Measuring modularity is important to understand the structure of networks, and has an important number of real-world implications. However, several measures exists to assess the modularity, and give both different modularity values and different modules composition. In this article, I propose an a posteriori measure of modularity, which represents the ratio of interactions between members of th...

The mechanisms by which modularity emerges in complex networks are not well understood but recent reports have suggested that modularity may arise from evolutionary selection. We show that finding the modularity of a network is analogous to finding the ground-state energy of a spin system. Moreover, we demonstrate that, due to fluctuations, stochastic network models give rise to modular network...

Modularity is an important topological attribute for functional brain networks. Recent human fMRI studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co-occurring with fluctuations in network properties of functional connectivity, estimated over short time intervals. ...

Compositionality provides the foundation of software modularity, reusability and separate verification of software components. One of the known difficulties, when separately verifying components, is producing compositional proofs for progress properties of distributed systems. This paper presents a new composition theory based on reasoning about temporary interference. The approach is axiomatic...

In this paper we propose an engineering approach for fast prototyping of a HMM-based speech recognizer. The presented system has been specifically used for words and sentences recognition of a regional variety of Italian. Experimental results have been obtained using the AVIP corpus of Italian spontaneous speech. The prototyping system is based on the "Khoros" software framework, which offers t...

Some limitations have been identified in current software process modelling languages (SPML) concerning expressiveness, standarization, modularity and flexibility. We present a SPML called PROMENADE which aims at improving the above-mentioned features. Expressiveness is enhanced in PROMENADE by the use of a basic catalogue of precedence relationships (instead of transitions), the ability to def...

Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper proposes an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community...

This contribution to the ongoing discussion of division algorithms for residue number systems (RNS) is based on Newton iteration for computing the reciprocal. An extended RNS with twice the number of moduli provides the range required for multiplication and scaling. Separation of the algorithm description from its RNS implementation achieves a high level of modularity, and makes the complexity ...

Agent-based control structures provide flexible and emergent solutions to complex nonlinear problems benefiting from properties such as modularity, adaptability, scalability and robustness. One such problem is product grade transitions in distributed process. The framework proposed earlier (Tetiker, 2006a) is extended by adding several layers of agents to control species percentage distribution...

Most join algorithms can be extended to reduce wasted work when several tuples contain the same value of the join attribute. We show that separating detection of duplicates from th~ir exploitation improves modularity and makes it easier to implement whole families ofhierarchy-e~ploitingjoin algorithms that avoid duplication. The technique is also used to provide an execution technique for star-...