An information-theoretic approach to combining object models

This paper introduces a new method for combining different object models. By determining a configuration of the models, which maximizes their mutual information, the proposed method creates a unified hypothesis from multiple object models on the fly without prior training. To validate the effectiveness of the proposed method, experiments are conducted in which human faces are detected and localized in images by combining different face models.     

An information-theoretic approach to quantitative association rule mining

Quantitative association rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike boolean association rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the high-confidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.     

Overfitting revisited: an information-theoretic approach to simplifying discrimination trees

Abstract

This paper describes a method of simplifying inductively generated discrimination trees using a measure of tree quality based on the principle of information economy, which takes into account both the size of the tree and the size of the outcome data after (notional) encoding by that tree. Results of testing this method on a selection of data sets show that it has some practical advantages over previously used techniques for tree-pruning. Some of the theoretical implications of the present method are also discussed.     

无线传感器网络中一种优化的隐式跨层传输控制算法

为了提高无线传感器网络的传输质量,对Ad Hoc网络中一种隐式逐跳的跨层拥塞控制协议做了改进.通过路由层、MAC层与传输层合作,使得隐式逐跳的传输控制协议在无线传感器网络中能够适用.路由层采用静态路由协议,设置2个目的接收节点来防止洪泛产生.在传输层通过引入相对信息熵理论实现隐式确认,以提高数据传输的可靠性.在MAC层根据节点距拥塞区域的跳数来决定信道竟用的退避时间.跨层设计有效地解决了无线传感器网络中的传输控制问题.通过Matlab仿真证明:与现有的传输层协议相比,改进的方法能降低网络的负载,增加网络的吞吐量,减少网络的延迟和丢包率.     

综合属性选择和删除的属性约简方法

属性约简能有效地消除信息冗余,广泛应用于人工智能、机器学习.通过实例指出基于辨识矩阵的经典的属性约简方法存在不能得到约简的可能性,仍具有冗余性.因此,提出了综合属性选择和删除算法的辨识矩阵属性约简方法,并有效解决该问题.通过UCI标准数据集验证表明,新方法比经典方法进一步减少了属性的个数,凸显其实用性和有效性.     

An Information Theory Framework for the Analysis of Scene Complexity

In this paper we present a new framework for the analysis of scene visibility and radiosity complexity. We introduce a number of complexity measures from information theory quantifying how difficult it is to compute with accuracy the visibility and radiosity in a scene. We define the continuous mutual information as a complexity measure of a scene, independent of whatever discretisation, and discrete mutual information as the complexity of a discretised scene. Mutual information can be understood as the degree of correlation or dependence between all the points or patches of a scene. Thus, low complexity corresponds to low correlation and vice versa. Experiments illustrating that the best mesh of a given scene among a number of alternatives corresponds to the one with the highest discrete mutual information, indicate the feasibility of the approach. Unlike continuous mutual information, which is very cheap to compute, the computation of discrete mutual information can however be quite demanding. We will develop cheap complexity measure estimates and derive practical algorithms from this framework in future work.     

信息论在异常检测中的应用

异常检测能有效地检测出新类型的攻击,但是目前大多数入侵检测使用的异常检测技术都缺乏理论指导,通常是经过反复的实验建立检测模型,这样建立的模型只适用于某些环境,不具有通用性。该文提出了一种可以指导异常检测的理论基础———信息论,介绍了应用在异常检测中的几种信息论的方法,包括熵,条件熵,相对(条件)熵,信息量。利用这些方法可以指导异常模型建立过程和解释模型性能。最后该文还通过举例说明了这种理论的可行性和必要性。     

语言熵理论与应用模型：英语语义辅助理解工具设计基础

Adapting the needs for english education of China, by the concept of Shannon's information entropy, this paper proposes the theory of language entropy and its application model: fish-eye driver model. This work develops initial basis for computer-aided English semantic understand. The paper also discusses three complex problems that need deeper research.     

An adaptive PNN-DS approach to classification using multi-sensor information fusion

In this paper, an adaptive neural network approach to classification which combines modified probabilistic neural network and D-S evidence theory (PNN-DS) is proposed. It attempts to deal with the drawbacks of information uncertainty and imprecision using single classification algorithm. This PNN-DS approach firstly adopts a modified PNN to obtain posteriori probabilities and make a primary classification decision in feature-level fusion. Then posteriori probabilities are transformed to masses noting the evidence of the D-S evidential theory. Finally advanced D-S evidential theory is utilized to gain more accurate classification results in the last decision-level fusion. In order to implement PNN-DS, covariance matrices are firstly employed in the modified PNN module to replace the singular smoothing factor in the PNN’s kernel function, and linear function is utilized in the pattern of summation layer. Secondly, the whole scheme of the proposed approach is explained in depth. Thirdly, three classification experiments are carried out on the proposed approach and a large amount of comparable analyses are done to demonstrate the effectiveness and robustness of the proposed approach. Experiments reveal that the PNN-DS outperforms BPNN-DS, which provides encouraging results in terms of classification accuracy and the speed of learning convergence.     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

An eigenstructure assignment approach to FDI for the industrial actuator benchmark test

This paper examines the robustness in modelling uncertainties of an observer-based fault-detection and isolation scheme applied to the industrial actuator benchmark problem. A linear dynamic model is used, and the observer is designed using eigenstructure assignment. Fault detection is achieved with a fixed threshold. The design and test example is an electromechanical system, subject to an actuator fault, a sensor fault and a load disturbance. The paper outlines the design, application, and an evaluation of the robustness of the method.     

基于压缩域的视频关注对象提取

针对视频压缩域的对象分割问题,提出了一种视频对象精确提取方法.先将视频对象的颜色空间进行均值偏移和区域生长,得到分块的视频图像.同时对视频编码过程中所产生的运动矢量统计熵值,提取出视频中能引起人眼关注的运动对象.最后利用提出的对象轮廓精确分割方法,提取视频关注对象.实验表明该算法能精确和完整地分割出视频关注对象,并对视频中关注对象的切换、物体的非刚性形变有很好的鲁棒性.     

An experience-based system supporting inventory planning: A fuzzy approach

The study aims to extend the Uncapacitated Fuzzy Single Item Lot Sizing Problem (known as F-USILSP) model and extend it for inventory planning. The F-USILSP model is a good choice when there is no statistical data collection, but where there is verbal or qualitative information from experts with experience. Previously, the mixed integer linear programming (MILP) relied on the crisp assumption which hinders the use of the F-USILSP. In this paper, a Possibility Approach is adapted to convert the F-USILSP to a mathematically solvable equivalent crisp USILSP (EC-USILSP). The EC-USILSP model is tested with a case. The organization under study is a petrochemical company power plant with trapezoidal fuzzy demand and triangular fuzzy unit price. The overall results show that the EC-USILSP is more practical and exhibits more flexibility when there is a need to add more realistic situations.     

基于信息增益的特征词权重调整算法研究

传统权重公式TFIDF忽略了词语在集合中的分布比例,针对TFIDF的这个缺点,把信息增益公式引入文本集合中并提出IF*IDF*IG,取得了较好的效果。在分析中发现单纯把信息增益引入文本集合并不能完全解决词语分布对词语权重的影响。从文档类别层次上考虑,把信息论中信息增益应用到文本集合的类别层次上,提出了一种改进的权重公式tf*idf*IGc,用改进的权重公式来衡量词语在文本集合的各个类别中分布比例上的差异,进一步弥补传统公式的不足。实验对比了改进的公式tf*idf*IGc和IF*IDF*IG的实验效果,实验证明tf*idf*IGc权重公式在表现词语权重时更有效。     

信息技术与中职教学英语的学科整合

二十一世纪现代化社会发展对英语教学提出了新的要求,本文以建构主义理论为指导,在其先进科学的视角下探讨多媒体信息技术与中职英语学科的有机整合,使得中职英语学习与教学达到更高水平.     

A Shrink Wrapping Approach to Remeshing Polygonal Surfaces

Due to their simplicity and flexibility, polygonal meshes are about to become the standard representation for surface geometry in computer graphics applications. Some algorithms in the context of multiresolution representation and modeling can be performed much more efficiently and robustly if the underlying surface tesselations have the special subdivision connectivity. In this paper, we propose a new algorithm for converting a given unstructured triangle mesh into one having subdivision connectivity. The basic idea is to simulate the shrink wrapping process by adapting the deformable surface technique known from image processing. The resulting algorithm generates subdivision connectivity meshes whose base meshes only have a very small number of triangles. The iterative optimization process that distributes the mesh vertices over the given surface geometry guarantees low local distortion of the triangular faces. We show several examples and applications including the progressive transmission of subdivision surfaces.     

依据建构主义理论探索大学英语网络教学模式

提出了结合信息技术,整合课堂教学、自主学习、第二课堂的大学英语网络教学模式。借助网络平台,教师在课堂教学中作为知识传授者的身份逐渐弱化,作为课堂活动组织者的作用日益凸显,课堂教学之外,教师更成为学生自主学习和英语课外活动的监督者和指导者。采用这种网络化的教学模式有利于在大学英语教学中实践建构主义学习理论、提高学生语言应用能力和自主学习能力。     

An observer and signal-processing approach to FDI for the industrial actuator benchmark test

In order to apply the best fault-detection and diagnosis scheme, it is required to investigate the process model profoundly and the kinds of faults to be detected. Especially, the process excitation and the effect of the fault being considered play an important role. This is the starting point for the choice of one of the various model-based fault-detection methods. According to this strategy, two different approaches, an observer-based and a signal-based approach, are selected for the two given faults of the benchmark task. It is shown that the use of adaptive thresholds can significantly improve the performance of the fault-detection scheme with respect to the false alarm rate and the delay in detection.