An end-to-end model for chinese calligraphy generation

Multimedia Tools and Applications - A Chinese calligraphy copybook usually has a limited number of Chinese characters, far from a whole set of characters needed for typesetting. Therefore, there is...     

An end-to-end QoS control model for enhanced internet

This paper describes an end-to-end QoS(Quality of Serviece) control model for distributed multimedia computing on enhanced Internet,and gives the design and implementation of this model including hosts and routers.The architecture,the mathematical definition about QoS parameters,and the mapping between Integrated Service(IS)and Differentiated Service(DS) are discussed in this paper,The simulation shows that this model can improve the performance of audio streams when it is used in IPhone system.     

An analytical model for end-to-end communication channel over PLCN based on QBDs

The performance analysis of packet loss in end-to-end communication channel is the basis of adaptive end-to-end protocol design. However, existing analytical models of packet loss cannot provide quantitative information of lossy channel systems. In this paper, an analysis on balance state of end-to-end communication channel over probabilistic lossy communication networks (PLCN) is performed firstly. We then discuss the process of modeling end-to-end communication channel over PLCN using Quasi Birth–Death models (QBDs) in detail. Finally, some characteristics of end-to-end communication channel, such as the stability of channel and the performance of channel packet loss, are analyzed. The experimental results show that the proposed model can analyze quantitatively the stability of end-to-end communication channel and the packet loss probability during time interval [0,t]. The results in the work are helpful for designing adaptive end-to-end communication protocols.     

一种定性定量信息转换的不确定性模型——云模型*

目前,许多领域的研究进入到复杂系统阶段。从复杂系统描述与评价的角度来讲,严格、精确的数学描述几乎是不可能做到的,只有将定性定量的知识加以变换与集成才能获得关于系统的定性定量相结合的完整描述。因此,建立一个定性定量的不确定性转换模型,实现语言值与数值的互换,是复杂系统研究迫切需要解决的问题,也是其他领域研究需要解决的问题。从定性定量转换研究现状出发,综述了由李德毅院士提出的定性定量转换的不确定性模型——云模型。从云模型提出背景出发,讨论了云模型的理论基础、发展过程与研究现状;综述了云模型基本理论研究进展情况,云模型在质量评价、模式识别、控制等领域的研究进展。通过研究发现,云模型是定性定量转换的有力工具,并且已经比较成功地得到了应用,取得了重要的研究成果。如何进一步深化理论研究,研究不同领域中定性定量转换应用问题是加强和促进云模型研究的有效途径。     

An analytical model for multi-epidemic information dissemination

Contemporary distributed systems usually involve the spreading of information by means of ad-hoc dialogs between nodes (peers). This paradigm resembles the spreading of a virus in the biological perspective (epidemics). Such abstraction allows us to design and implement information dissemination schemes with increased efficiency. In addition, elementary information generated at a certain node can be further processed to obtain more specific, higher-level and more valuable information. Such information carries specific semantic value that can be further interpreted and exploited throughout the network. This is also reflected in the epidemical framework through the idea of virus transmutation which is a key component in our model. We establish an analytical framework for the study of a multi-epidemical information dissemination scheme in which diverse ‘transmuted epidemics’ are spread. We validate our analytical model through simulations. Key outcomes of this study include the assessment of the efficiency of the proposed scheme and the prediction of the characteristics of the spreading process (multi-epidemical prevalence and decay).     

Unsupervised group matching with application to cross-lingual topic matching without alignment information

We propose a method for unsupervised group matching, which is the task of finding correspondence between groups across different domains without cross-domain similarity measurements or paired data. For example, the proposed method can find matching of topic categories in different languages without alignment information. The proposed method interprets a group as a probability distribution, which enables us to handle uncertainty in a limited amount of data, and to incorporate the high order information on groups. Groups are matched by maximizing the dependence between distributions, in which we use the Hilbert Schmidt independence criterion for measuring the dependence. By using kernel embedding which maps distributions into a reproducing kernel Hilbert space, we can calculate the dependence between distributions without density estimation. In the experiments, we demonstrate the effectiveness of the proposed method using synthetic and real data sets including an application to cross-lingual topic matching.     

An end-to-end communication architecture for collaborative virtual environments

Synchronous collaboration in virtual reality spaces has specific requirements that differ from those of other application data, leading to a different communication approach. Update messages corresponding to the change in the state of a shared object must be communicated both reliably and in a timely manner among users. While a lot of research has been done in terms of transmission of update messages representing the motion of avatars and objects, very few works focus on collaboration itself. Here, we present an architecture that supports tightly coupled collaborative tasks to be performed efficiently in virtual environments. The architecture consists of an application-general layer which is mapped into a communication protocol. An implementation is also presented as proof of concept.     

An end-to-end network for irregular printed Mongolian recognition

International Journal on Document Analysis and Recognition (IJDAR) - Mongolian is a language spoken in Inner Mongolia, China. In the recognition process, due to the shooting angle and other...     

An improved feature transformation method using mutual information

The feature transformation is a very important step in pattern recognition systems. A feature transformation matrix can be obtained using different criteria such as discrimination between classes or feature independence or mutual information between features and classes. The obtained matrix can also be used for feature reduction. In this paper, we propose a new method for finding a feature transformation-based on Mutual Information (MI). For this purpose, we suppose that the Probability Density Function (PDF) of features in classes is Gaussian, and then we use the gradient ascent to maximize the mutual information between features and classes. Experimental results show that the proposed MI projection consistently outperforms other methods for a variety of cases. In the UCI Glass database we improve the classification accuracy up to 7.95 %. Besides, the improvement of phoneme recognition rate is 3.55 % on TIMIT.     

An efficient algorithm for the tensor product model transformation

The tensor-product (TP) model transformation was proposed recently as a numerical and automatically executable method which is capable of transforming linear parameter varying (LPV) state-space models into the higher order singular value decomposition (HOSVD) based canonical form of polytopic models. The crucial disadvantage of the TP model transformation is that its computational load explodes with the density of discretization and the dimensionality of the parameter vector of the parameter-varying state-space model. In this paper we propose a new algorithm that leads to considerable reduction of the computation in the TP model transformation. The main idea behind the modified algorithm is to minimize the number of discretized points to acquire as much information as possible. The modified TP model transformation can readily be executed on a regular computer efficiently and concisely, especially in higher dimensional cases when the original TP model transformation fails. The paper also presents numerical examples to show the effectiveness of the new algorithm.     

面向跨语言文本分类与标签推荐的带标签双语主题模型的研究

针对日渐丰富的跨语言的文字信息资源与新闻报道及科技文献中的多标签数据,为了挖掘跨语言间的相关性及数据属性间的关联性,提出了带标签双语主题模型,应用于跨语言文本分类与标签的推荐。首先,假设科技文献中的关键词与摘要部分有着内容上的相关性,对关键词进行提取,并进行标签化,进而把标签对应于主题模型中的主题,实例化“潜在”的主题;其次,利用带标签双语主题模型对摘要部分进行了训练迭代;最后,对新加入的文档进行跨语言文本分类及标签的推荐。实验结果表明,跨语言文本分类任务中micro-F1达到94.81%,推荐的标签也较好地体现出语义上的相关性。     

Distributed multimedia information systems: an end-to-end perspective

Emerging Web-based applications require distributed multimedia information system (DMIS) infrastructures. Examples of such applications abound in the domains of medicine, entertainment, manufacturing, e-commerce, as well as military and critical national infrastructures. Development of DMIS for such applications need a broad range of technological solutions for organizing, storing, and delivering multimedia information in an integrated, secure and timely manner with guaranteed end-to-end (E2E) quality of presentation (QoP). DMIS are viewed as catalysts for new research in many areas, ranging from basic research to applied technology. This view is a result of the fact that no single monolithic end-to-end architecture for DMIS can meet the wide spectrum of characteristics and requirements of various Web-based multimedia applications. One size does not fit all in this medium of communication. Management of integrated end-to-end QoP and ensuring information security in DMIS, when viewed in conjunction with real world constraints and system-wide performance requirements, present formidable research and implementation challenges. These challenges encompass all the sub-system components of a DMIS. The ultimate objective of achieving a comprehensive end-to-end QoP management relies on the performance and allocation of resources of each of the DMIS sub-system components including networks, databases, and end-systems. In this paper, we elaborate on these challenges and present a high level distributed architecture aimed at providing the critical functionality for a DMIS.

A method for generating rules for cross-lingual transliteration

This paper suggests a method for generating the rules of cross-lingual transliteration. The method is based on analysis of a parallel corpus of proper names. The method is language-independent and does not require large training corpora. The results of experiments on different languages are provided.     

An information model of light quantization

This paper presents the basic ideas and concepts for the development of information models of optical objects. Quantitative ratios among regularities in trigonometric spectral analysis as a possible connection between the projection of natural (point-source) radiator and absorbing atomic section are shown. This approach has been applied to the correlation between these patterns and atomic spectroscopy (specifically, terms and ionization potentials of neutral atoms with s and p shells. This has made it possible to build information models of radiation and atomic absorption on certain principles of field continuum quantization.     

An end-to-end generative network for environmental sound-based covert communication

Environmental sounds, everyday audio events that do not consist of music or speech data and are often more diverse and chaotic in their structure, have proven to be a promising type of carrier signals to carry out covert communication as they occur frequently in the natural environment, e.g., marine communication by mimicking dolphin or sea lion whistles. However, a mass collection of the carrier signals still remains a challenging task. Recently proposed generator models represented by Generator Adversarial Nets (GAN) have provided an effective way to synthesize environmental sounds. In this study, an end-to-end convolutional neural network (CNN) is proposed to directly transform the randomly sampled Gaussian noise into environmental sound that contains the secret message. The proposed network structure is composed of upsampling groups and orthogonal quantization layer, which can simultaneously realize factor analysis and information embedding. The design of the orthogonal quantization layer to complete the message embedding task is inspired by spread spectrum, model-based modulation, and compensative quantization. The underlying idea in this study is to treat the secret message as the constraint information in the generative model with the aim of maximizing the complete data model. The alternating back-propagation algorithm is used to train the overall network. Experimental results show that the proposed scheme can generate realistic environmental sounds that convey secret messages, while guaranteeing a high degree of communication reliability.

     

An exact end-to-end blocking probability algorithm for multicast networks

We consider the calculation of blocking probabilities in multicast trees with dynamic membership. We extend the work by Karvo et al., where an approximate algorithm based on the reduced load approximation (RLA) was given to calculate end-to-end blocking for infinite sized user populations in multicast networks. The new algorithm for calculating end-to-end call blocking exactly for an arbitrary sized user population is based on the known blocking probability algorithm in hierarchical multiservice access networks, where link occupancy distributions are alternately convolved and truncated. We show that the algorithm can be applied to multicast trees embedded in a network with an arbitrary topology carrying also non-multicast traffic. The resource sharing of multicast connections, however, requires the modification of the algorithm by introducing a new type of convolution, the OR-convolution. In addition, we discuss several different user population models for which the algorithm is applicable.     

An analytical model of information dissemination for a gossip-based protocol

We develop an analytical model of information dissemination for a gossiping protocol that combines both pull and push approaches. With this model we analyse how fast an item is replicated through a network, and how fast the item covers the network. We also determine the optimal size of the exchange buffer, to obtain fast replication. Our results are confirmed by large-scale simulation experiments.     

Design of an end-to-end method to extract information from tables

This paper plans an end-to-end method for extracting information from tables embedded in documents; input format is ASCII, to which any richer format can be converted, preserving all textual and much of the layout information. We start by defining table. Then we describe the steps involved in extracting information from tables and analyse table-related research to place the contribution of different authors, find the paths research is following, and identify issues that are still unsolved. We then analyse current approaches to evaluating table processing algorithms and propose two new metrics for the task of segmenting cells/columns/rows. We proceed to design our own end-to-end method, where there is a higher interaction between different steps; we indicate how back loops in the usual order of the steps can reduce the possibility of errors and contribute to solving previously unsolved problems. Finally, we explore how the actual interpretation of the table not only allows inferring the accuracy of the overall extraction process but also contributes to actually improving its quality. In order to do so, we believe interpretation has to consider context-specific knowledge; we explore how the addition of this knowledge can be made in a plug-in/out manner, such that the overall method will maintain its operability in different contexts.The opinions expressed in this article are the responsibility of the authors and do not necessarily reflect those of Banco de Portugal.     

Fuzzy conceptual indexing for concept-based cross-lingual text retrieval

Cross-lingual text retrieval (CLTR) is a technique for locating relevant documents in different languages. The authors have developed fuzzy conceptual indexing (FCI) to extend CLTR to include documents that share concepts but don't contain exact translations of query terms. In FCI, documents and queries are represented as a function of language-independent concepts, thus enabling direct mapping between them across multiple languages. Experimental results suggest that concept-based CLTR outperforms translation-based CLTR in identifying conceptually relevant documents.