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: EFFECTIVE ATTRIBUTED NETWORK EMBEDDING WITH INFORMATION BEHAVIOR EXTRACTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: This is a revised manuscript and the authors have revised most of the comments raised by the reviewers. The quality of manuscript has been signficantly improved.
Experimental design: Experimental evaluation has been improved.
Validity of the findings: The proposed TNE model improves the performance of existing embedding models.
Additional comments: NA


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: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 1
Reviewer: 1

Output:
Basic reporting: This study investigated security and privacy awareness among academics in Saudi Arabia
relating to the use of video conferencing apps.

Authors theoretical claim seems to be acceptable but there is no novelty in the work. The authors try to replicate the same story throughout the paper.
Experimental design: A survey experiment research design is employed in this paper
Validity of the findings: There are few experimental findings in the paper and it is not sufficient. Need more experimental analysis on the data.
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: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: Since video conference apps are common among all kinds of professions and why this survey is targeted at academicians?
What is the logic behind the calculation of the security awareness score?
Is this study proposes any particular application as a safer one out of this study?
Validity of the findings: The considered sample size should have been higher to come to one conclusion.
Possibility of having high bias due to the non respondents and sample size
age group wise findings should have been included
What was the duration of data collection? Collection through WhatsApp, LinkedIn, Telegram, and Twitter is a viable way to reach academicians?
What is being considered as Non-STEM category as Math and Science also considered in STEM category
Additional comments: Results based discussion should be much more elaborated based on the findings
The metrics used should be properly explained
Sample questionnaire can be included


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: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 1
Reviewer: 3

Output:
Basic reporting: This study examines the factors that influence academics' actual awareness of security and privacy in relation to videoconferencing apps. The data are based on a recent survey and PLS-SEM is used. This is a timeless study and SEM is appropriate. Some results are not plausible. For example, the results that perceived security awareness is negatively related to actual security and privacy awareness are a bit strange. The document needs improvement in all aspects. Below there are a number of comments that must be addressed
Main comments
Introduction:  Please try to improve the description of the purpose of the research. Please add one sentence on the data and methods used (PLS-SEM). Please also clearly state the contribution of the research. Is it the first study on privacy and security issues of video conferencing apps?
Conceptual background: Section 2 “e-learning in Saudi Arabia” should be improved. The paragraph on internet speed does not fit.  Section: Video conferencing in online learning. Please cite more recent references. Video conferencing apps greatly improved. References from 1976 are not helpful here.
The theoretical background must be improved: Privacy issues cannot be explained by privacy concerns (see H3). There is a correlation by definition. The theoretical framework is inadequate. Think clearly about what is the dependent variable and what is the independent variable. From my point of view there are too many hypotheses.
Experimental design: PLS-SEM cannot estimate causal relationships. What you estimate are correlations. You need to be more careful in the interpretation of results (e.g., not writing ”x impacts y ”, or ”has effect on ”) and generally create awareness that a correlation does not necessarily indicate causation. Apart from acknowledging that endogeneity might be present, there are also ways to deal with endogeneity, such as instrumental variables, control function approaches, propensity score matching, experiments and natural experiments. Hair et al. (2020) note that four conditions must be met to assume causality: (1) Time sequence —the cause must occur prior to the effect; (2) Covariance —changes in the hypothesized independent variable are associated with changes in the dependent variable; (3) Non-spurious associations — the relationship is not due to other variables that may affect cause and effect; and (4) Theoretical support — a logical explanation for the relationship.

Reference
Hair, J. F., Jr , Howard, M. C. , & Nitzl, C. (2020a). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109 , 101–110 .
Pesämaa, O., Zwikael, O., HairJr, J., & Huemann, M. (2021). Publishing quantitative papers with rigor and transparency. International Journal of Project Management, 39(3), 217-222
Validity of the findings: Method: PLS-SEM is used. Yes it is correct that PLS-SEM is estimated by least squares. Please add information on the stata command. Robustness checks are missing. Please report some validity tests (Degree of correlation between constructs, Path significance -Assesses how strongly two constructs relate to each other, Coefficient of determination	- Measure of the predictive accuracy of the structural model,  Effect size- Measure of the impact of the exogenous construct on the endogenous one, Predictive relevance	- Measure of a model's predictive power and Robustness checks).
The article needs to be thoroughly edited and proofread by a qualified language editor. There is an illogical flow of ideas and a lack of coherence. Sections are disjointed without any link between them.
Additional comments: no comment.


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

Input:
Title: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 2
Reviewer: 1

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


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

Input:
Title: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 2
Reviewer: 2

Output:
Basic reporting: The comments are given in the previous review are addressed by the authors
Experimental design: Ok
Validity of the findings: Ok
Additional comments: Nil


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

Input:
Title: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 2
Reviewer: 3

Output:
Basic reporting: Some of the hypotheses are trivial and self-explanatory: check them out: Perceived security awareness has a higher awareness of the security and privacy settings of video conferencing apps. I suggest to de-emphasise this.
.
Experimental design: "We used a survey experiment to determine "
Please rewrite. This paper applied PLS-SEM to a stand alone survey. This is not an experimental design.
Validity of the findings: PLS-SEM
Please report all the needed validity tests for the measurement and structural model. This part is not complete.
Additional comments: Please follow the style guidelines
Alammary, A., A. Carbone, and J. Sheard. 2016. “Blended Learning in Higher Education: Delivery Methods Selection.” In Twenty-Fourth European Conference on Information Systems (ECIS 2016). İstanbul,Turkey.
Alammary, Ali, Moneer Alshaikh, and Areej Alhogail. 2021. “The Impact of the COVID-19 Pandemic on the Adoption of e-Learning among Academics in Saudi Arabia.” Behaviour & Information Technology, September, 1–23. https://doi.org/10.1080/0144929x.2021.1973106


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: AWARENESS OF SECURITY AND PRIVACY SETTINGS IN VIDEO CONFERENCING APPS AMONG FACULTY DURING THE COVID-19 PANDEMIC
Review round: 3
Reviewer: 1

Output:
Basic reporting: The revised version looks good to me
Conclusions
Please provide information on the limitations
The text is not always smooth. The language can be improved.
See an example:
Privacy concerns are significantly associated with higher awareness only for STEM faculty, while attitudes toward ICT for teaching and learning is negatively associated with such awareness among faculty with more than 10 years of experience.

->
Privacy concerns are significantly associated with higher awareness only among teachers of STEM subjects, while attitudes towards ICT for teaching and learning are negatively associated with such awareness among teachers with more than 10 years of experience.
Experimental design: PLS-SEM is standard. All tests are reported
Validity of the findings: OK
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: AN EMPIRICAL BAYES APPROACH TO STOCHASTIC BLOCKMODELS AND GRAPHONS: SHRINKAGE ESTIMATION AND MODEL SELECTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The papers appears to meet all the basic reporting requirements of the journal. Regarding the literature review, I only have one minor concern (see bullet point #1 in my list in the section ‘Additional comments’).
Experimental design: The papers appears to meet all the experimental design requirements of the journal.
Validity of the findings: The papers appears to meet all the requirements of the journal around validity of the findings.
Additional comments: The paper proposes a simple yet effective empirical Bayes estimator for the block connectivity matrix in stochastic blockmodel, conditional on an estimate of the community structure. The authors also discuss implications of their work in terms of graphon estimation, using the stochastic blockmodel approximation to graphons. The paper is well presented and the methodologies clearly explained. The methods presented in this article are particularly relevant for the communities interested in network analysis and community detection, therefore I believe the paper would fit well within the aims and scope of the journal. Some of my main concerns are summarised below:

1. The problem of correctly selecting the number of communities K is mentioned, but existing methods for estimation of K are not discussed. I believe a brief literature review on criteria for model selection should be included, considering that this is one of the central aspects of the work. For example, in the context of spectral graph clustering, a good reference for this would be Yang et al. (JCGS, 2021).
2. Page 4, before equation (12) — A Jeffreys prior is chosen for the partition probabilities, but no discussion is provided on the reasoning behind this choice and potential implications. What is the effect of the prior parameters \tau on the performance of the estimation procedure for K? It would be good to comment on this (maybe examining limit cases such as \tau to 0 or to infinity — or via simulations).
3. The simulations presented in the section ‘Results / Simulation’ (page 7 onwards) are fairly simple: the simulated networks are strongly assortative and the block connectivity probabilities are highly homogeneous. It is reasonable and expected to see a shrinkage estimator to have a better performance under this scenario. It would be interesting to see how the performance changes when heterogeneous block connectivity probabilities are used (maybe adding such additional simulations to the supplementary material).
4. Lines 182 and 188, page 8 — The two simulation scenarios are referred to as ‘dense’ and ‘sparse’. Commonly, sparsity for graphs is defined as the number of nodes grows to infinity and the number of edges grows sub-quadratically (see e.g. Caron and Fox, JRSSB, 2017). Here, the term ‘sparse’ appears to be used instead to mean that the setting for model 2 is just sparser than model 1. I believe that this should be clarified to avoid confusion for the readers. It might be appropriate to simply report the  expected edge density under the two parameter settings, to better appreciate the difference between the two simulations.
5. The ‘Real data examples’ section on page 15 onwards assumes that the node labels available for the email network and political blogs network are actually representative of network connectivity. Often in real world networks, the assumption of a stochastic blockmodel is limiting, since it assumes extremely simple IID binomial within-community degree distributions — degree-corrected stochastic blockmodel (Karrer and Newman, 2011) might therefore be appropriate in the presence of within-community degree heterogeneity. Since the labels are used to assess the effectiveness of the proposed methodologies, would it be possible to evaluate (maybe via hypothesis testing) whether the blocks obtained from the available labels are representative of a stochastic blockmodel? In my opinion, this would make the real data analysis more convincing.
6. The choice of possible values of K for the email network appears to be only between 30 and 50. Many times in practice much smaller numbers of communities are sought. It would be interesting to see the performance of the models also for K starting from 1 as in the political blogs example, if possible.

Minor details:
The hyperreferences in the PDF document are not displayed correctly — see e.g. Figure ?? on page 5, line 130; (Section ) on page 7, lines 175-176.

References:
- Caron, F. and Fox, E.B. (2017), Sparse graphs using exchangeable random measures. J. R. Stat. Soc. B, 79: 1295-1366. https://doi.org/10.1111/rssb.12233
- Congyuan Yang, Carey E. Priebe, Youngser Park & David J. Marchette (2021) Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering, Journal of Computational and Graphical Statistics, 30:2, 422-441, DOI: 10.1080/10618600.2020.1824870
- B Karrer, MEJ Newman (2011), Stochastic blockmodels and community structure in networks, Physical Review E 83 (1), 016107


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: AN EMPIRICAL BAYES APPROACH TO STOCHASTIC BLOCKMODELS AND GRAPHONS: SHRINKAGE ESTIMATION AND MODEL SELECTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The article is mostly well-written, however there are a few sentences which are not clear (please see specific comments below) and improvements can be made in the section on future work. The literature cited is relevant however it does not reflect the breadth of research done on the topic. Thus, I would recommend the authors to find the latest articles on this topic without necessarily restricting to Bayesian techniques. More can be done to provide a deeper context to the problem. A simple example illustrating the requirement for shrinkage with existing graphon estimation techniques say would be helpful. The paper is structured well and figures, tables etc are clear.
Experimental design: The research question is well defined and meaningful, however, as mentioned above, a specific example would be helpful in providing context and understanding the relevance of the shrinkage problem.
Investigations have been performed via simulations and two data sets (please see specific comments regarding this). The description of methods used for simulations and data analysis should be further clarified with the exact steps mentioned clearly.
Validity of the findings: Benefit to literature is clearly stated but is not fully evident from existing analyses and presentation. Some further work and a clear reporting should help confirm the validity of the findings and also make the contribution of wider interest.
Additional comments: Specific comments:

p.1. line 40: clarify symmetry/undirected networks
p.2, line 74: `Empirical Bayes method is usually seen to have better performance when estimating many similar and variable quantities' :
-What does 'similar' and 'variable' mean exactly and why is this the case briefly?
- p.2, line 76: How `small' is the network size that you refer to here?
-p.5, line 130: missing figure number (??). Please check carefully throughout the document for referencing.
-p.7, line 175-176: labels of sections missing
-p.15, line 267-8: need rephrasing it seems. Not clear.
-p.2, around eqn 2.: is it assumed that n nodes are divided into K blocks, each allowed to be of a different size? If yes, please include it in the description here.
-p.5, line 130: Can there be scenarios where L-BFGS may not succeed in finding the global maxim? Please comment on this in the paper.
-p.6: what is the motivation for using ICL (and e.g. not Bayes factors)?
p.11: simulations on graphon estimation. What does the graphon model look like? Can a heatmap be included? Results using another graphon example with a different structure would be useful (google scholar results for `graphon estimation' brings up latest articles, many of which report simulation comparisons for a variety of graphon structures).
p.11, line 247: the performance of your method over competing methods is reported to be significant in the sparse case. Is this expected? If so, why?

- With only the K specified, how are the sizes of different clusters determined ( if they are assumed to be unequal in general)?
- In practice multiple labelings of nodes (i.e. different Z's) may be available from different clustering methods. What is the best way to proceed given this?
- How sensitive is your approach to different Z's in practice? This is not clear from the simulations.
- How may the range of K determined in practice? For example, why is K chosen to be at least 30 and at most 50 in the first data example?
- What are the labels z's in eqn (25). It is mentioned that these are  'known labels of the nodes' - what does this mean exactly? Are these the ground truth labels or labels estimated from an existing graph clustering algorithm?

My main concern is the simulation and data analyses reported to validate the findings. The MSEs can give a different picture and sometimes be deceptive. I would recommend plotting heatmaps of the estimates from each method directly for a visual confirmation that all methods are working as expected. Further, I understand that this is a Bayesian technique and thus it is fair to include comparisons with other Bayesian methods, however given the vast literature on this topic, it is important that the proposed method is compared with atleast one other non-Bayesian approach (e.g. network histogram of Olhede & Wolfe, 2014  is commonly used).

Secondly, why is it fair to use the department-wise labels as the ground truth in the first data example? There may be clusters based on multiple attributes or a combination of them and it is difficult to define ground truth based on node attributes in general. Is there a justification for this approach in this particular case/data set? One of the commonly used methods is to compare the mean squared prediction errors (as in recent graphon estimation papers in statistics for example).

It would be useful to see the values that the shrinkage parameter assumes to get insights on what determines estimation of connection probabilities in the two data sets. It would be useful to report the estimated shrinkage \eta's if possible.


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: AN EMPIRICAL BAYES APPROACH TO STOCHASTIC BLOCKMODELS AND GRAPHONS: SHRINKAGE ESTIMATION AND MODEL SELECTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: The papers appears to meet all the basic reporting requirements of the journal.
Experimental design: The papers appears to meet all the experimental design requirements of the journal.
Validity of the findings: The papers appears to meet all the basic validity of the findings requirements of the journal.
Additional comments: The revised version of the manuscript addressed the concerns that I expressed in my previous review. I thank the authors for implementing the suggested edits.


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: ENSEMBLE OF ADAPTED CONVOLUTIONAL NEURAL NETWORKS (CNN) METHODS FOR CLASSIFYING COLON HISTOPATHOLOGICAL IMAGES
Review round: 1
Reviewer: 1

Output:
Basic reporting: Your work is good, but there are many typos. The English of the article should be checked by an expert. I've marked some of the misspellings in the PDF. I added a comment. You must comply with the warnings. The abstract article is a little weak compared to its content. It should summarize the article in full. Shapes must be developed. All suggested citations should be included. There are too many repeated citations in the article. The article should be rearranged as incoming.
Experimental design: The Experimental Result section of the article is a bit weak compared to the other sections. This section is the heart of the article. This is the part that belongs to you. Therefore, this section should be developed. The resolution of the figures should be increased. The biggest shortcoming is that there is no confusion matrix. There are calculations, but there are no confusion matrices used to make these calculations. Add at least the confusion matrix of the model you propose (the most successful model, E-CNN). Include the ROC curve if possible. These will enable the reader to better understand and evaluate the article.
Validity of the findings: As stated in the previous comments, more detailed analysis is required to prove the validity of the findings.
Additional comments: Correct the article by considering the warnings and comments in the PDF uploaded to the system. Also take into account the comments made in general.


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: ENSEMBLE OF ADAPTED CONVOLUTIONAL NEURAL NETWORKS (CNN) METHODS FOR CLASSIFYING COLON HISTOPATHOLOGICAL IMAGES
Review round: 1
Reviewer: 2

Output:
Basic reporting: Figures' quality is not enough.
(For more detail, see attachments)
Experimental design: The methods, especially how the ensemble learning was applied in this study, was not explained in detail.
(For more detail, see attachments)
Validity of the findings: The experimental results (training length of networks) is not enough to compare methods.
It was not provided how many samples were used to compute the statistical p-values.
(For more detail, see attachments)
Additional comments: The motivation of this study is promising. However, it is not easy to make sure of the superiority of the proposed model with such short training.


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: ENSEMBLE OF ADAPTED CONVOLUTIONAL NEURAL NETWORKS (CNN) METHODS FOR CLASSIFYING COLON HISTOPATHOLOGICAL IMAGES
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper has some drawbacks:
1. The overview of the related papers should be expanded. It's a very popular research area. I suggest the authors evaluate these papers related to Transfer Learning and Ensemble Learning Techniques:
https://www.nature.com/articles/s41598-021-93783-8
https://ieeexplore.ieee.org/document/9107128
2. The presentation of Figures 7, 8 should be improved.
Experimental design: What image preprocessing technique do the author use?
The authors should present confusion matrices for each dataset.
Validity of the findings: The authors used only one dataset for testing and validation. It's not enough.
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: ENSEMBLE OF ADAPTED CONVOLUTIONAL NEURAL NETWORKS (CNN) METHODS FOR CLASSIFYING COLON HISTOPATHOLOGICAL IMAGES
Review round: 2
Reviewer: 1

Output:
Basic reporting: All requested corrections have been carefully made. The article can be published in my opinion.
Experimental design: All requested corrections have been carefully made. The article can be published in my opinion.
Validity of the findings: All requested corrections have been carefully made. The article can be published in my opinion.
Additional comments: All requested corrections have been carefully made. The article can be published in my opinion.


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: ENSEMBLE OF ADAPTED CONVOLUTIONAL NEURAL NETWORKS (CNN) METHODS FOR CLASSIFYING COLON HISTOPATHOLOGICAL IMAGES
Review round: 2
Reviewer: 2

Output:
Basic reporting: The manuscript meets this journal's standards
Experimental design: The manuscript meets this journal's standards
Validity of the findings: The manuscript meets this journal's standards
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: ENSEMBLE OF ADAPTED CONVOLUTIONAL NEURAL NETWORKS (CNN) METHODS FOR CLASSIFYING COLON HISTOPATHOLOGICAL IMAGES
Review round: 2
Reviewer: 3

Output:
Basic reporting: The authors addressed all my comments. The paper can be accepted in the present form.
Experimental design: -
Validity of the findings: -
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: SFEDCHAIN: BLOCKCHAIN-BASED FEDERATED LEARNING SCHEME FOR SECURE DATA SHARING IN DISTRIBUTED ENERGY STORAGE NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper provides a comprehensive review of research work on privacy-preserving data sharing in the introduction and related literature, and introduces the importance of secure joint modeling in distributed energy storage networks. This paper innovatively proposes a scheme called SFedChain based on blockchain and federated learning to realize the safe sharing of data in distributed energy storage network, which has strong practicability and logic. This paper is innovative in application, considers the scenario of data sharing in distributed energy storage networks, and expounds the importance of data privacy protection. In terms of method and theoretical innovation, this paper realizes efficient joint modeling by combining federated learning to transform data sharing problems into machine learning problems, and uses blockchain technology to record to ensure security, making it more practical. In joint modeling, it is logical to ensure the security and improve the accuracy of joint modeling through the designed encryption parameter upload review mechanism and reputation-based dynamic weight consensus protocol. The experimental results demonstrate the effectiveness and applicability of the method. The research problem in this paper is meaningful, and the method is innovative. The sequence structure is clear and the paper has a good organization structure. However, the manuscript still needs to be modified before it is published. I have several questions listed as follows:
1)There are some Language problems and typos or grammar errors in this manuscript. Many of them could cause difficulties to the readers.
2)The author proposes to use the consortium chain to trace the source of the joint modeling process. Why does this article apply the consortium chain instead of the public chain or private chain? Please give the author a detailed explanation.
3)In the simulation of the attack experiment in Figure 9, the authors should give a more detailed experimental setup.
Experimental design: In order to solve the problem of effective data management in the distributed energy storage network, this paper establishes a multi-party data security sharing and privacy protection mechanism, and effectively solves the possible threats to the model through a series of security analysis. This paper uses real benchmark data sets and conducts comparative test analysis to verify that the improved blockchain consensus mechanism and incentive algorithm can effectively improve the accuracy of federated learning. By simulating different proportions of attack intensities, the anti-interference ability of the model is verified. The method has certain novelty.
Validity of the findings: In the experimental part of this paper, the theoretical results are reasonable. For the proposed threat model, a reliable solution is provided through security analysis. At the same time, benchmark experiments and comparative verifications demonstrate the effectiveness and robustness of the model, and realize the secure sharing and privacy protection of data in distributed energy storage networks.
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: SFEDCHAIN: BLOCKCHAIN-BASED FEDERATED LEARNING SCHEME FOR SECURE DATA SHARING IN DISTRIBUTED ENERGY STORAGE NETWORKS
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this work, the authors have proposed a deep learning scheme called SFedChain in distributed energy storage networks to ensure the secure sharing of data between distributed devices and improve the training accuracy of federated learning models.
In the new scheme, the three-in-one blockchain architecture is designed for on-chain storage and computation of a large number of model parameters, which is different from the current small-capacity data storage method mainly based on ledgers. It provides an effective solution to the big data storage bottleneck problem of model training parameters in energy storage scenarios.
In addition, this scheme designs a secure federated learning model to ensure the security of power data sharing among distributed energy storage nodes. It also solves the problem of aggregator single-point failure and malicious attack in traditional federated learning, and effectively improves the accuracy of the joint model through the designed dynamic weight method.
Finally, a series of safety analysis proved the reliability of the scheme. Comparative experiments and performance analysis show the efficiency and anti-interference of the scheme.
This paper is well–written. The proposed scheme is quite rational and has a rigorous theoretical reasoning process. The originality is clear. The technology adopted is advanced. The objective evaluation results are reported. The solution is suitable for problem solving in distributed energy storage networks. Hence I would like to recommend to accept the paper for publication.
Experimental design: The experimental design is good.
Validity of the findings: The findings are valid.
Additional comments: 1, In the first paragraph of chapter 1, the “blockchain” is written as “blockchian”. Please proofread carefully.

2, In section 2 of chapter 2, the explanation of 〖ID〗_DH and 〖ID〗_TR are not given when it appears at the first time.

3, In chapter INTRODUCTION, page 2, line 99 - "privacy", use capital "P". Line 118, "In Section 2, We give implementation", Use the small letter "we".

4, The text in Figure 1 is too small, please use a high-quality vector image and adjust the font size.


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: SFEDCHAIN: BLOCKCHAIN-BASED FEDERATED LEARNING SCHEME FOR SECURE DATA SHARING IN DISTRIBUTED ENERGY STORAGE NETWORKS
Review round: 2
Reviewer: 1

Output:
Basic reporting: I have no comment.
Experimental design: I have no comment.
Validity of the findings: I have 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: FAST AND ACCURATE FACE RECOGNITION SYSTEM USING MORSCMS-LBP ON EMBEDDED CIRCUITS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Fails in 'Professional article structure, figures, tables. Raw data shared'.
All the figures and tables are not embedded in the manuscript.
So this manuscript is very hard to read and understand.
Experimental design: no comment
Validity of the findings: Fails in 'All underlying data have been provided; they are robust, statistically sound, & controlled'.
The proposed method was only compard with SOTA methods on the Grimace dataset.
Compared with other SOTA methods, how about the performance on the Faces 95 and Faces 96 datasets?
Additional comments: All the figures and tables are not embedded in the manuscript.
So this manuscript is very hard to read and understand.
The proposed method was only compard with SOTA methods on the Grimace dataset.
Compared with other SOTA methods, how about the performance on the Faces 95 and Faces 96 datasets?


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: FAST AND ACCURATE FACE RECOGNITION SYSTEM USING MORSCMS-LBP ON EMBEDDED CIRCUITS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The English language using is a little poor, and this paper t is very hard to read and understand. The authors may miss the  related latest work, such as: Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3067464
Experimental design: None
Validity of the findings: The authors may miss the compared results on the Faces 95 and Faces 96 datasets.
Additional comments: None


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: FAST AND ACCURATE FACE RECOGNITION SYSTEM USING MORSCMS-LBP ON EMBEDDED CIRCUITS
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: All of my concerns have been addressed. This version may be considered to 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: FAST AND ACCURATE FACE RECOGNITION SYSTEM USING MORSCMS-LBP ON EMBEDDED CIRCUITS
Review round: 2
Reviewer: 2

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


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

Input:
Title: RULE-RANKING METHOD BASED ON ITEM UTILITY IN ADAPTIVE RULE MODEL
Review round: 1
Reviewer: 1

Output:
Basic reporting: Author proposed novel method to find interesting patterns using lift ratio which incorporates both utility and frequency of itemsets in a database. The major highlight of this article is that authors proposed rule ranking method to extract utility based association rules. The article is structured and written well. Though this work addresses the issues of the existing work in utility based data mining, few corrections needs to be done.

1.1. Introduction should clearly addresses the issues of existing works.
1.2. The comprehensive review on utility based data mining should be done. The following recent references should be investigated and cited.
i) https://www.inderscienceonline.com/doi/abs/10.1504/IJITM.2015.066056
ii) https://www.tandfonline.com/doi/full/10.1080/08839514.2014.891839
iii) http://www.cai2.sk/ojs/index.php/cai/article/view/1333
iv) https://ieeexplore.ieee.org/abstract/document/6416812/
v) https://link.springer.com/article/10.1007/s11036-019-01385-6
vi) https://link.springer.com/article/10.1007/s12652-020-02187-5
vii) https://publications.waset.org/9997900/a-distributed-approach-to-extract-high-utility-itemsets-from-xml-data
viii) https://link.springer.com/article/10.1007/s11063-022-10793-x

1.3. Equations should be represented by using equation numbers.
1.4. The flow of algorithm should be clearly explained.
1.5. The algorithm needs to be written clearly with necessary indentations.
Experimental design: 2.1 Experimental results needs to be explained elaborated manner.
2.2 Dataset description is not available.
2.3 Experimental results should be supported with necessary graphs by considering various factors such as number of rules generated, support, utility, confidence etc.
Validity of the findings: 3.1 The validity and the findings is not clearly explained.
3.2 Statistical analysis can be performed.
Additional comments: The proposed work can be explained with suitable example. Author can use small dataset for this explanation.


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: RULE-RANKING METHOD BASED ON ITEM UTILITY IN ADAPTIVE RULE MODEL
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: The authors proposed a method for ranking the rules based on the lift ratio value, which was derived using the item's frequency and utility. I would ask the authors for clarification of some issues before acceptance of this paper. Therefore, I recommend a revision.
•	Please, you should add a comparative study. A comparison between your algorithm and others should be added.
•	The limitation(s) of the association rules mining methodologies proposed in this work should be extensively discussed.
•	When the pseudo-codes of the proposed method are examined, it is seen that concepts such as D, S, X, and U are written in a different language. Please write them all in the same language and give pseudocodes as "appendix".
•	Complexity analysis should be done.
•	In Figure 1 "eksternal" should be fixed as "external"
•	You are using a specific database in Table 1. Please indicate your reasons for choosing these datasets.
•	Number of transactions is small in all datasets. A larger data set should be added and analysis should be performed on large data sets.
•	I'd like to see a more detailed analysis of the proposed algorithm's scalability. What are the main theoretical and practical benefits of the proposed algorithm? What about memory consumption and problem dimensions (especially big data)?
•	"II.2 and II.3" used in " Tables II.2 and II.3 show that the number… " expression should be corrected.
•	Is the support value given as a percentage in the test results? Not mentioned in the article?
•	The authors mentioned some studies in the literature (such as ELECTRE, ELECTRE II, AHP). They should also compare the proposed method with the existing methods in the literature.
•	The properties of the datasets used are already given in Table 1. It is given again in Table 2. If it will be given in Table 1 separately, it should be removed from Table 2. Table 1 is not needed if Table 2 will also be given.


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

Input:
Title: RULE-RANKING METHOD BASED ON ITEM UTILITY IN ADAPTIVE RULE MODEL
Review round: 2
Reviewer: 1

Output:
Basic reporting: Authors made significant efforts in improving the manuscript. But, few references i have suggested which are closely associated with their work are not studied and cited. Hence, I suggest authors to study and revise the paper accordingly.
Experimental design: Authors made significant efforts in improving the manuscript. But, few references i have suggested which are closely associated with their work are not studied and cited. Hence, I suggest authors to study and revise the paper accordingly.
Validity of the findings: Authors made significant efforts in improving the manuscript. But, few references i have suggested which are closely associated with their work are not studied and cited. Hence, I suggest authors to study and revise the paper accordingly.
Additional comments: Authors made significant efforts in improving the manuscript. But, few references i have suggested which are closely associated with their work are not studied and cited. Hence, I suggest authors to study and revise the 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: A MULTI-STAGE HEURISTIC METHOD FOR SERVICE CACHING AND TASK OFFLOADING TO IMPROVE THE COOPERATION BETWEEN EDGE AND CLOUD COMPUTING
Review round: 1
Reviewer: 1

Output:
Basic reporting: The variables need be well defined:
1.	The authors defined different variables with multiple letters, i.e., in_i, map_{i,j}, bw_j, nft_i, etc, which are confusing. Commonly, ‘in_i’ in an equation means ‘i’ * ‘n_i’. Therefore, try to define a variable with a single letter.
2.	There are multiple variables used in this manuscript. Adding a notation table to list all symbols/notations could improve its readability.
3. There is a highly related work [R1] on service caching and task offloading in MEC, which should be well cited.
[R1] "Joint Optimization of Service Caching Placement and Computation Oﬄoading in Mobile Edge Computing Systems," IEEE Transactions on Wireless Communications, vol. 19, no. 7, pp. 4947-4963, July 2020.
Experimental design: The performance evaluation of the proposed algorithm is not sufficient.
4.	With respect to the contribution of this work, since the proposed algorithm MSHCO is a heuristic algorithm, what is the performance gap between MSHCO and the global optimal algorithm? The numerical studies only show that MSHCO outperforms some existing benchmarks. However, we have no idea how good enough is the algorithm, which is essential for potential readers who want to continue studying this topic. Better provide an upper bound or the exhaustive search optimal.
5.	This work considers head deadline requirements of tasks, where each task is associated with a deadline d_i. How do you handle the tasks if the deadline cannot be met? Will these unfinished tasks resources for the proposed MSHCO? How about other benchmark algorithms evaluated in the simulations?
6.	In the performance evaluation, besides the comparisons with other benchmarks, the analysis on the structure of MSHCO is necessary. For example, how do these three stages of the algorithm affect its performance?
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: A MULTI-STAGE HEURISTIC METHOD FOR SERVICE CACHING AND TASK OFFLOADING TO IMPROVE THE COOPERATION BETWEEN EDGE AND CLOUD COMPUTING
Review round: 1
Reviewer: 2

Output:
Basic reporting: Correct the punctuation error in line 37, used comma two times

On line 56, grammar error “But these works have some issues must be address”

The sentence on line 69, 70, 71, there is single sentence which should be avoided.

In Line 87, “The section” should be replaced with “The next section” or “The second section”

Line 96 have also grammar problem, should be rectified

Please define the sentence on line 199, “and exams the next task”

Another grammar mistake on line 199-200-201 “Otherwise, MSHCO tries to rented a new CS”. “MSHCO rents a new CS with found type”

I think whole paragraph from line 196-205 should be revised because of grammar problems.

Another grammar issue on line 208-209 “There are two situations when offloading a task to an ES, the requested service has or 209 not cached on the ES”

Please also revise sentence on line 217-218.

There are no snapshots regarding experimental work, moreover authors should compare their results in tabular format.

Please also elaborate the comparison of user satisfaction in more detail.
Experimental design: In this paper, the authors have proposed a method to address the  joint service caching and task offloading problem in edge-cloud computing environments, to improve the cooperation between edge and cloud resources. They have proposed the method in three steps including formulating  the problem into a mix-integer nonLinear programming followed by proposing a  a three-stage heuristic method for solving the problem in polynomial time. Finally , they focused on improving the performance of tasks offloaded to the cloud, by re-offloading some tasks from cloud resources to edge resources.
Following are my major questions
It seems to be a stand alone study without the comparison with any state of the art, please try to justify your method with a concrete comparison with some similar kind of latest study. The same should also be discussed in the abstract.

Please briefly justify the potential benefits of the proposed system.
Please make sure to cite the references for the equations if they are not owned by you
What are the formation basis and correctness justifications of Algorithm 2 & 3 ?

Because the cloud & edge computing is the hot area of research these days and there are a lots of test beds available, therefore, authors are strongly recommended to perform a real study instead of the simulations of proposed model. This way , they will have a better chance to prove the authenticity of their work.
Validity of the findings: It seems to be a stand alone study without the comparison with any state of the art, please try to justify your method with a concrete comparison with some similar kind of latest study. The same should also be discussed in the abstract.

What are the formation basis and correctness justifications of Algorithm 2 & 3 ?

Because the cloud & edge computing is the hot area of research these days and there are a lots of test beds available, therefore, authors are strongly recommended to perform a real study instead of the simulations of proposed model. This way , they will have a better chance to prove the authenticity of their work.
Additional comments: please make sure to remove the language  and grammatical errors through out the manuscript.


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

Input:
Title: AN ENHANCED GENETIC FOLDING ALGORITHM FOR PROSTATE AND BREAST CANCER DETECTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript has presented a clear meaning and has utilised professional English at most of the contents. However, there are concerns regarding the methodology and dataset:
1. how is the Genetic Folding algorithm developed in terms of its novelty, targeted issues in the dataset?
2. The dataset appears to be small rather than comprehensive to reflect the effectiveness of the algorithm. One another better dataset example can be:
"Using deep learning to enhance cancer diagnosis and classification"
3. The figures could not exclusively identify the details for the paper.
4. The algorithms could not be translated properly with its current form.
5. Please consider to make the reference clickable. For some reasons, I could not find the reference for Table 5.
Experimental design: Overall, it appears to be a work representing the quality of student project report, which has sufficiently reflected the terms and knowledge for building classifiers for particular dataset.
However, it fails to reflect the studied research questions, particularly it is difficult to understand the challenge to build a qualified and SOTA classifier for the given dataset. Meanwhile, it lacks sufficient details concerning the novelty and completeness of the utilised method. It appears to be an application work.
Validity of the findings: It is not strong enough to support the conclusion as SOTA performance for the given dataset.
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: AN ENHANCED GENETIC FOLDING ALGORITHM FOR PROSTATE AND BREAST CANCER DETECTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this MS the authors apply an evolutionary Genetic Folding (GF)
algorithm to the binary classification of prostate cancer malignancy from clinical tumour features.
The development and improvement of tumour stratification algorithms is an active area of research where advances might strongly benefit cancer diagnosis and treatment.

The authors provide a clear and concise overview of prostate cancer diagnosis and grading approaches, both clinical and computational.

The authors then propose and implement a SVM with GF-kernel based prostate cancer classifier (GF-SVM), arguing that GF has been shown to outperform other evolutionary algorithms.

A few passages should be edited using more appropriate terminology (e.g. line 174 should read "Instances were classified as...").

There are a few typos I would recommend addressing before publication (e.g. repetition at lines 78-78, line 134 should reference Mezher et. al 2010).
Experimental design: no comment
Validity of the findings: The authors state (lines 205,206) that GF-SVM "demonstrated a significant performance improvement over the existing models in this domain." However, the authors have failed to perform any sort of statistical analysis to demonstrate that their method is significantly better, or even different from, the established methods (e.g. see [1]). I would recommend the authors included a pairwise analysis of all alternative models in Fig. 2C versus GF-SVM (e.g. via a binomial McNemar test [2] over at least 10 holdout shuffle replicates). Without this I do not believe this manuscript meets the "statistically sound" criterion and I cannot recommend it for publication.

[1] Nicholls, Anthony. "Confidence limits, error bars and method comparison in molecular modeling. Part 1: the calculation of confidence intervals." Journal of computer-aided molecular design 28.9 (2014): 887-918.
[2] McNemar, Quinn, 1947. "Note on the sampling error of the difference between correlated proportions or percentages". Psychometrika. 12 (2): 153–157
Additional comments: The authors collect and made available a tabular dataset of 8 clinical features across 100 prostate cancer patients, as well as a repository for the Genetic Folding Open source library. Unfortunately, they do not provide the reader with any way of rapidly and conveniently reproducing their models and results (e.g. via a colab or jupyter notebook). I believe this would greatly help their model's chances of being re-used by others in the community.


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: AN ENHANCED GENETIC FOLDING ALGORITHM FOR PROSTATE AND BREAST CANCER DETECTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: Thanks for the revision efforts. It is found that more details have been added, however, it lacks a section of describing the distinguishable technical contributions.
Moreover, the reference is very limited. Please consider including more up-to-date reference, such as:
Weakly supervised prostate tma classification via graph convolutional networks;
Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome;
Supervised machine learning model for high dimensional gene data in colon cancer detection;
A survey on machine learning approaches in gene expression classification in modelling computational diagnostic system for complex diseases;
OncoNetExplainer: explainable predictions of cancer types based on gene expression data;
A Novel Statistical Feature Selection Measure for Decision Tree Models on Microarray Cancer Detection;
Selecting features subsets based on support vector machine-recursive features elimination and one dimensional-Naïve Bayes classifier using support vector machines for classification of prostate and breast cancer.
Experimental design: Please refer to the mentioned reference regarding the experimental design. The p-value will be desired to evaluate the effectiveness of the model.
Validity of the findings: It could be validated if the source code and datasets are publicly available.
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: AN ENHANCED GENETIC FOLDING ALGORITHM FOR PROSTATE AND BREAST CANCER DETECTION
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have expanded their model performance analysis by including a pairwise model accuracy comparison among a set of alternative SVC kernels and their proposed GF kernel in Fig. 2C,E. This analysis shows that GF outperforms all other evaluated kernels on these classification problems. I believe the manuscript now satisfies this journal's criterion for publication. However, I would personally still recommend the authors included some sort of pairwise statistical test in this analysis (e.g. Mann-Whitney, McNemar) to robustly assess the significance of the observed improvement in performance.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: no comment


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

Input:
Title: PPE DETECTOR: A YOLO-BASED ARCHITECTURE TO DETECT PERSONAL PROTECTIVE EQUIPMENT (PPE) FOR CONSTRUCTION SITES
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is, in general, written well, used formalism is explained, and the problem is stated clearly. Also, the application seems to be interesting. Unfortunately, there are three points that need to be addressed.

a) Abstract: the sentence "For the detection algorithm, this study has used the You Only Look Once (YOLO) family's anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval." is not valid. The study does not bring a comparison with other SOTA detectors. The only comparison is made with YOLOv5-x. Based on general knowledge and benchmarks such as https://paperswithcode.com/sota/object-detection-on-coco benchmark, scaled YOLOv4 should yield better performance than YOLOx. The currently best detector is Swin, which still performs with a reasonable time.

b) Section 4: there is no motivation to implement your own haze/rain/low-light augmentations when all of them and many more are available through Albumentations? See https://albumentations-demo.herokuapp.com/ Based on it, Section 4 is useless and can be removed.

c) Section 5: it describes widely used evaluation metrics that are a gold standard in object detection and are well known by researchers. Therefore, there is no need to explain it in detail. It is enough to state you use IOU and mAP; thus, the section can be removed.
Experimental design: no comment
Validity of the findings: Here, it is necessary to state that two major issues were found. It is needed to address them carefully, mainly the second issue.

a) Repository:
-	There is no readme in the repository regarding the described functionality. There is only a general readme forked from the original YOLOx repository. It must be described how to run the training script (which one is it) to replicate your results.
-	I did not find training data or link to them.
-	The model_test.ipynb functionality in the repository is nothing but a long error report. In the end, there is a test on seven images only without their visualization or mAP evaluation.
In summary, the correctness of the scheme cannot be confirmed.

b) Data:  there is a question about the correctness of the labels. I examined several images from 'test-n.zip' and observed that:
- gettyimages-88655418-612x612_jpg.rf.6f0a5bd6a8ac6cf7a77d8a24b814e8df there are two person, two glasses, two helmets, and two vests. Label includes only one glass and one person.
- ppe_0579_jpg.rf.17a45729452bdb75971919d2630f0e21.jpg one orange helmet and one head are missing in the label
- ppe_0994_jpg.rf.98234bc61dda7a1fb4e54e7e02ade2a0.jpg the label for the bottom-right person is missing
- vitolda-klein-lAqSzwr5eQc-unsplash_jpg.rf.774701a69123aa712e43b4fc4deb1ed0.jpg the label misses orange helmet
- 000221_jpg.rf.8dff87700f9373a3eff79e1f9f55f273.jpg label person is missing for all people. That holds for all (8) images from this 'subseries.'
- gettyimages-83455052-612x612_jpg.rf.ea5cd5e8c3044737116b908a6139fd6b.jpg the label misses two helmets
...and many more.

Based on this fact, the results published in the papers are not trustworthy because they are computed for a highly noisy and inconsistent dataset. The dataset must be fixed, and the whole experiment section must be recalculated.
Additional comments: There are three minor comments:

a) Is 'Md. Ferdous' full name?

b) The sentence "In construction sites more than 71% injury appears than 32 in all other industries." needs to be rephrased.

c) Formula 4: there is written 'Where, G and P are the prediction and ground truth bounding boxes respectively'. It is formally correct, but it is better to mark P as prediction and G as ground truth and not contrariwise.


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: PPE DETECTOR: A YOLO-BASED ARCHITECTURE TO DETECT PERSONAL PROTECTIVE EQUIPMENT (PPE) FOR CONSTRUCTION SITES
Review round: 1
Reviewer: 2

Output:
Basic reporting: This review paper demonstrates a YOLO-based architecture to detect personal protective equipment for construction sites. The structure of the article is organized well however, some changes need to be done to improve it.

Some suggestions for further improvement:

The Introduction section is well-written, and I propose to schematize all the discussion in several paragraphs (to facilitate the reading): (1) motivations, (2) the overall approach, (3) main contributions.
However, the main challenges to the field and the needs and benefits of this study are missed in the introduction.

A general discussion of the limitations and expectations of the proposed model should be inserted.
Experimental design: Adding some effects such as haze or low light on images, will not change the features of the image significantly. I suggest creating a night-time dataset and adding it to the existing one.

The authors mentioned that the effected images are used only for testing. What about the original images of the effected images? If the same original images are used for training, then testing with the same images with just a small effect is not a good idea because in that case, the model will just memorize the images.

Please specify what is the main differences between CHV and CHVG datasets. Is it the same datasets just the number of classes are increased or the HAZE, RAIN, and LOW-LIGHT images are included as well?

Has exactly the same YOLOX model been used in this article or have the authors made any changes to it? This needs to be explained in the paper clearly.

Line 120: there are two “also” which are not necessary and can be removed.

Line 146: Please explain why DarkNet53 has been used as pre-trained weights in this model.

Line 186 and 188: Authors need to determine why they set these parameters.

Line 194: The image size needed to be explained in the dataset section.

Line 259: Please determine why the NMS value is set to 0.65
Validity of the findings: The evaluation metrics section is explained very well, however, the discussion section is too short and needs to be expanded.

It would be good if authors upload their code on GitHub and add the link in the paper.
Additional comments: No additional comments


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

Input:
Title: PPE DETECTOR: A YOLO-BASED ARCHITECTURE TO DETECT PERSONAL PROTECTIVE EQUIPMENT (PPE) FOR CONSTRUCTION SITES
Review round: 2
Reviewer: 1

Output:
Basic reporting: I appreciate that the authors carefully processed all my comments and improved the paper significantly. It is clear, easily readable, and correct.
Experimental design: I have no critical comments in this section.
Validity of the findings: The authors fixed the issue with the dataset labels. The findings are now supported by the data and I have no more critical comments.
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: PPE DETECTOR: A YOLO-BASED ARCHITECTURE TO DETECT PERSONAL PROTECTIVE EQUIPMENT (PPE) FOR CONSTRUCTION SITES
Review round: 2
Reviewer: 2

Output:
Basic reporting: No Comment
Experimental design: No Comment
Validity of the findings: No Comment
Additional comments: No additional comments


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

Input:
Title: THE ROLE OF METACOGNITION IN PROMOTING DEEP LEARNING IN MOOCS DURING COVID-19 PANDEMIC
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper aims at  addressing role of metacognition in promoting deep learning via MOOCs. The work targets to answer two research questions. The work reported though not novel is acceptable contribution as the attempt is considerable.
Experimental design: Three aspects Critical thinking, Connecting concepts and Creating new concepts are considered as evaluation strategy. The questionnaire  enclosed along with manuscript is considered for review.

In my view, A detailed analysis w.r.t Anova would have been better instead of just stating the outcome. Some more w.r.t analysis and key findings would add value. Authors can address these in revision.
Validity of the findings: Computation enclosed in excel file support for validation. Conclusion is relatable to discussion done in the paper. The approach though not novel is acceptable attempt.
Additional comments: A more comparative evaluation is preferred as present version is limited.

Usually, analysis is expected but analysis part is very limited

These two points may be considered


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

Input:
Title: THE ROLE OF METACOGNITION IN PROMOTING DEEP LEARNING IN MOOCS DURING COVID-19 PANDEMIC
Review round: 1
Reviewer: 2

Output:
Basic reporting: its an attempt to explore the Role of Metacognition in Promoting Deep Learning in MOOCs during COVID-19 Pandemic. deep learning is not elevated in detail. data set taken for experimentation is  not  mentioned in detail.
Experimental design: needs more explanation about dataset taken and its features. how they have trained the model and how they tested. utilization of deep learning model to be discussed. Results generated from the proposed idea are limited
Validity of the findings: somewhat okay but dataset size and features to be enhanced. model should be more scalable with more variety of data and with higher dimensions are able to process in dynamic platform.
Additional comments: more investigation to be done on idea(methodology), dataset taken and projecting the role of deep learning in MOOCs


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

Input:
Title: THE ROLE OF METACOGNITION IN PROMOTING DEEP LEARNING IN MOOCS DURING COVID-19 PANDEMIC
Review round: 1
Reviewer: 3

Output:
Basic reporting: As per my observation, the studies were done on covid -19 and are related to the title given. Participants were students at the department of home economics who were all at the seventh academic level. Based on their scores on the metacognition awareness inventory, they were divided into two experimental
groups, i.e high metacognition students and low metacognition students done with a small sample.
 if it is taken for a large sample will the same results will be appeared?
Experimental design: As per the Author's view  203 out of  260 quantitative MAI points, metacognition was graded into two groups, high 204 metacognition (HM ≥ 65 percent) and low metacognition (LM < 65 percent) in line with Aydın205 and Coşkun (2011); Redondo and López (2018).
In other words, based on their scores on the 206 MAI instrument, participants were divided into two experimental groups.
There is a scope for comparative study. is it possible to incorporate?
Validity of the findings: is it justifiable that the authors noted  that all participants have studied and passed the course's prerequisite 14 courses, mainly Research Methodology, Teaching & Statistics Principles, and Principles of
215 statistics. Performance can be upgraded
Additional comments: Check the Grammar again


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

Input:
Title: THE ROLE OF METACOGNITION IN PROMOTING DEEP LEARNING IN MOOCS DURING COVID-19 PANDEMIC
Review round: 2
Reviewer: 1

Output:
Basic reporting: All responses are addressed in the revised version of the manuscript.
Experimental design: Investigation is carried and method described are sufficient w.r.t revised version of manuscript.
Validity of the findings: Findings are validated with proper data.
Additional comments: All review comments are answered and incorporated appropriately.


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

Input:
Title: THE ROLE OF METACOGNITION IN PROMOTING DEEP LEARNING IN MOOCS DURING COVID-19 PANDEMIC
Review round: 2
Reviewer: 2

Output:
Basic reporting: manuscript is well organized and explored on important  point required in current context
Experimental design: methodology given well and implemented too to obtain specific outcomes
Validity of the findings: findings obtained from the proposed method are valid may be improved in the performance on various datasets/various data sizes
Additional comments: manuscript will be so worthy if mentioned Limitations are reduced and future directions are implemented further


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

Input:
Title: THE ROLE OF METACOGNITION IN PROMOTING DEEP LEARNING IN MOOCS DURING COVID-19 PANDEMIC
Review round: 2
Reviewer: 3

Output:
Basic reporting: Yes, the revision version is fine.
Experimental design: The results enclosed in revision are fine
Validity of the findings: Supporting data is provided for validation.
Additional comments: The revision version is acceptable.


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

Input:
Title: REAL-TIME DDOS FLOOD ATTACK MONITORING AND DETECTION (RT-AMD) MODEL FOR CLOUD COMPUTING
Review round: 1
Reviewer: 1

Output:
Basic reporting: The analysis of the findings is very interesting. Many challenges of this proposed method are covered. Literature reference is relevant, but does not cover all the topics, such as AI, and is not so up to date.
Experimental design: As a concept, it is well designed. I found the topic interesting and definitely an area of growth.
Validity of the findings: Despite the research effort and the achieved high rate of accuracy, there are a number of challenges that need to be addressed, such as execution-time for online learning of random forest algorithm, the lack of up-to-date real-world datasets for training, what is the rate of false-positive and false-negative, there are scalability issues or performance?
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: REAL-TIME DDOS FLOOD ATTACK MONITORING AND DETECTION (RT-AMD) MODEL FOR CLOUD COMPUTING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper is well structured and easy to read. Also, the use of English is quite good. The introduction provides a great, generalized background of the topic and the motivations for this study are clear.  A minor comment concerns the References part, where it would have been better to have used more recent literature with more up-to-date knowledge.
The experimental part is well written and is appropriate for the study.
To conclude, this research work apparently fulfills the purpose for which it was carried out. It would be interesting to see in the future, the use of more machine learning classifiers in building the Real-Time DDoS flood Attack Monitoring and Detection RT-AMD predicting model or even use more datasets for executing the same experiments.
For the above reasons I strongly recommend the acceptance of this paper.
Experimental design: The research question of this research is well defined and undoubtedly adds knowledge to an otherwise quite explored scientific area, that of attack monitoring and
detection in cloud computing. The methods used, although not innovative, have certainly been used in the right way and the overall investigation is performed to a high technical standard.
Validity of the findings: Throughout the research there is no replication of pre-existing knowledge.
Conclusions are both well stated and linked to original research question.
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: SOLVING THE CLUSTERED TRAVELING SALESMAN PROBLEM VIA TRAVELING SALESMAN PROBLEM METHODS
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, the authors investigate the clustered traveling salesman problem (CTSP), which is an extension of the popular TSP.

The paper is clear and the conclusions are supported by extensive computational results.

I do recommend to add at the references the following paper:

P.C. Pop, I. Kara and A. Horvat Marc, New Mathematical Models of the Generalized Vehicle Routing Problem and Extensions, Applied Mathematical Modelling, Elsevier, Vol. 36, Issue 1, pp. 97-107, 2012.

which presents some interesting MIP formulations of the CTSP, and the papers:

O. Cosma, P.C. Pop and L. Cosma, An effective hybrid genetic algorithm for solving the generalized traveling salesman problem, Lecture Notes in Computer Science, Vol. 12886, pp. 113-123, 2021.

P.C. Pop, O. Matei and C.M. Pintea, A two-level diploid genetic based algorithm for solving the family traveling salesman problem, in Proc. of GECCO 2018, Association for Computing Machinery, Kyoto, Japan, 2018.

These papers deal with problems closely related to CTSP: the generalized traveling salesman problem (GTSP) and the family traveling salesman problem (FTSP). It is important to mention these papers in the introduction, especially because the authors are using GTSP instances.
Experimental design: The authors used the transformation suggested by Chisman in order to transform the investigated problem into classical TSP, which then is solved using state-of-the-art algorithms.

The research questions are well defined and they are relevant.
Validity of the findings: The conclusions are well stated and they are clearly linked to the proposed research questions.

Therefore, taking into consideration all the mentioned aspects I do recommend to accept the paper for publication if the authors answer to the addressed comments.
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: SOLVING THE CLUSTERED TRAVELING SALESMAN PROBLEM VIA TRAVELING SALESMAN PROBLEM METHODS
Review round: 1
Reviewer: 2

Output:
Basic reporting: This manuscript investigates methods for solving the Clustered Traveling Salesman Problem (CTSP), specifically, by converting it to the Traveling Salesman Problem (TSP) that has been extensively studied. The experimental results essentially answer several of the questions proposed by the authors.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: There are several issues below that need further clarification by the authors:

1. In Section 1, in addition to introducing the literature involved in the relevant methods, the characteristics and scope of application of the corresponding methods should also be summarized.

2. Again in the Section 1, some references seem to be too old.

3. The focus of this paper is to explore the conversion of CTSP to TSP for solving. The conversion method adopted by the author and the considerations behind it should be described in more detail in Section 2.1.

4. On page 8, line 285, what does “Err” in “B-Err” stand for? Authors may consider using more appropriate abbreviations.

5. The author's research found that several TSP solvers have different performance for CTSP. However, it would also make sense to discuss the reasons behind these differences in further depth.

6. When comparing the results, some statistical methods can be added to compare the superiority of several methods, such as variance analysis, Friedman test, etc.


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

Input:
Title: SOLVING THE CLUSTERED TRAVELING SALESMAN PROBLEM VIA TRAVELING SALESMAN PROBLEM METHODS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors took into consideration all the comments and observations and as a consequence, I do recommend to accept the paper for publication.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: No additional comments


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

Input:
Title: SOLVING THE CLUSTERED TRAVELING SALESMAN PROBLEM VIA TRAVELING SALESMAN PROBLEM METHODS
Review round: 2
Reviewer: 2

Output:
Basic reporting: most of the comments have been addressed, just need to do proofreading before publication
Experimental design: most of the comments have been addressed, just need to do proofreading before publication
Validity of the findings: most of the comments have been addressed, just need to do proofreading before publication
Additional comments: No additional comments


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

Input:
Title: KINSHIP IDENTIFICATION USING AGE TRANSFORMATION AND SIAMESE NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: - Problem statement, solution provided and outcome should be clearly explained in abstract. The flow of information should be in order and connected.
- Description of big data in the beginning of introduction is not relevant and should be removed. Start from pictorial discussion.
- Clearly explain the problem statement in the introduction section.
- Contribution of paper mainly highlights the methodology not the contribution. Explain how the results and outcome of this research contributes to the body of knowledge.
- Remove paper organization section.
- "Joseph P.R. et al Introduced....." use standard style of reference citation in the text.
- "resemblance [8]. Zhang et al. (2015)" - follow only one style of referencing throughout the paper.
- Literature should be written in discussion form not simple descriptive form.
- "we present a model....." don't use I, we, our in research. use passive voice sentences.
Experimental design: In proposed work, use modeling form (graphical/mathematical etc) to explain the methodology and explain all the steps in sequence. Give research model.
- Support the methodology with references.
- Provide details of data set, its source and how much data is used for training and implementation?
- Equations are inline with text. Should be on separate lines, with standard equation format and numbering, and explain them.
- Properly define the design of methodology and explain it step by step.
Validity of the findings: - How the validity of the results checked?
- Explain results and show them with the proper technical format rather than simply mentioning them.
- Use tables, data, numbers and figures to explain the results.
-  Data given in tables should be well explained and justified.
- How results are significant?
- Conclusion should explain how results support the objectives. what is the significance of the study. Summarize the outcome of the research, its impact and applicability.
Additional comments: - Make grammatical corrections.


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: KINSHIP IDENTIFICATION USING AGE TRANSFORMATION AND SIAMESE NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: a)-
What was the specific reason for picking images of an age between 15-20 only?
b)-
What was the dataset size used in the experimental validation.
c)-
Why CNN-based deep relational network was used for features extraction from the facial images of the dataset?
Validity of the findings: a)-
Why the proposed model is taking 10 images of each member? why not 5 or 15 or more?

b)-
Any specific reason why Father-Daughter and Mother-Son accuracy in both Training and Validation Accuracy is lower as compared to Father-Son and Mother-Daughter?

c)-
Have we also compared any other performance factor other than accuracy from the other proposed models mentioned in Table1.
Additional comments: We can only say that the proposed model's performance is better than the previously proposed model (Table1). We cannot say that the previously proposed models failed to deliver improved performance.


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: KINSHIP IDENTIFICATION USING AGE TRANSFORMATION AND SIAMESE NETWORK
Review round: 1
Reviewer: 3

Output:
Basic reporting: English language corrections are needed at some places in the article. Proofreading is recommended. E.g. line 194: Spelling mistake ‘pre-processing’, Line 284: use figure 4 instead of diagram 4
Experimental design: no comments
Validity of the findings: no comment
Additional comments: Repetition of the basic working is observed in multiple sections. It is suggested that more details be added instead of repetitions of the basic working.
Theme of Figure 3 and figure 4 is the same.
Care must be taken when using abbreviations. It is suggested that the full form and its abbreviation be provided at the first appearance. Afterward only abbreviated form be used. E.g. RFIW –Full form and its abbreviation should be provided at first appearance only. Afterward only abbreviated form should be used. Lines 62, 82, 315. Similarly, at line 324 only the abbreviation LAT should have been used. Many other such examples are present.
All equations must be numbered and cited in the text.


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: QUANTUM READOUT AND GRADIENT DEEP LEARNING MODEL FOR SECURE AND SUSTAINABLE DATA ACCESS IN IWSN
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.Use of deep learning models with wsn is good for intrusion detection

2. For authentication and authorisation alone ML/DL are not required, however to minimise the false acceptance rate use of sophisticated algorithms is good technique
Experimental design: 3. It is generally preferable to use light weight techniques in wsn/ IoT systems , deep learning is time and resource intensive when it comes to training. If a certain protocol has to be followed for authentication ,violation of it leads to an alarm. To detect this violation an anomaly based model is used . The same can be implemented without a model by simply using a checklist that contains a exhaustive list of items that need to be covered before the device can be safely added to the network. So while the approach of the author seems to be effective for now , better algorithms will keep coming up. To setup an authentication based on checklist mechanism is however time resilient unlike a learning based method. Statistical analysis of data could be useful from time to time , but for wsn lesser data that needs to be processed the better.

4. Deep learning and machine learning models tend to work based on historic data and behaviour . Training the models when planned changes occur in the infrastructure is an operational overhead.
Validity of the findings: 5 gradient based algorithm is not the best approach in security mechanisms as , things need to be as deterministic as possible . Sometimes the position of a sensor or its configuration could be constantly changing in the IWSN . In such a case it's not clear how this system works.
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: QUANTUM READOUT AND GRADIENT DEEP LEARNING MODEL FOR SECURE AND SUSTAINABLE DATA ACCESS IN IWSN
Review round: 1
Reviewer: 2

Output:
Basic reporting: The English language of the paper is clear and readable.

•	There is a need to explain the authentication problem in more details, some of the IIoT networks are designed in a closed network where they have their own cables and devices and no one outside the network has any access to it, in this case the authentication problem needs to be clarified at what circumstances it is considered an issue. I suggest the authors provide an example to explain when/how the authentication step should be included in the design.

•	Line 67: “in this paper” is repeated.

•	Authors need to identify the terminologies earlier in the paper such as authentication rate, authentication acceptance false rate, and authentication time.

•	Figure 1 in the paper should me improved to tell more details about the system model; not much information can be extracted from the figure in its current design.

•	Line 213: “the gateway node has to approve the authenticity of the sensors. The gateway node verifies whether the sensor has been tampered with or not in order to guarantee the confidentiality and integrity of the data packets”. Authors are required to identify what the gateway node is, what type of data is gathered, and how the authentication, confidentiality, and integrity of the sensors are being verified?

•	Line 220: Extra word “input”

•	Figure 2 is meaningless and not clear, E.g., What is the input look like? Etc.,.

•	The paper has no example of where their design is applicable.

•	Line 237, “The QSAE checks for energy availability, authenticity and authorization of the sensor by employing the quantum readout function to ensure smooth communication between the sensor and the supervisory control unit with a minimum false acceptance rate”. How are the energy availability, authenticity, and authorization verified by the QSAE? Authors should mention that it will be explained in coming sections.

•	QRH should be used in full words for the first time then used as a short cut.

The approach of this paper in not fully explained with the technical and theoretical details, more details need to be explained.
Experimental design: •	Equation #7 should be Sj0 instead of Si0.

•	Line 296: The gradient-associating sensors is not identified anywhere earlier in the paper.

•	Equations should be after the parameters are identified for better understanding.

•	Why the number of the simulation runs is 10? No explanation of why this value is chosen, in the literature, values converged after 45 runs.
Validity of the findings: •	In all tables, the increase in values gradually with the increase in the number of sensors ; this is intuitively expected and should not be mentioned as a result. However results of optimizations are valid.
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: QUANTUM READOUT AND GRADIENT DEEP LEARNING MODEL FOR SECURE AND SUSTAINABLE DATA ACCESS IN IWSN
Review round: 2
Reviewer: 1

Output:
Basic reporting: All comment seems to be resolved except the following:

• There is a need to explain the authentication problem in more details, some of the IIoT networks are designed in a closed network where they have their own cables and devices and no one outside the network has any access to it, in this case the authentication problem needs to be clarified at what circumstances it is considered an issue. I suggest the authors provide an example to explain when/how the authentication step should be included in the design.

• Authors need to identify the terminologies earlier in the paper such as authentication rate, authentication acceptance false rate, and authentication time.

I think authors should submit a corrected copy that marked all text change in red, it will be easier to see the new updates.
Experimental design: 343-347 references and citations are required.
Validity of the findings: Conclusion is not sufficient, more details should be illustrated in this section.
Additional comments: No additional comments


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

Input:
Title: QUANTUM READOUT AND GRADIENT DEEP LEARNING MODEL FOR SECURE AND SUSTAINABLE DATA ACCESS IN IWSN
Review round: 3
Reviewer: 1

Output:
Basic reporting: All comments are resolved.
Experimental design: All comments are resolved.
Validity of the findings: All comments are resolved.
Additional comments: All comments are resolved.


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: BILINEAR POOLING IN VIDEO-QA: EMPIRICAL CHALLENGES AND MOTIVATIONAL DRIFT FROM NEUROLOGICAL PARALLELS
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. The expression of the article is clear and coherent. However, there are some typos. In line 106, 'abstracted' to 'abstract'. In line 423, 'eachother' to 'each other'. Some terms are not easy to understand, such as in line 455, 'ImageNet-style feature vectors'. Many experiment results in the tables are not rigorous, i.e., the baseline offsets in Tables 3 and 5 have no percentage sign.
2. The overall article is well organized. However, the structure in the discussion section is chaotic. The analysis of experimental results may be more reasonable in the experiment section, for readers to contact contextual content more easily.
Experimental design: 1. The experimental results are relatively single, only quantitative analysis results are shown. There are no more qualitative or analytical experimental results.
Validity of the findings: 1. All experimental results showed no improvement from baselines, which was a little bit strange.  I doubt the correctness of the experimental setup, for the BLP has its rationality as the author proposed and has obvious performance improvement in image QA tasks[1].
[1] Ben-younes, H., Cade`ne, R., Cord, M., and Thome, N. (2017). Mutan: Multimodal tucker fusion for visual question answering. 2017 IEEE International Conference on Computer Vision (ICCV), pages 655 2631–2639.
2. The insights given in the article are relatively basic, and the discussion is not deep enough. The author did not give an overall summary or analysis of the multiple BLP methods. The author gives a brief introduction of the neurological parallels, however, their theoretical inspiration is not clearly explained. In addition, the reasons why the BLP does harm on the video QA task didn't convince me very well. If it does do harm, what kind of interaction method should we use in the future for cross-modality representation need to be discussed further.
Additional comments: no comment


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

Input:
Title: BILINEAR POOLING IN VIDEO-QA: EMPIRICAL CHALLENGES AND MOTIVATIONAL DRIFT FROM NEUROLOGICAL PARALLELS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper focuses on the bilinear pooling problem in the context of Video-QA. The problem is indeed topical and subject to many research works. The topic is therefore appropriate and also important. The quality of the English language is not a problem and the paper is clearly written from both a literary and scientific point of view.
The structure of the manuscript is quite in accordance with the standard required by the journal and the figures are of good quality with good definition and do not suffer from any particular problem.
The datasets used are among the most appropriate and the availability of two of them is well indicated in the paper (TVQA and HME-VideoQA) but not for the others. There is no indication on the availability of the dual stream model proposed by the authors.
The title of the paper however raises an interesting and important question but the content of the paper does not provide any explanation or clear answer to the question induced by the title. This is one of the weaknesses of this paper that should be improved in order to have a real added value and a significant contribution of the work.
Experimental design: The subject matter is well within the scope of the paper and raises an important issue that is expressed in the title, namely "The limitations of bilinear pooling in video-QA". These limitations are indeed shown by means of experiments and comparisons with state-of-the-art methods. Unfortunately, there is no clear explanation of the underlying causes of this weakness of bilinear pooling except for a problem related to datasets, knowing that some of these datasets, such as MovieQA and YouTube2TextQA, seem, according to the literature, to provide the necessary for this kind of studies.
In my humble opinion, it would be necessary to explain the current failures of bilinear pooling which may be due to a problem of alignment of textual and video stream modalities with also temporal alignment or to the expression of queries. A conclusion on this subject would also be welcome and there are probably two among which one should decide. The first conclusion is that the bilinear pooling is not adapted to the problem by explaining why, but this conclusion, in my opinion is not the right one because it is enough to have the adequate datasets to solve part of the problem. The second conclusion should propose corrections to the existing models. The ideas proposed in the paragraph "PROPOSED AREAS OF RESEARCH" (line 607) remain mostly general and vague intuitions. More concrete and forceful proposals would give more weight to the paper.
The idea of demonstrating the weaknesses of a model is a very good approach in research, but again, these weaknesses should be clearly expressed, explained and alternatives or corrections proposed.
Validity of the findings: The scientific findings are at this stage quite light and need to be improved. The preceding comments may guide the authors in improving their contributions. The authors claim that, to their knowledge, they are the first to describe how important neurological theories, namely the dual coding theory and the two-stream vision model, are related to the history (and background) of modern multimodal deep learning practices. The thinking is certainly good but needs more formal framework and mathematical expression to be effectively exploited.

My suggestions to authors
1. Try to explain clearly where the current weaknesses of bilinear pooling lie and not just the dataset problem.
2. In the light of these weaknesses propose ideas for improvement or correction.
3. Formalize their idea of the dual coding theory and the dual stream vision model so that it can have a significant contribution in this area.
Additional comments: No additional comments