A Shopping Negotiation Agent That Adapts to User Preferences
This paper describes a shopping negotiation agent that can adapt user preferences and automatically negotiate with its counter party on behalf of a user it represents. The agent is built on a basis of the proposed negotiation model, the enhanced extended Bazaar model, which is a sequence of decision making model of negotiation with exploiting common knowledge, public information, and game theory. Since different users can have different preferences, it is important for the agent to have adaptation to different user preferences. This can be achieved by acquiring user preferences, tracing user's behavior on Web and mapping the behavior to a set of the preference parameters, creating the negotiation model class, and generating an instance negotiation model object with new/updated preference parameters.

title: Towards a Natural Internet-Based Collaborative Environment with Support of Object Physical and Social Characteristics
abstract: Objects in this article refer to sharable applications, such as a whiteboard and a video player, used by multi-users who are in different sites and have computers connected to networks. The objects are important elements in our Internet-based desktop collaborative system, called virtual collaboration room (VCR). We argue that a natural collaborative environment should be developed in a framework of using both a room metaphor and an object metaphor, i.e., emulating the fundamental characteristics of real rooms and real objects, respectively. This article gives the first systematic specifications of object physical and social characteristics, and discusses how to exploit and implement the object characteristics in VCR. A preliminary prototype of platform independent real-time audio/video communications among multiple users is also described. It can be used together with VCR.

title: Peer Collaboration Modes and Private Room Services in TOMSCOP
abstract: This paper presents the implementation of peer group management services in a pure P2P groupware framework called TOMSCOP (technology of multiuser synchronous collaboration platform). To support a variety of computer-based collaborative activities and styles, a user in a group can be assigned a role or identity such as a discusser peer, a chair peer, a presenter peer, a player peer, or an observer peer-Based on the above identities and their control service, we developed three collaboration modes, i.e., discussion mode, presentation mode and rotation mode, for smoothly manipulating shared applications in their different usages. Moreover we designed and implemented the public and private room services for users to create suitable collaboration rooms that match theirs privacy requirements. These modes and services not only provide more functions to TOMSCOP users, but also support software developers for easily and flexibly making P2P shared applications with different purposes

title: TOMSCOP - a synchronous P2P collaboration platform over JXTA
abstract: We focus on design and implementation of a synchronous P2P (peer-to-peer) collaboration platform called TOMSCOP (Technology Of Multiuser Synchronous Collaboration Platform). Based on the elementary peer group services offered by the JXTA general framework, TOMSCOP provides the extra four types of services: synchronous message transportation, peer room administration, peer communication support and application space management. By using the four services, different kinds of shared applications for various specific purposes can be relatively easily developed and associated collaborative cyber spaces or communities can be quickly built across the JXTA virtual network overlaid on top of the existing physical networks.

title: Entropy Penalized Automated Model Selection On Gaussian Mixture
abstract: Gaussian mixture modeling is a powerful approach for data analysis and the determination of the number of Gaussians, or clusters, is actually the problem of Gaussian mixture model selection which has been investigated from several respects. This paper proposes a new kind of automated model selection algorithm for Gaussian mixture modeling via an entropy penalized maximum-likelihood estimation. It is demonstrated by the experiments that the proposed algorithm can make model selection automatically during the parameter estimation, with the mixing proportions of the extra Gaussians attenuating to zero. As compared with the BYY automated model selection algorithms, it converges more stably and accurately as the number of samples becomes large.

title: Interval-valued reduced order statistical interconnect modeling
abstract: We show how recent advances in the handling of correlated interval representations of range uncertainty can be used to predict the impact of statistical manufacturing variations on linear interconnect. We represent correlated statistical variations in RLC parameters as sets of correlated intervals, and show how classical model order reduction methods - AWE and PRIMA - can be re-targeted to compute interval-valued, rather than scalar-valued reductions. By applying a statistical interpretation and sampling to the resulting compact interval-valued model, we can efficiently estimate the impact of variations on the original circuit. Results show the technique can predict mean delay with errors between 5-10%, for correlated RLC parameter variations up to 35%.

title: Adaptive Video Streaming over P2P Multi-Hop Path