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

Input:
Title: SYNTAX AND PREJUDICE: ETHICALLY-CHARGED BIASES OF A SYNTAX-BASED HATE SPEECH RECOGNIZER UNVEILED
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper still needs a thorough proofreading in a number of points.
For example, the paragraph in ll.34-45 is very confusing and not really easy to follow, especially the discussion on censorship and free speech. I see the point, but the very notion of censorship here seems to have first a positive and then a negative connotation. The authors should try to organize the section such that concepts and ideas flow in a more logical way.
In addition, the authors didn't fully addressed reviewers' request to introduce an operational definition of HS. They did, in fact, add references to relevant literature on this matter, but failed to provide a clearer picture of the phenomenon. Highlighting - as the authors rightfully did - the struggles in finding a common definition and the blurry boundaries with related phenomena is perfectly fine, but there actually are some distinguishing traits in HS (e.g. incitement to hate and violence against a specific target), and they should be properly defined in the paper.

- l.107-8: "the above measures of prejudice for learned models fail to capture another important fact:
learned models can be “right from the wrong reasons”": this statement could be further expanded and motivated

Other typos and errors spotted here and there:
- l.89: "inside a neural networks"
- l.92: "Afro American English" -- African-American
- l.99: "untended bias" - - unintended bias (?)
- l.159: "statistical functions Kamishima et al. (2012)" -- (Kamishima et al, 2012)
- ll.243-5: "Finally, we generated our political datasets filtering from the Kaggle dataset holding only the tweets geolocated in the US, all the tweets with at least one of the hashtags present in the lists." -- this sentence should be rephrased
- l.251: "the two datasets made up the corpus actually reflect" -- the two datasets that make up (?) the corpus
- l.293: "proposional to its class" -- proportional
- l.304: "since it is for all different with greater or lesser knowledge of syntax, machine learning [...]" -- please rephrase this sentence fragment
- l.309: "led to the definition of the next." -- next one
- l.310: "questions [...] depends on" -- depend
- Appendix A, l.507: "states those constitute" -- that constitute
Experimental design: -  Sect. 3.4.3: The list of hashtags used to filter the tweets should be made available, maybe along with the supplementary material in Appendix A
- Sect. 4.1 and the Blind/Inside-Out/Prejudice tests: the authors in the rebuttal responded to reviewers' comments by motivating the difficutlies in getting a proper pool of annotators, but the main point in those comments didn't have to do with the number of annotators - which is more than reasonable - but with the sample size (i.e. the number of annotated items) used for the analysis, which is very small. With respect to this point, no action has apparently been taken.
Also, for a more systematic description of the annotation process in that step, as well as in the creation of the BLM and US Election datasets, I recommend the authors following the scheme and guidelines proposed here:
https://techpolicylab.uw.edu/wp-content/uploads/2021/11/Data_Statements_Guide_V2.pdf
While this can be done in a concise way, I believe that addressing all the applicable points in these guidelines would help both the authors and the reader in getting a clearer and more exhaustive picture of the data at hand.
Validity of the findings: - Sect. 4.2, l.340: It is not clear what kind of "gap" the authors refer to, when commenting on results in table 2; more in general, the whole sentence should be rephrased
- l. 354-5: the authors say that BERTbase is more prone to classify tweets as offensive in the Democratic and Republican datasets, but according to Fig.3 it's the opposite (HS tag is preferred, between HS and offensive language, especially in the Republican dataset)
- Sect. 4.3 (and examples in Appendix C): the authors' response to reviewer #3 on this point should be made more explicit in the paper as it would help the reader understand what actually motivated the perturbation analysis and why the authors picked precisely those examples.
Additional comments: Despite the tentative improvements made to the revised version, the paper still needs further adjustments in order to be considered for full acceptance.
Nonetheless, the work is of great interest to the community, as it sheds light on how and to what degree syntax can be helpful in HS detection. Therefore, I highly recommend to the authors that they address reviewers' comments more 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: COMPOSITE LEARNING TRACKING CONTROL FOR UNDERACTUATED MARINE SURFACE VESSELS WITH OUTPUT CONSTRAINTS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This article solves the composite learning tracking control for the underactuated marine surface vessels. In particular, the output constraints are addressed in the control design procedure. The under-actuated ship motion control is a challenging problem. This article is well written and intersing. The composite learing technique is employed such  that the control system has the learning capability. It can be considiered to be accepted after some revisions.

Somme comments are given as follows:
1. It seems that the mass parameters m_jj should be known. But, some other parameters are unknown, Some remarks are needed for the parameter determine.

2. The output constraints should be explained frome a practical viewpoint.

3. The ocean disturbances acting on the ships are assumed to be bounded and then are attenuated with the NN approximation errors. It has some conservativenss. Some disturbance estimtion scheme has been provided such as 10.1109/TITS.2021.3054177. Authors should be mentioned this issue in the context or lie in the future research.
Experimental design: No
Validity of the findings: No
Additional comments: No


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

Input:
Title: COMPOSITE LEARNING TRACKING CONTROL FOR UNDERACTUATED MARINE SURFACE VESSELS WITH OUTPUT CONSTRAINTS
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper develops a composite learning trajectory tracking control scheme for underactuated marine surface vehicles (MSVs) with unknown dynamics and time-varying disturbances under output constraints. The considered issue is interesting and the paper has an acceptable structure and presentation. However, the following comments are needed to be considered in the revision:
1.It is suggested to supplement these more related works of composite adaptive, the advantages of composite learning control scheme can be clearly understood by readers.
2.How to choose the design parameters of the controller selected, in the simulation? Please give some remarks about it.
3.Please add some explanations of how the MSV obtains its positions, orientations, velocities and angular velocities..
4.It is suggested to supplement a block diagram of the control architecture
Experimental design: 1. An extensive comparative study may help the authors to improve the paper presentation.
2. The format of Figures should be unified.
Validity of the findings: The trajectory tracking control for underactuated marine surface vehicles subject to unknown dynamics and time-varying disturbances under output constraints is considered. The problem investigated is practical and valuable.
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: COMPOSITE LEARNING TRACKING CONTROL FOR UNDERACTUATED MARINE SURFACE VESSELS WITH OUTPUT CONSTRAINTS
Review round: 2
Reviewer: 1

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: COMPOSITE LEARNING TRACKING CONTROL FOR UNDERACTUATED MARINE SURFACE VESSELS WITH OUTPUT CONSTRAINTS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The article meets the PeerJ criteria and should be accepted as is.
Experimental design: The article meets the PeerJ criteria and should be accepted as is.
Validity of the findings: The article meets the PeerJ criteria and should be accepted as is.
Additional comments: No additional comments


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

Input:
Title: DETECTING PHISHING WEBPAGES VIA HOMOLOGY ANALYSIS OF WEBPAGE STRUCTURE
Review round: 1
Reviewer: 1

Output:
Basic reporting: - The problem statement should be included implicitly in the introduction section. No need to repeat it again in the Method section.
- It is mentioned that (in abstract and other section): "the proposed method can accurately locate the family of phishing webpages and can detect phishing webpages efficiently".
However, the proposed method is based on clustering which has high computational cost. Please justify how the proposed method is more efficient?
- is it possible to discuss the benefits of this kind of detection method comparing to using ML methods?
Experimental design: - The experimental design is described well. However, the time of detection should discuss the clustering time which is necessary to perform this method.
Also discuss the cost/time of generating the fingerprint?

- Will the fingerprint will be the same even you use different websites in the testing? When it should be updated? Will the detection rate will be enhanced if we use more websites in the training? Discuss this.
Validity of the findings: - The validation (not evaluation) of the proposed method is not clear. Example: How you ensure that the proposed fingerprint is valid?

The authors compared the proposed method using different parameters (for the same method). Is it possible to compare the performance of this proposed method with other previous methods to show the enhancements obtained?
Additional comments: - The best values in all tables should be highlighted (bold) to make the comparison easy.


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

Input:
Title: DETECTING PHISHING WEBPAGES VIA HOMOLOGY ANALYSIS OF WEBPAGE STRUCTURE
Review round: 1
Reviewer: 2

Output:
Basic reporting: Certain sections of the paper require major revision before the manuscript can be considered for publication.
Experimental design: Necessary references to the existing techniques should be provided in the experimental result section. Moreover, a comparative analysis of the proposed technique needs to be carried out with other techniques proposed in the literature to validate the effectiveness of the proposed model
Validity of the findings: No Comment
Additional comments: Some sections of the paper require proofreading to fix grammatical and typographical 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: DETECTING PHISHING WEBPAGES VIA HOMOLOGY ANALYSIS OF WEBPAGE STRUCTURE
Review round: 2
Reviewer: 1

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


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

Input:
Title: SELF-SUPERVISED RECURRENT DEPTH ESTIMATION WITH ATTENTION MECHANISMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The author developed a detph estimation algorithm using self-supervised attention mechanisms. By adopting ConvGRU layers, the networks can capture temporal information even better. Also, the proposed algorithm is precisely compared with the state-of-the-art algorithms in various aspects.

In the introduction section, the author organized the related works clearly, which is quite reasonable to me.
Experimental design: 1. Table 1 contains too much infomation. Some of them are useful to some readers but it appears to weaken the arguments. The author needs to simplified the table for more clarity.

2. If the author can upload the codes in the github, then the previous comments can be removed. The author can upload detailed information in the github rather than describing many details of the networks in the paper.
Validity of the findings: 1. The table 3 is not clearly explained. The author needs to specify which algorithms are based on single images.

2. The proposed method shows worse performance than some single image baselines even though it uses much information. Then, what is the advantage of using the proposed algorithm beyond the performance?

3. How long does it take in a single feedforward pass? The author assumes that the proposed method has a potential to be used in autonomous driving environments. Then the algorithm should be ran in real-time, otherwise the paper fails to show its validity.

4. What is the performance of a depth estimation algorithm without using temporal information? The author needs to show the comparison results in the ablation study as the author states that using temporal information is the key of the algorithm.

5. In the conclusion section, the author compares the performance with Monodepth2, which seems to me that it is not on the right section. The author can move the statement to the discussion section for clarity.

6. In the conclusion section, the author mentioned Frechet Inception Distance (FID) as an evaluation metric. What is the meaning of it? Can it be a valid metric for comparision? If it is, what is the purpose of selecting FID as a metric?
Additional comments: This paper applies convGRU with self-attention mechansim, which is quite novel to me. However, the result sections fails to illustrate the validity of the proposed framework as the advantage of the algorithm is not clearly described.


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

Input:
Title: SELF-SUPERVISED RECURRENT DEPTH ESTIMATION WITH ATTENTION MECHANISMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The writing overall is comprehendible but the authors can improve upon by better conforming to professional standards of expressions. There are a few expressions throughout the manuscript that do not seem suitable for academic writing; "a bunch of" would be one example. The authors also have a mixed usage of British and American English, such as 'regularisation' and 'regularization', or color. I suggest that the authors proofread the manuscript and address such issues prior to the final submission.

The literature review and introduction seem to provide sufficient background and context regarding the subject. While most figures are able to provide enough context to the readers and are placed well, the figure describing the ConvGRU block (Fig. 2), appears to be identical to that of a conventional GRU block. The difference between a ConvGRU block and a conventional GRU block are adequately described in lines 263 through 269, as well as Equations 8 through 11, hence Fig. 2 may be unnecessary as GRU blocks are typically well-known throughout the field.
Experimental design: The problem formulation seems to be well defined, yet some sections lack to provide enough logical reasoning behind the selection of each method.

While each of the methods separately are described with sufficient detail, it would be clearer for the readers if the authors outline the overall methodology, in terms of presentation of the contents in the methodology section. This will also help with the transition from each subsection to another,

The experimental details are well described and comprehensive enough for replication.
Validity of the findings: The ablation study seems to provide a detailed analysis and justification for the models/methods used in this work. However, the authors should better emphasize the contributions of this paper as the results generally seem to be less convincing to the potential audience. Although the results do suggest that the usage of temporal information can enhance the performance as the authors claim, the proposed methodology performs worse compared to most of the 'u-seq' or models (which are in the same category), and the qualitative comparison of the view synthesis quality was done with a model from a different category. Despite the relative weakness in terms of RMSE compared to other models within the same category, conducting a qualitative comparison with one of the state-of-the-art methods and showing how similarly the proposed method can perform would be help potential readers to be more persuaded. Another suggestion would be to point out and highlight any other advantages the proposed methodology has over the other methods within the same category to compensate for the weakness in performance.

Additionally, for Fig. 5, what are the ranges of the error maps? While the proposed methodology does seem to have less intensive values in the error map, it would be worth indicating the levels by having a colorbar next the the plots in J through O.
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: SELF-SUPERVISED RECURRENT DEPTH ESTIMATION WITH ATTENTION MECHANISMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The responses of the author have removed my questions. It could give a better understanding to potential readers if some of the contents are reinforced.
Experimental design: no comment
Validity of the findings: 1. The author mentioned that 'supervised learning methods are very heady and self-supervised learning methods are light.' This could be seen as a hasty generalization mistakes as the computational burden varies with the structure and design of networks. If there are any references that support your claim, then adding them in the introduction section could eliminate the concerns.

2. The captions (or y-axis) of the figures 6 and 8 are mislabeled. The explaination of those two figures are not matched with the one from the captions. The author should check the 'D, E, F' and 'G, H, I' parts.

3. The computational speed of the proposed algorithm is quite astonishing and can be applicable to real-time, which could be considered as an advantage of using your algorithm. It would be much better if this contents are emphaized in the manuscript.
Additional comments: The author mentioned that the codes are included in the original paper but It would be better to add the link of the code in the manuscript so that the potential readers can refer your methods easily.


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

Input:
Title: SELF-SUPERVISED RECURRENT DEPTH ESTIMATION WITH ATTENTION MECHANISMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The writing overall has been improved, though the authors could proofread the manuscript carefully prior to the final submission. For example in the first sentence of Section 4, the authors have written "we first our experimental setup", and it seems like there could be a missing verb. Again, it is recommended that the authors review the manuscript for any grammatical or spelling errors.
Experimental design: No comment
Validity of the findings: 1. Though the authors claim the contributions of the proposed methodology to be of utilizing new network structures and having new training strategies, how are these finding reflected in terms of performance? As the authors describe in Section 5.2, the proposed model performs worse than most of the multi-frame and sequential approaches, possibly weakening the authors' claim that incorporating temporal information could be beneficial. Novelty alone may not be sufficient for the justification and necessity of this method, and the fact that the proposed method perhaps underperforms most of the state-of-the-art methods would be less convincing to potential readers. Thus it is highly recommended that the authors either (1) highlight any advantages the proposed method holds over other existing methods (why this method might be useful despite worse performance) or (2) enhance the performance of the model such that acceptable performance is achieved.

2. The qualitative comparison with Monodepth 2 does not help in terms of convincing potential readers the usage of the proposed method. One of the main contributions that the authors claim are that sequential/temporal information can be utilized; it would logically make more sense to have a comparison between another sequential model.
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: SELF-SUPERVISED RECURRENT DEPTH ESTIMATION WITH ATTENTION MECHANISMS
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: SELF-SUPERVISED RECURRENT DEPTH ESTIMATION WITH ATTENTION MECHANISMS
Review round: 3
Reviewer: 2

Output:
Basic reporting: The authors have seemed to remove all typos and grammatical errors.
Experimental design: No comment
Validity of the findings: The authors have seemed to address previous comments and questions in the revised manuscript and rebuttal form.
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: OPTIMAL POWER ALLOCATION FOR A WIRELESS COOPERATIVE NETWORK WITH UAV
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper studies the optimal power allocation strategy in wireless networks where the energy-constrained relay nodes through RF energy from the UAVs. The addressed problem is interesting and the proposed solution seems effective. However, there are some weaknesses in the paper that the authors must particularly pay attention and handle:
(1) (Line 461 on page 15) The format of references is inconsistent, for example, the caption case of a reference article is inconsistent.
(2) The logic and English usage of this paper are poor. The quality of written English should be improved.
Experimental design: The paper refers to the need to find a suitable α to make a compromise between the outage probability and the throughout, is it necessary to give the appropriate α in the form of simulation?
Validity of the findings: no comment
Additional comments: (1) (Line 2 on page 1) "UAV cooperative wireless DF relay network." What is DF?
(2) The expression of the formula in this paper is wrong, and the physical meaning of some letters is unclear.
(3) (Line 224 on page 7 and line 242 on page 8) Why the second level headings B and C are the same?


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

Input:
Title: OPTIMAL POWER ALLOCATION FOR A WIRELESS COOPERATIVE NETWORK WITH UAV
Review round: 1
Reviewer: 2

Output:
Basic reporting: Yes
Experimental design: Yes
Validity of the findings: Yes
Additional comments: 1. In the system model, why choose DF protocol instead of AF protocol? Any justification.

2. Why Rayleigh channel, especially when Los link exists.

3. UAV itself has very limited energy, so it will very impractical to use it as an energy source to power the relay node. The authors must provide substantial evidence to show the effectiveness.

4. What are the computation complexity of solutions used to solve the identified optimization problems?

5. Please provide more baseline strategies used for baselines in simulation.

6. Presentation:
(1) Undefined abbreviation in abstract "DF"
(2) Too many typos and grammar problems.
(3) Please put the figures at appropriate places instead of at the end of the manuscript.
(4) Provide a table to include all parameters to better help the reviewer understand.


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

Input:
Title: OPTIMAL POWER ALLOCATION FOR A WIRELESS COOPERATIVE NETWORK WITH UAV
Review round: 2
Reviewer: 1

Output:
Basic reporting: Good
Experimental design: Good
Validity of the findings: Good
Additional comments: Thanks for addressing my comments. Here, more justifications are needed for the following two questions:

(1) Why Rayleigh channel, especially when Los link exists. Since LOS links, the Ricean channel will be more suitable instead of Rayleigh. After reading the authors' feedback, I am not still convinced about the use of the Rayleigh channel.
(2) Instead of only text, could the authors provide some simulation results for the baseline strategy.
(3) Please insert the table of all parameters somewhere in the paper instead of listing it only in the response letter.


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

Input:
Title: THE USE OF STATISTICAL AND MACHINE LEARNING TOOLS TO ACCURATELY QUANTIFY THE ENERGY PERFORMANCE OF RESIDENTIAL BUILDINGS
Review round: 1
Reviewer: 1

Output:
Basic reporting: In the present work, the authors applied and compared two machines learning models for better prediction of heating load (HL) and cooling load using eight input variables namely, Building Size, Floor Height, Glazing Area, Wall Area, Window to Wall Ratio, Win Glazing U-value, Roof U-value, and the External Wall U-value. The proposed models were; the multilayer perceptron neural network (MLPNN) model and the multiple linear regression model (MLR). The two models were developed using more than 3840 building samples, and three modelling strategies were analyzed based on different splitting ratios: 70/30, 80/20, 90/10. Three performances metrics were used for models evaluation and comparison: the RMSE, MAE and R2. The investigation showed that the MLPNN was more accurate than the MLR, which is a logical finding.

The focus of the paper is very interesting, worthy of investigation and relevant to the PeerJ Journal. The necessary information’s for such study were provided by the authors and clearly presented. The writing style is good and the reader can easily understand what the authors tried to explain and to demonstrate. I have no problem about the paper structure, except some figures that I estimate they are non-value added as there are any comments of discussion about containing information in these figures (5-11). I would like to follow the positive aspect of the paper by mentioning some weak points. Indeed, the section results and discussion seems still to be the weak part of the investigation and it has significantly contributed to degrading the overall paper quality, information losses, worsened understanding of the overall results of the investigation and has not contributed to an increased paper reading.  In conclusion, the paper needs in depth amendments before to be accepted.
1.	Theoretical description of the MLPNN should be presented and clearly described.
2.	In such modelling study, a comparison between the models having different input combinations should be conducted (at least six combinations).
3.	The contribution of the input variables calculated as a percent ratio can help in better understanding the manuscript, see for example the connection weights approach (CW-AP) (Olden and Jackson 2002; Olden et al. 2004) and the Garson approach (G-AP) (Garson 1991).
4.	Obtained results should be deeply discussed.
5.	The scatterplot between measured and predicted data should be provided (only for the testing data).
6.	The following figures are necessary: boxplot, violin plot, and Taylor diagram (only for the testing data).
Experimental design: see comments above
Validity of the findings: see comments above
Additional comments: see comments above


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

Input:
Title: THE USE OF STATISTICAL AND MACHINE LEARNING TOOLS TO ACCURATELY QUANTIFY THE ENERGY PERFORMANCE OF RESIDENTIAL BUILDINGS
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper presents a new building energy consumption dataset and investigates the impact of eight input variables on residential buildings heating load (HL) and cooling load (CL), respectively.  A variety of classical and non-parametric statistical analytic tools were used to find the most strongly associated input variables with each of the output variables. Then, using the performance measures Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2), two machine learning statistical methods to estimate HL and CL were compared: Multiple linear regression (MLR) and Multilayer perceptron (MLP).

The contribution of the paper is interesting and relevant to building energy research community. The proposed solution and evaluation methodology are original. However, the manuscript still can be improved through the consideration of the following aspects:

1)	The authors failed to motivate their proposed work in the Introduction. The paragraph should be added to the introduction for highlighting the applications of the building energy consumption datasets, such as energy prediction, energy disaggregation (or non-intrusive load monitoring), anomaly detection, fault diagnosis, etc. To that end, I suggest to the following references when you address this comments:
-	Identifying key determinants for building energy analysis from urban building datasets
-	Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
-	A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data
-	Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability
-	Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
2)	The literature review is very terse and some new articles discussing building energy consumption datasets are missing, such as :
-	Building power consumption datasets: Survey, taxonomy and future directions
-	A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks
Where a new dataset, called Qatar University dataset (QUD) has been presented.
3)	The drawbacks and limitations of the proposed work should be discussed in the conclusion along the most important findings.
4)	The paper should be carefully proofread as there are some typos and grammatical issues.
Experimental design: The experimental design is well discussed in the paper. the latter explains well the procedure used to gather data and validate the dataset with two machine learning models.
Validity of the findings: More discussions are required to highlight the drawbacks and limitations of the proposed approach, and the future directions to overcome these issues.
Additional comments: No additional comments


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

Input:
Title: THE USE OF STATISTICAL AND MACHINE LEARNING TOOLS TO ACCURATELY QUANTIFY THE ENERGY PERFORMANCE OF RESIDENTIAL BUILDINGS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The article is clear, but requires a slight correction of the English language. Literature references are sufficient. Results supported by examples.
Experimental design: The paper is an interesting approach in terms of forecasting energy consumption. Nevertheless, in the methods based on machine learning, an essential element is the training and validation set. The effectiveness of forecasting and the thesis mainly formulated depends on selecting criteria, the amount of data, and the appropriate data selection. Therefore, the universality of the algorithm has some limitations.
Validity of the findings: 1) I propose to describe in more detail on what basis and the scope of selecting input data for the given research problem results.
2) The authors could refer to other machine learning methods and justify the choice of the methods presented.
Additional comments: No additional comments


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

Input:
Title: THE USE OF STATISTICAL AND MACHINE LEARNING TOOLS TO ACCURATELY QUANTIFY THE ENERGY PERFORMANCE OF RESIDENTIAL BUILDINGS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The have significantly improved the paper and the necessaries revisions were correctly done. The paper is ready for publication, no further revision is necessary. well
Experimental design: well conducted
Validity of the findings: well presented and discussed
Additional comments: The have significantly improved the paper and the necessaries revisions were correctly done. The paper is ready for publication, no further revision is necessary.


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

Input:
Title: THE USE OF STATISTICAL AND MACHINE LEARNING TOOLS TO ACCURATELY QUANTIFY THE ENERGY PERFORMANCE OF RESIDENTIAL BUILDINGS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper has been significantly improved after revision.
Experimental design: The experimental design part has been improved after revision.
Validity of the findings: The main findings of the paper are innovative.
Additional comments: The authors have addressed all my comments, I have no further suggestions.


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

Input:
Title: DEEP LEARNING-BASED ELECTROCARDIOGRAM RHYTHM AND BEAT FEATURES FOR HEART ABNORMALITY CLASSIFICATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript is well-written, has good presentation, and thoroughly covers the literature.
Experimental design: The manuscript should be clearer in stating the novelty of the proposed method versus the existing state-of-the-art. Also, the architecture of the proposed network should be more clearly presented.
Validity of the findings: Results seem good, and the experiments use plenty of databases. However, the comparison with the state-of-the-art is flawed and does not suffice to assess the relative quality of the proposed method. The most promising literature approaches should be tested in the same conditions as the proposed method to directly compare their performance results.
Additional comments: The exact contribution of this work is not very clear. Throughout the abstract and introduction, the authors state that the literature is composed of either rhythm or beat-based methods, which leads me to think that this would be a hybrid method combining rhythm and beat features. However, both tasks are addressed separately, which was already studied in (Nurmaini 2020) and (Tutuko 2021). The introduction should state, more clearly, what is the difference between this and the previous works.

The figure 3, depicting the CNN architecture, needs to be improved. As it stands, it seems there is a FC neuron (in the bottom) for each convolutional layer, and somehow information flows forward and backward through the model using those neurons. Also, it is not clear what the orange squares represent.

Were the state-of-the-art methods (in Table 7) evaluated with the same datasets as the proposed method? And the same data train-test splits? If not, and since they even consider different number of target classes, the results are not really comparable. Hence, we do not have a robust way to assess if the proposed method is, in fact, better than the state-of-the-art. Authors should implement a couple of the most promising literature methods and test them in the same conditions as the proposed method.

In figure 6, since the AUC is very close to 1, perhaps it would be useful to use log-log scale axes for the ROC curve. Or, there could be a box zooming in on the [1.0, 1.0] area, so readers can see clearly the difference between the curves.


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

Input:
Title: DEEP LEARNING-BASED ELECTROCARDIOGRAM RHYTHM AND BEAT FEATURES FOR HEART ABNORMALITY CLASSIFICATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this manuscript, Darmawahyuni and colleagues evaluated the performance of 1D-CNN model on ECG classification. The authors reported that their model demonstrated high classification performance on both rhythm and beat abnormalities. The reviewer agrees with the clinical importance of simultaneously identifying ECG waveforms and rhythms, but unfortunately the authors' method fails to achieve their goal.

The authors stated that they were able to classify 24 heart abnormalities (9 rhythms and 15 beats), but in reality, they only repeated the binary classification 24 times. This is the main drawback of this study. As a side note, the reviewer believes that confusion matrices, not ROC curves, should be used to evaluate the performance of multiclass classification.

The definition of the classifications for cardiac rhythm is also incorrect. According to the authors, the rhythm abnormality includes “Myocardial Infarction”, Myocarditis”, “Heart Failure”, “Valvular heart disease”, “Hypertrophy” and “Cardiomyopathy”, none of these are abnormalities in heart rhythm. Furthermore, since myocardial infarction and heart failure often coexist, and valvular disease and cardiac hypertrophy can also coexist, classifying them as separate classes (based on ECG alone) is obviously a wrong approach. In such a complex task, it is impossible for all classification models to achieve AUROC 1.0, as shown in Figure 6.

The reviewers speculate that these mistakes may be partly due to the lack of clinicians among the authors. This manuscript needs to be radically revised from the research design.
Experimental design: Experimental design is inappropriate as described above.
Validity of the findings: Findings are unreliable as described above.
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: DEEP LEARNING-BASED ELECTROCARDIOGRAM RHYTHM AND BEAT FEATURES FOR HEART ABNORMALITY CLASSIFICATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: The manuscript is well-written, has a good presentation, and thoroughly covers the literature.
Experimental design: The experiments have been correctly and thoroughly defined and conducted.
Validity of the findings: Results seem good, and the experiments use plenty of databases. The comparison with the state-of-the-art remains flawed and does not suffice to assess the relative quality of the proposed method. The most promising literature approaches should be tested in the exact same conditions as the proposed method to directly compare their performance results.
Additional comments: I thank the authors for their response to my comments. I believe most have been addressed, although one key concern has been avoided (and the manuscript's quality has suffered because of that).

Direct comparison with the state-of-the-art is fundamental. If you have more classes than the literature, or other things are different (random seeds, etc.), then implement state-of-the-art methods and test them in your exact scenario. If there are many competing works in the literature, select at least the 1-2 best to have this direct benchmarking. This is essential to have a real fair comparison and to really understand if, where, and how your method is best.


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

Input:
Title: DEEP LEARNING-BASED ELECTROCARDIOGRAM RHYTHM AND BEAT FEATURES FOR HEART ABNORMALITY CLASSIFICATION
Review round: 3
Reviewer: 1

Output:
Basic reporting: The manuscript is well-written, has a good presentation, and thoroughly covers the literature.
Experimental design: The experiments have been correctly and thoroughly defined and conducted.
Validity of the findings: Results seem good, and the experiments use plenty of databases. The manuscript now includes direct comparison with state-of-the-art methods to objectively illustrate the superiority of the proposed method.
Additional comments: I thank the authors for addressing my comments. I believe it is now publishable. Congratulations on a good piece of research!


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

Input:
Title: RESEARCH ON REMOTE SENSING IMAGE EXTRACTION BASED ON DEEP LEARNING
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper mainly focused on the classification of remote sensing images by carrying out atmospheric calibration, band combination, image fusion and other data enhancement methods for Landsat 8 satellite remote sensing data to improve the data quality. In addition, deep learning and spatial/channel attention are applied to remote sensing image block segmentation in this paper. Although the experiments show the effectiveness of this method, there are still some major concerns that need to be carefully clarified and revised before considering a possible publication.
Experimental design: 1.The proposed method consists of several important components, so please add ablation experiments to evaluate the importance of each component. How each component contributes to the final performance gain? Especially the AC-CBAM compared to CBAM.
2.“Results and discussions” section is too short. Please give more experimental results and deep discussions of the results. Especially the deep reasons for the gains of the results.
Validity of the findings: 1.There exist many grammar errors throughout the paper, which severely disturbs the readability. I strongly suggest the author to carefully revise the paper for English grammar, the choices of words, the sentence structure, and the use of articles (a native speaker is strongly recommended for this task).
2.In my opinion, the novelty of this paper is somewhat marginal, so the authors need to describe the main contributions of this paper clearly in the Introduction section.
3.What is the full name of AC-CBAM? Abbreviations should be defined at first mention and used consistently thereafter.
4.The authors should clearly highlight the difference between CBAM and AC-CBAM.
5.A deep literature review should further given, especially regarding the topics involved in this paper. Therefore, the reviewer suggests discussing the advances by citing some references, e.g., “When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs”, “Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities”, and “Feature enhancement network for object detection in optical remote sensing images”.
6.There is a typo in the caption of Table 6.
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: RESEARCH ON REMOTE SENSING IMAGE EXTRACTION BASED ON DEEP LEARNING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The traditional remote sensing image segmentation technology cannot make full use of the rich spatial information of the image, the workload is too large, and the accuracy is not high enough. Aiming at these problems, this paper carried out atmospheric calibration, band combination, image fusion and other data enhancement methods for Landsat 8 satellite remote sensing data to improve the data quality. In addition, deep learning is applied to remote sensing image block segmentation in this paper. Based on the full convolutional neural network of codec structure, AC-CBAM innovative structure is proposed, and the optimization module of integrated attention and sliding window prediction method are adopted to effectively improve the segmentation accuracy. This paper has done some work, however, there are at least the following problems.

1)	 The format of English is nonstandard, for example, in Introduction, the first paragraph  is too long, while other paragraphs are too short. In addition, in row 33,“In the experiment of test data, the models mIoU, mAcc and aAcc in this paper reach 97.34%, 98.66% and 98.67% respectively, which is 1.44% higher than DNLNet (95.9%), which provides a reference for deep learning to realize the automation of remote sensing land information extraction”, it is unusual for“ which” appearing twice continuously, etc.
2)	The English expression is not accurate enough,  much of the translations fails to conform to English expression and culture connotation, resulting in poor readability,  and it is best to find professionals to improve the language expression.  For example, in Row 50 of 4.3 Slide Window Prediction , “And many other constraints and prior information to improve the performance of semantic segmentation”.  In row 290/ 291, “finally got good spatial  resolution and retained the images of the multispectral image information, to ultimately improve  the effect of plot extraction have good role in promoting” , etc.
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: DNN-BASED MULTI-OUTPUT MODEL FOR PREDICTING SOCCER TEAM TACTICS
Review round: 1
Reviewer: 1

Output:
Basic reporting: > The authors proposed to use Deep Neural Networks and feature engineering to predict the soccer tactics of teams, such as formations, game styles, and game outcome based on soccer dataset.

> The authors stated that experimental results demonstrated significant improvements of the proposed model compared to baseline models.

> The manuscript is literature supported.

> A new English revision is encouraged, e.g:

Line 24: “the proposed model, which obtain” , missing “s”
Line 66: “We propose Multi-Output” , missing “a”
Line 67: “game outcome; The” , missing ".”
Line 76: “We propose deep learning” , missing “a”
Line 195: “determining a team’s tactics” , remove “a”
Experimental design: > Source code and data source were provided. Also, Python was used as programming language, which helps to reach a bigger audience.

> “Our model trains a dataset consisting of 380 games and tests the 233 model with a dataset consisting of 38 games.”

It seems that hold-out validation was applied, or a single cross-validation. However, it is well-known that there are other more robust validation methods, such as leave-one-out and k-fold cross-validation. Also, the raw data provided in https://github.com/Lee-Gunju/Multi-Output-Model-for-Soccer-based-on-DNN-and-Feature-Engineering-for-Predicting-Team-Tactics do not seem difficult to process.

Can the authors extend the experimental study? For example, by adding new datasets and using a different validation method.

> “Moreover, we have developed the visualization system”

Apologies, during the revision, the provided system was unavailable (http://recsys.cau.ac.kr:8092/). Please check the provided figures.
Validity of the findings: > After reviewing and testing the provided source code (https://github.com/kecau/Multi-Output-model-for-Soccer).

Please update how the data is loaded in Line 10, since xlrd has explicitly removed support for anything other than xls files. You can follow this link https://stackoverflow.com/questions/65254535/xlrd-biffh-xlrderror-excel-xlsx-file-not-supported.

> Source code is missing from the file “Multi-Output model for Soccer.py”. For example, when executing your script the following error is seen:  “NameError: name 'train_dt' is not defined”.

> Why the 2nd output (game styles) used a softmax instead of a sigmoid activation?

> Regarding the game outcome performance, the results achieved are discrete (49.39% recall). However, the authors surpassed the baseline models. The authors should extend the experimental analysis in order to better support their proposal.

> Please include the individual sensitivity rates achieved for every outcome in the analysis in order to have a complete picture of your proposal, e.g. win, draw, loss, offensive, defensive.

> The conclusion should be stated after performing more experiments with new datasets and state-of-the-art models, if possible.
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: DNN-BASED MULTI-OUTPUT MODEL FOR PREDICTING SOCCER TEAM TACTICS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The general writing style in this paper is legible and understandable but the authors sometimes make common grammar mistakes such as omitting the "s" in first person singular verbs or omitting some articles that should be present.

The Introduction section is well written and describes the motivation and the objective of the study. I believe that the sentence about representing each segmented position by 0 or 1 (lines 59-61) is difficult to understand: where are these characteristics represented and how are they related to each position?

The references along the article are appropriate.

The article follows the common section structure in computer science (and more loosely the PeerJ standard), including a section for related work, sections to explain the proposed solution and a section for results and discussion.

Figures are of good quality in general. I would suggest adjusting the darkness of the green color used in Figures 1 and 4 (make it lighter so that there is a higher contrast between the background and the foreground text) as well as making legends in Figure 4 way larger, as they are currently illegible. Figure 3 should include a description of the numbers in parentheses across the image, are they the dimension of each layer output? The colors applied to the layers should be described as well if they hold some meaning, e.g. are all red layers a dense feed forward operation?

Original data appears to have been collected from a website, although there is no apparent direct download for the datasets, so the authors must have composed the dataset themselves out of the statistics available. The dataset has been published by the authors in a GitHub repository.
Experimental design: This article seems to fit within the scope of journal PeerJ Computer Science, as it applies widely known machine learning methods to a concrete problem and develops improvements on the models and a specific solution to tackle the training and prediction for this kind of data.

The authors aim to tackle a problem of modeling (european) football plays using more modern tools (neural networks) that have not been used yet for this objective.

The fundamental part of the model description is Figure 3 which details the structure of the neural network used, but I think it could be clearer if it could somehow indicate the number of actual dense layers that are used (I believe it could be every red and yellow layer, but it should be clearer if some researcher wants to reproduce this work).

I think that the learning rate used in the Adam optimizer (0.01, Table 6) is much higher than the standard 0.001, is there a reason behind this?
Validity of the findings: The resulting metrics seem reasonable and the stated conclusions are supported by the results.

Since the baseline model performs better than your newly proposed model (when both are without feature engineering), could it improve further than yours when the engineered features are provided?
Additional comments: My initial concern with the approach of the authors was that feature engineering is supposed to be less useful with deep learning models but, in this case, the results show that this step is key in finding a well-performant model. My question now is whether the feature engineering is providing more improvements than the predictor itself and, as a result, if one could achieve even better metrics by providing the engineered metrics to another predictor (e.g. the baseline). The overall quality of the article, however, is good and I would consider it ready for acceptance after fixing the details I indicated above.


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

Input:
Title: DNN-BASED MULTI-OUTPUT MODEL FOR PREDICTING SOCCER TEAM TACTICS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The manuscript is acceptable in its current form.
Experimental design: The authors have responded all the previous queries/revisions.
Validity of the findings: The authors surpassed the baseline models and included a full performance analysis of their proposal.
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: DNN-BASED MULTI-OUTPUT MODEL FOR PREDICTING SOCCER TEAM TACTICS
Review round: 2
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors have addressed all of my concerns adequately in the rebuttal. I no longer hold any issues against the publication of the article.


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

Input:
Title: TEMPORAL NETWORK EMBEDDING FRAMEWORK WITH CAUSAL ANONYMOUS WALKS REPRESENTATIONS
Review round: 1
Reviewer: 1

Output:
Basic reporting: No comments. Language is in general clear. Sufficient background is given. Terms are defined before use.
Experimental design: Experimental designs make sense and cover a wide range of problems. Experimental details are sufficient.
Validity of the findings: The findings are valid. Experimental results support the findings.
Additional comments: N/A


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

Input:
Title: TEMPORAL NETWORK EMBEDDING FRAMEWORK WITH CAUSAL ANONYMOUS WALKS REPRESENTATIONS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Easy to understand, interesting, relevant and convicing.
Experimental design: well defined, relevant, meaningful and convincing.
Validity of the findings: The findings are well presented, easy to understand and convincing. Also the conclusions are well stated and clearly support the assumption given before.
Additional comments: As the authors correctly present, many machine learning tasks for graphs are increasingly prevalent, especially in link prediction and node classification, and indeed the challenge becomes greater when analyzing the dynamic (i.e., temporal) network. Therefore, in this paper, the authors propose a new approach to learn dynamic representations based on temporal graph networks by introducing a message generation function by extracting causal anonymous paths. To evaluate their novel approach, the authors present a benchmark pipeline for evaluating temporal network embeddings. Truly, this is a very interesting, relevant and also important approach. The authors also nicely demonstrate the applicability of their model in the real downstream graph of one of the leading European banks performing credit scoring based on transaction data - which is a good application.

This reviewer is very positive and recommends the acceptance of this article and only recommends to make a few minor improvements:

1) page 4, section 3.1. line 157, indeed graph neural networks are enormously important for this approach and some very good background work has also been listed, here for completeness a very recent and already highly cited related work should be cited and that is by Holzinger et al (2021), Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI, https://doi.org/10.1016/j.inffus.2021 .01.008 - this reference is in fact an important indication for the interested reader of the possibility of traceability and understandability (which is currently subsumed under explainability), which is also interesting for further work in the context of this application domain (baking, finance).

2) Figure 1 is very good, but the small text in the boxes (this reviewer printed the work on paper) is practically impossible to read. (Figure 2 is perfect)

3) Table 1 is also difficult to read

4) Figure 3 needs a legible caption and for faster reader acquisition a better caption. (Figure 4 again is good)

Generally a very good paper and this reviewer congratulates the 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: TEMPORAL NETWORK EMBEDDING FRAMEWORK WITH CAUSAL ANONYMOUS WALKS REPRESENTATIONS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper is well organized but the presentation could be improved. References can provide sufficient field background. The article structure, figures and tables are clear, and the raw data and code are also provided.
In addition, there are some problems with this paper.

Q1: The second half of Equation 2 is somewhat difficult to understand, and I hope to see a more intuitive definition of “the t-timed k-hop neighborhood”.

Q2: The calculation of time difference t (Line 274, Page 7/18) is not given, which can be further explained. If t is calculated directly, perhaps it means that the earlier the node appears, the higher the probability of being selected?

Q3: In Equation 12, there are two feature vectors, how are they obtained? In addition, what is the difference between the feature vectors at different times?

Q4: In Section 3, the paper gives abbreviations for some methods (such as DeepWalk, EvolveGCN and CAW), but not for others, and I suggest consistency in formatting.

Q5: There are also some new works can be introduced in Section 3, including but not limited to the following works.
[1] Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21).
[2] Meng Liu and Yong Liu. 2021. Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21).
[3] Menglin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang, and Irwin King. 2021. Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21).
[4] Hu, Linmei and Li, Chen and Shi, Chuan and Yang, Cheng and Shao, Chao. 2020. Graph neural news recommendation with long-term and short-term interest modeling. Information Processing & Management.

Q6: A method abbreviation (such as DeepWalk) may help your paper spread more effectively.
Experimental design: The paper's research matches the aims and scope of the journal. Research question well defined, relevant, and meaningful. The paper gives the raw data and code, and provides enough details to reproduce the model. The paper conducts experiment on several datasets and discusses the experimental results to demonstrate the validity of the work.
In addition, there are some problems with this paper.

Q7: The paper conducts experiment on link prediction and node classification tasks in transductive and inductive settings. I would like to see the construction details for the downstream tasks described further.

Q8: In Line 348, Page 10/18, the paper mentions that “the number of negative samples is equal to the number of edges”. I am interested to see a further explanation of it.
Validity of the findings: The paper presents an innovative combination of CAW and TGN methods, and the experimental results demonstrate its effectiveness. All underlying data have been provided, and conclusions are well stated.
In addition, there are some problems with this paper.

Q9: The paper seems to embed CAW as a module in the structure of TGN, does it lead to a high complexity? I would like to see a discussion on the complexity of the model.
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: TEMPORAL NETWORK EMBEDDING FRAMEWORK WITH CAUSAL ANONYMOUS WALKS REPRESENTATIONS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have taken into account the reviewer's comments and addressed them appropriately, and the reviewer would now recommend that the paper be accepted.
Experimental design: n.a.
Validity of the findings: n.a.
Additional comments: Of course run the usual final spell checks when going into production.


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

Input:
Title: TEMPORAL NETWORK EMBEDDING FRAMEWORK WITH CAUSAL ANONYMOUS WALKS REPRESENTATIONS
Review round: 2
Reviewer: 2

Output:
Basic reporting: All my questions have been explained in the revision. No other comments.
Experimental design: Experimental designs make sense and cover a wide range of problems. Experimental details are
sufficient.
Validity of the findings: The findings are valid. Experimental results support the findings.
Additional comments: N/A


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given 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 DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 1
Reviewer: 1

Output:
Basic reporting: Line 31: Solar radio burst is (not originates from) radio wave signal. Solar radio bursts originate from energetic electrons.
Line 32: Unit of frequency should be Hz. Meter kilometer-wave is the wavelength.
Line 36: Height (or heliocentric distance) of solar radio bursts is not determined by propagation characteristics, it’s determined by emission mechanism and background plasma property.
Line 40: Description not correct, the author of “Tan et al 2007” is not “Australian astronomers”. And “Tan et al 2007” in the reference list doesn’t have referred journal volume information.
Line 62: [Scholars] -> [researches] or [previous works] or [works in literature]. (this problem happened multiple times in the paper, please correct them thorough)
Line 71: Citation form: Author-year
Line 92: gray-scale value after normalization could be out of the range [0,255], does here include change of data type (from uint8 to another type), or how to rebin the normalized value to the range of [0,255]
Line 115: Redundant description of a common method widely used in computer vision, please reduce the description length and cite the original paper of morph-close [Image Analysis and Mathematical Morphology by Jean Serra (1982)]
Line 140-202: Redundant description of CNN, please replace this part with some citation (e.g: ImageNet classification with deep convolutional neural networks 2012 [proceedings-neurips] A Krizhevsky et al) or any textbook introducing CNN, GRU is well described in the paper of [Cho, Kyunghyun, et al 2014]
Line 217: delete line break
Line 309-316: not the conclusion of this paper.

 [For the basic concept of solar radio bursts, I highly recommend the author to read the book: Introduction to Solar Radio Astronomy and Radio Physics]
Experimental design: (1)  I wouldn’t suggest the authors claim they proposed a “new” method or model, as the CNN-GRU network is already widely used, for example:
 [Hybrid CNN-GRU model for high efficient handwritten digit recognition 2019 Vantruong Nguyen] and [Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network]
(2)  The description of the dataset is important, please elaborate on the data preparation procedure: how the data is labeled and segmented, input size of the network is 120*120 while the data size is 120*2520. Please state how 120*2520->120*120 is achieved, either one dynamic spectrum is divided into 21 standalone images or the image is rescaled? Downsampling rescale will cause the loss of resolution and the loss of fine structures. (This paper is about fine structures, hence should be sensitive to resolution, so I wouldn't suggest any sort of downsampling before the input of the network).
(3) The detail of affine transformation needs to be elaborated.
(4)  Affine transformation may not be appropriate in the context of solar radio bursts, because the type of solar radio burst is determined by its shape and frequency drift rate, and affine transformation of the dataset may change the shape and frequency and create a dynamic spectrum that won’t exist in the real world. This will make the model perform largely differently in the authors’ dataset and fresh new data.
Validity of the findings: (1)	In this work, the output information is limited: [Event/No-event/Calibration]. Namely the existence of solar radio bursts. The output doesn’t include the information of the type of solar radio bursts and the position in the dynamic spectrum. But actually, the information of [what type, when, and which frequency range] is more useful.

There are already methods that can obtain the type and time-frequency information of radio bursts:
https://www.frontiersin.org/articles/10.3389/fphy.2021.646556/full
https://www.swsc-journal.org/articles/swsc/pdf/2018/01/swsc170092.pdf
https://meetingorganizer.copernicus.org/EGU2020/EGU2020-5109.html

if the method can not be improved to output the type and time-frequency information, please at least discuss it in [future work]

(2) There should be a discussion session

(3) It would be better if the event list in this work can also be published
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 DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 1
Reviewer: 2

Output:
Basic reporting: Comments to the Author

A paper documenting a deep learning technique for the fine structure recognition of solar radio spectrum burst is welcomed. Your manuscript contains much useful information, but I found some  aspects requiring more thorough expressed. Here are my specific comments and suggestion：

1) The title may be propitiate to change to "solar radio burst spectrum".
2) Line 39-40, Line 309-316 have little relevance with the subject of this paper. I suggest removing them.
3) Line 192-193, eq(12)-eq(13) have bad display.
4) Line 216-217, Line 299-301 should be concatenated in one line.
5) Line 248, FN is defined twice.
6) Line 286, Line 289, it is hardly to see the where the Ref.12 locates in Table 6.
7) Some abbreviations  should be fully explained when appearing in the first time. Such as CGRU, CBIGRU, TPR, FPR, TP, FN.
8) Line 319, what does the unbalanced sample distribution mean? It should be explained more thoroughly in advance.
Experimental design: No comments.
Validity of the findings: The author draws a conclusion that CGRU in this paper is more sensitive to small features of solar bursts than other methods. If it was drawn from Table 6, I suggest the author adds more support data such as the classification to complex features or structures in all kinds of solar bursts to make the conclusion solid.
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: A DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 1
Reviewer: 3

Output:
Basic reporting: The manuscript proposes a CGRU model for solar spectrum image analysis, especially for the recognition of burst radio spectrum. Owing to the preprocessing and GRU, experiments demonstrates that the proposed method is very efficient with high accuracy. Therefore, it gives new light on solar image understanding. However, it would be better to further explain the following:
1)	Give the overview of the model architecture or/and the table of model hierarchy structure to clearly show that the model is much simple and efficiency;
2)	Some typing errors need attentions, such the equation (12) and (13). The “FN” in line 249th  seems be a mistake of “TN”.
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 DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 2
Reviewer: 1

Output:
Basic reporting: Line 176, what’s the meaning of left right rotation, is it left and right polarization?

Figure 1, the y-axes should be frequency (MHz GHz), x-axes should be UT time.

Line 204, resolution should be MHz per pixel or sec per pixel, 120*120 is image size

Caption of Figure 4 is not complete.
Experimental design: False negative cases are important, please check case by case if there is event which is not marked in the original dataset.
Validity of the findings: The SBRS is open public:
https://nadc.china-vo.org/data/data/sbrs/
https://sun.bao.ac.cn/SHDA_data/
SBRS has no restrictions to publish data processing results, including the science results and event catalog. The event list is an important part of method validation.
Additional comments: The author has addressed some of the comments, I still have some concerns about the manuscript and results


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given 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 DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 2
Reviewer: 2

Output:
Basic reporting: I am glad to see the author gave quick responses and made appropriate revisions based on all reviewers' suggestions.  The missing abbreviations and concepts are explained, and the wrong formats are fixed as well.  The conclusions are re-organized and re-expressed so the tables and figures seem to justify them.

There are still some typo and small grammar  errors in the document. I think they could be corrected through some automation tools before published. Anyway, the article is suggested to be accepted and published.
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: A DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 3
Reviewer: 1

Output:
Basic reporting: The author has addressed most of my previous problems, these are some minor suggestions:

Line 30: kHz to sub THz
Line 31: background radiation -> quiet sun. background radiation usually refers to ‘The cosmic microwave background’ in astronomy
Line 40: channel noise -> RFI (radio frequency interference)
Line 92: eliminate -> reduce, instrument noise can never be perfectly removed.
Line 203: limit -> rescale or map.
Line 259: eliminate -> reduce, (this is used multiple times, please correct all)
Line 269: the radio signal changes on the dynamic spectrogram -> the morphology of the radio bursts in dynamic spectrum

I would suggest accepting the manuscript after minor revision.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: No additional comments


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

Input:
Title: A DEEP LEARNING METHOD FOR THE RECOGNITION OF SOLAR RADIO BURST SPECTRUM
Review round: 4
Reviewer: 1

Output:
Basic reporting: No further suggestions, the manuscript can be accepted
Experimental design: no comment
Validity of the findings: no comment
Additional comments: No additional comments


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

Input:
Title: A THREE-STAGE HEURISTIC TASK SCHEDULING FOR OPTIMIZING THE SERVICE LEVEL AGREEMENT SATISFACTION IN DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING
Review round: 1
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: I would suggest the authors to incorporate the following very minor changes in the manuscript

1. I would suggest you to add a "motivation" sub-section and "contributions" sub-section in the "Introduction" section.

2. In line 73, the authors should add the reference of the paper from where the EDF technique is cited.

3. Similarly, on lines 245, 246, 248, 250, and 256 references to FF, FFD, EDF, BF, LSTF are missing, cite the papers from where these techniques are used in the current 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 THREE-STAGE HEURISTIC TASK SCHEDULING FOR OPTIMIZING THE SERVICE LEVEL AGREEMENT SATISFACTION IN DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING
Review round: 1
Reviewer: 2

Output:
Basic reporting: Authors present three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing in this paper. Article is well written, and authors have done great job. However, there are some very serious question and comments which need to be addressed.
•	Abstract: According to authors, they proposed the technique to solve the problem in reasonable time. What will be considered reasonable time?
•	THREE-STAGE HEURISTIC TASK SCHEDULING: In the proposed technique, priority order is (i) clouds, (ii) edge servers, and (iii) local devices. Shouldn’t it be other way around? If they task can be executed on the device, it should run on the device. If it can’t, it should be placed to edge servers to reduce latency and overall cost (which will be paid to cloud service provider) and in the end if task cannot be placed on edge servers, due to limited resources, it should be forwarded to cloud. This mechanism will improve overall resource utilization of user device and edge server. Moreover, it will remove latency, cloud placement cost, and migration and rescheduling costs.
•	THREE-STAGE HEURISTIC TASK SCHEDULING: In 2nd stage, tasks are shifted to device which can be accommodated on the device. Why its not done in first place?
•	THREE-STAGE HEURISTIC TASK SCHEDULING: According to authors, last stage improves the resource cost. How will it improve resource cost? what about cost of such frequent migrations? why a smart technique is not devised which places the workload intelligently and reduces the cost of rescheduling and migration?
Experimental design: •	Experimental Design, please provides the details/specifications of tasks, VMs and physical machines in tabular form.
•	Experimental Design, please provide the proper reference number of all the selected techniques. How are these selected existing techniques closely related to your work? All of them are based on bin packing techniques. Why other state of the art, heuristic-based techniques are not considered for the comparison? Some of these selected techniques are very old and outdated.
•	Experimental Design, please provide the formula of each selected performance metric.
•	Results: According to authors, they achieved 59% better SLA satisfaction. How this satisfaction is achieved? And have they considered performance / SLA degradation due to migration? If yes, what is its impact? if no, why didn't you considered such an important consideration?
Validity of the findings: •	Results: According to authors, they achieved 59% better SLA satisfaction. How this satisfaction is achieved? And have they considered performance / SLA degradation due to migration? If yes, what is its impact? if no, why didn't you considered such an important consideration?
•	Results: Figures 2, 3, 4 and 5. What does y axis show in these graphs? Does it represent the graph heading or caption? Results are confusing. Values of y axis are in percentage or numbers? Please provide the proper axis titles in each graph along with the units.
•	Results: SLA Satisfaction, “In contrary, other methods prefer local resources or nearby edge resources, aiming at providing the best performance for each task with minimal resource costs. But these methods result in several local and nearby edge resources that are used by some tasks which can be finished by the cloud, and these resources can be reserved for processing other tasks whose demands cannot be satisfied by the cloud.”, there are techniques that place the tasks on local device or edge, but when the real-time tasks with latency limitation is to be offloaded, they are placed (and given priority) on the local device/edge and non-real time tasks hosted on those machines are moved to next layer of hierarchy.
•	Results: Resource utilization of existing techniques shouldn’t be high? They are utilizing local and edge resources first compared to cloud.
•	Results: Cost Efficiency of proposed technique should be much higher compared to existing techniques as it first places the tasks on cloud rather than utilizing local resources.
•	Results: Number of migration, performance degradation due to migrations and cost of migrations should be considered in performance evaluation process.
•	Results: Rationale behind the result discussion is very weak.
Additional comments: •	Related Work: limited related work. should be extended by including 2021 techniques.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given 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 THREE-STAGE HEURISTIC TASK SCHEDULING FOR OPTIMIZING THE SERVICE LEVEL AGREEMENT SATISFACTION IN DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING
Review round: 1
Reviewer: 3

Output:
Basic reporting: Problem that is tackled in proposed paper is interesting and important, however proposed manuscript suffers from some drawbacks, which are listed below.

According to my opinion proposed manuscript should undergo major revision before acceptance.
Experimental design: Visualization of results should be improved - consider using box and whiskers diagrams, swarm plots, etc. Also, please include convergence speed graphs, since the convergence is very important indicator of system performance.
Validity of the findings: Proposed method should be compared with SOTA (state-of-the-art) metaheuristics methods. Please, examine literature and include few metaheuristics approaches.

Moreover, statistical tests should be executed to prove significant results improvements over other methods.
Additional comments: Abstract should be completely reformulated to highlight main ideas and contributions of the proposed research. Abstract should emphasize a problem that is being solved, importance of the problem, employed methods and achieved results along with methods used in comparative analysis.

Your abstract does not highlight the specifics of your research or findings but contains too much background information. Some details of your research would be nice. An abstract with some details helps show the impact of your research. To aid in this, here is one of many good articles concerning crafting an abstract https://writing.wisc.edu/handbook/assignments/writing-an-abstract-for-your-research-paper.

It is not the best practice to use abbreviations in the abstract. Moreover, method names should not be capitalized.

Writing paper in the 1st person does not sound scientifically. Please rephrase all sentences into the 3rd person.

Introduction should be clearly presented to highlight main ideas and motivation behind the proposed research. Please include and clearly state research question and contributions of proposed study in Introduction.

Literature review should be improved to include  metaheuristics-based approaches. There are many literature sources where the cloud-edge task scheduling problem was solved by using metaheuristics. You may consider the following reference:

https://www.mdpi.com/574168

Related works section should be included after Introduction, alternatively as a subsection within Introduction.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given 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 THREE-STAGE HEURISTIC TASK SCHEDULING FOR OPTIMIZING THE SERVICE LEVEL AGREEMENT SATISFACTION IN DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: I am satisfied with the manuscript now as the asked changes have been incorporated by the authors. Hence this manuscript may 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: A THREE-STAGE HEURISTIC TASK SCHEDULING FOR OPTIMIZING THE SERVICE LEVEL AGREEMENT SATISFACTION IN DEVICE-EDGE-CLOUD COOPERATIVE COMPUTING
Review round: 2
Reviewer: 2

Output:
Basic reporting: Dear Authors,

thank you for addressing my comments.

However, I still noticed some minor English language and technical issues, therefore please read once again your manuscript thoroughly before publication.

All the best
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Dear Authors,

thank you for addressing my comments.

However, I still noticed some minor English language and technical issues, therefore please read once again your manuscript thoroughly before publication.

All the best


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

Input:
Title: PERFORMANCE IMPACT OF PRECISION REDUCTION IN SPARSE LINEAR SYSTEMS SOLVERS
Review round: 1
Reviewer: 1

Output:
Basic reporting: - The paper is clear and well-organized
- Sections are of appropriate sizes
- Intro. & Background are fine
- Figures are relevant
Experimental design: - The authors compared SpMV in single and double precisions on Intel CPU and NVidia GPU
- They also compared LU using PARDISO, cuSPARSE and SuperLU in single vs. double
- The fact that the authors are not performing a study for their own solver is also valuable to ensure a fair comparison
Validity of the findings: - The authors wrote that subnormal numbers can be an issue to achieve performance, and they illustrate with meaningful results
- They explained that such numbers can propagate during the computation and hurt the performance for numerous instructions
- They demonstrate that expecting a speedup of 2 just by using float instead of double is not possible in the majority of cases
- They pointed that incomplete factorization in float vs double has a lowest speedup compared to SpMV, which could motivate the implementation of a more efficient strategy
Additional comments: I think the paper deserves to be published as it provides interesting results that are beneficial for the community.
From my side, there are only a few points that should be addressed before having a final version.

- One thing that I had faced in the past is the poor implementation of single-precision kernels (in the MKL especially).
It might not be true anymore, but as the authors rely on the MKL/cuSPARSE as a "black-box", they are considering
that the kernels are as optimized in both cases, which might not be true.
I would like the authors to ensure that the differences they are seeing are not coming from a difference in the optimization level.
With this aim, I would suggest the following.
The authors could convert a small dense matrix that fit in the L1 cache in the CSR format and apply operations
(such as SpMV) on it (hundreds or thousands of times) such that it will provide a good view of the raw performance
of the kernels when the memory transfers are negligible.
The authors could then simply add a sentence in the manuscript to state if there is a difference or not (and
update the corresponding sections if needed).
This is just one way to do it, and of course, the authors could use any other strategy to test that the MKL
or cuSPARSE kernels are optimized similarly.

- I would appreciate in "EXPERIMENTAL SETUP" to have an idea of how the numbers were extracted,
for example, are they average of X executions? Or the median?

- Similarly, I imagine that the first data that will be used by the kernels are not in the L1 cache.
I would like the authors to confirm this point in "EXPERIMENTAL SETUP".

- I do not understand this sentence "because they are usually processed at the software level,"
My first assumption is to disagree with this statement. If I program in assembly a code
that does a dot product, it will work also with subnormal numbers without
the need to add anything "at the software level".
However, I think that instructions can have higher latency (need more clocks)
when subnormal numbers are used (or NaN as Intel in the past, etc.).
I would like the authors to clarify this point.

- I would suggest replacing "CSR" with "MXCSR" in the comments of Line 200 (to ensure no
ambiguity with CSR storage). I also suggest adding a number and a legend to this code
in order to describe what it does (without the need to go in the main text).

- I can imagine that the discussion concerning the bytes per values (12b/nnz ~ 8b/nnz)
is also valid for other operations than the SpMV.
I would consider that many of the symbolic/transformation stages are memory bound
and would not benefit from the shift from double to single (because it only reduces
the matrix memory by 1/3 and not 1/2).
But after a careful read, the authors made it clear by giving explanations
and stating that these stages are not related to computation
(so I think it will be clear for all readers).

- "rpecision." -> "precision."


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

Input:
Title: PERFORMANCE IMPACT OF PRECISION REDUCTION IN SPARSE LINEAR SYSTEMS SOLVERS
Review round: 1
Reviewer: 2

Output:
Basic reporting: It would be better to add the definitions of "solve" in Figures 7 and 8, as well as Figures 9 and 10, because they seem to have different meanings.
Experimental design: This paper studies the effect of replacing double-precision arithmetic in solving sparse linear system with single-precision. The investigation about the mechanism how subnormal numbers are generated and caused poor speedup is interesting. Also, the finding of the lack of parallelism and floating-point arithmetic in the analysis and reordering phases of the solvers as the reasons of much lower speedup is reasonable.
However, it would be better if the authors could add some detailed analysis on the characteristics of matrices. For example, the matrix ss1 is small but shows low ratio of "Reordering & Analysis" ratio in Figure 7. This may caused the better speedup in Figure 1. So, the readers will be interested in why this matrix has shown different behavior.
Also, discussions on the results for iterative solvers are limited. It would be better to add the reasons why it has shown less speedup on GPU than CPU. In addition to that, there should be more description about the reason why the speedup of ILU is not significant.
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: PERFORMANCE IMPACT OF PRECISION REDUCTION IN SPARSE LINEAR SYSTEMS SOLVERS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors carefully answer the questions/remarks, and I think the paper can now be accepted.

Again, the paper is clear and well-written.
Experimental design: The experimental part is rigorous and well done. Moreover, the code is publicly available.
Validity of the findings: The study of the effect of subnormal numbers, through the linear systems solvers, is important in the context of increasing use of mixed precision by the community.
Additional comments: None.


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

Input:
Title: PERFORMANCE IMPACT OF PRECISION REDUCTION IN SPARSE LINEAR SYSTEMS SOLVERS
Review round: 2
Reviewer: 2

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


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

Input:
Title: COMPARATIVE ANALYSIS OF MAGNETIC INDUCTION BASED COMMUNICATION TECHNIQUES FOR WIRELESS UNDERGROUND SENSOR NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: There are few format and spelling problems, and also unprofessional when claim "accurate predictions" without giving data. Examples are as follows:
1. "Table 1. Comparative analysis of EM wave, Direct MI, MI waveuide and 3-D Coil MI waveguide communcation methods" misspelling of "Waveguide" and "communication".
2. Line 29, WUSN, needs to be expended as it’s first time appears.
3. Line 137, “…accurate predictions of …”, define what is the (accurate) range, resolution and sensitivity of the prediction?
4. Line 306, “the required number of underground transceiver nodes may be reduced to a significant level”, at what level?
5. Line 311, “MI waveguide mechanism offers huge reduction of path loss”, what is the loss level?
Experimental design: As the statements from the Introduction and from the Survey Methodology are inconsistent, the authors should decide "What is the objective of this paper?"
In line 90 of the introduction, the authors described that “This literature review is needed and intended to do the comparative analysis of all the techniques so as to enable the users to find the optimal solution of MI based WUSNs for respective applications”,  while in line 107 of the Survey Methodology, “The purpose of this paper is to review the usage of magnetic induction approach in comparison with conventional EM wave approach for WUSNs and subsequently review all available MI techniques used for the same. ”
The authors should decide whether to compare the techniques among MI approach or compare the MI with the conventional EM wave approach?
Validity of the findings: 1. This paper is a literature review of  magnetic induction mechanism for WUSNs, however the authors should point out what is the novelty of this paper?
2. The authors summarized the comparision of EM wave, Direct MI, MI waveguide and 3-D Coil MI waveguide communication methods in Table 1. However, clear numbers or range for parameters should be provided instead of "Higher/ High/ Lower/ Low" as in table 1.
Additional comments: This paper is a literature review of  magnetic induction mechanism for WUSNs, the authors compared the three techniques in magnetic induction approach, and with the classical electromagnetic wave.
 However, the authors can further improve the manuscript in several aspects:
 1. Your introduction needs to support the objective. At the beginning of the introduction, the authors detail discussed the classical electromagnetic wave approaches (3 paragraphs), and their disadvantages. The authors should decide what is the main objective of this paper? (Comparative analysis of magnetic induction? as in title) or (comparison of MI with classical electromagnetic wave as described in Methodology?) The introduction should work for introducing the main objective.
2. Conclusions are lack of data support. e.g. line 228-243, 301-343
3. What are the main challenges in magnetic induction based communication?
4. Mixed of capital and lower case in sentences. e.g. in title "Comparative Analysis of Magnetic induction based Communication for wireless underground sensor networks", line 19 "......Electromagnetic wave (EM)......", line 23 "......Electromagnetic wave (EM)......" line 188,......
5. line 166. indentation of f)......