Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

A Life-Cycle Analysis of the Thermal Energy Transfer in Prototypical Air Force Office Building Construction Using Best Value Insulation Standards

The United States Department of Defense (DoD) possesses over 560,000 buildings and structures around the world which require electricity to maintain and operate. The energy costs associated with the operations of these building is approximately $4 billion per year. Sustainable infrastructure management is a crucial opportunity to improve and establish a prudent, manageable, and successful DoD budget. This research identified, modeled, and simulated thermal energy-efficient standards in building construction in order to recognize the best value standards as opportunities for potential cost savings. EnergyPlus and OpenStudio Building Performance Simulation (BPS) software was used to model the energy flow into and out of buildings to determine the annual energy costs for two prototypical DoD office buildings developed by the Pacific Northwest National Laboratory. The simulation inputs of building size, location, and insulation materials were varied to determine their effects on the energy cost. The results showed that exceeding construction code with R-15 wall insulation was consistently the most cost effective. Exceeding the construction code with R-60 roof insulation was more cost effective in the large facility located in the cold and mild climates. Lower than construction standard roof insulation was more cost effective in hot climates and in mild climates for the small facility. The research results indicate that designers, engineers, and policy makers in the Air Force should consider facility life-cycle costs to lower annual facility sustainment costs. Accepting the construction code without performing an energy flow analysis of the facility during the design phase forfeits the opportunity to improve the life-cycle energy cost

IMPORT TENDERS AND BIDDING STRATEGIES IN WHEAT

Bidding competition plays an important role in price discovery and the determination of suppliers in international grains. In this paper we analyze international bidding competition for wheat for a specific importer. Tender data over the period 1993-1999 were analyzed and bid functions estimated by class of wheat (hard red spring, hard amber durum, and hard red winter denoted as HRS, HAD, and HRW, respectively) and by selling firm. A stochastic simulation model was developed to determine the optimal bid and to analyze factors affecting bidding behavior and competition. The tender data indicated there was a surprisingly wide range of bids. Variation of bids across firms submitted for individual HRS tenders had standard deviations that ranged from $5/mt or less in a number of tenders to as high as$22/mt. Tenders for HAD show similar variability. Tenders for HRW showed higher variability yet with standard deviations of bids between $30 and$40/mt. These results show much greater variability than is normally ascribed to competition among international grain sellers. The spread between participants' bids and cost indicators ranged widely across firms. Optimal bids and expected payoffs were derived for a prototypical bidder competing against the existing incumbents. Using this as a base case, we analyzed the impacts of the number of competitors, information, and cost differentials. In each case, we quantified the likely impact on optimal bids and expected payoffs. In addition, there were three particularly interesting extensions from conventional auction models that were examined. One was the impact of the option to the seller of supplying wheat from Canadian origins. Effects of Canadian offers in bid functions were not statistically different from U.S. origins. The effect however, was interpreted as an increase in the number of random bidders within a tender. The effect of this was to reduce optimal bids for HRS by $0.50/mt. This suggests that the effect of Canadian origin as an option is minimal when the Canadian Wheat Board (CWB) sells through accredited exporters. The second interesting effect was that of correlated bids. Results indicated a high degree of correlation among bidders which had the effect of increasing the probability of winning, optimal bids, and expected profits. Finally, we explored the prospective impacts of the winner's curse on optimal bids. Results suggest that in light of the winner's curse, bidders should raise their bids; in the case of HRS, from a high of 1.9% to 7.7% to correct for bias in value estimation, to a low of 0.2% to 3.1% when considering money left on the table. These results have a number of implications. The simulations improve our understanding of a very important mechanism of procurement and competition in international grain trading. For buyers, tendering is useful particularly if there is temporal variability in costs and they vary across supply firms, if the number of bidders is large, and if information about bidders is transparent and bidders' offers are less correlated. Finally, for sellers, auctions can result in intense competition among participants. Being low cost is essential to success in this form of competition. Sellers that are not low cost should avoid auctions to be successful, and bidders should make adjustments to their bids to account for the winner's curse.auction, bidding, wheat tenders, optimal bid, U.S., Canada, Marketing,

Enhancing Supply Chain Reliability through Agent-Based Supply Chain Event Management

Supply Chain Event Management (SCEM) is an approach to the monitoring of supply chains. It observes specific events and exceptions in real-time and then alerts managers if problems occur. This paper presents an architecture for an SCEM system based on intelligent software agents, Auto-ID technologies and mobile user interfaces. The motivation for this approach is to enhance existing SCEM solutions by exploiting up-to-date technologies. It delegates the task of automated problem solving when disruptions in supply chains occur to software agents

Distributed Learning System Design: A New Approach and an Agenda for Future Research

This article presents a theoretical framework designed to guide distributed learning design, with the goal of enhancing the effectiveness of distributed learning systems. The authors begin with a review of the extant research on distributed learning design, and themes embedded in this literature are extracted and discussed to identify critical gaps that should be addressed by future work in this area. A conceptual framework that integrates instructional objectives, targeted competencies, instructional design considerations, and technological features is then developed to address the most pressing gaps in current research and practice. The rationale and logic underlying this framework is explicated. The framework is designed to help guide trainers and instructional designers through critical stages of the distributed learning system design process. In addition, it is intended to help researchers identify critical issues that should serve as the focus of future research efforts. Recommendations and future research directions are presented and discussed

Analyzing the Effects of Load Distribution Algorithms on Energy Consumption of Servers in Cloud Data Centers

Cloud computing has become an important driver for IT service provisioning in recent years. It offers additional flexibility to both customers and IT service providers, but also comes along with new challenges for providers. One of the major challenges for providers is the reduction of energy consumption since today, already more than 50% of operational costs in data centers account for energy. A possible way to reduce these costs is to efficiently distribute load within the data center. Although the effect of load distribution algorithms on energy consumption is a topic of recent research, an analysis-framework for evaluating arbitrary load distribution algorithms with regard to their effects on the energy consumption of cloud data centers is still nonexistent. Therefore, in this contribution, a concept of a simulation-based, quantitative analysis-framework for load distribution algorithms in cloud environments with respect to the energy consumption of data centers is developed and evaluated