The Experts below are selected from a list of 290775 Experts worldwide ranked by ideXlab platform
Philipp Beckerle - One of the best experts on this subject based on the ideXlab platform.
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how cognitive Models of human body Experience might push robotics
Frontiers in Neurorobotics, 2019Co-Authors: Tim Schurmann, Betty J Mohler, Jan Peters, Philipp BeckerleAbstract:In the last decades, cognitive Models of multisensory integration in human beings have been developed and applied to model human body Experience. Recent research indicates that Bayesian and connectionist Models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive Models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive Models support the understanding of how the modulating factors of human body Experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user’s individual body Experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body Experience Models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.
Splett, Nathan S. - One of the best experts on this subject based on the ideXlab platform.
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Development and Evaluation of a Credit Scoring Model for the Farm Credit System
2024Co-Authors: Splett, Nathan S.Abstract:155 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.Credit scoring Models were developed to identify the credit risk in term and operating loans for the principal purposes of evaluating portfolio quality and monitoring risk at the loan and portfolio levels in the Farm Credit System. Experience based (lender) and statistical (academic) approaches were used jointly. Initially, Experience term and operating loan Models were developed, utilizing financial ratios from the Farm Financial Standards Task Force (FFSTF) as the explanatory variable measures. Then, logit regression was employed to estimate term and operating loan Models, utilizing classifications and variable measures from the Experience Models as the dependent and independent variables, respectively. The procedure compared the Experience model and statistical model on the basis of the variable measures and classifications, and the FFSTF measures were evaluated with respect to their applicability in credit scoring Models. Results from the Experience and Logit Models' applications indicated similarity in the variable measures selected and classifications, and indicated that the FFSTF measures were appropriate in the Models.The credit scoring accuracy of the Experience Models was evaluated across structural characteristics of farm businesses, recognizing that trade-offs may be necessary between credit scoring accuracy and the range of structural characteristics over which the Models apply. The structural characteristics were described by three "size" categories, including total assets, value of farm production, and total acres operated; five farm business "types", including grain, hogs, dairy, beef/feeder cattle, and poultry; tenure; location (state); and loan size. Results of the Models' application among the categories indicated that various structural characteristics may influence credit scores.Term and operating loans were used to develop term and operating loan Models, respectively. The term and operating loan Models were applied to operating and term loans, respectively, to determine if two Models were necessary. The Models place different weights on the variable measures, and the loan data were different in terms of their financial structures, advocating the use of separate Models. Classifications varied substantially in the cross-application of the Models, indicating that separate term loan and operating loan Models were appropriate, rather than using a single model.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
Jan Peters - One of the best experts on this subject based on the ideXlab platform.
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how cognitive Models of human body Experience might push robotics
Frontiers in Neurorobotics, 2019Co-Authors: Tim Schurmann, Betty J Mohler, Jan Peters, Philipp BeckerleAbstract:In the last decades, cognitive Models of multisensory integration in human beings have been developed and applied to model human body Experience. Recent research indicates that Bayesian and connectionist Models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive Models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive Models support the understanding of how the modulating factors of human body Experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user’s individual body Experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body Experience Models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.
Antonio Cuadra Sánchez - One of the best experts on this subject based on the ideXlab platform.
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Quality of Experience Models for Multimedia Streaming
International Journal of Mobile Computing and Multimedia Communications, 2010Co-Authors: Vlado Menkovski, Georgios Exarchakos, Antonio Liotta, Antonio Cuadra SánchezAbstract:Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience (QoE) is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction Models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning Models outperform other statistical methods achieving accuracy greater than 90%. These Models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these Models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction Models, the authors have adopted Online Learning techniques that update the Models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction Models that are an indispensible component of a QoE-aware management service.
Alan C Bovik - One of the best experts on this subject based on the ideXlab platform.
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towards perceptually optimized adaptive video streaming a realistic quality of Experience database
IEEE Transactions on Image Processing, 2021Co-Authors: Christos G Bampis, Ioannis Katsavounidis, Teyuan Huang, Chaitanya Ekanadham, Alan C BovikAbstract:Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and objective QoE prediction Models. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. The new database focuses on low bandwidth conditions which are more challenging for bitrate adaptation algorithms, which often must navigate tradeoffs between rebuffering and video quality. Using our database, we study the effects of multiple streaming dimensions on user Experience and evaluate video quality and quality of Experience Models and analyze their strengths and weaknesses. We believe that the tools introduced here will help inspire further progress on the development of perceptually-optimized client adaptation and video streaming strategies. The database is publicly available at http://live.ece.utexas.edu/research/LIVE_NFLX_II/live_nflx_plus.html .
