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    • 21. 发明申请
    • CLIPRANK: RANKING MEDIA CONTENT USING THEIR RELATIONSHIPS WITH END USERS
    • CLIPRANK:使用与最终用户的关系排名媒体内容
    • US20090132519A1
    • 2009-05-21
    • US12120209
    • 2008-05-13
    • Priyang RathodMithun SheshagiriSimon J. GibbsAnugeetha Kunjithapatham
    • Priyang RathodMithun SheshagiriSimon J. GibbsAnugeetha Kunjithapatham
    • G06F17/30
    • G06F17/30038
    • A method of ranking a plurality pieces of media content is provided. Each of the plurality pieces of media content has at least one relationship with at least one of a plurality of users. Each of the plurality of users has at least one relationship with at least one of the plurality pieces of media content. Each of the plurality pieces of media content is associated with a weight, each of the plurality of users is associated with a weight, and each relationship is associated with a weight. For each of the plurality pieces of media content and each of the plurality of users, recursively calculating and updating the weight associated with the piece of media content or the user until a difference between the weights associated with the plurality pieces of media content and the plurality of users calculated during a current iteration and the weights associated with the plurality pieces of media content and the plurality of users calculated during a previous iteration is less than a predefined threshold. The weight associated with a piece of media content or a user is calculated based on the weights of the at least one relationship and the weights of the at least one piece of media content or the at least one user with which the piece of media content or the user has the at least one relationship. Ranking the plurality pieces of media content according to their respectively associated weights.
    • 提供了一种对多个媒体内容进行排序的方法。 多个媒体内容中的每一个与多个用户中的至少一个具有至少一个关系。 多个用户中的每一个与多个媒体内容中的至少一个具有至少一个关系。 多个媒体内容中的每一个与权重相关联,多个用户中的每一个与权重相关联,并且每个关系与权重相关联。 对于多个媒体内容中的每一个和多个用户中的每一个,递归地计算和更新与该片媒体内容或用户相关联的权重,直到与多个媒体内容和多个媒体内容相关联的权重之间的差异 并且与多个媒体内容相关联的权重以及在先前迭代期间计算的多个用户的权重小于预定义的阈值。 基于所述至少一个关系的权重以及所述至少一个媒体内容的权重或者所述至少一个用户可以计算与一段媒体内容或用户相关联的权重, 用户具有至少一个关系。 根据各自的相关权重对多个媒体内容进行排序。
    • 22. 发明授权
    • Hardware acceleration of web applications
    • Web应用程序的硬件加速
    • US09424089B2
    • 2016-08-23
    • US13492761
    • 2012-06-08
    • Simon J. GibbsTasneem G. BrutchWon Jeon
    • Simon J. GibbsTasneem G. BrutchWon Jeon
    • G06F9/46G06F9/50G06F9/455
    • G06F9/5027G06F9/45508G06F2209/5017Y02D10/22
    • In a first embodiment of the present invention, a method for enabling hardware acceleration of web applications is provided, comprising: parsing a web page using a scripting engine, wherein the web page necessitates running a web application; accessing one or more Application Program Interfaces (APIs) that provide parallelization, and distribute tasks of the web application among multiple cores of a multi-core central processing unit (CPU) or graphical processing unit (GPU), wherein the accessing uses a compute context class that, when instantiated, creates a compute context object that acts as a bridge between the scripting engine and the one or more APIs; and creating one or more kernels to operate on the multiple cores.
    • 在本发明的第一实施例中,提供了一种用于启用web应用的硬件加速的方法,包括:使用脚本引擎来解析网页,其中所述网页需要运行web应用; 访问提供并行化的一个或多个应用程序接口(API),并且在多核心中央处理单元(CPU)或图形处理单元(GPU))的多个核心之间分发web应用的任务,其中访问使用计算上下文 类,当实例化时,创建一个计算上下文对象,充当脚本引擎和一个或多个API之间的桥梁; 并创建一个或多个内核来操作多个内核。
    • 26. 发明授权
    • Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments
    • 包括弹性计算系统和环境在内的计算系统和环境的执行分配成本评估
    • US08560465B2
    • 2013-10-15
    • US12710204
    • 2010-02-22
    • Sangoh JeongSimon J. GibbsXinwen ZhangAnugeetha Kunjithapatham
    • Sangoh JeongSimon J. GibbsXinwen ZhangAnugeetha Kunjithapatham
    • G06N5/00
    • G06N5/02G06F9/5066
    • Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.
    • 公开了用于在弹性计算环境中分配用于执行的可执行代码的单独可执行部分的技术。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。 机器学习可用于自动确定是否将可执行代码(例如,Weblet)的每个单独部分分配给计算系统(例如,计算设备)的内部计算资源或动态可扩展计算资源的外部资源 (例如,云)。 作为示例,状态和偏好数据可以用于训练监督学习机制,以允许计算设备自动地将可执行代码分配给弹性计算环境的内部和外部计算资源。