206564985
Optimizing digraph-latency based biometric typist verification systems: inter and intra typist differences in digraph latency distributions
Umphress and Williams have shown that individual differences in digraph latency may provide a means of accurately verifying the identity of computer users. The present research refined this technique by exploring inter and intra subject differences in digraph latency distributions. Experiment 1 showed that there is marked heterogeneity in the latency with which individual subjects type different digraphs. Consequently, it was found that typist verification accuracy improved when a digraph-specific index of the distance between test and reference digraph latencies was employed. Experiment 1 also showed the utility of nonlinear modelling as a tool to establish optimum verification parameter settings. Experiment 2 showed that the use of a common low-pass temporal filter cutoff setting for all typists when screening digraphs is unwise. It was found that there is a significant interaction between subjects and filter settings such that verification accuracy may improve if subject-specific filter settings are used.
5705405
Robust face-voice based speaker identity verification using multilevel fusion
In this paper, we propose a robust multilevel fusion strategy involving cascaded multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motion-depth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3%, respectively, for biometric person authentication task.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
10947752
Noise Clustering-Based Speaker Verification
The normalisation method for speaker verification proposed in this paper is based on the idea of the noise clustering method in fuzzy clustering. The proposed method can reduce false acceptance errors and apply to all current normalisation scores. Experiments performed on the ANDOSL and YOHO speech corpora show better results for the proposed method.
35465720
Reliability score based multimodal fusion for biometric person authentication
In this paper, we propose a robust multilevel fusion strategy involving cascaded fusion of hybrid multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motiondepth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric person authentication task.
987011
Similarity normalization for speaker verification by fuzzy fusion
Abstract Similarity or likelihood normalization techniques are important for speaker verification systems as they help to alleviate the variations in the speech signals. In the conventional normalization, the a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the theory of fuzzy measure and fuzzy integral to combine the likelihood values of the cohort speakers in which the assumption of equal a priori probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. We illustrate the performance of the proposed approach by testing the speaker verification system with both the conventional and the fuzzy algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker verification system using the fuzzy integral is more flexible and more favorable than the conventional normalization method.
751960
Performance Evaluation Methodologies for Abstract Data Type Implementation Techniques
Abstract The runtime management system for a programming language must allow the efficient manipulation of complex objects, such as instances of abstract data types (ADTs). We describe an implementation model for ADTs based on coroutine objects (i.e. as an alternative to ad hoc modifications of the runtime stack). We evaluate the performances of variants of our implementation model using two different approaches: a benchmarking technique which monitors actual costs for test programs and a “pseudo-execution” technique which estimates costs based on idealized instruction timings. The results of our comparison indicate that the two forms of evaluation give very similar cost estimates, supporting the use of the (cheaper) pseudo-execution technique. The overall performance results identify applications in which our coroutine implementation model has definite strengths and weaknesses. Finally, our implementation approach is compared with modified stack implementations used in other ADT systems.
27343383
Anatomische Abbildung elektrischer Aktivität des ZNS
7586216
Linear Regression Models for Solvent Accessibility Prediction in Proteins
The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.
47337297
probeBase: an online resource for rRNA-targeted oligonucleotide probes
Ribosomal RNA-(rRNA)-targeted oligonucleotide probes are widely used for culture-independent identification of microorganisms in environmental and clinical samples. ProbeBase is a comprehensive database containing more than 700 published rRNA-targeted oligonucleotide probe sequences (status August 2002) with supporting bibliographic and biological annotation that can be accessed through the internet at http://www.probebase.net. Each oligonucleotide probe entry contains information on target organisms, target molecule (small- or large-subunit rRNA) and position, G+C content, predicted melting temperature, molecular weight, necessity of competitor probes, and the reference that originally described the oligonucleotide probe, including a link to the respective abstract at PubMed. In addition, probes successfully used for fluorescence in situ hybridization (FISH) are highlighted and the recommended hybridization conditions are listed. ProbeBase also offers difference alignments for 16S rRNA-targeted probes by using the probe match tool of the ARB software and the latest small-subunit rRNA ARB database (release June 2002). The option to directly submit probe sequences to the probe match tool of the Ribosomal Database Project II (RDP-II) further allows one to extract supplementary information on probe specificities. The two main features of probeBase, 'search probeBase' and 'find probe set', help researchers to find suitable, published oligonucleotide probes for microorganisms of interest or for rRNA gene sequences submitted by the user. Furthermore, the 'search target site' option provides guidance for the development of new FISH probes.
14972044
Service-Oriented Adaptation in Ubiquitous Computing Environments
Ubiquitous computing environments may offer dynamically discoverable services that can be utilized by adaptive applications to improve their functionality and quality of service. We have developed a general software development framework for context-aware adaptive applications. In this paper we present our novel modeling concepts that extend an existing component-based modeling approach by service-oriented adaptation concepts. With our new extensions a dynamically discovered service may replace an application component if it increases the overall utility of the application. We discuss open questions and point to potential solution approaches.