Complexity Metrics for Systems Development Methods and Techniques

So many systems development methods have been introduced in the last decade that one can talk about a ¿methodology jungle¿. To aid the method developers and evaluators in fighting their way through this jungle, we propose a systematic approach for measuring properties of methods. We describe two sets of metrics which measure the complexity of single diagram techniques, and of complete systems development methods. The proposed metrics provide a relatively fast and simple way to analyse the descriptive capabilities of a technique or method. When accompanied with other selection criteria, the metrics can be used for estimating the relative complexity of a technique compared to others. To demonstrate the applicability of the metrics, we have applied them to 36 techniques and 11 methods

Dynamic Compositional Neural Networks over Tree Structure

Tree-structured neural networks have proven to be effective in learning semantic representations by exploiting syntactic information. In spite of their success, most existing models suffer from the underfitting problem: they recursively use the same shared compositional function throughout the whole compositional process and lack expressive power due to inability to capture the richness of compositionality. In this paper, we address this issue by introducing the dynamic compositional neural networks over tree structure (DC-TreeNN), in which the compositional function is dynamically generated by a meta network. The role of meta-network is to capture the metaknowledge across the different compositional rules and formulate them. Experimental results on two typical tasks show the effectiveness of the proposed models.Comment: Accepted by IJCAI 201

Modal logics are coalgebraic

Applications of modal logics are abundant in computer science, and a large number of structurally different modal logics have been successfully employed in a diverse spectrum of application contexts. Coalgebraic semantics, on the other hand, provides a uniform and encompassing view on the large variety of specific logics used in particular domains. The coalgebraic approach is generic and compositional: tools and techniques simultaneously apply to a large class of application areas and can moreover be combined in a modular way. In particular, this facilitates a pick-and-choose approach to domain specific formalisms, applicable across the entire scope of application areas, leading to generic software tools that are easier to design, to implement, and to maintain. This paper substantiates the authors' firm belief that the systematic exploitation of the coalgebraic nature of modal logic will not only have impact on the field of modal logic itself but also lead to significant progress in a number of areas within computer science, such as knowledge representation and concurrency/mobility