Abstract ：

OBJECTIVE(#br) To assess the trends in non-melanoma skin cancer (NMSC) incidence in Hong Kong from 1990 to 2019 and the associations of age, calendar period, and birth cohort, to make projections to 2030, and to examine the drivers of NMSC incidence. (#br)METHODS(#br) We assessed the age, calendar p...

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age-period-cohort analysis decomposition Non-melanoma skin cancer projection

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Abstract ：

在目前的图像实时采集方法中，对采集到的图像信号的噪声滤波不足，影响采集图像的质量。因此，提出基于计算机技术的高速串行光纤图像实时采集方法设计研究。采用计算机技术中值滤波法，对采集到的图像进行数据滤波去噪。通过Mallat算法将去噪后的图像进行分解并重构小波函数，获得完整的图像数据信号，进一步分析和变换重构后的小波函数。通过链码方式提取图像的轮廓和特征链码，实现图像采集。实验结果显示，设计的图像采集方法采集到的数据受噪声影响更低，信噪比改善因子的均值分别为-5.9762和-4.6691,并且图像质量更好，因此，证明研究方法满足设计目的，有效提高了采集图像的质量。

Keyword ：

Mallat算法 图像轮廓结构 图像实时采集 噪声滤波 中值滤波法

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随着税收优惠政策数量的迅速增加，纳税人面对海量的税收优惠政策难以快速定位与自身相关的优惠内容，导致许多纳税人没有享受到应该享受的优惠政策。基于预训练语言模型BERT与规则处理相结合的方法实现了对税收优惠政策法规的表示、关键要素抽取和税收优惠的可视化查询，使纳税人可以快速准确地定位与自身相关的税收优惠信息，并对结果进行可视化展示。实验结果表明，关键要素抽取性能优越，税收优惠政策查询快速直观，可有效缓解海量税收优惠信息过载。

Keyword ：

可视化 税收优惠政策 信息抽取 预训练语言模型

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Twitter bot detection has become an important and challenging task to combat misinformation and protect the integrity of the online discourse. State-of-the-art approaches generally leverage the topological structure of the Twittersphere, while they neglect the heterogeneity of relations and influence among users. In this paper, we propose a novel bot detection framework to alleviate this problem, which leverages the topological structure of user-formed heterogeneous graphs and models varying influence intensity between users. Specifically, we construct a heterogeneous information network with users as nodes and diversified relations as edges. We then propose relational graph transformers to model heterogeneous influence between users and learn node representations. Finally, we use semantic attention networks to aggregate messages across users and relations and conduct heterogeneity-aware Twitter bot detection. Extensive experiments demonstrate that our proposal outperforms state-of-the-art methods on a comprehensive Twitter bot detection benchmark. Additional studies also bear out the effectiveness of our proposed relational graph transformers, semantic attention networks and the graph-based approach in general. Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Keyword ：

Artificial intelligence Graphic methods Information services Semantics Social networking (online) Topology

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Abstract ：

Twitter bot detection has become an important and challenging task to combat misinformation and protect the integrity of the online discourse. State-of-the-art approaches generally leverage the topological structure of the Twittersphere, while they neglect the heterogeneity of relations and influence among users. In this paper, we propose a novel bot detection framework to alleviate this problem, which leverages the topological structure of user-formed heterogeneous graphs and models varying influence intensity between users. Specifically, we construct a heterogeneous information network with users as nodes and diversified relations as edges. We then propose relational graph transformers to model heterogeneous influence between users and learn node representations. Finally, we use semantic attention networks to aggregate messages across users and relations and conduct heterogeneity-aware Twitter bot detection. Extensive experiments demonstrate that our proposal outperforms state-of-the-art methods on a comprehensive Twitter bot detection benchmark. Additional studies also bear out the effectiveness of our proposed relational graph transformers, semantic attention networks and the graph-based approach in general. Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outliers and heavy-tailed noises. Understanding modal regression’s theoretical behavior can be fundamental in learning theory. Despite significant progress in characterizing its statistical property, the majority of the results are based on the assumption that samples are independent and identical distributed (i.i.d.), which is too restrictive for real-world applications. This paper concerns the statistical property of regularized modal regression (RMR) within an important dependence structure - Markov dependent. Specifically, we establish the upper bound for RMR estimator under moderate conditions and give an explicit learning rate. Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain. This result shed a new light on characterizing the theoretical underpinning for robust regression. Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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Video service providers have deployed dynamic video bitrate adaptation services to fulfill user demands for higher video quality. However, fluctuations and instability of network conditions inhibit the performance promotion of adaptive bitrate (ABR) algorithms. Existing rule-based approaches fail to guarantee accurate throughput estimates, and learning-based algorithms are considerably sensitive to the variability of network. Therefore, how to gain effective and stable throughput estimates has become one of the critical challenges to further enhancing ABR methods. To eliminate this concern, we propose PRIOR, an ABR algorithm that fuses an effective throughput prediction module and a state-of-the-art multi-agent reinforcement learning method to provide a high quality of experience (QoE). PRIOR aims to maximize the QoE metric by straightforwardly utilizing accurate throughput estimates rather than past throughput measurements. Specifically, PRIOR employs a light-weighted prediction module with attention mechanism to obtain effective future throughput. Considering the excellent features introduced by the HTTP/3 protocol, we apply PRIOR to trace-driven simulations and real-world scenarios over HTTP/1.1 and HTTP/3. Trace-driven emulation illustrates that PRIOR outperforms existing ABR schemes over HTTP/1.1 and HTTP/3, and our prediction module can also reinforce the performance of other ABR algorithms. Extensive results on real-world evaluation demonstrate the superiority of PRIOR over existing state-of-the-art ABR schemes. © 2022 ACM.

Keyword ：

Deep learning Forecasting HTTP Hypertext systems Learning algorithms Learning systems Multi agent systems Quality of service Reinforcement learning Video streaming

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随着我国大数据、人工智能、移动互联网、云计算、物联网、区块链等新一代信息技术的发展,会计行业在迭代升级中逐步实现了数字化管理.高等职业教育会计专业人才培养模式迫切需要与时俱进,以适应数字经济时代的人才需求.通过对当前高职院校会计专业人才培养模式存在的主要问题和内在原因进行研究,试图准确定位高职会计人才培养目标和合理设置课程体系,寻求改革和创新高职院校会计专业人才培养模式.

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为提高审计系统的智能化推理和预测能力.文中提出了基于BP审计网络的人工智能审计系统平台,在对人工智能开发方式和神经网络方法进行融合分析的基础上,开展了智能化审计模式分析和系统设计分析,并且以不同类型的两类企业为试验对象开展了实证研究.结果表明,BP人工神经网络主要应用于非线性分析,对于解决复杂审计问题具有非常好的适用性;该模型对餐饮类企业判别正常纳税企业32个,异常纳税18个,准确率为95％;对于服装类企业判断正常纳税企业25个,异常25个,准确率84％.文中的研究对智能设计工作提供了一定参考.

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MOOC热潮的到来给大学各学科领域的教学带来了机遇和挑战。不同学科有其不同的教学特点,不同学科的学生对MOOC的认同程度也有较大差异。针对这种差异,文章对MOOC在理工类,文史类,医学类、艺术类、体育类专业教育中的应用进行分析和探讨。指出在教学创新中,各学科应针对不同专业的学生为MOOC课程设定个性化的培养方案,开展个性化的教学步骤及教学内容。以满足不同层次不同领域学生的学习需求。

Keyword ：

高等教育 教学创新 教育改革 慕课

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