Abstract: Methods and apparatus for determining whether a media presentation device is in an on state or an off state are disclosed. A disclosed example method comprises determining contribution values from at least one of a signal measured from a sensing device or an output signal accessed from the presentation device, wherein the contribution values are indicative of a state of a presentation device. Summing, via a logic circuit, a first plurality of the contribution values corresponding to a first measurement cycle to generate a first intermediate fuzzy score for the first measurement cycle. Storing the first intermediate fuzzy score in a buffer including a plurality of intermediate fuzzy scores corresponding to respective measurement cycles. Combining, via the logic circuit, the intermediate fuzzy scores corresponding to a first time period to form a final fuzzy score.

Abstract: Methods and apparatus relating to techniques for incremental network quantization. In an example, an apparatus comprises logic, at least partially comprising hardware logic to determine a plurality of weights for a layer of a convolutional neural network (CNN) comprising a plurality of kernels; organize the plurality of weights into a plurality of clusters for the plurality of kernels; and apply a K-means compression algorithm to each of the plurality of clusters. Other embodiments are also disclosed and claimed.

Abstract: Techniques are disclosed for training of factorization machines (FMs) using a streaming mode alternating least squares (ALS) optimization. A methodology implementing the techniques according to an embodiment includes receiving a datapoint that includes a feature vector and an associated target value. The feature vector includes user identification, subject matter identification, and a context. The target value identifies an opinion of the user relative to the subject matter. The method further includes applying an FM to the feature vector to generate an estimate of the target value, and updating parameters of the FM for training of the FM. The parameter update is based on application of a streaming mode ALS optimization to: the datapoint; the estimate of the target value; and to an updated summation of intermediate calculated terms generated by application of the streaming mode ALS optimization to previously received datapoints associated with prior parameter updates of the FM.

Abstract: Record clustering is performed by learning from verified clusters which are used as the source of training data in a deduplication workflow utilizing supervised machine learning.

Abstract: Methods and systems for decoding communication protocols having an unknown structure. In the disclosed embodiments, a decoding system analyzes network traffic that uses such a communication protocol, and semi-automatically generates a structured template for decoding the protocol. In an example embodiment, the traffic comprises HTTP transactions used in some unknown variant of a Web-based e-mail or social network application, and the system generates an Extensible Markup Language (XML) template for parsing such transactions. The system enables an analyst to review sample transactions, and identify target components of the protocol that contain target information of interest. The system typically generates a set of rules with the assistance of the analyst.

Abstract: A user activity pattern may be ascertained using signal data from a set of computing devices. The activity pattern may be used to infer user intent with regards to a user interaction with a computing device or to predict a likely future action by the user. In one implementation, a set of computing devices is monitored to detect user activities using sensors associated with the computing devices. Activity features associated with the detected user activities are determined and used to identify an activity pattern based on a plurality of user activities having similar features. Examples of user activity patterns may include patterns based on time, location, content, or other context. The inferred user intent or predicted future actions may be used to provide improved user experiences, such as personalization, modifying functionality of user devices, or providing more efficient consumption of bandwidth or power.

Abstract: Methods and apparatus related to determining an inquiry to provide to a user based on deficient information related to a plan of the user. Deficient information may be determined based on an insufficient association between a desired information item of the plan and a set of information items that are determined from one or more sources associated with the user. In some implementations, the user may provide, responsive to the inquiry, additional information related to the deficient information and a suggestion may be provided to the user based on the additional information.

Abstract: A method may include selecting a particular entity from a knowledge graph when a level of connectivity between entities in the knowledge graph that are neighbors to the particular entity is above a certain level and determining whether the particular entity is in a character string.

Abstract: The present design is directed to a system for detecting and adjusting qualitative contexts across multiple dimensions for multiple actors with cognitive computing techniques including a series of periodic execution components configured to operate over full or partial sets of received data, the series of periodic components comprising a peer to peer analyzer configured to detect anomalous behaviors among work-specific peer actors sharing similar types tasks, an actor behavior analyzer configured to examine change in an actor's behavior over time by comparing the similarity of past behavior and current behavior, a rate of change predictor configured to study changes in behavior over time for peer to peer performance according to the peer to peer analyzer, actor behavior change according to the actor behavior analyzer, and actor correlation analysis, and a semantic rule analyzer configured to encode conditional, provisional, cognitive, operational, and functional knowledge, and a plurality of signal managers.

Abstract: A method, system and computer product for performing storage maintenance is described. A training set for storage volume reclamation is received. The training set for storage volume reclamation contains sets of storage parameters for storage volumes and corresponding user decisions whether the storage volumes are reclaimable. The training set is used to train a machine learning system to recognize reclaimable candidate storage volumes. The trained machine learning system is used to determine that a candidate storage volume for reclamation is likely a reclaimable storage volume.

Abstract: An event clustering system that has an extraction engine in communication with a managed infrastructure. A signalizer engine includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The signalizer engine determines one or more common characteristics or features from events. The signalizer engine uses the common features of events to produce clusters of events relating to the failure or errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. A feedback signalizer functor is provided that is a supervised machine learning approach to train to reproduce a situation. In response to production of the clusters one or more physical changes in a managed infrastructure hardware is made, where the hardware supports the flow and processing of information.

Abstract: A method and system of creating a model for large scale data analytics is provided. Training data is received in a form of a data matrix X and partitioned into a plurality of partitions. A random matrix T is generated. A feature matrix is determined based on multiplying the partitioned training data by the random matrix T. A predicted data {tilde over (y)} is determined for each partition via a stochastic average gradient (SAG) of each partition. A number of SAG values is reduced based on a number of rows n in the data matrix X. For each iteration, a sum of the reduced SAG values is determined, as well as a full gradient based on the sum of the reduced SAG values from all rows n, by distributed parallel processing. The model parameters w are updated based on the full gradient for each partition.

Abstract: Described is a system for selecting among intelligence elements of a neural model. An intelligence element is selected from a set of intelligence elements which change group attack probability estimates and processed via multiple operations. A semantic memory component learns group probability distributions and rules based on the group probability distributions. The rules determine which intelligence element related to the groups to select. Given an environment of new probability distributions, the semantic memory component recalls which rule to select to receive a particular intelligence element. An episodic memory component recalls a utility value for each information element A procedural memory component recalls and selects the information element considered to have the highest utility. A list of intelligence elements is published to disambiguate likely attackers.

Abstract: Certain embodiments involve generating or optimizing a neural network for risk assessment. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used both for accurately determining risk indicators or other outputs using predictor variables and for determining adverse action codes explaining the predictor variables' effect or an amount of impact on the risk indicator.

Abstract: A reinforcement learning processor specifically configured to train reinforcement learning agents in the AI systems by the way of implementing an application-specific instruction set is disclosed. The application-specific instruction set incorporates ‘Single Instruction Multiple Agents (SIMA)’ instructions. SIMA type instructions are specifically designed to be implemented simultaneously on a plurality of reinforcement learning agents which interact with corresponding reinforcement learning environments. The SIMA type instructions are specifically configured to receive either a reinforcement learning agent ID or a reinforcement learning environment ID as the operand. The reinforcement learning processor is designed for parallelism in reinforcement learning operations. The reinforcement learning processor executing of a plurality of threads associated with an operation or task in parallel.

Abstract: A method for determining a transfer apparatus based on user preferences and at least a transfer apparatus archive includes receiving, by a computer device, at least a transfer invocation and user preferences, generating for each candidate transfer apparatus, performance prognoses corresponding to the user preferences, wherein generating each performance prognoses comprises receiving a candidate transfer apparatus archive, training, as a function of the candidate transfer apparatus performance archive and a supervised machine-learning process, a candidate transfer apparatus model, generating performance prognoses as a function of the candidate transfer apparatus model and the at least a transfer invocation, selecting a candidate transfer apparatus as a function of the user preferences, generating an objective function of the user preferences, wherein the objective function outputs a ranking of performance prognoses and selecting a candidate transfer apparatus which maximizes the ranking, and providing the sele

Abstract: Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.

Abstract: Systems and methods are disclosed for dynamically analyzing and providing the quality of one or more content items at the time, or substantially close to the time, they are received by a data processing system. The systems and methods described herein can maintain and update the quality score for improving previously created content items after they have been published. The one or more content items can include one or more assets (e.g., one or more headlines, one or more descriptions, images, video, etc.). The data processing system can use numerical analysis methods to determine an overall quality (e.g., estimated clicks) of the content items received by the data processing system using a trained model.

Abstract: Certain embodiments involve generating or optimizing a neural network for risk assessment. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used to determine a relationship between each of the predictor variables and a risk indicator. The neural network can be optimized by iteratively adjusting the neural network such that a monotonic relationship exists between each of the predictor variables and the risk indicator. The optimized neural network can be used both for accurately determining risk indicators using predictor variables and determining adverse action codes for the predictor variables, which indicate an effect or an amount of impact that a given predictor variable has on the risk indicator. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the risk indicator score.

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of managing regulatory questions. The systems and methods receive a question having words and phrases. The systems and methods identify keywords in the question using a knowledgebase. The systems and methods determine closely related questions based on the identification, the closely related questions having answers associated with each question of the closely related questions. The systems and methods perform machine learning on the answers of the determined closely related questions.