This paper presents an algorithmic approach to acquiring the influencing relationships among users by discovering implicit influencing group structure from smartphone usage. The method assumes that a time series of users' application downloads and activations can be represented by individual inter-personal influence factors. To achieve better predictive performance and also to avoid over-fitting, a latent feature model is employed. The method tries to extract the latent structures by monitoring cross validating predictive performances on approximated influence matrices with reduced ranks, which are generated based on an initial influence matrix obtained from a training set. The method adopts Nonnegative Matrix Factorization (NMF) to reduce the influence matrix dimension and thus to extract the latent features. To validate and demonstrate its ability, about 160 university students voluntarily participated in a mobile application usage monitoring experiment. An empirical study on real collected data reveals that the influencing structure consisted of six influencing groups with two types of mutual influence, i.e. intra-group influence and inter-group influence. The results also highlight the importance of sparseness control on NMF for discovering latent influencing groups. The obtained influencing structure provides better predictive performance than state-of-the-art collaborative filtering methods as well as conventional methods such as user-based collaborative filtering techniques and simple popularity.

We develop a new class of stochastic Petri net: non-regenerative stochastic Petri net (NRSPN), which allows the firing time of its transitions with arbitrary distributions, and can automatically generate a bounded reachability graph that is equivalent to a generalization of the Markov renewal process in which some of the states may not constitute regeneration points. Thus, it can model and analyze behavior of a system whose states include some non-regeneration points. We show how to model a system by the NRSPN, and how to obtain numerical solutions for the NRSPN model. The probabilistic behavior of the modeled system can be clarified with the reliability measures such as the steady-state probability, the expected numbers of visits to each state per unit time, availability, unavailability and mean time between system failure. Finally, to demonstrate the modeling ability and analysis power of the NRSPN model, we present an example for a fault-tolerant system using the NRSPN and give numerical results for specific distributions.

Behavior modeling of objects is critical in object-oriented design. In particular, it is essential to preserve integrity constraints on object behavior in application environments where objects of various classes dynamically interact with each other. In order to provide a stable design technique, a behavior model using the notion of the life cycle schema of a class is proposed. To model the aspect of behavioral abstraction of objects, the notion of schema refinement together with a diagrammatic representation technique is also defined. In this framework, a formalization of behavior constraints on objects which interact with each other is proposed together with its graphical representation. Verification rules of consistency of behavior constraints are also discussed. In order to perform certain functions, several partner objects of the same or different classes should collaborate establishing client-server relationships. The contract of a class is defined as a collection of responsibilities of a server class to a client class where each responsibility is specified in the form of the script. To achieve a high degree of systems integrity, a procedure to derive scripts from behavior constraints on collaborating partners is developed. It is also critical to evenly distribute responsibilities to partner objects. A delegation is placing a whole or a part of responsibilities of an object in charge of other objects. Based on the design principle delegation along the aggregation hierarchy,' a unified design approach to delegation that enables to reorganize scripts in constraints preserving way is proposed.