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: DEPENDENCY MANAGEMENT BOTS IN OPEN-SOURCE SYSTEMS—PREVALENCE AND ADOPTION
Review round: 2
Reviewer: 1

Output:
Basic reporting: Thank you to the authors for providing this detailed revision. I would reinforce that I find the topic is very important and timely. I also think the study is well-executed, and the paper is well-written, easy to read and understand. In addition, the problem is well-motivated.

Due to the detailed revision version provided by the authors, I recommend paper acceptance. The authors addressed all the priority and significant review points I have raised. I see no major issues related to the approach followed in conducting the study or even the results' presentation.
Experimental design: There are no further suggestions for improvement.
Validity of the findings: Overall, the paper is clean, readable, addresses a significant problem, presents valid results grounded on the data provided.
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: DEPENDENCY MANAGEMENT BOTS IN OPEN-SOURCE SYSTEMS—PREVALENCE AND ADOPTION
Review round: 2
Reviewer: 2

Output:
Basic reporting: -
Experimental design: -
Validity of the findings: -
Additional comments: The authors have significantly improved the manuscript by restructuring and condensing the previous draft.


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

Input:
Title: A NOVEL HYBRID-BASED APPROACH OF SNORT AUTOMATIC RULE GENERATOR AND SECURITY EVENT CORRELATION (SARG-SEC)
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article uses clear, unambiguous, technically correct text, having a good introduction clearly mentioning the disadvantages of the manual snort rules generation. However, the paper is a little bit long and wordy and in some sections is exaggerated.
Literature is not sufficient to show the disadvantages of existing automatic snort reules generation and the need for current work that fills the gap. For example, see https://doi.org/10.1109/IranianCEE.2016.7585840 and https://doi.org/10.1109/ICITEED.2015.7409013 work is also related to this, many more example exists.
The structure of the paper is not bad, the figures and tables look fine.
The results partly support the hypothesis proposed by the authors.
Experimental design: With much detail design of the experiment is outlined in order to evaluate the proposed snort automatic rule generator and security event correlation. However, there is no proper explanation and discussion of attack types under which SARG-SEC is evaluated. Attack type is needed to explain under which the proposed strategy is evaluated.
Validity of the findings: In the abstract from lines 50-52” It is evident from the experimental results that SARG-SEC has demonstrated impressive performance and can provide competitive advantages compared to other related approaches”. But not linked with a conclusion as there is a comparison shown with other related approaches.

The author needs to show the comparison of manual rules results Vs auto-generated snort rules results.
Additional comments: No additional comments


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

Input:
Title: A NOVEL HYBRID-BASED APPROACH OF SNORT AUTOMATIC RULE GENERATOR AND SECURITY EVENT CORRELATION (SARG-SEC)
Review round: 1
Reviewer: 2

Output:
Basic reporting: How to define the snort rules is one the critical issue when deploying snort-based IDS.
This paper made a good trial to automatically generating the  rule for such IDSs, which I think is  an interesting,challenging and meaningful trial.
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: A NOVEL HYBRID-BASED APPROACH OF SNORT AUTOMATIC RULE GENERATOR AND SECURITY EVENT CORRELATION (SARG-SEC)
Review round: 1
Reviewer: 3

Output:
Basic reporting: The authors present a novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC). In general, the work is interesting and the proposed methodology is adequately explained. The authors rely on Snort, a well known Network Intrusion Detection System (NIDS). Although the contributions of the paper are mentioned in the introductory part, is not very clear how the paper is differentiated with respect to other works. Moreover, the introductory part can be further enhanced, including some statistics about NIDS and real cybersecurity incidents. The authors provide a detailed background on NIDS and Snort. Finally, despite the fact that the evaluation results demonstrate the efficiency behind this work some further clarification should be provided
Experimental design: Although the authors present a detailed overview about NIDS and Snort, the paper does not include a paper about similar works. Some indicative references are given below. Moreover, it is not very clear why the authors chose Snort? why not other signature-based NIDS like Suricata.

[1] Radoglou-Grammatikis, Panagiotis, et al. "SPEAR SIEM: A Security Information and Event Management system for the Smart Grid." Computer Networks 193 (2021): 108008.

[2] Grammatikis, Panagiotis Radoglou, et al. "SDN-Based Resilient Smart Grid: The SDN-microSENSE Architecture." Digital 1.4 (2021): 173-187.

[3] Grammatikis, Panagiotis Radoglou, et al. "Secure and private smart grid: The spear architecture." 2020 6th IEEE Conference on Network Softwarization (NetSoft). IEEE, 2020.

[4] Sekharan, S. Sandeep, and Kamalanathan Kandasamy. "Profiling SIEM tools and correlation engines for security analytics." 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2017.

[5] Suarez-Tangil, Guillermo, et al. "Providing SIEM systems with self-adaptation." Information Fusion 21 (2015): 145-158.

[6] Suarez-Tangil, Guillermo, et al. "Automatic rule generation based on genetic programming for event correlation." Computational Intelligence in Security for Information Systems. Springer, Berlin, Heidelberg, 2009. 127-134.

[7] Kotenko, Igor, et al. "Parallelization of security event correlation based on accounting of event type links." 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2018.

[8] Fedorchenko, Andrey, and Igor Kotenko. "IOT Security event correlation based on the analysis of event types." Dependable IoT for Human and Industry: Modeling, Architecting, Implementation 147 (2018).

[9] Ferebee, Denise, et al. "Security visualization: Cyber security storm map and event correlation." 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS). IEEE, 2011.

[10] Dwivedi, Neelam, and Aprna Tripathi. "Event correlation for intrusion detection systems." 2015 IEEE International Conference on Computational Intelligence & Communication Technology. IEEE, 2015.
Validity of the findings: Although the authors provide a lot of figures related to the effectiveness of the proposed methods, it is not clear how the authors reflect the validity of the findings. For instance, the authors can compare their implementation with other works, other IDS and Security Information and Event Management (SIEM) systems, using some baseline metrics.
Additional comments: The paper should be re-checked entirely about potential writing errors and typos.


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

Input:
Title: A NOVEL HYBRID-BASED APPROACH OF SNORT AUTOMATIC RULE GENERATOR AND SECURITY EVENT CORRELATION (SARG-SEC)
Review round: 1
Reviewer: 4

Output:
Basic reporting: Spelling mistakes such as the one in line 594 should be avoided where "A priori" is misspelt as "Apriorior". In addition, The grammar within this article needs another round of proofreading. In line 176, instead of just "propose", one could use "to propose". In the same line, the conjunction doesn't really do justice to an intuitive reading of the line and feels awkward. Multiple such issues were observed all over the manuscript.
The figures and graphs look somewhat low in quality (both in terms of resolution and the font used). It's advised to export these in 300 dpi for publishable quality.
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: A NOVEL HYBRID-BASED APPROACH OF SNORT AUTOMATIC RULE GENERATOR AND SECURITY EVENT CORRELATION (SARG-SEC)
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors addressed all the comments. Therefore, the paper is accepted for publication
Experimental design: The authors addressed all the comments. Therefore, the paper is accepted for publication
Validity of the findings: The authors addressed all the comments. Therefore, the paper is accepted for publication
Additional comments: The authors addressed all the comments. Therefore, the paper is accepted for publication


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

Input:
Title: A NOVEL HYBRID-BASED APPROACH OF SNORT AUTOMATIC RULE GENERATOR AND SECURITY EVENT CORRELATION (SARG-SEC)
Review round: 2
Reviewer: 2

Output:
Basic reporting: Looks good
Experimental design: Looks good
Validity of the findings: 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: DEVELOPMENT OF A STOCK TRADING SYSTEM BASED ON A NEURAL NETWORK USING HIGHLY VOLATILE STOCK PRICE PATTERNS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper proposes a pattern-based stock trading system using ANN-based deep learning and utilizing the results to analyze and forecast highly volatile stock price patterns. Three highly volatile price patterns containing at least a record of the price hitting the daily ceiling in the recent trading days are defined. The implications of each pattern are briefly analyzed using chart examples.

Here, the training of the neural network has been conducted with stock data filtered in three patterns and trading signals were generated using the prediction results of those neural networks. Using data from the KOSPI and KOSDAQ markets, this paper shows that the proposed pattern-based trading system can achieve better trading performances than domestic and overseas stock indices.
Experimental design: 1. How the experimental environment has been developed?
2. Why the dataset is divided into four parts?
Validity of the findings: 1. How the accuracy is 96.23%?
2. The proposed scheme must be compared with at least two existing schemes.
3. There must be a discussion on how the results are generated. Technical details are missing.
Additional comments: 1.	Motivations of the paper are not clear.
2.	Contributions must be represented point-wise.
3.	In the "Related Works" section, the existing schemes must be discussed one by one. Delete the limitations from this section and add them in the Introduction section.
4.	How the price pattern is chosen.
5.	The proposed scheme is unstructured. divide the proposed scheme in many sub-sections, and then, discuss the entire proposed scheme under each sub-section.
6.	Equations and figures are not represented properly. All the key terms must be defined.
7.	It is hard to identify the novelty of the proposed work.
8.	The English language is very poor. Never use I, we, or our in a Research Article.
9.	The organization of the paper is poor. Add section number.
10. Important references are missing. Add the following references:
“Efficient algorithm for big data clustering on single machine”, CAAI Transactions on Intelligence Technology, vol. 5, no. 1, pp. 9-14, 2020.
“Ensemble algorithm using transfer learning for sheep breed classification”, Proc. of the 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, Timisoara, Romania, pp. 199-204, 19-21 May 2021.
“Enhanced neural network based univariate time series forecasting model”, Distributed and Parallel Databases, 2021. DOI: 10.1007/s10619-021-07364-9
“Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement”, CAAI Transactions on Intelligence Technology, vol. 5, no. 3, pp. 184-192, 2020.
“Fast and secure data accessing by using DNA computing for the cloud environment”, IEEE Transactions on Services Computing, 2020. DOI: 10.1109/TSC.2020.3046471
“Feature selection approach using ensemble learning for network anomaly detection”, CAAI Transactions on Intelligent Technology, vol. 5, no. 4, pp. 283-293, 2020
“Securing multimedia by using DNA based encryption in the cloud computing environment”, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3s, 2020. DOI: https://doi.org/10.1145/3392665
“Nonlinear neural network based forecasting model for predicting COVID-19 cases”, Neural Processing Letters, 2021. DOI: 10.1007/s11063-021-10495-w
“IFODPSO-based multi-level image segmentation scheme aided with Masi entropy”, Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 7793-7811, 2021. DOI: https://doi.org/10.1007/s12652-020-02506-w


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: DEVELOPMENT OF A STOCK TRADING SYSTEM BASED ON A NEURAL NETWORK USING HIGHLY VOLATILE STOCK PRICE PATTERNS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The author didn’t properly introduce the problem correctly. There is lot of Finance related technical information that’s presented to the reader from the beginning without properly clarifying what it is. Author should work on easing the reader into the topic.

The quality of the figures presented are not publishing ready and should be redone  as a high quality image
Experimental design: Each stock market index uses proprietary methods to determine which companies or investments to include and that can change over time. How is that handled here?
Validity of the findings: This is a time series prediction problem, why are we not using a variation of LSTM’s? LSTM’s  almost always guarantee better results than DNN. Authors should experiment with modern methods rather than leaving it at DNN
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: DEVELOPMENT OF A STOCK TRADING SYSTEM BASED ON A NEURAL NETWORK USING HIGHLY VOLATILE STOCK PRICE PATTERNS
Review round: 1
Reviewer: 3

Output:
Basic reporting: Impact of volatility on the assets prices changes play a crucial for traders/investors for capturing positive returns on their investments and with the advancement of technology revolution crept into the stock markets arena. Since the turn of the century financial markets have undergone two major changes, (a) massive information (b) speed of strategy execution. The paper under the review speaks of the same tone for picking up a strategy and to discover a price amidst of volatility. But the paper need to speak conceptually more on drawback in the current existing patters while proposing for the new trading mechanism.
Experimental design: 1.	Provide the complete implementation process?
2.	Does any specific reason for datasets is divided into more parts?
Validity of the findings: 1.	How was 96.23% accuracy arrived at, what was the measuring method used to arrive at 96.23% accuracy?
2.	The proposed scheme must be compared with at least ‘four’ traditional methods.
3.	Need to add more technical novelty details.
Additional comments: 1.	Motivation of the paper was not clear.
2.	Need more reference on related work, on the other hand  with what ever reference works were quoted the authors haven’t touched up on criticism/drawback/limitations in the related works.
3.	Research work is silent in recommending a particular method/process for measuring accuracy.


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: DEVELOPMENT OF A STOCK TRADING SYSTEM BASED ON A NEURAL NETWORK USING HIGHLY VOLATILE STOCK PRICE PATTERNS
Review round: 2
Reviewer: 1

Output:
Basic reporting: This paper proposes a pattern-based stock trading system using ANN-based deep learning
and utilizing the results to analyze and forecast highly volatile stock price patterns.
Experimental design: The experimental design is convincing.
Validity of the findings: The results show the efficiency of the proposed scheme.
Additional comments: The paper can be accepted.


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

Input:
Title: DEVELOPMENT OF A STOCK TRADING SYSTEM BASED ON A NEURAL NETWORK USING HIGHLY VOLATILE STOCK PRICE PATTERNS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The author has been clear in presenting his thoughts and the literature reference are sufficient .
Experimental design: 1. Author have provided the complete implementation process;
2. The author has rectified the specific reason for division of the datasets.
Validity of the findings: 1.Validation are presented much better way in substantiating the argument.
2. They had clearly compared with few methods that added novelty to their approach.
Additional comments: Motivation for taking up the paper and summering  the paper are clear.


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: DETECTING RACISM AND XENOPHOBIA USING DEEP LEARNING MODELS ON TWITTER DATA: CNN, LSTM AND BERT
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors of this paper have focused on a very important aspect of our society, the increasing popularity of racism and xenophobia over social media. More specifically, the authors focus on the Twitter platform and the Spanish language where they use three different deep learning models, CNN, LSTM, and BERT with the last one being the most effective during experimentation.
The paper is well structured and well written.

This is another important paper in the area that utilizes NLP and behavioural characteristics, showcasing existing problems. However, there are a few issues that the reviewer would like to raise with the authors.

The contributions of the paper are three, the assembly of the datasets, the experimentations on top of the datasets, and last the critical comparison and analysis.
A table at the end of the related literature can help the authors summarize their novelties and the potential reader understand them.
Experimental design: The hardware and software specifications of the testbed environment are not mentioned and they should be for the replicability of the results.
Validity of the findings: The discussion section should be renamed to critical evaluation and comparison of the results. In the same section, the authors should further explain their results, not just demonstrate the numbers. They should clarify why each methodology had different results and how they can be used potentially in an automated environment.

There is no mention of introduced overhead if any between the different deep learning models.
Additional comments: There is no mention of privacy. This work clearly demonstrates that Cambridge Analytica is still alive and we can export people’s behavioural characteristics without their consent just by acquiring publicly available data (e.g. https://doi.org/10.1109/MC.2018.3191268 , https://doi.org/10.1109/UIC-ATC.2013.12 ,  https://doi.org/10.3390/make2030011 ).


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: DETECTING RACISM AND XENOPHOBIA USING DEEP LEARNING MODELS ON TWITTER DATA: CNN, LSTM AND BERT
Review round: 1
Reviewer: 2

Output:
Basic reporting: This manuscript proposes several deep learning models to classify Spanish texts on xenophobia and racism. Among the objectives of the research presented are to generate a dataset of Spanish-language tweets labelled as xenophobic or racist, or not. To carry out this research, the authors have compiled their own dataset which they have shared with the rest of the scientific community and, on this, they have applied CNN, LSTM and BERT models. The manuscript is well-structured, state-of-the-art, scientifically justified, reproducible and novel.
The research focuses on generating models with a Spanish dataset which, a priori, is often difficult to justify. However, the authors mention other similar research such as HaterNet and HatEval, highlighting the possible weaknesses of these experiments and thus justifying the experiments presented in this manuscript.
Experimental design: At the methodological level, the authors mention the methodology applied, how they obtained the dataset and, in addition, they have shared the jupyter notebooks containing the code of the models generated and the results obtained.
Validity of the findings: The authors need to address some minor weaknesses before the manuscript can be considered for publication in a journal.

1. Although CNN, LSTM and BERT techniques are indicated in the title, CNN and LSTM techniques are not mentioned in the abstract. It is recommended that the authors modify the abstract by adding this information and other interesting information regarding the final results obtained, for example, the % accuracy obtained in the models.

2. To improve the interpretability of the results, authors are advised to add some of the figures of the confusion matrices corresponding to the models that obtained the best results.

3. On the other hand, they should revise the manuscript and modify some concepts that appear written in two different ways, for example "tagged" and "labelled".
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: DETECTING RACISM AND XENOPHOBIA USING DEEP LEARNING MODELS ON TWITTER DATA: CNN, LSTM AND BERT
Review round: 1
Reviewer: 3

Output:
Basic reporting: This paper evaluates different approaches to detect hate speech motivated by racism or xenophobia in Spanish using Twitter data. The structure of the manuscript is clear and there is an extensive literature review. The authors justifies the need of an adapted model for supervised text classification in Spanish to detect hateful messages particularly aimed at migrants or non-caucasian people.

I consider however that the rational is still poor and needs to create arguments in the next directions:
-If the problem is language-based, what other developments have shown that native models for hate speech detection are better that general ones? This is, showing examples of attempts in languages different from English that have a good performance (and not only in Spanish).
-Compare the results of this paper to the best performances obtained in other languages.

This would make the manuscript more useful for international audiences.

Regarding previous attempts, the authors mention HaterNet and HatEval, but are missing Pharm. This project develops deep learning models to classify tweets with hate speech towards migrants and refugees in Spanish, Greek and Italian. In Spanish, they manually labelled more than 12,000 tweets finding 1,390 hateful messages to build the classification model with RNN. This approach does not use BERT, but still gets good metrics (f1-score=0.87 for Spanish). The classification interface (http://pharm-interface.usal.es) offers more information (probably the training corpus under request too) and these two papers:
Vrysis, L.; Vryzas, N.; Kotsakis, R.; Saridou, T.; Matsiola, M.; Veglis, A.; Arcila, C.; Dimoulas, C. (2021). A Web Interface for Analyzing Hate Speech. Future Internet, 13(3), 80. https://doi.org/10.3390/fi13030080
Arcila, C., Sánchez, P., Quintana, C., Amores, J. & Blanco, D. (2022, Online preprint).  Hate speech and social acceptance of migrants in Europe:  Analysis of tweets with geolocation. Comunicar, 71.

On the other hand, what is the goal of point 2.2? Why is it called "Sentiment analysis"? Do the authors refer probably to text classification? The title is confusing since SA is not the scope of this paper.
Experimental design: The method is correctly included in the paper and shows that the research is in line with the scope of the Journal. The models are well described, although I am not sure if most of the details are necessary for a Computer Science specialized audience (i.e. description of what BERT is or definition of te evaluation metrics).
The training corpus seem to be labelled by specialists in Psychology and has specific examples of racism and xenophobia. This is a good value of the paper. Though, the reliability of this manual classification is not reported (i.e. inter-coder reliability measure), which seriously compromise the quality of the ad hoc data set. This is extremely important since this test can tell if different coders were really considering the same as hate, and then validated the qualitative category.
In addition, there is a relevant privacy concern in the Twitter data. In Zenodo, the tweets include the usernames and the given label (hate/ no hate). This does not meet the ethical and data protection standards for Twitter analysis.
Validity of the findings: The findings are relevant for the hate speech detection field, since they can be used to build better models in different languages. In special, the use of BETO and the verification of its enormous advantage can help other researchers and practitioners to create better models in the future.
My concern is the validation of the results in other different data. This is, for example, collecting new Twitter data (within another timeframe or word filters) and validate the obtained models. I consider this external validation phase extremely important for ML models.
Regarding the conclusions, I think that the limitations and future research should be better developed. Is just the size of the dataset the only limitation? What specific applications can generate this model? How can the model deteriorate in time with the use on new words/sentences? How often this model should be re-trainned to be really useful in real-life applications?
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: DETECTING RACISM AND XENOPHOBIA USING DEEP LEARNING MODELS ON TWITTER DATA: CNN, LSTM AND BERT
Review round: 2
Reviewer: 1

Output:
Basic reporting: The paper was already clear and its quality was improved according to the comments.
Experimental design: The experimental design was improved according to the comments. New measurements  were added as requested.
Validity of the findings: The impact of the findings was addressed and extra discussion was added.
Additional comments: All comments were addressed.


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: DETECTING RACISM AND XENOPHOBIA USING DEEP LEARNING MODELS ON TWITTER DATA: CNN, LSTM AND BERT
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have accomplished all the suggested improvements and changes.
Under my opinion the paper must be accepted in the current state.
Experimental design: ok
Validity of the findings: ok
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: DETECTING RACISM AND XENOPHOBIA USING DEEP LEARNING MODELS ON TWITTER DATA: CNN, LSTM AND BERT
Review round: 2
Reviewer: 3

Output:
Basic reporting: The authors have addressed my main concerns and the manuscript is much better now. The missing  reference can be found in:
Arcila, C., S nchez, P., Quintana, C., Amores, J. & Blanco, D. (2022, Online preprint). Hate speech and social acceptance of migrants in Europe: Analysis of tweets with geolocation. Comunicar, 71. https://www.revistacomunicar.com/index.php?contenido=detalles&numero=71&articulo=71-2022-02&idioma=en
Experimental design: The authors have addressed my main concerns in the experimental design.
Validity of the findings: The authors have addressed my main concerns in the validity of findings.
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: STARMC: AN AUTOMATA BASED CTL* MODEL CHECKER
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper presents the ideas and algorithms behind the starMC tool, one of the few CTL* in existence. The tool uses decision diagrams for its symbolic implementation and accepts Petri nets as input. The implementation is evaluated in extensive experiments, using many models and formulas. This paper builds on an earlier demo paper, and extends it with separate logical and implementation views of the tool. Furthermore, it contains extensive references to original works, from which the ideas originated. The experimental evaluation is more rigorous than in the previous paper.

The paper is very clearly written and pleasant to read. The paper is mostly self-contained. There are plenty of references to previous work; the related work section covers more than two pages. I think this gives a lot of insight into the history of CTL* model checking and also its current state. The source code and files used for the experiments are available online, which is commendable.
Experimental design: The main questions posed in the paper are whether it's possible to design and implement a CTL* model checker and whether techniques like variable ordering and the extraction of counter-examples can be applied to it. As mentioned in the intro, the authors only answer the first half of this question, the second half is left for future work.

In my view, the setup of the experiments is appropriate for the question that the researchers try to answer. The set of benchmarks is based on the widely used MCC collection of models and formulas. The experimental results indicate that this is a varied set of benchmarks, which helps validity. My only criticism here is that the first research question relates to models of 'industrial interest', while it's not clear that such models are contained in the MCC benchmark set.
Validity of the findings: I think the main achievement of this paper is not the finding that CTL* model checking is possible in practice, but the fact that there is now a tool to do just that. The conclusion that starMC can be used for large state spaces is fair, given the experimental results. The authors also highlight the shortcomings of their tool.
Additional comments: The paper was truly enjoying to read, and I think that not much needs to be done for acceptance. I do have a few small remarks, which are listed below. The paper can be accepted when these are addressed.

Minor remarks/typos:
- p2, l92: distint -> distinct
- Sect. 2.2: in my opinion, it's nice to be consistent with singular/plural of abbreviated nouns. You use the abbreviation MDD both for singular and plural, but for plural of MxD, you use MxDs.
- p3: it might help the reader if you emphasize that the 'fully reduced rule' of skipping levels does not apply to MxDs. For example, the MxD of T0 does not restrict the relation between P2 and P2' if interpreted as fully reduced.
- Def. 7: braces for sets in the wrong place; should be Q = S \cup {Spre}, with Spre \notin S.
- p8, l287: s1 -> s_1
- Alg.3, l11: it was not immediately clear to me that this line is a comment. Perhaps there is a better syntax?
- p13, l363: Some aspects of this procedure needs -> Some aspects of this procedure need
- p13, l370: if M ⊗ A is a BA [..] weak or terminal -> if M ⊗ A is a weak or terminal BA
- p13, l374: "whenever Qi ≤ Qj , there is no transition from Q j to Q i." Perhaps this should be the other way around? Otherwise the partial order is allowed to be the identity relation.
- p13, l385: "onto the marking of s j." -> Do you mean "onto the marking of s_i"?
- Alg. 5, l9: perhaps you mean S = S'?
- p16, l528:  "Most computations of the model checker works with DD" -> Most computations of the model checker work with DDs
- p17, l548: It is not immediately clear what "2n + 2 resp." refers to.
- Tables 1, 2 and 3: "Both terminates" -> Both terminate
- p23, l742: "LTSmin is faster then starMC" -> than


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: STARMC: AN AUTOMATA BASED CTL* MODEL CHECKER
Review round: 1
Reviewer: 2

Output:
Basic reporting: - The treatment of deadlocks is unclear. On one hand, a Kripke structure is defined (lines 169-170 and 173-175) to be deadlock-free, enforcing infinite paths. On the other hand, the experiments section reports "stuttering enabled for LTL, and disabled for CTL" (line 631-632), which seems inconsistent with the given definition for a Kripke structure, and begs the question of what then the decision is for CTL* and whether the decision for LTL and CTL follows that of line 631.

- Fairness is presented in a confusing manner. The acceptance sets of the constructed GBA are used as "fairness constraints" (line 14 of Algorithm 4), but given that I have limited knowledge of and experience with fairness assumptions, why this is the case is completely opaque to me. A clarification either way would likely help make the theory much more digestible to a reader not familiar with the original work by Emerson and Lei. The role of the fairness assumption could also be clarified to make the algorithm more transparent.

- Another avenue for improving transparency of the algorithm is a more thorough overview of the algorithm before going into the details. Lines 300-314 attempt this, but the takeaways from the example are not particularly clear, as lines 303-310 mainly demonstrate how the example cannot be checked just with LTL or CTL checking. More elaboration on how the different subformulae are dealt with would be nice here.

- The there are a handful of typos. Definition 3 lists that $s \models a$ iff $a \in L(a)$ when clearly it should be $a \in L(s)$, and Algorithm 5 writes $\mathbf{repeat} ... \mathbf{until} S \neq S'$ as a fixed-point computation, when it probably should be $\ldots \mathbf{until} S = S'$. The example on line 302 has a formula mentioning APs alpha and beta, but the text describe an AP gamma which is not present.

- Algorithm 5 details a function $CheckE_{fair}G$ that returns a sat-set, while other functions that compute sat-sets are called e.g. $SatCTL*$ (algorithms 2, 4).
Experimental design: - The comparison to LTSmin is not particularly convincing. The paper does not appear to give a convincing argument why it is sane to consider the state space generation step an important part of the verification procedure. Line 638/639 specifies 60 seconds for state space generation and 60 seconds per formula, but the paper focuses mainly on the formula verification half of this, in which case the state space generation step is not important. Because of this, the comparison to LTSmin should probably have been restricted to models where both starMC and LTSmin generated state spaces, rather than models where either of them did. The text also places some emphasis on the worst-case exponential translation to $\mu$ calculus done in LTSmin, it should be clarified whether this time was contained in the 60 seconds for state space generation or the 60 seconds per query.

- The method of constructing CTL* queries from MCC should be noted already on line 633, right now it's first introduced on line 735-738. The sentence on line 633 should also be revised, it seems to have grammatical issues.

- For sake of completeness the used Spot/Meddly versions and LTSmin version should be noted.

- It seems “unfair” that LTSmin gets 4 cores. Sylvan can utilize 4 cores, but it seems be an unfair comparison for starMC (which is single core) and comparing the underlying DD framework rather than the algorithm (translation via mu-calculus vs. the Emmerson-Lei Algorithm).
Validity of the findings: - The experimental setup in its is missing. The generated data is provided, but the experiment cannot be repeated w/o significant effort by a peer. This is particular problematic as LTSmin is forced to adopt a specific variable-ordering, which is known to be a significant factor in the efficiency of symbolic modelcheckers.

- As the author motivates their work by the rareness of existing CTL* modelcheckers for which executable code is available, the authors should ensure that their entire setup is provided in a repeatable format. This would include both source-code, models and scripts for execution and data-collection. Currently links are provided to the raw result (but not scripts, models or queries) along with links to mutable/volatile places on the internet. A link to a permanent data-storage service such as Zenodo is strongly recommended.

- "the experiments conducted in the testing phase indicate that starMC can
solve (significantly) more models than LTSmin" is a strong statement given the selection-criteria of the models. The followup comment on the speed of LTSmin also seems arbitrary, given the difference in resource allocation for the two tools.
Additional comments: The paper is recommended to be revised, in particular the experimental section.

Furthermore, the text as a whole could have been written more clearly and needs an additional proof-reading pass to make it clearer, a few examples are provided in the notes.


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: STARMC: AN AUTOMATA BASED CTL* MODEL CHECKER
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper is detailed and gives mostly details on the implementation of the starMC tool which is part of the greatSPN graphical petri net tool, although it can also be used standalone. The paper describes the tool and the implemented algorithms in sufficient detail. There is plenty of background and there is also a reasonable section on the related work in the literature. The work is self-contained. Not many proofs are included but this is not necessary since there are no true new algorithms proposed, merely existing algorithms that have been implemented using symbolic methods.

Virtual machine with the tool, csv summaries of the benchmarks etc can be downloaded, although it is a bit tricky because the link in the review form is broken.
Experimental design: Research questions are sufficient and the conclusions appear supported by the empirical evidence. They appear to be replicable. I would however recommend that the authors store an artifact of the benchmarks, raw data, processing scripts on an external platform such as Zenodo for long term persistence.
Validity of the findings: As reported in section 2, I think the conclusions are supported by the evidence, they appear to be scientifically accurate.
Additional comments: I found a minor spelling error "Sumamry" in the caption of Table 3. Other than that the paper is reasonable. I would personally have appreciated more discussion of variable orderings and their impact because I like that in BDD papers, but in this case I can see that it does not add much to 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: STARMC: AN AUTOMATA BASED CTL* MODEL CHECKER
Review round: 2
Reviewer: 1

Output:
Basic reporting: This paper presents the ideas and algorithms behind the starMC tool, one of the few CTL* in existence. The tool uses decision diagrams for its symbolic implementation and accepts Petri nets as input. The implementation is evaluated in extensive experiments, using many models and formulas. This paper builds on an earlier demo paper, and extends it with separate logical and implementation views of the tool. Furthermore, it contains extensive references to original works, from which the ideas originated. The experimental evaluation is more rigorous than in the previous paper.

The paper is very clearly written and pleasant to read. The paper is mostly self-contained. There are plenty of references to previous work; the related work section covers more than two pages. I think this gives a lot of insight into the history of CTL* model checking and also its current state. The source code and files used for the experiments are available online, which is commendable.
Experimental design: The main questions posed in the paper are whether it's possible to design and implement a CTL* model checker and whether techniques like variable ordering and the extraction of counter-examples can be applied to it. As mentioned in the intro, the authors only answer the first half of this question, the second half is left for future work.

In my view, the setup of the experiments is appropriate for the question that the researchers try to answer. The set of benchmarks is based on the widely used MCC collection of models and formulas. The experimental results indicate that this is a varied set of benchmarks, which helps validity. My only criticism here is that the first research question relates to models of 'industrial interest', while it's not clear that such models are contained in the MCC benchmark set.
Validity of the findings: I think the main achievement of this paper is not the finding that CTL* model checking is possible in practice, but the fact that there is now a tool to do just that. The conclusion that starMC can be used for large state spaces is fair, given the experimental results. The authors also highlight the shortcomings of their tool.

The experimental results are easy to reproduce with the virtual machine the authors provided. The VM includes a script for running a subset of the experiments in a reasonable amount of time. The results appear to match those in the paper.
Additional comments: I want to thank the authors for carefully addressing all the comments I listed for the initial version.


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

Input:
Title: AN ENSEMBLE BASED APPROACH USING A COMBINATION OF CLUSTERING AND CLASSIFICATION ALGORITHMS TO ENHANCE CUSTOMER CHURN PREDICTION IN TELECOM INDUSTRY
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors proposed churn prediction model is a hybrid model that is
based on the combination of clustering and classification algorithms using ensemble. First,
different clustering algorithms i.e K-means, K-medoids, X-means, and Random clustering
are evaluated individually on two churn prediction datasets. Then hybrid models are
introduced by combining clustering with seven different classification algorithms
individually and then evaluation is performed using ensembles. The author has done some good work but still, need improvements :

1- Abstract didn't contain much information about the proposed ensemble model. it contains not needed information as compared to meaningful information please update it.

2- there is no need to add a single sub-section 1.1 heading. just simply add a contribution.

3- Visualization is very poor.

4- In section 3.4.8 Ensemble classifiers author mentions ensemble classifiers voting, bagging, AdaBoost, and stacked without proper architecture discussion.

5- The flow of experiments should be added with diagrams.

6- Consider these ensemble architectures in literature and in comparison with your proposed model.

Rupapara, V., Rustam, F., Shahzad, H.F., Mehmood, A., Ashraf, I. and Choi, G.S., 2021. Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification using RVVC Model. IEEE Access.

Jamil, R., Ashraf, I., Rustam, F., Saad, E., Mehmood, A. and Choi, G.S., 2021. Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. PeerJ Computer Science, 7, p.e645.

Rustam, F., Ashraf, I., Mehmood, A., Ullah, S. and Choi, G.S., 2019. Tweets classification on the base of sentiments for US airline companies. Entropy, 21(11), p.1078.

Rustam, F., Mehmood, A., Ullah, S., Ahmad, M., Khan, D.M., Choi, G.S. and On, B.W., 2020. Predicting pulsar stars using a random tree boosting voting classifier (RTB-VC). Astronomy and Computing, 32, p.100404.
Experimental design: The experimental design of the manuscript is not clear.
Please add a graphical representation of the proposed methodology.
Hyperparameter setting of models should be in tabular form.
How you tune your models, which method you used
Validity of the findings: Why author used Accuracy precision, recall, and f1 score for clustering method evaluation?
There is a big difference in accuracy and F1 score value which shows model overfitting please justify it.
Compare your proposed approach results with the above-mentioned studies.
Additional comments: Dear Editor
I hope you are doing well.
The manuscript is good needs some changes I have mention above
kind regards


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

Input:
Title: AN ENSEMBLE BASED APPROACH USING A COMBINATION OF CLUSTERING AND CLASSIFICATION ALGORITHMS TO ENHANCE CUSTOMER CHURN PREDICTION IN TELECOM INDUSTRY
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors proposed an approach for customer churn prediction. They proposed a hybrid model using the cluster and classification algorithms. The proposed research is evaluated on two different benchmark telecom data sets obtained from GitHub and Bigml platforms.  The author needs improvement to make the manuscript significant.

How the author combines cluster and classification algorithms please describe it in more detail.
The combination of several models will increase the complexity so you work on the tradeoff between accuracy and efficiency.
Evaluate the clustering model in terms of RMSE, and MSE, MAE.
Why you selected these two datasets?
Apply some validation techniques to show the significance of the proposed approach in terms of standard deviation.
What is the impact of preprocessing on the model's performance?
Have you done some variation in preprocessing?
Apply state of the arts deep learning models LSTM, CNN GRU in comparison.
Update the literature review with recent studies.

Rupapara, V., Rustam, F., Shahzad, H.F., Mehmood, A., Ashraf, I. and Choi, G.S., 2021. Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification using RVVC Model. IEEE Access.
Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A. and Choi, G.S., 2021. A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. Plos one, 16(2), p.e0245909.
Omar, B., Rustam, F., Mehmood, A. and Choi, G.S., 2021. Minimizing the overlapping degree to improve class-imbalanced learning under sparse feature selection: application to fraud detection. IEEE Access, 9, pp.28101-28110.
Rustam, F., Ashraf, I., Shafique, R., Mehmood, A., Ullah, S. and Sang Choi, G., 2021. Review prognosis system to predict employees job satisfaction using deep neural network. Computational Intelligence, 37(2), pp.924-950.
Experimental design: No comments
Validity of the findings: No comments
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: AN ENSEMBLE BASED APPROACH USING A COMBINATION OF CLUSTERING AND CLASSIFICATION ALGORITHMS TO ENHANCE CUSTOMER CHURN PREDICTION IN TELECOM INDUSTRY
Review round: 2
Reviewer: 1

Output:
Basic reporting: The revised version is good and the authors also resolve all my concerns, its good now for publication.
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: AN ENSEMBLE BASED APPROACH USING A COMBINATION OF CLUSTERING AND CLASSIFICATION ALGORITHMS TO ENHANCE CUSTOMER CHURN PREDICTION IN TELECOM INDUSTRY
Review round: 2
Reviewer: 2

Output:
Basic reporting: Authors have done good work in revision and resolved all issues mentioned by me
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: ACCESSIBILITY CHALLENGES OF E-COMMERCE WEBSITES
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper is well written. In addition to the introduction and conclusion, it is composed of method and material, results and discussion sections Hence, It is well structured.
The literature references are relevant and recent. However, almost of theme are reports and guides from international organizations like WHO. In fact, it will be more suitable to cite academic papers.
The authors follow a clear methodology. The obtained results are analyzed and discussed in order to give solution to the accessibility problem.
Solutions of accessibility are described in the conclusion section. I think that it is more suitable to put in the discussion section. Also, I think that the authors have to describe in details the followed methodology and the proposed solutions.
The authors have to explain some abbreviations (like ARIA attributes, A, AA and AAA levels).
I think that the introduction is very long. The authors touch on many subjects to show the importance of the problem (like COVID-19, e-commerce, accessibility). I suggest to rewrite the introduction to be more consistent.
I remark that there is no related work section presented in this paper !!!
Experimental design: The paper presents the accessibility challenges of e-commerce websites. The authors through this study have presented many contributions: 1 - correlation between the ranking of e-commerce websites and the accessibility, 2 - the most significant accessibility barriers and neglected principles  3 - some proposed solutions to improve the accessibility of e-commerce websites. I think that the research is well designed. Also, the research question is relevant, especially during COVID-19 period where e-commerce is considered as a main solution to apply confinement.
The applied methodology is rigorous. Also, the authors have mentioned the main limits of this methodology which consists mainly in the inability of automated tools to test content accessibility.
I think, that the authors have to explain in details the followed methodology.
Comparing to some cited works (like Oliveira et al (2020)), , I did understand exactly the novelty of this study. Is it the proposition of solutions to the accessibility? because the same tools (WAVE) and field (E-commerce) are used in both studies.
Validity of the findings: The obtained results are very important and they allow improving e-commerce websites (and indirectly improving the global economy). The first remark of the authors is the correlation between e-commerce websites ranking and the accessibility. This remark guides the business sector to improve the accessibility of their websites to improve their rank. Also, the authors describe the main accessibility barriers and the most neglected principles of this attribute. Moreover, the authors presented the solutions of this issue.
In term of novelty and originality, the authors have mentioned several recent works that are deal with this problem (using the same tools and applied in the e-commerce field). I think, that the authors have to mention clearly the novelty and originality points comparing to these studies. I suggest adding related work section to study this point.
The authors provided all data  required to replicate.
In future works, the authors suggest using in their future work a hybrid method by applying automated tool with manual review method. I think that it is difficult to test the accessibility of 50 websites manually. Also, the manual method causes subjectivity problems.
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: ACCESSIBILITY CHALLENGES OF E-COMMERCE WEBSITES
Review round: 1
Reviewer: 2

Output:
Basic reporting: The manuscript titled: Accessibility challenges of e-commerce websites, evaluates the accessibility of 50 e-commerce web sites in the top rankings based on classification proposed by ecommerceDB. The evaluation was made under WAVE tool. MS Excel pivot table tool was applied for analyzing the accessibility of selected e-commerce. In constrast, the normality test and the correlation between the ranking of e-commerce websites and accessibility barriers were analyzed using IBM SPSS (v 25).
Overall, the authors’ contribution was not pretty clear since the study analyses the outputs of WAVE tool on 50 top ranking e-commerce web sites. The proposed Methodology for evaluating e-commerce websites needs further highlights. For exemple, in the third phase: Define the test scenario, from a software engineering point of view there is no provided test. scenario. Also, Is there any potential back-tracking in the proposed Methodology in the case where WAVE does not exhibit a relevant information (potential failures, new accessibility improvements purposes, etc) ? Also, in Phase 2, which user categories were selected? it should be discussed.

The authors are strongly invited to make a comparison between their findings and the study of (Xu, 2020) which is the unique work presented in the State-Of-The-Art section that uses WAVE-based evaluation of 45 e-commerce website.
I should be interesting, for authors, to highlight the WAVE analysis method and the additional modification indicated in WCAG-EM 1.0 since it impacts the results and the proposed accessibility improvements.
In table 2 the column headings were not explained (Errors, Contrast Errors, Alerts, Features, Structural Elements and ARIA). For instance, the authors indicate errors and contrast errors, what is the difference between them? Also, it seems that ARIA, Structural Elements, Features and Alerts are warning barrier types. I suggest to explain these concepts when introducing table2..
Table 3 is presented in summative manner and needs more clarification about Success criteria and Level. Success criteria presents number with X.X.X format which is not clear (the meaning of the first, second and third number is missing). Also, level takes A or AA values which is even not explained before table3. In short, success criteria according to WCAG 2.1 should be outlined first in the appropriate position in the paper.

The authors should mention how Accessibility barriers have been identified? if the identification process belongs to WAVE findings, it must be noted.
In table 4, the authors are invited to justify why the Normality testing is required? Also, the result of normality test should be commented either the data follow the normal distribution or not (i.e., Lilliefors test)
In the discussion section, in addition to what is raised about accessibility barriers (low vision, ocular degenerative diseases, cardiovascular accidents, etc), the Hardware limitations were not discussed. Sometimes end-users’ devices (smartphone, tablets, …) may hide some significant information on the web site based on automatic brightness functionality which is proposed mainly for preserving human vision. Interstitial advertising was not discussed as well.
In table 5, when calculating the Pearson correlation, the authors should provide at which level the correlation is significant. Using SPSS, the significance level is defaulted by 0.01, it there any modification of that level?
Page 11, line 282, (In analyzing the accessibility of e-commerce websites, we applied descriptive statistics …)  the authors should clarify which type of descriptive statistics is used: univariate or multi-variate
Finally, as a native question: Is it possible to take into account web applications by WAVE-based evaluation?
Although figures, tables are well labelled and described, some minor issues are:
•	Line 94 the title: The state of the art is missing since the what follows discuss literature review.
•	Second paragraph after the figure 1: (there are many; many people with hearing roblems)-> redundance
•	Table3, figure 5, figure 6 and figure 7 captions are written in bold. It should be submitted to the template.
•	(…, a sample of the top 50 e-commerce websites was taken from the EcommerceDB ranking site)   recurrent phrase in the manuscript. (In Introduction section, In Materials & Methods section, in results section)
•	Page 8, line 125, (Our research proposes an automatic review method using the WAVE Web Accessibility Evaluation Tool (15) to solve …)  what (15) stands for? Is it about the tool version?
•	page 7, ligne 85: WCAG 2.1 (World Wide Web Consortium, 2018) consists of 4 principles, 13 guidelines and conformance criteria, plus an undetermined number of suitable techniques  Is there really an undetermined number? Can it be limited?
•	Page 7, line 88: Principle 1, related to perceptibility, refers to information, and user interface components should be presented most simply. Principle 2 focuses on operability - it comprises the user interface components, and navigation should be between each page   the difference between principle 1 and 2 is not clear.
•	The first paragraph in discussion section, (it was revealed that there is a need for web developers and web designers to apply WCAG 2.1 …) : incomplete phrase, a descriptive word is missing after WCAG 2.1 like : guidelines, principles, requirements.
•	Page 13, line 340, (This research can guide developers and designers of e-commerce websites to spread the use of WCAG 2.1 (6), ..) what does (6) stands for ?
Experimental design: Rigorous investigation is performed in a simplest way. Overall, the authors’ contribution was not pretty clear since the study analyses the outputs of WAVE tool on 50 top ranking e-commerce web sites. The proposed Methodology for evaluating e-commerce websites needs further highlights. The introduction should also highlights what are the gaps left by existing solutions and how the presented proposal addresses them.
Validity of the findings: In conclusion section, the provided recommendations are suitable for any type of web sites. I would have liked that they are dedicated to e-commerce sites since the core matter of the study is the Accessibility issue in e-commerce websites. Generally, e-commerce websites present images of products. I suppose that an image processing issue should be tackled.
The authors suggest future works in two places in the manuscript: before and within the conclusion section. It should be better to combine suggested future works in the end of conclusion section.
Additional comments: No additional comments


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

Input:
Title: ACCESSIBILITY CHALLENGES OF E-COMMERCE WEBSITES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Overall, the raised comments are handled.

Paper formatting needs more adjustments. As well as English improvement.

For example, in Page 10, line 211 : (This phase starts the evaluation process is defined, the level of compliance with WCAG 2.1 for the evaluation is defined (World Wide Web Consortium, 2018). Should be reformulated.

In Materials & Mathods section, page 195, (We used (WebAIM, 2021) Web Accessibility Evaluation Tool (WAVE) ) -> the citation should be putted after the  tool name.
Experimental design: The authors are invited to justify why they put the works ((Villa & Monzón, 2021), (Pollák et al., 2021) and (Paștiu et al., 2020)) to denote the impact of COVID-19 on the growth of e-commerce websites? Is there a need for making that? Also, the main issue of the paper is the accessibility not the impact of COVID-19 on the evolution of e-commerce websites. I suggest to make just a short paragraph in the form: according to ((Villa & Monzón, 2021), (Pollák et al., 2021) and (Paștiu et al., 2020)) the COVID-19 impacts the growth of e-commerce Websites in the way …..) not to describe each work separately.

In the Literature review of COVID-19, e-commerce, and web accessibility section, I suggest to remove (COVID-19, e-commerce, and web accessibility) and to keep only the title: Literature review
From the line 103 to 136, the authors expose the WCAG 2.1 principles in the literature review section after introducing COVID-19 works related to Web accessibility. I suggest to put a brief description of WCAG principles in the introduction section and restructure it better or to make a separated section that outlined WCAG principles.

The paper contribution is well designed.
Validity of the findings: The previous remarks and suggestions have been reviewed.
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: ACCESSIBILITY CHALLENGES OF E-COMMERCE WEBSITES
Review round: 3
Reviewer: 1

Output:
Basic reporting: I have just one last remark regarding the order of sections: it should be better to put Web accessibility principles section (from line 92 to 125) after the literature review section and before Materials & Methods section since it’s inappropriate in scientific writing to make sections intertwined. I think that my suggestion in the last review was not clearly understood.

For the rest, I’m satisfied and I think that the raised comments have been handled.
Experimental design: /
Validity of the findings: /
Additional comments: /


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

Input:
Title: CURRENT APPROACHES FOR EXECUTING BIG DATA SCIENCE PROJECTS—A SYSTEMATIC LITERATURE REVIEW
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript presents a systematic review of frameworks for executing big data science projects. The contributions of the article are well presented. The manuscript concisely summarizes the current state of the art in data science project management frameworks. Furthermore, the findings and conclusions derived from the systematic review are of interest to the research community on organizational and management frameworks for big data science projects.

The structure of the article is correct, and there are sufficient background details and literature references provided. The article introduces the topic adequately. The lack of established and mature methodologies justifies the need for information about how data science projects are organized, managed, and coordinated. Thus, it adequately justifies the need for a systematic review in this field.

The paper is well written and well organized, uses professional, clear, and unambiguous English. Figures and tables in general are comprehensive and helpful.
Experimental design: The content of the article is consistent with the journal's Aims and Scope. Furthermore, the methodology for the systematic review is described with sufficient detail: both the search space, the search strategy, and the selection criteria & procedure are well justified and described to be reproducible by another investigator (given access to the mentioned digital libraries).
The references are correctly cited. The review is also organized logically into coherent paragraphs.

The reviewer noted a possible mismatch in the total number of papers:

- In Figure 1, in the Google Scholar search space, the number of retrieved studies is 37600. However, the total number of retrieved papers in Table 1 is 44600 (9200 + 17800 + 17600). Is this mismatch due to repeated articles? This difference should be clarified.

- In line 262, it is mentioned that "After executing the title and abstract screen, 98 papers were selected for candidates for primary studies.". The same number of articles (98) appears in Figure 1. However, the total number of candidate papers in table 2 is 135 (52+18+24+36+5). Again, is this mismatch due to repeated articles on different digital libraries? This difference should be clarified.
Validity of the findings: The novelty of the study is assessed in the manuscript. The conclusions are appropriately stated, connected to the three research questions, and are supported by the results. The conclusion identifies very interesting future directions for research in the field. The limitations of the study are also presented in the Discussion section.

- Lines 299-304: "While the six themes that we identified in our SLR are all relevant to project execution, there was a wide range in the number of papers published for the different themes. Exploring this ratio of publications across the different themes provides a high-level view of research activity for the different themes. In other words, the number of articles for a particular theme is indicative of the current focus, with respect to current research efforts for that theme regarding the execution of data science projects."
The reviewer found this paragraph not straightforward and difficult to read, with the same idea repeated twice. Therefore, the reviewer recommends simplifying this paragraph and making it easier to understand.
Additional comments: Typography errors:
- Lines 511-513: "Another limitation is that while authors explored ACM Digital Library, IEEEXplore, Scopus, ScienceDirect, and Google Scholar databases, which index high impact journals and conference papers from IEEE, ACM, SpringerLink, and Elsevier to identify all possible relevant articles.": The while clause has not been appropriately used; there is no second part to the sentence.
- Line 387: the text should be in bold
- Caption Figure 1: csteps => steps
- Table 4: There are unnecessary parentheses on Extension / CRISP-DM / Primary Studies


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: CURRENT APPROACHES FOR EXECUTING BIG DATA SCIENCE PROJECTS—A SYSTEMATIC LITERATURE REVIEW
Review round: 1
Reviewer: 2

Output:
Basic reporting: This is a very interesting piece of research.
The use of English was clear, professional, and unambiguous.
The review of literature is carried out following a logical approach and a consistent methodology. Reasonable exclusions are adequately justified.
The structure of the paper is good, however, Table 3 shows missing references in the first two lines (Error displayed)
The article is aligned with the aims and scope of the journal and potentially impactful as its novelty is identified in a broad review (based on recent and up to date research) of the approaches for executing big data science projects. However, the article is generically assessing the methods and approaches underestimating the importance (and relevance) of the industries in which big data can be applied.
The introduction adequately presents the subject and it supports enough the research rationale and scope.
Experimental design: Rigorous investigation, excellent description of the methods and appropriate citations.
Validity of the findings: The validity of the findings is supported by adequate and complete methodology.
I encourage the authors to further investigate the industries (sectors) where (and why) the big data can be better applied.
Conclusions are well stated, however, they can be more developed. (Gaps and limitations adequately identified)
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: DBFU-NET: DOUBLE BRANCH FUSION U-NET WITH HARD EXAMPLE WEIGHTING TRAIN STRATEGY TO SEGMENT RETINAL VESSEL
Review round: 1
Reviewer: 1

Output:
Basic reporting: - The english writing is extremely problematic and hard to follow, the grammatical errors are all over the article. The submission should be thoroughly refined and proofread.
- The description in the section of related works is not informative enough to outline the pros and cons of different approaches and their design choices, thus being hard to well clarify the contribution of the proposed method.
Experimental design: - The train/test split is not well reported in the experimental settings. There are only descriptions on the cross-validation, but we have no idea what are the number of testing samples in each dataset. The authors should better clarify if they follow exactly the same experimental settings as other baselines used for making comparison.
Validity of the findings: - Although the reported performance of the proposed method is superior to various baselines on three datasets (i.e. DRIVE, STARE, an CHASE), the improvement is actually quite marginal. The ablation study for verifying the contribution of design choices (e.g. random channel attention, hard example weighting) shows also the same problem to have only incremental (almost neglectable) improvement with respect other model variants.
- The authors should clarify if they adopt the same binarization threshold for all the baselines while using F1 score, Sn, Sp, ACC, G-Mean, and MCC metrics for evaluation. As now the proposed method "chooses the threshold that has the highest F1-score as the optimal threshold value" (stated in Line 281), it would be inappropriate and unfair since the threshold is chosen according to the evaluation result (based on testing data?).
Additional comments: This paper proposes to tackle the problem of segmenting retinal vessel from the fundus image. The proposed method is stemmed from the U-Net architecture and extended to consist of one encoder and two decoder branches, where one encoder is trained with the typical cross-entropy objective while another one is trained by the loss function that weights more on the hard pixels (e.g. the boundary pixels of vessel). Moreover, the authors propose a "random channel attention" mechanism serving as regularization to improve the model training. The output of two decoder branches are merged by a fusion layer to produce the final segmentation output.

Pros:
+ The idea of adopting the hard example/pixels is not new but shown to be beneficial for the task of retinal vessel segmentation. The regularization based on "random channel attention" mechanism also shows better (but quite marginal) performance.
Cons:
In addition to the issues as commented in "Basic reporting", "Experimental design", and "Validity of the findings", here comes more problems that the authors should take into consideration for improving their paper.
- The performance of each decoder branch should be reported in order to better distinguish between the boost contributed to fusion layer and the hard example/pixels.
- During the ablation study for making comparison with respect to focal loss and dice loss, the parameter settings for the focal loss should be clarified.
- The superior performance in comparison to other baselines seems to mostly stem from the network with larger capacity, as the model variant of using single branch (in Table 2) already outperforms many baselines. There should be more detailed discussion/comparison on the model capacity among the proposed method and baselines for better clarifying the contribution of the proposed method.

Overall, as there exists the issues of having marginal improvement, unclear and potentially problematic experimental settings, insufficient experimental results, and bad paper organization/writing, I regret to suggest the rejection for this 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: DBFU-NET: DOUBLE BRANCH FUSION U-NET WITH HARD EXAMPLE WEIGHTING TRAIN STRATEGY TO SEGMENT RETINAL VESSEL
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper proposed an automatic fundus image retinal vessel segmentation (RVS) method, consisting of a 2-branch fusion U-Net with the proposed random channel weighting regularization mechanism. While a branch was trained with segmentation criterion, the other was trained with morphological mask weighted criterion. Related works were categorized and compared with the proposed methods in reasonable manners. Figures about different parts of proposed method were shown with clarity.

However, the experiments demonstrated no significant proof that the proposed methods (regularization and weighting loss) performed better than the existing deep learning methods. Also, there were a lot of gramatically errors that need to be fixed.
Experimental design: The experiments consisted of baselines and ablation comparisons on 3 different datasets, which were techincally sound and adequate. Also, the description of the method and all settings of experiments were provided with sufficient details.
Validity of the findings: The proposed method should be competitively effective, but better not to be claimed superior to the exsisting methods. According the the results, the improvements of DBFU-Net over other regularization methods(L2, Dropout, etc), training losses (Dice, Focal), and existing deep learning segmentation baselines were quantitatively neglegable (<1%). Moreover, the necessity of two branches were kind of sceptical, as applying mask weighting loss with smaller "weight" in Eq. 3 on a single U-Net could be equal to the effect of fusing two branches. Last but not least, the computational cost of generating "morphological mask weighted cross entropy loss" should be greater than Focal and Dice loss because of the additional erosion/dilation operations.
Additional comments: The authors addressed a good problem to solve, as automatic RVS should be beneficial for ophthalmology disease screening. A fairly amount of experiments were conducted to show the performance of the proposed method. However, the following are a few cons to be fixed:

    1. According to the results, the performance and improvement of the proposed methods over other exisiting methods should be claimed conservative.
    2. Additional experiments should be conducted to better demonstrate the contribution of this work:
        2.1. single branch U-Net vs. DBFU-Net, using the same loss functions (mask weighted, Focal, DICE)
        2.2. single branch U-Net vs. DBFU-Net, using the same regularizations (RCA, dropout, etc.)
    3. There were too many grammatical errors. I strongly suggest the authors proofread it thoroughly, e.g.
	1.1. Missing “a” and “the”
	1.2. Wrong usage of comma and multiple verbs in a sentense
	1.4. Fig. 2, Func. 3
	1.5. Typos, e.g.
		Line 61. patient -> procedure
		Line 188. define -> definition
		Line 424. feature -> future


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: DBFU-NET: DOUBLE BRANCH FUSION U-NET WITH HARD EXAMPLE WEIGHTING TRAIN STRATEGY TO SEGMENT RETINAL VESSEL
Review round: 2
Reviewer: 1

Output:
Basic reporting: - The english writing is still problematic as the grammatical errors are still all over the article even in the revision parts. The submission should be thoroughly refined and proofread.
- The categorization in the section of related works seems awkward, the boundary between non-learning-based methods and some unsupervised methods is unclear.
Experimental design: no comment
Validity of the findings: - If the authors claim that their design choices (e.g. random channel attention, hard example weighting) can be combined with the techniques proposed in other articles to get better results, there should be such experiments in the revision.
Additional comments: This paper proposes to tackle the problem of segmenting retinal vessel from the fundus image. The proposed method is stemmed from the U-Net architecture and extended to consist of one encoder and two decoder branches, where one encoder is trained with the typical cross-entropy objective while another one is trained by the loss function that weights more on the hard pixels (e.g. the boundary pixels of vessel). Moreover, the authors propose a "random channel attention" mechanism serving as regularization to improve the model training. The output of two decoder branches are merged by a fusion layer to produce the final segmentation output.

Pros:
+ The idea of adopting the hard example/pixels is not new but shown to be beneficial for the task of retinal vessel segmentation. The regularization based on "random channel attention" mechanism also shows better (but quite marginal) performance.
Cons:
Please refer to my comments on "Basic reporting" and "Validity of the findings".

Overall, I suggest the major revision for this 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: DBFU-NET: DOUBLE BRANCH FUSION U-NET WITH HARD EXAMPLE WEIGHTING TRAIN STRATEGY TO SEGMENT RETINAL VESSEL
Review round: 2
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: 1. The author had clarified the incremental contributions of this paper: RCA regularization, hard example weighting train strategy, and DBFU-Net. Also, the results were claimed to be comparable with existing methods.

2. The paper had been proofread.

3. Additional experiments were conducted and the results were demonstrated to be competitive to the existing methods. A follow-up concern is that your tables got some "-" (missing data). I suggest the author fill them up as some might alter the expectation (e.g. Table 7 [21, 22] might be better than the proposed method).


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

Input:
Title: ULTRASOUND IMAGE DENOISING USING GENERATIVE ADVERSARIAL NETWORKS WITH RESIDUAL DENSE CONNECTIVITY AND WEIGHTED JOINT LOSS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors of the article named "Ultrasound Image Denoising Using Generative Adversarial Networks with residual dense connectivity and weighted joint loss" propose a novel method based on the GAN network for denoising of speckle noise in images. They train and evaluate their method using the SNR and SSIM metrics. They compare the results with the other state-of-the-art methods in image denoising and prove their GAN outperforms other methods.

The language is clear, consistent, and professional English. The literature overview is sufficient, however, there could be a few improvements and additional references. On line 78 you write "(...) methods (...), which 'are' divided into two categories." If it is common to divide these methods in these categories then use a reference, if not, write "could be" instead of "are". There should be a citation for the SSIM. In line 252 you should cite all of the named models.

The article is well structured. Figures could be improved. Figures 9 and 12 are mentioned in the article as "Figure", others are mentioned as "Fig.". The authors should fix that. In figure 12, the authors should add what dataset does the bar plot refers to.
Experimental design: Research is well defined and structured. The results are interesting and well presented. However, in Table I. authors should add the average value and standard deviation. Models should be trained multiple times to avoid accidental results.

The authors should mention what does the skip pathway refers to, concatenation or addition? Authors could try to squeeze the whole model in one or two figures for better clarity and generally improve Figure 1.

Methods to which the GAN is compared could be better described.
Validity of the findings: The article proposes original work, it is impactful and has some novelty. All data has been provided. Conclusions are well stated and limited to supporting results.
Additional comments: The general impression of the article is good, I hope the authors will submit the review. The headline of the article is based on the ultrasound images. However, the method has only been visually evaluated on the ultrasound dataset, all analysis has been done on some other publicly available datasets.


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

Input:
Title: ULTRASOUND IMAGE DENOISING USING GENERATIVE ADVERSARIAL NETWORKS WITH RESIDUAL DENSE CONNECTIVITY AND WEIGHTED JOINT LOSS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Literature needs improvement. Writing style is OK, but Figures are poorly drawn, requires directional arrows to illustrate the dataflow
Experimental design: OK
Validity of the findings: Its OK. Please refer to my final comments related to the noise levels
Additional comments: Major Concerns :

P8 Lin-79- The author classify the Wavelet method as a Frequency domain techniques, which is absurd. Wavelets provide both time and frequency. This statement/ and preceding ones should be modified  accordingly

P9, Lin 106. What does modified UNET mean. Provide reasonable information about the modifications. Also there are many UNET variations which need to be mentioned in literature - RDAU-NET, RDA-UNET-WGAN, ADID UNET, UNET++, VGGUNET, Ens4B-UNet etc. Though these models are NOT directly related to denoising, it is worth mentioning these models atleast in the literature

P10 Lin 138 - The Speckle Noise Model requires more explanation. The current information is minimalistic.

P11. Lin 155 - Please change loss of image information to loss of spatial information

P11 Lin 158 - Use of Residual blocks and Dense nets are known to avoid vanishing gradient problem, as stated in RDAUNET and ADID UNETS, hence what additional improvement does RDCB provide

P14, Lin 212. I am confused as to why the author is explaining  VGG 19 model ?. I feel the VGG part can be removed or rewritten in way that is related to the proposed architecture

P15, Lin 245, Is Visual effect ( the performance metric as explained  by the author ) is used elsewhere in the literature ? If so please cite those papers .

Figure 1 requires directions (arrows) to illustrate the flow the data. At the moment,  its really confusing to the readers to understand how the entire model works

Figure 12. Why does the noise levels vary between the same sigma value for PSNR and SSIM. Does noise level expressed in db should be same (eq 32db in PSNR and 0.8377 in SSIM) ?

In general the paper requires more additional literature in regards to UNETs and GANs. the authors have to clearly mention the term " Modified " that is used in their model


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: ULTRASOUND IMAGE DENOISING USING GENERATIVE ADVERSARIAL NETWORKS WITH RESIDUAL DENSE CONNECTIVITY AND WEIGHTED JOINT LOSS
Review round: 2
Reviewer: 1

Output:
Basic reporting: References are improved. Professional english is used.
Experimental design: Research question is now well defined. Methods description is improved as well as figures.  However, authors do not state how many times were experiments done to prove the robustnes of the methods, at least I do not see it.
Validity of the findings: Conclusions are well stated, and all underlying data has been provided.
Additional comments: If these small modifications are made the article is suitable for publishing, in my opinion.


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

Input:
Title: 2D FACIAL LANDMARK LOCALIZATION METHOD FOR MULTI-VIEW FACE SYNTHESIS IMAGE USING A TWO-PATHWAY GENERATIVE ADVERSARIAL NETWORK APPROACH
Review round: 1
Reviewer: 1

Output:
Basic reporting: - Clarity in the overall structure of manuscript sentences.
- Literary study can be further extended as there are no studies referred for the year 2021.
- Sufficiently detailed tables and figures which is a plus point.
- Abstract mentions "improved the encoder-decoder global pathway structure"; however, the abstract fails to mention the details of this improvement in terms of accuracy or operational workflow.
- Authors mention throughout the manuscript the need for the global structure; however, the reader can still do not get the idea of its importance and impact on the overall verification process.
- Line 459 mentions 20 illumination levels for the image dataset. Mention the details of these levels.
Experimental design: - Line 5,6,7 mentions there is a drop of 10% of recognition. Can authors present and validate this point through focused experiments and also show the gain of the proposed methods over the same set of images (for frontal-profile to frontal-frontal face verification)
Validity of the findings: - The results reported are promising. It would be great if the authors could add some more experiments to show the effect of 20 different illumination levels as for angles presented in Table 3.
Additional comments: - Related work should be summarised and a small paragraph should be added at the end of the section to discuss the key takeaway of literary study to facilitate the reader.
- Proposed method should be presented in one concrete table with all the operational steps involved (all in one place)

Overall the research is well-defined and has a high potential to fill the identified research gap.


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: 2D FACIAL LANDMARK LOCALIZATION METHOD FOR MULTI-VIEW FACE SYNTHESIS IMAGE USING A TWO-PATHWAY GENERATIVE ADVERSARIAL NETWORK APPROACH
Review round: 1
Reviewer: 2

Output:
Basic reporting: - English language should be improved. There still have some grammatical errors, typos, or ambiguous parts.

- "Introduction" is verbose, it could be revised to make it more concise.

- Quality of figures should be improved significantly. Some figures had a low resolution.
Experimental design: - Source codes should be provided for replicating the methods.

- Statistical tests should be conducted in the comparison to see the significance of the results.

- The authors should describe in more detail on the hyperparameter optimization of the models.

- Deep learning is well-known and has been used in previous studies i.e., PMID: 31920706, PMID: 32613242. Thus, the authors are suggested to refer to more works in this description to attract a broader readership.

- Cross-validation should be conducted instead of train/val split.
Validity of the findings: - The authors should compare the performance results to previously published works on the same problem/dataset.

- It lacks a lot of discussions on the results.

- The model contained a little bit overfitting, how to explain this case?
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: 2D FACIAL LANDMARK LOCALIZATION METHOD FOR MULTI-VIEW FACE SYNTHESIS IMAGE USING A TWO-PATHWAY GENERATIVE ADVERSARIAL NETWORK APPROACH
Review round: 2
Reviewer: 1

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


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

Input:
Title: INTEGER PARTICLE SWARM OPTIMIZATION BASED TASK SCHEDULING FOR DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING TO IMPROVE SLA SATISFACTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.	Abstract has to be rewritten. A concise and factual abstract is required.
2.	Problem/gap not properly highlighted in the abstract, also quantitative results/improvement missing in the abstract.
3.	There are a number of English language-related issues and need to be revised especially a lot of conjunctions used, sentences are too long and difficult to apprehend.
4.	A meta-heuristic algorithm …. Which can have a better performance than heuristic methods (line 45-47), how? Need some references or proper justification.
5.	According to the assumption at line 109 that tasks assigned to a core are executed based on EDF, in such a case how do you handle starvation?
6.	Do you think that branch and bound technique can’t be applied for large-scale problems (line 154)?
7.	The sentence “Then, their method used Kuhn-Munkres …” need to be rephrased (line 364)
8.	The sentence “Different from these  … “ need to be rephrased (line 393)
9.	No paper is found from 2021 and the single paper you have mentioned as of 2021 is actually published in 2018.
10.	Add papers from 2021
11.	Figure number should be mentioned (line 339)
Experimental design: 1.	The numbers of subscripts were used for variables and symbols are very high, reduce the number of subscript variables and symbols as few as possible. For this, you can divide the “problem formulation” section into subsections.

2.	Do you think inertia weight has a role in the PSO algorithm? Which variant of inertia weight you have used for your algorithm. Detail about inertia weight and other terms like r1, r2, c1, c2 are not given (lines 212-213).

3.	Complexity of the proposed approach has been calculated but not compared with their counterparts.

4.	Which encoding scheme you have used? (Figure 2)

5.	“Referring to published ……” Only mentioning that it is taken from the literature is not sufficient, you need to provide proper references (lines 247-250).
Validity of the findings: 1.	References not mentioned for the first three approaches (FF, FFD, and EDF) used for comparisons (lines 253, 254-255, and 256-258)? You need to provide proper references.
2.	How the computing time of task on the Cloud can be greater than on the Edge? (lines 330-331)
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: INTEGER PARTICLE SWARM OPTIMIZATION BASED TASK SCHEDULING FOR DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING TO IMPROVE SLA SATISFACTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors claimed that his approach has better resource efficiency. However, the results concerning resource efficiency are not meaningful. The authors are required to obtain results concerning Average resource utilization which will be between 0 to 1 or 0 to 100 %.
All the figures are having no y-axis caption. Similarly, some of the results seem the same even their titles are different. For instance, Figure 3 (a) and Figure 3 (c).
The authors are required to add a section results and discussion that should show why their proposed approach is better than the existing and results need to be clearly described.
Experimental design: The authors claimed that his approach has better resource efficiency. However, the results concerning resource efficiency are not meaningful. The authors are required to obtain results concerning Average resource utilization which will be between 0 to 1 or 0 to 100 %.
All the figures are having no y-axis caption. Similarly, some of the results seem the same even their titles are different. For instance, Figure 3 (a) and Figure 3 (c).
The authors are required to add a section results and discussion that should show why their proposed approach is better than the existing and results need to be clearly described.
Validity of the findings: The results need to be discussed properly.
Additional comments: No additional comments