Abstract: The present disclosure presents system and methods for obtaining multimodal input data of a subject, wherein the multimodal input data comprises at least two input modalities of data; extracting features from the multimodal input data; learning multimodal signal correlations and source latent distribution alignment from the extracted features of the multimodal input data; training and optimizing a multimodal machine learning algorithm on input labeled data to learn local features of each modality of the multimodal input data; and/or executing, by a computer system, the trained multimodal machine learning algorithm to predict a cognitive state or disorder of the subject using the learned multimodal signal correlations and source latent distribution alignment.

Abstract: System, methods, and algorithms are disclosed to carry out real-time video scene parsing and indexing in conjunction with query-based retrieval of geographically distributed object-attribute relationships. A distributed video analytics query mechanism is disclosed that involves swarms of small deep neural networks at embedded-AI edge devices, which can quickly perform initial feature detection and extraction and also re-identification of features or object in a cooperative manner. Then, the high-volume edge inference may fall back to the query computing model in a cloud, which performs complementary large scale up processing and result generation. The final decision, labelling, and scene investigation may be done by humans after interpreting the query results. This approach can provide the benefit of low communication costs (edge to cloud) compared to continually offloading parallel streams of edge devices, such as video, to the cloud.

Abstract: Evaluating the engagement and attentiveness of persons in a group by preparing a streaming video of the group, extracting faces of the persons in the group, aligning the extracted faces, assigning an identity to each of the faces, recognizing the emotional state of each face by a temporal emotion recognition module, and estimating the attentiveness of each person based on the recognized emotional state of each face of each person. The estimated attentiveness may also include recognizing the gaze of each person.

Abstract: Evaluating the engagement and attentiveness of persons in a group by preparing a streaming video of the group, extracting faces of the persons in the group, aligning the extracted faces, assigning an identity to each of the faces, recognizing the emotional state of each face by a temporal emotion recognition module, and estimating the attentiveness of each person based on the recognized emotional state of each face of each person. The estimated attentiveness may also include recognizing the gaze of each person.

Abstract: A computer implemented approach which simplifies the task of a user in using commodity pricing information in making decisions on purchasing of items. The user is provided a suitable interface using which the constituent commodities and the required respective quantities for each item may be specified. The digital processing system then automatically (without requiring manual entry by the user) retrieves the current prices of the commodities, and provides various types of information which simplifies the purchasing decisions. According to one aspect, alerts are provided when the prices of a commodity does not satisfy pre-specified limits or when the aggregate cost of commodities required for one unit of the item does not satisfy a pre-specified limit. Similarly, information is presented which simplifies hedging decisions and buy or make decisions.