International Business Machines Corporation

Jeff Hsueh-Chang Kuo

Chih-Wen Su

Ying-Chen Yu

June-Ray Lin

A method, computer system, and a computer program product for smart SDN is provided. The present invention may include recording and clustering a pod's behavior to generate a behavior transition model for the pod. The present invention may include watching a behavior of the pod and comparing the behavior to the generated behavior transition model. The present invention may include triggering a network policy change based on determining that the behavior of the pod is a misbehavior.

Smart SDN for intrusion prevention

Embodiments of the present invention are directed to a computer-implemented method for identifying advertisements in a video. The method includes obtaining a plurality of copies of the video and processing each of the plurality of copies of the video, wherein the processing identifies a plurality of iframes in each of the plurality of copies of the video. The method also includes comparing the plurality of iframes of each of the plurality of copies of the video. The method further includes identifying one or more common portions of each of the plurality of copies of the video one or more advertisement in the video, based on the comparison.

Identifying advertisements embedded in videos

A method, computer program product, and computer system for receiving, by a computing device, one or more user preferences for a video clip. The one or more user preferences may be matched with one or more branches of the video clip, wherein each of the one or more branches may include one or more segments of the video clip. The one or more segments of the video clip may be selected from the one or more branches based upon, at least in part, matching the one or more user preferences with the one or more branches of the video clip. The video clip may be composed based upon, at least in part, the one or more user preferences.

System and method for adaptive video

Methods, systems and computer program products for flagging abnormal videos are provided. Aspects include training an image recognition model based on a plurality of images that depict one or more of a plurality of subjects. Aspects also include generating a normal subject relationship graph representing normal relationships between the plurality of subjects by applying the image recognition model to a plurality of training videos and a test subject relationship graph representing test relationships between subjects depicted in a test video by applying the image recognition model to the test video. Each normal relationship is associated with a strength value. Responsive to determining that a difference between a strength value associated with a first normal relationship and a strength value associated with a corresponding first test relationship exceeds a predetermined threshold, aspects include flagging the test video as being abnormal.

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Taipei, Taiwan

Jeff Hsueh-Chang Kuo