一种提高Vicsek模型收敛效率的新方法

针对Vicsek模型收敛速度较慢和一致程度较低的问题,利用动态网络的拓扑结构并结合复杂网络中度的概念,提出一种以度为权重提高Viesek模型收敛效率的新方法.进一步以动态网络的度的幂指数得到模型的推广形式,该指数的范围为[0,∞).仿真实验结果表明,改进后模型的收敛效率优于原模型,且收敛效率随着指数的增加而增大.     

基于免疫和模糊模式识别的检测器生成模型

分析了在检测器生成过程中引入模糊模式识别的原因,提出了一种基于免疫原理和模糊模式识别的检测器生成模型,该模型的核心算法是基于隶属度的检测器选择算法.详细描述了该算法的算法流程和检测器对非我抗原集合隶属度的计算算法,并简述了初始检测器生成算法和分层检测策略.设计了仿真试验,并将实验结果同传统方法进行了比较,详述了仿真实验中各个参数选择的依据和理由,试验结果表明新模型呈现较高的检测率和低的误检率.     

Network Design with Edge-Connectivity and Degree Constraints

We consider the following network design problem; Given a vertex set V with a metric cost c on V, an integer k≥1, and a degree specification b, find a minimum cost k-edge-connected multigraph on V under the constraint that the degree of each vertex v∈V is equal to b(v). This problem generalizes metric TSP. In this paper, we show that the problem admits a ρ-approximation algorithm if b(v)≥2, v∈V, where ρ=2.5 if k is even, and ρ=2.5+1.5/k if k is odd. We also prove that the digraph version of this problem admits a 2.5-approximation algorithm and discuss some generalization of metric TSP.     

基于XQuery的数据集成研究

介绍了XML和其查询语言XQuery的基础,提出一个以XQuery为基础的数据集成框架XBD I,该框架屏蔽底层数据源的特性,对异构数据提供虚拟的XML视图。详细介绍了XBD I框架的设计原则及其关键技术,对和框架实现密切相关的中介器、数据服务等理论进行了说明。     

基于范畴论的素描数据模型

针对关系数据库语义表达的不足,讨论了一种语义数据模型--基于范畴论的素描数据模型,从素描数据模型的形式化定义出发,以一种非形式化的方法分析了素描数据模型的基本概念,为更好地使用素描数据模型提供了基础.     

Relational-Convergent Transformer for image captioning

Image captioning describes the visual content of a given image by using natural language sentences, and plays a key role in the fusion and utilization of the image features. However, in the existing image captioning models, the decoder sometimes fails to efficiently capture the relationships between image features because of their lack of sequential dependencies. In this paper, we propose a Relational-Convergent Transformer (RCT) network to obtain complex intramodality representations in image captioning. In RCT, a Relational Fusion Module (RFM) is designed for capturing the local and global information of an image by a recursive fusion. Then, a Relational-Convergent Attention (RCA) is proposed, which is composed of a self-attention and a hierarchical fusion module for aggregating global relational information to extract a more comprehensive intramodal contextual representation. To validate the effectiveness of the proposed model, extensive experiments are conducted on the MSCOCO dataset. The experimental results show that the proposed method outperforms some of the state-of-the-art methods.     

基于可信度变化趋势的音频分割算法

提出基于可信度变化趋势的音频分割算法。采用定长滑动窗检测结构减少累积错误,在窗内计算各音频帧的可信度,根据可信度的变化趋势检测跳变点,以避免阈值选择和硬门限判决造成的误检。实验结果表明,该算法的分割性能优于基于KL2距离、基于隐马尔可夫模型、基于贝叶斯信息准则和基于熵变化趋势的音频分割算法。     

Relational generative topographic mapping

The generative topographic mapping (GTM) has been proposed as a statistical model to represent high-dimensional data by a distribution induced by a sparse lattice of points in a low-dimensional latent space, such that visualization, compression, and data inspection become possible. The formulation in terms of a generative statistical model has the benefit that relevant parameters of the model can be determined automatically based on an expectation maximization scheme. Further, the model offers a large flexibility such as a direct out-of-sample extension and the possibility to obtain different degrees of granularity of the visualization without the need of additional training. Original GTM is restricted to Euclidean data points in a given Euclidean vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows us to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks.     

On graph reasoning

In this paper, we study the (positive) graph relational calculus. The basis for this calculus was introduced by Curtis and Lowe in 1996 and some variants, motivated by their applications to semantics of programs and foundations of mathematics, appear scattered in the literature. No proper treatment of these ideas as a logical system seems to have been presented. Here, we give a formal presentation of the system, with precise formulation of syntax, semantics, and derivation rules. We show that the set of rules is sound and complete for the valid inclusions, and prove a finite model result as well as decidability. We also prove that the graph relational language has the same expressive power as a first-order positive fragment (both languages define the same binary relations), so our calculus may be regarded as a notational variant of the positive existential first-order logic of binary relations. The graph calculus, however, has a playful aspect, with rules easy to grasp and use. This opens a wide range of applications which we illustrate by applying our calculus to the positive relational calculus (whose set of valid inclusions is not finitely axiomatizable), obtaining an algorithm for deciding the valid inclusions and equalities of the latter.     

粗关系数据库查询模型

针对粗关系数据库中数据的特性,提出一种不确定性数据的存储方法。基于汉明距离的一种变式,计算元组间距离,构成距离矩阵,根据距离矩阵将相同或相近的元组归类,从而有效地对表中的元组进行索引。借助粗集中的上、下近似,通过计算用户查询的数据与粗关系数据库中数据的相似度,查询出用户所需的数据。结合以上方法构建粗关系数据库查询模型,设计相应的查询算法并应用于实例中。