Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment.
Automatic service composition in mobile and pervasive computing faces many challenges due to the complex nature of the environment.
Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation.
As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time.
Cognitive computing systems require human labeled data for evaluation, and often for training.
Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd.