An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Texture descriptor combining fractal dimension and artificial crawlers

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the details richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that each agent can interact with the environment and each other. Since this swarm system alone does not achieve a good discrimination, we developed a new method to increase the discriminatory power of artificial crawlers, together with the fractal dimension theory. Here, we estimated the fractal dimension by the Bouligand-Minkowski method due to its precision in quantifying structural properties of images. We validate our method on two texture datasets and the experimental results reveal that our method leads to highly discriminative textural features. The results indicate that our method can be used in different texture applications.Comment: 12 pages 9 figures. Paper in press: Physica A: Statistical Mechanics and its Application

Wearable performance

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2009 Taylor & FrancisWearable computing devices worn on the body provide the potential for digital interaction in the world. A new stage of computing technology at the beginning of the 21st Century links the personal and the pervasive through mobile wearables. The convergence between the miniaturisation of microchips (nanotechnology), intelligent textile or interfacial materials production, advances in biotechnology and the growth of wireless, ubiquitous computing emphasises not only mobility but integration into clothing or the human body. In artistic contexts one expects such integrated wearable devices to have the two-way function of interface instruments (e.g. sensor data acquisition and exchange) worn for particular purposes, either for communication with the environment or various aesthetic and compositional expressions. 'Wearable performance' briefly surveys the context for wearables in the performance arts and distinguishes display and performative/interfacial garments. It then focuses on the authors' experiments with 'design in motion' and digital performance, examining prototyping at the DAP-Lab which involves transdisciplinary convergences between fashion and dance, interactive system architecture, electronic textiles, wearable technologies and digital animation. The concept of an 'evolving' garment design that is materialised (mobilised) in live performance between partners originates from DAP Lab's work with telepresence and distributed media addressing the 'connective tissues' and 'wearabilities' of projected bodies through a study of shared embodiment and perception/proprioception in the wearer (tactile sensory processing). Such notions of wearability are applied both to the immediate sensory processing on the performer's body and to the processing of the responsive, animate environment. Wearable computing devices worn on the body provide the potential for digital interaction in the world. A new stage of computing technology at the beginning of the 21st Century links the personal and the pervasive through mobile wearables. The convergence between the miniaturisation of microchips (nanotechnology), intelligent textile or interfacial materials production, advances in biotechnology and the growth of wireless, ubiquitous computing emphasises not only mobility but integration into clothing or the human body. In artistic contexts one expects such integrated wearable devices to have the two-way function of interface instruments (e.g. sensor data acquisition and exchange) worn for particular purposes, either for communication with the environment or various aesthetic and compositional expressions. 'Wearable performance' briefly surveys the context for wearables in the performance arts and distinguishes display and performative/interfacial garments. It then focuses on the authors' experiments with 'design in motion' and digital performance, examining prototyping at the DAP-Lab which involves transdisciplinary convergences between fashion and dance, interactive system architecture, electronic textiles, wearable technologies and digital animation. The concept of an 'evolving' garment design that is materialised (mobilised) in live performance between partners originates from DAP Lab's work with telepresence and distributed media addressing the 'connective tissues' and 'wearabilities' of projected bodies through a study of shared embodiment and perception/proprioception in the wearer (tactile sensory processing). Such notions of wearability are applied both to the immediate sensory processing on the performer's body and to the processing of the responsive, animate environment

Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular data-set, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity