Although, most of phenomena change over time, there has been an attempt to model the phenomena of real world assuming a static nature for them. Even when changes occurred in the phenomena, it is restricted to identify and assess the changes over a period of time. These static modeling can be applied when phenomena with a long change period are considered. Since late 1980s, and by increasing geo...

Restricted Boltzmann machines (RBMs) are a powerful generative modeling technique, based on a complex graphical model of hidden (latent) variables. Conditional RBMs (CRBMs) are an extension of RBMs tailored to modeling temporal data. A drawback of CRBMs is their consideration of linear temporal dependencies, which limits their capability to capture complex temporal structure. They also require ...

Multiple-valued logics [2] provide an interesting alternative to classical boolean logic for modeling and reasoning about systems. By allowing additional truth values, they support the explicit modeling of uncertainty and disagreement. In order to do temporal reasoning over multiple-valued systems, we must extend a classical temporal logic to the multiple-valued case. For instance, the branchin...

in this article the methodology proposed by li and wang for mixed qualitative and quantitative modeling and simulation of temporal behavior of processing unit is reexamined and extended to more complex case. the main issue of their approach considers the multivariate statistics of principal component analysis (pca), along with clustered fuzzy digraphs and reasoning. the pca and fuzzy clustering...

The need for modeling causality, beyond mere statistical correlations, for meaningful application of data mining to real world problems has been recognized widely. The framework of Bayesian networks, along with the related causal networks, is well suited for the modeling of causal structure, and its applicability to various application domains has been well investigated. Many of these applicati...

Cellular automata (CA) modelling is one of the recent advances in spatial–temporal modeling techniques in the field of growth dynamics. Spatio-temporal modeling of growth patterns has gained more importance in the recent years especially in the field of crystal growth, urban growth, biological growth etc. It has become an interest for researchers to study the model on spatial and temporal dynam...

This paper describes our solution for the video recognition task of the Google Cloud & YouTube-8M Video Understanding Challenge that ranked the 3rd place. Because the challenge provides pre-extracted visual and audio features instead of the raw videos, we mainly investigate various temporal modeling approaches to aggregate the frame-level features for multi-label video recognition. Our system c...

It is always attractive and challenging to explore the intricate behavior data and uncover people’s motivations, preference and habits, which can greatly benefit many tasks including link prediction, item recommendation, etc. Traditional work usually studies people’s behaviors without time information in a static or discrete manner, assuming the underlying factors stay invariant in a long perio...