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

Input:
Title: TOWARDS AN ARABIC SIGN LANGUAGE (ARSL) CORPUS FOR DEAF DRIVERS
Review round: 2
Reviewer: 3

Output:
Basic reporting: The manuscript need to be checked again with respect to issues on spelling and style. (for ex. Line No 32 - spelling of publish need to be checked again)

Uniformity in listing the references is expected.

Also it is suggested to cite the latest references in the manuscript.

In general, Basic reporting is ok.
Experimental design: This research focus on communicating with persons with hearing and speech impairment especially with drivers. The research question could have been presented well.

Methodology could have been presented in detail.
Validity of the findings: The time complexity of the proposed methodology could have been discussed.

Accuracy is not discussed.

Conclusion is ok.
Additional comments: This research focus on communicating with persons with hearing and speech impairment. what motivates the author to restrict with only drivers. Even the passengers with hearing and speech impairment need to communicate with a driver who is not like that. Title of the manuscript can be checked again with respect to this.

Line No 152 need to be checked. deaf people can communicate, but they cannot understand what others say.

The time complexity of the proposed methodology could have been discussed.

Accuracy could have been discussed.

The manuscript need to be checked again with respect to issues on spelling and style. (for ex. Line No 32 - spelling of publish need to be checked again)

Uniformity in listing the references and uniformity in citing the references is expected. Some of the articles cited are not listed in the reference. Also few papers in reference are not cited.

Also it is suggested to cite the latest references in the manuscript.


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

Input:
Title: SYNTACTIC MODEL-BASED HUMAN BODY 3D RECONSTRUCTION AND EVENT CLASSIFICATION VIA ASSOCIATION BASED FEATURES MINING AND DEEP LEARNING
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, authors presented a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, authors applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets
Experimental design: Experimental Design is Ok. But some more technical revisions are required in this paper with regard to Algorithm and Flowchart of the proposed methodology.
Validity of the findings: Data and Analysis experimentation based finiding are Ok.
Additional comments: The Paper needs the following Major Revisions:

1. Introduction should be more broad with regard to Problem Definition and even the scope of the paper. More information needs to be discussed in the paper.

2. Related works- In every related works, the stress should be on the novelty proposed, the explanation of the technique and even on experimental conclusions. At the end of related works, highlight in 9-15 lines what overall technical gaps are observed that led to the design of the proposed methodology.

3. Add the Algorithm, Flowchart and Steps of working of the proposed methodology.

4. Add some performance analysis section to this paper.

5. Add future scope to this 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: SYNTACTIC MODEL-BASED HUMAN BODY 3D RECONSTRUCTION AND EVENT CLASSIFICATION VIA ASSOCIATION BASED FEATURES MINING AND DEEP LEARNING
Review round: 1
Reviewer: 2

Output:
Basic reporting: Why not compare all methods on three data sets in Table 9?
Experimental design: no comment
Validity of the findings: no comment
Additional comments: 1 According to the performance scores given in the tables, the results of the proposed method are compelling. But from a technical perspective, it is mainly a combination of existing technologies, and the contributions are not obvious. I hope that the manuscript will highlight the contributions of this paper in the revised version, omitting unnecessary formulas.
2 Please discuss the failure cases and the limitations of the proposed method.
3 Why not compare all methods on three data sets in Table 9?
4 “To find the knees points we take the midpoint between the human-body midpoint and the two feet points. Eq. (6) demonstrates the human knee points;” According to this manuscript at lines 204-206, the denominator in formula (1) is 2?
5 My personal suggestion is to intersperse the Figures in the manuscript.
6 Some paragraphs are indented too much (such as the first paragraph), and some are not indented.


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: SYNTACTIC MODEL-BASED HUMAN BODY 3D RECONSTRUCTION AND EVENT CLASSIFICATION VIA ASSOCIATION BASED FEATURES MINING AND DEEP LEARNING
Review round: 1
Reviewer: 3

Output:
Basic reporting: 1. The writing in this article lacks proper structure, has various grammatical mistakes, and somethings are not clearly formulated.
2. The mathematical notation used in the manuscript is not easy to parse and doesn't follow standard notations, which makes it difficult for users to understand the ideas in detail. This also results in various redundancies throughout the manuscript.
I have marked some of them in the paper and attached them. Authors should make the manuscript easy for readers to follow.
3. Limitations and future works are not discussed.
4. All the figures and tables are added towards the end of the manuscript and are not placed correctly. Captions for the figures are not self-explanatory.
5. Some variables are not defined before they are used (see the attachment).
6. Relevant missing work: Openpose, Cao et. al.
7. Feature generation steps have various ambiguities and require clarification(see the attachment).
8. Algorithms should be re-written and some of them(like Algorithm 2) are not even required.  Indention is not followed in algorithms
Experimental design: 1. Goal and idea of the method is interesting. Authors propose various features like pose, joint angle, silhouette, etc, and use these for gait-event classification. Although all the features used seem important for gait classification, there can be some redundant features and some might enable wrong correlation.  I would be very interested to see a comprehensive analysis of feature selection.
2. Since the method section is not clear, even though the techniques used in the method are trivial methods, I fear, paper cannot be exactly reproduced. This is because some things were not clear.
3. I have marked specific questions in the manuscript.
Validity of the findings: 1. Datasets used in the paper are publicly available datasets and authors provide detailed evaluation for various types of motion.

2. Limitations and future works should be discussed in brief. Authors do not explain the limitations of their method like multi-step data preprocessing step, time etc.

3. Authors use CNN for classification, although CNN is powerful features extractors, how do author justify their choice of not using CNN based features? A comparative study or justification is required.

4. How is this pipeline of even gait classification better than OpenPose based 2d joint detection + gait classification layer. This is the very basic experimental baseline that comes to my mind, given OpenPose performs very well for most of the datasets and generalizes well.
Additional comments: I have corrected some grammatical mistakes and anomalies, not all. So you should carefully read the manuscript again along the same line.


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: SYNTACTIC MODEL-BASED HUMAN BODY 3D RECONSTRUCTION AND EVENT CLASSIFICATION VIA ASSOCIATION BASED FEATURES MINING AND DEEP LEARNING
Review round: 2
Reviewer: 1

Output:
Basic reporting: In this paper, authors presented a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, authors applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets.
Experimental design: Experimental design is Ok and falls under aim and scope of the paper.
Validity of the findings: Yes, the revised findings are Ok.
Additional comments: The Paper stands Accepted with no further rrevisions.


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: SYNTACTIC MODEL-BASED HUMAN BODY 3D RECONSTRUCTION AND EVENT CLASSIFICATION VIA ASSOCIATION BASED FEATURES MINING AND DEEP LEARNING
Review round: 2
Reviewer: 2

Output:
Basic reporting: Following the suggestions and requests of all reviewers, the authors have updated the manuscript. The problem statement, technical gaps, experiments, algorithm, and flow charts have been updated/added. This improves the initial submission by a great margin.
The paper reads well now.
Experimental design: no comment
Validity of the findings: My previous concern of using CNN not just for classification but also for features has not been answered well. Also, authors do now validate their method against the openpose+CNN classifier baseline.

They only provide some argument, which doesn't seem strong enough. I would recommend the authors add openpose+CNN classifier baseline results in the paper, even if their method performs worse than this. It will be useful for readers to draw a conclusion.
Additional comments: My only request is that the authors add the above baseline(section 3) in the paper.


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

Input:
Title: A FAST VECTORIZED SORTING IMPLEMENTATION BASED ON THE ARM SCALABLE VECTOR EXTENSION (SVE)
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper proposes a fast vectorized sorting implementation based on the ARM scalable vector extension (SVE). It ports the author's previous work about the AVX-SORT algorithm to SVE, defines a new Bitonic-sort variant using SVE, and implements an efficient Quicksort variant using OpenMP tasks.

Strengths:

The description is clear. The work seems technically sound and leads to impressive experimental results with raw data shared.

Weaknesses:

(1) It is good the author declares this work is to port the previous work in AVX to SVE and cites the paper "A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel Skylake". There is no cover letter so that the reviewer is not certain whether it is allowable to directly reuse figures 1, 2 and 3. Besides, it is not clear the difference between this paper and the reference paper.

(2) This paper is mainly about SVE, so it is better to not only mention AVX in the related work.

(3) The performance figures, especially figure 4 and figure 7, are very hard to read; e.g., there are two markers that are too similar in figure 4, and lines with the same color but different width in figure 7.

(4) As declared in the paper, there are three contributions. It would be good if the author could detail the last one, "Implement an efficient Quicksort variant using OpenMP tasks", because there are only very brief descriptions about this in the paper.

(5) What's the difference between SVE-Bitonic and SVE512-Bitonic? Is SVE512-Bitonic a version of SVE-Bitonic with fixed vector length? If so, the performance is a little confusing as the performance of SVE512-Bitonic will not be worse than SVE-Bitonic, right?

(6) In figure 7, there are several lines of different numbers of threads are overlapped, which means the overheads and benefits of different numbers of threads on the same problem are the same. The reviewer was wondering why this happens.

(7) It would be a plus if the author could define how good the improvement is by using SVE in this paper, e.g., comparing it to the theoretical one instead of only to AVX.
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: A FAST VECTORIZED SORTING IMPLEMENTATION BASED ON THE ARM SCALABLE VECTOR EXTENSION (SVE)
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1.  This paper need to add more information about Scalable Vector Extension in introduction and related part. Especially in section 2.2 and 2.3.  It feels like the author is writing a paper about AVX instead of SVE.  In section 2.2.1 why use avx512 as an example instead of SVE? SVE related work and publications are not mentioned in section 2.3.

2.  Figure 4 is very confusing and hard to read. Choose different lines and markers will be better.
Experimental design: 1. In line 365 the author claims that "This means that considering a static vector size of 512 bits, with compare-exchange indices hard coded and no loops/branches, does not provide any benefit, and is even slower for more than 70 values."

The author needs to provide more information and details to support this assumption, because normally extra branching instruction may need extra cycles. It will be helpful if the author can provide low level explanation, such as assemble code snippets.  Also some performance tools

2. The vector length of  A64FX is configurable, I would like to see the results of the same experiments by setting vector length to 256 bits, then we can evaluate performance benefits with different vector length.
Validity of the findings: "We also demonstrate that an implementation designed for a fixed size of vectors is less
efficient", I would like to see more evidence to support this conclusion.
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 FAST VECTORIZED SORTING IMPLEMENTATION BASED ON THE ARM SCALABLE VECTOR EXTENSION (SVE)
Review round: 2
Reviewer: 1

Output:
Basic reporting: Thanks for the authors' detailed response, and there is one remaining question. In the description of Figure 7, it's said "using more than 8 threads does not provide any benefit", which is not true when the number of values is large, as shown in the figure.

Minor:

(1) Figure 7 is split so that maybe markers could be used to differentiate lines instead of line width.

(2) there is only one subsubsection (2.2.1), and maybe it's better to remove it.
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: NFAD: FIXING ANOMALY DETECTION USING NORMALIZING FLOWS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The introduction could be more detailed, such as adding the reason why the proposed method  in the manuscript works.

I suggest that the authors add more recent and closely related references on the same problem, such as:

(1) Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection, IEEE Transactions on Neural Networks and Learning Systems, 2021.
(2) Deep Weakly-supervised Anomaly Detection. arXiv preprint: 1910.13601 (2019).

I also encourage the authors to consider comparing their performance with those of the two citations I recommended. The authors had use an early version of the second recommended citation as a comparing method(devnet); the first citation has a superior performance than that of devnet on the same problem.

The first paper is my own paper. And the second one has no conflict of interest with me.

The manuscript is not well formated in the current version. Also, there are some typos and errors in the content, e.g., the caption of some tables are wrongly represented as "Figure X.".

Excludingly, this manuscript basically meets the requirements.
Experimental design: In the experiments, the analysis of influencing factors is missing, and the author can add some experiments to this regard.
Validity of the findings: More experiments are suggested to conduct to show the effect of the proposed method as well as the major factors that may have impact on its performance.
Additional comments: I attempted to run the code, but an error occured. Maybe there's something wrong with my environment or my network. But I hope the authors make sure the code could run automatically without any additional change.

Also for this code, the authors are suggested to provide more detailed instructions.


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: NFAD: FIXING ANOMALY DETECTION USING NORMALIZING FLOWS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Clear and unambiguous, professional English used throughout.
The language is correct and the article is written in clear and professional English.
Literature references, sufficient field background/context provided.
The literature on related papers could be more explored. Just a few numbers of papers were cited, and the article mentions just a few methods of AD, specially based on classification, while there are others as ones based on Clustering, distance and density (as in F. Meng, G. Yuan, S. Lv, Z. Wang, and S. Xia, “An overview on trajectory outlier detection,” Artificial Intelligence Review, vol. 52, no. 4, pp. 2437–2456, 2019).
Professional article structure, figures, tables. Raw data shared.
The structure doesn’t follow the format suggest by the journal. It combines “Results” and “Discussion” in just one item, what makes the paper less clear, because there are discussions presented before results. Result are present in 3 paragraphs after Figure 4 and before figures 7 to 9.
In Figure 1, specially the last diagram is too hard to see the tail p-values. Figures 4, 5, 6, 7, 8 and 9 are not figures but table. They should shave been mentioned as tables not figures.
Figures 8 and 9 are misplaced in the text. They are in “Conclusion”, but they should be included in “Results and Discussions” as figures 4 to 7 are.
In figure 7 and 8, the best results are bolded as in other figures. They should have been bolded despite the best results are not provided by the proposed algorithm.
All appropriate raw data have been made available in accordance with our Data Sharing policy.
Self-contained with relevant results to hypotheses.
The article proposes a new technique to address DA problem. The problems are well stated, and the hypothesis is tested, showing relevant results, even the ones that the proposed algorithm doesn’t performance well.
The submission is ‘self-contained,’ and represents an appropriate ‘unit of publication’. It also includes all results relevant to the hypothesis.
Formal results should include clear definitions of all terms and theorems, and detailed proofs.
In topic 3, when authors state that normalizing flows generative mode aims to fit the exact probability distribution of data, they don’t mention that normalizing flows provides exact inference and log-likelihood evaluation as its merits (D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” in Advances in Neural Information Processing Systems, 2018, pp. 10 215–10 224). It is important because when the model is trained in topic 4, it is trained by standard for normalizing scheme of maximization the log-likelihood, which is not described previously when normalizing flows are described (Topic 3).
Experimental design: Original primary research within Aims and Scope of the journal.
The submission is within Aims and Scope of the journal, because proposes a new algorithm to AD task, a field related to Computer Sciences. The paper is a Research Article, in which a well stated hypothesis is tested and the results of these tests are presented.
Research question well defined, relevant & meaningful. It is stated how research fills an identified knowledge gap.
The research question is well defined and also the article scope. However, in the second line of “Problem Statement” item, the statement “In practice, while such samples are, indeed, most likely to be anomalous, often some
anomalies might not be distinguishable from normal samples” doesn’t have any reference.
Also, the effect of the number of anomaly samples in the training data should be addressed more deeply, as in Exploring normalizing flows for Anomaly Detection (Pathak, C. 2019), as this effect is one of the most important issues in the paper.
Rigorous investigation performed to a high technical & ethical standard.
The investigation must have been conducted rigorously and to a high technical standard. The research must have been conducted in conformity with the prevailing ethical standards in the field.
Methods described with sufficient detail & information to replicate.
Methods should be described with sufficient information to be reproducible by another investigator.
Validity of the findings: Impact and novelty not assessed. Meaningful replication encouraged where rationale & benefit to literature is clearly stated.
Conclusions are based in a metric presented in tables, so we can see clearly where the proposed algorithm surpass others and where it doesn’t. However, the metric employed is not clearly described. We infer that authors use accuracy, but this option is not justified.
All underlying data have been provided; they are robust, statistically sound, & controlled.
The data is provided and it was possible to rerun the experiment.
Conclusions are well stated, linked to original research question & limited to supporting results.
The conclusions are appropriately stated, and connected to the original question investigated, and are supported by the results, as based in obtained results described in figures 4 to 9. The proposed algorithm outperformances many tested algorithms in most of situations, except when dialing with images dataset.
Additional comments: As general conclusion, the paper can be published after mandatory corrections, specially these described in item 1.


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: NFAD: FIXING ANOMALY DETECTION USING NORMALIZING FLOWS
Review round: 2
Reviewer: 1

Output:
Basic reporting: It seems that the authors have addressed most my concerns. I suggest to a minor revision for the manuscript in its current form. The comments are as follows:1
1. for the two references, "(1) Feature Encoding with AutoEncoders for Weakly-supervised
Anomaly Detection, IEEE Transactions on Neural Networks and
Learning Systems, 2021. (2) Deep Weakly-supervised Anomaly
Detection. arXiv preprint: 1910.13601 (2019)."
What is the major technical differences between the references and the proposed method? I guess that the authors did not display the bad cases for the proposed  method, which the authors should present and highlight them.
2.  some references are not complete, e.g., ““Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., and Liu, L. (2021). Feature encoding with autoencoders for weakly-supervised anomaly detection.””, which lacks the publication title and etc.
"241 Chen, R. T. Q., Behrmann, J., Duvenaud, D., and Jacobsen, J.-H. (2019). Residual flows for invertible
242 generative modeling."
3 a detailed lists of revisions are needed in the response letter for each coments in the previous review, so that the reviewer can see the revisions more clear.
Experimental design: please see the above
Validity of the findings: please see the above
Additional comments: no


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

Input:
Title: EVENT CLASSIFICATION FROM THE URDU LANGUAGE TEXT ON SOCIAL MEDIA
Review round: 1
Reviewer: 1

Output:
Basic reporting: The title of the paper is  “Event classification from text existing on social media”
it's not mentioned in the title and abstract, that the data is in Urdu language !!!
Experimental design: In the lines 182-183 the authors mentioned that “
 In comparison, we used sentences of Urdu language for classification and explored the textual features of sentences. We disclosed all textual features in detail in this paper that were not reported ever in state-of-art.”
Comment :
The authors didn’t mention in details all the textual features for the Urdu language, and they didn't provide enough examples for each feature so the reader can understand it.

the authors didn't add a section for the machine learning algorithms they used in the paper, or even add a references for these algorithms!!!!

the authors didn't mentioned the stemmer or POS tool they used in the experiments.

the authors mentioned "The dataset consists of more than 1 million (1,02,962) labeled instances of twelve (12)
22 different types of events."  what was the size of training data , and the size for testing data!!!!

the authors didn't include any example for their work!!!
Validity of the findings: the authors didn't experiment all choices of the extracted features, and they just did the experiment on "title and last 4 word" !!! only

the tables 7-10 not explained.
 All the figures not explained.

the findings not clear and accurate!!!
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: EVENT CLASSIFICATION FROM THE URDU LANGUAGE TEXT ON SOCIAL MEDIA
Review round: 1
Reviewer: 2

Output:
Basic reporting: The author works in the text classification domain and performs an experiment on the event classification in the Urdu language. The author used state of the art machine learning models and show that the KNN outperform all machine learning models. The author used a dataset contain 1 million records and 12 different labels taken from social me dia.
Experimental design: 1-The author used TF-IDF and count vectorizer for features extraction. Why author used only TF-IDF and BoW count vectorizer? the author should use word2vec in comparison with  TF-IDF and count vectorizer see the article "Rustam, F., Ashraf, I., Mehmood, A., Ullah, S. and Choi, G.S., 2019. Tweets classification on the basis of sentiments for US airline companies. Entropy, 21(11), p.1078."

2- Author should check the IDF formula again.
3- KNN is best performer when features set will be small. Find a supporting article for that where knn outperform on a large features set.
4- Author used state of the art method which have already lots of used in classification what is the novality of author work. work in specific domain is not a novality.
Validity of the findings: No comment
Additional comments: 1- The experimental diagram should follow the full experimental flow.
2- author work on specific language how to deal with preprocessing of text there should be a more clear discussion about the library and self-generated library if anything used by the author.
3- the author should perform a comparison with results without preprocessing of text.
4- grammar should be checked thoroughly.


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: EVENT CLASSIFICATION FROM THE URDU LANGUAGE TEXT ON SOCIAL MEDIA
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: The accuracy for the ML algorithms with the features , are not reasonable , increasing from 17% to 85% , then the final results 99% !!!
The authors didn't mention any weaknesses of their model.

In many English and Arabic researches , the SVM and KNN , were the best ML algorithms , but in your research they are 's not, can the authors mention the reasons!!
Additional comments: The examples should be written in Urdu , and English language as well,


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

Input:
Title: EVENT CLASSIFICATION FROM THE URDU LANGUAGE TEXT ON SOCIAL MEDIA
Review round: 2
Reviewer: 2

Output:
Basic reporting: Comments are below
Experimental design: Comments are below
Validity of the findings: Comments are below
Additional comments: The author asks to revise the manuscript to improve the quality of the article and for this, I have mentioned some comments for the author. Lots of comments have cooperated but still, there are some weak areas in the manuscript.
1- Abstract's first 3 and 4 lines are too general and the overall abstract should be more attractive. best performers' results should be added to the abstract.

2- Results section should contain more detail about the results and represent the significance of the models.


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: EVENT CLASSIFICATION FROM THE URDU LANGUAGE TEXT ON SOCIAL MEDIA
Review round: 4
Reviewer: 1

Output:
Basic reporting: No comments
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: EVENT CLASSIFICATION FROM THE URDU LANGUAGE TEXT ON SOCIAL MEDIA
Review round: 4
Reviewer: 2

Output:
Basic reporting: The author presented the event classification for the Urdu language text existing on social media and news channels. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. Title, Length, and last-4-words of a sentence are used as features to classify events. The Term Frequency-Inverse Document Frequency (tf-idf) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. The author resolves the comments but still lots of things should be improved especially the abstract. such as:

These mention two sentences are the same in the abstract why? Random Forest (RF), Decision Tree, and k-Nearest Neighbor outperformed among the other classifiers. Random Forest and K-Nearest Neighbor are the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively.

The Abstract didn't contain detail about contribution and methodology. it's too general and short.
Experimental design: No commets
Validity of the findings: The dataset is imbalanced then the results are too significant justifies that the models are no overfitted on majority class data.

The author gives the confusion matrix report in terms of TP, TN, FP, and FP.. These terms are useful for binary classification how they adjust them in multiclass classification. add some visual infromation.
Additional comments: No comemnt


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: SMALL FACIAL IMAGE DATASET AUGMENTATION USING CONDITIONAL GANS BASED ON INCOMPLETE EDGE FEATURE INPUT
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, a new image-to-image translation framework using conditional GANs is proposed, which can generate diverse photorealistic images from limited edge features after training with a small number of training images.The article has a certain degree of innovation and novelty, the experiment is relatively sufficient, and the overall content is complete, but there are still some problems that the author hopes to correct：
1）Whether the pictures involved in the article can be directly reflected in the text to facilitate reading and understanding;
2）The quality of the picture is not very high, it is recommended to replace it, and some of the picture content is redundant, and it is recommended to delete it;
3）Figure 16 compares the KID and FID values of different methods within 500 sheets, and the difference between the results of the other two methods and the method in this article is getting smaller and smaller. Can the author compare more images, or briefly Explain the change trend of subsequent FID and KID results;
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: SMALL FACIAL IMAGE DATASET AUGMENTATION USING CONDITIONAL GANS BASED ON INCOMPLETE EDGE FEATURE INPUT
Review round: 1
Reviewer: 2

Output:
Basic reporting: It is better to include a paragraph providing details about the structure of the paper (could be the last paragraph of the Introduction section).

The related work section is very comprehensive (good work!). Include some details of additional image data augmentation works would further improve the related work section.

Indicate potential future works of this research in the conclusion section.
Experimental design: Structuring the methods section is very important to enhance the readability of the paper. It is better to include individual subsections to three major parts of the proposed method.

Lack of supportive arguments is the primary concern in the results section. Authors will get benefitted by including a comprehensive discussion on both qualitative and quantitative results.

An ablative study is a must in this case to express the significance of each module.
Validity of the findings: Highlight the limitations of the work is important for researchers in the field to explore more in this domain of interest. Discuss some of them in the results 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: SMALL FACIAL IMAGE DATASET AUGMENTATION USING CONDITIONAL GANS BASED ON INCOMPLETE EDGE FEATURE INPUT
Review round: 1
Reviewer: 3

Output:
Basic reporting: There contains grammatical errors and typos in the manuscript. The authors should re-check and revise carefully.
Experimental design: The research question is well defined. Methods are described in full detail.
Validity of the findings: The findings are valid, and can be easily replicated.
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: SMALL FACIAL IMAGE DATASET AUGMENTATION USING CONDITIONAL GANS BASED ON INCOMPLETE EDGE FEATURE INPUT
Review round: 2
Reviewer: 1

Output:
Basic reporting: Great improvement. The article is well written, well structure and I believe the level of novelty is good. I really don't see anything especially to comment and I rather accept it within the current form.
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: SMALL FACIAL IMAGE DATASET AUGMENTATION USING CONDITIONAL GANS BASED ON INCOMPLETE EDGE FEATURE INPUT
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have carefully revised the manuscript according to previous reviewers' feedback.
Experimental design: Improved to the expected quality.
Validity of the findings: Improved to the expected quality.
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: BENGSENTILEX AND BENGSWEARLEX: CREATING LEXICONS FOR SENTIMENT ANALYSIS AND PROFANITY DETECTION IN LOW-RESOURCE BENGALI LANGUAGE
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: The discussion of the experiment needs to be more in-depth, and the content of the experiment needs to be more detailed and complete. Please analyze the advantages of the proposed method and supplement the related experiments.
Validity of the findings: no comment
Additional comments: 1. Your most important issue
Q1: Are the methods presented in this paper the most advanced and original, what are the main challenges of this work, and these need to be further discussed in the relevant work.
Q2: The discussion of the experiment needs to be more in-depth, and the content of the experiment needs to be more detailed and complete. Please analyze the advantages of the proposed method and supplement the related experiments.
2. The next most important item
Q3: 3.3.2 What is the meaning of the PMI formula and its variables? Please give specific explanation.
Q4: 3.3.3 “After applying approach 2, our dataset contains around 38000 pseudo-labeled reviews. We then employ PMI and POS tagger in a similar way to phase 2. However, since this phase utilizes pseudo-labeled data instead of the true-label data, we set a higher threshold of 0.7 for the class label assignment.”  Why is the threshold set to 0.7?
Q5: 5.1.1 “If the total polarity score of a review is below 0, we consider it as a positive prediction; if the final score is below 0, we consider it as negative;” In which case does the score below 0?
3. The least important points
Q6: Table reference error in section 3.1.2, Table3 should be changed to Table1.
Q7: 3.1.2 Incorrect data in Table1, the data in the last column of the last row in Table 1 should be 10410.
Q8: 5.1.2 “Among the three translated lexicons, VADER classifies 46.60% reviews correctly, while AFINN and Opinion Lexicon provide 48.65% and 41.95% accuracy, respectively.” The accuracy of AFINN in Table 4 is 31.65%, which does not agree with the description in the text.
Q9: CONCLUSION “We made both BengSentiLex and BengSwearLex publicly available for the researchers in Sen (2020)” This sentence lacks punctuation.


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: BENGSENTILEX AND BENGSWEARLEX: CREATING LEXICONS FOR SENTIMENT ANALYSIS AND PROFANITY DETECTION IN LOW-RESOURCE BENGALI LANGUAGE
Review round: 1
Reviewer: 2

Output:
Basic reporting: The article has been provided in clear and professional English, as well as a sufficient organized sections. The tables and Figures are also well created.
Experimental design: The approach seems to have a promising results. Although the approaches have been used in previous papers, they showed a good insight on the specific language (Bengali).
Validity of the findings: The authors have provided well-designed conclusion, as well as defining the data with good statistics.
Additional comments: Here is a few suggestion that can improve the article:
1- The scope of the "related works" section requires a more thorough details.

i) Some essential parts of the paper, such as "supervisory characteristics" (supervised methods, semi-supervised methods, and unsupervised methods) can be discussed.

ii) the following paper has also done a similar work. it is recommended to review and compare the method with the provided method in the paper. https://www.sciencedirect.com/science/article/abs/pii/S0950705120305529

iii) Section 3.2.1, the second paragraph is relatively discussed the related works, which can be replaced in the "related works" section.

2- For better referencing the readers, it is recommended to reference the Equations with numbers.
3- For better understanding of the readers, a translation for Figure 5 is required.
4- Also, the source of the dataset (Youtube reviews) is needed to be cited.
5- The compared methods are mostly traditional methods for the experiments. authors must provide state-of-the-art methods for comparisons. (having LSTM, Bi-LSTM, CNN, Attention model, BERT model, etc.) - though not all of them, but to compare some of them.

The general idea behind the paper is a novel approach for Bengali language and hope the authors find the comments useful and make the necessary changes for the acceptance.


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

Input:
Title: BENGSENTILEX AND BENGSWEARLEX: CREATING LEXICONS FOR SENTIMENT ANALYSIS AND PROFANITY DETECTION IN LOW-RESOURCE BENGALI LANGUAGE
Review round: 1
Reviewer: 3

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: line 231, Table 3 -> Table 1
line 235, numbers do not add up to the numbers tabulated in Table 1 -> last row, last column cell?
line 249, will translation sufficiently map all types of sentiments from one to another language?
line 254, reference for NMT
line 364, which is the machine learning model used in SGD algorithm? Similary in line 515.


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: BENGSENTILEX AND BENGSWEARLEX: CREATING LEXICONS FOR SENTIMENT ANALYSIS AND PROFANITY DETECTION IN LOW-RESOURCE BENGALI LANGUAGE
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The following questions should be addressed by the authors:
1) line 280 typo error
2) line 301 typo error
3) line 320, was there any statistical analysis carried out?
4) line 363, how many languages and list them?
5) line 405, what are the input features for the various ML classifiers? covered in section 5.2.2 but not in section 4!
6) line 408 and 409, svm is a binary classifier and not a multiple class classifier.
7) line 489, which type of word embedding is used, is it CBOW or count based?
8) line 529, how is the polarity score computed?


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: BENGSENTILEX AND BENGSWEARLEX: CREATING LEXICONS FOR SENTIMENT ANALYSIS AND PROFANITY DETECTION IN LOW-RESOURCE BENGALI LANGUAGE
Review round: 2
Reviewer: 2

Output:
Basic reporting: 1. The concept is clear.
2. In line no. 280, "sophisti. cated" - spelling mistake.
3. In figure 2, both Phase 2 and 3 consist of lexicon building block (you may indicate flow of phase 2 and 3 work in this diagram)
4. In line 383, 4.4.2 Phase 2: Lexicon generation from labeled data - you mentioned like that. But in figure 2, phase 2 input is taken from both labeled and unlabeled like that. Rectify this.
Experimental design: 1. To improve the readability, the author requires to provide a good data flow/ sequence diagram in understanding the “Service Level Agreement”
Validity of the findings: 1. In the introduction, the findings of the present research work should be compared with the recent work of the same field towards claiming the contribution made. , kindly provide several references to substantiate the claim made in the abstract (that is, provide references to other groups who do or have done research in this area).
2.	Try to concise the conclusion.
3.	Discuss the future plans with respect to the research state of progress and its limitations.
Additional comments: 1.	In the Introduction section, the drawbacks of each conventional technique should be described clearly.
2.	You should emphasize the difference between other methods to clarify the position of this work further.
3.	The Wide ranges of applications need to be addressed in the Introduction
4.	Add the advantages of the proposed system in one quoted line for justifying the proposed approach in the Introduction section.


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

Input:
Title: USING MACHINE LEARNING ANALYSIS TO INTERPRET THE RELATIONSHIP BETWEEN MUSIC EMOTION AND LYRIC FEATURES
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, the authors address the emotional effect of features extracted from song's audio and lyrics on human listeners. In particular, the main goal of the paper is to use NLP techniques to see the role of music's audio/lyrics can raise any emotional response on the listeners.

The paper is well written and clear. The context and background information is supported by literature references. The paper is well structured.

I would like to make the following comments about the paper:

1) The authors could stress the definitions for the "musical" terms used in the paper, such as: melody, lyrics, audio, valence, arousal;

2) How can someone tell if a music is positive or negative without lyric? In the literature, is there a study relating sound features to the emotional response?

3) The authors mention that the results may depend on the country where the study is conducted since the local culture may affect the way an individual interprets the music. In which extent the results presented here may be generalized to all countries?

4) What is the difference between music emotion (Panda et al. 2013) and the idea of emotion perception in music? I think the authors could emphasize how and why this work is different from Panda et al. 2013.

5) Is there an explanation for the correlation between arousal and the total number of words of a song? Do you think that the duration of the music play a significant role? In other words, is there a threshold (minimum number of words / duration) that has to be reached so a music can trigger an emotional response?

6) The authors could explain how the numbers 2371 and 18 (in r(2371) and t(18), respectively) were obtained.

7) The authors could discuss about the use of "extensive" features (that may depend on the song size) instead of "intensive" ones (normalized, that may not depend on the song size attributes such as the number of words). If an emotional response (like arousal) is triggered by the song size, does it mean that a longer song will trigger a more intense emotional response? I wonder this relationship is always linear, in the sense that a song that is too long (too short) will trigger the same kind of response that a medium size song. It would be nice to see a two dimensional histogram showing the intensity of the emotional response and the size of the music.
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: USING MACHINE LEARNING ANALYSIS TO INTERPRET THE RELATIONSHIP BETWEEN MUSIC EMOTION AND LYRIC FEATURES
Review round: 1
Reviewer: 2

Output:
Basic reporting: General

Taking into account the possibility that this manuscript does get published, I think it appropriate to include a few brief comments.
- The manuscript seems somewhat with grammatical/syntax and typographical problems. I leave it to the authors to resolve these copyediting problems by actually thoroughly reading the manuscript. Problems of this sort should definitely not appear in print.

Title
It is concise and conveys the topic of research.

Abstract and Conclusions sections included are basically the same text.
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: USING MACHINE LEARNING ANALYSIS TO INTERPRET THE RELATIONSHIP BETWEEN MUSIC EMOTION AND LYRIC FEATURES
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: The authors addressed the points I raised previously, thus I recommend the publication of the paper.


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

Input:
Title: USING MACHINE LEARNING ANALYSIS TO INTERPRET THE RELATIONSHIP BETWEEN MUSIC EMOTION AND LYRIC FEATURES
Review round: 2
Reviewer: 2

Output:
Basic reporting: Thank you for trusting in my work. All proposed suggestions were provided in this last version. I'd like to suggest some proof-editing:
At line 229, including a period at the end of the paragraph
At line 309, include a space between Table and S2 words - Supplemental Materials TabelS2
At line 341, replace the capital letter T for lowercase t
 In order to apply a default format of the numbers used in the manuscript, I’d suggest including 0 before all occurrences of the decimals numbers (sometimes is used .01, .111, and 0.01)
Experimental design: no comment
Validity of the findings: no comment
Additional comments: All proposed suggestions were provided in this last version. I'd like to suggest some proof-editing:
At line 229, including a period at the end of the paragraph
At line 309, include a space between Table and S2 words - Supplemental Materials TabelS2
At line 341, replace the capital letter T for lowercase t
 In order to apply a default format of the numbers used in the manuscript, I’d suggest including 0 before all occurrences of the decimals numbers (sometimes is used .01, .111, and 0.01)


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: PREDICTION OF COVID-19 EPIDEMIC SITUATION VIA FINE-TUNED INDRNN
Review round: 1
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: I think the details of data processing in the experimental design, including all variables from the preprocessing and coding of the original obtained data to how to train and test, should be clearer and add more details.
Validity of the findings: I suggest that the discussion of the results should be more fully compared with the previous studies. Please clarify and add.
Additional comments: The structure of this article is complete and professional, with clear diagrams and tables, and a clear sharing of materials. However, I think the reference literature, especially the collection of the literature on the epidemic, is slightly insufficient. Not only must we collect more literature about COVID-19, but even from the perspective of public health, I think it is necessary to go back to the Spanish flu... etc. The relevant literature on the spread of the epidemic is discussed in order to complete the discussion in the background.
For your research design, I think the details of data processing in the experimental design, including all variables from the preprocessing and coding of the original obtained data to how to train and test, should be clearer and add more details.
Finally, I suggest that the discussion of the results should be more fully compared with the previous studies. Please clarify and add.


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: PREDICTION OF COVID-19 EPIDEMIC SITUATION VIA FINE-TUNED INDRNN
Review round: 1
Reviewer: 2

Output:
Basic reporting: Many flaws in the article that are needed to be addressed by authors.
Experimental design: Need to be revised in terms of phases and draw a main figure the tell the whole story.
Validity of the findings: No benchmark scenario is found, so how authors can prove that their study better than existing ones.
Additional comments: The authors presented very traditional  study which I think it is more to evaluation rather than development. However, several limitations are exist:
- The title is too general and it reflects the contribution more to evaluation rather than development study ?
- The abstract is too long and need to be revised in the context of academic article.
- Authors mentioned "Therefore, it is necessary to propose a model to predict and analyze the development tendencyof COVID-19, and to summarise the meaningful and positive policies"
this is too general challenge which was handled by many studies. Furthermore, the contribution of the study is very poor. Moreover, the authors need to be specific about the contributions of the study.
- Comparison with state of the art studies also is missing.
-  The related works section is missing. Thus, the study failed to fill up to highlight the gap of existing 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: PREDICTION OF COVID-19 EPIDEMIC SITUATION VIA FINE-TUNED INDRNN
Review round: 2
Reviewer: 1

Output:
Basic reporting: Authors have addressed well all issues and the articles in the present format can be accepted for publication.
Experimental design: Well organized research methodology
Validity of the findings: Robust validation scenario
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: CAPPLANT: A CAPSULE NETWORK BASED FRAMEWORK FOR PLANT DISEASE CLASSIFICATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: In the methodology section, authors have mentioned that CapPlant is a real deep learning architecture because it uses end-to-end learning. However, in research paper end-to-end learning is not explained and discussed. It would be better if the authors can explain the concept of end-to-end learning in the paper.
Experimental design: Figure 3 shows the learning curves obtained while calculating training and validation accuracy and it gives an idea that model was trained for 200 epochs. In contrast, Table 1 lists 100 epochs.  Author should explain that why model was trained for 200 epochs but model till 100 epochs was used for testing and validation.

Please cite Plant Village dataset in Table 2.
Validity of the findings: It would be helpful if the authors can include the values of standard deviation values along with the accuracies reported in Table 4.
Additional comments: Overall the paper is written and organised well.


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

Input:
Title: CAPPLANT: A CAPSULE NETWORK BASED FRAMEWORK FOR PLANT DISEASE CLASSIFICATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors propose a model CapPlant to classify diseases in plants using deep learning model. They try to solve this problem by incorporating capsule layer with convolutional layer. The paper is generally well written and structured.  Literature review is comprehensive and authors managed to successfully discuss the importance of their research, from both a theoretical and an applied perspective. The statistical analysis been performed appropriately and rigorously. Manuscript is presented in an intelligible fashion and written in standard English. The manuscript is technically sound, and the results support the conclusions.
However, I would recommend following modifications:
1.	In introduction author has mentioned that “exploiting capsule layer enables the model to capture relative spatial and orientation relationship between different entities of an object in image”. How?
2.	In Table 2, author has already assigned class labels, these labels should be used in Table 4 & Table 5 instead of class names while discussing F1 score, Precision and Recall of each class.
3.	Paper lacks detailed model summary of proposed CapPlant model. There should be a visual illustration that shows type of layer(input, CNN, Capsule, Dense etc ) input tensor and output tensor of a complete 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: CAPPLANT: A CAPSULE NETWORK BASED FRAMEWORK FOR PLANT DISEASE CLASSIFICATION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper presents the CapPlant model to predict whether it’s healthy or contain some disease underpins deep learning based architecture. Usage of Capsule layer as the final layer convolutional neural network to include spatial information about the entities is a novel idea.

The paper provides a detailed description of the existing state of the art techniques along with their limitations.

The results for the experiments have been described in detail. The approach is showing statistically sound results.

Some Minor changes:
In Line 49-58, the authors mention that “These models have some major drawbacks, for instance one major issue with some of these models is in targeting less number of crops and diseases, secondly they are presenting results using limited or none evaluation metrices. In  this research, a deep learning architecture; CapPlant is developed using CNN along with capsule network to classify and detect any disease found in plants accurately.”

It would be nice to elaborate a bit the evaluation metrics that the existing techniques are not focussing on and the ones that CapPlant is dealing with.
Experimental design: Usage of the Capsule layer as part of the CNN is quite a novel and original idea.

The process of the approach has been provided in detail. Experimental methodology and the experimental results for both the proposed approach and the existing techniques are provided. A comparative analysis is done. The approach shows statistically sound results with a better accuracy score when compared to the other approaches.

For the purpose of reproducibility, it would be nice to make available the code in the form of Git repository.
Validity of the findings: The dataset is available.

The findings of the experiments comply with the main contribution of the paper.

Threats to Validity for the CapPlant approach should be 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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: .
Experimental design: .
Validity of the findings: .
Additional comments: •	This paper discusses the image memorability using Deep CNN and the work presented in this paper reads well and is logically structured in a clear narrative. The paper has a descriptive title, abstract, and keywords. Still, I have the following comments and observations that need to be accommodated or addressed in this paper:
•	It is better to state the main motivations and contributions of this paper more clearly in the introduction. The novelty of the proposed work should be better emphasized.
•	In One Cycle Learning, the use of the words Cycle, iterations, and epoch seems ambiguous and not properly explained.
•	It is also not clearly mentioned in what way One Cycle Learning leads to better model fitting.
•	In addition, the authors should include the details about the hyper-parameter settings.
•	The evaluation metrics mention that the rank correlation of -1 represents complete disagreement, which I don't think is the case. The statement can be rechecked and rewrite accordingly.
•	Regarding the image classification and segmentation, more literature needed to give a provide stronger backing to your claims. Check the below work!!
CL Chowdhary, et al., Analytical study of hybrid techniques for image encryption and decryption, Sensors 20 (18), 5162, 2020.
J Tamang, et al. Dynamical properties of ion-acoustic waves in space plasma and its application to image encryption - IEEE Access, 2021


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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: 1. The English language in the paper has to be polished.
2. What are the limitations of the existing works that motivated the current work?
3. Discuss the drawbacks of the current work in the conclusion.
4. There must be maximum two paragraphs in conclusion section. The first paragraph is for briefly discussing the entire paper and the second paragraph is for discussing some future works.
5. Short form must be defined as properly when used for the first time.
6. Never use I, we, you, our, etc. in a research article. Use one “Tense” to write the entire paper. “Present Tense” is preferable.

7. All the figures, tables, equations and references must be cited in the text.
8. References must be cited sequentially starting from 1, then 2, then 3 and so on.
9. The discussion is very important in the research paper. Nevertheless, this section is short and should be presented completely.
10. Please cite below references in introduction and related work.

 “Median filtering forensics scheme for color images based on quaternion magnitude-phase cnn
“CNN approaches for classification of Indian leaf species using smartphones
An efficient image analysis framework for the classification of glioma brain images using CNN approach,
Early detection of diabetic retinopathy using PCA-firefly based deep learning 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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 1
Reviewer: 3

Output:
Basic reporting: - Paper is well written. Authors should add a little background of the study and limitations of the existing works and clearly explain the contributions at the end of the introduction.
- Spell out each acronym the first time used in the Abstract as well as the body of the paper.
- The objectives of this paper need to be polished. Contribution list should be polished at the end of the introduction section and last paragraph of the introduction should be the organization of the paper.
Experimental design: - The authors should clearly mention all parameters used to evaluate the performance of the model.
- The procedures and analysis of the data are seen to be unclear.
- Authors should clearly describe splitting criteria.
_ Should discuss more about the dataset.
Validity of the findings: - Major contribution was not clearly mentioned in the conclusion part.
- The discussion is very important in research paper. Nevertheless, this section is short and should be presented completely.
Additional comments: - Please improve the overall readability of the paper.
- Authors should add the most recent reference:
1) Byte-level object identification for forensic investigation of digital images, 2020 International Conference on Cyber Warfare and Security (ICCWS), 1-4
2) Cross corpus multi-lingual speech emotion recognition using ensemble learning, Complex & Intelligent Systems, 1-10
3) CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network using CNN and Attention-based GRU, IEEE Transactions on Network Science and Engineering


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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 1
Reviewer: 4

Output:
Basic reporting: The article looks good. The detailed comments are mentioned in the attached file.
Experimental design: Some improvements are required, are mentioned in the attached file.
Validity of the findings: The experiments performed are valid, some more comparisons are required to show it more better.
Additional comments: All comments are mentioned in the attached file.


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

Input:
Title: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 1
Reviewer: 5

Output:
Basic reporting: This paper addresses the interesting question of what makes an image memorable and uses state-of-the-art computational models in its quest to better understand why certain images are remembered better than others. The paper introduces a novel neural network architecture which can be used to better approximate, relative to other models, the memorability of images. I enjoyed this paper and think it has the ability to contribute a useful model to the literature. In addition to proposing a novel model, the authors utilize a clean approach consisting of publicly available datasets to provide support for the utility of their neural network design and its applicability to other researchers. I do, however, have a few concerns about the paper in its current form. Below I have listed these specific concerns, as well as suggestions as to how, in my opinion, the paper could be improved. I have organized these concerns relating to both theoretical considerations and content, as well as writing and style.

1) I would recommend revising your writing with respect to memorability, so that it is a bit cleaner and easier for the reader to follow. In lines 91-92 you describe “measure an individual's probability of remembering an image”, but then later you discuss that memorability is a property of the image. Try to be clearer about whether the memorability you are focused on in this work is a property of the image or of a person. The phrasing in line 95 implies that memorability could be tied to the person, rather than the image.

2) In line 200: “The main innovation in ResNet”, is this your innovation or the work of the creators of ResNet? If so, please cite.

3) Pertaining to lines 115-116, in the previous paragraphs you discussed how memorability could vary between people, provide a bit more detail about how this memorability score here was annotated.

4) In line 174, what is the “human performance” you are referring to here? Can you describe where this comes from? And then later in line 410, is this the same human accuracy referenced here? If not, please describe.

5) Pertaining to lines 373-375, can you explain more about how real-time deployment performance can drastically improve for objects and scenes that are not in the new datasets?

6) In line 107, what type of deployment do you mean here? Are you referring to the application of it?

Please include citations to support the various claims made in lines:
⁃	Line 89-90
⁃	Line 104-109
⁃	Line 134 (and remove the phrase “which was already mentioned“)
⁃	Line 487 - 493

A few instances of repeated words or phrasing
⁃	Line 399-400 “cross-validation purposes”
⁃	Line 497-499 “humans’ capability…”

Re-word/Re-phrase to make comprehension easier for the reader
⁃	Line 41 “a hardest problem”
⁃	Line 89 & 334 “prove”
⁃	Line 110-111 rephrase “was given”
⁃	Line 122-123 “greedy algorithm” -- I am not sure what you mean by this
⁃	Line 159-160 and 165-166 both statements “propose” the new model. Can you remove one of these to make this clearer?
Experimental design: The methods and approach you described are technically sound and fits with the general scope of this journal.

1) In line 45 in the Abstract, you note the “information from the hidden layers” — could you speak to what type of information is stored in each layer? Can you connect this to the qualitative section later in the paper where you discuss contributions to memorability such as the presence of an object within the scene and the presence of a human, etc.
2) Can you provide data and/or a quantifiable measure to support the claims in lines 481-485. For example, instead of stating that manually you observed a difference with respect to the presence of a human or not, you could quantify the average memorability for images containing a human vs without a human.
3) In line 436, please report the ground-truth values here like you did in line 432 for the Top 10
Validity of the findings: The findings and data reported are appropriate, as well as the figures to support findings. Though I would recommend adding to the description of, Figures 5, 6, & 7, to state where the reported performance comes from (e.g., averaged across, etc.). Additionally, the axis labels in Figure 9 are nearly impossible to read. Though not extremely important, it would be helpful to provide a figure with higher resolution so that they can be read.

Conclusions are well stated and impact of the novel model is described.

1) When testing with the other models, did you use the same 5-set cross-validation approaches across all models? Please provide more details to confirm the similarity in the approaches so that it is easier to compare the various models and to be confident that differences in consistency of the predicted scores are due to the models themselves, not just the other processes/steps.

2) In line 111, is this the dataset that you refer to later as the Isola dataset? If so, please label it here on the first time you introduce it. If not, what makes this different? In line 328, the same comment -- is this the Isola dataset?

3) In the supplementary material you provide the link to the LaMem dataset, however, can you also add how to access the Isola dataset?
Additional comments: Minor writing suggestions
1) A few instances of unnecessary capitalization throughout, for example:
	Line 84 “Image Memorability”
	Line 440 “Computer Vision tasks”
	Line 491 “Image Aesthetics”
2) Line 202, the use of ResNet-50 is inconsistent, please use the same phrasing throughout so it is easier for the reader to follow
3) Line 276 “it’s” should be “its”
4) Line 418, the word “close” appears twice
5) Line 161 “ResMet-Net” should be “ResMem-Net”
6) 508 “per-trained” model should be “pre-trained 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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: .
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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This research work "ResMem-Net: Memory based deep CNN for image memorability estimation" is proposes.   The research domain addressed in this research paper is novel, and the results are satisfactory. But it needs the below improvements:

1.	Reduce unnecessary text from the paper.
2.	The authors should discuss more on deep CNN.
3.	The Experiment discussion should be written more in-depth, precise, and concrete, such as what questions were resolved? How can the proposed method solve these problems? The most recent works should be discussed in the related work section.
5.	The objectives of this paper need to be polished. The contribution list should be polished at the end of the introduction section, and the last paragraph of the introduction should be the organization of the paper.
6.	Reduce the size of the introduction.
7.	Contributions at the end of the introduction section should be polished.
8.	Relevant literature reviews of the latest similar research studies on the topic at hand must be discussed.
9.	The quality of the figures is not good.
10	Don’t use “Its,” ‘I, your’ and any informal text in the paper.
11.	There are some grammar and typo errors.


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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 2
Reviewer: 3

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


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

Input:
Title: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 2
Reviewer: 4

Output:
Basic reporting: Good
Experimental design: Good
Validity of the findings: Good
Additional comments: All corrections are addressed by authors.


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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 2
Reviewer: 5

Output:
Basic reporting: I appreciate the authors' diligence and am satisfied with how they have addressed a majority of my concerns and incorporated my feedback and suggestions to improve the manuscript. I do have two minor concerns (described below) that I think could be addressed to further improve the manuscript. First, while I see how the authors addressed my concerns and provided additional details to help clarify their intention with the use of the term "image memorability", I still have a remaining question as to whether they are trying to portray that image memorability is a property of the image or more a reflection of the individual who is viewing the image? I feel like the authors could expand this around lines 112 and 113, perhaps by adding a bit more to the sentence in 114 and 115 to help make this clearer.
Experimental design: I would ask the authors to provide a citation/support for the claims made in line 854. Additionally, it would be helpful if they could add some statistics pertaining to the findings of violence and image aesthetics relating to memory, lines 854 - 856. It would be helpful if they followed the same format that they had used previously when reporting about the impact of the presence of humans on memorability.
Validity of the findings: No further recommendations or suggestions. Nicely done!
Additional comments: Minor writing suggestions
1) Line 220, “Suport” should be “Support”
2) Line 492-493, missing closing parenthesis “)”


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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 3
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: RESMEM-NET: MEMORY BASED DEEP CNN FOR IMAGE MEMORABILITY ESTIMATION
Review round: 3
Reviewer: 2

Output:
Basic reporting: No comments
Experimental design: No comments
Validity of the findings: No comments
Additional comments: No further recommendations or suggestions. Nicely done!


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

Input:
Title: IMPROVEMENT OF NUCLEAR FACILITIES DNP3 PROTOCOL DATA TRANSMISSION SECURITY USING SUPER ENCRYPTION BRC4 IN SCADA SYSTEMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: no comment
Additional comments: The authors have made changes according to the reviewers comments. Now the manuscript is in a good shape and presentation. However, please check again the English. For example, sentence in the last paragraph of Section 2, line 188-189.

 As for comparison to the proposed method, as shown in Table 1.


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: IMPROVEMENT OF NUCLEAR FACILITIES DNP3 PROTOCOL DATA TRANSMISSION SECURITY USING SUPER ENCRYPTION BRC4 IN SCADA SYSTEMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors proposed a method of improving DNP3 security by introducing BRC4 encryption. This combines Beaufort encryption, in which plain text is encrypted by applying a polyalphabetic substitution code based on the Beaufort table by subtracting keys in plain text, and RC4 encryption, a stream cipher with a variable-length key algorithm. Authors contribute to improving the security of data transmission and accelerating key generation. The work by the author is good and the presentation of data is also good.
Experimental design: Experiment design is explained well.
Validity of the findings: For Table 1 Hybrid security approach to data transmission
Which filter used by the author to find the studies for the comparison?
Additional comments: The author has done good work.
They should check grammatical errors in the manuscript.
The quality of images should be improved.


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

Input:
Title: IMPROVEMENT OF NUCLEAR FACILITIES DNP3 PROTOCOL DATA TRANSMISSION SECURITY USING SUPER ENCRYPTION BRC4 IN SCADA SYSTEMS
Review round: 1
Reviewer: 3

Output:
Basic reporting: Tests are carried out by key space analysis, correlation coefficient analysis, information entropy analysis, visual analysis, and time complexity analysis.The results show that to secure encryption processes from brute force attacks, a key of at least 16 characters is necessary. IL data correlation values were IL1 = -0.010, IL2 = 0.006, and IL3 = 0.001, respectively, indicating that the proposed method (BRC4) is better than the Beaufort or RC4 methods in isolation. Meanwhile, the information entropy values from IL data are IL1 = 7.84, IL2 = 7.98, and IL3 = 7.99, respectively, likewise indicating that the proposed method is better than the Beaufort or RC4 methods in isolation. Both results also show that the proposed method is secure from MITM attacks. Visual analysis, using a histogram, shows that ciphertext is more significantly distributed than plaintext, and thus secure from MITM attacks. The time complexity analysis results show that the proposed method algorithm is categorized as linear complexity.
Experimental design: Visual analysis, using a histogram, shows that the distribution of the ciphertext is significantly 462more varied than the plaintext, and thus it is secure from MITM attacks. Time complexity 463analysis shows that the proposed method is categorized as linear complexity.
Validity of the findings: no comment
Additional comments: Findings will be useful in the foundations and evaluation of support systems . This version is good in this form but most of the recent references are missing and English of the article is also poor. Comparisons between methods are also missing. Further, please improve these portion, add some significance points of pros and cons of the work and revise the formatting of the references. After the critical revision, this paper can be 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: IMPROVEMENT OF NUCLEAR FACILITIES DNP3 PROTOCOL DATA TRANSMISSION SECURITY USING SUPER ENCRYPTION BRC4 IN SCADA SYSTEMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: The authors have addressed the concerns during the first round of review process.


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: IMPROVEMENT OF NUCLEAR FACILITIES DNP3 PROTOCOL DATA TRANSMISSION SECURITY USING SUPER ENCRYPTION BRC4 IN SCADA SYSTEMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The manuscript by the author still needs improvement, The presentation of data in the manuscript is not good.  Lots of points are resolved by the authors in revision.
Experimental design: No comment
Validity of the findings: no comment
Additional comments: Refer to my previous 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: IMPROVEMENT OF NUCLEAR FACILITIES DNP3 PROTOCOL DATA TRANSMISSION SECURITY USING SUPER ENCRYPTION BRC4 IN SCADA SYSTEMS
Review round: 2
Reviewer: 3

Output:
Basic reporting: Now, this paper can be accepted 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: EFFECT ON SPEECH EMOTION CLASSIFICATION OF A FEATURE SELECTION APPROACH USING A CONVOLUTIONAL NEURAL NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is very well written however at several places poor sentence structure makes the paper difficult to read and create ambiguities.  Professional proof reading is required before publication.
Experimental design: The contribution of the paper is the field is appreciable but the following shortcomings and must be addressed. .

In their proposed approach the authors have used the AlexNet for feature extractions. This network architecture is too old and has been superseded by several newer CNN based feed forward networks. Particularly the use of 11X11 convolutions in the AlexNet has been mostly discontinued and 3x3 kernels are used in most current networks.

The authors have used the transfer learning approach by using a pretrained AlexNet. However, they have not clearly mentioned the dataset used for pretraining of the Alexnet.

The authors do not specified the details of deep learning python libraries (e.g. Tensorflow/Keras or Pytorch) etc. in section 4.2.

In sub-section 5.0.1 the the number of neurons, activation functions and loss functions used to train the MLP classifier have not been specified. Similarly, hyperparameters used  for the other methods are also not mentioned.
Validity of the findings: The findings have been evaluated by using the well known metrics in the field and conclusion is well stated.
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: EFFECT ON SPEECH EMOTION CLASSIFICATION OF A FEATURE SELECTION APPROACH USING A CONVOLUTIONAL NEURAL NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: Language needs to be improved including the following particular issues:
Line 71: ‘have been founded’ should be ‘have been found’
Line 77-78:  'Therefore' opening the first two consecutive sentences
Line 109: ‘Emotions are categorized into two approaches’ should be ‘Emotions are categorized using two approaches’
Section 2.1 and literature review in general presents various kinds of techniques and their results without a chronological sequence
Experimental design: The method proposed in this paper extracts Alexnet based deep learning features from spectrogram for speech and then uses correlation based feature selection before feeding multiple shallow classifiers for emotion recognition. However the manuscript should address following two main issues:

1st issue:
A similar method is proposed by ‘Melissa N., et al 2017 and 2020’. They treat the speech spectrogram as an RGB image and uses Alexnet for feature extraction to classify emotions without the ‘feature selection step though.
The manuscript has not referred to these papers at all. The authors must highlight how the proposed method is different or better from the mentioned work.

Stolar, Melissa N., et al. "Real time speech emotion recognition using RGB image classification and transfer learning." 2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2017.

Lech, Margaret, et al. "Real-time speech emotion recognition using a pre-trained image classification network: Effects of bandwidth reduction and companding." Frontiers in Computer Science 2 (2020): 14.

2nd issue:
The experimental setup for feature selection from speech features is not explained in detail. Both sections 3.4 and 3.5 provide the generic foundation of feature selection techniques without any explanation of implementation in the proposed setup.
Validity of the findings: The results section presents confusion matrices for different datasets without explaining their significance. The manuscript should justify the classification metrics analyzed in results in terms of their significance e.g. what are the consequences of classifying a particular emotion as another and what the confusion matrices aim to highlight.

Tables 3-6 present accuracies for different classifiers without mentioning ‘accuracy’ in the table tile or any axis

Line 398 in conclusion suggests that authors aim to test the results in cloud and edge computing environments. What is the rationale for such tests?
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: EFFECT ON SPEECH EMOTION CLASSIFICATION OF A FEATURE SELECTION APPROACH USING A CONVOLUTIONAL NEURAL NETWORK
Review round: 2
Reviewer: 1

Output:
Basic reporting: NA
Experimental design: 1.	The authors now cited Melissa N. Stolar; Margaret Lech 2017 and 2020 which follow a similar approach to the proposed method however the similarities and differences in the proposed approach with these references in particular and other approaches, in general, must be highlighted.
.

2.	The authors have explained in the response letter their rationale and strategy for feature selection from speech. However, it will be helpful for replication, etc. to add the implementation details in the manuscript itself about the pipeline for extracting spectrogram features and then selecting the most suitable.
Validity of the findings: As a response to the justification for classification metrics used in the results section e.g. the confusion matrix, the authors have referred to Table 3 which compares the nature of speech datasets. The suggestion here was to explain the reasons for using certain classification metrics/confusion matrices, what they indicate and what are the implications of the obtained results in real-world scenarios.
Additional comments: NA


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

Input:
Title: IMPROVING SCHEDULING PERFORMANCE IN CONGESTED NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors have used two terminologies to refer to the network nodes, Serving Area Points and Point of Presence. The author needs to discuss if there are differences between the two types of nodes or they provide the same role. If the same role is provided by them, the author should use consistent terminology in different sections.  Authors should note that the term POP is more common and known to the research community than SAP.
Experimental design: On page 8, line 243, authors have mentioned two types of the Erlang Distributions B and C. Authors needs to clarify what Distribution was used in the experiment and  the underlying reasons.

On  Page 10, Line  296  authors have  used the  term Traffic Intensity  provided the TI values Table 3 used in experiments.  Authors  should   clarify what  mathematical  models were used to  calculate the traffic intensities.
Validity of the findings: On Figure 5, authors  have  provided the ASP traffic profiling with showing how the changes are propagated to upstream nodes. Authors needs  clarify whether the symmetric or asymmetric behavior is used for the  upstream and downstream traffic. Furthermore,  authors need discuss the approach  if the upstream and downstream data rates are different, that is the usual approach for the internet traffic.
Additional comments: It is stated on the page 10  line 315 that FOB process has been used in the experiments. Authors should clarify why this process has been used and why not the ASP or HNUB? And whether the results   would be same with either of the approach.
 On page 11, line 330 the terms MNC and CNC are  defined, however the  how  theses conditions were simulated  in the experiments, author needs to clarify.
 On page 18,  line 560 the node role has been discussed,  author needs to clarify how these roles are communicated to  nodes or algorithm instances.
On page 26, authors have discussed the potential use of machine learning algorithms with TIPS, authors  need to clarify whether the traffic forecasting techniques proposed in the literature are  capable to  provide prediction of the TI values.