Abstract: A computer-implemented method and system for generating a predictive model include a computation engine learning a predictive model having missing or crippled data. A processor applies a formulated problem of missing or crippled data based learning to the predictive model. The computation engine reduces one or more tasks associated with the predictive model to a quadratically constrained quadratic problem (QCQP). The computation engine characterizes one or more solutions associated with the QCQP, where each of the one or more solutions is a loss function value.

Abstract: Obtain, as input, in electronic form, structured information including tasks for a plurality of occupations in a plurality of industries over a length of time; compute, from the structured information, a time series of normalized occupation task shares over the length of time; train a computerized machine learning model, on the time series, to predict future task shares for the plurality of occupations in the plurality of industries; and, with the trained computerized machine learning model, predict the future task shares.

Abstract: Obtain, using at least one hardware processor, data characterizing a physical system governed by a physical conservation law. Apply, using the at least one hardware processor, contrastive learning to the data to automatically capture system invariants of the physical system. Employ, using the at least one hardware processor, a neural projection layer to guarantee that a corresponding dynamic machine learning model preserves the captured system invariants. Optionally, predict performance of the physical system using the corresponding dynamic machine learning model.

Abstract: A computer implemented method determines skill shares for skills from job advertisements. A skill share for a skill identifies a number of times a skill has appeared in job advertisements during a given period of time. The computer implemented method creates a time series of skill demand using the skill shares. The computer implemented method extracts embeddings from job advertisements for an occupation using natural language processing. The computer implemented method clusters the skills using the embeddings to form skill clusters. The computer implemented method defines a training dataset using as a time series of skill demand for all skills within a cluster containing a selected skill to be predicted. The computer implemented method trains a time series prediction model using the training dataset.

Abstract: In one aspect of the invention, there is a computer-implemented method including: detecting, by a processor set of a first sensor agent, sensor data from one or more sensors comprised in the first sensor agent; determining, by the processor set, an own series of estimates, based on the sensor data; transmitting, by the processor set, the own series of estimates; receiving, by the processor set, at least one additional series of estimates from additional sensor agents; restoring, by the processor set, in response to detecting that a second sensor agent of the additional sensor agents has become disconnected and then re-connected, the transmitting of the series of estimates to the second sensor agent and the receiving of the series of estimates from the second sensor agent; and outputting, by the processor set, based on the own series of estimates and the additional series of estimates, a series of consensus estimates.

Abstract: One or more computer processors create a fully convolution network (FCN) comprising a plurality of 1×1 convolutions. The one or more computer processors append linear mapping layer (LM) to created FCN. The one or more computer processors capture a plurality of features utilizing multi-scale dilated convolutional kernels from the linear mapped FCN (LM-FCN). The one or more computer processors apply an average pool layer to the captured plurality of features along a temporal axis of a dilated convolutional kernel within the LM-FCN. The one or more computer processors predict a classification for subsequent time-series data utilizing the pooled plurality of features.

Abstract: Providing a trained reinforcement learning (RL) model by formulating a decision process problem for the RL model, defining at least one of a logarithmic loss function for the RL model and defining an initiation point for the RL model according to an optimized spectral norm of the RL model, training the system according to the logarithmic loss function or from the initiation point, and providing the trained RL model.

Abstract: Using a first sensor device in a network of sensor devices, sensor data is measured. A second sensor device comprising a trusted sensor device is selected from the network of sensor devices. A gain matrix is updated. Using the gain matrix, the sensor data, and a second parameter estimate received from the second sensor device, a first parameter estimate is updated. The first parameter estimate comprises an estimate of a parameter of a model representing the first sensor device. Using the gain matrix, an estimation error covariance matrix and a cross-variance matrix are updated. Using the updated first parameter estimate, second sensor data measured by the first sensor device is adjusted.

Abstract: In aspects of the disclosure, a method comprises training, by a computing system, a dynamics model of a cooperative multi-agent reinforcement learning (c-MARL) environment. The method further comprises processing, by the computing system, a perturbation optimizer to generate a state perturbation of the c-MARL environment, based on the dynamics model. The method further comprises selecting one or more agents of the c-MARL system as having enhanced vulnerability. The method further comprises attacking, by the computing system, the c-MARL system based on the state perturbation and the selected one or more agents.

Abstract: A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

Abstract: An embodiment for identifying skill adjacencies and skill gaps to generate reskilling recommendations. The embodiment may receive input from a user including candidate details and a job description. The embodiment may automatically extract a first set of skill keywords from the candidate description and a second set of skill keywords from the job description. The embodiment may automatically input the first and second set of skill keywords into a first type of word embedding model and a second type of word embedding model to automatically generate word embeddings. The embodiment may automatically compare the generated word embeddings and calculate cosine similarity scores for the first and second set of skill keywords.

Abstract: One or more group-specific aggregate losses, one or more group-agnostic aggregate losses, and a joint loss are computed. A regularizer loss is computed based on the one or more group-specific aggregate losses and the one or more group-agnostic aggregate losses. One or more group-specific models are trained based on the one or more group-specific aggregate losses. A feature extractor is updated based on the regularizer loss and a joint classifier is updated based on the joint loss.

Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can identify a plurality of constraints on states of data and actions of data associated with a data model. Embodiments of the present invention can then identify constraints on safety policy parameters associated with a computing device. Embodiments of the present invention can then convert the identified constraints into a uniform domain syntax that considers coupled and decoupled constraints and introduce buffer data within the converted constraints, wherein the buffer data filters outlier constraints within the plurality of constraints. Embodiments of the present invention can then dynamically generate optimal safety policies associated with the computing device based on the remaining constraints.

Abstract: A method for creating a question answering system includes receiving user stories, wherein each of the user stories is structured as a plurality of first phrasal entities within a template; applying a Natural Language Processing to discover first data relationships between the first phrasal entities and first context relationships between the first phrasal entities; constructing a knowledge graph that captures second data relationships and second contextual relationships of a plurality of second phrasal entities; enriching the KG by linking the first phrasal entities to the second phrasal entities to form enriched phrasal entities in the KG; receiving a selection of ones of the enriched phrasal entities for completing a story template; identifying a technical requirement based on the selection of the enriched phrasal entities; and training a model matching at least one of the user stories to the technical requirement.

Abstract: A mechanism is provided in a data processing system to implement a personalized patient engagement engine.

Abstract: A method for training control software to reinforce safety constraints using visual inputs includes performing template matching for each object in an image of a reinforcement learning (RL) agent's action space using a visual template for each object wherein each object in the RL agent's action space is detected, mapping each detected object to a set of planar coordinates for each object in the RL agent's action space, determining a set of safe actions for the RL agent by applying a safety specification for the RL agent's action space to the set of variables for coordinates for each object in the RL agent's action space, outputting the set of safe actions to the RL agent for a current state of a RL procedure, and preventing the RL agent from executing an action that is unsafe, before the RL agent takes an action.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate data protection distributed learning are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a local parameter component that employs an agent to compute local model parameters based on data of the agent. The computer executable components can further comprise a global parameter component that employs the agent to estimate a global model parameter based on the local model parameters and model parameters of one or more neighbor agents.

Abstract: Method and apparatus that includes receiving a query describing an aspect in a video, the video including a plurality of frames, identifying multiple proposals that potentially correspond to the query where each of the proposals includes a subset of the plurality of frames, ranking the proposals using a graph convolution network that identifies relationships between the proposals, and selecting, based on the ranking, one of the proposals as a video segment that correlates to the query.

Abstract: Embodiments of the disclosure provide a reinforcement learning model configured to receive state data (e.g., image state data) and determine candidate actions (e.g., environment navigation actions, environment modification actions, etc.) based on the received state data. Embodiments of the disclosure further provide an object detector configured to generate symbolic state data (e.g., safety relevant data) from the state data. Accordingly, as described herein, a safety system can update a dynamical safety constraint based on the symbolic state data, as well as filter the actions determined by the reinforcement learning model and select an action to be executed based on the dynamical safety constraint. For instance, the safety system classifies each action (e.g., each candidate action determined by the reinforcement learning model) in each symbolic state as either “safe” or “not safe” based on the dynamical safety constraint (e.g., and a safe action may be selected and executed).

Abstract: Obtain, as input, in electronic form, structured information including tasks for a plurality of occupations in a plurality of industries over a length of time; compute, from the structured information, a time series of normalized occupation task shares over the length of time; train a computerized machine learning model, on the time series, to predict future task shares for the plurality of occupations in the plurality of industries; and, with the trained computerized machine learning model, predict the future task shares.