An analysis of BioHashing and its variants

As a result of the growing demand for accurate and reliable personal authentication, biometric recognition, a substitute for or complement to existing authentication technologies, has attracted considerable attention. It has recently been reported that, along with its variants, BioHashing, a new technique that combines biometric features and a tokenized (pseudo-) random number (TRN), has achieved perfect accuracy, having zero equal error rates (EER) for faces, fingerprints and palmprints. There are, however, anomalies in this approach. These are identified in this paper, in which we systematically analyze the details of the approach and conclude that the claim of having achieved a zero EER is based upon an impractical hidden assumption. We simulate the claimants’ experiments and find that it is not possible to achieve their reported performance without the hidden assumption and that, indeed, the results are worse than when using the biometric alone.     

A cooperative recurrent neural network for solving L(1) estimation problems with general linear constraints

The constrained L(1) estimation is an attractive alternative to both the unconstrained L(1) estimation and the least square estimation. In this letter, we propose a cooperative recurrent neural network (CRNN) for solving L(1) estimation problems with general linear constraints. The proposed CRNN model combines four individual neural network models automatically and is suitable for parallel implementation. As a special case, the proposed CRNN includes two existing neural networks for solving unconstrained and constrained L(1) estimation problems, respectively. Unlike existing neural networks, with penalty parameters, for solving the constrained L(1) estimation problem, the proposed CRNN is guaranteed to converge globally to the exact optimal solution without any additional condition. Compared with conventional numerical algorithms, the proposed CRNN has a low computational complexity and can deal with the L(1) estimation problem with degeneracy. Several applied examples show that the proposed CRNN can obtain more accurate estimates than several existing algorithms.     

Diversity-based combination of non-parametric classifiers for EMG signal decomposition

Non-parametric classification procedures based on a certainty measure and nearest neighbour rule for motor unit potential classification (MUP) during electromyographic (EMG) signal decomposition were explored. A diversity-based classifier fusion approach is developed and evaluated to achieve improved classification performance. The developed system allows the construction of a set of non-parametric base classifiers and then automatically chooses, from the pool of base classifiers, subsets of classifiers to form candidate classifier ensembles. The system selects the classifier ensemble members by exploiting a diversity measure for selecting classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between base classifier outputs, i.e., to measure the degree of decision similarity between base classifiers. The pool of base classifiers consists of two kinds of classifiers: adaptive certainty-based classifiers (ACCs) and adaptive fuzzy k-NN classifiers (AFNNCs) and both utilize different types of features. Once the patterns are assigned to their classes, by the classifier fusion system, firing pattern consistency statistics for each class are calculated to detect classification errors in an adaptive fashion. Performance of the developed system was evaluated using real and simulated EMG signals and was compared with the performance of the constituent base classifiers and the performance of the fixed ensemble containing the full set of base classifiers. Across the EMG signal data sets used, the diversity-based classifier fusion approach had better average classification performance overall, especially in terms of reducing classification errors.     

An efficient concept-based retrieval model for enhancing text retrieval quality

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval.     

An ASN.1 Model and Integration Approach for Structured Text

A language for manipulating ASN.1 print files is defined as an addition to the ASN.1 data definition language as specified in the ISO standards 8824 and 8825. The language has syntax similar in spirit to SQL, and provides analogous operations. Together, ASN.1 and the manipulation language define a model that can represent attribute hierarchies, multivalued attributes, optional attributes, both ordered and unordered attributes and tuples, and unnamed attributes—constructs that are absent in the traditional relational model. In addition, the proposed model allows the semantics of order, homogeneity, optionality, choosability, and uniqueness to be attached to constructs and construct elements. The approach is well suited for modeling data where form is important, such as text and flat file databases having ad hoc formats. It also supports modeling relations. The primary motivation for developing this language is to provide a common model for a database integration approach where some of the databases are in the form of formatted flat file text, while others are relational. Although the approach is general and can be applied in many different areas, scientific database and tool integration was the primary motive for its development.     

Evolving comprehensible neural network trees using genetic algorithms

This paper presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it can be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem. The article is published in the original. Elsayed Mostafa. Received the B.Sc. degree in electrical (Communication) Eng., Cairo University at 1967. Dipl.-Ing, and Dr-Ing. from Stuttgart University at 1977, 1981 respectively. He is a member of ECS and EEES. He is currently a professor of electronic circuits, Faculty of Engineering, University of Helwan. Amr Kamel. Graduated from Computer Department, Faculty of Engineering of Ain Shams University, Egypt in 1999, and studying M.Sc. degree in computer engineering from the Faculty of Engineering of Helwan University. His special fields of interest include neural networks and genetic algorithms. Alaa Hamdy. Was born in Giza in Egypt, on August 17, 1966. He graduated from the Telecommunications and Electronics Department, Faculty of Engineering and Technology of Helwan University, Cairo, Egypt in 1989. He received the M.Sc. degree in computer engineering from the same university in 1996 and the Ph.D. degree from the Faculty of Electrical Engineering, Poznan University of Technology, Poland in 2004. Currently he is working as a lecturer in the Faculty of Engineering of Helwan University. His special fields of interest, include image processing, pattern analysis, and machine vision.     

Nonlinear analysis of an elastic cable under harmonic excitation

The nonlinear behavior of an elastic cable subjected to harmonic excitation is studied and solved. The method of multiple scales perturbation is applied to analyze the response of the nonlinear system near the simultaneous principle primary and internal resonance. The stability of the proposed analytic nonlinear solution near the simultaneous primary-internal resonance is studied and the stability condition is investigated. The effect of different parameters on the steady state responses of the vibrating system is studied and discussed using frequency response equations. The numerical solutions and chaotic response of the nonlinear system of the elastic cable for different parameters are also studied.     

Design of a new efficient and secure multi-secret images sharing scheme

We propose a new (n,n) multi-secret images sharing scheme that provides high level of provable security with fast sharing and reconstruction procedures. It uses simple Boolean operations conjointly with a secure stream cipher and a cryptographic hash function in order to enable an efficient sharing of n secret images among a set of n different participants. This approach overcomes the security weakness detected in existing similar schemes, and provides additional advantages such as high sensitivity to alterations and ability to share heterogeneous images having diverse resolutions. Obtained experimental results show the effectiveness and robustness of the method compared to existing schemes, particularly its ability to ensure higher security level with competitive computational performances.     

Visualization of dynamic fiber-matrix interfacial shear debonding

To visualize the debonding event in real time for the study of dynamic crack initiation and propagation at the fiber–matrix interface, a modified tension Kolsky bar was integrated with a high-speed synchrotron X-ray phase-contrast imaging setup. In the gage section, the pull-out configuration was utilized to understand the behavior of interfacial debonding between SC-15 epoxy matrix and S-2 glass fiber, tungsten wire, steel wire, and carbon fiber composite Z-pin at pull-out velocities of 2.5 and 5.0 m s^?1. The load history and images of the debonding progression were simultaneously recorded. Both S-2 glass fiber and Z-pin experienced catastrophic interfacial debonding whereas tungsten and steel wire experienced both catastrophic debonding and stick–slip behavior. Even though S-2 glass fiber and Z-pin samples exhibited a slight increase and tungsten and steel wire samples exhibited a slight decrease in average peak force and average interfacial shear stress as the pull-out velocities were increased, no statistical difference was found for most properties when the velocity was increased. Furthermore, the debonding behavior for each fiber material is similar with increasing pull-out velocity. Thus, the debonding mechanism, peak force, and interfacial shear stress were rate insensitive as the pull-out velocity doubled from 2.5 to 5.0 m s^?1. Scanning electron microscope imaging of recovered epoxy beads revealed a snap-back behavior around the meniscus region of the bead for S-2 glass, tungsten, and steel fiber materials at 5.0 m s^?1 whereas those at 2.5 m s^?1 exhibited no snap-back behavior.