experiments made in the work are on the following nodes:
a. Relational Entity of the Nhatos Model
b. Profile of Participating Teams
c. Recommendations Made by Project
d. Recommendations Through Analysis of Context Histories
2. The RESTFull API Application provides a communication channel between the Hybrid Application and 421 the Context Storage application has been implemented using right approach, however, more consistency in analysis is needed
3. Learning phase steps to be explained in better way
Validity of the findings: findings have been validated and aps have been consistent
the data used and provided is consistent, however, more comparative study is needed
Additional comments: need to have relook at comparative study


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: TOWARDS UBIQUITOUS REQUIREMENTS ENGINEERING THROUGH RECOMMENDATIONS BASED ON CONTEXT HISTORIES
Review round: 1
Reviewer: 2

Output:
Basic reporting: # Literature work should be improved in the paper as it is very limited. Add one or Two table of comparison along with line diagram of existing work.

# The proposed methodology and design must be compared with existing work. Dedicated separate sub-section for this. Also, use block/line diagram for such proposed methodology.
Experimental design: # The work is under the Aims and scope of this journal and also, presented with scientific contribution of this study as use of the similarity analysis of this work.

# The article proposed  "Nhatos" which is a computational model for ubiquitous requirements management . This is very appreciated.

# The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations. This is justified
Validity of the findings: # Conclusion of this paper can be written in systematic way
Additional comments: No additional comments