Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
In software for embedded systems, the frequent use of interrupts for timing, sensing, and I/O processing can cause concurrency faults to occur due to interactions between applicat...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
—We have recently proposed a novel receiver for Ultra-Wide-band Impulse-Radio communication in bursty applications like Wireless Sensor Networks. The receiver, based on the princ...
Coevolution has often been based on averaged outcomes, resulting in unstable evaluation. Several theoretical approaches have used archives to provide stable evaluation. However, t...