Abstract: A method, system, and computer program product for software package update handling are provided. The method installs an initial set of software packages in a virtual environment. A package dependency graph is generated representing independent software packages and dependent software packages of the initial set of software packages. One or more software packages are updated with one or more updated software packages to generate a subsequent set of software packages. A compatibility check is performed on the subsequent set of software packages. The method generates an update prerequisite package based on the compatibility check.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to compliance mapping, and more particularly to aggregated mapping of one or more sets of context-based compliance data with standard compliance data, such as from a target domain and one or more associate domains. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a mapping component that can map a compliance control for a target domain based on a model trained by an active learning process that incorporates a plurality of contexts representing relationships between entities and associate domain specific dependencies.

Abstract: An approach for training a machine learning model within a carbon budgetary constraint may be provided. The approach may include receiving a carbon budget constraint, for training a machine learning model. The approach may also include generate a training plan for the machine learning model within the carbon budget constraint. Generating the training plan may include sampling the search space of the machine learning model and identifying hyperparameters that will have the greatest effect on the accuracy of the machine learning model. The approach may also include monitoring carbon emissions of the machine learning model training plan. Further, the approach may include updating the training plan of the machine learning model based on the monitored carbon emissions.

Abstract: Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

Abstract: An apparatus, a method, and a computer program product are provided that combine policy compliance with vulnerability management to provide a more accurate risk assessment of an environment. The method includes training a policy machine learning model using a first training dataset to generate a policy machine learning model to produce mitigation technique classifications and training a vulnerability machine learning model using a second training dataset to generate a vulnerability machine learning model to produce weakness type classifications. The method also includes mapping the mitigation technique classifications to attack techniques to produce a policy mapping and mapping the weakness type classifications to the attack techniques to produce a vulnerability mapping. The method further includes producing a risk assessment of a vulnerability based on the policy mapping and the vulnerability mapping.

Abstract: A request to execute a sequentially randomized controlled trial (sRCT) that relates to a subject regarding a population of humans is received. Datapoints from the population that are needed for the sRCT and factors that define the population as fitting the sRCT are identified. It is detected that a human within an interaction satisfies the factors and therefore is part of the population. During the interaction, the datapoints from the human that are needed for the sRCT are gathered in response to detecting that the human is part of the population, and treatment is randomly assigned if the interaction is also a treatment decision point.

Abstract: Techniques regarding neuromodulation are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include a mapping component that can generate a stimulus map by mapping a stimulus parameter to a response from an entity to application of a neuromodulating stimulus, with the first neuromodulating stimulus being applied to the entity based on the first stimulus parameter, to therapeutically cause or prevent a sensation.

Abstract: Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

Abstract: A latent bio-signal prediction can be obtained from at least one bio-signal from a consumer grade health monitoring device is described. A user's bio-signal readings from a health monitoring device can be analyzed by a model trained with the health records of a plurality of individuals. The model can be further personalized to user based on the individual traits of the user. The model can analyze the bio-signal and generate a latent bio-signal prediction. A latent bio-signal prediction can be transmitted to an electronic device for a user or health professional to monitor.

Abstract: An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to transform inter-arrival times between a plurality of input events into discrete time symbols and map the input events and the discrete time symbols using a dictionary to output a time gram representing a temporal dimension between a sequences of events.

Abstract: An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to transform inter-arrival times between a plurality of input events into discrete time symbols and map the input events and the discrete time symbols using a dictionary to output a time gram representing a temporal dimension between a sequences of events.

Abstract: An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to compute an inter-arrival time between a plurality of input events and computes a sequence of events, a transformation device configured to transform the inter-arrival time between the plurality of input events into discrete time symbols, and a mapping device configured to map the input events and the discrete time symbols using a dictionary to output a time gram.

Abstract: An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to compute an inter-arrival time between a plurality of input events and computes a sequence of events, a transformation device configured to transform the inter-arrival time between the plurality of input events into discrete time symbols, and a mapping device configured to map the input events and the discrete time symbols using a dictionary to output a time gram.

Abstract: A method of pre-fetching and preparing content in an information processing system is provided. The method includes the steps of generating at least one content pre-fetching policy and at least one content preparation policy, wherein each of the policies are at least in part a function of context information associated with a user. The content is pre-fetched based on information contained within the at least one content pre-fetching policy. Once the content has been pre-fetched, it is prepared based on information contained within the at least one content preparation policy. The context information associated with the user includes at least one of the user's usage patterns, current location, future plans and preferences.

Abstract: Techniques are disclosed for determining and validating provenance data in such data stream processing systems. For example, a method for processing data associated with a data stream received by a data stream processing system, wherein the system comprises a plurality of processing elements, comprises the following steps. Input data elements and output data elements associated with at least one processing element of the plurality of processing elements are obtained. One or more intervals are computed for the processing element using data representing observations of associations between inputs elements and output elements of the processing element, wherein, for a given one of the intervals, one or more particular input elements contained within the given interval are determined to have contributed to a particular output element. In another method, intervals are specified, and then validated by comparing the specified intervals against intervals computed based on observations.

Abstract: A method of pre-fetching and preparing content in an information processing system is provided. The method includes the steps of generating at least one content pre-fetching policy and at least one content preparation policy, wherein each of the policies are at least in part a function of context information associated with a user. The content is pre-fetched based on information contained within the at least one content pre-fetching policy. Once the content has been pre-fetched, it is prepared based on information contained within the at least one content preparation policy. The context information associated with the user includes at least one of the user's usage patterns, current location, future plans and preferences.

Abstract: At least one embodiment of the invention addresses the problem of accidental user operation (e.g., dialing) of a mobile communication device such as a mobile telephone. In this regard, embodiments of the invention provide a proactive solution for preventing unintended operation of a mobile device based on various types of input data of the device, most notably sensory data.

Abstract: Techniques are disclosed for maintaining and processing provenance data in such data stream processing systems. For example, a method for processing data associated with a data stream received by a data stream processing system, wherein the system comprises a plurality of processing elements, comprising the following steps. A portion of data associated with the data stream is maintained. The maintained data comprises inputs to each processing element that contributed to an output of each processing element. In response to an alert generated by one of the processing elements, a scheduler is triggered to determine when a pre-calculation of a prospective query related to the alert should be executed. In response to the scheduler, at least a portion of the maintained data is used to determine a set of data that contributed to the alert such that the alert-contributing set of data can be used to respond to the prospective query upon arrival thereof.

Abstract: A method is provided for performing a classification. The method includes ranking a plurality of features of a training set according to how closely they are correlated to their corresponding classifications, extracting a plurality of features of from input data, selecting a subset of the plurality of features such that a computational resource cost of the subset is less than a predefined computational resource maximum and a degree of utility achieved by a classification of the subset by a selected classifier is optimized and exceeds a predefined utility minimum, predicting one of the features of the sensor data that is not selected for the subset of features from a predefined number of past samples of the feature and adding the predicted feature to the subset of features, and classifying, by a processor, using the selected classifier and the resulting subset of features.

Abstract: The invention provides methods and apparatus for establishing a connection between a mobile client and an external computer network such that the client computer network connection provides the illusion of seamless connectivity. In one aspect of the invention data is processed in a wireless communication network and is received from a access point at a gateway to an external computer network. Access points are disposed in the vicinity of the path of the mobile client. In another aspect of the invention data is processed in a wireless communication network and is received at an onboard proxy from an access point. The data is then transmitted to a mobile client.