Abstract: A general-purpose approach to solving the core problems of detecting and predicting the actions of invisible actors, and the consequential challenges of intervention and prevention. The operational forecasting system is applied to data gathered from complex systems. The operational forecasting system uses novel early-warning signals that are based on anomalous behaviors of actors/agents that are observed, as they respond to those unobserved actors that are the source of systemic change. The operational forecasting system targets predicting when an event will occur, before it does, based on the anomalous behaviors of observed actors responding to those invisible actors that are creating the perturbation (i.e. the murmuration).

Abstract: A summarization system includes: K embedding modules configured to: receive K blocks of text, respectively, of a document to be summarized; and generate K first representations based on the K blocks of text, respectively, where K is an integer greater than 2; a first propagation module configured to generate second representations based on the K first representations; a second propagation module configured to generate third representations based on the second representations; an output module configured to select ones of the K blocks based on the third representations; and a summary module configured to generate a summary of the document from text of the selected ones of the K blocks.

Abstract: The present invention relates to a user knowledge tracing method with more improved accuracy, and an operating method for a user knowledge tracing system including a plurality of encoder neural networks and a plurality of decoder neural networks includes: inputting exercise information to a k-th encoder neural network and inputting response information to a k-th decoder neural network; generating query data, which is information on an exercise for which a user is to predict a correct answer probability, by reflecting a weight to the response information and generating attention information to be used as a weight for the query data by reflecting the weight to the exercise information; and training the user knowledge tracing system by using the attention information as the weight for the query data.

Abstract: A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

Abstract: A system is provided. The system includes a first platform including a first platform level agent configured to direct one or more actions of the first platform based on at least one of a selected target or a selected goal. The system also includes a computer system in communication with the first platform level agent. The computer system programmed to a) execute a supervisor level agent configured to select at least one of a target or a goal for one or more platforms including the first platform, b) receive targeting information including one or more targets, c) receive platform information for the one or more platforms, d) select, by the supervisor level agent, a target of the one or more targets based on the target information and the platform information, and e) transmit, to the first platform level agent, the selected target.

Abstract: An information handling system includes a data store configured to store an account associated with a first alert and a second alert. A processor may receive the first alert and the second alert, map the first alert and the second alert to a first node and a second node of a causality graph, traverse the causality graph starting from the sink node to the source node to determine an association between the first alert and the second alert, and generate the account based on the association between the first alert and the second alert.

Abstract: A method, system, and computer program product for managing model updates at multiple data centers hosting a same machine learning model obtain a plurality of first feature profiles input to a first implementation of a first machine learning model and a plurality of first model states determined from processing a model input with the first implementation; determine that a first model policy associated with the first machine learning model is satisfied, based on the plurality of first model states including a set of model states defined by the first model policy and the plurality of first feature profiles including a set of feature profiles defined by the first model policy; and in response to determining that the first model policy is satisfied, provide the plurality of first model states and the plurality of first feature profiles for updating at least one second implementation of the first machine learning model.

Abstract: An electronic device includes at least one processor configured to obtain user data associated with a plurality of devices from multiple data sources. The at least one processor is also configured to determine a static weight for each of the plurality of devices based on at least one source of the multiple data sources. The at least one processor is further configured to identify a portion of the plurality of devices that represents the plurality of devices based on the static weight and a dynamic weight. In addition, the at least one processor is configured to determine the dynamic weight for each of the portion of the plurality of devices while the portion of the plurality of devices is identified, where the dynamic weight is based on one or more sources of the multiple data sources.

Abstract: Methods are provided for deploying machine learning operations within existing storage devices for streamlining various calibration processes. Machine learning operations are specifically designed to generate inference data as a substitute for various measurements taken during calibration. These operations may be verified through additional sample measurements and rolled back when the results of the machine learning operations are outside of a range of approved values. Storage devices designed to utilize machine learning methods within calibration processes can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing an application stored in the non-volatile memory and receiving a request for measurement data from the application.

Abstract: List-based entity name detection implementations are described that detect entity names in electronic textural documents. In one implementation, unknown entity names are detected. In another implementation, ambiguous entity names are detected and disambiguated. In yet another implementation, generic entity names are detected and associated with an applicable species entity name.

Abstract: One embodiment of the present application sets forth a method for playback of a sense-making operation. The method includes receiving first session data that includes a set of timeline steps. Each timeline step included in the set of timeline steps corresponds to a user action performed on a data set. The method further includes receiving a playback command to display a first sequence of timeline steps included in the set of timeline steps. The method further includes rendering a first graph for display based on at least one timeline step included in the first sequence of timeline steps.

Abstract: A method includes segmenting updates associated with a record into a set of update subsets and generating first and second vectors based on first and second update subsets using a first neural network. The first update subset is associated with a first session and a timestamp, and the second update subset is associated with a second session. The method includes determining a first output using a second neural network based on the first and second vectors and a time difference between the first and second sessions. The method includes selecting a segment of a periodic time interval based on the timestamp, determining a second output using a third neural network based on a ratio based on the segment and the periodic time interval, and generating a characterizing vector using a fourth neural network based on the first and second outputs.

Abstract: A computer-implemented method for managing an installed base of multiple artificial intelligence (AI) modules, each AI module transforming input data into output data by an internal processing chain, the input data including data that characterize a state and/or the behavior, of at least one physical plant, apparatus, or system, the behavior of the internal processing chain being determined by a set of configuration parameters trainable based on training input data and corresponding reference data to which the internal processing chain is to map the training input data, the method including: obtaining, from multiple AI modules and/or domains in which the AI modules are applied, pairs of input data and corresponding output data, and/or pairs of training input data and corresponding reference data, and/or configuration parameters, the data and/or parameters relating to same or sufficiently similar operating situations of the AI modules according to a predetermined quantitative similarity criterion.

Abstract: The present invention relates to controlling a 2D application in a mixed reality application. In order to provide further simplification in terms of operating devices and equipment for the medical personnel, a device (10) for controlling a 2D application in a mixed reality application is provided. The device comprises a 2D application interface (12), a processor (14), a mixed reality application interface (16) and a control interface (18). The 2D application interface receives data representative of a 2D application. The application comprises interactive elements providing commands for user control of the 2D application. The processor identifies and extracts the interactive elements based on the data representative of the 2D application. The processor also translates the interactive elements to user interface features of a mixed reality display application.

Abstract: Embodiments of the invention are directed to computer-implemented methods of labeling unlabeled electronic information. In a non-limiting embodiment of the invention, the computer-implemented method includes receiving, using a processor system, an unlabeled error log (EL) having an EL format. A set of unlabeled EL keywords are extracted from the unlabeled EL. For each unlabeled EL keyword, the processor system uses the unlabeled EL keyword to extract an electronic document (ED) sentence from an ED based on a determination that the ED sentence is relevant to the unlabeled EL keyword. One or more ED keywords are extracted from the ED sentence. A deep neural network of the processor system is used to predict an ED sentence label for the ED sentence, an ED keyword label for the ED keyword, and an EL keyword label for the unlabeled EL keyword.

Abstract: A computer-implemented method is disclosed for compressing a deep neural network (DNN) model by DNN weight pruning to accelerate DNN inference on mobile devices. The method includes the steps of (a) performing an intra-convolution kernel pruning of the DNN model wherein a fixed number of weights are pruned in each convolution kernel of the DNN model to generate sparse convolution patterns; (b) performing inter-convolution kernel pruning of the DNN model to generate connectivity sparsity, wherein inter-convolution kernel pruning comprises cutting connections between given input and output channels of the DNN model to remove corresponding kernels; and (c) training the DNN model compressed in steps (a) and (b).

Abstract: A contact center system can track and evaluate dialogue data, telephony data, and/or application usage data associated with communication sessions between customers and representatives. The contact center system can identify communication sessions that have dialogue data containing keywords of one or more keyword categories, such as keyword categories associated with perceptions of high customer effort, and/or based on values of other key performance indicators. Users can use the contact center system to investigate the dialogue data, telephony data, and/or application usage data for identified communication sessions, for example to identify opportunities to train representatives to use alternate language during communication sessions, revise procedures in the contact center, or otherwise reduce perceptions of customer effort.

Abstract: A facility for providing an XR device-based tool for cross-platform content creation and display is disclosed. The facility causes a definition of a mixed reality (MR) procedure to be presented by an editing MR device in a virtual application window. Input is received via the editing MR device. The facility interprets the received input as one or more editing actions performed with respect to the MR procedure definition and alters the MR procedure definition accordingly. The facility creates a device-independent representation of the MR procedure based on the altered MR procedure definition.

Abstract: The present disclosure relates generally to sharing audio, including while the electronic device is connected to a first external device via a communication link, wherein the electronic device is configured to provide audio data to the first external device via the communication link, detecting an indication that a physical proximity between the electronic device and a second external device, different from the first external device, satisfies a proximity condition; in response to detecting the indication that the physical proximity between the electronic device and the second external device satisfies the proximity condition, displaying, via the display device, a first user interface including a first affordance; detecting an input corresponding to selection of the first affordance; and in response to detecting the input corresponding to selection of the first affordance, initiating a process to provide audio data concurrently with the first external device and the second external device.

Abstract: Computer technology for content association in file editing. The method includes providing one or more files including a first element currently being edited and including at least one other element of a different format to the first element. The method analyzes changes to the first element to obtain information relating to the changes and analyzing the contents of at least one other element. The method identifies any of the other elements that are correlated elements to the first element by determining an association between the contents of the other element and the changes of the first element and instigates a corresponding change to the contents of any correlated elements.