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: COMPARISON OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN PROMOTER PREDICTION ACROSS DIVERSE SPECIES
Review round: 1
Reviewer: 3

Output:
Basic reporting: •	The report is well-written in a clear language and a good structure and flow of information
•	In the introduction, the paragraph that starts at line 89, it is mentioning some of the genomics applications and not covering all of them; such information should be clearly state
Experimental design: •	Data extraction was not explained well and needed more details
•	90/10 data division for training and testing is subject to overfitting, and the performance of the testing, in this case, is questionable
•	Random Forest (RF) is not a representation of ML models, so elaborate on why you selected it?
•	Ceasing your experiments, as mentioned in section Comparison of Feature Encoding Techniques at line 226, because the training time was very high is not enough to draw your conclusion
•	Regarding comparing with existing techniques, it is not a fair comparison in which you should unify the datasets and no just report the published results
Validity of the findings: •	You have mentioned one of the challenges that were in previous studies is choosing the negative data, yet I do not think shuffling it would solve the
•	I think having 1000 long sequence is too long to the extent that you might lose some of the important features and include irrelevant ones
Additional comments: •	You have mentioned one of the challenges that were in previous studies is choosing the negative data, yet I don’t think shuffling it would solve the
•	Have you checked the sequences and removed the duplicates or 100% identical sequences?
•	I would recommend a visual representation of your ML/DL models with the parameters that were used
•	The subtitle “Feature extraction” might be changed to data representation or data preprocessing


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: COMPARISON OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN PROMOTER PREDICTION ACROSS DIVERSE SPECIES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Comments addressed by the authors.
Experimental design: Reviewer’s original comment 1:
Considering the amount of samples (35,000 and 6,000), I would recommend using
cross-validation to assess the performance of the models. Are the positive/negative
samples ratio 1:1, if so, what would be the results of testing the entire genome for
TSS where the number of negative samples is much larger than positive samples.
With this test, the functionality of the method would be demonstrated.

Authors’ response: We agree with the reviewer that the number of samples would require
performance assessment of the models for each species. Number of positive and
negative samples are indeed in ratio 1:1. In order to validate the performance
assessment we also tested the model generated using shuffled sequences as negative
dataset on a large number of randomly selected non-TSS sequences from human
genome. We found that the model functioned equally well with this dataset validating
efficacy of models (Please refer to the line number: 281).

Reviewer:
Can authors please explain why only 593 random sequences from the human genome were considered as negative test set?
Also, please be accurate in the dataset sizes. In the manuscript, it is mentioned approximately 35,000 promoter sequences for each A. Thaliana and Human, and 6,000 for yeast. However, in the files I found 41671, 61546, and 6125 for A. Thaliana, Human, and yeast, respectively.
Finally, for reproducibility of data extraction, please indicate genome references, annotation used, and any other considerations.

-------
Reviewer’s comment 2:
Can authors please explain if and how the hyperparameters of each ML/DL model
were tuned? Did authors tune these parameters using only the training data? did
authors use validation set?

Authors’ response: Typically, hyperparameters are tuned using grid search method, but due to
the amount of data and challenges with preprocessing, we did hyperparameter tuning
empirically. Commonly tuned hyperparameters were: Network layers, Filter size,
Kernel regularization, Activation function, Loss function, Optimizer, Number of
epochs, Learning rate, Decay rate, Batch Size. The line number 281 of the revised
manuscript refers the cross-validation set.

Reviewer:
To be certain that no biases were introduced in the results (due to data leakage while tuning the models), it is essential to describe how the parameters of the models were tuned. Normally, a portion of the training data is exclusively reserved for tuning these parameters (validation set). Finally, the test portion of the data is only used once for reporting the final results of the model. If parameters were empirically set by using the test set directly, then there is a data leakage in the approach. Please clarify these details in the manuscript and add a tale with the final model parameters used.

-------
Reviewer’s comment 3:
In line 288, authors mentioned that using a synthetic background dataset resulted
in model with high sensitivity and specificity. Could this be because of the
methodology for shuffling these promoters regions? did shuffling cause a random
distribution of nucleotides? For this, an evaluation of the models with real genome
sequences would be needed.

Author's response: The shuffling did cause a random distribution of nucleotides keeping the overall nucleotide composition same. As suggested by reviewer we have evaluated the
model with real genome sequence data as described in section “training of ML/DL
model with random genome sequences”. Please refer to the line number 281.

Reviewer:
In line 283, the authors mentioned that a ~66,000 sequences dataset combining the different considered organisms was created. If I understand correctly, the results discussed in lines 285-294 are obtained by using the same 90%/10% data split for training/testing. If so, and related to my previous (parameter tuning), if the parameters were empirically set by optimizing the performance on the test set (without a validation set) then results would be biased and models would not necessarily generalize to unseen sequences with the reported results.
Validity of the findings: Reviewer’s original comment 4:
Considering the number of samples (35,000 and 6,000), I would recommend using
cross-validation to assess the performance of the models. Are the positive/negative
samples ratio 1:1, if so, what would be the results of testing the entire genome for
TSS where the number of negative samples is much larger than positive samples.
With this test, the functionality of the method would be demonstrated.

Authors' response: We agree with the reviewer that the number of samples would require
performance assessment of the models for each species. Number of positive and
negative samples are indeed in ratio 1:1. In order to validate the performance
assessment we also tested the model generated using shuffled sequences as negative
dataset on a large number of randomly selected non-TSS sequences from human
genome. We found that the model functioned equally well with this dataset validating
efficacy of models (Please refer to the line number: 281).

Reviewer:
I still consider 10% of testing data from an already small dataset may not represent the generalization capabilities of the model. Moreover, 593 random human non-promoter sequences may be too few. This is especially important to validate considering that no details are given on how parameters were tuned during the training of the model and which data was used.
Additional comments: The authors addressed most of my comments and I am overall satisfied with the results. However, I have concerns that are critical to be addressed. Below, I write my original comment, followed by Author’s response and my current concerns.


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: COMPARISON OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN PROMOTER PREDICTION ACROSS DIVERSE SPECIES
Review round: 2
Reviewer: 2

Output:
Basic reporting: The comments were handled.
Experimental design: The provided answers were good enough.
Validity of the findings: The issues were clarified.
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: COMPARISON OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN PROMOTER PREDICTION ACROSS DIVERSE SPECIES
Review round: 3
Reviewer: 1

Output:
Basic reporting: The authors addressed all of my concerns.
Experimental design: Please note that methods should be described with sufficient information to be reproducible by other researchers. Having said that, data selection and pre-processing remain unclear in the manuscript.
From the last reply from the authors:
"The dataset uploaded were the 41671, 61546, and 6125 in size. However, after preprocessing and data cleaning process the dataset were reduced to 35949, 37957 and 6042, respectively.  Out of these, for experimentation we have randomly selected 35000, 35000 and 6000 sequences for A. thaliana, human and yeast, respectively."

I still consider these details need to be clarified in the manuscript.
Moreover, the data available at GitHub only contain the raw sequences without any preprocessing. I suggest authors explain this process in the manuscript and also upload a "raw data" and "processed data" folders in the GitHub link.
Validity of the findings: The authors addressed all of my concerns.
Additional comments: I am satisfied with the reviewed manuscript.


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

Input:
Title: SOFTWARE EVOLUTION: THE LIFETIME OF FINE-GRAINED ELEMENTS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is very well written. Its English is professional, clear and unambiguous.

The academic literature references give enough background and context of the problematic and the context of the research.

The structure (intro, methods, results and discussion, threats, related work and conclusions) is solid, figures and tables are clear.

The raw data and the software are shared in a replication package. As by now, the replication package is provided in a single 3.6GB compressed file (that uncompresses to 66GB). Given that it contains several elements, i.e., the data and the tool, I think it would be a good idea to offer them separately, esp. when 99% corresponds to the repos as I see and the rest to the scripts and stats. The README describes the stats, but the tool (i.e., the many scripts) under the src directory are not documented - I would ask the authors to do it.

The paper is self-contained, with relevant results to the hypotheses presented. At this point it is mainly quantitative, with few qualitative analysis, but I understand that this is to some extent outside of the scope of this work.
Experimental design: The paper addresses an original research, that is within the aims and scope of the journal.

The research questions have been well designed, are relevant and meaningful. The authors elaborate conveniently how their work fills a knowledge gap in the software maintenance and evolution research field.

The research methodology is sound, and it has been performed with high technical and ethical standards. The research shown requires a major non-trivial engineering effort (e.g., the token-based approach in addition to just considering lines, or the git branch flattening), that is presented correctly in the paper and that is available to other researchers in the replication package. I've already pointed out some limitations in the replication package that I would the authors to address.

The methods and sources used are described with detail in order to make a replication possible. In case other researchers want to replicate the study, the replication package is available and contains even the analyzed repositories.
Validity of the findings: All underlying data have been provided. The findings are robust, statistically sound, and controlled.

Conclusions are well stated, linked to the original research question and limited to supporting results.

The research is mainly quantitative, so there is speculation on the reason for the results. However, the speculative part -some of the discussion points- is small, and can be clearly identified even if it shares section with results. I understand that this is a task for further research, and not necessarily has to be included in this work, which already contributes significantly with new scientific knowledge.
Additional comments: The inclusion and removal of large amounts of external code has been reported previously in software evolution studies. My feeling is that they affect severely the results obtained, as it is the case for Handbrake. This is mostly external code and may be considered differently (e.g., in some software growth studies some authors filter these ripples out). I would ask the authors to elaborate a little bit more about it -- at least, it should be included as one of the threats to validity.

Other, minor issues:

Line 99: constructs. drivers of change.

Line 252: copied to renamed --> copied or renamed?

Line 328: if we have line --> if we have a line

Lines 503, 549, 617: Section )

Line 516: Sections 6 and 6

Line 558: All lines of code in these projects die young -> Really they do not die... they are still there, maybe in a zombie state as the project is discontinued, but have not been removed.


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: SOFTWARE EVOLUTION: THE LIFETIME OF FINE-GRAINED ELEMENTS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Regarding the literature references of software evolution, although the paper describes macroscopic and microscopic studies, I think the paper misses the discussion of other elements of source code like methods:
- Jennifer Bevan, E. James Whitehead, Sunghun Kim, and Michael Godfrey. 2005. Facilitating software evolution research with kenyon. In Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering (ESEC/FSE-13).
- Thomas Zimmermann. 2006. Fine-grained processing of CVS archives with APFEL. In Proceedings of the 2006 OOPSLA workshop on eclipse technology eXchange (eclipse '06).
- Hideaki Hata, Osamu Mizuno, and Tohru Kikuno. 2011. Historage: fine-grained version control system for Java. In Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution (IWPSE-EVOL '11).

The idea of creating a synthetic Git repository where files are stored as the targeted elements was originally presented in the paper:
- Hideaki Hata, Osamu Mizuno, and Tohru Kikuno. 2011. Historage: fine-grained version control system for Java. In Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution (IWPSE-EVOL '11).

Minor:
In the section of Internal Validity and External Validity, there are some minor issues:
- No section name in "(Section )"
- "Section 6 and 6"
Experimental design: Regarding the diff used in this study, a recent study reported that among the diff algorithms built into Git, Histogram is better than Myers:
Nugroho, Y.S., Hata, H. & Matsumoto, K. How different are different diff algorithms in Git?. Empir Software Eng 25, 790–823 (2020).
In light of this finding, please discuss the problems with using traditional diff in this study.

As for the death of lines, I think line death would also occur by deleting the file to which the line belongs. I could not understand how to measure the lifetime of lines when files are removed. Please explain in such a case.
Validity of the findings: Regarding the tool lifetime, although it is included in the replication package, I think it is valuable to be presented as a separate project in GitHub.
Additional comments: This paper studies the evolution of source code lines and tokens in 89 Git repositories. To conduct this study, the authors presented methods of Git history simplification, survival tracking, token history analysis, and analysis of moved lines. Standard parametric models in survival analysis were built to model the lifetime of source code lines and tokens.

The description of the method is almost complete, and the publication of the tools and dataset is a valuable contribution. There were just a few manageable problems, but they can be mostly solved by adding the description.


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: SURVEY ON GRAPH EMBEDDINGS AND THEIR APPLICATIONS TO MACHINE LEARNING PROBLEMS ON GRAPHS
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this work, the author reviews several graph embedding methods from several perspectives. It is an interesting and popular field of research.
Experimental design: It is interesting to find some experiment in a survey paper. The author designs some interesting experiments to compare different embedding methods.
Validity of the findings: no comment
Additional comments: I have some high-level concerns over the paper. The paper is not well-organized and still needs some polish. It is not easy to follow sometimes. I suggest the authors spend some time polishing the paper. Some of them are listed below:
1) Introduction section lack pf some high-level paper structure explanation.
2) Section Preliminaries has no section number
3) The paper writing style is inconsistent in the paper.


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

Input:
Title: SURVEY ON GRAPH EMBEDDINGS AND THEIR APPLICATIONS TO MACHINE LEARNING PROBLEMS ON GRAPHS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The writing of this paper is clear and easy to understand. The author provides relatively sufficient references, even though some pretty recent graph embedding techniques are not included. This is expected since current graph research community are blowing up.

I think the paper structure can be further improved. I feel like I was reading a tutorial, or some kind of literature reviews.

The result verify authors' claim. but the table 4 seems to be not completed (lacking last columns). Additionally, what is LR, RF means? What is GBM? for Table 3, 4, and 5, adding its corresponding references would be better. Without references, I am not sure how consistent with the original one.
Experimental design: Authors compare different methods on the examined dataset.  However, compare with discussions before experiments, the experimental design seems to be limited regarding to the dataset: The author has mentioned multiple hypothesis, including heterogenous network, attributed network, variety of applications, network compression, etc. while the experimental only shows on CORA and CITESEER. These two citation dataset does not reflect network heterogeneity, and these dataset does not either consider attributes of each vertices.
Validity of the findings: Since this is another survey of graph embedding, I do expect authors would provide deeper insights among listed publications. Although the author claims that "Our survey is not limited to specific model types and provides an overview from different angles", I haven't found this survey deep dive existing pain points: For example, industrials often face very large graph so that the adjacent matrix cannot even fit into the memory, how to do graph embedding in such case; graph neural network is also facing over-smoothing problem as well; etc. I believe if such topics are surveyed, the contribution of this paper would be more significant.
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: SURVEY ON GRAPH EMBEDDINGS AND THEIR APPLICATIONS TO MACHINE LEARNING PROBLEMS ON GRAPHS
Review round: 1
Reviewer: 3

Output:
Basic reporting: There are a lot of grammatical errors and typos in this manuscript. The authors should re-check and revise carefully. For examples:
- ... a family of automated graph feature engineering techniques have been proposed in different streams of literature
- This paper focus on the four most popular tasks on graphs ...
- ...

How did the authors select the range of selecting papers? It needs to be described clearly.

There are some mistakes in article structure. For example, the authors have section "3.2.1", but no "3.2.2".

Deep neural networks have been used in previous works such as PMID: 31277574 and PMID: 32613242. Therefore, the authors should refer more works in this description to attract broader readership.
Experimental design: The authors used 3 benchmark datasets, but why did they only show 2 datasets in Table 2?

How did the authors tune the optimal hyperparameters of their deep learning models?

The authors should explain clearly on the UMAP idea as well as parameter setting for UMAP generation.

It is good to have an UMAP to analyze the clusters of the data, but why did the authors not include the UMAP information into the models to help improving the performance? The authors could show a comparison between with and without UMAP features.

The authors mentioned Walklets and GraRep models were better than the others. However, I have checked that the performance from the others were also good such as Diff2Vec, GCN, or GraphSage. Therefore, what is the metric to determine the performance of UMAP analyses?

Performance metrics (i.e., Recall, Precision, or Accuracy) have been used in previous works such as PMID: 31987913 and PMID: 28643394. It is suggested to refer more works here.

The authors should release source codes for replicating the results.
Validity of the findings: Discussions are weak. The authors should discuss more on the findings and comparisons to previous works.
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: SURVEY ON GRAPH EMBEDDINGS AND THEIR APPLICATIONS TO MACHINE LEARNING PROBLEMS ON GRAPHS
Review round: 2
Reviewer: 1

Output:
Basic reporting: I think the author has addressed all the comments from the reviewers properly. This paper is in better shape now. My only two concerns are:
1) reader may get confused about the paper structure after reading the introduction part. The author stated that they present "how different network properties affect graph embedding quality on most popular machine learning problems on graphs." It seems this paper focused on the graph network instead of the graph embedding techniques.
2) I suggest the author do proofread before publication.
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: SURVEY ON GRAPH EMBEDDINGS AND THEIR APPLICATIONS TO MACHINE LEARNING PROBLEMS ON GRAPHS
Review round: 2
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: My previous comments have been addressed satisfactorily.


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: SOLUTION STRATEGY BASED ON GAUSSIAN MIXTURE MODELS AND DISPERSION REDUCTION FOR THE CAPACITATED CENTERED CLUSTERING PROBLEM
Review round: 1
Reviewer: 1

Output:
Basic reporting: This is a well-written and structured paper on the use of gaussian mixtures to address the issue of capacitated clustering. The problem is clearly stated and the method properly presented as an alternative way to solve this problem with less computational time
Experimental design: Expertiment were properly designed and sufficiently detailed, minor aspects  of alternative testing instances are described.
Validity of the findings: Results appear to be sound. Maple code is clean and easy to understand.
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: SOLUTION STRATEGY BASED ON GAUSSIAN MIXTURE MODELS AND DISPERSION REDUCTION FOR THE CAPACITATED CENTERED CLUSTERING PROBLEM
Review round: 1
Reviewer: 2

Output:
Basic reporting: see general comments
Experimental design: see general comments
Validity of the findings: see general comments
Additional comments: The paper proposes a new method to solve CCCP, called DRG. It is competitive against other methods in the literature.

The proposed method can beat all the non-benchmark methods in terms of accuracy. However, it seems for instance when N <= 10000 TS remains pretty competitive against DRG, so it is not quite clear to me under what circumstances one should consider DRG over other methods. For instance, what's the most common range of N in practical applications?

Also Figure 6 is a bit confusing because the legend says the red curve is DRG, while in the plot it is labeled as GMM.

Some typos in the paper:
All the section numbers seem to be missing such as in Line 81, 163, and 183.


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: SOLUTION STRATEGY BASED ON GAUSSIAN MIXTURE MODELS AND DISPERSION REDUCTION FOR THE CAPACITATED CENTERED CLUSTERING PROBLEM
Review round: 1
Reviewer: 3

Output:
Basic reporting: This project is logically laid out and technically sound. It is also an enjoyable reading. The work should be completed as to demonstrate that the references are up to date (some performance benchmarks are almost ten years old).
Experimental design: Furthermore, albeit the performance of the proposed method is deemed novel and competitive, it fails to completely dominate other reported methods.  Under what circumstances would an experimenter choose the proposed method over the other methods? This is particularly important considering that, in real life, the problem at hand is more strategic and does not quite critically requires the fastest possible heuristic solution (as it would be the case for an operational problem).
Validity of the findings: One important issue that must be discussed is the following: if the proposed method is meant to minimize spread, why doesn´t it completely dominate (in that criterion) the other methods that did not consider that objective explicitly?

Additionally, an appendix on graphical validation would help a lot to appreciate the kinds of solutions set forth by the proposed method.
Additional comments: In general, a probabilistic approach might be more complex than a deterministic approach. For this reason, more conclusive evidence of superior performance might be needed to accept the proposed method as an attractive competitor to other 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: SOLUTION STRATEGY BASED ON GAUSSIAN MIXTURE MODELS AND DISPERSION REDUCTION FOR THE CAPACITATED CENTERED CLUSTERING PROBLEM
Review round: 1
Reviewer: 4

Output:
Basic reporting: In this study the author proposed an integrated approach based on Gaussian mixture models and dispersion reduction to solve  the capacitated centered clustering problem (CCCP). For this purpose, the author employed the proposed methodology to some CCCPs and the obtained results were compared with some methods in litarature. To my opinion, this study seems nice and interesting and can be accepted for publication. But, it needs some modification before its acceptance.
Experimental design: The experimental section is adequate and I think this part is good.
Validity of the findings: The findings seems appropriate and nice.
Additional comments: I ask the author to consider the following comments.

1. The introduction is weak and literature review should be improved. There are some papers which are related to this study and have not been cited. For instance,

a) Hadi-Vencheh, A., & Mohamadghasemi, A. (2013). An integrated AHP–NLP methodology for facility layout design. Journal of Manufacturing Systems, 32(1), 40-45.
b) Niroomand, S., Hadi-Vencheh, A., Şahin, R., & Vizvári, B. (2015). Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems. Expert Systems with Applications, 42(19), 6586-6597.
c) Mohamadghasemi, A., & Hadi-Vencheh, A. (2012). An integrated synthetic value of fuzzy judgments and nonlinear programming methodology for ranking the facility layout patterns. Computers & Industrial Engineering, 62(1), 342-348.

2. There are some typo-grammatical errors through the text.
3. What is/are the disadvantage(s) of the proposed method?


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: SOLUTION STRATEGY BASED ON GAUSSIAN MIXTURE MODELS AND DISPERSION REDUCTION FOR THE CAPACITATED CENTERED CLUSTERING PROBLEM
Review round: 2
Reviewer: 1

Output:
Basic reporting: see general comments
Experimental design: see general comments
Validity of the findings: see general comments
Additional comments: The authors have addressed my comments for the previous version. One suggestion is that the new references maybe included in the final version as well.


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

Input:
Title: SOLUTION STRATEGY BASED ON GAUSSIAN MIXTURE MODELS AND DISPERSION REDUCTION FOR THE CAPACITATED CENTERED CLUSTERING PROBLEM
Review round: 2
Reviewer: 2

Output:
Basic reporting: In this version the author addressed all of my comments, hence I recommend acceptance of this study.
Experimental design: It seems correct and adequate.
Validity of the findings: The findings are correct based on my judgement.
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: USING ALGORITHMIC TRADING TO ANALYZE SHORT TERM PROFITABILITY OF BITCOIN
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: A weakness of the manuscript is the unclear objective of the paper. 5 research questions are presented without explanation why these questions are relevant. So it is not clear if the paper is about to find the best algorithm for a specific asset (BTC) or to compare profitability of specific assets using the algorithms.

Please do more explanation why you use this 5 research quesitions. And make the objective of the paper clearer.
Validity of the findings: no comment
Additional comments: 1.	The algorithms 1 – 4 generate buy and sell signals even if it not possible to buy or sell the assets. Please change your algorithms in a way that they consider previous prices and only generate buy or sell signals, if it is possible to buy respectively sell.  You cannot sell without buying before. Specially for algorithm 4 you even describe, that buy and sell signals are generated if two moving averages cut each other (lines 192 – 195). Nevertheless, in algorithm 4, signals are generated, if one moving average is higher/lower than the other.
2.	In line 206 – 208 you describe that algorithm 4 generate signals without a cut of the two moving averages. Please be consistent.
3.	In this context, table 2 should be adjusted. Here you show the number of signals which are generated over the whole data set per algorithm. There is a large number of buy and sell signals but the number of completed transactions is very small. It seems you assume that if the last day of a trading period occurs, it is not necessary to sell all units bought before. This is a typical assumption in the literature of competitive algorithms which you refer to. Note, if there is no need to sell the units which are bought before, your benchmark (the buy and hold strategy) is in disadvantage. Specially, if transactions costs are considered.
4.	Further it is not clear, if you take the return of trading periods into account in which only buy signals are generated.
5.	If all bought units have to be sold at the last day of a trading period, this should also be noted in algorithms 1 – 4.
6.	Why is FLMA the only algorithm which is not presented?
7.	Another important issue with your manuscript is the relevance of your research questions. The five research questions are listed in the lines 141 – 145 without any explanation why these questions are relevant.
8.	One unanswered question is how do you determine m and M for RP?
9.	Figure 2 shows the transactions per algorithm. Figure 1 shows the effect of transactions costs. Because the results of figure 1 depend on the findings of figure 2, I would change the order of figures and show figure 2 first. Further, this change in order gives you the opportunity to discuss the findings of figure 1. This could be very helpful, because at the first sight it seems, that the algorithms with a low number of transactions, haven’t a lower impact from transaction costs.
10.	Table 2 is a summary of the selected scenarios but not of the algorithms. In fact, you use six algorithms, in two different settings (15 days, 30 days). Further, it is not clear, why you choose different parameters for the algorithms VLMA and FLMA for 15 days and 30 days. Why do you not use the four combinations of parameter for 15 and 30 days?
11.	In line 203 you define e_t again, without using it after that.
12.	Please define the first two variables in line 209 before using it.
13.	Please define the variables i and f before using it. Line 226 -228. Why are you not using T?
14.	Please define the variable T before using it. (First use in algorithm 2)
15.	Please define the variable q_t before using it. (First use in algorithm 2). Do you mixed up q_t and e_t?


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: USING ALGORITHMIC TRADING TO ANALYZE SHORT TERM PROFITABILITY OF BITCOIN
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors investigated the short term profitability of Bitcoin cryptocurrency against two fiat currencies, namely Euro and Yen.
The analysis was conducted implementing both reservation price algorithms and moving average based trading techniques over an eight year period, ranging from January 1, 2011 to 31 December, 2018.
The results were compared with those obtained with the classical buy-and-hold trading technique and were evaluated in terms of the (Geometric) average trading period return criterion.
This study highlights that on average, in short term Yen is more profitable than Bitcoin and Euro, although the answer also depends on the choice of the algorithm.

The article is clearly written and the analyses seem to be performed rigorously. Raw data and code are provided by the authors and easily accessible. For these reasons, I think that the paper would be of interest for the readership of PeerJ Computer Science.
However, there are some minor changes that must be addressed for the article to be ready for publication.

The "Introduction" of the paper should be proofread. I suggest to move the content of section 2 (Bitcoin - A brief overview) to the "Introduction" section, describing in a more detailed way the Blockchain technology, focusing more on its main feature of public ledger. The authors made a comparison between Bitcoin cryptocurrency and two fiat currencies, namely Euro and Yen. I think that they didn't highlight the main problem of this comparison, which is also the main feature that distinguish their markets, i.e. fiat currencies depends only on classical macroeconomic variables while cryptocurrencies are virtual currencies based on Blockchain technology and therefore their market also depends on variables related to the technology itself. Please go into this in more detail in the "Introduction" section.
Figures 1 and 2 shows the results obtained for the Bitcoin cryptocurrency. If there are no length limits, why not also report the figures for Euros and Yen? Please, report all results.

The purpose of this paper is to ascertain the short term profitability of Bitcoin comparing it with those of the Euro and Yen fiat currencies. This comparison gives a sense of how different BTC is. Actually, to make this analysis more robust I would include the analysis of another cryptocurrency in order to have an equal comparison, for example (Bitcoin, Ethereum) VS (Euro, Yen). The choice of BTC and ETH is quite straightforward, since they are the two most valuable cryptocurrency at the moment. Please, expand the experiment with one more cryptocurrency.
- Page 4, table 1. I suppose the data are in USD, but please always report the unit of measure or specify it in the table description.
- Page 7, section 5.2. The authors should be more accurate in the description of the chosen evaluation criterion. Equation 6 doesn't represent the Average Trading Period Return, which is the simple arithmetic mean of returns, but instead the Geometric Average Trading Period Return, that is the geometric mean of returns. Furthermore, the authors should explain how this evaluation criterion should be use in this study. For example, when dealing with time series prediction we use MAE (Mean Absolute Error) error to evaluate results. A lower MAE means better performance, while a worse MAE means worse results. Please, clarify these points.
- Page 7, lines 221-224, line 235. Page 9, line 285. The authors should explain how and why they choose this values. Did the authors use or follow a particular criterion for the choice of these values? For example, when dealing with clustering the number of cluster to build is usually chosen because of the Elbow method. This is a critical issue that the authors should clarify.
- Page 11, Figure 2. Please, choose "Signal" or "Signals".
- Page 11, line 330. "whereas moving based algorithms...". Is "whereas moving average based algorithms..." what the authors would like to say?


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: USING ALGORITHMIC TRADING TO ANALYZE SHORT TERM PROFITABILITY OF BITCOIN
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Thank you for taking so many of my improvement suggestions into account.

I have no further issues with the manuscript.

Good Work!


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

Input:
Title: USING ALGORITHMIC TRADING TO ANALYZE SHORT TERM PROFITABILITY OF BITCOIN
Review round: 2
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors have provided a clear and detailed explanation of all the points raised.  For this reason I believe that the article is now ready for publication.


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

Input:
Title: A NOVEL IOT-BASED HEALTH AND TACTICAL ANALYSIS MODEL WITH FOG COMPUTING
Review round: 1
Reviewer: 1

Output:
Basic reporting: - This paper proposes an analysis model as a paper that applies Fog Computing-based IoT to the sports field.

- It is necessary to organize the research part of the existing research into a separate chapter. In addition, it is necessary to add a comparative evaluation part of this study and the existing one.
Experimental design: - As a result of analysis through simulation, it is necessary to verify experimentally how the analysis is excellent in terms of efficiency and security with existing research results.

- In addition to the logical argument that the analysis of data collected by the sensor at the Fog layer provides low-latency by being feedback directly to the device layer, it is also necessary to add simulation results to support this.

- We need a detailed explanation of the analysis model, which is the main topic of this paper. For example, what AI algorithms are used to provide the results of the analysis to the players and speculators.
Validity of the findings: - It is necessary to organize the evaluation contents by comparing and analyzing the existing relevant research results.

- Further analysis of providing security will also need to be added by applying FLAT. In particular, analysis is required from the perspective of security parameters in the field of sports.

- And as a solution to define and solve security issues that need to be solved in the sports field, an explanation is needed from the integrated perspective of security provided by Blockchain-based SDN controller and FLAT.
Additional comments: No additional comments


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

Input:
Title: A NOVEL IOT-BASED HEALTH AND TACTICAL ANALYSIS MODEL WITH FOG COMPUTING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper proposes "A Novel IoT-Based Health and Tactical Analysis Model with Fog Computing". The idea is novel, however, the paper has a lot of technical mistakes that needs to be revised. The paper needs an extensive proofreading and professional English should be used. The paper has no mathematical formulation that can verify the effectiveness of the proposed algorithm. The author has used the SDN approach, however, there are no details of how the algorithm interacts with the controller. The results are not well drawn and written.
Experimental design: The experimental design needs more explanation. Which simulator is used for emulating the SDN.
Validity of the findings: The graphs and results are not professionally written and well explained. There is not flow and link between the algorithm and result section.
Additional comments: The author paper proposes "A Novel IoT-Based Health and Tactical Analysis Model with Fog Computing". The idea is novel, however, the paper has a lot of technical mistakes that needs to be revised.
1. The paper needs an extensive proofreading and professional English should be used.
2.  The paper has no mathematical formulation that can verify the effectiveness of the proposed algorithm. 3. The author has used the SDN approach, however, there are no details of how the algorithm interacts with the controller.
4.The results are not well drawn and written.
5. The algorithm is not well defined, how will be the delay reduced using Fog computing approach.
6. What is role of sdn controller with Fog computing, how will fog computing receive the optimal paths.
7. Is fog computing intelligent or the SDN controller.


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

Input:
Title: A NOVEL IOT-BASED HEALTH AND TACTICAL ANALYSIS MODEL WITH FOG COMPUTING
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: Please explain the security process in more detail from the perspective of FLAT and SDN controller.
Additional comments: Please review the sentences more about the overall logic of the paper.


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

Input:
Title: A NOVEL IOT-BASED HEALTH AND TACTICAL ANALYSIS MODEL WITH FOG COMPUTING
Review round: 2
Reviewer: 2

Output:
Basic reporting: The revision of the paper has been well addressed according to my comments. It can be acceptable.
Experimental design: The simulation details have been added and graphs have been well explained according to my concern.
Validity of the findings: Findings are validated.
Additional comments: The comments have been well addressed and the paper can 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: A NOVEL IOT-BASED HEALTH AND TACTICAL ANALYSIS MODEL WITH FOG COMPUTING
Review round: 3
Reviewer: 1

Output:
Basic reporting: Good
Experimental design: Good
Validity of the findings: It can be acceptable.
Additional comments: It can be acceptable.


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-FLOW-BASED ADAPTION OF THE SYSTEM-THEORETIC PROCESS ANALYSIS FOR SECURITY (STPA-SEC)
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper claims that there are limitations with STPA-Sec predominantly because it lacks IT-centric information. It is not clear to me why this is a limitations, I would suggest changing the wording to instead claim that the STPA-DFSec method is an augmentation that reorients the scope of STPA-Sec. However, the authors don't address similar literature that has unified these two views using STPA:

 A Preliminary Design-Phase Security Methodology for Cyber–Physical Systems, Systems

Systems‐theoretic security requirements modeling for cyber‐physical systems, Systems Engineering

An Ontological Metamodel for Cyber-Physical System Safety, Security, and Resilience Coengineering arxiv

A Top Down Approach for Eliciting Systems Security Requirements for a Notional Autonomous Space System

(This has little bearing on my assessment of the work and authors should not feel forced to cite this work; cite it only if you find it important to cite in the context of your work)

I believe the rest of the paper is okay in terms of basic reporting.
Experimental design: The work is described in sufficient detail, but I would like to see more of an application of the different transformation steps as in how is does the model start, get manipulated and end. Also I found some of the table notation confusing (e.g., P_CA1_ICA1) but I can't particularly think of a way of fixing this that is universal. I suggest these are modified in the paper to something more descriptive potentially with a quasi mathematical form (notice NOT mathematical merely using supscripts and superscripts – one of the benefits of STAMP is that it does not pretend to be mathematical).
Validity of the findings: I would like to see publicly accessible model that does the things (even if it does them in text documents) and see all the transformations in an online git repository. This would increase the likelihood that the results are valid and can be replicated in another system. This is also generally a lacking resource from STAMP so I think it could increase the visibility of the paper. If this is a toy example it should be said explicitly in the paper and still be offered publicly online in totality (I understand that paper appendices might do the same job but STAMP is difficult to enclose in tables in letter paper).
Additional comments: I personally like the idea and I think the paper illustrates the application of this work. I think as a potential augmentation to STAMP when IT-centric security properties are needed this is good work. But when is such a case? When one would use STAMP but want to capture confidentiality, integrity and availability? The paper could do a better job motivating this augmentation.


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-FLOW-BASED ADAPTION OF THE SYSTEM-THEORETIC PROCESS ANALYSIS FOR SECURITY (STPA-SEC)
Review round: 1
Reviewer: 2

Output:
Basic reporting: 'No comment'
Experimental design: However some further clarifications should be given by the authors on a Functional Interaction Structure (FIS) based on data flows is created  to interpret how the system works from the perspective of data flows.
- Further justification is needed for the interaction between the general FIS components and the related "functions".
-This interaction is done in real time?
-A better description for the how the function database can be extended and refined by the  development team to make the database more practical and realisitic case studies.
-How the network vulnerabilities (presence of evendroppers, spoofing attacks, RF jamming etc.) affects the  data-flow-based  structure for information security considerations.
-Social aspects and human factors can also be included in the proposed analysis.
Validity of the findings: 'No comment'
Additional comments: In this paper, the authors proposed a data-flow-based adaption of the STPA-Sec (named STPA-DFSec) to overcome the mentioned limitations and elicit security constraints systematically. The authors analytically desctribe the methodoly for the STPA-DFSec method to overcome the  security-critical issues (e.g. data confidentiality) and  identify the information security concepts. The main novelty of this paper is that the proposed methodology overcomes the original STPA-Sec limitations that it cannot cover more information-security centric properties such as confidentiality. The conclusions of this work are well sated and linked to original research.

However some further clarifications should be given by the authors on a Functional Interaction Structure (FIS) based on data flows is created  to interpret how the system works from the perspective of data flows.
- Further justification is needed for the interaction between the general FIS components and the related "functions".
-This interaction is done in real time?
-A better description for the how the function database can be extended and refined by the  development team to make the database more practical and realisitic case studies.
-How the network vulnerabilities (presence of evendroppers, spoofing attacks, RF jamming etc.) affects the  data-flow-based  structure for information security considerations.


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-FLOW-BASED ADAPTION OF THE SYSTEM-THEORETIC PROCESS ANALYSIS FOR SECURITY (STPA-SEC)
Review round: 2
Reviewer: 1

Output:
Basic reporting: The paper should go through a (very) minor editorial review, quotation marks are the wrong way and some again very minor problems with English.

Figure 4 and 5 should be made bigger.

Everything else is acceptable for a paper on a high level systems theory paper like STPA.
Experimental design: Researchers show how they augment STPA and clearly state that it is a toy example so I am not expecting something more extensive.
Validity of the findings: The findings seem to follow the contributions stated in the paper.
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: DATA-FLOW-BASED ADAPTION OF THE SYSTEM-THEORETIC PROCESS ANALYSIS FOR SECURITY (STPA-SEC)
Review round: 2
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: This paper proposes a data-flow-based adaption of the STPA-Sec (named STPA-DFSec) to overcome the mentioned STPA-Sec’s limitations. Μost of the reviewers' comments were covered by the authors' answers. The main motivation of this article is that the proposed STPA-DFSec follows the general STPA framework but introduces a data-flow-based structure for information security considerations. An additional contribution is that in the "Step 4 - Identification of loss scenarios" in which the possible causes of IFB are identified, new guidewords are defined for loss scenarios caused by external factors. However, the loss scenario ‘Transmission channel is monitored illegally’ in Table 5 can not be attributed to just eavesdropping attack but also to other Denial of Service (DoS) attacks.


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: ON THE CLASSIFICATION OF MICROSOFT-WINDOWS RANSOMWARE USING HARDWARE PROFILE
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors proposed a ransomware detection method based on machine learning algorithms, and their model achieved accuracy of 0.97, which is good classification performance. However, some issues should be handled before publication.
1.	In the end of the Abstract, you mentioned that the best accuracy was 0.97, it is suggested to present which classification algorithm achieved that accuracy.
2.	How were the values of the features in Table 2 collected? Do you mean you run the ransomware in your PC and recorded the feature values, or the values were available online? Please give a more explicit description.
3.	If the features are related with the hardware environment, how can the trained classification models be applied to the features from a different hardware environment?
4.	The performance evaluation was based on hold-out validation, but it is preferred to use cross validation because your dataset is small, which only includes 160 cases.
5.	The explanation of classification models is too short which only occupied 13 lines, and there is no novelty. You need to present the detailed description for your algorithms. Deep learning methods are popular nowadays, Please discuss the methods in 'Pathological Brain Detection based on AlexNet and Transfer Learning' and 'Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm'. Why not use deep networks?
6.	There is a miss typing in equation 4, line 362. The ‘x’ should be multiple operation.
7.	Please give a comprehensive comparison of your method and state-of-the-art approaches.


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: ON THE CLASSIFICATION OF MICROSOFT-WINDOWS RANSOMWARE USING HARDWARE PROFILE
Review round: 1
Reviewer: 2

Output:
Basic reporting: .
Experimental design: .
Validity of the findings: .
Additional comments: Article: "On the Classification of Microsoft-Windows ransomware using hardware profile" (#52627)

The paper is quite well written; its reasoning is rather sound.

However, it seems to me that not enough technical explanations are provided:

1. It would be interesting to know exactly how the number of malware applications selected by the authors (80 non-ransomware and 80 ransomware) compares to the number of malware applications available in VirusSare database.
Are other open sources available besides VirusShare? (I do not know this, but I suspect there are...) I think the authors made a 50%-50% dataset in order to get a balanced classification problem. However, on what principles was their choice [of non-ransomware] based? Did they build an as large as possible dataset? I would kindly ask they to give us a survey of the VirusShare collection.

2. I ask myself whether the high value obtained for F-measure -- minimum 0.94, maximum 0.97 -- indicate (or not) the presence of over-fitting. (This question is in a certain measure related to the previous one: is the dataset very comprehensive or too much restricted?)

3. For how long the selected applications were ran in the "protected" mode / environment (in order to set the values for the selected HPC feature)?
And how much [varying] this time would affect the F-measures obtained for the 4 classifiers?

4. What about the parameters set for instance for Random Forests and Extreme Gradient Boosting?

How were they set?
Nothing is said in the paper about datasets used for validation (aka parameter tuning). What the authors say is that 80% of the collected set of malware from VirusShare was used for training and 20% for testing / prediction. Did they run the algorithms with implicit values of the parameters?

5. Which Extreme Gradient Boosting implementation did the authors use? (While Chen et al. 2015 publication present XGBoost, other versions of Extreme Gradient Boosting algorithms exist, some of them being more recent, and very powerful...)

4. Could we have access to the training and test datasets used/designed by the authors? (They say nothing about this issue.)

Other issues:

An overall remark:
------------------
The description of the research itself -- pages 7-11 -- is under-represented; it amounts to less than 1/2 of the whole paper.

With respect to the 'Related work' section (pages 5-7):
-------------------------------------------------------
Most of the references there are from 2014-2017; only one is from 2020, but none from 2018-2019!
I went through the list of [titles of] papers made available by VirsuShare (the main/only resource used by the authors of the present paper!) at https://virusshare.com/research, and I saw that 52 of them contain the term 'ransomware': 10 from 2020, 15 from 2019, 17 from 2018 and 10 from 2016.
It seems to me that the authors did not update sufficiently their previous research (see L.468-469).

Some specific / "pointed" remarks:
----------------------------------

Table 1:
It should be updated!
It contains only one reference from 2020, none from 2018-2019(!!), two from 2017 and several from 2016, 2015 and 2013. Again, it seems to me that it is too "close" to the paper published by [almost the same group of] authors back in 2017.

L.413:
The same result (0.97 F-measure) was obtained by Extreme Gradient Boosting (not only by Random Forests). Why do the authors not mention it?

L.85-86: "lack of...":
I would not agree with the authors' assertion.
(Although I am not an expert in the malware detection field, I had in the past a substantial co-operation with malware experts...)

L.232-233:
There is no contradiction here with what was written on the lines L.114-118?

L.312: Figures 2a and 2b -> Figures 3A and 3B
L.378: Figure 3 - Figure 4
L.385: Figure 4 - Figure 5

L.402: "results of 0.97" -> F-measure results of 0.97

L.413:
Do you really mean "accuracy" (twice!)
Based on the context, I would say "F-measure".

L.345-353:
I am not sure whether the notions presented here should be explained, because they are well-known in the Machine Learning community.
The same apply to lines L.374-376.
The only reason I see for including them is to make the paper self-contained.

English language issues:
----------------------------------

L.93: "surveyed" ...what exactly?
L.99: Compare to -> Compared to
L.117: that use -> that uses
L.387: over-fitting problem -> the over-fitting problem
L.423: "to deal against ransomware to new grounds" ...What do you want to say?

Conclusion:
-----------
I would recommend a decision in the 'weak accept' to 'weak reject' interval.


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: ON THE CLASSIFICATION OF MICROSOFT-WINDOWS RANSOMWARE USING HARDWARE PROFILE
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The revised paper looks better and my comments are answered carefully. I recommend to accept it.


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: ENHANCEMENT OF SMALL DOPPLER FREQUENCIES DETECTION FOR LFMCW RADAR
Review round: 1
Reviewer: 1

Output:
Basic reporting: The English of the paper is better, and the charts basically meet the requirements.
Experimental design: This paper studies the problem of slow target detection, which has good significance.
Validity of the findings: The method proposed in this paper is novel ， but the advantages of this method are not described clearly. There is a lack of in-depth theoretical and experimental research and verification, and lack of comparison with other methods.
Additional comments: The problems studied in this paper have good practical significance. However, the method studied in this paper lacks in-depth theoretical and experimental verification, and the comparison between the proposed method and other methods is also insufficient.The suggestions are as follows::
1. The whole theoretical process of the research method is given.
2. In this paper, the target velocity range of the proposed method should be given, and explicit simulation and practical verification examples should be given.
3. In this paper, the relationship between detection method and target SINR should be given.


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

Input:
Title: ENHANCEMENT OF SMALL DOPPLER FREQUENCIES DETECTION FOR LFMCW RADAR
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1- The purpose of the paper is ambiguous and not clear relative to the mentioned
    references and previously published work :-
     - Reference no. 6
     - "Detection improvement of off-pin targets in FMCW radars", October 2019IOP
         Conference Series Materials Science and Engineering 610:012048, DOI:
        10.1088/1757-899X/610/1/012048.  Not included in the references.
2- For the proposed filter:
  - The reason of choosing its coefficients and order is not clear, and needs more proof.
  - The reason for putting it before the first FFT or the second FFT needs explanation.
  - References [7] and [8] are not found in the paper body.
Experimental design: 1- Research questions are not well defined.
2- Results of Fig (10) is the same as that proposed in [6] . No extra enhancement.
3- According to Fig (10), the mentioned enhancement in Fig. (11) has no meaning.
4- Hardware design and implementation was not described and its results are not sufficient.
Validity of the findings: 1- No novality was found in this article.
2- Results in conclusion need to be verified.
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: ADID-UNET—A SEGMENTATION MODEL FOR COVID-19 INFECTION FROM LUNG CT SCANS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This manuscript proposes a novel methodology for segmenting the covid-19 infections  This manuscript is written well.
Read the paper carefully to remove the typos and grammatical errors
Some sentences are not understandable to the reviewers Example :Summary of CT scan segmentation methods in COVID-19 application based on deep learning in this paper.
This line has grammatical error
The authors has to explain what are the features extracted for the purpose of segmentation?
Figure 15.Visual comparison of the segmentation results of COVID-19 lung infection should be explained
Check the performance measure as it confuses the reviewers. Make it more clear in the abstract
Experimental design: Experimental results were satisfactory. But in the abstract , the performance measure is mentioned as 80% where ares in the MS, it is different. Check this throughout the MS
Results were clearly explained
Reviewers were in dilemma to validate the accuracy since the proposed methodology has many metrics used to validate the accuracy..
The supplementary files should be executable so that the reviewers can validate the results since this works belongs to Covid-19. Python files were not sufficient.
Submit the coding as Matlab file so that the reviewers can validate the proposed methodology and its accuracy
Validity of the findings: Findings were good. But different types of results were confusing the reviewers.
Arrange all the results in its appropriate sections.
A thorough experiment is done
Reviewers has to validate the proposed methodology via the Coding
All the data were provided. Provide the Matlab coding as supplementary file
Additional comments: This manuscript proposes a novel methodology for segmenting the covid-19 infections  This manuscript is written well.
Read the paper carefully to remove the typos and grammatical errors
Some sentences are not understandable to the reviewers Example :Summary of CT scan segmentation methods in COVID-19 application based on deep learning in this paper.
This line has grammatical error
The authors has to explain what are the features extracted for the purpose of segmentation?
Figure 15.Visual comparison of the segmentation results of COVID-19 lung infection should be explained
the performance measure is mentioned as 80% where ares in the MS, it is different. Check this throughout the MS
Check the performance measure as it confuses the reviewers. Make it more clear in the abstract
Submit the coding as Matlab file so that the reviewers can validate the proposed methodology and its accuracy.
Arrange the results accordingly.


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: ADID-UNET—A SEGMENTATION MODEL FOR COVID-19 INFECTION FROM LUNG CT SCANS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The topic and manuscript appears interesting theoretically and in terms of results, however the paper requires improvements in terms of the following comments:
1) The main manuscript should be checked for typos
2) The results, should be better detailed in one section so that it can be easily refereed as well as understandable to the readers.
3) There were more results and it should be validated by the reviewers.
4) The executable program of the proposed methodology should must be presented as supplementary file. Change the current file format.
5) The validation would be better supported if you would add  the executable programs and also with some image datasets.
6) Transitions from section to section should be smoother. Intermediate results can be changed to the results and discussion section.
Experimental design: Experiments were performed well. Research question is well defined.
Methods were described well
Validity of the findings: The findings were novel
All the data related to the validity is provided
Conclusions are well stated
Additional comments: 1) The main manuscript should be checked for typos
2) The results, should be better detailed in one section so that it can be easily refereed as well as understandable to the readers.
3) The executable program of the proposed methodology should must be presented as supplementary file. Change the current file format.
5) The validation would be better supported if you would add  the executable programs and also with some image datasets.
6) Transitions from section to section should be smoother. Intermediate results can be changed to the results and discussion section.


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

Input:
Title: ADID-UNET—A SEGMENTATION MODEL FOR COVID-19 INFECTION FROM LUNG CT SCANS
Review round: 2
Reviewer: 1

Output:
Basic reporting: All the revisions were done
Experimental design: All the revisions were done
Validity of the findings: All the revisions were done
Additional comments: I can find that all my comments were updated in the revised manuscript


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

Input:
Title: ADID-UNET—A SEGMENTATION MODEL FOR COVID-19 INFECTION FROM LUNG CT SCANS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The manuscript is revised based on my previous comments
Experimental design: The manuscript is revised based on my previous comments
Validity of the findings: The manuscript is revised based on my previous comments
Additional comments: The manuscript is revised based on my previous comments. All the revisions were done.


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: ARTIFICIAL NEURAL NETWORK WITH TAGUCHI METHOD FOR ROBUST CLASSIFICATION MODEL TO IMPROVE CLASSIFICATION ACCURACY OF BREAST CANCER
Review round: 1
Reviewer: 1

Output:
Basic reporting: The English in the report is generally of a high quality. However, there are a number of occasions where the authors confuse the technical terms with respect to the number of hidden layers, and the number of the neurons in a hidden layer, which causes significant confusion.

Literature is patchy. There are significant gaps in wider application of the technique of Taguchi to the design of Artificial Neural Networks (although there is considerable coverage of ANNs applied to Breast Cancer Screening). There are serious concerns regarding assertions made with respect to ‘killer diseases’ and breast cancer being the biggest cancer killer of women (WHO and CDC sources suggest that lung cancer is the biggest killer. The literature used by the author to support their claim does not appear to be peer-reviewed). One of the references that the cite as using ANNs to classify cancer actually describes biomarkers for identifying dyslexia.

Professional Structure could be improved. There is significant repetition throughout the document. There are too many figures, not all of which add to the narrative of the document, and some of the background information on Taguchi and ANNs would be better positioned as supplementary material (in my opinion).

The hypothesis is potentially self-contained and relevant but hasn’t been exploited to its full potential.

Results are clearly expressed. However, there are significant flaws in the experimental design which casts doubt on how meaningful they are.
Experimental design: The subject material is within scope for PeerJ Computer Science. The research question is relevant and sufficiently defined.

The investigation, however is not rigorous from a technical perspective. The application of Taguchi to identify the number of neurons does not appear necessary, as the authors consider only one parameter (the number of hidden neurons) and then only present results for half of the experiments suggested by their own Taguchi.

The methods are sufficiently detailed, although in my opinion are not applied appropriately.
Validity of the findings: It is my opinion that the findings of the analysis are not valid with respect to the original research intention.

Although the results show that the 15 neuron ANN outperforms other models with respect to correct classification of malignancies and non-malignancies, this is simply as a consequence of a brute force approach with respect to the number of hidden nodes and ignores other parameters in the ANN which may impact on optimisation, such as threshold function and momentum. It is not a result of a Taguchi analysis. The paper actually details the difference in performance of a 15 Neuron hidden layer ANN due to split between training, test and validation of the breast cancer dataset. It therefore does not demonstrate the power of Taguchi in the design of ANN, and so does not meet the objectives as stated in the introduction. Conclusions drawn which indicate otherwise are therefore spurious.
Additional comments: The issues that the authors set out to address are important and highlight the power of technology in supporting clinical decisions in complex, potentially life-threatening conditions. However, the methodological approach is flawed, and Taguchi hasn’t been applied appropriately, in my opinion. The paper shows that via an iterative approach, the authors have arrived at an ANN architecture that outperforms other validated models on an established dataset, and needs to be repositioned as such, It is misleading to the readers to state the design is a consequence of a robust Taguchi analysis.


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

Input:
Title: ARTIFICIAL NEURAL NETWORK WITH TAGUCHI METHOD FOR ROBUST CLASSIFICATION MODEL TO IMPROVE CLASSIFICATION ACCURACY OF BREAST CANCER
Review round: 1
Reviewer: 2

Output:
Basic reporting: Overall Acceptable.

1) It would be better if the author could cite more recent published papers. Currently, there's no cited paper published after 2019.

2) The information density of table II is very low. The author can consider using figures instead of that table.
Experimental design: Acceptable
Validity of the findings: 1) Line 337-338, "In addition, 70% of the dataset for training, 15% for validation, and 15% for testing is the optimal partitioning…". The choice of the data partition benefits the model proposed by the author. It is unclear that the results are valid due to the selection bias risk. It would be better if the author could compare the proposed model to other existing classification models with other data partitions.

2) Table III shows the results from 30 different classification simulations using the WDBC dataset with their corresponding percentage errors. The author presents the average of the tests' accuracy. However, the variance of the results is also worth showing. This is also a common problem for other tables.

3) Line 257-258, "A two-layer feed-forward neural network, Gradient Descent with Adaptive Learning Rate (GDA) optimizer with Sigmoid activation function and Softmax Output Neurons, was used to classify the datasets with adequate neurons in the hidden layer." The author uses the GDA optimizer with Sigmoid activation function and Softmax Output Neurons as the model's components. The reason for choosing them is not mentioned in the paper. Adaptive gradient descent algorithms such as Adagrad, Adadelta, RMSprop, and Adam are state of the art and more acceptable optimizers. Also, there could be different activation and output function. It would be better to discuss the choice.
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: ARTIFICIAL NEURAL NETWORK WITH TAGUCHI METHOD FOR ROBUST CLASSIFICATION MODEL TO IMPROVE CLASSIFICATION ACCURACY OF BREAST CANCER
Review round: 2
Reviewer: 1

Output:
Basic reporting: Acceptable
Experimental design: Acceptable
Validity of the findings: Acceptable
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: CLASSIFICATION OF BOTNET ATTACKS IN IOT SMART FACTORY USING HONEYPOT COMBINED WITH MACHINE LEARNING
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, the authors presented an experiment where honeypot is combined with machine learning to detect botnet in the smart factory.

The paper well is well written in terms of English, but the overall structure of the paper is not well organised. The presentation of the paper needs to be improved for better understanding of readers.

Instead of writing your views in the form of long sentences, authors should follow the following points:

1. Better to use more bullet points to discuss problem and solutions separately.
2. Use a preliminary section, where describe several terms/definitions used throughout the paper.
3. Use tables in the related work so that reader can easily compare the state of art work results. No one wants to read long sentences to compare the results of the paper.
4. Try to draw some figures to explain your point of views and several attacks.

Overall, please improve to presentation of the paper to make it more interesting.
Experimental design: Authors uploaded files related to code of the experiment. However, I suggest to upload them on github and also mention some instructions to implement them like standard projects. However, if not willing to publicly share the code in such case authors should upload some instruction to verify the codes and dataset.
Validity of the findings: Results presented by authors are naive and can be verified.
Additional comments: Please improve the overall presentation of the paper as mentioned above.


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

Input:
Title: CLASSIFICATION OF BOTNET ATTACKS IN IOT SMART FACTORY USING HONEYPOT COMBINED WITH MACHINE LEARNING
Review round: 1
Reviewer: 2

Output:
Basic reporting: Authors proposed a honeypot combined machine learning model that is proved to be highly feasible for detecting botnet in the smart factory. The paper is interesting, and the work itself is novel.

However, I strongly recommend authors to work properly on the presentation of the paper.
Experimental design: The experimental design of the paper is good, and it presents important results.
Validity of the findings: All the results can be verified from the available code and therefore, no issue with the validity of the article. However, pls also upload instruction to run the code.
Additional comments: The topic of the paper is interesting and relevant but not well organised in terms of presentation. Several typos and grammar mistakes are available and require a proper proofread. Also, please work more on the formatting of the paper.

Also, I don't know why the authors removed all figures and table at the end of the article. It's better to post at the same place where you gave a reference. The current view of the paper seems like essay and therefore not interesting for technical and quick readers.


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: CLASSIFICATION OF BOTNET ATTACKS IN IOT SMART FACTORY USING HONEYPOT COMBINED WITH MACHINE LEARNING
Review round: 2
Reviewer: 1

Output:
Basic reporting: Authors added several figures and make the whole paper readable by general audience.
Experimental design: As I mentioned in previous major review, experimental design is ok.
Validity of the findings: Validity of finding is ok.
Additional comments: Authors improved the paper substantially and added several improvements and therefore no more further comments. Paper can be accepted in its present form.


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: CLASSIFICATION OF BOTNET ATTACKS IN IOT SMART FACTORY USING HONEYPOT COMBINED WITH MACHINE LEARNING
Review round: 2
Reviewer: 2

Output:
Basic reporting: Authors proposed all changes as mentioned in previous review report.
Experimental design: The experimental design of the paper is good, and it presents important results. No further comments.
Validity of the findings: Instructions are added for verifying code. No further comments.
Additional comments: Authors made all changes and presentation of the paper seems good and therefore paper can be accepted in present form.


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: RESEARCHING COVID-19 TRACING APP ACCEPTANCE: INCORPORATING THEORY FROM THE TECHNOLOGICAL ACCEPTANCE MODEL
Review round: 1
Reviewer: 1

Output:
Basic reporting: (1) Note that all acronyms/abbreviations must be defined the first time they appear in the abstract, main text, and in figures or tables. What is the TAM model? What is PLS-SEM? Unclear. Please specify.

(2) Covid-19 should be always capitalized (the lower case was spotted in some places in the manuscript).

(3) As a survey paper, appropriate references to related works are not covered sufficiently in the list, especially for the contact tracing part. A systematic review of the global deployment status of contact tracing apps is recommended as below.

J. Li and X. Guo, “Global Deployment Mappings and Challenges of Contact-tracing Apps for COVID-19,” SSRN Electronic Journal, May 2020. doi: 10.2139/ssrn.3609516 Available at https://doi.org/10.2139/ssrn.3609516
Experimental design: (1) The Materials & Methods section should be expanded to describe with sufficient information to be reproducible by other investigators.

(2) Missing clarification for this in the results section: "reliability and validity of the measurement model was tested," How did you test it?
Validity of the findings: Overall, the manuscript seems to be an application of the methods used. It adds very little to knowledge already published. The reported results and the discussions are normative and minimal, most of them have been reported/discussed online in the last few months. The authors should consider highlighting the contribution/novelty of this work.

Discussion and conclusion need to be rearranged. Most of the content in the Conclusion part should be migrated into the Discussion section.
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: RESEARCHING COVID-19 TRACING APP ACCEPTANCE: INCORPORATING THEORY FROM THE TECHNOLOGICAL ACCEPTANCE MODEL
Review round: 1
Reviewer: 2

Output:
Basic reporting: * The title is not clear. I suggest changing it into "Evaluating the acceptance of COVID-19 tracing application using an extended technology acceptance model" to reflect the content of the manuscript.

* The use of the term, "COVID-19" should be consistent throughout the entire manuscript. In some parts, the authors used "Covid-19" and in others, "COVID-19".

* In the abstract, avoid using "unbelievably valuable". This would be left to be judged and perceived by the readers, not the authors of the study.

* In the introduction, before embarking into the research objective, the logical stance of the problem statement needs to be fully clarified.

* In the LR, the section "Covid-19 APP" is very short and misleading. It should be further revised to include the previous mobile Apps that have been used to curb the pandemic. Using a Table to list the previous studies would be an advantage.

* The full form of TAM is "Technology acceptance model" not "technological acceptance model". Please check through the entire manuscript.

* Under the "TAM" section, why it is important to extend the TAM with the specified variables like Trust, Perceived Risk, and privacy concerns? This is an essential point that needs to be discussed and supported by recent references.

* Under the "Research model and hypotheses development" section, the hypotheses should be placed under each factor in the model. Don't accumulate all of them at the end of the section. Some readers will be confused while reading.
Experimental design: * Under the "Materials & Methods" section, you are required to answer the following:
- What is the response rate?
- What is the sampling technique?
- There might be some bias when you collect data from different means like email and social networks. How did you handle this issue? (you might use the common method bias (CMB)).
- Why PLS-SEM is used? Why not CB-SEM?

* Remove "composite" from the caption of Table 2.

* For discriminant validity, you have relied on Fornell and Larcker test only. This criterion has been criticized in the past few years. You are required to test and report the results of HTMT.
Validity of the findings: * There should be a dedicated section for theoretical and practical implications. This could be added before the conclusion section.

* In the conclusion section, some short paragraphs need to be merged together.

* The items of your survey should be provided in the Appendix to allow further replications of similar studies in the future.
Additional comments: * Please refer to the following references in your manuscript:
- Arpaci, I., Al-Emran, M., Al-Sharafi, M. A., & Shaalan, K. (2021). A Novel Approach for Predicting the Adoption of Smartwatches Using Machine Learning Algorithms. In Recent Advances in Intelligent Systems and Smart Applications (pp. 185-195). Springer, Cham.
- Arpaci, I., Alshehabi, S., Al-Emran, M., Khasawneh, M., Mahariq, I., Abdeljawad, T., & Hassanien, A. E. (2020). Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic. CMC-COMPUTERS MATERIALS & CONTINUA, 65(1), 193-203.
- Mezhuyev, V., Al-Emran, M., Ismail, M. A., Benedicenti, L., & Chandran, D. A. (2019). The acceptance of search-based software engineering techniques: An empirical evaluation using the technology acceptance model. IEEE Access, 7, 101073-101085.


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: RESEARCHING COVID-19 TRACING APP ACCEPTANCE: INCORPORATING THEORY FROM THE TECHNOLOGICAL ACCEPTANCE MODEL
Review round: 1
Reviewer: 3

Output:
Basic reporting: In this paper, the authors use an extended TAM model to investigate whether citizens would be willing to accept and adopt a mobile application and understand the relationship of citizens’ perceived risk of getting COVID-19, trust, privacy concern, perceived ease of use and perceived usefulness of the app, attitude and behavioral Intention will be examined. This is a very interesting study with practical applications and proper statistical analysis. Overall, this is a clear, concise, and well-written manuscript. Several specific comments are as follows.

Introduction:
“This means that the factors which indicate the intention to use and adoption of this type of app by future users must be investigated. The main objective of this study is to answer the question "Would users be willing to use an APP that would alert them if they have been in contact with anybody infected with COVID?””
Such a statement may be over-generalized. Why does this study explore whether citizens would be willing to accept and adopt a mobile application that indicates if they have been in contact with people infected with COVID-19? Please provide some references.

Literature Review:
For the quality of the literature review, please provide a context for why based on the extended TAM model, citizens are willing to accept and adopt a mobile application to stay in touch with people infected with COVID-19 (including citizens' perceptions of the risks of getting COVID-19, trust, privacy concerns, ease of use and usefulness of the application, attitudes and behavioral intentions).

Research model and hypotheses development:
Please move the hypotheses H1-H8 into each section (e.g. Ease of Use and Perceived Usefulness, Trust...etc), and make the hypotheses more explicit.
In the measurement of this study, please provide the reference for the questionnaire and the reliability of each construct.

Results:
Need to report descriptive statistics for the measures; for example, have the normality assumption and the multivariate normality satisfied for the purpose of structural equation modelling in this paper?

Discussion:
Besides the descriptions of the results of the Hypotheses, more discussion should be offered in more depth.

Conclusions:
Based on these findings, have the current data (yours and others) supported or differentiated? Need to link the results to extant literature, clearly articulate the knowledge gained as results of this study and how this knowledge can be used.
Experimental design: Research model and hypotheses development:
Please move the hypotheses H1-H8 into each section (e.g. Ease of Use and Perceived Usefulness, Trust...etc), and make the hypotheses more explicit.

In the measurement of this study, please provide the reference for the questionnaire and the reliability of each construct.
Validity of the findings: Results:
Need to report descriptive statistics for the measures; for example, have the normality assumption and the multivariate normality satisfied for the purpose of structural equation modelling in this paper?

Discussion:
Besides the descriptions of the results of the Hypotheses, more discussion should be offered in more depth.

Conclusions:
Based on these findings, have the current data (yours and others) supported or differentiated? Need to link the results to extant literature, clearly articulate the knowledge gained as results of this study and how this knowledge can be used.
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: RESEARCHING COVID-19 TRACING APP ACCEPTANCE: INCORPORATING THEORY FROM THE TECHNOLOGICAL ACCEPTANCE MODEL
Review round: 2
Reviewer: 1

Output:
Basic reporting: Further English editing and proofreading should be conducted.
Some of the grammatical errors as mentioned in the first round have yet been fixed, e.g. the "Covid-19" should be capitalized, including in the title and Figure 1.
Experimental design: Most of the concerns have been answered and corrected.
Validity of the findings: After a previous review process from which the manuscript has been revised, the present reviewer appreciates the present version and the effort that the authors put into the revision.
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: RESEARCHING COVID-19 TRACING APP ACCEPTANCE: INCORPORATING THEORY FROM THE TECHNOLOGICAL ACCEPTANCE MODEL
Review round: 2
Reviewer: 2

Output:
Basic reporting: This paper provides evidence of the use of a well-known method on a meaningful set of data; both the findings and the application of the successful application of the method are worth sharing with the PeerJ Computer Science community.
Experimental design: The method is appropriate and the research question well defined, relevant & meaningful.
Validity of the findings: The Results are properly and critically described.

Please check and adjust the following values to 0.7?
"The reliability and validity of the measurement model was tested analyzing Crombach’s Alpha, Composite Reliability and Average Variance, which meant that the latent variables needed to have a minimum value of 0'7 to be acceptable [63]."
Additional comments: This revised version is much improved as the author(s) have really taken on board the reviewers' comments and suggestions. The paper is now much more meaningful and worth sharing with the PeerJ Computer Science community.