Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Comparison of nitrogen adsorption at 77 K on non-porous silica and pore wall of MCM-41 materials by means of density functional theory

Nitrogen adsorption on a surface of a non-porous reference material is widely used in the characterization. Traditionally, the enhancement of solid-fluid potential in a porous solid is accounted for by incorporating the surface curvature into the solid-fluid Potential of the flat reference surface. However, this calculation procedure has not been justified experimentally. In this paper, we derive the solid-fluid potential of mesoporous MCM-41 solid by using solely the adsorption isotherm of that solid. This solid-fluid potential is then compared with that of the non-porous reference surface. In derivation of the solid-fluid potential for both reference surface and mesoporous MCM-41 silica (diameter ranging front 3 to 6.5 nm) we employ the nonlocal density functional theory developed for amorphous solids. It is found that, to out, surprise, the solid-fluid potential of a porous solid is practically the same as that for the reference surface, indicating that there is no enhancement due to Surface curvature. This requires further investigations to explain this unusual departure from our conventional wisdom of curvature-induced enhancement. Accepting the curvature-independent solid-fluid potential derived from the non-porous reference surface, we analyze the hysteresis features of a series of MCM-41 samples. (c) 2005 Elsevier Inc. All rights reserved.

On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing

We have undertaken two-dimensional gel electrophoresis proteomic profiling on a series of cell lines with different recombinant antibody production rates. Due to the nature of gel-based experiments not all protein spots are detected across all samples in an experiment, and hence datasets are invariably incomplete. New approaches are therefore required for the analysis of such graduated datasets. We approached this problem in two ways. Firstly, we applied a missing value imputation technique to calculate missing data points. Secondly, we combined a singular value decomposition based hierarchical clustering with the expression variability test to identify protein spots whose expression correlates with increased antibody production. The results have shown that while imputation of missing data was a useful method to improve the statistical analysis of such data sets, this was of limited use in differentiating between the samples investigated, and highlighted a small number of candidate proteins for further investigation. (c) 2006 Elsevier B.V. All rights reserved.

Parapsychology on the couch: The psychology of occult belief in Germany, c. 1870-1939

This article considers the attempts of academic psychologists and critical occultists in Germany during the late nineteenth and early twentieth centuries to construct a psychology of occult belief. While they claimed that the purpose of this new subdiscipline was to help evaluate the work of occult researchers, the emergence of a psychology of occult belief in Germany served primarily to pathologize parapsychology and its practitioners. Not to be outdone, however, parapsychologists argued that their adversaries suffered from a morbid inability to accept the reality of the paranormal. Unable to resolve through experimental means the dispute over who should be allowed to mold the public's understanding of the occult, both sides resorted to defaming their opponent. (c) 2006 Wiley Periodicals, Inc.

Tool support for generating passive C++ test oracles from object-z specifications

A test oracle provides a means for determining whether an implementation behaves according to its specification. A passive test oracle checks that the correct behaviour has been implemented, but does not implement the behaviour itself. In previous work, we have presented a method that allows us to derive passive C++ test oracles from formal specifications written in Object-Z. We describe the "Warlock" prototype tool that supports the method. Warlock is built on top of an existing Object-Z type checker and generates oracle code for a substantial subset of the Object-Z language. We describe the architecture of Warlock and its application to a number of Object-Z specifications. We also discuss its current limitations.

The use of counterfactual scenarios as a means to assess policy deadweight:an Irish case study

The evaluation of industrial policy interventions has attracted increasing policy and academic attention in recent years. Despite the widespread consensus regarding the need for evaluation, the issue of how to evaluate, and the associated methodological considerations, continue to be issues of considerable debate. The authors develop an approach to estimate the net additionality of financial assistance from Enterprise Ireland to indigenously owned firms in Ireland for the period 2000 to 2002. With a sample of Enterprise Ireland assisted firms, an innovative, self-assessment, in-depth, face-to-face, interview methodology was adopted. The authors also explore a way of incorporating the indirect benefits of assistance into derived deadweight estimate issue which is seldom discussed in the context of deadweight estimates. They conclude by reflecting on the key methodological lessons learned from the evaluation process, and highlight some pertinent evaluation issues which should form the focus of much future discussion in this field of research.

Development of an intelligent quality management system using fuzzy association rules

In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization’s success. In recent years, attention has been focused on intelligent systems which have shown great promise in supporting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to overcome the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ‘‘distributed process mining” feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems.

Sensitivity of peptide conformational dynamics on clustering of a classical molecular dynamics trajectory

We investigate the sensitivity of a Markov model with states and transition probabilities obtained from clustering a molecular dynamics trajectory. We have examined a 500 ns molecular dynamics trajectory of the peptide valine-proline-alanine-leucine in explicit water. The sensitivity is quantified by varying the boundaries of the clusters and investigating the resulting variation in transition probabilities and the average transition time between states. In this way, we represent the effect of clustering using different clustering algorithms. It is found that in terms of the investigated quantities, the peptide dynamics described by the Markov model is sensitive to the clustering; in particular, the average transition times are found to vary up to 46%. Moreover, inclusion of nonphysical sparsely populated clusters can lead to serious errors of up to 814%. In the investigation, the time step used in the transition matrix is determined by the minimum time scale on which the system behaves approximately Markovian. This time step is found to be about 100 ps. It is concluded that the description of peptide dynamics with transition matrices should be performed with care, and that using standard clustering algorithms to obtain states and transition probabilities may not always produce reliable results.

Odour pollution problems within the district of the Wrekin council

This thesis is concerned with various aspects of Air Pollution due to smell, the impact it has on communities exposed to it, the means by which it may be controlled and the manner in which a local authority may investigate the problems it causes. The approach is a practical one drawing on examples occurring within a Local Authority's experience and for that reason the research is anecdotal and is not a comprehensive treatise on the full range of options available. Odour Pollution is not yet a well organised discipline and might be considered esoteric as it is necessary to incorporate elements of science and the humanities. It has been necessary to range widely across a number of aspects of the subject so that discussion is often restricted but many references have been included to enable a reader to pursue a particular point in greater depth. In a `fuzzy' subject there is often a yawning gap separating theory and practice, thus case studies have been used to illustrate the interplay of various disciplines in resolution of a problem. The essence of any science is observation and measurement. Observation has been made of the spread of odour pollution through a community and also of relevant meterological data so that a mathematical model could be constructed and its predictions checked. It has been used to explore the results of some options for odour control. Measurements of odour perception and human behaviour seldom have the precision and accuracy of the physical sciences. However methods of social research enabled individual perception of odour pollution to be quantified and an insight gained into reaction of a community exposed to it. Odours have four attributes that can be measured and together provide a complete description of its perception. No objective techniques of measurement have yet been developed but in this thesis simple, structured procedures of subjective assessment have been improvised and their use enabled the functioning of the components of an odour control system to be assessed. Such data enabled the action of the system to be communicated using terms that are understood by a non specialist audience.

Neurite outgrowth by the alternatively spliced region of human tenascin-C is mediated by neuronal alpha7beta1 integrin

The region of tenascin-C containing only alternately spliced fibronectin type-III repeat D (fnD) increases neurite outgrowth by itself and also as part of tenascin-C. We previously localized the active site within fnD to an eight amino acid sequence unique to tenascin-C, VFDNFVLK, and showed that the amino acids FD and FV are required for activity. The purpose of this study was to identify the neuronal receptor that interacts with VFDNFVLK and to investigate the hypothesis that FD and FV are important for receptor binding. Function-blocking antibodies against both alpha7 and beta1 integrin subunits were found to abolish VFDNFVLK-mediated process extension from cerebellar granule neurons. VFDNFVLK but not its mutant, VSPNGSLK, induced clustering of neuronal beta1 integrin immunoreactivity. This strongly implicates FD and FV as important structural elements for receptor activation. Moreover, biochemical experiments revealed an association of the alpha7beta1 integrin with tenascin-C peptides containing the VFDNFVLK sequence but not with peptides with alterations in FD and/or FV. These findings are the first to provide evidence that the alpha7beta1 integrin mediates a response to tenascin-C and the first to demonstrate a functional role for the alpha7beta1 integrin receptor in CNS neurons.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.