题目：An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

作者：S. Muthubalaji et al.

来源：Big Data Mining and Analytics, vol. 7, no. 2, pp. 399-418.

摘要：Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid, which also supports to obtain a variety of technological, social, and financial benefits. There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies, along with data processing and advanced tools. The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security. The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. Then, a Kernel based Extreme Neural Network (KENN) technique is used to anticipate security vulnerabilities more effectively. The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. Moreover, several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection- Kernel based Extreme Neural Network (AEFS-KENN) big data security framework. The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5% with precision and AUC of 99% for all smart grid big datasets used in this study.

题目：A Cloud-Edge Collaboration Framework for Generating Process Digital Twin

作者：B. Shen et al.

来源：IEEE Transactions on Cloud Computing, vol. 12, no. 2, pp. 388-404.

摘要：Tracking the process of remote task execution is critical to timely process analysis by collecting the evidence of correct execution or failure, which generates a process digital twin (DT) for remote supervision. Generally, it will encounter the challenge of constrained communication, high overhead, and high traceability demand, leading to the efficient remote process tracking issue. Existing approaches can address the issue by monitoring or simulating remote task execution. Nevertheless, they do not provide a cost-effective solution, especially when unexpected situation occurs. Thus, we proposed a new cloud-edge collaboration framework for process DT generation. It addresses the efficient remote process tracking issue with a real-virtual collaborative process tracking (RVCPT) approach. The approach contains three patterns of real-virtual collaboration for tracking the entire process of task execution with a coevolution pattern, identifying unexpected situations with a discrimination pattern, and generating a process DT with a real-virtual fusion pattern. This approach can minimize tracking overhead, and meanwhile maintains high traceability, which maximizes the overall cost-effectiveness. With prototype development, case study and experimental evaluation show the applicability and performance advantage of the new cloud-edge collaboration framework in remote supervision.

题目：基于联邦学习的政务大数据平台应用研究

作者：吴坚平、陈超超、金加和、吴春明

来源：大数据, 2024, 10(3): 40-54.

摘要：当前数字政府建设已进入深水区，政务大数据平台作为数据底座支撑各类政务信息化应用，其隐私数据的安全性和合规性一直被业界广泛关注。联邦学习是一类解决数据孤岛的重要方法，基于联邦学习的政务一体化大数据平台应用具有较高的研究价值。首先，介绍政务大数据平台及联邦学习应用现状；然后，分析政务大数据平台面临的隐私数据的采集、分类分级、共享三大管理挑战；接着，阐述基于联邦学习的推荐算法和隐私集合求交技术的解决方法；最后，对政务大数据平台隐私数据的未来应用进行了总结和展望。

题目：基于深度学习的医学多模态数据融合方法在肿瘤学中的进展和挑战

作者：蔡程飞、李军、焦一平、王向学、郭冠辰、徐军

来源：数据与计算发展前沿, 2024, 6(3): 3-14.

摘要：在肿瘤学中，患者有一系列的临床数据，从放射学、组织学、基因组学到电子健康记录。不同数据模式的整合为提高诊断和预后模型的稳健性和准确性提供了机会，使人工智能在临床实践发挥重要作用。本文将探讨深度学习技术以及其在肿瘤医学数据中的应用，并研究肿瘤学领域多模态数据融合方法的潜在影响和重要发现。多模态数据能够更好地发现与患者治疗响应、预后效果相关的信息，从而构建更加鲁棒的深度学习模型。深度学习已经在医学领域取得了显著的进展，特别是在肿瘤学研究中处理多模态医学数据。这些进展为临床提供了更准确、更快速的工具来进行肿瘤的检测、分割、分类和预后预测，同时也面临很多挑战亟须解决。

题目：政务数据标识技术研究进展及下一代政务数据标识体系

作者：王昀、郭毅峰、苏晓亮、周武爱、张皖哲、许大虎、周强、冯建华

来源：大数据, 2024, 10(3): 3-15.

摘要：政务数据标识是建设全国一体化政务大数据体系的一项基础性工作。对数据标识技术的研究进展进行了总结，比较了不同数据标识技术编码规则的异同，并进一步总结了政务数据标识及应用进展。结合政务数据所具有的权责明确、安全性要求高、兼容性需求强等特点，提出了下一代政务数据标识体系Gcode。Gcode由外部码、内部码和安全码3个部分组成。其中，外部码兼容了统一社会信息用代码，内部码建立了“机构部门-系统-数据”的关联关系，安全码通过引入区块链技术实现防伪验真。Gcode具有权责明确、兼容性强、安全性高等特点，能够支持政务数据跨层级、跨地域、跨系统、跨部门、跨业务共享，可有力推动实现政务数据“一数一源”。

题目：深度学习在口腔种植影像学中的应用：研究进展与挑战

作者：郑懿诺、孙沐毅、张虹云、张婧、邓天政、刘倩

来源：数据与计算发展前沿, 2024, 6(3): 41-49.

摘要：系统性地回顾和总结深度学习在口腔种植领域的研究进展，包括口腔影像处理、种植体系统检测以及口腔种植预后的应用。基于深度学习在口腔种植领域的研究，按照研究方向进行分类总结，阐述相关研究的主要研究内容及结论。深度学习技术在口腔种植领域已取得显著成就。口腔影像中的智能分割和识别技术提高了口腔医生的诊断准确性和工作效率，而口腔种植体系统的自动化检测有助于更快速地了解患者的口腔情况。此外，深度学习还在口腔种植预后的预测中发挥关键作用，帮助医生提前干预并改善治疗结果。深度学习在口腔种植领域具有巨大潜力，有助于推动口腔种植更加精准高效，为口腔医生赋能。