We propose a novel method based on Trust Attitudes COmparison (TACO method), which derives adjusted reputations compliant with the behavioral patterns of the evaluators and eliminates the subjectivity from the trust ratings. With the TACO method, all participants have comparable opportunities to choose trustworthy transaction partners, regardless of their trust dispositions.
COBISS.SI-ID: 1536297667
We propose an Advanced Model for Efficient Workload Prediction in the Cloud (AME-WPC), which combines statistical and learning methods, improves accuracy of workload prediction for cloud computing applications and can be dynamically adapted to a particular system. We address the workload prediction problem with classification as well as regression. Experimental results demonstrate that combining statistical and learning methods makes sense and can significantly improve prediction accuracy of workload over time.
COBISS.SI-ID: 1536396227
Introducing CEP concepts to the Service Oriented Architecture (SOA) provides an opportunity to enhance the capabilities of SOA. A model that supports the CEP usage in SOA where the actual pattern recognition can be done by any external CEP Engine is proposed. We also define a new service type-a Complex Event Aware (CEA) service that automatically reacts to complex events specified in its interface.
COBISS.SI-ID: 9754196
We propose a novel efficient and reliable gait authentication approach. It is based on the analysis of accelerometer signals using higher order statistics. Gait patterns are obtained by transformation of acceleration data in feature space represented with higher order cumulants. The proposed approach is able to operate on multichannel and multisensor data by combining feature-level and sensor-level fusion.
COBISS.SI-ID: 1536253635
Complex real-world networks commonly reveal characteristic groups of nodes like communities and modules. These are of value in various applications, especially in the case of large social and information networks. However, while numerous community detection techniques have been presented in the literature, approaches for other groups of nodes are relatively rare and often limited in some way. We present a simple propagation-based algorithm for general group detection that requires no apriori knowledge and has near ideal complexity.
COBISS.SI-ID: 10333012