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 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: There are provided a system, a method and computer program product providing a data-to-model challenge platform for enabling bidders publicly build, test, evaluate, validate and optimize AI models on proprietary data while at the same time avoiding the need to grant them access to the data itself. Rather, the bidder develops analytics on the enterprise’s data to solve a task desirable to the organization and submits the analytics for evaluation against other bidders. The offering organization evaluates all submissions from the bidders and rank submissions against each other using the same metrics, wherein the metrics for selecting the winning bidder can include response time, number and rate of attempts to solve the analytics, quality of results, code compactness, team size etc. Allowing the submissions of the bidders to be visible only to the offering organization can occur subject to an agreement that the offering organization and the bidder sign.

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 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, computer program products, and systems are presented. The methods include, for instance: obtaining a style feed including a plurality of original works by an author. An author-style model for the author is built based on the style feed by use of a selected neural network, and a publication is generated in the style of the author based on the author-style model.

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: A process for monitoring binding events, a coating compound, and a device for monitoring binding events are disclosed. The process includes providing two reservoirs that contain an electrically conductive fluid, wherein the reservoirs are separated by a membrane having a nanopore. The process also includes selecting a target compound, selecting a ligand based on the selection of the target compound, and preparing a coating that includes the coating compound, wherein the coating compound includes the ligand and a moiety that immobilizes the ligand. Additionally, the process includes applying the coating to the inner surface of the nanopore, adding the target compound to a conductive liquid in the first chamber, establishing a voltage gradient across the membrane, and electrically monitoring translocation of the target molecule. The device includes the reservoirs and the membrane having the coated nanopore.

Abstract: A method and system for improving a machine learning technical support applicant selection process is provided. The method includes retrieving and analyzing, in real time, job role data, resume based data, and sensor data associated with a plurality of individuals. A specialized memory repository is generated within a specified portion of a hardware memory device and results of the analysis are stored within the specialized memory repository. Self-learning software code for executing a job applicant selection process is generated and a plurality of current resumes and previously generated time-based resumes are retrieved for current job applicants for a specified position. The current resumes are analyzed with respect to the previously generated time-based resumes and associated inconsistencies are determined. A first job applicant is selected and the self-learning software code is modified.

Abstract: A recommendation method, system, and computer program product include monitoring a patient using a plurality of sensors, receiving patient information including a comfort level corresponding to a sensor of the plurality of sensors, determining a relevance of each sensor of the plurality of sensors to at least one health conditions of the patient, and determining at least one sensor of the plurality of sensors to disconnect based on the comfort level and the relevance of each sensor.

Abstract: A cognitive coaching method; detecting one or more real-time specific action occurrences during one or more game phases; generating, with a heat map generator, one or more real-time heat maps with one or more specific action label based on the detected one or more real-time specific action occurrences; training a heat map image classifier with an analytics module; transposing one or more historical heat maps comprised in a historical heat map library onto the one or more generated real-time heat maps, in real time, to generate strategic insight on one or more opposing teams; analyzing, using a predictive module, a future potential outcome based on the one or more transposed heat maps; and signaling, to a user, a recommended corrective action deployment strategy based on the analysis of the future potential outcome.

Abstract: A health-monitoring method, system, and computer program product include operating at least one sensor of a health-monitoring device having a plurality of sensors, detecting a health condition event that requires operation of an additional sensor of the plurality of sensors to monitor the health condition event, activating the additional sensor of the health-monitoring device, and deactivating the additional sensor when the health condition event that requires the second sensor is no longer detected by the detecting.

Abstract: Nanofluidic passages such as nanochannels and nanopores are closed or opened in a controlled manner through the use of a feedback system. An oxide layer is grown or removed within a passage in the presence of an electrolyte until the passage reaches selected dimensions or is closed. The change in dimensions of the nanofluidic passage is measured during fabrication. The ionic current level through the passage can be used to determine passage dimensions. Fluid flow through an array of fluidic elements can be controlled by selective oxidation of fluidic passages between elements.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining sample essays, sample annotations corresponding to the sample essays, and a subject content for building a subject domain comprehension model and an essay annotation model, by use of one or more neural network. The nodes of the subject domain comprehension model and the essay annotation model are interconnected based on respective relevancies for automatically annotating student works according to a standard of review corresponding to submitting students.