30118530
The EventScope Remote Science Operations Tool: Using a Visualization of a Remote Location as an Interface for Robotic Exploration
Using visualizations based on orbital data and imagery, the EventScope Remote Science Operations Tool allows scientists to explore a remote location and control a rover in that location. Scientists create a rover plan by sticking virtual pins in the visualization. This paradigm for exploration is being developed in conjunction with the Life in the Atacama Project at Carnegie Mellon University. CR
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.
25044094
Generalised Fuzzy Hidden Markov Models for Speech Recognition
A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were performed on the TI46 speech data corpus. A significant result for isolatedword recognition performed on a highly confusable vocabulary consisting of the nine English E-set words is that, a 33.8% recognition error rate for the HMM-based system was reduced to 30.5%, 29.9%, 29.8% and 27.8%, respectively, by using the FCM-HMM, the FE-HMM, the NC-FE-HMM, and the NC-FCM-HMM-based systems.
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
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.
14749213
Verteilte Suche nach digitalen Wasserzeichen in eMule
ZusammenfassungZusammenfassung Filesharing erfreut sich einer immer weiter wachsenden Beliebtheit. Allerdings wird diese Technologie zunehmend zur illegalen Verbreitung von urheberrechtlich geschütztem Material genutzt. Dementsprechend existiert eine Vielzahl von Ansätzen um gegen diese Verbreitung vorzugehen. Eine Alternative zum Einsatz von restriktivem Digital Rights Management (DRM) ist die Einbettung digitaler Wasserzeichen zur Markierung von urheberrechtlich geschütztem Material. Dadurch ist eine etwaige Weitergabe von Medien zu ihrer Quelle zurückverfolgbar. Um diesen Ursprung identifizieren zu können, müssen allerdings zuerst die markierten Medien mit der entsprechenden Filesharing-Software heruntergeladen werden. Um der großen Anzahl an Dateien gerecht zu werden, wird hierzu eine skalierbare, verteilte Lösung zum Download dieser Dateien auf der Basis einer von Schlüsselwörtern gesteuerten Suche vorgestellt. Resultat dieser Arbeit ist ein System, welches unter guter Ausnutzung der zur Verfügung stehenden Ressourcen eine hohe Skalierbarkeit und Fehlertoleranz bietet.
17740711
Hierarchical data access in structured P2P-networks
Hierarchically structured organizations have clear and relatively short communication paths. However, one does not take advantage of this fact in modern peer-to-peer overlays. We show how a command and control order can be imposed on a given peer-to-peer network. This allows fast hierarchical data storage and data access. Maintaining a hierarchical organization structure upon a given distributed hashtable overlay network scales with O(logN), where N is the number of peers in the network. This approach can be used to benefit from hierarchical structure in any communication system, especially in first response scenarios.
13891540
MagicMeeting: A Collaborative Tangible Augmented Reality System
We describe an augmented reality (AR) system that allows multiple participants to interact with 2D and 3D data using tangible user interfaces. The system features face-to-face communication, collaborative viewing and manipulation of 3D models, and seamless access to 2D desktop applications within the shared 3D space. All virtual content, including 3D models and 2D desktop windows, is attached to tracked physical objects in order to leverage the efficiencies of natural two-handed manipulation. The presence of 2D desktop space within 3D facilitates data exchange between the two realms, enables control of 3D information by 2D applications, and generally increases productivity by providing access to familiar tools. We present a general concept for a collaborative tangible AR system, including a comprehensive set of interaction techniques, a distributed hardware setup, and a component-based software architecture that can be flexibly configured using XML. We show the validity of our concept with an implementation of an application scenario from the automotive industry.
7449534
probeBase—an online resource for rRNA-targeted oligonucleotide probes: new features 2007
probeBase is a curated database of annotated rRNA-targeted oligonucleotide probes and supporting information. Rapid access to probe, microarray and reference data is achieved by powerful search tools and via different lists that are based on selected categories such as functional or taxonomic properties of the target organism(s) or the hybridization format (fluorescence in situ hybridization or microarray) in which the probes were applied. Additional information on probe coverage and specificity is available through direct submissions of probe sequences from probeBase to RDP-II and Greengenes, two major rRNA sequence databases. A freely editable user comments field for each probe entry allows any user to add, modify or remove information or to report errors in real-time. probeBase entries increased from 700 to more than 1200 during the past three years. Several options for submission of single probes or entire probe sets, even prior to publication of newly developed probes, should further contribute to keeping probeBase an up-to-date and useful resource. probeBase is freely accessible at . Email correspondence can be addressed to probebase@microbial-ecology.net.
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.