P. Dube N. Halim K. Karenos M. Kim Z. Liu S. Parthasarathy D. Pendarakis H. Yang In this paper, we present Harmony, a holistic messaging middleware for distributed, event-driven systems. Harmony supports various communication paradigms and heterogeneous networks. The key novelty of Harmony is the unified provision of endto-end quality of service, security, and resiliency, which shields the appl...

Under the Bayesian Ying–Yang (BYY) harmony learning theory, a harmony function has been developed on a BI-directional architecture of the BYY system for Gaussian mixture with an important feature that, via its maximization through a general gradient rule, a model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper further pro...

Many harmony systems impose conditions on the triggers and targets of harmony that involve prosodic features such as stress, length, or peripherality in the prosodic word domain. Such conditions serve to distinguish a `strong' anchor (SA) from a `weak' anchor (WA) for a given feature, and are responsible for asymmetries in the class of segments that may trigger or undergo harmony. Yet, language...

Background. An important outcome from the debate on whether wellness equals happiness, is the need of research focusing on how psychological well-being might influence humans' ability to adapt to the changing environment and live in harmony. To get a detailed picture of the influence of positive and negative affect, the current study employed the affective profiles model in which individuals ar...

This paper proposes a multimedia presentation system Harmony which can deal with temporal and active media such as motion video, computer animation, and music sounds. Harmony is based on the notion of a hyperobject which integrates a hypertext system with an object-oriented framework. Each object is considered a node as is usual in hypertext systems, and the relations between objects are repres...

In many applications, independence of event occurrences is assumed, even if there is evidence for dependence. Capturing dependence leads to complex models, and even if the complex models were superior, they fail to beat the simplicity and scalability of the independence assumption. Therefore, many models assume independence and apply heuristics to improve results. Theoretical explanations of th...