A synthesis technique of general petri nets

The general Petri net (GPN) is useful for modeling flexible manufacturing systems with multiple robots and workstations [15] and for parallel programs [8]. A problem of using reachability analysis for analyzing Petri nets (PN) is the large number of states generated. Most of the existing synthesis techniques do not deal with GPN. Koh et al.[15] invented a synthesis technique for GPN. We propose to improve their achievement by adding the simple Arc-ratio rules to Yaw's knitting technique [37, 38, 39] based on the notion of structure relationship together with new path generations, which mark the most distinct feature compared with other approaches. The synthesis rules and procedures of how to update the temporal matrix and structure synchronic distance are presented. The Arc-ratio rules for GPN are also presented. One can successfully synthesize complicated Petri nets using these rules. An example to synthesize a Petri net in [15] is illustrated. The correctness of each synthesis rule with an appropriate Arc-ratio rule for GPN is proved.     

A dual latent class unfolding model for two-way two-mode preference rating data

In unfolding for two-way two-mode preference ratings data, the categorization of the set of individuals while the categories are represented in a low dimensional space may be an advisable procedure to facilitate their understanding. In addition to considering groups of individuals of a similar preference pattern, homogeneous groups of objects are also considered, such that within each group there are clustered objects perceived to have similar attributes. A dual latent class model is proposed for a matrix of preference ratings data, which will partition the individuals and the objects into classes, and simultaneously represent the cluster centers in a low dimensional space, while individuals and objects retain their preference relationship. Both the categories achieved and the unfolding configuration are estimated to be simultaneously optimal, by means of a conditional maximum likelihood estimation procedure, in a simulated annealing framework that enables us to take a statistical decision about the parameters of the model. The adjusted BIC statistic is employed to test the number of mixture components, and the dimensionality of the representation. Real and artificial data sets are analyzed to illustrate the model’s performance.     

基于耦合相关度的空间数据查询结果自动分类方法

由于空间数据库通常蕴含海量数据,因此一个普通的空间查询很可能会导致多查询结果问题。为了解决上述问题,提出了一种空间查询结果自动分类方法。在离线阶段,根据空间对象之间的位置相近度和语义相关度来评估空间对象之间的耦合关系,在此基础上利用概率密度评估方法对空间对象进行聚类,每个聚类代表一种类型的用户需求;在在线查询处理阶段,对于一个给定的空间查询,在查询结果集上利用改进的C4.5决策树算法动态生成一棵查询结果分类树,用户可通过检查分类树分支的标签来逐步定位到其感兴趣的空间对象。实验结果表明,提出的空间对象聚类方法能够有效地体现空间对象在语义和位置上的相近性,查询结果分类方法具有较好的分类效果和较低的搜索代价。     

The category-theoretic solution of recursive metric-space equations

It is well known that one can use an adaptation of the inverse-limit construction to solve recursive equations in the category of complete ultrametric spaces. We show that this construction generalizes to a large class of categories with metric-space structure on each set of morphisms: the exact nature of the objects is less important. In particular, the construction immediately applies to categories where the objects are ultrametric spaces with ‘extra structure’, and where the morphisms preserve this extra structure. The generalization is inspired by classical domain-theoretic work by Smyth and Plotkin.For many of the categories we consider, there is a natural subcategory in which each set of morphisms is required to be a compact metric space. Our setting allows for a proof that such a subcategory always inherits solutions of recursive equations from the full category.As another application, we present a construction that relates solutions of generalized domain equations in the sense of Smyth and Plotkin to solutions of equations in our class of categories.Our primary motivation for solving generalized recursive metric-space equations comes from recent and ongoing work on Kripke-style models in which the sets of worlds must be recursively defined. We show a series of examples motivated by this line of work.     

Document retrieval using fuzzy-valued concept networks

This paper presents a new method for document retrieval using fuzzy-valued concept networks, where the relevant degrees between the concepts in a fuzzy-valued concept network are represented by arbitrary shapes of fuzzy numbers. There are two kinds of relevant relationships between any two concepts in a fuzzy-valued concept network, i.e., fuzzy positive association and fuzzy negative association. The relevant matrices and the relationship matrices are used to model the fuzzy-valued concept network. The elements in a relevant matrix represent the relevant degrees between concepts. The elements in a relationship matrix represent the relevant relationships between concepts. Furthermore, ne also allow users' queries to be represented by arbitrary shapes of fuzzy numbers and to use fuzzy positive association relationship and fuzzy negative association relationship for formulating their queries for increasing the flexibility of fuzzy information retrieval systems. We also present an information retrieval method in the Internet environment based on the network-type fuzzy-valued concept network architecture.     

一种协同过滤式零次学习方法

深度学习算法在很多有监督学习任务上达到了令人满意的结果，但其依赖于大量标注样本，并且使用特定类别训练的分类器，只能对这些类别进行分类.零次学习希望计算机像人类一样，能够结合历史经验与知识进行推理，无需使用大量新类别样本训练，便可达到识别新类别的效果.发现了零次学习任务存在“冷启动”以及矩阵稀疏两个特点，这些特点在推荐任务中同样存在.受推荐任务启发，将零次图像分类任务建模为矩阵填充问题，借鉴推荐领域中协同过滤算法，将稀疏的样本标签矩阵视为非稀疏的视觉特征矩阵和类别特征矩阵的内积结果，进而实现对新类别样本的分类预测.此外，构建了基于类间语义关联的语义图结构，使用图神经网络进行已知类别和新类别之间的知识迁移，以较小代价为类别学得准确的语义特征.在3个经典零次学习数据集上分别进行传统零次学习和广义零次学习实验，实验结果表明：提出的协同过滤式零次学习方法能够有效提升分类精度，且训练代价较小.     

体数据中物体之间结构关系的描述方法

体数据中不同的物体之间存在不同的结构关系,如隐藏关系、拓扑关系等,不同结构关系是影响用户理解体数据的重要特征之一。从分析体数据中物体之间关系入手,提出一个有效的体绘制应当定义一个从数据到绘制的映射,以便将数据中物体之间的每种关系映射到绘制空间。然后提出描述体数据中物体之间典型结构关系的体绘制方法。在绘制空间对物体之间的不同关系的描述增强了用户对体数据的理解,并拓展了体绘制在体数据分析中的功能。     

空间聚类与方向关系的融合技术研究

在大数据量的环境下,传统空间数据的空间关系仅描述两个空间物体,从而出现数据存储冗余,检索速度慢等问题。提出改进的聚类算法对空间物体聚类,再在聚类结果的基础上表示空间物体的方向关系。提出了基于密度的K-均值算法和空间聚类与方向关系融合的新方法。所提方法增强了空间数据库对空间数据对象的空间方向关系的智能处理能力,节省了存储空间,提高了数据的查询速度。     

一种基于概念可辨识矩阵的概念约简方法

基于布尔因子分析的概念约简能够保持形式背景的二元关系不变。借鉴概念格中基于可辨识矩阵求解属性约简的思想,在形式背景上定义概念可辨识矩阵,基于此给出保持二元关系不变的概念约简方法。首先,在形式背景上定义一种新的可辨识矩阵,称之为概念可辨识矩阵。该矩阵的行和列都是形式概念,矩阵的每个元素是由属于所在行的形式概念的所有对象和属性对,但不属于所在列的形式概念的对象和属性对构成的集合。其次,研究概念可辨识矩阵与概念协调集之间的关系,利用概念可辨识矩阵给出概念协调集的判定方法。然后,利用概念可辨识矩阵详细讨论核心概念、相对必要概念和不必要概念的特征,进而分别给出判断这3类形式概念的方法。最后,给出基于概念可辨识矩阵寻找概念约简的步骤。     

On two sequences of sets of mappings of abstract sets into a Dedekind ring

This paper establishes some interrelations between a finite sequence of sets of mappings of an abstract set S into complete residue systems of pairwise relatively prime elements of any Dedekind ring and the corresponding sequence of sets of mappings of the set S into the complete residue system corresponding to the product of these elements. A relationship is revealed between the established interrelation and Lang’s theorem on isomorphic factor rings. A string model is presented that is an interpretation of structures investigated in the cases of the ring of integers and a one-element set S. It is shown that the results obtained can be used for computing the number of combinatorial objects defined in terms of finite residue rings.     

Categorical top-<Emphasis Type="Italic">k</Emphasis> spatial influence query

The influence of a spatial facility object depicts the importance of the object in the whole data space. In this paper, we present a novel definition of object influence in applications where objects are of different categories. We study the problem of Spatial Influence Query which considers the contribution of an object in forming functional units consisting of a given set of objects with different categories designated by users. We first show that the problem of spatial influence query is NP-hard with respect to the number of object categories in the functional unit. To tackle the computational hardness, we develop an efficient framework following two main steps, possible participants finding and optimal functional unit computation. Based on this framework, for the first step, novel and efficient pruning techniques are developed based on the nearest neighbor set (NNS) approach. To find the optimal functional unit efficiently, we propose two algorithms, an exact algorithm and an efficient approximate algorithm with performance guarantee. Comprehensive experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques.     

基于最短路径的道路网络k近邻查询处理

针对基于空间道路网络的k近部查询处理,提出了分布式移动对象更新策略以有效减少服务器计算代价,利用基于内存的空间道路网络部接矩阵、最短路径矩阵结构和移动对象哈希表索引分别对道路网络无向图与移动对象进行存储管理。提出了基于最短路径度量的网络扩展搜索(SPNE)算法,以通过裁剪网络搜索空间来减少k近部查询搜索代价。实验表明,SPNE算法的性能优于传统的NE和MKNN等k近邻查询处理算法。     

视触觉融合的增强现实三维注册方法

增强现实技术是近年来人机交互领域的研究热点。在增强现实环境下加入触觉感知,可使用户在真实场景中看到并感知到虚拟对象。为了实现增强现实环境下与虚拟对象之间更加自然的交互,提出一种视触觉融合的三维注册方法。基于图像视觉技术获得三维注册矩阵;借助空间转换关系求解出触觉空间与图像空间的转换关系;结合两者与摄像头空间的关系实现视触觉融合的增强现实交互场景。为验证该方法的有效性,设计了一个基于视触觉增强现实的组装机器人项目。用户可触摸并移动真实环境中的机器人零件,还能在触摸时感受到反馈力,使交互更具真实感。     

深度矩阵分解推荐算法

协同过滤推荐算法中的矩阵分解因其简单、易于实现,得到了广泛的应用.但是矩阵分解通过简单的线性内积建模用户和物品之间的非线性交互关系,限制了模型的表达能力.为此,He等人提出了广义矩阵分解模型,通过非线性激活函数和连接权重,将矩阵分解推广到广义矩阵分解,为模型赋予建模用户和物品间的二阶非线性交互关系的能力.但是广义矩阵分解模型是一个浅层模型,并不能很好地建模用户和物品间高阶交互关系,一定程度上可能会影响模型性能.受广义矩阵分解模型启发,提出了深度矩阵分解模型（deep matrix factorization,简称DMF）,在广义矩阵分解模型的基础上引入隐藏层,利用深层神经网络来学习用户和物品间高阶交互关系.深度矩阵分解模型不仅解决了简单内积的线性问题,同时还能够建模用户和物品间的高阶交互,具有很好的表达能力.此外,在MovieLens和Anime两个数据集上进行了大量丰富的对比实验,验证了模型的可行性和有效性;同时,通过实验确定了模型的最优参数.     

Automatic text summarization using latent semantic analysis

In the paper, the most state-of-the-art methods of automatic text summarization, which build summaries in the form of generic extracts, are considered. The original text is represented in the form of a numerical matrix. Matrix columns correspond to text sentences, and each sentence is represented in the form of a vector in the term space. Further, latent semantic analysis is applied to the matrix obtained to construct sentences representation in the topic space. The dimensionality of the topic space is much less than the dimensionality of the initial term space. The choice of the most important sentences is carried out on the basis of sentences representation in the topic space. The number of important sentences is defined by the length of the demanded summary. This paper also presents a new generic text summarization method that uses nonnegative matrix factorization to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of topic space and further weighting of each topic using sentences representation in topic space. The proposed method shows better summarization quality and performance than state-of-the-art methods on the DUC 2001 and DUC 2002 standard data sets.     

容差近似空间的广义概念格模型研究

在粗糙集合理论中,近似空间概念格之间存在着有趣的对应关系,利用概念格研究知识的约简和发现。更直观和更有效,但已有的概念格模型是基于近似空间的等价类划分的。等价类划分过于苛刻,扩展的基于容差关系的近似空间具有更广泛的意义,但目前未见有相应的格模型被提出。该文提出了容差近似空间的一种格模型,称为广义概念格,给出了定义,描述了建立方法和由它产生规则的原则,讨论了空间复杂性问题,并且与其它相近方法做了比较     

Group-theoretical vector space model

This paper presents a group-theoretical vector space model (VSM) that extends the VSM with a group action on a vector space of the VSM. We use group and its representation theory to represent a dynamic transformation of information objects, in which each information object is represented by a vector in a vector space of the VSM. Several groups and their matrix representations are employed for representing different kinds of dynamic transformations of information objects used in the VSM. We provide concrete examples of how a dynamic transformation of information objects is performed and discuss algebraic properties involving certain dynamic transformations of information objects used in the VSM.     

移动空间安全区域关键字查询算法

随着时代的飞速发展,人们对智能生活的追求不断提高,空间查询也被人们愈来愈重视。移动空间关键字查询,作为一种主要的连续空间查询类型,受到了广泛的研究。在最新的顶尖会议文刊中,提出了一种新的查询类型,称为移动集合空间关键字查询(MCSKQ)。这种类型的查询不断报告一组对象,这些对象在查询移动时共同覆盖查询关键字。同时,返回的对象也必须靠近查询对象并且彼此靠近。计算精确的结果集是一个NP-hard的问题。为了降低查询处理的成本,本文提出了基于安全区域技术的算法,在查询对象移动时,保持精确的结果集。在其基础上,本文基于MCKSQ的思想提出新的优化策略,以降低查询处理成本的方法。     

Modeling complex longitudinal consumer behavior with Dynamic Bayesian networks: an Acquisition Pattern Analysis application

Longitudinal consumer behavior has been modeled by sequence analysis. A popular application involves Acquisition Pattern Analysis exploiting typical acquisition patterns to predict a customer’s next purchase. Typically, the acquisition process is represented by an extensional, unidimensional sequence taking values from a symbolic alphabet. Given complex product structures, the extensional state representation rapidly evokes the state-space explosion problem. Consequently, most authors simplify the decision problem to the prediction of acquisitions for selected products or within product categories. This paper advocates the use of intensional state definitions representing the state by a set of variables thereby exploiting structure and allowing to model complex, possibly coupled sequential phenomena. The advantages of this intensional state space representation are demonstrated on a financial-services cross-sell application. A Dynamic Bayesian Network (DBN) models longitudinal customer behavior as represented by acquisition, product ownership and covariate variables. The DBN provides insight in the longitudinal interaction between a household’s portfolio maintenance behavior and acquisition behavior. Moreover, it exhibits adequate predictive performance to support the financial-services provider’s cross-sell strategy comparable to decision trees but superior to MulltiLayer Perceptron neural networks.