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

Input:
Title: TOWARDS UBIQUITOUS REQUIREMENTS ENGINEERING THROUGH RECOMMENDATIONS BASED ON CONTEXT HISTORIES
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper titled, "Towards ubiquitous requirements engineering through recommendations based on context histories" proposes a technique to recommend requirements on the basis of context histories of projects. The paper is generally well written and covers literature in an adequate manner. The authors also provide a comparison of related works with the proposed approach and the developed tool in tabular form (Table 1) which shows the usefulness of the Nhatos model as compared to other similar methods in the literature.

The figures in the paper are generally in good shape however, Figures 1 and 3 give a blurred look when the pdf of paper is zoomed in for better reading perhaps their quality can be improved.

The raw data has been shared and the interpretation of the approach and results are self contained. However, I could not open the links (https://github.com/robsonklima/nhatos api and
https://github.com/robsonklima/nhatos front end) which is a requirement for this journal that a working software version should be available in an online repository.
Experimental design: The research proposed in this paper is definitely within the scope of the journal and the research questions identified are well defined, relevant and meaningful filling the gap of recommending requirements on the basis of context histories of projects. However, I have the following questions for the authors:

1) The basic project information was in Portuguese and it was translated to English using the Google Cloud Translation API. The accuracy of this API is 100%, how did the authors make up for any discrepancies in the translation and how potentially this would have impacted the results currently produced by Nhatos?

2) On page 6 of the paper at line 234, the authors talk about the semantic similarity of requirements during the analysis of requirements. However, it will be great if they can shed some light on which semantic similarity measures for NLP have been used and why plus whether there was any syntactic analysis done prior to it or not?
Validity of the findings: Since, I was unable to find and download from these two links (https://github.com/robsonklima/nhatos API and https://github.com/robsonklima/nhatos front end), therefore, it is difficult to comment on the replication and reproducibility of results. In the next version I would expect that the link is live and the files retrievable from these links.
On page 14, Table 3, the data provide show acceptance percentages of five different projects none of which is above 68.7% the last row of the table shows the approval percent which is computed to be 75.1%. How is this approval percent computed and how does it go well above the accepted requirements percentage?
Second last row of Table 3, shows an acceptance percent value of 61,6 which should be 61.6
The conclusions are well stated and linked to research questions along with providing possible future directions. However, my final question authors is how much would we save in terms of time by using Nhatos model specifically given the fact that it some times generates recommendations too many to be assessed by the team members as mentioned on page 15, lines 529-30?
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: GRAPH COLORING USING THE REDUCED QUANTUM GENETIC ALGORITHM
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript aims to solve the graph coloring problem using the Reduced Quantum Genetic Algorithm. The general structure of the manuscript is respectable with good professional English and clear links between sections. However, there are some weaknesses in the basic reporting that must be revised, from which we can briefly cite the following points:
The list of references, especially in the literature review section needs to be updated.  Authors are advised to use more recent references.
Some terms and definitions need to be detailed. For instance, quantum computing is not well defined and detailed although it is a fundamental concept in the solving approach.
Some assumptions were cited by the authors without any explanation or justification. For example, authors said in lines 37 and 38 ‘On the other hand, quantum computation is very powerful for solving various problems due to its 38 specific properties and phenomena such as entanglement, interference, and exponential parallelism. (Need to add a reference to justify).
Experimental design: The results section is very short and does not contain depth analysis to prove the efficiency of the proposed approach. No Rigorous investigation was performed in the manuscript. The results section needs to be revised with consideration to the following points:
•	To add a comparative study of the results (and not metrics) with similar solving approaches applied to the same problem (the graph coloring problem).
•	To perform a statistical analysis to validate the high performance of the proposed approach.
•	Apply the solving approach to more practical, complex, real-life problems and not only the graph coloring problem.
Validity of the findings: No innovative points and contributions can be easily found in the proposed approaches. Authors clearly cited in the Methodology section (lines 162, and 163, 167 and 168) that they will use the Reduced Quantum Genetic Algorithm proposed by Udrescu et al. (2006), So what is the contribution of the authors? Authors do not present their contribution clearly - where is the novelty comparing to existing methods? In addition, the proposed solving algorithm  must beapplied to more applicable, real-life problems to prove its effeciency and strengh.
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: GRAPH COLORING USING THE REDUCED QUANTUM GENETIC ALGORITHM
Review round: 1
Reviewer: 2

Output:
Basic reporting: Need Clear Definitions
Experimental design: The authors have to compare the proposed algorithm with more number of existing algorithms
Validity of the findings: Novelty not assesed
Additional comments: Review Comments:
In this work, the authors proposed the reduced quantum genetic algorithm for graph coloring. Although, the approach could be of interest but there are some major concerns which should be addressed.
1.	The Figure 8 and 9 are not clear. I suggest the authors to provide the axis titles that makes more understanding of the figures for readers.
2.	In Line No.281, the authors mentioned “adders need 2 qubits” and in Line No: 283, the authors mentioned “4 qubits are used for fitness value representation”. How the parameter values are set to specific threshold value? The authors have to justify the parameter values.
3.	In Line No. 311, the authors mentioned that the proposed algorithm determines the chromatic number in Figures 17 and 18. But the details are not explained in the Figures description.
4.	The authors didn’t mention about the system specification.
5.	The authors should make available their software with documentation for its usage and examples for testing in a public repository like GitHub.
6.	Limitations of the proposed study have to be included in the manuscript.
7.	The authors shown the experimental results for their proposed algorithm. However, they have not compared with other recent optimization algorithms such as Modified Cuckoo algorithm, ABC optimization algorithm, Memetic Teaching–Learning-Based Optimization algorithm and so on. I suggest the authors to compare the proposed algorithm with more number of existing algorithms.
•	Mahmoudi, Shadi & Lotfi, Shahriar. (2015). Modified Cuckoo Optimization Algorithm (MCOA) to solve Graph Coloring problem. Applied Soft Computing. 33. 10.1016/j.asoc.2015.04.020.
•	Dökeroğlu, Tansel & Sevinç, Ender. (2021). Memetic Teaching-Learning-Based Optimization Algorithms for Large Graph Coloring Problems. Engineering Applications of Artificial Intelligence. 102. 10.1016/j.engappai.2021.104282.
•	R. S. Tomar, S. Singh, S. Verma and G. S. Tomar, "A Novel ABC Optimization Algorithm for Graph Coloring Problem," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 257-261, doi: 10.1109/CICN.2013.61.
•	Aragón Artacho, F.J., Campoy, R. Solving Graph Coloring Problems with the Douglas-Rachford Algorithm. Set-Valued Var. Anal 26, 277–304 (2018). https://doi.org/10.1007/s11228-017-0461-4

8.	I suggest the authors to provide the significance of the proposed algorithm to the society.

This manuscript standard is not suitable for publication in this esteemed journal. Hence, the manuscript needs “major revision” for further steps.


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: GRAPH COLORING USING THE REDUCED QUANTUM GENETIC ALGORITHM
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have addressed the concerns I have raised adequately (expect the third comment of the Experimental Design section) and they have improved the paper largely. I think the paper is now accepted and publishable.
Experimental design: The experimental design was improved.
Validity of the findings: The findings section was improved by using the comparative study.
Additional comments: No additional comments


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

Input:
Title: PRESB-NET: PARAMETRIC BINARIZED NEURAL NETWORK WITH LEARNABLE ACTIVATIONS AND SHUFFLED GROUPED CONVOLUTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: pros:
The authors present a new binary network architecture, namely PresB-Net, which utilizes channel-shuffle and group binary convolution operations to increase the capacity of binary features without introducing extra computing costs. Besides, the proposed "Biased PReLU+LN+Biased PReLU+BN" module seems to solve the imbalance between different channel groups. Overall, the approach may shed some light on the design of compact binary networks and accurate BNN training.

I personally think the proposed method is simple yet effective. The technique part of this paper is easy to follow. The background and related works have been thoroughly discussed, such as group convolution, channel shuffle, and binary convolutions.

cons:
Since "group conv+channel shuffle" is a common setting in compact network design, I'm not sure whether PresB-Net is the first attempt to double the input channel number? The proposed "Basic block" seems to be a combination of ShuffleNet and DenseNet.
Experimental design: cons:
It would be better to fully evaluate the performance of PresB-Net, though it has achieved noticeable improvements over baseline results on CIFAR-100. Besides, I expect an ablation study shown in table form. The current manuscript makes it hard to understand the differences between the listed terms. Last but not least, the authors present a new architecture, however, I found no ablation study on the network design, e.g., why use "Biased PReLU+LN+Biased PReLU+BN" instead of "Biased PReLU+LN+BN"?
Validity of the findings: cons:
The authors focus on "fast output computation with low hardware costs" yet no real speedup over the FP32 counterpart has been reported in this paper. Note that when the mean and variance of LN are computed at inference time, the memory overhead and extra computing costs can be unacceptable in real-world applications.
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: PRESB-NET: PARAMETRIC BINARIZED NEURAL NETWORK WITH LEARNABLE ACTIVATIONS AND SHUFFLED GROUPED CONVOLUTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper proposes PresB-Net, which applies shuffled grouped convolution to expand the channel with reduced computation resources. The proposed normalization approach can solve the imbalance between groups in the grouped convolution. Using the biased PreLU activation function with a learnable slope and binary activation with biases to improve performance. PresB-Net consists of stacked basic and expand blocks, the blocks have shortcuts for each binarized grouped convolution.
The proposed model can enhance Top-1 final accuracy 1.6%–2.4% over existing ReActNet and AresB-Net on CIFAR.

However, there are some issues in this paper which are listed as follows in the form of questions and suggestions.

1.	Why is “performance degradation is the most critical problem in BNN models”.
2.	The length of the paper is too small and the Section of experiments is rough. It is suggested to add some experimental contents, such as verifying your model on ImageNet, comparing with AresB-Net, ReActNet, MobileNet and ShuffleNet.
3.	Whether using PreLU in ResNet can also improve performance? Whether the performance improvement of PresB-Net is related to PreLU?
4.	“Therefore, the grouped binarized convolution results are normalized in both channel-wise and batch-wise manner. The proposed normalization approach allows the normalized convolutional results in both channel-wise and batch-wise manners, which can consider data correlations between groups.”  Can the output of layer normalization and batch normalization be added directly?  “consider data correlations between groups”, it needs to further explain how to consider relevance.
5.	“solves the imbalance between groups arising from shuffled grouped convolution by considering the correlation of groups and allows the results of each group to follow a normal distribution, thereby increasing performance by balanced group and increasing the convergence.”  How to solve imbalance between groups?
6.	Whether can use basic and expand blocks to stack different network models?
7.	Fig. 10 and 11 are too rough.
8.	In the experiment, the reasons of the comparison results were not described.
9.	The parameters of the model were not compared in the experiment.
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: PRESB-NET: PARAMETRIC BINARIZED NEURAL NETWORK WITH LEARNABLE ACTIVATIONS AND SHUFFLED GROUPED CONVOLUTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: I have carefully read the authors' feedback and comments from other reviewers. The authors have reflected the revision in the final version, hence I raise the overall score to an acceptance.
Experimental design: Whether the experiment results on CIFAR-100 are consistent with ImageNet? Note that the authors mainly conduct experiments on CIFAR-100 and it would be better to evaluate the correlation coefficient between them.
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: PRESB-NET: PARAMETRIC BINARIZED NEURAL NETWORK WITH LEARNABLE ACTIVATIONS AND SHUFFLED GROUPED CONVOLUTION
Review round: 2
Reviewer: 2

Output:
Basic reporting: This paper proposes PresB-Net, which applies shuffled grouped convolution to expand the channel with reduced computation resources. The proposed normalization approach can solve the imbalance between groups in the grouped convolution. Using the biased PreLU activation function with a learnable slope and binary activation with biases to improve performance. PresB-Net consists of stacked basic and expand blocks, the blocks have shortcuts for each binarized grouped convolution. The proposed model can enhance Top-1 final accuracy 1.6%–2.4% over existing ReActNet and AresB-Net on CIFAR.
Experimental design: none
Validity of the findings: none
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: PERSON IMAGE GENERATION THROUGH GRAPH-BASED AND APPEARANCE-DECOMPOSED GENERATIVE ADVERSARIAL NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: There is a misspelling .....Apperance..even in the Title.
It should be taken care of before submission.
Experimental design: No comment
Validity of the findings: No comment
Additional comments: General Comments
This paper is concerned with personal image generation through graph-based networks along with decomposed particular attributes.
The authors put efforts to propose a generative adversarial network based on the graph.
Both qualitative and quantitative experiments were performed to confirm the proposed method.
The paper is interesting and will be beneficial to a sizable amount of researchers and students.
However the tying error even in the title of the paper ……Apperance …..
should be taken care of before submission.
Such errors are also seen in the text (For example Line 120)
Particular Comments.
1. Line 78 and Lines 93-94, the two statements about the proposed method may confuse the readers.  Are they the same or different contributions?
2. Lines 147-148 Can we get the 18 joint points automatically?
3. I wonder how we can know the probabilities defined in equation 11 are non-zero.
The experimental results seem to be significant.


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: PERSON IMAGE GENERATION THROUGH GRAPH-BASED AND APPEARANCE-DECOMPOSED GENERATIVE ADVERSARIAL NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors should check the manuscript once again by themselves.
- First of all, there are spelling mistakes in the title, and the capitalization rules are not consistent.
- The single-byte space after the commas and periods are not consistent.
- The operators that are supposed to be Hadamard products are written as e.
- Section 3.1 and 3.2 have the same titles. Perhaps 3.1 should be called "Encoder".
Experimental design: The method is written in a relatively clear manner, including mathematical expressions, but it does not correspond to Figure 3.
- For example, there is no mention of AdaIN, which is included in the Texture Block in the figure.
- Also, the text says that the Pose Block contains GCN, but the Pose Block in the figure is not drawn in such a way as to show this.

There is no mention of how the three Losses are combined.
If the three are to be added together, it should be clearly stated as such.
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: PERSON IMAGE GENERATION THROUGH GRAPH-BASED AND APPEARANCE-DECOMPOSED GENERATIVE ADVERSARIAL NETWORK
Review round: 2
Reviewer: 1

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


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

Input:
Title: PERSON IMAGE GENERATION THROUGH GRAPH-BASED AND APPEARANCE-DECOMPOSED GENERATIVE ADVERSARIAL NETWORK
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: COMPARING SUPERVISED AND UNSUPERVISED APPROACHES TO MULTIMODAL EMOTION RECOGNITION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The writing of the paper manuscript is clear and well organized. But there are some problems to be addressed. The citation of some references should be complete, including pages (e.g. Srinivasan, R., & Martinez, A. M. (2018).)  Some of the figures should be resized (e.g. Figure 6).
Experimental design: The implementation of the methods should be described with sufficient detail for replication, including the setting of parameters. More recently state-of-the-art methods on supervised and unsupervised learning should be considered for comparison.
Validity of the findings: The experimental results are provided. But the contribution and novelty of the article are not clear.
Additional comments: The authors investigated the supervised and unsupervised emotion classifier performance. The comments should be addressed before further processing.


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: COMPARING SUPERVISED AND UNSUPERVISED APPROACHES TO MULTIMODAL EMOTION RECOGNITION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The content of this article is acceptable on the general with novel research topic and abundant detailed experimental results. Yet it still requires certain revisions to meet the publication standard, which are listed below in several aspects.
1. Language description: The descriptions in this article are unclear and hard to unsderstand at multiple places, such as Lines 56-57, 105-110, 229-231, 237-239, 372-374, etc. Please check the language used in the article.
2. Literature references: This article provided various prior works and references, however, the methods compared in the experiments are not fully included. For example, the late fusion methods mentioned (maximum rule, sum rule, product rule, weight criterion, rule-based, and model-based methods) have not been referenced, and the early fusion methods have not been explicited presented. Please properly cite all evaluated works in this paper.
3. Article structure: This article is organized in clear and reasonable structure. However, the discussion section should be more compact and conclusive, highlighting the major findings and contributions of this article.
Experimental design: Various experiments have been conducted for this research. Minor improvements are suggested as follows:
1. The optimal numbers of clusters for the traditional unsupervised approaches are said to be 2 or 6 through CH index and Silhouette score analysis (Lines 220-225). Experimental results should be presented to validate this statement.
2. In feature extraction and pre-processing section, certain data cleaning process has been conducted (Line 148). Please explain the reason for such process.
3. Multiple features for AUs are computed as video features (Lines 154-157), please give explicit definition or computational detail of each feature.
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: COMPARING SUPERVISED AND UNSUPERVISED APPROACHES TO MULTIMODAL EMOTION RECOGNITION
Review round: 1
Reviewer: 3

Output:
Basic reporting: Very good paper. A few slight comments:
I understand that one of the strength of this study is to investigate the variety of emotional categories and to show the importance of each features (voice + face).

A little more introduction should be discussed in comparison with previous studies (see https://github.com/EvelynFan/AWESOME-MER) that adopted a multimodal approach.

Knowledge of features goes to application to robots. I felt that it would be good if there was a discussion about Human-robot interaction in the Discussion section.

Barrett et al (2019: Line 80-81) strictly criticizes emotion recognition in emotional categories like this paper. Citing Keltner is preferable as a position, so I recommend to cite it (e.g., https://psycnet.apa.org/record/2017-30838-004; http://emotionresearcher.com/the-great-expressions-debate/)
Experimental design: no comment
Validity of the findings: "actor and gender specific aspects also contributed to clustering"
What are the implications of this finding?
The Unsupervised method seems just to report the characteristics of the stimulus (GEMEP). I want the author to clarify that necessity.
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: COMPARING SUPERVISED AND UNSUPERVISED APPROACHES TO MULTIMODAL EMOTION RECOGNITION
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have enhanced the article with more introductions and discussions.
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: ADVERSARIAL EXAMPLE DEFENSE BASED ON IMAGE RECONSTRUCTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. The paper is easy to understand, but the English language should be improved on grammar and tense. Some examples include lines 241-242, 273, 288-289, and the inconsistency of tense used in Section experiment results.

2. The authors missed some background information on adversarial attacks and defense methods. In the experiment, you used DeepFool attack and compared your proposed method with Pixel Defend, Feature Squeezing, and ComDefend but you didn't introduce these methods in Section Background.

3. The structure of this paper is good. Figures are relevant to the content of the paper. Some problems are in Table 3. The names of the first two columns should be "network" and "method". The caption of Table 3 doesn't provide sufficient information to help understand this table. Specifically, why for each cell there are two numbers (e.g., 93%/93%)? I also don't find the explanation in the paper.

4. I thank you for providing the source code, but you need also to provide a Readme file to describe how to use the code. In addition, there are many Chinese characters in the code and filenames, which should be replaced with English words.  The authors should also provide pre-trained models to ensure the experimental results are reproducible.
Experimental design: 1. The paper proposed an adversarial defense method based on image compression and reconstruction. However, the detailed structure of the image reconstruction network was not introduced. In the provided source code I saw some different network architectures used on different datasets. The authors should introduce them in the paper.

2. In lines 214-216, the authors described they generated adversarial examples and used them with clear examples to train the image reconstruction network. However, in the source code (cs-59969-MNIST-002.zip/train_turn_defense.ipynb), I found only adversarial examples were used. The authors should double-check the used method and describe it clearly and accurately.

3. The authors conducted experiments on evaluating the performances of the proposed method, the transferability of the proposed methods, and the comparison of the proposed method with other defense methods. The research questions are well defined and meaningful. However, the experiment setting should be improved in some ways.

First, ImageNet or tiny-imagenet dataset should be used to evaluate the proposed method because it is the most widely used and most complicated dataset for image classification.

Second, for the comparison of the proposed method and other defense methods, more similar defense methods based on image reconstruction such as HGD mentioned in the paper could be considered to compare.
Validity of the findings: 1. For the experiment of comparing the proposed method with other defense methods, only result on F-MNIST was reported. The results on the other two datasets should be provided to prove the proposed method is better than other methods in general.
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: ADVERSARIAL EXAMPLE DEFENSE BASED ON IMAGE RECONSTRUCTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The introduction section demands to be more convincing. Try to structure the introduction section with four paragraphs as follows: i) State the motivation and clearly define the problem to be solved. ii) Make a thorough discussion of the state-of-the-art. iii) Describe your proposal in fair context to other published methods highlighting advantages and disadvantages of these methods. iv) Clearly pinpoint the novelty/contribution of your proposal and briefly describe your findings.
Experimental design: The performance of CNN strongly depends on an optimum structure of a network. The training structure in Figure 4 needs to be self-contained such as number of layers, height and width of each layer.

Networks with defense method show degraded performance for clean images in Table 2. How do you validate this result?
Validity of the findings: Network models in supplement files were not possible to test. It is needed to provide comprehensive readme files to run and test source codes, models and dataset.

Is there any limitation of the proposed methodology?
Additional comments: The manuscript is overall well written. If there are weaknesses, as I have noted above which need be improved upon before publication.


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

Input:
Title: ADVERSARIAL EXAMPLE DEFENSE BASED ON IMAGE RECONSTRUCTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: 1. The paper proposed an adversarial defense method by combining image compression and image reconstruction models. The background of adversarial attack and defense was introduced in detail. The authors conducted comprehensive experiments to evaluate the performance of the proposed method against common adversarial attacks and compared the method with existing defenses. The experiment result shows that the proposed method achieves good performance.

2. There are a few tense inconsistencies in the Section background, which should be revised.

3. In the Section Approach, the overview of the defense method should be described more clearly. The authors could consider showing how the defense method works from taking as the input of the original image to reporting whether the image is an adversarial example in Figure 4. Now Figure 4 just shows the process until the image reconstruction. I cannot easily know how to use the output image to detect adversarial examples from the Figure.
Experimental design: The authors conducted experiments on evaluating the performances of the proposed method, the transferability of the proposed methods, and the comparison of the proposed method with other defense methods. The experiment results show the proposed method outperforms other baseline defenses. In Table 4, the author should highlight (bold) the best experiment results, which is better to help compare the performances of these methods.
Validity of the findings: The experiment results are well evaluated. The authors provided the source code and detailed instructions for reproducing the experiment.
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: ADVERSARIAL EXAMPLE DEFENSE BASED ON IMAGE RECONSTRUCTION
Review round: 2
Reviewer: 2

Output:
Basic reporting: The revised manuscript has been improved and addressed all the concerns accordingly.  The usage of English language is satisfactory.
Experimental design: The experiential design and analysis is satisfactory in the revised manuscript.
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: ADVERSARIAL EXAMPLE DEFENSE BASED ON IMAGE RECONSTRUCTION
Review round: 3
Reviewer: 1

Output:
Basic reporting: The revised manuscript has addressed my questions accordingly.
Experimental design: Experiments are solid in the revised manuscript.
Validity of the findings: no 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: WHO PARTICIPATES IN COMPUTER SCIENCE EDUCATION STUDIES? A LITERATURE REVIEW ON K-12 SUBJECTS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Is the review of broad and cross-disciplinary interest and within the scope of the journal?

The authors have focused their literature review on three flagship conference proceedings in order to highlight the ways in which researchers describe, or not, the demographics of the students who participated in the studies. The authors maintain that this is important to study for two reasons: first, to inform policy and support replications, and second, to identify demographic attributes that are important for the field to report. As such, the literature review holds broad appeal and ought to be of interest to those working in the multidisciplinary computer science education field.

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

The authors note that similar review work has been completed (McGill et al., 2018; 		Schlesinger et al., 2017), although they seem to indicate that their review is distinct in		that it evaluates additional demographic information, disability status in particular. I am		not certain that the authors have convinced me that this sensitive information can be		widely and appropriately asked across K-12 studies, at least those that occur in the		United States.

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

The Introduction is well written, situates the review among related research, then			appropriately narrows its focus on a brief overview of this study’s process and purpose.
Experimental design: Is the Survey Methodology consistent with a comprehensive, unbiased coverage of the subject? If not, what is missing?

The authors do a commendable job of detailing their process and justifying their use of		the three conference proceedings. They do note in Limitations that there was no attempt		at inter-rater reliability, or double-coding. This does seem potentially problematic as the		authors indicated they deduced information context, rather than from details overtly		stated in the studies. It may be that one researcher missed vital information or			misunderstood. Such double checking is important to the validity of the authors’ claims.

Additionally, the authors do not address what safeguards were in place to ensure that an		intervention was not submitted to and accepted at more than one of these conferences		and therefore those data were considered more than once.

Are sources adequately cited? Quoted or paraphrased as appropriate?

The authors cite appropriately, although I am used to alphabetizing in-text citations when		there is more than one cite. Relatedly, there are several places (lines 61, 69, and 74)		where the formatting of the citation is incorrect.

Is the review organized logically into coherent paragraphs/subsections?

The review is organized well and logically flows from Introduction to Conclusion.
Validity of the findings: Is there a well developed and supported argument that meets the goals set out in the Introduction?

	I provide more specific information below in General Comments, but overall I am			struggling to see the feasibility of including disability status as a socio-demographic		metric. I understand the authors’ contention that such a characteristic needs to be		considered for generalizability purposes; however, the reality of receiving such			information, at least in the US, is highly complicated and would not lend itself to		 	generalizability. This aside, the authors complete their literature review as they set out in		the Introduction.

I am not certain that the statement “Both for race/ethnic background and for disabilities a		pattern can be seen where these categories are only reported on when deviating from		the majority” is supported. Is it that these characteristics are only reported when they		differ from the majority or could it be that these socio-demographic metrics are			problematic to collect?

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

The Conclusion identifies suggestions for making consistent across CSEd studies the		ways we report on demographics. What the authors need to include are real ways to		balance the tension between sharing empirical findings about specific students in K-12		studies when the data we are able to collect might be at the school level only.

I feel the authors could strengthen their argument and the paper overall by delving more		deeply into the articles that report on disability status, as this seems to be their major		interest, especially those not focused solely on a certain population of students (ie., blind		students). How did these articles report on these disabilities? What was their institutional		review policy? How did they obtain this information (ie., from the students, teachers, or		parents)? What major findings/conclusions did they offer? The number of articles that fall		into this category is quite small, so more closely exploring them may help draw out and		support your contention that disability status is a metric essential to report. Given this		small number, perhaps it would be wise to broaden your search beyond the flagship		conferences. In other words, the authors could share their literature review findings,		including the small number of articles that report on disability status, and then provide a		detailed overview of articles in CSEd at large (conferences and journals) that do report		on disability status.
Additional comments: The following are line item/section comments regarding grammar, formatting, or general structure:

Line 5 (Abstract): if space permits, it may be helpful to include the year range
Lines 25, 38, and 50: authors appear to use ‘pre-college,’ ‘pre-university,’ and ‘K-12’ interchangeably. I think it would read better if one phrase were used; K-12 is most common. These phrases appear throughout the article.
Line 99: it seems the RQ should end as “... in K-12 programming” and not into
Line 156: grade 6 is included twice in both elementary and middle school; where did you ultimately include it?

Gender: is it important to include the gender options the studies permitted the participants to select, or that --% of studies permitted students to opt out or select Other? Also, the finding that males are overrepresented is not terribly surprising, but I feel this interacts with the context of the studies. For example, did the studies where males were overrepresented take place in elective classes or optional experiences such as camps?

Race/Ethnic Background
Line 178: the sentence that begins on line 177 and includes “studies is represented in,” is poorly worded or missing a reference to a figure.
“This is further confirmed by the large number of studies that report on mixed-racial experimental groups, as Figure 3 depicts, which does not correspond with how mixed classrooms typically are in the US (Stroub and Richards, 2013)”: what does this mean? What are mixed classrooms and how are they typically organized/studied in the US?

SES and Disabilities
I have combined these two subsections as my concerns with them are similar.
As a US researcher, I have always used NSLP (free and reduced school lunch eligibility) as a proxy for SES. This follows because we have never found it appropriate to ask students about their family’s SES and this information is publicly available at the school level. When working with a single class or a subset of students in class, it feels inappropriate to report out the school level SES.

No university IRB would approve US researchers asking K-12 students, deemed a vulnerable population, about their disability status. FERPA regulations prohibit school personnel from disclosing such information at an individual level to outside researchers. It may be that a teacher could tell a researcher something generic, such as “of the 30 students in this classroom, 5 have IEPs and 4 have 504 plans.”

Line 216: “However, given the aforementioned prevalence of individuals with a disability, it seems unlikely that the participant groups of the other 128 papers did not include any of these children”: of course these classrooms included students with disabilities, but the practice of mainstreaming and not delineating students by ability, likely contributed to the small number of studies that report on this characteristic

Lines 273-4: the wording of the final sentence is problematic. You have set up a juxtaposition whereby poor inner city cannot include well-educated. Don’t conflate education and income here. Something like “it could be a school in a poor inner city area or a high-income neighborhood” would be better.


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: WHO PARTICIPATES IN COMPUTER SCIENCE EDUCATION STUDIES? A LITERATURE REVIEW ON K-12 SUBJECTS
Review round: 1
Reviewer: 2

Output:
Basic reporting: In the introduction, the authors adequately introduce the topic by providing sufficient background on pre-university programming education and comprehensively discussing related work on demographics in computer science education studies. They clearly distinguish their work from prior research (e.g., by McGill et al. (2018)) and identify gaps. The desideratum on which the authors build is that there is no comprehensive research on the demographics of computer science education studies between 2014 and 2018.

Overall, the paper is well structured and contains detailed sections on methodology, results, and conclusions. The argumentation is comprehensive throughout and easy to follow. Relevant previous literature is referenced appropriately. Figures and tables are appropriately labelled and well described in the respective sections/paragraphs.

The paper is written in professional, unambiguous English. The text is technically correct and follows professional standards of courtesy and expression.

Suggestions for improvement:

- In the “Pre-university Programming Education” section, two improvements should be made to the citation style: (1) (Yang et al., 2015) for example […] -> Yang et al. (2015) for example; (2) Aivaloglou and Hermans (Aivaloglou and Hermans, 2016) performed an […] -> Aivaloglou and Hermans (2016) performed an.

- Authors should consider submitting the figures in a better resolution as the diagrams provided are slightly blurred. Please, add a dot after each of the figure labels.

- Please, reference Figure 2 in the text. Currently, there is no reference to it in the text.

- Please, use CSed abbreviation consequently.
Experimental design: The paper is a literature review with a clear formulated study design. It aims to address the wide computer science education community which is in line with the aims and scope of the PeerJ journal.

In the methodology section, the authors clearly articulate the research question (What are the demographics of subjects who participate in CSEd studies into K-12 programming?) and comprehensively describe the procedure for literature selection, narrowing the scope of the selected literature to studies of the pre-university target group with the focus on programming education. The selection of demographic categories is clearly reasoned, and the description of the analysis process is extensively described. The paper also includes a detailed discussion of the limitations of the literature review. All raw data is provided for better understanding and comprehension of the results, which I found helpful.

Suggestions for improvement:

- Although the authors refer to computer science education in the title of the paper, the literature used for the review is limited to programming education only. However, computer science education is much broader than learning programming. Please, include this information as a limitation and explain in the introduction why you focused on programming education as one of the aspects of computer science education.

- In the introduction, the authors justify the selection of SIGCSE, ITiCSE and ICER by arguing that these are “flagship conferences” in the CSEd community. Please, support this argument with references.

- In presenting the results in the “Race/Ethnic Background” section, the authors go beyond describing the results (“This leads us to hypothesize that reporting bias is at play here”). However, since this argument is already about evaluating the results, I recommend moving it to the “Ethical and Legal Considerations” section.
Validity of the findings: The findings are appropriately structured and presented according to the categories proposed in the survey methodology section. In the conclusions, the authors reflect on the findings based on the research question and the state of the arts presented in the introduction. The authors also identify future directions relevant to the entire computer science education community, suggesting, for example, the development of standards/guidelines for collecting and reporting demographic information in empirical studies of K-12 CSed.

Overall, I found the conclusions to be well articulated and linked to the original research question. The authors fully achieved the objectives stated in the introduction and survey methodology.
Additional comments: I find the results of this literature review enriching for research in computer science education. Many thanks to the authors of this work!


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

Input:
Title: WHO PARTICIPATES IN COMPUTER SCIENCE EDUCATION STUDIES? A LITERATURE REVIEW ON K-12 SUBJECTS
Review round: 2
Reviewer: 1

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


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

Input:
Title: WHO PARTICIPATES IN COMPUTER SCIENCE EDUCATION STUDIES? A LITERATURE REVIEW ON K-12 SUBJECTS
Review round: 2
Reviewer: 2

Output:
Basic reporting: In the introduction, the authors adequately introduce the topic by providing sufficient background on pre-university programming education and comprehensively discussing related work on demographics in computer science education studies. They clearly distinguish their work from prior research (e.g., by McGill et al. (2018)) and identify gaps. The desideratum on which the authors build is that there is no comprehensive research on the demographics of computer science education studies between 2014 and 2018.

Overall, the paper is well structured and contains detailed sections on methodology, results, and conclusions. The argumentation is comprehensive throughout and easy to follow. Relevant previous literature is referenced appropriately. Figures and tables are appropriately labelled and well described in the respective sections/paragraphs.

The paper is written in professional, unambiguous English. The text is technically correct and follows professional standards of courtesy and expression.

The authors included suggestions for improvement from the first review.
Experimental design: The paper is a literature review with a clear formulated study design. It aims to address the wide computer science education community which is in line with the aims and scope of the PeerJ journal.

In the methodology section, the authors clearly articulate the research question (What are the demographics of subjects who participate in CSEd studies into K-12 programming?) and comprehensively describe the procedure for literature selection, narrowing the scope of the selected literature to studies of the pre-university target group with the focus on programming education. The selection of demographic categories is clearly reasoned, and the description of the analysis process is extensively described. The paper also includes a detailed discussion of the limitations of the literature review. All raw data is provided for better understanding and comprehension of the results, which I found helpful.

The authors included suggestions for improvement from the first review.
Validity of the findings: The findings are appropriately structured and presented according to the categories proposed in the survey methodology section. In the conclusions, the authors reflect on the findings based on the research question and the state of the arts presented in the introduction. The authors also identify future directions relevant to the entire computer science education community, suggesting, for example, the development of standards/guidelines for collecting and reporting demographic information in empirical studies of K-12 CSed.

Overall, I found the conclusions to be well articulated and linked to the original research question. The authors fully achieved the objectives stated in the introduction and survey methodology.
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: RECOMMENDATION ALGORITHM BASED ON ATTRIBUTED MULTIPLEX HETEROGENEOUS NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.	Novelty of idea is not clear. What is your main impact from the research and results? Make clear presentation also in relation to recent ideas in the field of deep learning.
2.	Related models to present: MobileGCN applied to low-dimensional node feature learning, Self-attention negative feedback network for real-time image super-resolution.
3.	Revise abstract to better show your main achievement and results.
Experimental design: 1.	Proposed in fig. 1 – fig. 2 model is not clear. What is the main input to this model? How do you preprocess info on the input?
2.	What features are considered by your model? It is not presented how do you make them from the input. Do you use normalization before processing?
3.	How is the model of graph in fig. 3 constructed?
4.	Why Adam was used in your model? Did you test other algorithms?
Validity of the findings: 1.	It is not necessary to use probability in eq. (15) – eq. (16). Do you really use probability in your model? Can you show in which place of model it is used?
2.	Your model needs comparisons to other model and comparisons on other data.
3.	It is not clear what is your research hypothesis for amazon or youtube data. We read about results but what actually do you want to verify?
Additional comments: Images need better quality, since resolution is low and will have bad outlook in paper


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

Input:
Title: RECOMMENDATION ALGORITHM BASED ON ATTRIBUTED MULTIPLEX HETEROGENEOUS NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper had proposed the recommendation algorithm based on attributed multiplex heterogeneous network. The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. The proposed framework SSN_GATNE-T was tested on two different types of datasets, Amazon and YouTube. The proposed algorithm improves the three evaluation indexes, especially the evaluation index F1. I suggest this paper be returned to authors for some related revisions, I hope those follow comments will help the authors to improve this paper.
Experimental design: I suggest the authors amend related description and depict with experiments.
Validity of the findings: 1.	The abstract and conclusion need to be improved. The abstract must be a concise yet comprehensive reflection of what is in your paper. Please modify the abstract according to “motivation, description, results and conclusion” parts. I suggest extending the conclusions section to focus on the results you get, the method you propose, and their significance.
2.	What is the motivation of the proposed method? The details of motivation and innovations are important for potential readers and journals. Please add this detailed description in the last paragraph in section I. Please modify the paragraph according to "For this paper, the main contributions are as follows: (1) ......" to Section I. Please give the details of motivations.
3.	The description of manuscript is very important for potential reader and other researchers. I encourage the authors to have their manuscript proof-edited by a native English speaker to enhance the level of paper presentation.
4.	Please update references with recent paper in CVPR, ICCV, ECCV et al and Elsevier, Springer. In your section 1 and section 2, I suggest the authors amend several related literatures and corresponding references in recent years. For example: The improved image inpainting algorithm via encoder and similarity constraint (The Visual Computer); Research on image inpainting algorithm of improved total variation minimization method (Journal of Ambient Intelligence and Humanized Computing); The image annotation algorithm using convolutional features from intermediate layer of deep learning (Multimedia Tools and Applications); Image super-resolution reconstruction based on feature map attention mechanism (Applied Intelligence).
5.	Please check all parameters in the manuscript and amend some related description of primary parameters. In section 3, please write the proposed algorithm in a proper algorithm/pseudocode format with Algorithm. Otherwise, it is very hard to follow. Some examples here: https://tex.stackexchange.com/questions/204592/how-to-format-a-pseudocode-algorithm
6.	The section 2 is too short, and I suggest the authors amend the details of background and motivation. The main section of manuscript is section 3. I suggest the authors amend related depict of proposed method.
Additional comments: no


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: RECOMMENDATION ALGORITHM BASED ON ATTRIBUTED MULTIPLEX HETEROGENEOUS NETWORK
Review round: 1
Reviewer: 3

Output:
Basic reporting: Recommender systems have been widely used in the real world. In addition to collaborative filtering, heterogeneous information networks (HINs) have attracted more attention in recent years. HIN-based recommendation algorithms have also been proved to be effective in a few practical recommendation scenarios. This work combines HINs and multiplex networks, a compound network made up of two or more sub-networks, to construct an attributed multiplex HIN for item recommendations. Besides, this work proposes an attention mechanism that can work well with the proposed network to recommend appropriate items for the target user. Basic reporting comments include:
1. This article is well organized. The presentation of this paper can be further improved with the help of native speakers of English.
2. All the figures of this article could be replaced with high-resolution ones.
3. In the Introduction section, the motivation of this work is not clear. Why did the work combine HINs and multiplex networks? The authors should point out the background, shortcomings of the existing models, and challenges.
4. A few key references of the related work were not included and analyzed in the second section. The authors should carefully review the studies on HIN-based embedding and multiplex network embedding and add those missing works in the revised manuscript.
[1] Fenfang Xie, Angyu Zheng, Liang Chen, Zibin Zheng: Attentive Meta-graph Embedding for item Recommendation in heterogeneous information networks. Knowl. Based Syst. 211: 106524 (2021)
[2] Léo Pio-Lopez, Alberto Valdeolivas, Laurent Tichit, Élisabeth Remy, Anaïs Baudot: MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach. Sci. Rep. 11: 8794 (2021)
[3] Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu Tang, Qing He: Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network. WWW 2020: 785-795
[4] Zhijun Liu, Chao Huang, Yanwei Yu, Baode Fan, Junyu Dong: Fast Attributed Multiplex Heterogeneous Network Embedding. CIKM 2020: 995-1004
[5] Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang: Representation Learning for Attributed Multiplex Heterogeneous Network. KDD 2019: 1358-1368
[6] Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi: Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism. AAAI 2019: 946-953
[7] Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi: Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework. CIKM 2019: 549-558
5. The concept of multiplex networks has been formally defined in the field of network science. However, the authors did not formulate it in this article. How is the attention mechanism applied to the proposed network that is essentially a multiplex network?
Experimental design: This study experimented on two real-world datasets and demonstrated the effectiveness of the proposed approach in terms of three evaluation metrics. The reviewer’s concerns on experimental design include:
1. The selected baselines have been proposed for three years. The authors should add more recent approaches (e.g., the above refs. [2], [4], and [5]) based on attributed multiplex heterogeneous networks to compare with the proposed approach.
2. In addition to the parameter setting of the proposed approach, the authors should introduce the settings of each baseline’s hyperparameters, which may impact the performance of each baseline.
3. The implementation code of the proposed approach is not available for research on the Internet.
4. In addition to the ablation experiment, the authors should discuss the impacts of some critical hyperparameters (e.g., the dimension of embeddings and the layer number of a multiplex network) on model performance.
Validity of the findings: 1. The motivation of the Fridman test is not clear. For example, what are the actual meanings of node1 and node2? Why did the Fridman test on the two datasets obtain two different results, i.e., the original hypothesis was retained on the Youtube dataset, while the original hypothesis was rejected on the Amazon dataset.
2. If possible, the authors should add a subsection to discuss some potential threats to the validity of this work, including construct validity, internal validity, and external validity.
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: RECOMMENDATION ALGORITHM BASED ON ATTRIBUTED MULTIPLEX HETEROGENEOUS NETWORK
Review round: 2
Reviewer: 1

Output:
Basic reporting: i think paper is revised
Experimental design: i think paper is revised
Validity of the findings: i think paper is revised
Additional comments: i think paper is revised


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: CONTEXT-BASED SENTIMENT ANALYSIS ON CUSTOMER REVIEWS USING MACHINE LEARNING LINEAR MODELS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Clear and unambiguous
Literature review is fair
Figures and Tables are clear
Experimental design: Novelty  is lacking only application of existing algorithm with GPU based infrastructure
Validity of the findings: Good  choice of algorithm and datasets
Additional comments: 1. Paper does not reveal whether multiple modalities have been used for sentiment Analysis. It can be mentioned.
2. Sarcasm is not part analysis and can be added


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: CONTEXT-BASED SENTIMENT ANALYSIS ON CUSTOMER REVIEWS USING MACHINE LEARNING LINEAR MODELS
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1. The article is clear in understanding and standard structure has been used.

2. Sufficient literature has been surveyed but lacks standard journal references like IEEE transactions, Elsevier  springer etc.

3. The data set used is standard datasets,
Experimental design: 1.Sentiment analysis using machine learning is one of the upcoming area and  is within aim and scope.

2. Extensive investigation is performed with machine learning and deep learning models.

3.  I could see only fasttext comparison with amazon review and all other datasets mentioned, but LSVM and SA-BLSTM of existing literature is not shown for amazon, yelp dataset.
Validity of the findings: 1. findings provided in the table are valid.

2. Conclusion should highlight the accuracy measure of the proposed model
Additional comments: Highlight what is the % of performance evaluation in abstract also


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: CONTEXT-BASED SENTIMENT ANALYSIS ON CUSTOMER REVIEWS USING MACHINE LEARNING LINEAR MODELS
Review round: 2
Reviewer: 1

Output:
Basic reporting: 1. The article is clear in understanding and standard structure has been used.

2. Sufficient literature has been surveyed but lacks standard journal references like IEEE transactions, Elsevier springer etc.

3. The data set used is standard datasets,
Experimental design: 1.Sentiment analysis using machine learning is one of the upcoming area and is within aim and scope.

2. Extensive investigation is performed with machine learning and deep learning models.
Validity of the findings: 1. findings provided in the table are valid.
2. Deep learning method has been tried.
Additional comments: 1. All suggestions mentioned in the previous review has been incorporated.


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: SIM-DRS: A SIMILARITY-BASED DYNAMIC RESOURCE SCHEDULING ALGORITHM FOR MICROSERVICE-BASED WEB SYSTEMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper tackles the resource scheduling problem in web systems using a dynamic resource allocation/scheduling method, which has long been a prevalent model and commonly adopted for microservice composition in the domain of distributed computing.

The paper formulated a combinatorial optimization problem and proposed a heuristic scheduling algorithm to find feasible scheduling schemes by directly adopting existing solutions to similar problems.
Experimental design: A benchmark named TeaStore is used for experimental evaluation. TeaStore has been widely used for performance evaluation of microservice-based applications.
Validity of the findings: The performance evaluation shows that the proposed approach outperforms other methods, which is quite expected since the existing methods (which are known to be better) are used.
Additional comments: Resource scheduling in distributed computing environment is well-known to be of interest of researchers in the related area and has been investigated extensively in literatures. Although this paper includes some interesting ideas and results, the following issues need to be addressed before acceptance.

Strengths:
1.	This work offers an interesting idea, i.e., solutions to similar problems often share certain structures, which is instructive for solving scheduling problems and could avoid unnecessary overhead of solving problems from scratch.
2.	The performance evaluation shows positive results that make the idea worthy of further investigation and adoption.

Weaknesses:
1.	A formal complexity study of the formulated problem “DRS-MWS” is missing.
2.	The related work section should be better summarized, e.g., the “rule-based approach” and “heuristic approach” contains too much work in a single paragraph and these two approaches may contain some overlap and their distinction is not well described. Also, a complete refresh of the related work with more recent literatures would be appreciated.
3.	Eq. 9 seems to be messed up, please correct


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

Input:
Title: SIM-DRS: A SIMILARITY-BASED DYNAMIC RESOURCE SCHEDULING ALGORITHM FOR MICROSERVICE-BASED WEB SYSTEMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This paper presents Sim-DRS, a similarity-based heuristic scheduling algorithm for microservice-based web systems that aims to find viable scheduling schemes by utilizing solutions to similar problems. More specifically, the Dynamic Resource Scheduling for Microservice-based Web Systems (DRS-MWS) is formulated as a combinatorial optimization problem, and the authors propose the Sim-DRS to solve the DRS-MWS problem. The experimental results show the superiority of Sim-DRS over three state-of-the-art scheduling algorithms.

Overall, the article is well organized and its presentation is good. The interesting idea is to obviate a fresh start in dynamic resource scheduling by incorporating previously-known viable solutions to similar problem.

However, some minor issues still need to be improved:
1. The introduction section should firstly provide an idea of the resource scheduling process before the analysis of existing methods.
2. The words in Figure 1 are not very clear, and the contents in Table 2 and Table 3 are not clearly displayed. Please improve the quality of figures and tables.
3. There are some typos and grammar errors. It is recommended that the authors should proof read the manuscript before submission.


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: SIM-DRS: A SIMILARITY-BASED DYNAMIC RESOURCE SCHEDULING ALGORITHM FOR MICROSERVICE-BASED WEB SYSTEMS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper is easy to read and the whole process is complete and neat. More specifically, the contribution of the work consists of three key components: (1) This paper describes DRS-MWS as a combinatorial optimization problem, which clarifies the processing logic of the original problem; (2) This paper proposes Sim-DRS to solve DRS-MWS. It uses prior knowledge, that is, to directly use feasible solutions to similar problems to find a promising scheduling plan, thus effectively improving the efficiency of the algorithm; (3) This article uses a well-known microservice benchmark called TeaStore to evaluate the performance of Sim-DRS through extensive experiments under different scheduling time constraints.
Experimental design: The Sim-DRS works well in experiments, and the research results obtained should be very effective, very useful to practitioners and to promote future research directions..
Validity of the findings: This paper proposes Sim-DRS, which uses the identification and combination of previously known feasible solutions to similar problems as initial values, and uses evolutionary algorithms to quickly find promising decision-making solutions for dynamic resource scheduling.
Additional comments: To improve the paper, I have some comments though:
1. There are some formatting issues in the paper. For example, the   in formula (6) should be left aligned; the result ( ) in formula 9 can try to give the reader.
2. Some symbols are not clearly defined. For example, the " " in equation (8) is unclear. If it is the dot product of the matrix, it is recommended to use " ", if it is the outer product or Hadamard product, it is also recommended to use the corresponding mainstream symbols to express; it is recommended to consider replacing   (in line 180) with   for more clarity. Similar issues are also recommended to be modified, etc.
3. Unify the expression of images in the text, such as "Figure 3" in line 233 and "Fig. 5" in line 330.


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: SIM-DRS: A SIMILARITY-BASED DYNAMIC RESOURCE SCHEDULING ALGORITHM FOR MICROSERVICE-BASED WEB SYSTEMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: My previous comments have been addressed in this version. I do not have any further comments.
Experimental design: My previous comments have been addressed in this version. I do not have any further comments.
Validity of the findings: My previous comments have been addressed in this version. I do not have any further comments.
Additional comments: My previous comments have been addressed in this version. I do not have any further 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: SIM-DRS: A SIMILARITY-BASED DYNAMIC RESOURCE SCHEDULING ALGORITHM FOR MICROSERVICE-BASED WEB SYSTEMS
Review round: 2
Reviewer: 2

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


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

Input:
Title: COMPARATIVE ANALYSIS OF MACHINE LEARNING APPROACHES TO ANALYZE AND PREDICT THE COVID-19 OUTBREAK
Review round: 1
Reviewer: 1

Output:
Basic reporting: The current study does not explain why these models were selected for comparison. Specifically, it lacks a review or summary of the existing studies that have applied forecasting models for the COVID-19 pandemic. Please see the existing papers in PubMed/LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?text=forecasting%20model). The methods are from traditional mathematical models and recurrent neural networks other than ANN. The study should discuss them in detail and select representative models with reasonings.
Experimental design: First, the study should explain the data split and cross-validation in more detail. How was the cross-validation performed? Since this is time-series data, the split by time is critical. How does the 10-fold cross-validation reflect the timeline?

Second, a more religious design should be made for the forecasting model. Currently, the spanning between the train and testing is too close. The training set contains the data up to 7th Nov 2020, whereas the testing data is directly from 8th Nov 2020. Arguably, if the data of 7th Nov 2020 is available, it is not too challenging to predict the 8th even using a simple model. And it is not very useful because there is not much time for interventions or prevention can be done in advance. The study could quantify how the model forecasts in a week or earlier in advance.
Validity of the findings: The study lacks a thorough discussion on the limitations. For example, there are many factors (prevention, vaccinations etc) that could impact the case growth. Such factors cannot be captured in the models.
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: COMPARATIVE ANALYSIS OF MACHINE LEARNING APPROACHES TO ANALYZE AND PREDICT THE COVID-19 OUTBREAK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The manuscript size should be reduced, by improving the writing style. For example, information included in Tables, should not be also mentioned in the main body of the manuscript in such detail. E.g., lines 269-281, lines 283-296, lines 301-313, etc.

Replace these details with a more qualitative and descriptive discussion of the overall findings.

The literature review on the topic is very poor at the moment, and should be enhanced.

The connection of this work with previously published work in the Journal should be established.

The manuscript needs very thorough editing and proofreading.

It would be best to remove the gray background of the figures.

The green in Figure 2 is too bright; replace it with a smoother one (use the same blue and red as in Figure 1).
Experimental design: The research question is well defined, and it contributes to the international literature on the topic. The methods are adequately described.
Validity of the findings: This work has merit to be an important contribution to the topic of using machine learning methods for COVID-19 forecasting, by comparing several such methods.
Additional comments: This is an interesting manuscript that has a contribution to the international literature of COVID-19 forecasting, by comparing machine learning approaches towards this direction. There are some issues that need to be addressed before the manuscript is ready for publication.


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

Input:
Title: COMPARATIVE ANALYSIS OF MACHINE LEARNING APPROACHES TO ANALYZE AND PREDICT THE COVID-19 OUTBREAK
Review round: 2
Reviewer: 1

Output:
Basic reporting: The reviewers have addressed my concerns.
Experimental design: Thanks, reviewers for the efforts in addressing my comment #3. However, this comment has not been fully addressed. The statement 'Actually dividing the data into training and testing parts is used to evaluate the model performance.   The model that are trained and the data are tested is completely unseen to the model. ' made by the author is fine but is not enough for the evaluation on generalization. Splitting training and testing from the same dataset of course is a way to evaluate the performance. However, splitting from the same dataset will make the training and testing set share similar distributions or characteristics, which is not always the case when applying to new data. In this specific case of outbreak prediction, it is critical to apply to an external dataset that does not have overlapped periods to the training and testing datasets.
Validity of the findings: I do not have additional 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: COMPARATIVE ANALYSIS OF MACHINE LEARNING APPROACHES TO ANALYZE AND PREDICT THE COVID-19 OUTBREAK
Review round: 3
Reviewer: 1

Output:
Basic reporting: My last concern has primarily been addressed. Thanks authors for their dedicated efforts
Experimental design: N/A
Validity of the findings: N/A
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 LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 1
Reviewer: 1

Output:
Basic reporting: - Please improve the overall readability of the paper.
- The objectives of this paper need to be polished. Contribution list should be polished at the end of the introduction section and last paragraph of the introduction should be the organization of the paper.
- In the first four paragraphs of literature review section, the authors have presented a good references, but they need to present the recent and most updated references.
- Some Paragraphs in the paper can be merged and some long paragraphs can be split into two. (i.e., Related Work first paragprah of more than 2pages)
- Remomve the authors name from table 1 first column. Just give reference number.
Experimental design: - Please cite each equation and clearly explain its terms.
- Clearly highlight the terms used in the algorithm and explain them in the text.
- The procedures and analysis of the data are seen to be unclear.
Validity of the findings: - What are the evaluations used for the verification of results?
- Provide a comparison with existing studies.
Additional comments: - Make sure the Conclusion succinctly summarizes the paper. It should not repeat phrases from the Introduction!
- Authors should add the most recent reference:
1) CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network using CNN and Attention-based GRU, IEEE Transactions on Network Science and Engineering
2) Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network, IEEE Transactions on Intelligent Transportation Systems

lastly, Proofread the paper


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

Input:
Title: A LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 1
Reviewer: 2

Output:
Basic reporting: - Reference format must be standard in the whole paper.
Experimental design: - It is recommended that more information adds for simulation environments, parameters and data.
Validity of the findings: It is ok.
Additional comments: - "VANET security section" needs more detail and references for challenges and criteria identification.
- Response to reviewer must be in an individual file.


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 LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 1
Reviewer: 3

Output:
Basic reporting: propose an online/offline lightweight authentication scheme for VANET cross-domain system in IIoT. The proposed scheme utilizes an efficient AES-RSA algorithm to achieve integrity and confidentiality of the message. The offline joining is added to avoid remote network intrusions and the risk of network service interruptions. The Burrows Abdi Needham (BAN logic) logic is used to prove that this scheme is mutually authenticated. The system’s security has been tested using the well-known AVISPA tool to formally evaluate and verify its security.
Experimental design: propose an online/offline lightweight authentication scheme for VANET cross-domain system in IIoT. The proposed scheme utilizes an efficient AES-RSA algorithm to achieve integrity and confidentiality of the message. The offline joining is added to avoid remote network intrusions and the risk of network service interruptions. The Burrows Abdi Needham (BAN logic) logic is used to prove that this scheme is mutually authenticated. The system’s security has been tested using the well-known AVISPA tool to formally evaluate and verify its security.
Validity of the findings: propose an online/offline lightweight authentication scheme for VANET cross-domain system in IIoT. The proposed scheme utilizes an efficient AES-RSA algorithm to achieve integrity and confidentiality of the message. The offline joining is added to avoid remote network intrusions and the risk of network service interruptions. The Burrows Abdi Needham (BAN logic) logic is used to prove that this scheme is mutually authenticated. The system’s security has been tested using the well-known AVISPA tool to formally evaluate and verify its security.
Additional comments: •	The authors should emphasize the difference between other methods to clarify the position of this work further.
•	The Wide ranges of applications need to be addressed in Introductions
•	The objective of the research should be clearly defined in the last paragraph of the introduction section.
•	Add the advantages of the proposed system in one quoted line for justifying the proposed approach in the Introduction section.
•	The motivation for the present research would be clearer, by providing a more direct link between the importance of choosing your own method.
Under blockchain background, the following papers can be referred
A survey on blockchain for big data: Approaches, opportunities, and future directions
Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions


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 LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 1
Reviewer: 4

Output:
Basic reporting: 1.Suggestion:  Figure 1- The typical architecture of VANETs in page 2; TA has registered OBUs and RSUs. This can be added to the picture.

2. Suggestion: Page 3; Line#105,106 In the offline authentication, TA is not involved in the joining procedure since the information is preloaded prior.

The message is not coming out clearly what is been preloaded in advance. A pictorial representation can help

3. Correction: Page 9, Figure 3 - The AES-RSA algorithm work diagram; The final outcome after the comparison of private key and public key comparison is Data is secure not Date is secure

4. Correction: Page 13, The DAT then concatenated the signature with the message CTDTA−→RSU = Enc DTAaes {Signdta ∥ M′ }; The DTA (Domain Trust Authority) not DAT

5. Grammar: Page 16, the movement from RSU1 to RSU2 is occur dynamically; can be changed to does occur or is occurring
Experimental design: Question: Page 26; Line 726 - 730  does every RSU preload with vehicle credentials (rvj,SKvj,texp,Ri,TIDv) . What if there are 1000s of RSUs and 1000s of OBU crossing every day?

Recommendation: Please quote an example for man in the middle attack or traceability is a concern with an existing VANETs
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 LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 2
Reviewer: 1

Output:
Basic reporting: -
Experimental design: -
Validity of the findings: -
Additional comments: Accept


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

Input:
Title: A LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 2
Reviewer: 2

Output:
Basic reporting: - It is recommended to check the formats of figures, tables, and references in the journal's standard format for more readability.
- It is recommended that short sentences be written to make the article more readable.
- In the abstract and introduction, attributes are used relatively, for example, "more security" or "less costly." To better understand, it is necessary to either mention the studied parameters or compare them numerically and as a percentage.
- In the previous works section, it is necessary to check the table with explanations. Some papers are not mentioned in the table. For example, in line 178, a source from 2021 is given, which is not mentioned in the table.
Experimental design: no comment
Validity of the findings: - For equations used in section 6.1, no reference is mentioned, and on the other hand, they are used to compare methods. Therefore, if the authors extract the equations, they add other standard parameters for evaluations.
Additional comments: Given that there is no reference to equations, it seems that the evaluation part needs to be improved


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 LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 2
Reviewer: 3

Output:
Basic reporting: The authors have addressed all of my comments, paper can be accepted in the current form. Thank you for giving me this opportunity.
Experimental design: Good
Validity of the findings: Good
Additional comments: The authors have addressed all of my comments, paper can be accepted in the current form. Thank you for giving me this opportunity.


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 LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 2
Reviewer: 4

Output:
Basic reporting: Suggested contents have been modified.
Looks good
Experimental design: Suggested contents have been modified.
Looks good
Validity of the findings: data looks good.
Additional comments: No additional comments


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

Input:
Title: A LIGHTWEIGHT AND SECURE ONLINE/OFFLINE CROSS-DOMAIN AUTHENTICATION SCHEME FOR VANET SYSTEMS IN INDUSTRIAL IOT
Review round: 3
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: Authors have addressed the reviewer’s comments satisfactorily.


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

Input:
Title: A 3D DEEP LEARNING APPROACH TO EPICARDIAL FAT SEGMENTATION IN NON-CONTRAST AND POST-CONTRAST CARDIAC CT IMAGES
Review round: 1
Reviewer: 1

Output:
Basic reporting: This research suggests a U-net based CNN segmentation for epicardial fat, which is very challenging task.
Overall, the submitted manuscript seems to have reasonably described the related works and the necessity of the research.
However, I have a few concerns about the methods, the evaluation , and the discussion .
First, the materials and methods part does not seem to be written clearly. The author had better describe more clearly the contribution of their research. And, I suggest the authors that the materials and Methods part should have some sub sections for readability.
Second, I am not sure why the author presented the experimental result of 5-fold cross validation. Are the other experimental results not the cross validation results?
Then, the author had better report how to divide the training set and the test set.
Currently, it does not seem to be clear whether all the experimental results were cross validation based results or not.
Third, in the discussion part, the author had better analyze and infer why such promising results were produced. For example, attention gates and deep supervision module were used in your research. The author had better describe why those gate and module made the performance improved precisely.
Experimental design: As I mentioned above, the author presented the experimental result of 5-fold cross validation. Are the other experimental results not the cross validation results?
The final result reported in Table 3 are different from the results of Table 2. Are they separate ones?
If they are separate ones, I wonder how the author choose the optimal weight in experiments of Table 2.
Validity of the findings: The research presented very promising segmentation results.
However, it should be also noted that the number of the training case is not large enough.
So, I think this should be clearly reported in the manuscript.
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 3D DEEP LEARNING APPROACH TO EPICARDIAL FAT SEGMENTATION IN NON-CONTRAST AND POST-CONTRAST CARDIAC CT IMAGES
Review round: 1
Reviewer: 2

Output:
Basic reporting: - In summary, this paper presents a computational strategy for automatic segmentation of Epicardial fat (ECF) volume using deep learning computer vision, where the method employs non-contrast and contrast-enhanced CT images. It is a well-organized and well-written paper.

- Suggestion and Comments:

1- I would encourage the authors to clearly list the "Clinical Significance" and "Technical significance" of their work at somewhere within the "Introduction".

2- 3D UNet and even 3D attention-based UNet have been around for several years in the community. What kind of novelties this papers is carrying?

3- While for 2D images the UNet network(s) are computationally efficient, we know that 3D convolutions have huge storage requirements and therefore, end-to-end training is limited by GPU memory and data size. Have authors tried 2.5 UNet? If not, why?

4- The current work reports that it employed 15 radiologist to manually segment the data. However, I was not able to find the analysis of inter-rater agreement! I mean, the level of agreement among annotators. I would very much like to see the IoU and/or Dice coefficient to better understand the level of agreement.

5- How the authors did split the 220 patients to build a training, testing, and validation set?

6- I would encourage the authors to provide another comparative analysis using different number of slices plus different image scaling.
Experimental design: - Inter-rater agreement among those 15 radiologist is missing.

- Data stratification is missing.

- In general, the amount of data seems to be few, particularly to tackle one of the most difficult medical image segmentation tasks. Have they tried any type of data augmentation, at least for the training set?

-  Is the improvement level statistically significance?
Validity of the findings: To me, this problem is one the most challenging problem in medical image analysis and interpretation. To me, I expect to see more in deep implementation, more data that covers more diversity, and a generalize model that can be applied across different healthcare centers.
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 3D DEEP LEARNING APPROACH TO EPICARDIAL FAT SEGMENTATION IN NON-CONTRAST AND POST-CONTRAST CARDIAC CT IMAGES
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors sincerely responded the reviewers' comments and modified the manuscript.
Thank you for the opportunity to review this manuscript.
Experimental design: I wish the authors could include the analysis of inter-rater agreement.
Except that, I have 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: A 3D DEEP LEARNING APPROACH TO EPICARDIAL FAT SEGMENTATION IN NON-CONTRAST AND POST-CONTRAST CARDIAC CT IMAGES
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors revised their manuscript accordingly based on my comments and recommendations.
Experimental design: The authors revised their manuscript accordingly based on my comments and recommendations.
Validity of the findings: The authors revised their manuscript accordingly based on my comments and recommendations.
Additional comments: No additional comments