A Roadmap of Agent Research and Development

This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.  

云计算研究进展综述

对现有的云计算系统进行分析和总结，以期为下一步的研究指明方向。采用文献分析方法简述了云计算的定义、特点和基本结构，介绍了实现云计算的各项相关技术的研究现状,包括虚拟化技术、Web服务技术、编程模型，对已有的几个较有代表性的云计算系统进行了介绍并指出了当前云计算系统亟待解决的问题与下一步的研究方向，可为云计算相关研究提供参考。  

多媒体传感器网络及其研究进展

作为一种全新的信息获取和处理技术,多媒体传感器网络较之传统传感器网络更多地关注于音频、视频、图像等大数据量、大信息量媒体的采集与处理,在军事、民用及商业领域中具有广阔的应用前景.介绍了多媒体传感器网络的概念与特点,着重探讨了多媒体传感器网络所面临的挑战与国内外的研究进展,最后分析了当前亟待解决的问题,并展望了其未来的发展趋势.多媒体传感器网络是一种新的概念系统但也存在较多的问题需要解决,其研究具有很强的理论意义和实用价值.  

一种更简化而高效的粒子群优化算法

针对基本粒子群优化(basic particle swarm optimization,简称bPSO)算法容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,采用简化粒子群优化方程和添加极值扰动算子两种策略加以改进,提出了简化粒子群优化(simple particle swarm optimization,简称sPSO)算法、带极值扰动粒子群优化(extremum disturbed particle swarm optimization,简称tPSO)算法和基于二者的带极值扰动的简化粒子群优化(ext  

异构传感器网络的分布式能量有效成簇算法

为了延长网络的生存时间,需要设计能量有效的协议,以适应传感器网络的特点.成簇算法是传感器网络中减少能量消耗的一种关键技术,它能够增强网络的扩展性和延长网络的生存时间.研究了异构传感器网络中成簇算法在节省能量方面的性能,提出一种适应异构无线传感器网络的分布式能量有效的成簇方案.此方案基于节点剩余能量与网络节点的平均能量的比例来选举簇头节点.较高初始能量和剩余能量的节点比低能量节点拥有更多的机会成为簇头节点,从而使网络能量均匀消耗,延长网络的生存时间.模拟实验结果显示,与现有的重要成簇方案相比,新的成簇算法在异构网络下提供了更长的网络生存时间和更大的网络有效吞吐量.  

不确定性数据管理技术研究综述

随着数据采集和处理技术的进步,人们对数据的不确定性的认识也逐步深入,在诸如经济、军事、物流、金融、电信等领域的具体应用中,数据的不确定性普遍存在,不确定性数据的表现形式多种多样,它们可以以关系型数据、半结构化数据、流数据或移动对象数据等形式出现,目前,根据应用特点与数据形式差异,研究者已经提出了多种针对不确定数据的数据模型,这些不确定性数据模型的核心思想都源自于可能世界模型,可能世界模型从一个或多个不确定的数据源演化出诸多确定的数据库实例,称为可能世界实例,而且所有实例的概率之和等于1.尽管可以首先分别为各个实例计算查询结果,然后合并中间结果以生成最终查询结果,但由于可能世界实例的数量远大于不确定性数据库的规模,这种方法并不可行,因此,必须运用排序、剪枝等肩发式技术设计新型算法,以提高效率.文中介绍了不确定性数据管理技术的概念、特点与挑战,综述了数据模型、数据预处理与集成、存储与索引、查询处理等方面的工作.  

BoosTexter: A Boosting-based System for Text Categorization

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.  

机会网络

大量具备短距离通信能力的智能设备的出现推动了无线自组网应用的迅速发展.但在许多实际应用环境中,节点移动、网络稀疏或信号衰减等各种原因通常导致形成的网络大部分时间不连通.传统的移动自组织网络传输模式要求通信源和目标节点之间存在至少一条完整的路径,因而无法在这类环境中运行.机会网络利用节点移动形成的通信机会逐跳传输消息,以"存储-携带-转发"的路由模式实现节点间通信,这种完全不同于传统网络通信模式的新兴组网方式引起了研究界极大的兴趣.首先介绍机会网络的概念和理论基础,并给出了当前机会网络的一些典型应用,然后详细阐述了机会网络研究的热点问题,包括机会转发机制、移动模型和基于机会通信的数据分发和检索等,并简要叙述了机会网络的通信中间件、协作和安全机制以及机会网络新的应用等其他研究问题,最后进行总结并展望了机会网络未来一段时间内的研究重点.  

Text Classification from Labeled and Unlabeled Documents using EM

This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available.We introduce an algorithm for learning from labeled and unlabeled documents based on the combination of Expectation-Maximization (EM) and a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents, and probabilistically labels the unlabeled documents. It then trains a new classifier using the labels for all the documents, and iterates to convergence. This basic EM procedure works well when the data conform to the generative assumptions of the model. However these assumptions are often violated in practice, and poor performance can result. We present two extensions to the algorithm that improve classification accuracy under these conditions: (1) a weighting factor to modulate the contribution of the unlabeled data, and (2) the use of multiple mixture components per class. Experimental results, obtained using text from three different real-world tasks, show that the use of unlabeled data reduces classification error by up to 30%.  

Shape and motion from image streams under orthography: a factorization method

Inferring scene geometry and camera motion from a stream of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. We have developed a factorization method that can overcome this difficulty by recovering shape and motion under orthography without computing depth as an intermediate step.An image stream can be represented by the 2F×P measurement matrix of the image coordinates of P points tracked through F frames. We show that under orthographic projection this matrix is of rank 3.Based on this observation, the factorization method uses the singular-value decomposition technique to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively. Two of the three translation components are computed in a preprocessing stage. The method can also handle and obtain a full solution from a partially filled-in measurement matrix that may result from occlusions or tracking failures.The method gives accurate results, and does not introduce smoothing in either shape or motion. We demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions.