Detection of damaged files and measurement of similarity to originals using entropy graph characteristics

An information entropy graph shows the probabilities of each piece of information being included in a dataset as entropy values using information entropy. Well-known filetypes exhibit different information entropy graph characteristics; hence, they can be detected and differentiated using these characteristics. In this paper, a method that detects damaged files using information entropy graphs is proposed. The proposed method expands on conventional proposals that use only information entropy values to facilitate differentiation of different filetypes that present the same entropy values. In experiments conducted, patterns that have significance for analysis and detection were shown in the information entropy graphs of well-known files. In addition, even when files had damaged header, footer, or body regions, the similarity of the graph pattern was preserved, even though the entropy values differed. The proposed method also enables quantitative comparison of the similarity of files before and after damage with their original versions through graph pattern similarity tests.     

大数据文件和混合文件的多线程并行下载

在应用计算机模拟病例训练与考试系统的过程中,客户端时常需要在线下载许多大数据文件、音频和视频混合文件,系统响应速度是一个关键问题。研究了在RIA中实现多线程的技术方案,提出在多核计算机上有效实现多线程并行下载大数据文件、音频和视频混合文件的优化方法。算法分析与实验结果表明,提出的多线程并行下载技术能够加速计算机模拟病例系统模块的在线下载,显著优化了系统运行性能。     

Database maintenance efficiency using differential files

Severance and Lohman[1] proposed that all database amendments (i.e. additions, deletions and updates) are localized into a relatively small storage area, called a differential file. When the differential file grows sufficiently large, a reorganization incorporates all changes into a new generation of the database. This paper proposes a modification of the differential file approach in that the differential file only stores record additions. It is also concerned with the selection of the optimum reorganization points for both the original and the modified differential file approaches.     

基于有向图的二进制文件结构化比较机制研究

通过对二进制文件结构化比较方法的研究,提出了基于有向图的二进制文件结构化比较方法及实现算法.和基于指令比较的二进制文件比较方法相比,二进制文件结构化比较方法更注重二进制文件在逻辑结构上的变化,而且更加简单.     

Implementing an integrated test facility audit using differential files

Integrated test facility is a computer auditing technique whose value has been attested by auditors and researchers alike. However, this technique has several disadvantages. Two of these are cost of the audit procedure and reduced data base integrity for the users. It is shown how the concept of differential files can be used to facilitate an audit by integrated test facility. This new use of differential files can reduce the cost of an audit and allows the auditor to use a wider variety of audit transactions while not violating data base integrity during the audit.     

Comparing images using joint histograms

Color histograms are widely used for content-based image retrieval due to their efficiency and robustness. However, a color histogram only records an image's overall color composition, so images with very different appearances can have similar color histograms. This problem is especially critical in large image databases, where many images have similar color histograms. In this paper, we propose an alternative to color histograms called a joint histogram, which incorporates additional information without sacrificing the robustness of color histograms. We create a joint histogram by selecting a set of local pixel features and constructing a multidimensional histogram. Each entry in a joint histogram contains the number of pixels in the image that are described by a particular combination of feature values. We describe a number of different joint histograms, and evaluate their performance for image retrieval on a database with over 210,000 images. On our benchmarks, joint histograms outperform color histograms by an order of magnitude.     

Detection of encrypted executable files based on entropy analysis to determine the randomness measure of byte sequences

A method has been proposed for identifying malicious programs that use encryption as a disguise. In this paper, a modification of the statistical spectral test based on entropy analysis has been described.     

Efficient speaker identification using spectral entropy

In voice recognition, the two main problems are speech recognition (what was said), and speaker recognition (who was speaking). The usual method for speaker recognition is to postulate a model where the speaker identity corresponds to the parameters of the model, which estimation could be time-consuming when the number of candidate speakers is large. In this paper, we model the speaker as a high dimensional point cloud of entropy-based features, extracted from the speech signal. The method allows indexing, and hence it can manage large databases. We experimentally assessed the quality of the identification with a publicly available database formed by extracting audio from a collection of YouTube videos of 1,000 different speakers. With 20 second audio excerpts, we were able to identify a speaker with 97% accuracy when the recording environment is not controlled, and with 99% accuracy for controlled recording environments.

     

Enhancing density-based data reduction using entropy

Data reduction algorithms determine a small data subset from a given large data set. In this article, new types of data reduction criteria, based on the concept of entropy, are first presented. These criteria can evaluate the data reduction performance in a sophisticated and comprehensive way. As a result, new data reduction procedures are developed. Using the newly introduced criteria, the proposed data reduction scheme is shown to be efficient and effective. In addition, an outlier-filtering strategy, which is computationally insignificant, is developed. In some instances, this strategy can substantially improve the performance of supervised data analysis. The proposed procedures are compared with related techniques in two types of application: density estimation and classification. Extensive comparative results are included to corroborate the contributions of the proposed algorithms.     

Joint structural similarity and entropy estimation for coded-exposure image restoration

Multimedia Tools and Applications - We address the image deblurring using coded exposure which can keep image content that may be lost by a traditional shutter. In the restoration of a coded...     

一种改进的直觉模糊熵公理化定义和构造公式

针对现有的直觉模糊熵的公理化定义和构造公式未能全面体现直觉模糊集模糊性的缺陷进行分析, 提出一种改进的直觉模糊熵的公理化定义, 据此构造一个新的直觉模糊熵的计算公式, 并将该公式与现有直觉模糊熵公式进行比较. 算例分析表明, 所提出的熵公式能够更充分地反映直觉模糊集的不确定性和未知性程度.

     

基于结构熵的警示传播算法收敛性分析

收敛性是评价信息传播算法性能的重要指标,信息传播算法求解可满足性问题时,命题公式的结构特征影响算法的收敛性,具有复杂结构的命题公式,信息传播算法不总收敛。为了系统地对此现象给予理论解释,借助于结构熵的方法和技术,提出命题公式的结构熵模型及其度量方法,计算随机可满足性实例的结构熵。警示传播算法(WP)作为信息传播算法的基本模型,分析WP算法的收敛性对于研究其他信息传播算法的收敛性具有重要意义,分析了WP算法收敛性与结构熵之间的关系,给出WP算法收敛的判定条件。通过实验分析,该方法有效可行。     

A method for improving full text search using signature files

Efficiency of full text retrieval using signatures depends on the number of filtering and the reduction of the original text, but there has been no discussion how a signature is constructed keeping the worst-case filtering ratio. In order to consider this problem, we present a technique of constructing signatures by using an appearance probability of strings in a textual data. It enables us to retrieve any keywords in expected worst-case searching time.

A partial appearance probability is proposed because the overall probability for the whole text takes a lot of time building signatures. From the simulation result, it turns can't that the worst-case filtering ratio of the presented method can keep the expected ratio while that of the traditional method degrades zero.     

MI-File: using inverted files for scalable approximate similarity search

We propose a new efficient and accurate technique for generic approximate similarity searching, based on the use of inverted files. We represent each object of a dataset by the ordering of a number of reference objects according to their distance from the object itself. In order to compare two objects in the dataset, we compare the two corresponding orderings of the reference objects. We show that this representation enables us to use inverted files to obtain very efficiently a very small set of good candidates for the query result. The candidate set is then reordered using the original similarity function to obtain the approximate similarity search result. The proposed technique performs several orders of magnitude better than exact similarity searches, still guaranteeing high accuracy. To also demonstrate the scalability of the proposed approach, tests were executed with various dataset sizes, ranging from 200,000 to 100 million objects.     

Comparing images using the Hausdorff distance

The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image     

Comparing software prediction techniques using simulation

The need for accurate software prediction systems increases as software becomes much larger and more complex. We believe that the underlying characteristics: size, number of features, type of distribution, etc., of the data set influence the choice of the prediction system to be used. For this reason, we would like to control the characteristics of such data sets in order to systematically explore the relationship between accuracy, choice of prediction system, and data set characteristic. It would also be useful to have a large validation data set. Our solution is to simulate data allowing both control and the possibility of large (1000) validation cases. The authors compare four prediction techniques: regression, rule induction, nearest neighbor (a form of case-based reasoning), and neural nets. The results suggest that there are significant differences depending upon the characteristics of the data set. Consequently, researchers should consider prediction context when evaluating competing prediction systems. We observed that the more "messy" the data and the more complex the relationship with the dependent variable, the more variability in the results. In the more complex cases, we observed significantly different results depending upon the particular training set that has been sampled from the underlying data set. However, our most important result is that it is more fruitful to ask which is the best prediction system in a particular context rather than which is the "best" prediction system     

基于二维结构熵的置信传播算法收敛性分析

为了对置信传播(BP)算法在结构比较复杂的命题公式上有时会失效,常常表现为不收敛的现象给予理论解释,提出了基于警示传播的社区发现算法(WPLPA).依据BP算法在因子图上的信息迭代策略,利用WPLPA算法对命题公式所对应因子图的社区结构进行划分.借助二维结构熵的相关理论技术,建立了命题公式的二维结构熵度量模型,通过该模型分析了BP算法的收敛性与二维结构熵之间的关系,给出BP算法的收敛性判定条件,对BP算法的收敛性进行系统分析.实验分析证明该方法有效可行,能为信息传播算法的后续研究提供理论支持.     

Face recognition using kernel entropy component analysis

In this letter, we have reported a new face recognition algorithm based on Renyi entropy component analysis. In the proposed model, kernel-based methodology is integrated with entropy analysis to choose the best principal component vectors that are subsequently used for pattern projection to a lower-dimensional space. Extensive experimentation on Yale and UMIST face database has been conducted to reveal the performance of the entropy based principal component analysis method and comparative analysis is made with the kernel principal component analysis method to signify the importance of selection of principal component vectors based on entropy information rather based only on magnitude of eigenvalues.     

一种新的基于二维Tsallis熵的阈值方法

Tsallis熵首先出现在统计力学中。对于呈现远距离交互,长时间记忆以及具有不规则结构的物理系统来说,它的表达式中引入了一个实数q作为参数。在利用图像像素的灰度值和像素的邻域平均灰度值建立的二维直方图的基础上,提出了基于二维Tsallis熵的阈值方法;同时为解决计算复杂度高、运算时间长这一缺点,利用群体智能中的粒子群优化（PSO）算法来优化搜索分割阈值（t,s）的过程,其中t和s分别是图像的像素灰度阈值以及邻域平均灰度阈值。通过对真实图像的处理实验证明,该方法不仅能够对目标图像进行准确的分割,而且大大减少了运算时间。