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 THE DRIFTING DATA STREAMS USING HETEROGENEOUS DIVERSIFIED DYNAMIC CLASS-WEIGHTED ENSEMBLE
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper has several issues in writing and notation that should be addressed. Furthermore, there are important references missing. More details are provided in the general comments to the authors.
Experimental design: The analysis conducted has a decent protocol, yet, there are missing relevant works that should be used in the experimentation. Without proper assessment against state-of-the-art classifiers, it is impossible to determine if the results are conclusive.
Validity of the findings: Following the previous comments, it is impossible to determine whether the results are competitive against state-of-the-art ensemble classifiers. The manuscript, in its current state, does not allow a proper assessment.
Additional comments: C1: The manuscript lacks important references. For instance, in the introduction, authors bring forward a description for concept drifts, yet, no reference Is provided. Authors should add references for definitions that are not novel.

C2: Furthermore, I believe that some references are not entirely accurate. For instance, in the Background section, authors state the following: “The main idea of the ensemble model is based on the assumption that a set of classifiers together can achieve better performance than individual classifiers (Gama et al. (2008))”. Even though this reference is valid, there are seminal works like the one from Kuncheva that should be analyzed and acknowledged.
-	Kuncheva L.I., C.J. Whitaker, C.A. Shipp and R.P.W. Duin. Is independence good for combining classifiers?, Proc. 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000, 2, 168-171
-	Kuncheva L.I., C.J. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning, 51, 2003, 181-207

C3: Authors are also encouraged to conduct a thorough review of the text, as there are missing commas and also commas that should be removed. Some examples I extracted from the introduction but that occur in other parts of the manuscript:
-	The primary motivation of this study is to design  a model, capable of handling various types of drifts.
-	We assume, that the creation of a heterogeneous ensemble consisting of different base learners with adaptation mechanism which ensures the diversity of  its members can lead to a robust ensemble, able to handle the drifting data efficiently.
C4: Another important paper that should be acknowledged in terms of related works is HEFT-Stream. Despite the underlying goal is to overcome feature drifts, it is also an heterogeneous ensemble for data streams with concept drift detection.

C5: The notation used in the section that describes the proposed method should be revisited. For instance, authors use $E_1 – E_m$, which should be replaced with $E_1, \ldots, E_m$ as there is no subtraction involved. Another thing that should be clarified is the notation used in Figure 2: what does $X e \{1, \ldots, n\}$ stand for? Furthermore, there are sentences like “represented as a matrix $W m \times c$ which are readable but not formally correct. Authors are then encouraged to revisit the entire notation.

C6: Some questions and on algorithm 1: what is time? What is “treshold”? Shouldn’t it be threshold? Furthermore, is this the same as \theta?

C7: Authors must revisit the dataset descriptions provided in Table 2. Is the dataset said to be abrupt and gradual, or are the drifts abrupt and gradual? Furthermore, are the drifts real or synthetic? Please be consistent with what is depicted in the text and table label.

C8: I am highly interested in Figures 3 to 6. What exactly do they mean? How can we verify that the data distribution is changing? What do the colors and bullet sizes depict?

C9: There are relevant ensembles that are not being compared in the experiments section. Examples include ARF and KUE:
Gomes, H.M., Bifet, A., Read, J. et al. Adaptive random forests for evolving data stream classification. Mach Learn 106, 1469–1495 (2017). https://doi.org/10.1007/s10994-017-5642-8
Cano, A., Krawczyk, B. Kappa Updated Ensemble for drifting data stream mining. Mach Learn 109, 175–218 (2020). https://doi.org/10.1007/s10994-019-05840-z
Without a proper comparison with state-of-the-art ensembles, it is impossible to determine whether the results depicted in the paper are competitive.


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 THE DRIFTING DATA STREAMS USING HETEROGENEOUS DIVERSIFIED DYNAMIC CLASS-WEIGHTED ENSEMBLE
Review round: 1
Reviewer: 3

Output:
Basic reporting: The related works, presentation of the algorithm, style, structure of the article are acceptable. The language should be polished, because there are several errors, eg., in the abstract (the second sentence)
"Streams are often very dynamic, and its underlying" - their underlying ...
There are some parts of the article which are hard to understand. I suggest asking a native speaker to review the language or send the article to a proofreading company.
 The presented figures are clear, but I suggest changing fig.2 - to be more understandable for readers. Some of the definitions need to be more precise - I think that author should define data stream, concept drift, decision rule, etc.
Experimental design: The goals of the experiment are unclear - what research question should be answered during this research?
The experimental protocol is unclear. How the authors count the metrics - I assume that test-then-train protocol was used (which is appropriate for the chunk-based approach), but then they should answer the question of how online learners have been modified because they used e.g., online bagging.
There is a lack of statistical analysis of the results.
The authors mentioned that the scope of the experiments was wider (e.g., they run experiments for different chunk size)- I understand that not all results should be included, but then a link to the repository or supplementary material should be added.
Nowadays most of the researches are replicable research, then access to the code is required to allow the readers to repeat the experiments.
Validity of the findings: Because the research questions were not formulated, therefore the conclusions are not so precise. I propose to add (after experimental research section) lessons learned section, where the authors may analyze the behavior of their method.
The idea is similar to the horse racing approach - a few words about this is needed.
I propose to run several experiments on a more controllable environment to check how the proposed algorithm reacts to the different types, severity, and frequency of drifts. I think it will behave pretty well for slow changes.
There is a lack of discussion on the weights accumulations - why it is so important, how this will impact the quality of the proposed solution.
Some important state-of-art methods should be added to comparison,e.g., from Learn++ family.
Additional comments: see above


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

Input:
Title: CLASSIFICATION OF THE DRIFTING DATA STREAMS USING HETEROGENEOUS DIVERSIFIED DYNAMIC CLASS-WEIGHTED ENSEMBLE
Review round: 2
Reviewer: 1

Output:
Basic reporting: C1: The paper still requires proofreading. There are still some sentences that are somewhat unclear and some words that should be replaced. For instance, there are many places where “the” should be replaced either by “a” or “an”, and vice-versa.

C2: The description provided in lines 255 to 269 must be clarified. What is Q statistic? How is it computed? This might be common ground for researchers in the area, but authors cannot assume all readers are familiar with ensemble diversity quantification.

C3: Following my previous comment, line 265 is unclear as it mixes mathematical notation with textual descriptions to declare how weights are computed. Again, please be specific and clear on these notations.

C4: Line 10 of Algorithm 1 is unclear.

C5: Authors state in lines 369-371 that Gini index was used to compute feature importance. This is an interesting approach, yet, the computation of Gini index is not clarified. Please provide a mathematical formulation for this. This is relevant as Gini index is several times confused with Gini impurity.

C6: Figure 3 and how it should be read has been clarified in its caption (x and y axis, bullet sizes, etc), yet, this information should accompany lines 373 and 374.

C7: Regarding the y axis of Figure 3, should it be “Feature Indices” instead of “Features”?

C8: Please increase the font and legend sizes in Figures 4 to 6.
Experimental design: The experimental design has improved from the previous round. I am satisfied with the experimental protocol adopted expect for the comment below.

C1: In addition to providing the number of classes available in each dataset, please clarify the class ratio in each dataset. This is relevant as accuracy may not be appropriate for all datasets. For instance, KDD99 is imbalanced (without mentioning that it is highly biased according to [1], and thus, the assessment is flawed. It is important to make sure that the same does not happen with other datasets.

[1] M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009.
Validity of the findings: The claims are backed up by the results. Nonetheless, please refer to the comment above for class imbalance analyses.
Additional comments: In this revised version, the authors have addressed my previous review’s comments. Nonetheless, I still have a couple of concerns w.r.t. the presentation and analyses of the results that I have detailed in the specific 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 THE DRIFTING DATA STREAMS USING HETEROGENEOUS DIVERSIFIED DYNAMIC CLASS-WEIGHTED ENSEMBLE
Review round: 2
Reviewer: 2

Output:
Basic reporting: no comments - this version could be accepted with minor improvements, as a discussion on weights are still missing
Experimental design: chosen metrics are disputable, but I accept them
It is ok, that you added a few reference methods, but Learn++ should be also added, especially because you deal with online learning.
Validity of the findings: I still cannot find the discussion on the weights accumulations - why it is so important, how this will impact the quality of the proposed solution. The authors did not answer this.
Additional comments: see above


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

Input:
Title: BLOCKCHAIN AND SMART CONTRACT FOR IOT ENABLED SMART AGRICULTURE
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This is an excellent use case and road map on how to enable Ubiquitous computing in agriculture workloads now by integrating IoT devices and the data they can acquire and report upon.  Placing this data on a blockchain creates immutability and adds value to workflow by creating trust in transactions and adding a layer of truth to the workflow.  Great job!


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: BLOCKCHAIN AND SMART CONTRACT FOR IOT ENABLED SMART AGRICULTURE
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1) Most of the article is written in clear, professional English. Some minor grammatical corrections are needed as suggested in the annotated PDF attached to the review.

2) References are provided to prove that blockchain based smart contracts can be helpful in streamlining distribution and supply in agricultural sector

3) Professional article structure is followed. Figure 4 can be more understandable if order of steps is added.

4) Advantages and disadvantages of the proposed system are included in the paper which will be useful for future extensions or improvements of the proposed system.
Experimental design: 1) Research question is well defined by setting the context where a solution is required. Relevant previous work in similar context and evidence for the advantage of using blockchain and smart contracts are also included

2) Good overview of blockchain and smart contracts and their usecases is included in the paper. Application of blockchain based smart contracts in agricultural sector is provided with reference to previous work in the area.

It will be good to answer the following questions:

3) Why MQTT was chosen for the design?

4) What is the source of cryptocurrency to the different type of actors in the system so that they can pay for the gas cost while submitting transactions to blockchain network?
Validity of the findings: 1) It will be good to comment/document the code provided so that it is easy for reader to understand and use

2) How each batch is tracked in the supply and distribution chain can be useful to add in the design section? For example: barcodes or QR codes attached to the package? It will be good to answer the question.

3) It is mentioned that future goal is to deploy the system on their own blockchain. It will be good to mention the type of blockchain that will be used (Permissioned, public or hybrid). The type of blockchain can suggest how centralized or decentralized the whole system is.
Additional comments: Inclusion of IoT devices in the blockchain based agricultural food chain is a very interesting concept. It will be good to think about how these IoT devices can be authenticated , authorized and tracked in the system.


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: BLOCKCHAIN AND SMART CONTRACT FOR IOT ENABLED SMART AGRICULTURE
Review round: 1
Reviewer: 3

Output:
Basic reporting: Some small comments relating to a few references, and some of the language used, which could be improved in places...

“Contrarily, the farmers are the lowest paid seller in the whole chain. The price in retail shops is much higher, sometimes twice or thrice, than that the price sold by the farmers.”
Line 40, any reference here that can be cited?

Line 62 - missing word after mass

Eg line 80/81, Blockchain doesn’t need to be capitalised, it should be written “blockchain”

86 - should be “tamperproof”

(Nakamoto  2019) - is that the right reference?

140 - absurd not really the right word here - infeasible?

178 - By far, blockchain is the most secure way to deal with any kind of data. <⏤ I'm not sure about this claim

200 - usually would refer to author by last name, so Szabo not Nick here

211 - I don’t think Nakamoto 2008 is the correct reference here, for Ethereum

Various - check capitalisation of Remix and Solidity

495 - demonstration not demo (also other places)

Figure 10 - text is a bit small -
Experimental design: Seems good, meets criteria.
Validity of the findings: Seems good, well explained.
Additional comments: Thank you for your paper, which I enjoyed reading. It provides a good explanation of the situation being addressed, and the proposed solution. One thing that could be considered in the future is security/authenticity of the data from the IOT devices into the network. I particularly liked the very clear diagrams.


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: BLOCKCHAIN AND SMART CONTRACT FOR IOT ENABLED SMART AGRICULTURE
Review round: 2
Reviewer: 1

Output:
Basic reporting: I note that the latest revision has changed "Ethereum" to "ethereum" which I don't think is correct - as a name, Ethereum should have capital "E", as in the original submission.
Experimental design: No further comments.
Validity of the findings: No further comments.
Additional comments: Thank you for making the suggested changes, the paper is looking very good now.


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: NON-DESTRUCTIVE DETECTION OF HIGHWAY HIDDEN LAYER DEFECTS USING A GROUND-PENETRATING RADAR AND ADAPTIVE PARTICLE SWARM SUPPORT VECTOR MACHINE
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) is proposed for detecting and recognizing hidden layer defects in highways. This work is interesting. However, the following comments should be cosidered.

(1) Since the main work is to identify the types of road defects, it is necessary to increase the tracking of the progress of the road defect identification content. Plese describe the challenges faced in solving the problem of road defect identification in more details.
(2) In the parameter optimization processing stage, grid search (GS), particle swarm optimization (PSO), and adaptive mutation PSO are used for parameter optimization. It should provide richer and more accurate data for the three methods. Moreover, the authors should highlight the accuracy and superiority of parameter optimization of adaptive mutation PSO.
(3) The paper focuses on the identification of three types of defects, and the comparison of the three parameter optimization algorithms is given. However, it is better to add discussions on other road defects to discuss the effect. Further, the comparision the proposed methods and others methods is suggested.
(4) The literature review and related work are not adequately done.
(5) The K-L transformation is selected when the article selects features. The advange of this method should be added.
(6) When selecting features, which principles are used to select these features, and how do these features help the next step of the recognition work?
(7) More information about the shortcomings of the method and the problems faced in engineering application should be discussed.
(8) Other factors that affect the detection results during the detection should be explained, such as the detection of underground steel bars, which can also reflect echoes, and weather which can afeect the image detection results, and disturbance actors etc.
(9) There are some typos, please check the paper thoroughly. Moreover, it is noted that your manuscript needs carefully editing by someone with expertise in technical English editing paying particular attention to English grammar, spelling and sentence structure so that the goals and results of the study are clear to reader.


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: NON-DESTRUCTIVE DETECTION OF HIGHWAY HIDDEN LAYER DEFECTS USING A GROUND-PENETRATING RADAR AND ADAPTIVE PARTICLE SWARM SUPPORT VECTOR MACHINE
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1.In the introduction part of the article, you should mention the current application examples of some enterprises or academic institutions in road defect identification, reflecting the current research results.
2.The format of some symbols in the text is wrong and needs improvement.
3.There are grammatical and editorial errors in part of the article. English grammar needs to be improved. You can find professionals whose native language is English to improve.
Experimental design: 1.The article provides a comparison of three parameter optimizations, but the processing time and processing results of the three methods are not given in detail. Please add relevant content.
2.When using this method for data processing, which requires higher graphics materials, can the improved method reduce this requirement?
3.There are 100 sets of data samples for training and testing in the article. Are they representative? Will the classification effect be better if the amount of data is increased? I hope the author can try it and display the results obtained in the article.
4.The article does not explain the use of image processing methods, why use Gaussian filtering and K-L transformation, the author can summarize.
5.Mark the type of ground penetrating radar you are using. The data detected by different types of ground penetrating radar are different, which can better show the clarity of the article.
Validity of the findings: 1.Why the main discussion direction of this article is the problem of parameter optimization? Explain the author's work in this respect in detail.
2.More discussion and analysis of existing research results can be added to highlight the contribution of the research.
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: NON-DESTRUCTIVE DETECTION OF HIGHWAY HIDDEN LAYER DEFECTS USING A GROUND-PENETRATING RADAR AND ADAPTIVE PARTICLE SWARM SUPPORT VECTOR MACHINE
Review round: 1
Reviewer: 3

Output:
Basic reporting: 1. In the Abstract, at line 28, the authors use the abbreviation PSO without defining it in advance. It is recommended that the abbreviation should be defined when it is firstly used in the context, as what have been done for GPR and SVM at line 20 and 21.

2. In the Introduction, at line 44, the authors use the term “nondestructive”. However, in the title of this manuscript, it is “non-destructive”. Please format the term.

3. The resolution of the figures in this manuscript looks a little bit low. Could the authors replace them with clearer graphs? For those which are generated from MATLAB, it is recommended that the user use “Export” option in MATLAB to obtain the graphs, which would lead to clearer results, instead of using other snipping tools.

4. In the Image segmentation section, at line 93, the authors state that the image segmentation by Canny operator is demonstrated in Figure 2. While in Figure 2, both the result of image filtering in previous section and the result of Canny operator are shown. Please clarify this in the context to give an exact and complete description of Figure 2. Moreover, could the authors explain the difference between the two results (or among the three images) very briefly?

5. At line 93, the authors use the words “To retain only the necessary information”. Could the authors clarify this in a bit more detail? For example, what is the necessary or unnecessary information, or what is the difference between the two results in Figure 3?

6. Regarding the Figure 4 (C) in the manuscript, please clarify the units of the axes on the plots. Please briefly mention and explain the plots in the context. Please also clarify the units on other plots if applicable.

7. At line 107, it seems that the word “both” should be “all” here, or “both using” should be “using both”? (Grammar)

8. At line 174, regarding the sentence “Speed represents the speed of movement”, it is recommended that the first “Speed” could be replaced by “Velocity” in accordance with the word usage in the previous sentence so that the context would be clearer. (Or vice versa, replace the word Velocity with the word Speed in the previous sentence.)
Experimental design: 1. Equation (4) is the same as equation (3). It seems that equation (4) is given incorrectly and should be replaced by another one. Please check the equation.

2. In the Optimization of the SVM classifier parameters section, at line 154, the authors introduce two parameters c and g in the context. Please define or explain the two parameters here.
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: NON-DESTRUCTIVE DETECTION OF HIGHWAY HIDDEN LAYER DEFECTS USING A GROUND-PENETRATING RADAR AND ADAPTIVE PARTICLE SWARM SUPPORT VECTOR MACHINE
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: The revision is satisfied 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: NON-DESTRUCTIVE DETECTION OF HIGHWAY HIDDEN LAYER DEFECTS USING A GROUND-PENETRATING RADAR AND ADAPTIVE PARTICLE SWARM SUPPORT VECTOR MACHINE
Review round: 2
Reviewer: 2

Output:
Basic reporting: All of the questions were answered.
Experimental design: Every question is responded in this part.
Validity of the findings: The simulation results are given to support validity of the findings.
Additional comments: No additional comments


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

Input:
Title: NON-DESTRUCTIVE DETECTION OF HIGHWAY HIDDEN LAYER DEFECTS USING A GROUND-PENETRATING RADAR AND ADAPTIVE PARTICLE SWARM SUPPORT VECTOR MACHINE
Review round: 2
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors had responded to my previous comments carefully. The revised manuscript is organised very well which provides significant results in defects detection of highway hidden layers.

Only some small issues to add:

(1) In the Introduction section, from line 45 to 55 in the revised manuscript, the authors provide a literature review in this part. However, when reviewing references [2] and [3], the authors use the simple past tense. While in the part of [4]-[6], the simple present tense is used. Similar problem exists in the next paragraph. Please check the literature reviews and use the same form of the tenses.

(2) Similar to above, when describing the authors' designs and simulations, sometimes the authors use the present tense, such as at line 124, 189, 251, 265, etc. While in many other descriptions, the authors use the past tense. Please check the whole manuscript again to correct the forms of the tenses.


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: TODAY’S COMPUTING CHALLENGES: OPPORTUNITIES FOR COMPUTER HARDWARE DESIGN
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Overall, the paper is written well and provides a good survey.


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: TODAY’S COMPUTING CHALLENGES: OPPORTUNITIES FOR COMPUTER HARDWARE DESIGN
Review round: 1
Reviewer: 2

Output:
Basic reporting: This article really does an interesting job of reviewing three essential aspects of a computing device namely logic, memory and interconnect. Mentioning when the survey was conducted (Sept 2019) is impressive as future readers will get to know the state-of-art till that period. The introduction section provided enough background

It is quite difficult to follow the text in the three sections. It would be very beneficial to readers to put subheadings in each of logic, memory and interconnect sections. A figure/table/chart in the introduction section which summarizes the entire paper would be very beneficial to the readers. An example would be:
1) Logic
     i)....
     ii)....
2) Memory
     i)....
     ii)....

It is not clear what is the difference between this review article and other ones. It is worth mentioning the key differences with other surveys for each section (logic, memory, interconnect).

Figures looks convincing and depicts the content mentioned in different sections
Experimental design: The review is conducted in a very narrow space with a few details about each subspace. Few jargons like cryptocurrency syndrome are not quite clear.

Few suggestions to improve the paper: Avoid short (4-5 word) sentences and combine with the immediate ones. This article requires significant changes in terms of writing, clearing grammatical mistakes, and prepositions in sentences like: 1) only simulation, then we draw - incorrect sentence 2) In fact, a better way is reusing, instead of automating. Reuse is a bit easier than automation - two same sentences here are redundant
Validity of the findings: This paper definitely has a positive impact from a reader stand point as it summarized the different aspect of hardware design. The conclusion reflects the introduction and the future directions to explore in each component. It is within the scope of the goals initially set by the authors.

Limited future work and scope is mentioned in the paper which does not give a big picture to the readers to move forward. For example, the paper mentions that the introduction of 3D RAM solves the scaling problem, but does not provide other directions to explore for the researchers to continue in this field.
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: TODAY’S COMPUTING CHALLENGES: OPPORTUNITIES FOR COMPUTER HARDWARE DESIGN
Review round: 2
Reviewer: 1

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


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

Input:
Title: TODAY’S COMPUTING CHALLENGES: OPPORTUNITIES FOR COMPUTER HARDWARE DESIGN
Review round: 2
Reviewer: 2

Output:
Basic reporting: Thanks for adding the subheadings
Experimental design: Thanks for correcting all the sentences.
Validity of the findings: --
Additional comments: No additional comments


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

Input:
Title: PERSONA2VEC: A FLEXIBLE MULTI-ROLE REPRESENTATIONS LEARNING FRAMEWORK FOR GRAPHS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is mostly well written, there are a couple of minor typos (line 101 "explores" should be "exploring", line 151 "shown" should be "shows"). Nothing that a simple proofread won't fix. The literature references are adequate. Figures and tables are of acceptable quality. Overall, the article of is of good quality.

One thing I'd improve is in presenting the idea of multiple node embeddings more clearly. Specifically, it should be clearer the difference between this method and simply building different embeddings with different methods. In both cases we generate different vectors for the same node, but in persona2vec the different vectors aid the *same* task, while in the alternative, you build different vectors specialized to solve *different* tasks (structural equivalence vs clustering, for instance).
Experimental design: The experiments show some standard evaluation techniques for link prediction. I would love to see a couple of examples of predicted links -- picking a network and show the ten predictions with highest score and whether the nodes are actually connected. This is rarely done in link prediction papers, though.

Data & code is publicly available, which is a plus.
Validity of the findings: The main contribution vs the state of the art is the addition of persona edges and their weighting scheme. While this is shown to be effective in practice, I wonder whether this is significant enough. The improvement is statistically significant as shown in Table 2, but also relatively small in absolute terms.
Additional comments: In general, I'm happy with the paper. I'm suggesting major revision instead of minor only for one reason, which I think might significantly impact some methods and/or require some additional experiments. Specifically: could the author use persona2vec for multilayer link prediction? In this case, interlayer coupling connections would provide some natural persona edges, but it'd be interesting to use a persona-like edge addition and weighting scheme to them (if we have n layers, do we connect all layers to all other layers in a clique fashion, or could we ignore some connections? Which weight should each coupling link have? Does it depend on the degree of the persona in a give layer?). There are some embedding based multilayer link predictors, along with some non-embedding ones.


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

Input:
Title: PERSONA2VEC: A FLEXIBLE MULTI-ROLE REPRESENTATIONS LEARNING FRAMEWORK FOR GRAPHS
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1. The overall writing is good, however, there exist some typos, e.g.,

it show --> it shows in L158, model -> models in L28, please proofread the whole paper again to modify them accordingly.

2. The whole organizations of the paper are confusing, e.g. the related work is before the conclusion. I would advise the authors to move it after the introduction section.

3. Regarding the Persona graph embedding section, the authors should show more details, from the current version, I cannot see any detailed formulas w.r.t. this section.

4. A missed reference for graph embedding learning in CV community is 'region graph embedding for zero-shot learning published in ECCV20'
Experimental design: Experimental design is clear and sufficient.
Validity of the findings: The overal novelty is neat, e.g., the idea of using multiple features for one node is new.

One question is how to extend this idea to GCN formulation like in 'region graph embedding for zero-shot learning published in ECCV20'?
Additional comments: A good paper with minor langurage issues, and other minor drawbacks.


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: PERSONA2VEC: A FLEXIBLE MULTI-ROLE REPRESENTATIONS LEARNING FRAMEWORK FOR GRAPHS
Review round: 2
Reviewer: 1

Output:
Basic reporting: All mostly good, see general comments.
Experimental design: All mostly good, see general comments.
Validity of the findings: All mostly good, see general comments.
Additional comments: Thank you for addressing my comments. For most of the changes, I have no further comments.

One minor thing: in the Zachary network, panel d, I initially counted only 9 blue+red links, instead of 10. I needed a 200% zoom to find the tenth (the short blue edge from node 1). Perhaps make it a little bit more obvious!

Re: the significance of the results: I actually never doubted the results were statistically significant! I was just wondering if the improvement is noticeable in absolute terms. In any case, this is super minor so I have no further things to say about 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: PERSONA2VEC: A FLEXIBLE MULTI-ROLE REPRESENTATIONS LEARNING FRAMEWORK FOR GRAPHS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The same as last round.
Experimental design: The same as last round.
Validity of the findings: The same as last round.
Additional comments: The authors have addressed my concerns in this version. I would accept this paper 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: ARESB-NET: ACCURATE RESIDUAL BINARIZED NEURAL NETWORKS USING SHORTCUT CONCATENATION AND SHUFFLED GROUPED CONVOLUTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The paper proposed AresB-Net which achieves better accuracy with reduced costs. This network consists of basic blocks where the real-valued shortcut per each binarized convolution layer by adding the shortcut and concatenating it to output channels is added, and then the expanded channels are shuffled and split for the next grouped convolution. The experiments are conducted on CIFAR10, CIFAR100, and ImageNet with the comparison of other algorithms, and the proposed method does have less storage size with acceptable test accuracies. It seems that there exist some points that can be considered as a novel work, however, there are some confusing throughout the texts. The major comments are as follows:

(1) (Page 4 line 127-159) Even though I have sufficient background in CNN and BNN research, it has been very difficult for me to understand what exactly was done in the novel basic block. To make this work accessible, it would be necessary to carefully rework the presentation. For example, the output sizes of the basic block are explained thoroughly, however, it is recommended to add a table describing the output sizes of the basic block to make it more readily understandable.
(2) Please provide some more explanation or revise the terminologies. (line141 subblocki, line142 1/n Symbols *)
(3) Abbreviation of BinConv3x3 layer should be properly represented (Page 4 line 131).
(4) Please enrich the manuscript with references or detailed experimental results for these; The evaluation using LeakyReLU having better performances than those using normal ReLU (Page 4 line 151-154) and the better classification accuracy with BN layer every two subblocks (Page 4 line 158)
(5) In page 7 line 249, it is indicated that the speedup over real-valued NesNet-18 on CIFAR dataset can be over 44%. Please clarify.
(6) Please provide the correct reference for the calculation method of the FLOPS of convolution layers (Page 6 line 241).
(7) In Section Ablation studies, four case studies are described. However, experimental results are not really mentioned thoroughly. Please provide some more detailed results. For example, it is mentioned that slight accuracy enhancements were shown with the random erasing for data augmentation. Please provide more detailed experimental results and why you chose this method for data 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: ARESB-NET: ACCURATE RESIDUAL BINARIZED NEURAL NETWORKS USING SHORTCUT CONCATENATION AND SHUFFLED GROUPED CONVOLUTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: - For me, the section, ARESB-NET: ACCURATE RESIDUAL BINARIZED NEURAL NETWORK MODEL, is quite not easy to understand.  I think it would be better to rearrange the contents.

- Also it would be better to mention the reason for using the block for stride=2  and why the number of output channels is doubled.
Experimental design: - the results verify that the proposed network achieve compatible performance while reduces computational costs

- It would be better to add some experiments that can show the validity of block design (ex) comparison between using left and right group for skip connection, and accuracy and required storage when adding 1x1 convolution)

- Please be clear whether the experiments were conducted under the same conditions(using the same data augmentation technique?)

- Is the storage-saving effect coming from the absence of 1x1 convolution is enough to account for the reduction in accuracy? (the storage is not significantly different from Bi-RealNet-18)
Validity of the findings: - I recommend analyzing more the reason why the proposed network shows good performance without 1x1 convolution
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: ARESB-NET: ACCURATE RESIDUAL BINARIZED NEURAL NETWORKS USING SHORTCUT CONCATENATION AND SHUFFLED GROUPED CONVOLUTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: .
Experimental design: .
Validity of the findings: .
Additional comments: The manuscript is well-revised and the issues raised by the reviewer has been clarified. Therefore, the paper is now acceptable 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: ARESB-NET: ACCURATE RESIDUAL BINARIZED NEURAL NETWORKS USING SHORTCUT CONCATENATION AND SHUFFLED GROUPED CONVOLUTION
Review round: 2
Reviewer: 2

Output:
Basic reporting: In this paper, the author proposes AresB-Net which is a residual binarized CNN with improved accuracy. For this, a pyramid structure without downsampling convolution is suggested. And the performance
of AresB-Net is verified through several experiments.

- After the revision,  the readability has increased and the structure has become more clear.

- Also, the proposed network is clearly explained in detail and I could fully understand the method and its goals.

- The author provides proper figures and tables that call well explain the proposed model.
Experimental design: - The results verify that the proposed network achieve compatible performance while reduces computational costs

- The experimental environment, conditions, parameters, and the datasets the author uses are described well.

- The author properly designed the experiments. And the storage-saving effect and accuracy improvement effect of the proposed model are shown through the experiments.
Validity of the findings: In this paper, the author proposes AresB-Net, and explains the structure and characteristics of the proposed network in detail, The performance of this network is verified through various experiments on several datasets.
Additional comments: Thank you for your hard work and for giving a sincere response.


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: ADVANCED FEATURE SELECTION TO STUDY THE INTERNATIONALIZATION STRATEGY OF ENTERPRISES
Review round: 1
Reviewer: 1

Output:
Basic reporting: The abstract should clarify aspects such as Design/methodology/approach, Purpose, Findings, Practical implications and above all Originality/value of the manuscript. I don't think the reader understands very well the objectives of the manuscript and the aspects described above.
In the introduction, before embarking into the research objective, the logical stance of the problem statement needs to be fully clarified.
Note that all acronyms/abbreviations must be defined the first time they appear in the abstract, main text, and in figures or tables. For example: MNE.

The section on internationalization of companies should be a section called Literature Review. Authors could incorporate some table of the major papers, their scope, and the techniques used to study the research problem.

For the quality of the literature review, please provide a context for why based on the internationalization of companies.
From line 133 of this last section, I think they should move the whole text to Material & Methods.
I recommend the authors to update the text with the new works of Johanson, J. and Vahlne, J.-E.
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: Authors should justify in detail why they have chosen this data analysis technique and justify their choice with a list of similar papers in related research fields such as marketing or business economics.
Validity of the findings: Besides the descriptions of the results of the Hypotheses, more discussion should be offered in more depth.
Additional comments: The authors have produced a well-written and interesting manuscript, but it lacks adequate literature review.
The study of the problem addressed by this manuscript has a wide list of authors and works that are not cited and related in the article.
Although the main strength is the technique of data analysis, it does not adequately justify its choice. The discussion section should be improved, putting the results in relation to other similar research. The conclusions should be improved in depth.


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: ADVANCED FEATURE SELECTION TO STUDY THE INTERNATIONALIZATION STRATEGY OF ENTERPRISES
Review round: 1
Reviewer: 2

Output:
Basic reporting: More attention must be paid to the clarity of expression and readability, such as sentence structure, jargon use, acronyms, etc by a professional native speaker. For example in lines 47-48 the authors said: "In present paper, a combination of machine learning methods is proposed in present paper".

There is no sufficient field background/context provided. The literature review section needs to be further developed. On the one hand, from line 133 the variables to be used are described, so all this information, in my opinión, should be in the materials and method section. In this way, the literature review would be limited to only 55 lines, which is in my opinion very scarce.

On the other hand, and more importantly, there are numerous studies on internationalization that have used the concept of psychological distance and that have been published in scientific journals indexed in the Journal Citation Reports or Scimago and that are not included in their review section. from the literature such as: Barkema et al., 1996; Benito and Gripsrud, 1992; Berry et al .; 2010; Brewer, 2007; Clavel et al. 2018; Chikouni et al., 2017; Dikova, 2009; Ghemawat, 2001; Johanson and Wiedersheim-Paul, 1975; Hakanson et al., 2016; Klein and Roth, 1990; Kogut and Singh, 1988; Kostova, 1999; Nordstrom, 1991; Nordstrom and Vahlne, 1994; Padmanabán and Cho, 1999; Prime et al., 2009; Rovira and Tolstoi, 2014; Yildiz and Fey, 2016).

Likewise, the authors make statements without identifying those previous studies, such as (lines 96-99): "Thus, recent studies have shown that experience, at the company-level, and bilateral psychic distance, at the country one, are particularly important for the majority of Multinational Enterprises (MNEs) ". I suggest when the authors mention "recent studies" or "previous studies" include references to those articles.

Finally, with respect to the variables used, a justification is necessary about the reasons for including these variables and not others. For example: In Features from companies themselves, why is the export percentage not included? or why is the year in which they started their international activity not included? Both variables could partly explain their international experience and this would undoubtedly determine, according to influential authors in international literature such as Johanson and Vahlne, the choice of one country or another. To solve this, I suggest a greater and better justification of the variables used is necessary.
Experimental design: Regarding the experimental design: The methods are not described with sufficient information to be reproducible by another investigator. On the one hand, the article does not include how the different variables used have been measured. For example: how did you measure the differences on education enrollment and literacy between the two countries? Or how did you measure Vicarious Experience? The article could be improved if this essential information is included. On the other hand, I would recommend including a table with descriptive statistics that allow the reader to know the characteristics of the sample that has been used in the study.
Validity of the findings: In the results and discussion section, there is little discussion of the results (the authors only include approximately 3 bibliographic references). Most of that section is limited to results and is necessary develop much more the discussion. Thus, I recommend further development in the discussion of results. Additionally, perhaps it might be interesting to separate the results and discussion section into two different sections for clarity to the reader.
Additional comments: The article deals with an interesting topic that can be useful for the scientific and also business fields. However, there are certain issues that have been discussed previously and that in my opinion should be improved before the article is published. The methodology used and the results are interesting, but a more in-depth study of the subject that deals with internationalization is necessary. The main questions for improvement that I suggest are related to the literature review sections, justification for the use of these variables and their way of measuring, and also a greater discussion of the results that they contribute to the scientific and business community.
Best of lucks


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: ADVANCED FEATURE SELECTION TO STUDY THE INTERNATIONALIZATION STRATEGY OF ENTERPRISES
Review round: 2
Reviewer: 1

Output:
Basic reporting: ok
Experimental design: The authors have made detailed responses to all of the reviewers' comments. However, I believe that the method of analysis provided should be better explained to the reader.
For example, there are important issues that could be added as a list of papers that have used the same methodology. Clarify in more detail the software or tools used for the calculations made.
And adequately justify why it is the most appropriate methodology.
Validity of the findings: ok
Additional comments: If the authors expand on this information, the research work could be ready for definitive acceptance.


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: ADVANCED FEATURE SELECTION TO STUDY THE INTERNATIONALIZATION STRATEGY OF ENTERPRISES
Review round: 2
Reviewer: 2

Output:
Basic reporting: Authors should review the journal's publication standards in terms of format or formal aspects. On line 319, for example, the authors include a reference without brackets.

The literature review section has improved. However, I suggest that Table 1 should be further developed including more studies.
Experimental design: No comment
Validity of the findings: The authors have improved the discussion and results section, but should improve the conclusions section in depth. Conclusions should be well stated, linked to original research question & limited to supporting results.
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 HYBRID MULTI-OBJECTIVE WHALE OPTIMIZATION ALGORITHM FOR ANALYZING MICROARRAY DATA BASED ON APACHE SPARK
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper titled “A Hybrid Multi-Objective Whale Optimization Algorithm for Analyzing 2Microarray Data based on Apache Spark” is well structured and written. But there are some minor corrections to be carried out for publications:
1.	Clear and unambiguous professional used throughout.
2.	Professional article structure figures raw data shared.
3.	Some additional information regarding background context need to   be provided.
Experimental design: No comments
Validity of the findings: No comments
Additional comments: In the conclusion and future work, the author has not provided some future work relevant of the research carried out. It would be better if the author also provides some limitations of the proposed work which can be carried out as future 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: A HYBRID MULTI-OBJECTIVE WHALE OPTIMIZATION ALGORITHM FOR ANALYZING MICROARRAY DATA BASED ON APACHE SPARK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The structure of the paper should be improved to clearly differentiate previous work from the main contribution. All components of the MOWOATS algorithm should be described (solution initialization, crossover operator, selection mechanism…) in a background section prior to presenting the changes made to adapt MOWOATS to the multi-objective gene clustering problem (solution representation, objective functions and distributed implementation).

I strongly suggest including a methodology section to explain the main goal of each experiment, the characteristics of the datasets, the evaluation metrics, and the comparison methods. In particular, the authors should justify the choice of algorithms for comparison, including a short description of them, and full details of their parameter set up. Now this information is missing or blurred among the experimental sections.

Execution time should be provided in absolute terms too. Normalized values give useful information about relative improvements but hide the time unit which is also important. It is not the same to reduce from 6 days to 1 day, or from 6 seconds to 1 second. That will help to understand if the effort and cost in infrastructure is paid off. Speed-up rates could be computed as well to enrich the analysis of results.

The adaptation of MOWOATS to the Spark framework should be extended with more details, e.g. some pseudocode. This is one of the main contributions but explained in only 10 lines (207-217).
Experimental design: The manuscript does not include explicit research questions, what makes it difficult to understand the purpose of the experiments. Research questions usually help to guide the discussion of the main findings.

The algorithm is not completely new, and the problem domain has been addressed before too, so the focus of the paper should be put on the experiments. I highlight the number of algorithms selected for comparison and the use of public datasets of different complexities. However, some aspects of the experimental design are not clear:

- Explain why the evaluation metrics for small/medium datasets are different from large dataset.
- Try to apply all methods to all datasets unless it is not technically possible for any reason. In such a case, mention it explicitly.
- If execution time is relevant, and it is in this work, comparison methods should be executed under the same platform as well. Current comparison is only provided in terms of clustering performance.
- It is not clear whether the authors execute the comparison methods or their results are simply gathered from the original papers.
- When and to which datasets preprocessing is applied. Is that common for all methods or required by MOWOATS only?
- Minimum number of repetitions recommended for metaheuristics is 30 or more. Less runs could be valid in costly experiments but decrease technical soundness and should be discussed.
Validity of the findings: Clustering performance should be statistically assessed to confirm the significance of the results, and it is especially important when algorithms are randomized. In fact, it is not clear whether authors show best results (one run) or the average of 20 runs. In the second case, standard deviation should be included too.

Conclusions about execution time are somehow expected, and have less scientific value, because they are not compared to any baseline. The datasets seem not be so huge as initially stated, so the authors should clarify how much time a standard algorithm in one CPU could need to cluster them. It is true that the number of features is considerably high, but very few samples are included in each dataset, even in the largest ones. A reader needs more evidence to believe the problem is truly computationally expensive.

From my point of view, results should be analyzed from a trade-off perspective, as improvement in performance might not be so important if execution time and the infrastructure needed to run the method is prohibitive.
Additional comments: The paper addresses the problem of clustering gene expression profile by means of a whale optimization algorithm enhanced with tabu search and adapted to a multi-objective perspective. The main contribution is the implementation of the algorithm, which was previously proposed by the authors, in Spark. The technical contribution is good, but the paper lacks a more scientific contribution. Besides, according to the authors, other proposals of bio-inspired clustering implemented in Spark or other platforms already exist, so even the technical contribution might not be so novel. A series of experiments with different real-world and publicly available datasets is presented, and results are compared with similar algorithms. The authors provide source code, what is valuable for reproducibility purposes.

Some other comments:

1) The “Big Data” problem might not be so realistic considering the datasets finally used. Are these datasets the only available? For instance, the cited work by Hosseini and Kiani [1] mentions a synthetic dataset with up to 1 million genes.

2) Last paragraph in the introduction is not readable as numbers of sections do not appear.

3) PeerJ Computer Science is not a specialized journal for metaheuristics, so a short introduction to whale optimization could be useful for non-familiar readers.

4) Details of the tabu search (TS) hybridization are not provided but using this kind of search in a multi-objective algorithm is not trivial [2,3]. Even if it is part of previous work, the authors should briefly explain when TS is applied, how solutions from the swarm are chosen for local improvement, and the acceptance criterion when multiple objectives exist. I did not see nothing specific of TS in Algorithm 1 and its explanation.

5) Multi-objective algorithms are compared in terms of quality indicators that measure convergence and diversity of the Pareto front [4]. However, here only one solution is chosen as final solution for comparison, so the need of a multi-objective approach is not evident. The authors could have optimized a main objective, with secondary objectives, or aggregate values somehow if they do not want to provide the user with a set of solutions for his/her choice.

6) The algorithm looks for diversification by means of a crossover operator, which is not described in detail. For genetic algorithms, it is known that crossover promotes convergence, whereas mutation is better for diversification. To the best of my knowledge, standard whale optimization does not have these kinds of operators, as it works similar to particle swarm optimization (PSO), i.e. by updating swarm member positions. PSO has something similar to mutation to “disturb” the swarm and promote diversity, so I guess whale optimization might work under the same assumptions.

7) Figure 2 is not easy to interpret for reader without background on gene expression profiles. They should be explained in more detail.

8) Sentence in lines 352-354 claims about the quality of code, but this is something that cannot be inferred from the figure.

[1] B. Hosseini, K. Kiani. “FWCMR: A scalable and robust fuzzy weighted clustering based on MapReduce with application to microarray gene expression”. Expert Systems with Applications, vol. 91, pp. 198-210. 2018.
[2] N. Krasnogor, J. Smith. “A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues”. IEEE Transactions on Evolutionary Computation, vol. 9, no. 5, pp. 474–488, 2005.
[3] A. Jaszkiewicz, H. Ishibuchi, Q. Zhang. “Handbook of Memetic Algorithms”. Springer Berlin Heidelberg, 2012, ch. Multiobjective Memetic Algorithms, pp. 201–217.
[4] M. Li, X. Yao. “Quality evaluation of solution sets in multiobjective optimisation: A survey”. ACM Computing Surveys, article 26, March 2019.


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 HYBRID MULTI-OBJECTIVE WHALE OPTIMIZATION ALGORITHM FOR ANALYZING MICROARRAY DATA BASED ON APACHE SPARK
Review round: 1
Reviewer: 3

Output:
Basic reporting: This paper adapts an existing multi objective whale optimisation / tabu search algorithm for the Apache Spark framework, and applies it to microarray data. The paper is generally structured ok, and the experimental results look promising. The biggest problem is the lack of detail on the algorithm adaptation for Spark, one of the main contributions of the paper.

Literature review / background
- The literature review should include more background on Spark and why Spark is often chosen over Hadoop these days, for people unfamiliar with big data technologies.

Proposed method:
- description of the MOWOATS algorithm is ok, inclusion of pseudocode is good. Could motivate clearer why tabu search and whale optimisation are good for multi-objective problems.

Minor problems:
- Cross references within the manuscript need checking; e.g. in the introduction lines 106-114, the Section numbers are not showing.
- The references tend to blend into the text, as there are no brackets around the author names – this might be a document template problem? But it should be fixed, as it makes the text very difficult to read.
- Good that the code is provided as a supplemental file, but it should be better documented
Experimental design: The introduction clearly states the three main contributions of the paper. The second and third contributions (lines 87-92) are covered by the experimental section.

However, the main body of the paper contains very little on the first contribution - adapting MOWOATS for the Spark framework. Lines 207-217 are not sufficient, and this subsection needs to be significantly expanded. At a minimum, I would suggest including:
- how the data is divided amongst the computing nodes
- description of how the algorithm is adapted to run in Spark, including what computation is done on the individual nodes, and how are the results aggregated at the end.
- Spark 'pseudocode' showing the flow of the Spark implementation of the algorithm and Spark operations used.

The experimental results section could be improved by being organised better, such as:
- the entire experimental framework could be detailed at the beginning, including description of datasets, any preprocessing done, algorithms compared against etc., then below that give the results
- It is unclear what the section 'Preprocessing the biological datasets' (line 293) is describing, or why it is needed
Validity of the findings: - All datasets are publically available
- the results generally look promising - they show the advantage of MOWOATS for microarray data over other algorithms, and that the algorithm can be effectively implemented in Spark.
- for the results on the larger datasets, I don't think any of the comparison algorithms are designed for microarray data specifically. So, it would be interesting if you could also implement some of the methods from the smaller dataset experiments in Spark, and apply them to the larger datasets. This would give a better sense of the performance of the algorithm on large datasets, in terms of the quality of the generated clusters.
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 HYBRID MULTI-OBJECTIVE WHALE OPTIMIZATION ALGORITHM FOR ANALYZING MICROARRAY DATA BASED ON APACHE SPARK
Review round: 2
Reviewer: 1

Output:
Basic reporting: The paper now includes a background section as requested. The section introduces the applied techniques (tabu search, whale optimization), but I cannot find any paragraph explaining how tabu search is integrated in MOWOATS. The only element later mentioned is the elitist list, but this is a general mechanism in metaheuristics to keep best individuals, not a particular component of tabu search. I still fail to see how the authors apply multi-objective tabu search to solutions generated via whale optimization. I refer to the definition of neighborhood for this problem, which solutions are compared (best/random choice) and how (Pareto-based, objective aggregation, one objective only…) and which problem characteristics are considered to build and update the tabu list (solutions are repeated if they share the genotype/the phenotype/part of them).

As for crossover operator, the authors explain in lines 210-212 that it is applied to swarm members only, but this sentence does not explain which specific operation is implemented to combine the positions of the swarms and how many swarm members are involved.
Experimental design: Methodological aspects have been reorganized and a new section for evaluation criteria has been added. However, details of the comparison algorithms are still missing, in particular: short description of all methods (not only MOC-GaPBK), justification of their choice and parameter values. This information is essential to ensure that the comparison was planned under fair conditions.

The authors reply that “The results have been computed for different number of runs” to my comment about the number of executions and statistical validation. Considering that the algorithms need at most 11h to run over the biggest dataset, I do not see any reason to have different number of runs at least for smaller datasets. Also, the authors should detail if the comparison algorithms are randomized or not, and how many runs were reported in all cases. The authors do not explain why statistical validation is not included. I mean null hypothesis testing [1] to check if MOWOATS wins across all datasets for which the same group of algorithms is executed.

[1] J. Derrac, S. García, D. Molina, F. Herrera. “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”. Swarm and Evolutionary Computation. 2011.
Validity of the findings: The authors reply that comparison algorithms were not run by themselves over the same platform used for the evaluation of MOWOATS. Also, that it is not possible because they do not have access to the server anymore. So, execution time is not directly comparable, of course. While I understand the technical problem with the server, it implies that the analysis is somehow biased and not as useful as it should be for this kind of study. The authors should clearly state that comparison is based on values reported by the original papers and revise sentences that suggest they execute all algorithms (e.g. line 370). If possible, they should collect information about the platform and execution time in the original papers and try to build some discussion upon that. Otherwise, the reader only knows part of the story (MOWOATS is better in the evaluation metrics), but nothing can be inferred about how much extra time is needed to achieve the improvements.
Additional comments: The authors have rephrased many parts of the manuscript to incorporate most of the requested comments. However, there are still some aspects that have not been properly addressed or need further clarification.


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: UNDERGRADUATE DATA SCIENCE DEGREES EMPHASIZE COMPUTER SCIENCE AND STATISTICS BUT FALL SHORT IN ETHICS TRAINING AND DOMAIN-SPECIFIC CONTEXT
Review round: 1
Reviewer: 1

Output:
Basic reporting: Literature and references are well laid out.

I would suggest a more in-depth discussion about the differing definitions of data science. While it is true a full accounting of the term is beyond the scope of this paper, a single definition is not sufficient to represent this issue. I am specifically referencing "An exhaustive history of the term
and definitions is beyond the scope of this work, but we adhere to the following general
description: Data science draws on statistics and computer science to address questions in
various domains, such as biology, education, physics, business, linguistics, or medicine". I think what is missing is data science vs analytics vs data engineering. Nothing too deep, but given the nature of this particular field there should be more discussion.
Experimental design: I do not understand the reasoning behind "chose institutions comparable to our home institution". It seems completely reasonable to apply the frameworks to institutions that are outside this definition. It could even be more useful and important work to broader this.  It seems like the build up to this point is somewhat misleading since the title, abstract, etc do not draw notice to this point. I think there needs to be clarity on what this paper is proposing to study from the beginning if we maintain that the only schools in consideration are under this selection bias.

Looking at course titles and descriptions is weakness of the approach it is mentioned in the paper. While not possible for all of the programs, I would suggest that scoring select programs where additional information is available and comparing that against what is determined using only titles and descriptions would be valuable. For example, the authors should reach out to contacts at some subset of these programs and ask for help reviewing their program. This could be done in a manner such that it informs how consistent the scoring is done before and after this information is available.
Validity of the findings: Please see previous comments that I believe touch on the validity or at least in inferences we can draw from the analysis.
Additional comments: The study was well conducted and the paper is well organized. My main comment (and it is what I've tried to communicate in the specifics above) is that I am not sure how much of the analysis is accurate given the limitations on titles and descriptions. I feel there needs to be more work done to establish some understanding of this measurement uncertainty.


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: UNDERGRADUATE DATA SCIENCE DEGREES EMPHASIZE COMPUTER SCIENCE AND STATISTICS BUT FALL SHORT IN ETHICS TRAINING AND DOMAIN-SPECIFIC CONTEXT
Review round: 1
Reviewer: 2

Output:
Basic reporting: Comments about the data and code

- I commend the authors for depositing data and code on Zenodo! Bravo! I appreciate that there is a readme and a license. Some additional comments to make this useful. Please add additional information the readme, including descriptions of code files, what order in which they should be run. If possible, include a Rmarkdown file that calls the appropriate scripts and runs the analyses. You can show code blocks that would be useful to most readers (like the model fitting) but hide the data cleaning code blocks (allowing an interested reader to examine those separately).

- A general comment unrelated to the manuscript but to this code deposit. Do not include rm(list=ls()) at the top of each document. That's an outdated practice to clean the current working environment. Since you are using Rstudio projects, these should run in their own environments and not be contaminated by other projects. Don't use stop statements at the top of scripts.
- For the framework na, use another name in the code as na will cause problems in your code.
- Load the packages you'll use at the top of scripts.
- The code did not run right off the bat for me. I stopped fixing bugs to focus on the rest of the paper, but share the code with another R person who doesn't have access to your machine and see if they can run it.
Experimental design: no comment here but see general comments
Validity of the findings: no comment here but see general comments
Additional comments: J. Oliver and T. McNeil tackle an interesting question in this paper. Given the rapid proliferation of undergraduate programs in data science, what do they actually teach, are they comparable, and how do they compare against frameworks designed to evaluate them. As a member of one of the institutions described in the study, I was keen to learn insights into what is working and what's missing.

Very early in the background, after describing the growth of data science programs (13 to 50) and the lack of clear definitions, the authors are quick to point out that the programs lack coursework on ethical use of data/comms skills. This seemed very abrupt and additional background tying this back to the frameworks (NASEM) would be helpful. If the goal of the paper is to also demonstrate how the programs are not uniform, comparable, and heavily influenced by the departments running them (stats versus cs), it would also be helpful to cite a few examples of why lack of ethics training in data science has been problematic, especially in the context of workforce development. There are many to choose from (see weapons of math description for a general reference) but a few here would be helpful.

I appreciate that there is a limitations section in the methods, given that you relied solely on course titles and descriptions. I would appreciate more acknowledgement of this in the discussion. For example, reproducibility is rarely taught as a separate course  and the appetite for such courses is also currently limited even among graduate students/postdocs. I am aware of many data science courses at several of the institutions you have surveyed that incorporate elements of reproducibility in data science courses. Data science ethics may also be listed as discussion topics and not show up in general course descriptions. I believe it would be important to acknowledge this. A small recommendation is that you could suggest that a future study could topic model syllabi to assess against the two frameworks.

There is no mention of the tradeoff in covering domain specific skills while trying to build up foundational computing/statistical skills. The discussion covers a notable exception being UW HCI program. The dataset doesn't include the list of courses and descriptions before they were scored. Are those also available?

While I agree that it is critical to teach reproducibility at the undergraduate level, and their omission has cascading effects, it would be fair to say that those topics are more likely to be taught at the graduate level, where students apply these skills to domain problems. Reproducibility is already a hard sell. So it is not that the data science community doesn’t consider them critical for training, it just remains unrealistic for UG programs to cover everything.

I appreciate you acknowledgement that it is early days for data science undergraduate programs so it's not surprising that they all fall short of the NASEM guidelines.


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: UNDERGRADUATE DATA SCIENCE DEGREES EMPHASIZE COMPUTER SCIENCE AND STATISTICS BUT FALL SHORT IN ETHICS TRAINING AND DOMAIN-SPECIFIC CONTEXT
Review round: 2
Reviewer: 1

Output:
Basic reporting: No improvement necessary for publication.
Experimental design: No improvement necessary for publication.
Validity of the findings: No improvement necessary for publication.
Additional comments: No improvement necessary 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: UNDERGRADUATE DATA SCIENCE DEGREES EMPHASIZE COMPUTER SCIENCE AND STATISTICS BUT FALL SHORT IN ETHICS TRAINING AND DOMAIN-SPECIFIC CONTEXT
Review round: 2
Reviewer: 2

Output:
Basic reporting: Nothing further to add
Experimental design: Nothing further to add
Validity of the findings: Nothing further to add
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 SYSTEMATIC REVIEW OF SECURITY AND PRIVACY ISSUES IN THE INTERNET OF MEDICAL THINGS; THE ROLE OF MACHINE LEARNING APPROACHES
Review round: 1
Reviewer: 1

Output:
Basic reporting: This is a review paper on medical Internet of things. The author lists and analyzes the related research work on the security and privacy protection of medical Internet of things, but it still needs further analysis and summary, and lacks of refinement. Some spelling mistakes in the paper also need further research
Experimental design: This is a review paper on Internet of medical things.
Validity of the findings: This is a review paper on Internet of medical things.
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 SYSTEMATIC REVIEW OF SECURITY AND PRIVACY ISSUES IN THE INTERNET OF MEDICAL THINGS; THE ROLE OF MACHINE LEARNING APPROACHES
Review round: 1
Reviewer: 2

Output:
Basic reporting: the major issue that needs to be addressed, to have a better overview of the problem and a more complete paper, is the lack of a clear motivations for this work. It is mentioned how the IoMT is potentially exposed to cyber-attacks, and that ML can potentially solve this issue, but (i) the article should elaborate more on how and why the IoMT is specifically more exposed to such threats; (ii) what attacks can be made and why they would be problematic in the IoMT scenario; (iv) how ML can help solve them; (v) why is ML the best candidate for security and privacy in the IoMT, and why traditional security algorithms and countermeasures fail or are not taken into account (please, be specific and offer technical explanations or examples).

The article definitely needs to be revised, proofread, and corrected. The use of English is incorrect, and several sentences are difficult to understand or poorly written. Moreover, many typos are present in the paper especially in the first third.

See attached pdf for more details
Experimental design: The organization of the article is confusing, especially in the initial part (at least till page 16, line 412), and results difficult to follow for the reader. Having a clear overview of the problem, as stated above, would improve this aspect. Second, the other reason why it is confusing is because the first sections mixes together definitions, general aspects of IoT, IoMT, ML and details on security and privacy. The effort to make the paper readable by non-experts as well is noticeable, but there should be a better linking between sections and among paragraphs in each section.
Validity of the findings: As for the discussion at the end, it does not offer a good insight into how the techniques analyzed in the previous sections can be applied to the healthcare field, or what the challenges are. They should be connected better with the previous part of the paper.
Additional comments: see attached pdf


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

Input:
Title: A SYSTEMATIC REVIEW OF SECURITY AND PRIVACY ISSUES IN THE INTERNET OF MEDICAL THINGS; THE ROLE OF MACHINE LEARNING APPROACHES
Review round: 1
Reviewer: 3

Output:
Basic reporting: This article presents the design, execution and results of a SLR of (1) the use of using machine learning approaches (2) to address security and privacy issues (3) in IoMT.

* Clear and unambiguous, professional English used throughout.

The language is clear and mostly correct, with minor typos: (l.89) “rational” instead of rationale, (l.125) “principle tiers”, (T.5) “Does the paper is…”.

* Literature references, sufficient field background/context provided.

The references in introductory material are adequate.

* The article should include sufficient introduction and background to demonstrate how the work fits into the broader field of knowledge. Professional article structure, figures, tables. Raw data shared.

The abstract devotes too much length to motivation and none to the survey method (sources? filters?), execution (how many papers? How many filtered out?), and results (it only reports an observation without clear relation to the examined literature).
The Research Rationale (not Rational) proposes research questions left unnamed, but they are later referenced as RQ1 etc.
The Background section describes (1) several ways of organizing IoMT architectures and types of sensors; (2) security and privacy issues in IoMT (although confusing metrics with quality characteristics, e.g. Privacy); (3) machine learning and some uses in IoT; and (4) deep learning and big data in IoT security
The Survey Methodology sections is too insufficiente (see below).
The Results and Findings presents detailed demographic and approach results, but the latter are not organized in a coherent single schema (see below).
The Discussion summarizes all primary studies, but is organized in non-orthogonal categories.
Fig.1 seems unnecessary (in style and content).

* Is the review of broad and cross-disciplinary interest and within the scope of the journal?

The review addresses a topic at the intersection of security/privacy, machine learning and IoMT, all three topics that are within the journal scope; the topic and intersection are explained in the Introduction.  The target audience is explicited in Research Rationale

* Has the field been reviewed recently? If so, is there a good reason for this review (different point of view, accessible to a different audience, etc.)?

There are recent surveys of IoT security and privacy, but none of machine learning techniques applied to this problem.  The problem is narrow, but exploding on terms of published work, and deserves a specialized review.
Related work is reviewed but somewhat carelessly: (l.298) “Yang et al (Cui et al 2018)”, (l.311) “Sikder et al. (Newaz et al. 2020)”.

* Does the Introduction adequately introduce the subject and make it clear who the audience is/what the motivation is?

The Introduction explains the three survey topic elements: (1) IoMT, (2) privacy and security issues in #1, and (3) machine learning approaches to #2.  The paper structure is given at the end of section, but it does not fit the actual sections titles/matters.

* Formal results should include clear definitions of all terms and theorems, and detailed proofs.

N/A
Experimental design: * Methods described with sufficient detail & information to replicate.

The first major problem in this survey is the lack of replicability: the overall method is well described, but needs to have specific databases searched, dates, specific query strings (the mind map in Fig.2 is suggestive but insufficient), indicate how many papers were found from each source, how many were duplicated, how many were filtered out at each step…
Also, most filtering criteria (Table 5) seems inadequate: #3 is always true given the searched databases; #4 makes initial filtering depending on the RQ, a methodological tail-biting; #5 is irrelevant (if it were, the search should be only recent papers).

* Is the Survey Methodology consistent with a comprehensive, unbiased coverage of the subject? If not, what is missing?

The survey methodology is fairly standard, but the authors seem unaware of its methodological moorings and consequent steps, risks, reporting criteria, etc.; please check “Guidelines for conducting systematic mapping studies in software engineering: An update” (Petersen et al. IST 2015) or any of Barbara Kitchenham’s papers.

* Are sources adequately cited? Quoted or paraphrased as appropriate?

The sources are mixed up with the papers found in the review; it is important to preserve the distinction.
Several sources are mis-cited, e.g. “Sikder et al. (Newaz et al. 2020)”, which would be correct iff Sikder == Newaz.

* Is the review organized logically into coherent paragraphs/subsections?

The demographic data is presented in aggregated form, and there is no way to ascertain trends (e.g. how the journal-conference balance has changed).
The second major problem in this survey are the detailed results: they are carefully presented, but organized into thematic sections that are themselves not motivated, i.e. there is no single schema that accounts for them.  The survey introduced (in the Background) several IoMT architectures, several classifications of risks, and several kinds of machine learning… but this information is not used to organize the primary studies in some orthogonal classification.  Thus, some papers would fit in several categories (e.g. because they address several problems or use several techniques), but that cannot be visualized from the way the results are presented, and a researcher focused on a category would miss a paper that was deemed to “better fit” in another category.  A better presentation would be (for example) a matrix with all found papers indicating which categories they fit.
The Discussion of RQs is generally adequate, but the RQ4 is too shallow because the gathered data (organized by kind of attach etc.) is not useful for reasoning about quality characteristics (see “Security in Telehealth Systems From a Software Engineering Viewpoint: A Systematic Mapping Study” (Marquez et al Access 2020) for a survey organized along those lines).
Validity of the findings: * Is there a well developed and supported argument that meets the goals set out in the Introduction?

The study Limitations are not well addressed, and this is a key point in any systematic survey.  Please identify and address internal (consistency) and external (generalizability) validity threats.

* Does the Conclusion identify unresolved questions / gaps / future directions?

The Conclusions should summarize the key findings, which are actually well discussed in the paper itself (when revising the RQs).  Some of the most important implications, like the lack of datasets and the big block that this posits to machine learning, are mentioned in passing and without connecting back to actual primary studies.  The conclusions need a thorough revamping.
Additional comments: This article is timely and welcome, and it reflects a large amount of careful work.  However, its presentations has two problems that must be overcome:
* the methodological underpinnings of systematic reviews require greater formality in reporting, to allow reproducibility
* the results are organized in sections that do not reflect an overall classification, although several elements for such classification are proposed in the background section of the survey itself; as a result, non-narrow papers may end up recorded as fit to only one category


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 SYSTEMATIC REVIEW OF SECURITY AND PRIVACY ISSUES IN THE INTERNET OF MEDICAL THINGS; THE ROLE OF MACHINE LEARNING APPROACHES
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This paper proposed a systematic review of security and privacy issues in the Internet of medical things.  The recent research papers of IoMTs are reviewed and a critical analys are conducted on the selected papers.


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 SYSTEMATIC REVIEW OF SECURITY AND PRIVACY ISSUES IN THE INTERNET OF MEDICAL THINGS; THE ROLE OF MACHINE LEARNING APPROACHES
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have satisfactorily addressed the concerns and questions of the reviewers.
The outcome is a well written, clearly organized and easy to read and easy to follow article.
The motivations of the work are clear in the revised version of the article.

Most of the grammatical errors and technical incorrect text of the first version have been fixed.

Few typos still remains, and I'm listing some of them. I recommend "minor revisions" just to correct these mistakes and address a couple of minor points reported below. However, the paper is in good shape for publication and would not necessarily need to be re-reviewed.
Experimental design: The article is in Aims and Scope of the journal. The investigation performed and methodology are technically sound and objective.

One point the authors might want to clarify is relative to the search strategy used for selecting the research papers (Figure 2), and specifically how the 27 papers were removed at the end based on "abstract, conclusions, quality of paper".
It is clear that the criteria used are those in Table 6, however, citing a criterium such as "quality of paper" is confusing because it is a subjective one. The wording can be left without changes, but I suggest clarifying in the text what it exactly means.

The review is logically organized into coherent paragraphs/subsections.
Validity of the findings: The authors have improved the result discussion, and included tables and figures that help understand and visualize the outcome of their research.
Additional comments: The article has improved, and it was nice to read. However, here are some remaining typos that the authors might want to correct:

Line 34: a critical analysis were -> was
Line 62: body sensor -> body sensors
Line 63: (gateway) -> gateways
Line 118: are become
Line 165: are become
Line 173: in IoMT -> in the IoMT
Line 180: generated date of IoMT -> the IoMT
Line 181: IoMT -> the IoMT
Line 193: "no free lunch" is just an informal expression, not a formal theory, please correct accordingly.
Line 199: has been recently emerged
Line 266: difficult by -> difficult for?
Line 267: suspectable -> susceptible
Line 269: IoMT system facing -> the IoMT system is facing?
Line 294: in IoT -> in the IoT
Line 298: "works of using" doesn't read well
Line 302: of IoMT
Line 341: 500 papers were remained
Line 377: journal article -> journal articles
Line 379: were belong
Line 413: the true from false -> true from false
Line 427L the fake from -> fake from
Line 525: was failed -> failed
Line 571: requires -> require
Line 767/870: majority of -> the majority of
Line 850: who have -> who has
Line 858: articles that yet to be published - doesn't read well
Caption Table 2: different the IoMT -> different IoMT, reprsenst -> represents

Caption Fig.1: The entire Mind Map show The keywords used in the Research Information Template (RIT). The rectangle box at the middle represent -> shows/the keywords/rectangular/represents

I suggest adjusting the style of Fig. 7 to match Fig. 3 and Fig. 5. However, this is not required.


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 SYSTEMATIC REVIEW OF SECURITY AND PRIVACY ISSUES IN THE INTERNET OF MEDICAL THINGS; THE ROLE OF MACHINE LEARNING APPROACHES
Review round: 2
Reviewer: 3

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: The authors' changes, in response to mine and others' reviews, do satisfy well the concerns in my initial review.  Thanks for producing a significantly better article.


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: DETECTION OF MALICIOUS CONSUMER INTEREST PACKET WITH DYNAMIC THRESHOLD VALUES
Review round: 1
Reviewer: 1

Output:
Basic reporting: abstract needs to be revised.
References are not sufficient
Figure 6. Mitigation of Flooding Attack-There is some ambiguity in the figure
The article should include sufficient introduction and background to demonstrate how the work fits into the broader field of knowledge. Relevant prior literature should be appropriately referenced.
Experimental design: The investigation must have been conducted rigorously and to a high technical standard.
The proposed method should be well discussed.
comparison with the existing approaches need to be highlighted
Validity of the findings: conclusions need to be revised -
Using the
358 mitigation technique mentioned in this paper, the Network Service Manager at NDN Edge router can
359 enable a mechanism in which upon reaching a certain threshold value; it blocks that interface temporarily
360 from which the excluded interest packets are being generated---Revise this statement
Additional comments: There are many state-of-the-art mitigation strategies for the content poisoning attack, but some new attack-surfaces have emerged with these schemes’ advent.
Mention what are some attacks.
Clearly mentions what is the outcome of the proposed method
Revise the abstract
Making the threshold value dynamic, which was initially set by the network operator statically? But in many networks, it is set dynamically -Comment
CPA Detection and Mitigation Approaches-need more discussion
Comparisons of CPA Mitigation Approaches- give the summary in a tabular form. Readers will easily understand
In Figure 3. Detection of Flooding Attack during Content Poisoning Attack Mitigation-interest packet generated and after again there is a block Interest Packet or Excluded Interest Packet
Discard Interest Packet with Exclude Filter and Exit-what is the role of this block
There is no clear discussion on EXPERIMENTAL RESULTS –
How the proposed method is superior to other methods
There is no comparison with other existing approaches. When there is no good comparison how can authors claim the method works fine?
There is no discussion on vulnerabilities
What are the various attack surfaces and how can they be resolved.
Discuss various types of attacks
Clearly mention the difference between the manual threshold and dynamic threshold. Give in a tabular for how can the proposed methods overcome the drawbacks of the manual method.
The threshold value is kept as 3. What does it mean by 3 and how can it be restricted.
The number of References needs to be increased