In this paper we present FastAR, a software component capable of transforming Joomla based websites into AR-channels compatible with the most popular augmented reality browsers (i.e. Junaio, Layar, Wikitude). FastAR exploits the consistency of the data structure across multiple sites that have been developed using the same content management system, so as to automate the transformation process of an internet website to an augmented reality channel. The proposed component abstracts all related programming tasks and significantly reduces the time required to generate and publish AR-content, making the entire process manageable by non-experts. In verifying the usefulness and effectiveness of FastAR, we conducted a survey to solicit the opinion of users who carried out the installation and transformation process.

ImproveMyCity is an open source platform that enables residents to directly report to their public administration local issues about their neighborhood such as discarded trash bins, faulty street lights, broken tiles on sidewalks, illegal advertising boards, etc. The reported issues are automatically transmitted to the appropriate office in public administration so as to schedule their settlement. Reporting is feasible both through a web- and a smartphone-based front-end that adopt a map-based visualization, which makes reporting a user-friendly and intriguing process. The management and routing of incoming issues is performed through a back-end infrastructure that serves as an integrated management system with easy to use interfaces. Apart from reporting a new issue, both front-ends allow the citizens to add comments or vote on existing issues, which adds a social dimension on the collected content. Finally, the platform makes also provision for informing the citizens about the progress status of the reported issue and in this way facilitate the establishment of a two-way dialogue between the citizen and public administration.

When an infinite training set is used, the Mahalanobis distance between a pattern measurement vector of dimensionality D and the center of the class it belongs to is distributed as a chi(2) with D degrees of freedom. However, the distribution of Mahalanobis distance becomes either Fisher or Beta depending on whether cross validation or resubstitution is used for parameter estimation in finite training sets. The total variation between chi(2) and Fisher, as well as between chi(2) and Beta, allows us to measure the information loss in high dimensions. The information loss is exploited then to set a lower limit for the correct classification rate achieved by the Bayes classifier that is used in subset feature selection.

Abstract—An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. Index Terms—Audio activity detection, cross-correlation, crosspower spectral density, dialogue detection, indicator functions, speaker clustering. I.

In this paper, adaptive genetic algorithms are employed to search for the worst performing features with respect to the probability of correct classification achieved by the Bayes classifier in a first stage. These features are subsequently excluded from sequential floating feature selection that employs the probability of correct classification of the Bayes classifier as criterion. In a second stage, adaptive genetic algorithms search for the worst performing utterances with respect to the same criterion. The sequential application of both stages is demonstrated to improve speech emotion recognition on the Danish Emotional Speech database.

Emotional speech classification can be treated as a supervised learning task where the statistical properties of emotional speech segments are the features and the emotional styles form the labels. The Akaike criterion is used for estimating automatically the number of Gaussian densities that model the probability density function of the emotional speech features. A procedure for reducing the computational burden of crossvalidation in sequential floating forward selection algorithm is proposed that applies the t-test on the probability of correct classification for the Bayes classifier designed for various feature sets. For the Bayes classifier, the sequential floating forward selection algorithm is found to yield a higher probability of correct classification by 3% than that of the sequential forward selection algorithm either taking into account the gender information or ignoring it. The experimental results indicate that the utterances from isolated words and sentences are more colored emotional than those from paragraphs. Without taking into account the gender information, the probability of correct classification for the Bayes classifier admits a maximum when the probability density function of emotional speech features extracted from the aforementioned utterances is modeled as a mixture of 2 Gaussian densities

Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The sequential forward selection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the cross-validated correct classification score of one of the following classifiers: nearest mean and Bayes classifier where class pdf are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% /spl plusmn/ 3% correct classification rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those two-class emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them.