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The manuscript has been revised well. Congratulations!
[# PeerJ Staff Note - this decision was reviewed and approved by Vicente Alarcon-Aquino, a PeerJ Section Editor covering this Section #]
Please revise the manuscript accordingly based on the reviewers' comments (especially Reviewer 1).
Clear.
Clear.
I am still not clear on the role of randomization, although I get that the proposed SBP achieves better performance by limiting search space/paths. Please describe more on this. What happens if a fixed index within threshold out of the best pairs is chosen? Also, how does random distribution profile other than uniform affect the performance?
Thanks for the revision.
- In definition 1, "A is involutory MDS matrix" should be changed to "A is an involutory MDS matrix".
Good
Good
Dear Authors,
Please review the comments from two expert reviewers, and revise the manuscript to be considered for publication in PeerJ CS.
Thanks,
Woorham
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
Overall, the paper reads well.
Increase the Table font size, they are barely visible in print.
The modification of BP into your proposed algorithm comprises two steps; BP picks the pairs that minimize the sum of distances, and then resolves ties by choosing maximum Norm. In contrast, your algorithm 1) collects the pairs that minimize the distance sum “or” maximize Norm, then 2) resolves ties by a random decision.
1. How is the optimization result differ if you use the same criterion for picking the new base element with BP and resolve ties by your method of random decision?
2. Either way, you need a uniform random number generation. How much does it affect the running time? Can you compare with other algorithms?
3. How does the parameter chosenParam affect the result?
4. In your BDKCI, how much does the threshold value affect the performance? On what basis do you choose this value?
5. The optimization result should inherently differ in every run due to the random number. What is the worst case and the best case? Is the provided number in the paper average value of several runs?
6. The extraction results indeed show that your algorithm performs better, but what exactly is the reason behind this? I hardly find an intuitive reason.
- The numbering of examples should be revised, for example “Example 6” should be changed to “Example 1”.
- The paper does not have many theoretical contributions.
It is recommended to present the theoretical or theoretical basis to be able to come up with such algorithm 1.
- The results of the article are more meaningful in terms of practice.
- The authors should improve the paper to make new theoretical contributions.
In my opinion, the results of the paper are not enough to be published in the Q2 journals.
quite good
Quite good
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