System, method, and tangible computer-readable storage media are disclosed for providing a brokering service for compute resources The method includes, at a brokering service, polling a group of separately administered compute environments to identify resource capabilities and information, each compute resource environment including the group of managed nodes for processing workload, receiving a request for compute resources at the brokering service system, the request for compute resources being associated with a service level agreement (SLA) and based on the resource capabilities across the group of compute resource environments, selecting compute resources in one or more of the group of compute resource environments The brokering service system receives workload associated with the request and communicates the workload to the selected resources for processing The brokering services system can aggregate resources for multiple cloud service providers and act as an advocate for or a guarantor of the SLA associated with the workload

System and method of brokering cloud computing resources

In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different speakers interleave in the time domain. This RNN is naturally integrated with a distance-dependent Chinese restaurant process (ddCRP) to accommodate an unknown number of speakers. Our system is fully supervised and is able to learn from examples where time-stamped speaker labels are annotated. We achieved a 7.6% diarization error rate on NIST SRE 2000 CALLHOME, which is better than the state-of-the-art method using spectral clustering. Moreover, our method decodes in an online fashion while most state-of-the-art systems rely on offline clustering.

Fully Supervised Speaker Diarization

While many action recognition datasets consist of collections of brief, trimmed videos each containing a relevant action, videos in the real-world (e.g., on YouTube) exhibit very different properties: they are often several minutes long, where brief relevant clips are often interleaved with segments of extended duration containing little change. Applying densely an action recognition system to every temporal clip within such videos is prohibitively expensive. Furthermore, as we show in our experiments, this results in suboptimal recognition accuracy as informative predictions from relevant clips are outnumbered by meaningless classification outputs over long uninformative sections of the video. In this paper we introduce a lightweight ``clip-sampling'' model that can efficiently identify the most salient temporal clips within a long video. We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips. Furthermore, we show that this yields significant gains in recognition accuracy compared to analysis of all clips or randomly selected clips. On Sports1M, our clip sampling scheme elevates the accuracy of an already state-of-the-art action classifier by 7% and reduces by more than 15 times its computational cost.

/pdf/scsampler-sampling-salient-clips-from-video-for-efficient-277myfrtpo.pdf

SCSampler: Sampling Salient Clips From Video for Efficient Action Recognition

An on-demand compute environment comprises a plurality of nodes within an on-demand compute environment available for provisioning and a slave management module operating on a dedicated node within the on-demand compute environment, wherein upon instructions from a master management module at a local compute environment, the slave management module modifies at least one node of the plurality of nodes.

On-demand compute environment

A computer-based system provides secure, configurable access to computer network resources. A human-readable language is provided for defining access policy rules. Rules in this language are converted in an automated fashion into filters applied within the various subsystems and components in a multi-layer security system. Network users are authenticated by an access control security system that obtains basic information about that user. Based on the user ID, a set of abstract policies can be retrieved. The retrieved policies are associated with the user and the groups associated with that user. Based on the retrieved rules, a set of rules for multiple layers of the network are generated and applied to those subsystems. Two or more of the subsystems may be placed in series with different types of processing occurring in each of the subsystems, reducing the workload of subsequent subsystems.

Multilayer access control security system

A system and associated method for determining web analytics information. A Hypertext Transfer Protocol (HTTP) request for a target page referred from a referring page by a mouse click is processed to calculate web analytics data in the target page. The web analytics data regarding a mouse cursor position, a 2-dimensional information of the target page, and the URL of the referring page is transmitted to a web analytics server to generate the web analytics information for the referring page in format of a heat map of the referring page.

Determining web analytics information

A method and system for gathering enriched web server activity data in a global communications network in which requested information files are cached at a plurality of network devices. With the prevalence of web caching on the Internet, the origin web servers do not serve the majority of requests for web site content. A single pixel clear Graphics Image Format (GIF) request is added to the HyperText Markup Language (HTML) source file for a web page. Appended to the GIF request is a Common Gateway Interface (CGI) string of data that contains enhanced web activity data information, including the number of images ( hits') that have to be retrieved by a client browser to build the web page, and the referring identifier that resulted in access to the web page. The single pixel clear GIF request is not cacheable and results in the request being transmitted to the origin web server when the client browser interprets the HTML file. The enriched data is stored in log files at the origin web server to accumulate an accurate number of hits on the web page.

Gathering enriched web server activity data of cached web content

The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling, and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis), and game analytics to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player's name and the whole number, or analysts input allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament (2017 Masters) and two major international tennis tournaments (2017 Wimbledon and U.S. Open), successfully extracting highlights through the course of the sporting events. For the 2017 Masters, 54% of the clips selected by our system overlapped with the official highlights reels. Furthermore, user studies showed that 90% of the non-overlapping ones were of the same quality of the official clips for the 2017 Masters, while the automatic selection of clips for highlights of 2017 Wimbledon and 2017 US Open agreed with human preferences 80% and 84.2% of the time, respectively.

Automatic Curation of Sports Highlights Using Multimodal Excitement Features

A video stream measurement method and system. The method includes receiving by a computing system, a video stream comprising plurality of timestamps located at specified intervals of the video stream. The computing system broadcasts the video stream for a user and decodes a first time stamp broadcasted during a first time period. The computing system monitors a user interaction function performed by the user and associated with the video stream. The computing system generates and transmits a report comprising a description associated with the user interaction function. The computing system receives an analysis associated with the report.

Video stream measurement method and system

The present invention generally provides a way to analyze crowd noise to identify “highlights” or the like. Specifically, an audio stream containing crowd noise from an event (e.g., sporting event, political rally, religious gathering, etc) is captured (e.g., using microphones) and time coded. The audio stream is normalized based on geography and processed to remove undesired artifacts and to identify a set (at least one) of highlights. Based on at least one threshold, at least one highlight is selected from the set of highlights.

Crowd noise analysis

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