非受限文本中深层空间语义的识别方法

利用地理空间描述模型中的相关概念扩展自然语言中空间语义角色,通过空间语义角色标注、短语识别以及句法模式分析达到识别非受限文本中深层空间语义的目的。实验表明,该方法具有较好的准确率、召回率与通常的信息提取性能相当。     

Revealing deep semantic commercial patterns: Insights from urban landscape depiction

Commercial functions play a significant role in economic growth and sustainable development. Influenced by socioeconomic changes which were investigated based on commercial behaviours using big geo-data, urban environment shows polycentric structures and further leads to more complex commercial landscapes. Thus, research considered environmental consequences of urban growth to better understand the commercialization process. However, only general commercial areas or certain commercial retail locations were investigated with the effects of urban environmental landscapes in existing studies. The decomposition of the commercial patterns such as accommodation and catering functions remains undiscussed. To fill this gap, this paper proposes a framework to decompose commercial patterns based on insight from different urban landscapes. First, we depict urban landscapes using spatial metrics, including the density of points of interest (POIs), percentage of landscape of buildings, landscape shape index of buildings and density of roads. Then, we examine the spatial distribution of the decomposed commercial patterns by delineating potential deep semantic topics. Finally, a structural equation model is utilised to determine the relations among urban landscapes and commercial patterns. The results show that four types of urban landscapes exhibit different spatial patterns, revealing various perspectives of commercial characteristics. Meanwhile, the decomposed commercial patterns, including those for catering and accommodation functions, display a heterogeneous distribution. These commercial patterns are directly affected by various configurations of buildings, POIs and roads. On this basis, suggestions are offered to improve commercial pattern development, including integrating urban landscape construction, organising the commercial pattern distribution and enhancing the harmony between the urban environment and commercial land uses.     

以网络为中心的语义特征造型协同设计

在网络化同步协同设计环境中，如何实现用户系统之间的实时数据交换、维护特征语义的一致性，成为语义特征造型协同设计的关键问题。该文介绍了基于细胞元表示的语义特征模型，根据该造型技术的分层体系结构，基于对协同环境下特征操作的分析，采取了传输造型操作命令的策略，为解决特征语义一致性问题，提出了一种用操作特征管理器调度操作执行的方法，并给出了一个实例。     

Real-time landscape model interaction using a tangible geospatial modeling environment

Emerging technologies that combine the flexibility of digital landscape representation with easy-to-interpret 3D physical models open new possibilities for user interaction with geospatial data. A prototype tangible geospatial modeling environment lets users interact with landscape analysis and simulations using a tangible physical model. We introduce a concept that builds upon previous independent tangible user interface (TUI) and terrain analysis research and aims at more intuitive collaborative interaction with digital landscape data.     

语言结构引导的可解释视频语义描述

视频描述技术旨在为视频自动生成包含丰富内容的文字描述,近年来吸引了广泛的研究兴趣.一个准确而精细的视频描述生成方法,不仅需要对视频有全局上的理解,更离不开具体显著目标的局部空间和时序特征.如何建模一个更优的视频特征表达,一直是视频描述工作的研究重点和难点.另一方面,大多数现有工作都将句子视为一个链状结构,并将视频描述任务视为一个生成单词序列的过程,而忽略了句子的语义结构,这使得算法难以应对和优化复杂的句子描述及长句子中易引起的逻辑错误.为了解决上述问题,提出一种新颖的语言结构引导的可解释视频语义描述生成方法,通过设计一个基于注意力的结构化小管定位机制,充分考虑局部对象信息和句子语义结构.结合句子的语法分析树,所提方法能够自适应地加入具有文本内容的相应时空特征,进一步提升视频描述的生成效果.在主流的视频描述任务基准数据集MSVD和MSR-VTT上的实验结果表明,所提出方法在大多数评价指标上都达到了最先进的水平.     

Modeling Content for Semantic-Level Querying of Multimedia

Many semantic content-based models have been developed for modeling video and audio in order to enable information retrieval based on semantic content. The level of querying of the media depends upon the semantic aspects modeled. This paper proposes a semantic content-based model for semantic-level querying that makes full use of the explicit media structure, objects, spatial relationships between objects, events and actions involving objects, temporal relationships between events and actions, and integration between syntactic and semantic information.     

基于多域VQGAN的文本生成国画方法研究

随着生成式对抗网络的出现,从文本描述合成图像最近成为一个活跃的研究领域.然而,目前文本描述往往使用英文,生成的对象也大多是人脸和花鸟等,专门针对中文和中国画的研究较少.同时,文本生成图像任务往往需要大量标注好的图像文本对,制作数据集的代价昂贵.随着多模态预训练的出现与推进,使得能够以一种优化的方式来指导生成对抗网络的生成过程,大大减少了对数据集和计算资源的需求.提出一种多域VQGAN模型来同时生成多种域的中国画,并利用多模态预训练模型WenLan来计算生成图像和文本描述之间的距离损失,通过优化输入多域VQGAN的隐空间变量来达到图片与文本语义一致的效果.对模型进行了消融实验,详细比较了不同结构的多域VQGAN的FID及R-precisoin指标,并进行了用户调查研究.结果表示,使用完整的多域VQGAN模型在图像质量和文本图像语义一致性上均超过原VQGAN模型的生成结果.     

路网要素多尺度表达目标的层次关系分析

针对目前地理要素多尺度建模的热点问题,剖析构建多尺度空间数据模型的关键技术,以路网要素为例,挖掘地理要素多尺度抽象表达的机理,利用多尺度扩展E-R方法,提炼多尺度抽象表达目标间的各种尺度、层次、语义、映射等层次语义关系,实现GIS中地理要素多尺度概念模型的设计。     

基于语义描述的Web服务工作流集成

Web服务是可以由其它软件在Internet上访问的一种软件接口。Web服务工作流则是一组按一定顺序执行的Web服务的集合。如何集成一个Web服务工作流,特别是如何运用对Web服务的语义描述来为一个工作流来自动寻找和匹配Web服务,这是目前所急需解决的问题。介绍如何用语义Web服务本体论来集成工作流,同时提出一个基于语义Web服务工作流集成的模型。