HCC: Small: Integrating User Context and Conceptual Perceptions for Understandable Wireless Access on Open-Platform Smartphones

HCC：小型：集成用户上下文和概念感知，以在开放平台智能手机上实现易于理解的无线访问

• 批准号: 0917112
• 负责人: Guanling Chen
• 金额: $ 30万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917112&HistoricalAwards=false
• 关键词: HCC; Small; Integrating; User; Context

This project will provide algorithms and toolkits to empower end users for better application experience when using open-platform smartphones over dynamic wireless access networks. A novel model-based diagnosis engine helps users to understand the access problems as application performance degrades. The cognition-aware feedback completes an execution-evaluation loop for the users to construct correct conceptual perceptions of the troubleshooting process and the root cause. Smartphones with customized software will be distributed to conduct user studies and evaluate the user-interaction effectiveness when dealing with wireless access problems.The combination of formal causality and state models is leveraged to troubleshoot complex wireless access problems. A unique contribution is to integrate application-level user context with network-level diagnostic models, as user mobility and physical environment are important factors of wireless access performance. Machine-learning algorithms are used for robust context inference using noisy data from on-device sensors. Norman's cognition framework is used for iterative presentation and perception of the troubleshooting process on small-screen smartphones with limited input methods.This work fills in the gap of providing diagnostic tools to the mobile users. The success of this project will result in new perception tools, which can be disseminated directly to the public through mobile application stores, to significantly enhance the often-misleading signal indicator on smartphones today. An interactive Web-based education portal will be developed to better involve students and online course modules will be introduced through UMass Lowell's Division of Continuing Studies and Corporate Education to reach IT professionals.

该项目将提供算法和工具包，使最终用户在通过动态无线接入网络使用开放平台智能手机时获得更好的应用体验。新颖的基于模型的诊断引擎可帮助用户了解应用程序性能下降时的访问问题。认知感知反馈完成了用户的执行评估循环，以构建对故障排除过程和根本原因的正确概念认知。将分发带有定制软件的智能手机来进行用户研究并评估处理无线接入问题时的用户交互有效性。利用形式因果关系和状态模型的结合来解决复杂的无线接入问题。一个独特的贡献是将应用程序级用户上下文与网络级诊断模型集成，因为用户移动性和物理环境是无线接入性能的重要因素。机器学习算法用于使用来自设备传感器的噪声数据进行稳健的上下文推理。 Norman的认知框架用于在输入方式有限的小屏幕智能手机上迭代呈现和感知故障排除过程。这项工作填补了为移动用户提供诊断工具的空白。该项目的成功将带来新的感知工具，这些工具可以通过移动应用商店直接向公众传播，以显着增强当今智能手机上经常误导的信号指示器。将开发一个基于网络的交互式教育门户，以更好地吸引学生，并将通过麻省大学洛厄尔分校的继续学习和企业教育部门引入在线课程模块，以吸引 IT 专业人员。