Abstract: A method includes collecting data related to a key performance indicator (KPI) for a cell in a mobile network. The method includes aggregating the collected data for the KPI into a plurality of groups, wherein a first group of the plurality of groups comprises values of the KPI during a time period in a first day, a second group of the plurality of groups comprises values of the KPI during the time period in a second day preceding the first day, and a third group of the plurality of groups comprises values of the KPI during the time period in a third day preceding the second day. The method includes determining whether the cell is a sleeping cell based on a comparison of the first, second and third groups. The method includes labelling the cell as sleeping in response to a determination that the cell is sleeping.

Abstract: A system and method of landmark detection using deep neural network with multi-frequency self-attention is provided. The system includes an encoder network that receives an image of an object of interest as an input and generates multi-frequency feature maps as output. The system further includes an attention layer that receives the generated multi-frequency feature maps and refines the generated multi-frequency feature maps based on correlations or associations between the received multi-frequency feature maps. The system further includes a decoder network that receives the refined multi-frequency feature maps as a second input from the attention layer and generates a landmark detection result based on the second input. The landmark detection result includes a heatmap image of the object of interest and the heatmap image indicates locations of landmark points on the object of interest in the image.

Abstract: A system and method of landmark detection using emotion recognition in multimedia videos using multi-modal fusion based deep neural network is provided. The system includes circuitry and a memory configured to store a multimodal fusion network which includes one or more feature extractors, a network of transformer encoders, a fusion attention network, and an output network coupled to the fusion attention network. The system inputs a multimodal input to the one or more feature extractors. The multimodal input is associated with an utterance depicted in one or more videos. The system generates input embeddings as an output of the one or more feature extractors for the input and further generates a set of emotion-relevant features based on the input embeddings. The system further generates a fused-feature representation of the set of emotion-relevant features and predicts an emotion label for the utterance based on fused-feature representation.

Abstract: Provided are a method and system for verifying a liveness detection of a user. The method includes: obtaining a video of a user speaking a phrase in response to a question or a randomly generated phrase presented to the user; inputting video data and audio data of the obtained video to a first determination model to obtain a first determination indicative of whether a mouth movement of the user is synchronized with the audio data; inputting, to a second determination model, a first input corresponding to the audio data and a second input corresponding to a predetermined phrase, to obtain a second determination indicative of whether the predetermined phrase is spoken by the user; and determining whether the first determination indicates that the mouth movement is synchronized with the audio data and whether the second determination indicates that the predetermined phrase is spoken by the user.

Abstract: A method includes collecting data related to a key performance indicator (KPI) for a cell in a mobile network. The method includes aggregating the collected data for the KPI into a plurality of groups, wherein a first group of the plurality of groups comprises values of the KPI during a time period in a first day, a second group of the plurality of groups comprises values of the KPI during the time period in a second day preceding the first day, and a third group of the plurality of groups comprises values of the KPI during the time period in a third day preceding the second day. The method includes determining whether the cell is a sleeping cell based on a comparison of the first, second and third groups. The method includes labelling the cell as sleeping in response to a determination that the cell is sleeping.

Abstract: A system and method for end-to-end semi-supervised object detection is provided. The system retrieves labeled and unlabeled images from an image dataset and generates an input batch by application of image transformation(s) on the images. The system further generates a first result for each image of the input batch by application of a teacher neural network on the input batch. For an object in an unlabeled image of the batch, the first result includes candidate bounding boxes and scores for the boxes. The system determines a threshold score based on the scores and selects a foreground bounding box from the candidates. The system generates a second result by application of a student neural network on the unlabeled image and computes a training loss over the input batch based on the foreground bounding box and the second result. The system trains the student neural network based on the training loss.

Abstract: A system and method of landmark detection using emotion recognition in multimedia videos using multi-modal fusion based deep neural network is provided. The system includes circuitry and a memory configured to store a multimodal fusion network which includes one or more feature extractors, a network of transformer encoders, a fusion attention network, and an output network coupled to the fusion attention network. The system inputs a multimodal input to the one or more feature extractors. The multimodal input is associated with an utterance depicted in one or more videos. The system generates input embeddings as an output of the one or more feature extractors for the input and further generates a set of emotion-relevant features based on the input embeddings. The system further generates a fused-feature representation of the set of emotion-relevant features and predicts an emotion label for the utterance based on fused-feature representation.

Abstract: A system and method of landmark detection using deep neural network with multi-frequency self-attention is provided. The system includes an encoder network that receives an image of an object of interest as an input and generates multi-frequency feature maps as output. The system further includes an attention layer that receives the generated multi-frequency feature maps and refines the generated multi-frequency feature maps based on correlations or associations between the received multi-frequency feature maps. The system further includes a decoder network that receives the refined multi-frequency feature maps as a second input from the attention layer and generates a landmark detection result based on the second input. The landmark detection result includes a heatmap image of the object of interest and the heatmap image indicates locations of landmark points on the object of interest in the image.

Abstract: Provided are a method and system for verifying a liveness detection of a user. The method includes: obtaining a video of a user speaking a phrase in response to a question or a randomly generated phrase presented to the user; inputting video data and audio data of the obtained video to a first determination model to obtain a first determination indicative of whether a mouth movement of the user is synchronized with the audio data; inputting, to a second determination model, a first input corresponding to the audio data and a second input corresponding to a predetermined phrase, to obtain a second determination indicative of whether the predetermined phrase is spoken by the user; and determining whether the first determination indicates that the mouth movement is synchronized with the audio data and whether the second determination indicates that the predetermined phrase is spoken by the user.

Abstract: A system including one or more processors and a memory. The memory stores instructions that, when executed by the one or more processors, cause the system to receive, via a server, a first real time messaging protocol message from a hub application, the first real time messaging protocol message to control a medical; transmit a device driver command to control the medical device based on the first real time messaging protocol message; receive data from the medical device; and transmit, via the server, the data from the medical device to the hub application.

Abstract: A method includes transmitting a low-resolution stream, buffering a high-resolution stream in a buffer memory, receiving a request for a high-resolution media item, retrieving the high-resolution media item from the buffer memory based on the frame numbers, and transmitting the high-resolution media item. The request includes one or more frame numbers based on the low-resolution stream.

Abstract: A system including one or more processors and a memory. The memory stores instructions that, when executed by the one or more processors, cause the system to receive, via a server, a first real time messaging protocol message from a hub application, the first real time messaging protocol message to control a medical; transmit a device driver command to control the medical device based on the first real time messaging protocol message; receive data from the medical device; and transmit, via the server, the data from the medical device to the hub application.

Abstract: A method includes transmitting a low-resolution stream, buffering a high-resolution stream in a buffer memory, receiving a request for a high-resolution media item, retrieving the high-resolution media item from the buffer memory based on the frame numbers, and transmitting the high-resolution media item. The request includes one or more frame numbers based on the low-resolution stream.