The Experts below are selected from a list of 80310 Experts worldwide ranked by ideXlab platform
Helen Meng - One of the best experts on this subject based on the ideXlab platform.
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Predicting User Satisfaction in Spoken Dialog System Evaluation With Collaborative Filtering
IEEE Journal of Selected Topics in Signal Processing, 2012Co-Authors: Zhaojun Yang, Gina-anne Levow, Helen MengAbstract:We propose a collaborative filtering (CF) model to predict User Satisfaction in SDS evaluation. Inspired by the use of CF in recommendation systems, where a User's preference for a new item is assume to resemble that for similar items rated previously, we adapt the idea to predict User evaluations of unrated dialogs based on the ratings received by similar dialogs. Ratings of dialogs are gathered by crowdsourcing through Amazon Mechanical Turk. A reference baseline is provided by a linear regression model (LRM) based on the PARADISE framework. We present two versions of the CF model. First, the item-based collaborative filtering model (ICFM) clusters rated dialogs and builds an LRM for each cluster. The rating of an unseen dialog is predicted by the LRM of its most similar cluster. Second, the extended ICFM (EICFM) separates dialog features into User-related and system-related groups, to build LRMs for these separately. Experimental results on dialogs from the Let's Go! system show both ICFM and EICFM can significantly improve the proportion of variability explained by the LRM. We also demonstrate the generalizability of the CF model to a new dialog corpus from the systems in the Spoken Dialog Challenge (SDC) 2010.
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Collaborative filtering model for User Satisfaction prediction in Spoken Dialog System evaluation
2010 IEEE Spoken Language Technology Workshop, 2010Co-Authors: Zhaojun Yang, Gina-anne Levow, Baichuan Li, Yi Zhu, Irwin King, Helen MengAbstract:Developing accurate models to automatically predict User Satisfaction about the overall quality of a Spoken Dialog System (SDS) is highly desirable for SDS evaluation. In the original PARADISE framework, a linear regression model is trained using measures drawn from rated dialogs as predictors with User Satisfaction as the target. In this paper, we extend PARADISE by introducing a collaborative filtering (CF) model for User Satisfaction prediction and its corresponding extension. This prediction model is drawn from the idea of CF in recommendation systems, which uses information from near neighbors of an unrated dialog to predict its User Satisfaction. We also present the methodology of collecting User judgments on SDS quality with crowdsourcing through Amazon Mechanical Turk. Experimental results show that the CF approaches could distinctly improve the prediction accuracy of User Satisfaction.
Vijay Janapa Reddi - One of the best experts on this subject based on the ideXlab platform.
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Mobile CPU's rise to power: Quantifying the impact of generational mobile CPU design trends on performance, energy, and User Satisfaction
Proceedings - International Symposium on High-Performance Computer Architecture, 2016Co-Authors: Megan Halpern, Yuhao Zhu, Vijay Janapa ReddiAbstract:In this paper, we assess the past, present, and future of mobile CPU design. We study how mobile CPU designs trends have impacted the end-User, hardware design, and the holistic mobile device. We analyze the evolution often cutting-edge mobile CPU designs released over the past seven years. Specifically, we report measured performance, power, energy and User Satisfaction trends across mobile CPU generations. A key contribution of our work is that we contextualize the mobile CPU's evolution in terms of User Satisfaction, which has largely been absent from prior mobile hardware studies. To bridge the gap between mobile CPU design and User Satisfaction, we construct and conduct a novel crowdsourcing study that spans over 25,000 survey participants using the Amazon Mechanical Turk service. Our methodology allows us to identify what mobile CPU design techniques provide the most benefit to the end-User's quality of User experience. Our results quantitatively demonstrate that CPUs play a crucial role in modern mobile system-on-chips (SoCs). Over the last seven years, both single-and multicore performance improvements have contributed to end-User Satisfaction by reducing User-critical application response latencies. Mobile CPUs aggressively adopted many power-hungry desktop-oriented design techniques to reach these performance levels. Unlike other smartphone components (e.g. display and radio) whose peak power consumption has decreased over time, the mobile CPU's peak power consumption has steadily increased. As the limits of technology scaling restrict the ability of desktop-like scaling to continue for mobile CPUs, specialized accelerators appear to be a promising alternative that can help sustain the power, performance, and energy improvements that mobile computing necessitates. Such a paradigm shift will redefine the role of the CPU within future SoCs, which merit several design considerations based on our findings.
Ase Brandt - One of the best experts on this subject based on the ideXlab platform.
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activity and participation quality of life and User Satisfaction outcomes of environmental control systems and smart home technology a systematic review
Disability and Rehabilitation: Assistive Technology, 2011Co-Authors: Ase Brandt, Kersti Samuelsson, Outi Toytari, Annaliisa SalminenAbstract:Objective. To examine activity and participation, quality of life, and User Satisfaction outcomes of environmental control systems (ECSs) and smart home technology (SHT) interventions for persons with impairments.Method. A systematic review. Seventeen databases, three conference proceedings, and two journals were searched without language or study design restrictions covering the period January 1993 – June 2009. Reviewers selected studies, extracted data, and assessed the methodological quality independently.Result. Of 1739 studies identified, five effect studies and six descriptive studies were included. One study was on SHT and the remainder on ECS; functionalities were overlapping. The studies varied in most aspects, and no synthesis could be drawn. However, ECS/SHT tended to increase study participants' independence, instrumental activities of daily living, socialising, and quality of life. Two studies showed high User Satisfaction. The level of evidence was regarded as low, mainly due to small study ...
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mobility related participation and User Satisfaction construct validity in the context of powered wheelchair use
Disability and Rehabilitation: Assistive Technology, 2010Co-Authors: Ase Brandt, Svend Kreiner, Susanne IwarssonAbstract:Purpose. The aim of this study was to investigate the constructs of mobility-related participation and User Satisfaction, two important outcome dimensions within praxis and research on mobility device interventions.Method. To fulfill this aim, validity and reliability of a 12-item scale on mobility-related participation and a 10-item scale on User Satisfaction were examined in the context of older people's powered wheelchair use (n = 111). Rasch analysis and correlation analysis were applied.Results. Construct validity of both scales was confirmed. The reliability of the User Satisfaction scale was good, while the mobility-related participation scale was not optimal in discriminating between persons with a high degree of mobility-related participation. It was demonstrated that mobility-related participation and User Satisfaction are separate, not related constructs.Conclusions. It can be concluded that the investigated mobility-related participation and User Satisfaction constructs appear to be valid. Sin...
Zhaojun Yang - One of the best experts on this subject based on the ideXlab platform.
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Predicting User Satisfaction in Spoken Dialog System Evaluation With Collaborative Filtering
IEEE Journal of Selected Topics in Signal Processing, 2012Co-Authors: Zhaojun Yang, Gina-anne Levow, Helen MengAbstract:We propose a collaborative filtering (CF) model to predict User Satisfaction in SDS evaluation. Inspired by the use of CF in recommendation systems, where a User's preference for a new item is assume to resemble that for similar items rated previously, we adapt the idea to predict User evaluations of unrated dialogs based on the ratings received by similar dialogs. Ratings of dialogs are gathered by crowdsourcing through Amazon Mechanical Turk. A reference baseline is provided by a linear regression model (LRM) based on the PARADISE framework. We present two versions of the CF model. First, the item-based collaborative filtering model (ICFM) clusters rated dialogs and builds an LRM for each cluster. The rating of an unseen dialog is predicted by the LRM of its most similar cluster. Second, the extended ICFM (EICFM) separates dialog features into User-related and system-related groups, to build LRMs for these separately. Experimental results on dialogs from the Let's Go! system show both ICFM and EICFM can significantly improve the proportion of variability explained by the LRM. We also demonstrate the generalizability of the CF model to a new dialog corpus from the systems in the Spoken Dialog Challenge (SDC) 2010.
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Collaborative filtering model for User Satisfaction prediction in Spoken Dialog System evaluation
2010 IEEE Spoken Language Technology Workshop, 2010Co-Authors: Zhaojun Yang, Gina-anne Levow, Baichuan Li, Yi Zhu, Irwin King, Helen MengAbstract:Developing accurate models to automatically predict User Satisfaction about the overall quality of a Spoken Dialog System (SDS) is highly desirable for SDS evaluation. In the original PARADISE framework, a linear regression model is trained using measures drawn from rated dialogs as predictors with User Satisfaction as the target. In this paper, we extend PARADISE by introducing a collaborative filtering (CF) model for User Satisfaction prediction and its corresponding extension. This prediction model is drawn from the idea of CF in recommendation systems, which uses information from near neighbors of an unrated dialog to predict its User Satisfaction. We also present the methodology of collecting User judgments on SDS quality with crowdsourcing through Amazon Mechanical Turk. Experimental results show that the CF approaches could distinctly improve the prediction accuracy of User Satisfaction.
Megan Halpern - One of the best experts on this subject based on the ideXlab platform.
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Mobile CPU's rise to power: Quantifying the impact of generational mobile CPU design trends on performance, energy, and User Satisfaction
Proceedings - International Symposium on High-Performance Computer Architecture, 2016Co-Authors: Megan Halpern, Yuhao Zhu, Vijay Janapa ReddiAbstract:In this paper, we assess the past, present, and future of mobile CPU design. We study how mobile CPU designs trends have impacted the end-User, hardware design, and the holistic mobile device. We analyze the evolution often cutting-edge mobile CPU designs released over the past seven years. Specifically, we report measured performance, power, energy and User Satisfaction trends across mobile CPU generations. A key contribution of our work is that we contextualize the mobile CPU's evolution in terms of User Satisfaction, which has largely been absent from prior mobile hardware studies. To bridge the gap between mobile CPU design and User Satisfaction, we construct and conduct a novel crowdsourcing study that spans over 25,000 survey participants using the Amazon Mechanical Turk service. Our methodology allows us to identify what mobile CPU design techniques provide the most benefit to the end-User's quality of User experience. Our results quantitatively demonstrate that CPUs play a crucial role in modern mobile system-on-chips (SoCs). Over the last seven years, both single-and multicore performance improvements have contributed to end-User Satisfaction by reducing User-critical application response latencies. Mobile CPUs aggressively adopted many power-hungry desktop-oriented design techniques to reach these performance levels. Unlike other smartphone components (e.g. display and radio) whose peak power consumption has decreased over time, the mobile CPU's peak power consumption has steadily increased. As the limits of technology scaling restrict the ability of desktop-like scaling to continue for mobile CPUs, specialized accelerators appear to be a promising alternative that can help sustain the power, performance, and energy improvements that mobile computing necessitates. Such a paradigm shift will redefine the role of the CPU within future SoCs, which merit several design considerations based on our findings.
