题目：Research Progress of Quantum Artificial Intelligence in Smart City

作者：S. Wang, N. Wang, T. Ji, Y. Shi and C. Wang.

来源：Intelligent and Converged Networks, vol. 5, no. 2, pp. 116-133.

摘要：The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence (AI). As Moore's Law approaches its limit, it is imperative to break the constraints that are holding back artificial intelligence. Quantum computing and artificial intelligence have been advancing along the highway of human civilization for many years, emerging as new engines driving economic and social development. This article delves into the integration of quantum computing and artificial intelligence in both research and application. It introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum effects. The discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives: quantum supervised learning, quantum unsupervised learning, quantum reinforcement learning, and quantum deep learning. In an effort to address the limitations of smart cities, this article explores the formidable potential of quantum artificial intelligence in the realm of smart cities. It does so by examining aspects such as intelligent transportation, urban operation assurance, urban planning, and information communication, showcasing a plethora of practical achievements in the process. In the foreseeable future, Quantum Artificial Intelligence (QAI) is poised to bring about revolutionary development to smart cities. The urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers, constructing an efficient, stable, and adaptive hybrid computing architecture that integrates quantum and classical computing, preparing quantum data as needed, and advancing controllable qubit hardware equipment to meet actual demands. The ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities, robustness, excellent generalization capabilities, and interpretability.

题目：Improved Quantile Convolutional and Recurrent Neural Networks for Electric Vehicle Battery Temperature Prediction

作者：A. M. Billert, R. Yu, S. Erschen, M. Frey and F. Gauterin.

来源：Big Data Mining and Analytics, vol. 7, no. 2, pp. 512-530.

摘要：The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature. This work extends a method for the development of Quantile Convolutional and Quantile Recurrent Neural Networks (namely Q*NN). Fleet data of 225 629 drives are clustered and balanced, simulation data from 971 simulations are augmented before they are combined for training and testing. The Q*NN hyperparameters are optimized using an efficient Bayesian optimization, before the Q*NN models are compared with regression and quantile regression models for four horizons. The analysis of point-forecast and quantile-related metrics shows the superior performance of the novel Q*NN models. The median predictions of the best performing model achieve an average RMSE of 0.66°C and R 2 of 0.84. The predicted 0.99 quantile covers 98.87% of the true values in the test data. In conclusion, this work proposes an extended development and comparison of Q*NN models for accurate battery temperature prediction.

题目：我国政务数据分类分级实施关键问题与实践研究

作者：王跃、苏娜

来源：大数据, 2024, 10(3): 16-26.

摘要：数据分类分级是保障数据安全流通、推动数据价值释放的基础前提。聚焦政务数据分类分级这一政府数字化改革中的关键任务，采用基于理论的案例研究方法，基于各省级地方及部委公开发布的方案，对我国政务数据分类分级实施情况进行系统梳理与量化分析。总结了我国政务数据分类分级实施的四大关键过程与五大特点；从政务数据分类分级的特殊复杂性出发，提出我国政务数据分类分级实施存在整体目标定位不清、分类分级对象各异、分类分级关系割裂、安全分级标准不一4个问题，并提供应对方案；基于国家某部委政务数据分类分级实践，验证应对方案的科学性、有效性，为构建全国统一的政务数据分类分级体系提供参考。

题目：基于Rucio的高能物理网格数据管理的研究和应用

作者：张玄同、张晓梅、胡皓、王浩帆

来源：数据与计算发展前沿, 2024, 6(3): 58-66.

摘要：近年来，高能物理网格数据规模和用户需求产生重大变革，需要研究和应用新兴网格数据管理技术以应对需求变化。基于新型网格数据管理系统Rucio，利用其高伸缩性、模块化和可扩展性的软件特点，发挥其分布式数据恢复、自适应的数据复制的功能特性，为多个国内主导的国际合作实验设计了面向实验需求的网格数据管理解决方案。实现了分布式数据统一命名、数据增删改查等基础管理功能、多站点数据副本管理、原始数据分发管理、实验软件数据管理接口嵌入等多种功能，并先后进入了测试应用阶段。本研究为未来国内主导的国际合作的高能物理实验网格数据管理方案的设计和开发进行了探索和尝试，希望进一步在网格架构上开展深入研究，实现国内实验通用标准的网格数据管理方案。

题目：数据要素交易多边平台研究：现状、进路与框架

作者：吴江、袁一鸣、贺超城、钱龙、杜乐、缪佳蕊

来源：信息资源管理学报, 2024, 14(3): 4-20.

摘要：为响应国家政策，构建统一完善的数据要素流通交易市场，对当前数据要素的流通交易过程进行梳理分析，为数据要素平台的构建提供思路，对推进我国数据要素市场化配置与数字经济发展具有战略意义。通过案例分析与文献梳理分析了数据要素交易多边市场中的主体及其之间的逻辑关系，总结市场现状中的痛点问题，并基于价值链理论以及社会技术系统理论提出了数据要素交易多边平台的突破路径以及研究框架，以期为数据要素流通交易市场建设提供参考路径。

题目：孤独症人工智能诊疗进展及前沿

作者：王志永、刘晶晶、王新明、陈博文、聂伟、张瀚林、刘洪海

来源：数据与计算发展前沿, 2024, 6(3): 15-27.

摘要：近年来人工智能和传感技术的快速发展为孤独症的诊疗提供了新的手段和方向。本文围绕孤独症人工智能诊疗技术进行了文献综述，为未来工作提供参考。本文采用关键字检索的方法调研了来自主流会议和期刊的相关论文，并进行了总结归纳。从临床人工诊疗发展、基于人工智能的诊断研究、基于人工智能的干预研究三个方面分别介绍基于不同方法和传感技术的研究进展。孤独症人工智能诊疗有助于克服临床上的局限性，如主观性强、耗时长、资源紧缺等。相关文献多为近年来的工作，对更早期的工作可能存在遗漏。通过分析最新的人工智能方法对孤独症诊疗的影响，梳理孤独症人工智能诊疗的挑战和展望，有助于推动孤独症人工智能辅助诊疗理论和技术的发展。