Abstract: Provided are a computer program product, system, and method for training and using a sentiment machine learning module to determine a sentiment score. Haptic metric values are collected from haptic interfaces embedded in input devices users control to generate content. A training set associates a haptic metric value resulting from a user interacting with an input device to generate content and a sentiment score for the content. A sentiment machine learning module is trained to output the sentiment score in a training set from input comprising the haptic metric value. A haptic metric value received from an input device, used by an active user interacting with the interactive program, is inputted to the sentiment machine learning module to output a haptic sentiment score for the haptic metric value. The haptic sentiment score is provided to an interactive program to control the interactive program communications with the active user.

Abstract: A computer-implemented method of decoding video data in multiple resolution formats includes receiving an encoded video stream including a salient data and a non-salient data, the salient data having a higher resolution format than the non-salient data. The video stream is decoded into the non-salient data in a lower-resolution format and the salient data in the higher-resolution format. The non-salient data is reconstructed to a higher resolution format. The salient data and the reconstructed non-salient data are combined to form a video stream in the higher-resolution format of the salient data.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include identifying one or more joints of a user based on collected data and generating one or more 3D representations of the one or more joints of the user. Embodiments may further include anonymizing the one or more 3D representations, classifying one or more actions of the user based on the one or more 3D representations, wherein the classifying outputs an action score, and exporting at least one of the one or more actions and the action score.

Abstract: A computer-implemented method of encoding video streams for low-bandwidth transmissions includes identifying a salient data and a non-salient data in a high-resolution video stream. The salient data and the non-salient data is segmented. The non-salient data is compressed to a lower resolution. The salient data and the compressed non-salient data are transmitted in a low-bandwidth transmission.

Abstract: A method, a computer program product, and a computer system determine abnormal motion from a patient. The method includes receiving sensory data of the patient and a location in which the patient is present. The sensory data includes video data over a period of time the patient is being monitored. The method includes generating contextual information based on the sensory data. The contextual information is indicative of surroundings of the patient and characteristics of the location. The method includes generating motion information based on the sensory data. The motion information is indicative of movement of the patient in the location. The method includes generating contextual motion data by incorporating the contextual information with the motion information. The method includes determining the abnormal motion based on the contextual motion data.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Abstract: A first version of a model specified by a model execution request is executed, producing a first execution result. A second version of the model is selected according to an input data attribute specified by the model execution request. The second version of the model is executed, producing a first execution result. Using a natural language processing engine, responsive to the first execution result and the second execution result differing by more than a threshold amount, a natural language explanation of a difference between the first execution result and the second execution result is constructed.

Abstract: Weighted population code in neuromorphic systems is provided. According to an embodiment, a plurality of input values is received. For each of the plurality of values, a plurality of spikes is generated. Each of the plurality of spikes has an associated weight. A consumption time is determined for each of the plurality of spikes. Each of the plurality of spikes is sent for consumption at its consumption time.

Abstract: Aspects described herein include a method of generating three-dimensional (3D) spikes. The method comprises receiving a signal comprising time-series data and generating a first two-dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing one or more Parkinson's disease symptoms. The exemplary embodiments may include collecting data of a user's motion, extracting one or more features from the collected data, and assessing one or more Parkinson's disease symptoms of the user based on applying one or more models to the data.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Abstract: A computer-implemented method of encoding video streams for low-bandwidth transmissions includes identifying a salient data and a non-salient data in a high-resolution video stream. The salient data and the non-salient data is segmented. The non-salient data is compressed to a lower resolution. The salient data and the compressed non-salient data are transmitted in a low-bandwidth transmission.

Abstract: A method, a computer program product, and a computer system determine abnormal motion from a patient. The method includes receiving sensory data of the patient and a location in which the patient is present. The sensory data includes video data over a period of time the patient is being monitored. The method includes generating contextual information based on the sensory data. The contextual information is indicative of surroundings of the patient and characteristics of the location. The method includes generating motion information based on the sensory data. The motion information is indicative of movement of the patient in the location. The method includes generating contextual motion data by incorporating the contextual information with the motion information. The method includes determining the abnormal motion based on the contextual motion data.

Abstract: A machine learning model can be updated based on collected data (i.e., initially unlabeled data). The unlabeled data can be labeled based on comparisons to labeled data. The newly labeled data, referred to as “weak labeled data” (as it was labeled without direct input of a professional) can then be used as training data in order to retrain the machine learning model.

Abstract: A machine learning model can be optimized for deployment on a device based on hardware specifications of the device. An existing model is acquired and pruned to reduce hardware resource consumption of the model. The pruned model is then trained based on training data. The pruned model is also trained based on a collection of “teacher” models. Performance of the trained model is then evaluated and compared to performance requirements, which can be based on the hardware specifications of a device.

Abstract: Methods, systems and computer program products for automatically generating structured training data based on an unstructured document are provided. Aspects include receiving an unstructured document and a corresponding structured document that includes labeled portions. Aspects also include generating a parsed document that has one or more extracted objects by applying a parsing tool to the unstructured document. Aspects also include identifying one or more matching extracted objects by applying a matching algorithm to the structured document and the parsed document. Each matching extracted object is an extracted object of the parsed document that corresponds to a labeled portion of the structured document. Aspects also include annotating a region of the unstructured document that corresponds to the bounding box of the respective matching extracted object with a respective label of the corresponding labeled portion of the unstructured document.

Abstract: One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction.

Abstract: Aspects described herein include a method of generating three-dimensional (3D) spikes. The method comprises receiving a signal comprising time-series data and generating a first two-dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

Abstract: Methods, systems and computer program products for automatically generating structured training data based on an unstructured document are provided. Aspects include receiving an unstructured document and a corresponding structured document that includes labeled portions. Aspects also include generating a parsed document that has one or more extracted objects by applying a parsing tool to the unstructured document. Aspects also include identifying one or more matching extracted objects by applying a matching algorithm to the structured document and the parsed document. Each matching extracted object is an extracted object of the parsed document that corresponds to a labeled portion of the structured document. Aspects also include annotating a region of the unstructured document that corresponds to the bounding box of the respective matching extracted object with a respective label of the corresponding labeled portion of the unstructured document.

Abstract: Provided are embodiments for a computer-implemented method. The method includes receiving a sequence of image data, transforming objects in each frame of the sequence of the image data into direction vectors, and clustering the direction vectors based at least in part on features of the objects. The method also includes mapping the direction vectors for the objects in each frame into a position-orientation data structure, and performing tracking using the mapped direction vectors in the position-orientation data structure. Also provided are embodiments of a computer program product and a system for performing object tracking.