Singing synthesis with an evolved physical model

A two-dimensional physical model of the human vocal tract is described. Such a system promises increased realism and control in the synthesis. of both speech and singing. However, the parameters describing the shape of the vocal tract while in use are not easily obtained, even using medical imaging techniques, so instead a genetic algorithm (GA) is applied to the model to find an appropriate configuration. Realistic sounds are produced by this method. Analysis of these, and the reliability of the technique (convergence properties) is provided

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art

Integrating user-centred design in the development of a silent speech interface based on permanent magnetic articulography

Abstract: A new wearable silent speech interface (SSI) based on Permanent Magnetic Articulography (PMA) was developed with the involvement of end users in the design process. Hence, desirable features such as appearance, port-ability, ease of use and light weight were integrated into the prototype. The aim of this paper is to address the challenges faced and the design considerations addressed during the development. Evaluation on both hardware and speech recognition performances are presented here. The new prototype shows a com-parable performance with its predecessor in terms of speech recognition accuracy (i.e. ~95% of word accuracy and ~75% of sequence accuracy), but significantly improved appearance, portability and hardware features in terms of min-iaturization and cost

MODELING THE EFFECT OF BELIEF REVISIONS ON THE SUCCESS OF CO-BRANDING

This paper provides a normative guideline regarding the successful formation of co-branding alliances for both academic researchers and practitioners. We use the expectancy-value model to quantify the mechanism of belief revision in co-branding. Starting from this, an existing mathematical model is adapted in order to investigate (1) the influence of belief revisions on the necessary condition of a successful co-branding alliance (i.e., a sufficient amount of required expansion for the partnering brands) and (2) the existence of an ideal situation that ensures the success. The resulting propositions show that belief revisions can affect a brand�s intention with respect to a co-branding partnership. A simulation study demonstrates that an ideal situation exists when the partnering brands are similar in the magnitude of customers� belief revision, brand reputation, and customer loyalty. The present paper advances existing knowledge by relating the success of co-branding partnerships to consumer evaluations. Managerial implications and future research directions are also discussed.Belief revision, success of co-branding, consumer evaluations, mathematical modelling in marketing

Secure and robust multi-constrained QoS aware routing algorithm for VANETs

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service

Learning sound representations using trainable COPE feature extractors

Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio