Fuzzy cognitive maps (FCMs), as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the dynamic behavior of the investigated systems. However, due to defects in expression and architecture, the traditional FCMs and most of their relevant extensions are not applicable to classification problems. To solve this problem, this paper presents an approach that directly extends the model by translating the reasoning mechanism of traditional FCMs to a set of fuzzy if– then rules. Moreover, the proposed approach fully considers the contribution of the inputs to the activation of the fuzzy rules and quantifies the causalities using mutual subsethood, which works in conjunction with volume defuzzification in a gradient descent-learning framework. In this manner, our approach enhances the capability of the conventional FCMs to automatically identify membership functions and quantify causalities. Despite the increase in the number of tunable parameters, experimental results show that the proposed approach efficiently extends the application of the traditional FCMs into classification problems, while keeping the ability for prediction and approximation.

Multi-core programming presents developers with a dramatic paradigm shift. Whereas sequential programming largely allowed the decoupling of source from underlying architecture, it is now impossible to develop new patterns and abstractions in isolation from issues of modern hardware utilization. Synchronization and coordination are now manifested at all levels of the software stack, and developers currently lack the essential tools to even partially automate reasoning techniques and system configuration management. As a first stage to addressing this problem, this paper proposes a framework for a tool suite designed to partially automate the acquisition and management of static system visualization in a feedback loop with dynamic execution properties. This model enables developers to find a best fit system configuration, potentially reconciling resource contention and utilization tensions that are critical to multi-core platforms. The application of a prototype of this suite, Deja View, demonstrates how tool support can aid reasoning about causally related sets of changes across system artifacts.

No abstract available.