Abstract: Methods and devices for generating a customized video segment from a video are disclosed. The video is partitioned into video segments. For each respective video segment, a respective set of scores is computed, where each score represents a respective content feature in the respective video segment. A respective weighted aggregate score is computed for each respective video segment by applying, to each respective set of scores, a common set of weight values. A selected video segment is outputted as the customized video segment, where the selected video segment is selected from one or more high-ranked video segments having high-ranked weighted aggregate scores.

Abstract: A system for fluid flow verification comprises a video recording device that captures frames of actual fluid or fluid display, and a processor coupled to the recording device. The processor hosts fluid verification modules, including a fluid flow rate verification module, and a fluid color verification module. The fluid flow rate verification module is operative to receive the frames; binarize region of interest, and compute histogram of white/black pixels; count white pixels to detect actual fluid quantity; determine whether actual fluid quantity matches expected fluid quantity; if there is a match, measure fluid change rate; determine whether there is steady fluid flow; if not, report fluid flow rate anomaly. The fluid color verification module is operative to: receive binarized region of interest and histogram; detect fluid color by extracting sample color mask of pixels; determine whether detected fluid color matches expected fluid color; if not, report fluid color anomaly.

Abstract: A method for verifying data and control couplings of software under test comprises obtaining software architecture and design information for the software; extracting expected control coupling information based on an order in which software component calls are in the software architecture; extracting expected data coupling information from one software component to another software component in a sequence diagram; updating the expected control and data coupling information in an expected control and data coupling sequencing table; deriving actual control coupling information from execution trace data obtained from a requirements based test execution; deriving actual data coupling information from data trace information; and updating the actual control and data coupling information in an actual control and data coupling sequencing table.

Abstract: The disclosure provides a method and system of improvement in the real-time operation of a terminal station in an industrial facility using a machine learning-based self-adaptive system comprising obtaining real-time operations data and historical data stored in a local database or at a cloud-storage. The data relates to input parameters of the terminal station. The method includes inputting the input parameter to a machine learning configurable module of the machine learning-based self-adaptive system and analyzing the input parameter using dynamic machine learning models and algorithms to identify patterns to each of the input parameters. The method further includes evaluating the identified pattern against the real-time operations data obtained from the terminal station and predicting at least one output parameter based on the input parameter and the identified pattern against the real-time operations. Based on the prediction, adjusting the output parameter of the real-time operations data.

Abstract: Methods, systems, and media for image searching are described. Images comprising one query image and a plurality of candidate images are received. For each candidate image, a first model similarity measure from an output of a first model configured for scene classification to perceive scenes in the images is determined. Further, for each candidate image of the plurality of candidate images, a second model similarity measure from the output of a second model configured for attribute classification to perceive attributes in the images is determined. For each candidate image of the plurality of candidate images, a similarity agglomerate index of a weighted aggregate of the first model similarity measure and the second model similarity measure is computed. The plurality of candidate images based on the respective similarity agglomerate index of each candidate image are ranked and a first ranked candidate images corresponding to the searched images are generated.

Abstract: A system for fluid flow verification comprises a video recording device that captures frames of actual fluid or fluid display, and a processor coupled to the recording device. The processor hosts fluid verification modules, including a fluid flow rate verification module, and a fluid color verification module. The fluid flow rate verification module is operative to receive the frames; binarize region of interest, and compute histogram of white/black pixels; count white pixels to detect actual fluid quantity; determine whether actual fluid quantity matches expected fluid quantity; if there is a match, measure fluid change rate; determine whether there is steady fluid flow; if not, report fluid flow rate anomaly. The fluid color verification module is operative to: receive binarized region of interest and histogram; detect fluid color by extracting sample color mask of pixels; determine whether detected fluid color matches expected fluid color; if not, report fluid color anomaly.

Abstract: System and method of analyzing a video, comprising dividing the video into a set of successive basic units; generating semantic tags for the basic units using a set of hierarchical classifier nodes that comprise a parent classifier node and a plurality of child classifier nodes, wherein the basic units are each routed through selected child classifier nodes based on classification of the basic units by the parent classifier node; and generating a semantic topic for the video based on the semantic tags generated for the basic units.

Abstract: Methods and systems for processing an image are described. A saliency map is generated from the image. The saliency map indicates one or more salient portions of the image that have saliency values satisfying a saliency criterion. A scene graph is generated for at least the one or more salient portions. The scene graph represents a plurality of objects detected in the image. The scene graph further represents one or more relationships between each respective object pairs. One or more dataset entries associated with the image are generated. Each of the one or more relationships for each of the one or more object pairs is indicated by a respective dataset entry. The one or more dataset entries are stored in a first dataset.

Abstract: The disclosure provides a method and system of improvement in the real-time operation of a terminal station in an industrial facility using a machine learning-based self-adaptive system comprising obtaining real-time operations data and historical data stored in a local database or at a cloud-storage. The data relates to input parameters of the terminal station. The method includes inputting the input parameter to a machine learning configurable module of the machine learning-based self-adaptive system and analyzing the input parameter using dynamic machine learning models and algorithms to identify patterns to each of the input parameters. The method further includes evaluating the identified pattern against the real-time operations data obtained from the terminal station and predicting at least one output parameter based on the input parameter and the identified pattern against the real-time operations. Based on the prediction, adjusting the output parameter of the real-time operations data.

Abstract: Methods and systems for video segmentation and scene recognition are described. A video having a plurality of frames and a subtitle file associated with the video are received. Segmentation is performed on the video to generate a first set video frames comprising one or more video frames based on a frame-by-frame comparison of features in the frames of the video. Each video frame in the first includes a frame indicator which indicates at least a first start frame of the video frame. The subtitle file associated with the video is parsed to generate one or more subtitle segments based on a start and an end time of each dialogue in the subtitle file. A second set of video frames comprising one or more second video frames are generated based on the video frames of the first set of video frames and the e or more subtitle segments.

Abstract: Methods and systems for video segmentation and scene recognition are described. A video having a plurality of frames and a subtitle file associated with the video are received. Segmentation is performed on the video to generate a first set video frames comprising one or more video frames based on a frame-by-frame comparison of features in the frames of the video. Each video frame in the first includes a frame indicator which indicates at least a first start frame of the video frame. The subtitle file associated with the video is parsed to generate one or more subtitle segments based on a start and an end time of each dialogue in the subtitle file. A second set of video frames comprising one or more second video frames are generated based on the video frames of the first set of video frames and the e or more subtitle segments.

Abstract: Methods and systems for processing an image are described. A saliency map is generated from the image. The saliency map indicates one or more salient portions of the image that have saliency values satisfying a saliency criterion. A scene graph is generated for at least the one or more salient portions. The scene graph represents a plurality of objects detected in the image. The scene graph further represents one or more relationships between each respective object pairs. One or more dataset entries associated with the image are generated. Each of the one or more relationships for each of the one or more object pairs is indicated by a respective dataset entry. The one or more dataset entries are stored in a first dataset.

Abstract: One or more devices may receive a service request. The service request may be associated with providing a network service to a service location and may be associated with a service provider network. The one or more devices may create a virtual network function (VNF), associated with providing the network service, based on the service request. The VNF may be created such that the VNF is hosted by the device, and may be configured to provide the network service. The one or more devices may insert the VNF into the service provider network. The VNF may be inserted to interact with a physical device of the service provider network to allow the network service to be provided to the service location.

Abstract: A system may receive order information that may include information identifying a network service, associated with a service provider network, and a service location associated with the network service. The system may determine context information based on the order information that may include information associated with providing the network service to the service location via the service provider network. The system may generate a service order based on the context information. The system may create a virtual network function (VNF) based on the service request. The VNF may be created such that the VNF operates on a computing device associated with the system, and may be configured to provide the network service. The system may insert the VNF into the service provider network to cause the network service to be provided to the service location. The VNF may interact with a physical device of the service provider network.

Abstract: A device may receive order information that may identify a network service to be provided, via a service provider network, to a service location associated with a customer. The device may determine context information associated with providing the network service. The context information may be determined based on the order information and may include information associated with a virtual network function (VNF) that is to provide the network service to the service location. The device may determine provisioning details associated with the network service. The device may generate a service request that includes the context information and the provisioning details. The device may identify a cloud resource that is to host the VNF. The device may provide the service request to the cloud resource to cause the VNF to be created and inserted into the service provider network.

Abstract: One or more devices may receive a service request. The service request may be associated with providing a network service to a service location and may be associated with a service provider network. The one or more devices may create a virtual network function (VNF), associated with providing the network service, based on the service request. The VNF may be created such that the VNF is hosted by the device, and may be configured to provide the network service. The one or more devices may insert the VNF into the service provider network. The VNF may be inserted to interact with a physical device of the service provider network to allow the network service to be provided to the service location.

Abstract: A device may receive order information that may identify a network service to be provided, via a service provider network, to a service location associated with a customer. The device may determine context information associated with providing the network service. The context information may be determined based on the order information and may include information associated with a virtual network function (VNF) that is to provide the network service to the service location. The device may determine provisioning details associated with the network service. The device may generate a service request that includes the context information and the provisioning details. The device may identify a cloud resource that is to host the VNF. The device may provide the service request to the cloud resource to cause the VNF to be created and inserted into the service provider network.

Abstract: A system may receive order information that may include information identifying a network service, associated with a service provider network, and a service location associated with the network service. The system may determine context information based on the order information that may include information associated with providing the network service to the service location via the service provider network. The system may generate a service order based on the context information. The system may create a virtual network function (VNF) based on the service request. The VNF may be created such that the VNF operates on a computing device associated with the system, and may be configured to provide the network service. The system may insert the VNF into the service provider network to cause the network service to be provided to the service location. The VNF may interact with a physical device of the service provider network.