Abstract: An apparatus for a communication device, the apparatus may include a processor configured to: obtain channel metrics for a plurality of radio communication channels, each obtained channel metric is associated with a respective radio communication channel of the plurality of radio communication channels, generate a plurality of channel hopping sequences, each channel hopping sequence is representative of an allocation of the plurality of radio communication channels for a plurality of time slots, wherein a number of time slots allocated for each radio communication channel within each channel hopping sequence is based on the respective obtained channel metric, and select one of the plurality of channel hopping sequences based on a predefined criterion to communicate with a further communication device.

Abstract: A system and method for switching one or more User Equipment (UEs) 116A-N from a unicast mode to a broadcast or multicast mode to transmit a streaming media content to the UEs 116A-N is provided. The system includes, an Over-the-top (OTT) platform 104, a CDN 112, the UEs 116A-N, a cellular core network 202, one-to-many offload core 204, an analytics engine 206, a database 208, one-to-many transmitter 210, a real time switching module 212, a Cellular base station 214 and a user specified rules module 222. The analytics engine 206 continuously analyzes real-time and historical data stored in the database 208 to identify the UEs 116A-N that receive a streaming media content through the unicast mode and the streaming media content to be offloaded. An offload is a process by which certain portions of the streaming media content is shifted from the unicast mode to the broadcast or multicast mode.

Abstract: Methods of detecting and categorizing an action in an untrimmed video segment regardless of the scale of the action and the close proximity of other actions. The methods improve upon the prior art which either require trimmed video segments including only a single activity depicted therein, or untrimmed video segments including relatively few actions, persons, or objects of interest, thereby directing the classification. Instead, the methods utilize a plurality of tubelets used to represent discreet actions, persons, and objects of interest within the comprehensive untrimmed video segment. The tubelets are localized to correct for pixel-level foreground-background biases, which are then turned into short spatio-temporal action tubelets that are passed to a classification network to obtain multi-label predictions. After classification, the tubelets are be linked together to obtain the final detections with varying lengths, and the method merges the short action tubelets into final action detections.

Abstract: Methods of detecting and categorizing an action in an untrimmed video segment regardless of the scale of the action and the close proximity of other actions. The methods improve upon the prior art which either require trimmed video segments including only a single activity depicted therein, or untrimmed video segments including relatively few actions, persons, or objects of interest, thereby directing the classification. Instead, the methods utilize a plurality of tubelets used to represent discreet actions, persons, and objects of interest within the comprehensive untrimmed video segment. The tubelets are localized to correct for pixel-level foreground-background biases, which are then turned into short spatio-temporal action tubelets that are passed to a classification network to obtain multi-label predictions. After classification, the tubelets are be linked together to obtain the final detections with varying lengths, and the method merges the short action tubelets into final action detections.

Abstract: A system and method for switching one or more User Equipment (UEs) 116A-N from a unicast mode to a broadcast or multicast mode to transmit a streaming media content to the UEs 116A-N is provided. The system includes, an Over-the-top (OTT) platform 104, a CDN 112, the UEs 116A-N, a cellular core network 202, one-to-many offload core 204, an analytics engine 206, a database 208, one-to-many transmitter 210, a real time switching module 212, a Cellular base station 214 and a user specified rules module 222. The analytics engine 206 continuously analyzes real-time and historical data stored in the database 208 to identify the UEs 116A-N that receive a streaming media content through the unicast mode and the streaming media content to be offloaded. An offload is a process by which certain portions of the streaming media content is shifted from the unicast mode to the broadcast or multicast mode.

Abstract: This disclosure relates to utilizing a statistical model trained on character dimensions to determine a likelihood of a person purchasing a product. The method may include obtaining user-input data of a first person (e.g., textual-input data, survey-response data, offer information, or clickstream data associated with a first person). A character profile for the first person is derived using the user-input data and a psycholinguistic lexicon. A statistical model is generated based on the derived character profile of the first person. Second user-input data associated with a second person is obtained. The second user-input is applied to the statistical model to determine an output of the model (e.g., a statistical probability value that quantifies, for example, a predicted intention of the second person to purchase a particular product).

Abstract: This disclosure relates to utilizing a statistical model trained on character dimensions to determine a likelihood of a person purchasing a product. The method may include obtaining user-input data of a first person (e.g., textual-input data, survey-response data, offer information, or clickstream data associated with a first person). A character profile for the first person is derived using the user-input data and a psycholinguistic lexicon. A statistical model is generated based on the derived character profile of the first person. Second user-input data associated with a second person is obtained. The second user-input is applied to the statistical model to determine an output of the model (e.g., a statistical probability value that quantifies, for example, a predicted intention of the second person to purchase a particular product).

Abstract: System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.

Abstract: System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.