Abstract: Provided are a facial expression recognition method and system combined with an attention mechanism. The method comprises: detecting faces comprised in each video frame in a video sequence, and extracting corresponding facial ROIs, so as to obtain facial pictures in each video frame; aligning the facial pictures in each video frame on the basis of location information of facial feature points of the facial pictures; inputting the aligned facial pictures into a residual neural network, and extracting spatial features of facial expressions corresponding to the facial pictures; inputting the spatial features of the facial expressions into a hybrid attention module to acquire fused features of the facial expressions; inputting the fused features of the facial expressions into a gated recurrent unit, and extracting temporal features of the facial expressions; and inputting the temporal features of the facial expressions into a fully connected layer, and classifying and recognizing the facial expressions.

Abstract: The present disclosure provides a non-contact fatigue detection system and method based on rPPG. The system and method adopt multi-thread synchronous communication for real-time acquisition and processing of rPPG signal, enabling fatigue status detection. In this setup, the first thread handles real-time rPPG data capture, storage and concatenation, while the second thread conducts real-time analysis and fatigue detection of rPPG data. Through a combination of skin detection and LUV color space conversion, rPPG raw signal extraction is achieved, effectively eliminating interference from internal and external environmental facial noise; Subsequently, an adaptive multi-stage filtering process enhances the signal-to-noise ratio, and a multi-dimensional fusion CNN model ensures accurate detection of respiration and heart rate.

Abstract: Computer implemented systems are described that implement a differentiable neural architecture search (DNAS) engine executing on one or more processors. The DNAS engine is configured with a stochastic super net defining a layer-wise search space having a plurality of candidate layers, each of the candidate layers specifying one or more operators for a neural network architecture. Further, the DNAS engine is configured to process training data to train weights for the operators in the stochastic super net based on a loss function representing a latency of the respective operator on a target platform, and to select a set of candidate neural network architectures from the trained stochastic super net. The DNAS engine may, for example, be configured to train the stochastic super net by traversing the layer-wise search space using gradient-based optimization of network architecture distribution.

Abstract: According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase.

Abstract: According to various embodiments, a machine-learning based system for diabetes analysis is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and demographic data from a user interface. The processors are further configured to train at least one neural network based on a grow-and-prune paradigm to generate at least one diabetes inference model. The neural network grows at least one of connections and neurons based on gradient information and prunes away at least one of connections and neurons based on magnitude information. The processors are also configured to output a diabetes-based decision by inputting the received physiological data and demographic data into the generated diabetes inference model.

Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset that has initial data and is updated with new data is disclosed. The method includes performing a first training on the initial neural network architecture to create a first trained neural network architecture. The method additionally includes performing a second training on the first trained neural network architecture when the dataset is updated with new data to create a second trained neural network architecture. The second training includes growing one or more connections for the new data based on a gradient of each connection, growing one or more connections for the new data and the initial data based on a gradient of each connection, and iteratively pruning one or more connections based on a magnitude of each connection until a desired neural network architecture is achieved.

Abstract: According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase.

Abstract: According to various embodiments, a method for generating an optimal hidden-layer long short-term memory (H-LSTM) architecture is disclosed. The H-LSTM architecture includes a memory cell and a plurality of deep neural network (DNN) control gates enhanced with hidden layers. The method includes providing an initial seed H-LSTM architecture, training the initial seed H-LSTM architecture by growing one or more connections based on gradient information and iteratively pruning one or more connections based on magnitude information, and terminating the iterative pruning when training cannot achieve a predefined accuracy threshold.

Abstract: According to various embodiments, a method for locating the user of a mobile device without accessing global position system (GPS) data is disclosed. The method includes determining the last location that the user was connected to a wireless network. The method further includes compiling publicly-available auxiliary information related to the last location. The method additionally includes classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking. The method also includes estimating the location of the user based on sensory and non-sensory data of the mobile device particular to the activity classification of the user.

Abstract: According to various embodiments, a method for locating the user of a mobile device without accessing global position system (GPS) data is disclosed. The method includes determining the last location that the user was connected to a wireless network. The method further includes compiling publicly-available auxiliary information related to the last location. The method additionally includes classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking. The method also includes estimating the location of the user based on sensory and non-sensory data of the mobile device particular to the activity classification of the user.