An application of stochastic languages to fingerprint pattern recognition

The purpose of this paper is to present a stochastic syntactic approach for representation and classification of fingerprint patterns.The fingerprint impressions are subdivided into sampling squares which are preprocessed for feature extraction. First, a brief summary of application of a class of context-free languages for recognition of fingerprints is presented. Next, using the same set of features, a class of stochastic context-free languages was used to further classify the fingerprint impressions. The recognizers using the class of context-free and stochastic context-free languages are named the first-level and second-level classifiers, respectively. Experimental results in terms of real data fingerprints are presented.     

Fingerprint image analysis for automatic identification

Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.     

Computational modeling of fluorescence loss in photobleaching

Fluorescence loss in photobleaching (FLIP) is a modern microscopy method for visualization of transport processes in living cells. Although FLIP is widespread, an automated reliable analysis of image data is still lacking. This paper presents a framework for modeling and simulation of FLIP sequences as reaction–diffusion systems on segmented cell images. The cell geometry is extracted from microscopy images using the Chan–Vese active contours algorithm (IEEE Trans Image Process 10(2):266–277, 2001). The PDE model is subsequently solved by the automated Finite Element software package FEniCS (Logg et al. in Automated solution of differential equations by the finite element method. Springer, Heidelberg, 2012). The flexibility of FEniCS allows for spatially resolved reaction diffusion coefficients in two (or more) spatial dimensions.     

A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

Identification of drug–target interactions (DTIs) is critical for discovering potential target protein candidates for new drugs. However, traditional experimental methods have limitations in discovering DTIs. They are time‐consuming, tedious, and expensive, and often suffer from high false‐positive rates and false‐negative rates. Therefore, using computational methods to predict DTIs has received extensive attention from many researchers in recent years. To address this issue, in this paper, an effective prediction model is presented which is based on the information of drug molecular structure data and protein sequence data. It performs prediction with the following procedures. First, we transform the sequences of each target into a position‐specific scoring matrix (PSSM), such that the features can retain biological evolutionary information. We then use a feature vector of molecular substructure fingerprints to describe the chemical structure information of the drug compounds. Second, the Legendre moments algorithm is used to extract new features from the PSSM. Finally, a classification algorithm called rotation forest is used to perform prediction, we tested its prediction performance on four golden standard data sets: enzymes, G‐protein‐coupled receptors, ion channels, and nuclear receptors. As a result, the proposed method achieves average accuracies of 0.9026, 0.8260, 0.8703, and 0.7444 on these four data sets using five‐fold cross‐validation. We also compare the proposed method with the support vector machine and other existing approaches. The proposed model is proved to be superior to comparative methods, showing that it is feasible, effective, and robust for predicting potential DTI.     

Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs

The purpose of this paper is to introduce an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with insight and understanding of the key factors—algorithmic, subject-based, procedural, image quality, environmental, among others—that affect the matching performance of the biometric system under study. This proposed methodology consists of two steps: (1) the design and execution of orthogonal fractional factorial experiment designs which allow the scientist to efficiently investigate the effect of a large number of factors—and interactions—simultaneously, and (2) the use of a select set of statistical data analysis graphical procedures which are fine-tuned to unambiguously highlight important factors, important interactions, and locally-optimal settings. We illustrate this methodology by application to a study of VASIR (Video-based Automated System for Iris Recognition)—NIST iris-based biometric system. In particular, we investigated k = 8 algorithmic factors from the VASIR system by constructing a (2^6?1 × 3¹  × 4¹) orthogonal fractional factorial design, generating the corresponding performance data, and applying an appropriate set of analysis graphics to determine the relative importance of the eight factors, the relative importance of the 28 two-term interactions, and the local best settings of the eight algorithms. The results showed that VASIR’s performance was primarily driven by six factors out of the eight, along with four two-term interactions. A virtue of our two-step methodology is that it is systematic and general, and hence may be applied with equal rigor and effectiveness to other biometric systems, such as fingerprints, face, voice, and DNA.     

Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints

In this paper, we investigate the application of Evolving Trees (ET) for the analysis of mass spectrometric data of bacteria. Evolving Trees are extensions of self-organizing maps (SOMs) developed for hierarchical classification systems. Therefore, they are well suited for taxonomic problems such as the identification of bacteria. Here, we focus on three topics, an appropriate pre-processing and encoding of the spectra, an adequate data model by means of a hierarchical Evolving Tree and an interpretable visualization. First, the high dimensionality of the data is reduced by a compact representation. Here, we employ sparse coding, specifically tailored for the processing of mass spectra. In the second step, the topographic information which is expected in the fingerprints is used for advanced tree evaluation and analysis. We adapted the original topographic product for SOMs for ET to achieve a judgment of topography. Additionally we transferred the concept of U-matrix for evaluation of the separability of SOMs to their analog in ET. We demonstrate these extensions for two mass spectrometric data sets of bacteria fingerprints and show their classification and evaluation capabilities in comparison to state of the art techniques.     

Scale-space properties of the multiscale morphologicaldilation-erosion

A multiscale morphological dilation-erosion smoothing operation and its associated scale-space expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scale-space monotonic property for signal extrema is demonstrated. Scale-space fingerprints from this approach have advantages over Gaussian scale-space fingerprints in that: they are defined for negative values of the scale parameter; have monotonic properties in two and higher dimensions; do not cause features to be shifted by the smoothing; and allow efficient computation. The application of reduced multiscale dilation-erosion fingerprints to the surface matching of terrain is demonstrated     

Authorship attribution

This paper considers the problem of quantifying literary style and looks at several variables which may be used as stylistic “fingerprints” of a writer. A review of work done on the statistical analysis of “change over time” in literary style is then presented, followed by a look at a specific application area, the authorship of Biblical texts.     

A recurrent cooperative/competitive field for segmentation ofmagnetic resonance brain images

The gray-white decision network is introduced as an application of a recurrent cooperative/competitive network for segmentation of magnetic resonance (MR) brain images. The three-layer dynamical system relaxes into a solution where each pixel is labeled as either gray matter, white matter, or other matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a neurally inspired recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Applications of the network and its phase plane analysis are presented     

Haptic applications for molecular structure manipulation

We describe the application of haptic technology to enhance the information available in chemical systems, specifically related to computational drug design. These methods are designed to build upon the visual information presented by molecular viewers and add the sensation of touch, or force feedback. The addition of sensory input can aid in the analysis of molecular structures and the understanding of intermolecular interactions by delivering chemically relevant forces to the end user.     

Haptic applications for molecular structure manipulation

We describe the application of haptic technology to enhance the information available in chemical systems, specifically related to computational drug design. These methods are designed to build upon the visual information presented by molecular viewers and add the sensation of touch, or force feedback. The addition of sensory input can aid in the analysis of molecular structures and the understanding of intermolecular interactions by delivering chemically relevant forces to the end user.     

An application of algebraic topology to solid modeling in molecular biology

A recurring problem in solid modeling, computer graphics, and molecular modeling is the computation of the intersection of two objects. A general solution to this problem is obtained by applying two ideas of algebraic topology: (1) a chain complex, and (2) a boundary formula for the intersection of two objects. A general data structure for a chain complex made up of piecewise polynomial cells is described, as are algorithms for connectivity, containment and intersection. The basic ideas of this work are abstract, topological, and for the most part, independent of the shape and dimensionality of the objects. An application to structural molecular biology is presented. The application identifies convex and concave features of protein surfaces.     

一种合谋安全的数据库指纹编码与盗版追踪算法

在将数据库作为软件产品发行的应用场合,需要有相应的安全机制对叛逆用户的盗版行为进行约束.本文提出了一种基于数字指纹的关系数据库盗版追踪解决方案.以混沌二值序列将版权水印与用户指纹组合而成数据库指纹,在密钥的控制下嵌入数据库.基于混沌二值序列的随机性进行指纹提取与叛逆追踪.方案具有较高的合谋安全性,同时降低了指纹检测与叛逆追踪的运算复杂度.文中描述了数据库指纹编码与嵌入、指纹检测与提取算法,分析了算法的鲁棒性与叛逆追踪能力,并进行了实验验证.     

Extended Narrow Band FLIP for Liquid Simulations

The Fluid Implicit Particle method (FLIP) reduces numerical dissipation by combining particles with grids. To improve performance, the subsequent narrow band FLIP method (NB‐FLIP) uses a FLIP‐based fluid simulation only near the liquid surface and a traditional grid‐based fluid simulation away from the surface. This spatially‐limited FLIP simulation significantly reduces the number of particles and alleviates a computational bottleneck. In this paper, we extend the NB‐FLIP idea even further, by allowing a simulation to transition between a FLIP‐like fluid simulation and a grid‐based simulation in arbitrary locations, not just near the surface. This approach leads to even more savings in memory and computation, because we can concentrate the particles only in areas where they are needed. More importantly, this new method allows us to seamlessly transition to smooth implicit surface geometry wherever the particle‐based simulation is unnecessary. Consequently, our method leads to a practical algorithm for avoiding the noisy surface artifacts associated with particle‐based liquid simulations, while simultaneously maintaining the benefits of a FLIP simulation in regions of dynamic motion.     

Narrow Band FLIP for Liquid Simulations

The Fluid Implicit Particle method (FLIP) for liquid simulations uses particles to reduce numerical dissipation and provide important visual cues for events like complex splashes and small‐scale features near the liquid surface. Unfortunately, FLIP simulations can be computationally expensive, because they require a dense sampling of particles to fill the entire liquid volume. Furthermore, the vast majority of these FLIP particles contribute nothing to the fluid's visual appearance, especially for larger volumes of liquid. We present a method that only uses FLIP particles within a narrow band of the liquid surface, while efficiently representing the remaining inner volume on a regular grid. We show that a naïve realization of this idea introduces unstable and uncontrollable energy fluctuations, and we propose a novel coupling scheme between FLIP particles and regular grid which overcomes this problem. Our method drastically reduces the particle count and simulation times while yielding results that are nearly indistinguishable from regular FLIP simulations. Our approach is easy to integrate into any existing FLIP implementation.     

Geobase: a simple geographical information system on a personal computer

Spatially distributed data are often encountered in the biological sciences. Representation and analysis of such data requires specific tools. A simple geographical information system is presented, which allows representation and elementary analysis of geographically coded information. The system handles two kinds of data: maps and facts, where map data describe the basis on which the fact data are located. Maps consist of objects described through a set of coordinates, while for facts a coordinate pair is associated with an unlimited number of data records containing five fields: a date, an element from a list, a two-character code, an integer number and a real number. The input data can be displayed interactively on screen by logically combining selection criteria for each field. The facts corresponding to the selected criteria are either displayed as such, or are clustered and displayed as polygons or pies. A short example showing a possible application of the program is presented and advantages as well as limitations are discussed.     

VisualiSAR: a web-based application for clustering, structure browsing, and structure-activity relationship study

VisualiSAR is a program designed to display chemical structures, find similarities and differences between them, and highlight relationships that might exist. The program integrates cluster analysis for the grouping of structurally related compounds, modal analysis of molecular fingerprints for the sorting and highlighting of chemical features, and a Web-based interface for flexibility and ease of use. VisualiSAR has proved useful for a number of applications including the discernment of structure-activity relationships (SAR) of high-volume screening data, and general structure browsing. This article discusses the design of the tool and illustrates two applications.     

Digital Image Forensics via Intrinsic Fingerprints

Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.     

Practical Algorithms and Lower Bounds for Similarity Search in Massive Graphs

To exploit the similarity information hidden in the hyperlink structure of the Web, this paper introduces algorithms scalable to graphs with billions of vertices on a distributed architecture. The similarity of multistep neighborhoods of vertices are numerically evaluated by similarity functions including SimRank, a recursive refinement of cocitation, and PSimRank, a novel variant with better theoretical characteristics. Our methods are presented in a general framework of Monte Carlo similarity search algorithms that precompute an index database of random fingerprints, and at query time, similarities are estimated from the fingerprints. We justify our approximation method by asymptotic worst-case lower bounds: we show that there is a significant gap between exact and approximate approaches, and suggest that the exact computation, in general, is infeasible for large-scale inputs. We were the first to evaluate SimRank on real Web data. On the Stanford WebBase graph of 80M pages the quality of the methods increased significantly in each refinement step until step four