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The authors have generally responded well to previous concerns, improving clarity, expanding discussions on limitations, methodology, and applications, and proofreading the manuscript.
Remaining suggestions include enhancing the comparative analysis by adding recent references and explicitly comparing the framework with alternative trust models, as well as reinforcing the novelty of addressing trust in the Metaverse with clearer literature support.
[# PeerJ Staff Note - this decision was reviewed and approved by Vicente Alarcon-Aquino, a PeerJ Section Editor covering this Section #]
The manuscript is well-prepared for final acceptance, requiring only minor revisions. The authors have effectively responded to the editor's and reviewers' comments, enhancing the discussion on limitations, methodology, and real-world applications, while also improving language clarity. However, to strengthen the paper's relevance, they should include recent references from 2022 to 2024 and consider providing a comparative analysis of their trust computation framework against at least one alternative model in tabular form.
Since these are refinements rather than fundamental flaws, minor revisions should be sufficient before final acceptance.
1. Strong Response to Comments
- The authors have addressed most of the editor's and reviewers' concerns in a clear and structured manner.
- They have expanded the discussion on limitations, methodology, and real-world applications.
- The manuscript has been proofread for language improvements.
2. Remaining Areas for Improvement
Comparative analysis:
- The addition of recent references (2022–2024) enhances the paper's relevance. However, it might be useful to explicitly compare your framework with at least one alternative trust computation model in a table.
Literature review:
- If "trust in Metaverse" has not been tackled before, consider reinforcing this claim with a more explicit statement of novelty backed by existing literature.
No comment
No comment
No comment
The revised manuscript has improved significantly compared to the last version. However, please consider the following points in order to strengthen the work's impact:
1.Expand the discussion section to address limitations and broader implications.
2.Include more diverse testing scenarios in the simulations to enhance the robustness of the results.
3.Provide deeper methodological explanations for fuzzy logic to ensure accessibility to a wider audience.
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services if you wish - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Your revision deadline is always extended while you undergo language editing. #]
Clarity and Professional Language:
The manuscript uses professional English effectively however, certain sentences, especially in the abstract and conclusion, could be rephrased for clarity. For example, the abstract could benefit from a more concise summary of the methodology and main findings.
Literature References and Background:
The manuscript adequately references recent and foundational works in the field of Cyber-Physical-Social Systems (CPSS) and Metaverse applications. However, including citations from recent years (2022–2024) related to fuzzy logic applications in trust management would support its relevance.
Structure, Figures, and Tables:
The article is well-structured, following a logical flow from the introduction to the experimental results and conclusion. Figures and tables are informative, but some, like Figure 4, could use better label clarity and improved font size. Including a legend for Figure 4 would enhance comprehension.
Self-Contained and Relevant Results:
The manuscript is self-contained, with all major claims supported by the presented results. However, additional discussion on how the results address the stated hypotheses for example, enhancing trust in CPSS would strengthen this section.
Formal Results and Definitions:
Key terms like "trust index" and "fuzzy logic membership functions" are defined, but providing mathematical proofs or derivations (where applicable) for fuzzy rules would increase rigor.
Original Research and Scope:
The study presents original research aligning with the journal’s scope. It tackles the trust evaluation challenge in CPSS, focusing on Metaverse scenarios
Research Question and Knowledge Gap:
The manuscript clearly defines its research question:
"How can trust in CPSS be effectively evaluated using fuzzy logic frameworks?" It identifies gaps in existing methods, particularly the need for dynamic trust evaluations in Metaverse contexts. Expanding this discussion in the introduction would further clarify the novelty.
Methods and Replication:
The methods section is detailed but could be improved by explicitly stating the fuzzy logic rules used and their derivation.
Impact and Novelty:
The study proposes a five-tier Fuzzy Logic Trust Management Framework, which is novel. However, assessing its impact compared to existing frameworks requires further discussion. A comparative analysis table showcasing the advantages over prior models would highlight the contribution.
Conclusions and Future Work:
The conclusions are well-stated, summarizing how the proposed framework addresses the research question. However, the discussion on future work, particularly real-world validation and potential scalability issues, should be expanded.
The paper is publishable with revisions, primarily focusing on enhancing the clarity of visual aids, providing comparative analyses, and expanding the discussion on parameter selection.
After the second round of review, the authors did not make substantial revisions to address the reviewers' comments. Consequently, a "Major Revision" is required for the authors to enhance the manuscript. The authors should consider and address the following points:
1. In the "Introduction," the authors should review and include references from the last three years (2022-2024) to ensure the relevance of the literature.
2. In Table 1, the authors have not compared the proposed Fuzzy Logic approach with the most recent works (2022-2024). Currently, the comparison includes studies published in 1993, 2000, 2011, 2017, and 2019, which needs updating.
3. In the "Abstract," the authors should provide a more comprehensive overview to clearly convey the main contributions and findings of the paper.
4. In the "Simulation Results and Security Analysis" section (4.2), the content is relatively brief. The authors must indicate whether their results have been compared or benchmarked against other published works. Additionally, clarity on how the authors verified their results is essential.
Although the authors have revised the manuscript based on the reviewers' comments, there remain significant concerns regarding the incorporation of recent references and the need to provide compelling arguments supporting the use of fuzzy logic over other technical approaches in the analysis. The authors should address these issues thoroughly if they wish to resubmit the paper after further revision.
As the paper addresses CPSS, some latest references as provided before need to be added.
Though the paper claims its main contribution as the proposition of FTM for CPSS, I think it is better to include some convincing arguments to support the fuzzy logic. Even if the trust in Metaverse is not tackled before as the authors responded, then why not to choose other technical paths?
No comment.
After the first round of review, this paper is recommended to a "Major Revision" based on the reviewers' comments. Authors also consider the following comments.
1. In the "Abstract", authors should provide the quantitative performance achievement of the proposed work.
2. The discussions of the details of the raw data, how the data were processed and detailed analysis steps should be improved.
3. Some references were asked by a reviewer. Authors should check and make sure these references are relevant and useful to enhance the quality of this work. If this is not the case, please ignore these references.
Clarity and Professional Language:
The manuscript generally uses clear and professional language. However, there are some grammatical and syntactical errors that need to be corrected for better readability. Examples include:
- Abstract, line 35: "section II attains the total trust value." should be "Section II attains the total trust value."
- Introduction, line 73: "improve service delivery and user-platform server interaction." should be "improves service delivery and user-platform server interaction."
- Review the manuscript for grammatical errors and improve sentence structure for better readability. Consider professional editing services if necessary.
Introduction and Background:
The introduction provides a good context and background for CPSS and the relevance of trust management. However, it can be improved by clearly stating the novelty and unique contributions of this work compared to existing literature.
Literature Review:
The literature review is comprehensive and well-referenced, providing a solid foundation for the study. However, it could benefit from a more critical analysis of the cited works to highlight the gaps this study aims to fill.
Structure and Standards:
The structure conforms to the journal's standards, with clearly defined sections. Figures and tables are relevant and high quality, but they need to be consistently labeled and described in the text for better integration.
Research Question:
The research question is well-defined and addresses a relevant and meaningful gap in the knowledge about trust management in CPSS using fuzzy logic.
Methodology:
The methodology is detailed and provides enough information for replication. However, some sections could benefit from additional clarity. For example:
- In Section 4, "Proposed fuzzy logic trust management framework for cyber physical social system (FTM-CPSS)," the description of the five tiers could be more detailed with specific examples or case studies.
- In Section 5, the simulation setup and parameters need to be explained more thoroughly to understand the context and constraints of the experiments.
Data and Analysis:
The data provided is robust and the statistical analysis is sound. However, more details on the raw data and how it was processed would be beneficial. Including a supplementary file with raw data and detailed analysis steps may strengthen the validity of the findings.
Conclusions:
The conclusions are well-stated and logically follow from the results. They are appropriately linked to the research questions and findings. However, a discussion on the potential limitations of the study and suggestions for future research would add depth to the conclusions.
Strengths:
- The integration of fuzzy logic in trust management for CPSS is a novel and well-executed idea.
- The paper is comprehensive and covers various aspects of trust management, including security issues.
- The use of a five-tier framework provides a structured approach to evaluating trust.
Weaknesses:
- The paper has some grammatical and syntactical errors that need correction.
- The methodology section could be more detailed with examples and clearer explanations of the simulation setup.
- The discussion section lacks a critical analysis of the potential limitations and future research directions.
no comment.
Please see the additional comments.
Please see the additional comments.
The paper presents a tiered framework for the computation of trust in CPSS using fuzzy logic. The topic is important and interesting, but I have some major comments here after my careful reading.
1. According to the theme, the introduction part needs to put emphasis on the importance of trustworthiness in CPSS and the major existing problems about its research, rather than comprehensively listing many applications in different fields.
2. Currently, the Related Work section simply piles up all the literature. I suggest the authors make a further summarization and refinement, providing typical technical features for each paragraph.
3. Organization of the article needs to be improved, as several sections/subsections only contain one paragraph (such as Section 3, Section 5 and Section 6).
4. For the content, trustworthiness of CPSS is a quite complicated issue. To my view, it is the confidence that the system’s decisions and behaviors align with the user’s expectations. This involves many factors such as the algorithm’s interpretability and transparency, the system’s reliability, the human user’s preference, the privacy protection, etc. Therefore, metrics considered in this paper may only cover a limited range of its connotation. The authors, at least, are required to explicitly differentiate them and specify the scope of this study.
5. Another major problem is the experiment. The simulation results are quite simple. I suggest the authors consider some questions: 1) How to improve the rationality and advancement of the proposed analytical approach? (compare with other typical work?) 2) How to determine the membership degrees for fuzzy logic computation? As I see, the membership degree measures the user’s uncertainty, but current experiments do not involve human participants. How can they support the validity of the framework? 3) How to explain the experiment results? Can we get any significant conclusions?
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