We propose a computational model for the Component Process Model (CPM) of Scherer, the most recent and the most complete model of emotion in psychology. This one proposes to appraise a stimulus through a sequence of sixteen appraisal variables dealing with a large number of its characteristics. As CPM is very abstract and high level, it is not really used in affective computing and no formal mo...

In this paper, we propose an adaptive linear approach for time series modeling and steam line leakage detection. Weighted recursive least squares (WRLS) method is used for modeling. Interpretive variables of an adaptive model should be linearly correlated to ensure a robust model. In this paper it is ensured by examining eigenvalues and eigenvectors of the principal component analysis (PCA). Th...

In some domains of application, like software development and health care processes, a normative business process system (e.g. workflow management system) is not appropriate because a flexible support is needed to the participants. On the other hand, while it is important to support flexibility of execution in these domains, security requirements can not be met whether these systems do not offe...

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may ot...

The distributed process network (DPN) model allows for greater scalability and performance over a non-distributed process network model. This paper presents a distributed deadlock detection algorithm applicable to process networks. The algorithm efficiently detects both local and global deadlocks. In addition, the deadlock detection algorithm has minimal bandwidth and memory requirements. Lastl...

Today’s organizations are characterized by long-running distributed business processes, which involve different stakeholders and share common resources. One of the main challenges posed in such a highly distributed setting comes from the interference between different processes that are running in parallel. During execution of a business process, a data modification caused by some external proc...

The traditional model-based fault detection and isolation (FDI) rely on tacit assumption that the validity of process model is out of question for current process data. However, it happens in practice that a process might end up in regions for which the underlying model is not validated. This can result in false alarms. To avoid the risk, a validity index is suggested as a measure of confidence...

Vehicle detection and classification of vehicles play an important role in decision making for the purpose of traffic control and management. We propose a novel and efficient algorithm based on image processing using vertically positioned camera for vehicle detection and classification according to their size. The algorithm is based on different techniques including image differencing, thrshold...

An intrusion detection ensemble, i.e. a set of diverse intrusion detection algorithms employed as a group, has been shown to outperform each one those diverse algorithms employed individually. Moving along this line, we have devised an intrusion detection ensemble that inspects network packets that flow across the process control network of a digitally controlled physical system such as a power...

Stealth rootkits that hide themselves on victim systems pose a major threat to computer systems. They are usually evasive as they use sophisticated stealth techniques to conceal files, processes, kernel modules, and other types of objects, making it extremely challenging to detect their presence in the victim system. However, current detection techniques are mostly system-specific and ineffecti...