Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: An IoT system includes a computing module for controlling an integral function of the system and including an analysis unit and a machine learning unit. The analysis unit is capable of operational analysis and creating a predictive model and creating a predictive model according to the data analyzed. The machine learning unit has an algorithm function to create a corresponding learning model. An IoT module is electrically connected to the computing module to serve as an intermediate role. At least one detection unit is electrically connected to the IoT module and disposed in soil to detect data of environmental and soil conditions and sends the data detected to the computing module for subsequent analysis.

Abstract: Systems and methods include receiving data from a network that includes a plurality of network elements that operate at a plurality levels that include any of network layers and encapsulations; displaying any of the plurality of network elements as a three-dimensional icon on a network map; illustrating a plinth in the three-dimensional icon for each of the plurality of levels for each network element of the any of the plurality of network elements, wherein a plinth at each level indicates a corresponding network element participates in that level; and illustrating horizontal connectivity as links between any of the network elements via associated plinths.

Abstract: An approach is provided for accessing and managing data from multiple heterogeneous data sources, processing the data to generate processing results, and providing user access to the processing results via a portal. The portal provides unified intelligent views of the processing results across the heterogeneous data sources, providing a single source of facts and trends. Embodiments include controlling user access to the views based upon user credentials.

Abstract: The disclosure relates to methods and systems for predicting time of occurrence of future server failures using server logs and a stream of numeric time-series data occurred with a particular time window. Method performed by processor includes accessing plurality of server logs and stream of numeric time-series data, applying density and sequential machine learning model over plurality of server logs for obtaining first and second outputs, respectively, applying a stochastic recurrent neural network model over the stream of time-series data to obtain third output. The method includes aggregating first, second, and third outputs using an ensemble model, predicting likelihood of at least one future server anomaly based on the aggregating, and determining time of occurrence of the at least one future server anomaly by capturing server behavior characteristics using time-series network model. The server behavior characteristics include time-series patterns of the stream of numeric time-series data.

Abstract: A trust reality service brokering apparatus located on an edge cloud receives a context rule, analyzes event data of at least one physical entity connected to the edge cloud based on the context rule, and transmits an action command to a physical entity or virtual entity corresponding to the event when it is determined that an event has occurred according to an analysis result.

Abstract: Systems and methods are provided for receiving a first media content item associated with a first interactive object of an interactive message, receiving a second media content item associated with a second interactive object of the interactive message, generating a third media content item based on the first media content item and second media content item, wherein the third media content item comprises combined features of the first media content item and the second media content item, and causing display of the generated third media content item.

Abstract: Virtual Reality (VR) processing devices and methods are provided for transmitting user feedback information comprising at least one of user position information and user orientation information, receiving encoded audio-video (A/V) data, which is generated based on the transmitted user feedback information, separating the A/V data into video data and audio data corresponding to a portion of a next frame of a sequence of frames of the video data to be displayed, decoding the portion of a next frame of the video data and the corresponding audio data, providing the audio data for aural presentation and controlling the portion of the next frame of the video data to be displayed in synchronization with the corresponding audio data.

Abstract: A client device determines a local user gradient value based on a current user preference vector and a local item gradient value based on a current item feature vector. The client device updates a user preference vector by using the local user gradient value and updates an item feature vector by using the local item gradient value. The client device determines a neighboring client device based on a predetermined adjacency relationship. The local item gradient value is sent by the client device to the neighboring client device. The client device receives a neighboring item gradient value sent by the neighboring client device. The client device updates the item feature vector by using the neighboring item gradient value. In response to the client device determining that a predetermined iteration stop condition is satisfied, the client device outputs the user preference vector and the item feature vector.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which evaluates user data for premium financing status and dynamically renders graphical user interfaces. The server trains an artificial intelligence model based on historical user data. The artificial intelligence model comprises one or more data points with each data point representing one of a plurality of attributes and applies a logistic regression algorithm to identify a weight factor for each attribute. The server uses a dynamic algorithm to generate a score by combining the plurality of attributes based on the weight factors. The server receives responses regarding the scores that indicate the premium financing status of each case. The server retrains the artificial intelligence model to identify new weight factors based on negative responses data. The server automatically displays new scores calculated based on the new weight factors.

Abstract: A prediction rationale analysis apparatus includes a prediction model that stores prediction models designed or trained to solve common problems, a prediction execution part that makes a prediction on the prediction target based on each prediction model to derive a model prediction result, a prediction rationale analyzer that identifies a set of prediction models based on which the model prediction result identical or approximate to the external prediction result is derived from the prediction target, and infers, as a prediction rationale for the external prediction result, a property common to the prediction models belonging to the set, and a prediction rationale difference analyzer that compares a property of any prediction model with the prediction rationale for the external prediction result to derive a prediction rationale difference indicating a difference in prediction rationale between the prediction model and the external prediction, and outputs information based on the prediction rationale differenc

Abstract: Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Abstract: Systems and methods for generating a video including a plurality of graphic objects provided in a shared environment is described. The method includes acquiring, at a first computing device, a shared session identifier from a shared session manager, the shared session identifier being associated with a first user identifier, receiving a selection of a second user identifier, causing the shared session identifier and the first user identifier to be provided to a second computing device associated with the second user identifier, receiving as input, a first graphic object for rendering to a display associated with the first computing device, the first graphic object being associated with the first user identifier, receiving from a data synchronizer, a second graphic object associated with the second user identifier and the shared session identifier for rendering to the display associated with the second computing device, and generating a video including graphic objects.

Abstract: An optimization apparatus includes a memory; and a processor coupled to the memory and the processor configured to: compute a local solution for a combinatorial optimization problem based on a first evaluation function representing the combinatorial optimization problem, select a state variable group targeted by partial problems from the plurality of state variables based on a first state variable whose value at the local solution is a predetermined value among the plurality of state variables included in the first evaluation function, a weight coefficient representing a magnitude of an interaction between the plurality of state variables held in a storage unit, and input selection region information, search a ground state for a second evaluation function representing the partial problems for the selected state variable group, and generate a whole solution by updating the local solution based on the partial solutions acquired by the ground state search.

Abstract: Aspects of the disclosure include methods, apparatuses, and non-transitory computer-readable storage mediums for receiving media data. One apparatus includes processing circuitry that receives a media presentation description (MPD) file that includes an essential property descriptor for session-based dynamic adaptive streaming over hypertext transfer protocol (DASH). The essential property descriptor indicates a session-based description (SBD) file and includes a set of keys for a part of a uniform resource locator (URL) that is used for receiving the media data. The processing circuitry determines a respective value for each of the set of keys based on whether the respective value is included in the SBD file and modifies the URL based on the set of keys and the determined values.

Abstract: Operations for facilitating establishment of connections in an online network are disclosed. A set of connection recommendations for a first entity associated with the online network is accessed. For each connection recommendation in the set of connection recommendations, a ranking value associated with the connection recommendation is accessed, a utility value corresponding to the connection recommendation is determined, and an adjusted the ranking value for the connection recommendation is calculated. The utility value is a two-sided utility value that combines a prediction of a utility of the first entity and a prediction of a utility of a second entity with respect to a key performance indicator. A set of connection recommendations is communicated for presentation in an interactive user interface of a client device associated with the first entity in accordance with the adjusted ranking value of each connection recommendation.

Abstract: A method and system for intelligently notifying collaborators of activity on collaborative content include receiving a first indication of a first collaboration activity performed on a first document by a first collaborator among a plurality of first collaborators associated with the first document and responsive to receiving the first indication, obtaining first activity information for the first document comprising the first collaborative activity and one or more second collaborative activities performed on the first document. The method and system determine a lifecycle stage of the first document, analyze the first activity information and the lifecycle stage of the first document to identify one or more second collaborators among the plurality of first collaborators associated with the first document to notify of the first collaborative activity, and provide for displaying the first collaboration activity to the one or more second collaborators.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for distributed and/or decentralized data aggregation. A method includes determining a user's electronic credentials for a plurality of third party service providers. A method includes accessing each of a plurality of third party service providers, from a hardware device associated with a user, using the user's electronic credentials, to download data associated with the user from the third party service providers. A method includes aggregating downloaded data from third party service providers and providing one or more of alerts and messages to the user on the hardware device in response to the aggregated downloaded data.

Abstract: A computing system receives decrypts learned parameters of a computer-implemented predictive model stored in a computer-readable, non-volatile data store upon instantiation of a model process in volatile memory of the computing system. The predictive model is parameterized by learned parameters that are based upon private data of a user, wherein the learned parameters of the predictive model have been encrypted subsequent to training of the predictive model based upon the private data of the user. The computing system receives a request for a prediction associated with the user. Upon authorizing the request, the computing identifies the predictive model. The computing system, by way of the model process, generates the prediction based upon the request and the predictive model. The computing system outputs the prediction to a computing device associated with the user.

Abstract: Methods, systems, and media for presenting media content are provided.