A Rule Set to Detect Interference of Runtime Enforcement Mechanisms

Runtime enforcement aims at verifying the active execution trace of executing software against formally specified properties of the software, and enforcing the properties in case that they are violated in the active execution trace. Enforcement mechanism of individual properties may interfere with each other, causing the overall behavior of the executing software to be erroneous. As the number and the complexity of the properties to be enforced increase, manual detection of the inferences becomes an error-prone and effort-consuming task. Hence, we aim at providing a framework for automatic detection of interferences. As the initial steps to create such a framework, in this paper we first provide formal definitions of an enforcement mechanism and enforcement operators. Second, we define a rule set to detect the interference among properties

Anti-social behavior detection in audio-visual surveillance systems

In this paper we propose a general purpose framework for detection of unusual events. The proposed system is based on the unsupervised method for unusual scene detection in web{cam images that was introduced in [1]. We extend their algorithm to accommodate data from different modalities and introduce the concept of time-space blocks. In addition, we evaluate early and late fusion techniques for our audio-visual data features. The experimental results on 192 hours of data show that data fusion of audio and video outperforms using a single modality