BeerReview: A Blockchain-enabled Peer Review Platform

• URL: http://arxiv.org/abs/2405.20220v1
• Date: Thu, 30 May 2024 16:19:13 GMT
• Title: BeerReview: A Blockchain-enabled Peer Review Platform
• Authors: Guodong Jin, Zihan Zhou, Wenzheng Tang, Kanglei Yu, Hao Xu, Erwu Liu,
• Abstract summary: BeerReview is a blockchain-enabled peer review platform. It offers a robust solution, enabling experts and scholars to participate actively in the review process without concerns about plagiarism or security threats.
• Score: 9.059774441296247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an era of increasing concerns over intellectual property rights, traditional peer review systems face challenges including plagiarism, malicious attacks, and unauthorized data access. BeerReview, a blockchain-enabled peer review platform, offers a robust solution, enabling experts and scholars to participate actively in the review process without concerns about plagiarism or security threats. Following the completion of its alpha testing, BeerReview demonstrates the potential for expanded deployment. This platform offers improved convenience and more robust intellectual property protection within the peer review process with open source initiative.

Related papers

• Evaluating Copyright Takedown Methods for Language Models [100.38129820325497]
  Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. This paper introduces the first evaluation of the feasibility and side effects of copyright takedowns for LMs. We examine several strategies, including adding system prompts, decoding-time filtering interventions, and unlearning approaches.
  arXiv  Detail & Related papers  (2024-06-26T18:09:46Z)
• Decentralized Peer Review in Open Science: A Mechanism Proposal [0.0]
  We propose a community-owned and -governed system for peer review. The system aims to increase quality and speed of peer review while lowering the chance and impact of erroneous judgements.
  arXiv  Detail & Related papers  (2024-04-28T11:42:54Z)
• What Blocks My Blockchain's Throughput? Developing a Generalizable Approach for Identifying Bottlenecks in Permissioned Blockchains [0.3495246564946556]
  We develop a more unified and graphical approach for identifying bottlenecks in permissioned blockchains. We conduct in-depth case studies on Hyperledger Fabric and Quorum, two widely used permissioned blockchains.
  arXiv  Detail & Related papers  (2024-04-02T13:00:50Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews [24.875901048855077]
  We introduce a novel task of automatically identifying contradictions among reviewers on a given article. To the best of our knowledge, we make the first attempt to identify disagreements among peer reviewers automatically.
  arXiv  Detail & Related papers  (2023-10-28T11:57:51Z)
• Resilient Consensus Sustained Collaboratively [6.090550359575682]
  We present the design of our Power-of-Collaboration protocol, which guards existing PoS/BFT blockchains against long-range attacks. PoC guarantees fairness and accountability and only marginally degrades the throughput of the underlying system.
  arXiv  Detail & Related papers  (2023-02-05T07:33:57Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• BackdoorBench: A Comprehensive Benchmark of Backdoor Learning [57.932398227755044]
  Backdoor learning is an emerging and important topic of studying the vulnerability of deep neural networks (DNNs) Many pioneering backdoor attack and defense methods are being proposed successively or concurrently, in the status of a rapid arms race. We build a comprehensive benchmark of backdoor learning, called BackdoorBench.
  arXiv  Detail & Related papers  (2022-06-25T13:48:04Z)
• Ants-Review: a Protocol for Incentivized Open Peer-Reviews on Ethereum [0.0]
  We propose a blockchain-based incentive system that rewards scientists for peer-reviewing other scientists' work. Authors can issue a bounty for open anonymous peer-review on smart contracts called Ants-Review. If requirements are met, peer-reviews will be accepted and paid by the approver proportionally to their quality.
  arXiv  Detail & Related papers  (2021-01-22T23:32:41Z)
• What Can We Do to Improve Peer Review in NLP? [69.11622020605431]
  We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.
  arXiv  Detail & Related papers  (2020-10-08T09:32:21Z)
• An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms [76.9570531352697]
  We propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes.
  arXiv  Detail & Related papers  (2020-01-24T22:23:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.