ExamGuard:用于安全在线测试的智能合约

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mayuri Diwakar Kulkarni, Ashish Awate, Makarand Shahade, Bhushan Nandwalkar
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引用次数: 0

摘要

目前,教育领域正在经历一场深刻的变革,其主要驱动力是在线考试平台的广泛应用。本文将深入探讨如何利用智能合约来彻底改变在线考试的监控和执行,从而保证评估数据和考生活动的可追溯性。在此背景下,整合诸如 PoseNet 算法(源自 TensorFlow 模型)等先进技术成为一个关键组成部分。通过利用 PoseNet,该系统能有效识别考生的单人和多人面孔,从而确保考试环节的真实性和完整性。此外,COCO 数据集的加入有助于识别考试环境中的物体,进一步增强了系统监控考生活动的能力。这一平台不仅能确保数据的不变性,还能防止潜在的篡改情况,从而维护考试结果的可信度。通过利用智能合约,拟议的框架不仅简化了考试流程,还提高了透明度和完整性,从而解决了传统考试方法中遇到的固有挑战。这种技术整合的主要优势之一在于,它能够使考试程序现代化,同时在教育评估生态系统中加强信任和问责。通过利用智能合约的力量,教育机构可以减少对数据篡改和舞弊行为的担忧,从而营造一个更加安全可靠的考试环境。此外,区块链技术提供的透明度确保了考试结果的可验证性和可审计性,为利益相关者注入了信心,提高了评估过程的整体可信度。总之,智能合约的采用代表了教育评估领域的范式转变,为应对传统考试方法带来的挑战提供了全面的解决方案。通过采用 PoseNet 和区块链等先进技术,教育机构不仅可以简化考试程序,还能坚持最高的诚信和问责标准。因此,智能合约的整合在塑造在线考试的未来方面具有巨大的潜力,为建立一个更加高效、透明和可信的评估生态系统铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ExamGuard: Smart contracts for secure online test
The education sector is currently experiencing profound changes, primarily driven by the widespread adoption of online platforms for conducting examinations. This paper delves into the utilization of smart contracts as a means to revolutionize the monitoring and execution of online examinations, thereby guaranteeing the traceability of evaluation data and examinee activities. In this context, the integration of advanced technologies such as the PoseNet algorithm, derived from the TensorFlow Model, emerges as a pivotal component. By leveraging PoseNet, the system adeptly identifies both single and multiple faces of examinees, thereby ensuring the authenticity and integrity of examination sessions. Moreover, the incorporation of the COCO dataset facilitates the recognition of objects within examination environments, further bolstering the system's capabilities in monitoring examinee activities.of paramount importance is the secure storage of evidence collected during examinations, a task efficiently accomplished through the implementation of the blockchain technology. This platform not only ensures the immutability of data but also safeguards against potential instances of tampering, thereby upholding the credibility of examination results. Through the utilization of smart contracts, the proposed framework not only streamlines the examination process but also instills transparency and integrity, thereby addressing inherent challenges encountered in traditional examination methods. One of the key advantages of this technological integration lies in its ability to modernize examination procedures while concurrently reinforcing trust and accountability within the educational assessment ecosystem. By harnessing the power of smart contracts, educational institutions can mitigate concerns pertaining to data manipulation and malpractice, thereby fostering a more secure and reliable examination environment. Furthermore, the transparency afforded by blockchain technology ensures that examination outcomes are verifiable and auditable, instilling confidence among stakeholders and enhancing the overall credibility of the assessment process. In conclusion, the adoption of smart contracts represents a paradigm shift in the realm of educational assessment, offering a comprehensive solution to the challenges posed by traditional examination methods. By embracing advanced technologies such as PoseNet and blockchain, educational institutions can not only streamline examination procedures but also uphold the highest standards of integrity and accountability. As such, the integration of smart contracts holds immense potential in shaping the future of online examinations, paving the way for a more efficient, transparent, and trustworthy assessment ecosystem.
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来源期刊
Information Systems
Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
2.70%
发文量
112
审稿时长
53 days
期刊介绍: Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.
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