Abstract: A computerized method of classifying network accessible storage transactions at network accessible storage. The method comprises obtaining an client predictive security model for anomaly or malfunctioning detection, the client predictive security model is dynamically created by an analysis of a plurality of client transactions made to access target data stored in an client computing device, monitoring a plurality of network accessible storage transactions made to access a replica of the target data when the replica is stored in an network accessible storage, and classifying at least some of the plurality of network accessible storage transactions based on the client predictive security model.

Abstract: A system, method and computer program product for assessing field development plans selected based on a stochastic response surface, preferably, for hydrocarbon reservoir production. Assessment begins by assessing uncertainty associated with multiple decision variable configurations. A subset of realizations is selected. An individual surrogate is constructed for each subset realization. A reduced representative realization subset is determined, where the reduced subset is representative of the behavior/performance of all realizations of decision variable configurations.

Abstract: A spiking neural network (SNN) is defined that includes artificial neurons interconnected by artificial synapses, the SNN defined to correspond to one or more numerical matrices in an equation such that weight values of the synapses correspond to values in the numerical matrices. An input vector is provided to the SNN to correspond to a numerical vector in the equation. A steady state spiking rate is determined for at least a portion of the neurons in the SNN and an approximate result of a matrix inverse problem corresponding to the equation is determined based on values of the steady state spiking rates.

Abstract: A method for recommendation model integration includes obtaining, using a first model, multiple text scores ranking multiple software applications according to a first user submitted terms in a source software application, and obtaining, using a second model, multiple feature scores ranking the software applications according to multiple features of the software applications. The method further includes integrating the text scores and the feature scores to generate multiple combined scores for the software applications, and presenting, to a user, the software applications according to the combined scores.

Abstract: Provided in some embodiments is an artificial intelligence (“AI”) engine configured to work with a pedagogical programming language configured to enable an author to 1) define a mental model to be learned by an AI model, the mental model including an input, one or more concept nodes, one or more stream nodes, and an output, as well as 2) define one or more curriculums for training the AI model respectively on the one or more concept nodes. A compiler can be configured to generate an assembly code from a source code authored in the pedagogical programming language. An architect module can be configured to propose a neural-network layout from the assembly code. A learner module can be configured to build the AI model the neural-network layout. An instructor module can be configured to train the AI model on the one or more concept nodes respectively with the one or more curriculums.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine configured to work with a graphical user interface (“GUI”). The AI engine can include an architect module, instructor module, and learner module AI-engine modules. The GUI can be configured with a text editor and a mental-model editor to enable an author to define a mental model to be learned by an AI model, the mental model including an input, one or more concept nodes, and an output. The architect module can be configured to propose a neural-network layout from an assembly code compiled from a source code in a pedagogical programming language, the learner module can be configured to build the AI model from the neural-network layout, and the instructor module can be configured to train the AI model on the one or more concept nodes.

Abstract: According to one embodiment, a memory system includes a volatile memory, a power supply circuit, and a controller. The power supply circuit includes a first power supply path in which power supplied from a host device is supplied to the volatile memory, a second power supply path in which the power is supplied from the internal power supply to the volatile memory, and a switching device that switches between the first power supply path and the second power supply path. In response to an instruction for a transition to a low power consumption mode received from the host device, the controller outputs, to the switching device, an instruction to switch the power supply circuit from the first power supply path to the second power supply path.

Abstract: Provided is a tool life estimating device that enables estimation of a life of a tool used in a machine tool according to changes in machining conditions. The tool life estimating device includes a state observation unit that acquires machining information indicative of a status of the machining in a state where the life of the tool remains sufficiently, wherein the machining information is acquired from log data recorded while the machine tool is operated, and creates input data based on the machining information that has been acquired; a learning unit that constructs a learning model in which clusters of the machining information are created by unsupervised learning using the input data that has been created by the state observation unit; and a learning model storage unit that stores the learning model.

Abstract: According to some embodiments, an analytics computing environment data store may contain a set of electronic data records, each electronic data record being associated with a predictive analytic algorithm and including an algorithm identifier and a set of algorithm characteristic values. An analytics environment computer may receive an adjustment from a user associated with an enterprise, the adjustment changing at least one of the set of algorithm characteristic values for a predictive analytic algorithm. Deployment of the predictive analytic algorithm may then be initiated in an enterprise operations workflow and at least one result may be generated. The deployed predictive analytic algorithm may then monitor the result and generate an alert signal when the result exceeds a boundary condition.

Abstract: Node meta-model systems and processes are disclosed which provide a seamless run-time integration between disparate devices, objects, frameworks, and/or agents (“objects”) without a priori knowledge of the types of objects, their capabilities, accepted commands/events, and underlying frameworks, and without expending any development effort. By introducing a node meta model, each system, device, and framework is described using its capabilities, properties, events, and methods of communications and thus an agent, at run-time, can figure out how to integrate/communicate with these systems accurately and without any development effort.

Abstract: A server device has an AC power supply unit configured to convert an AC voltage received from an AC power supply to a DC voltage, and a DC power supply input part configured to receive supply of a DC voltage outputted by an AC power supply unit of an external power supply device. The server device runs at a DC voltage supplied by at least one of the AC power supply unit and the DC power supply input part.

Abstract: Mechanisms are provided for implementing a proximity based candidate answer pre-processor engine that outputs a sub-set of candidate answers to a question and answer (QA) system. The mechanisms receive a lexical answer type (LAT) and an entity specified in an input natural language question as well as an ontology data structure representing a corpus of natural language content. The mechanisms identify a set of candidate answers having associated nodes in the ontology data structure that are within a predetermined proximity of a node corresponding to the entity, and a sub-set of candidate answers in the set of candidate answers having an entity type corresponding to the LAT. The mechanisms output, to the QA system, the sub-set of candidate answers as candidate answers to the input natural language question for evaluation and selection of a final answer to the input natural language question.

Abstract: In one embodiment, a method includes, by one or more computing devices of an online social network, receiving, from a client system of a first user of the online social network, a first audio input from an unknown user, identifying one or more candidate users, wherein each candidate user is a user of the online social network within a threshold degree of separation of a known user, calculating, for each candidate user, a probability score representing a probability that the unknown user is the candidate user, wherein the probability score is based on a comparison of the first audio input to a voiceprint of the candidate user stored by the online social network, wherein each voiceprint comprises audio data for auditory identification of the candidate user, and identifying one of the candidate users as being the unknown user based on the calculated probability scores of the candidate users.

Abstract: A computing device in accordance with an example includes a first operating system and a second operating system. The computing device includes a controller to select one of the first and second operating systems based on a mode of the computing device, where the first and second operating systems are executed substantially in parallel on a processor of the computing device.

Abstract: A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.

Abstract: Systems and methods for generating a slate of ranked items are provided. In one example embodiment, a computer-implemented method includes inputting a sequence of candidate items into a machine-learned model, and obtaining, in response to inputting the sequence of candidate items into the machine-learned model, an output of the machine-learned model that includes a ranking of the candidate items that presents a diverse set of the candidate items at the top positions in the ranking such that one or more highly relevant candidate items can be demoted in the ranking.

Abstract: Methods and apparatus are provided for data operation pre-processing with a probabilistic estimation of operation value. An exemplary method comprises extracting feature values from a data set; identifying a set of operations that previously processed data sets comprising the extracted feature values; determining whether to execute an operation from the set of operations before an explicit request for the execution of the operation based on a probabilistic evaluation of a value of pre-processing the operation; and executing a set of instructions for the operation when it is determined that the operation is to be executed. The set of instructions comprises, e.g., stopping execution of other operations being executed, freeing resources required by the operation, and/or allocating resources required by the operation.

Abstract: This document describes techniques (400, 500, 600) and apparatuses (100, 700) for implementing sensor-based near-field communication (NFC) authentication. These techniques (400, 500, 600) and apparatuses (100, 700) enable a computing device (102) to detect, in a low-power state, environmental variances indicating proximity with an NFC-enabled device (104) with which to authenticate. In some embodiments, various components of a computing device (102) in a sleep state are activated to process environmental variance(s), perform authentication operations, and/or an indicate initiation of authentication operations to a user.

Abstract: A feature extraction is performed on transaction data to obtain a user classification feature and a transaction classification feature. A first dimension feature is constructed based on the user classification feature and the transaction classification feature. A dimension reduction processing is performed on the first dimension feature to obtain a second dimension feature. A probability that the transaction data relates to a risky transaction is determined based on a decision classification of the second dimension feature, where the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.

Abstract: A machine learning module may generate a probability distribution from training data including labeled modeling data correlated with reflection data. Modeling data may include data from a LIDAR system, camera, and/or a GPS for a target environment/object. Reflection data may be collected from the same environment/object by a radar and/or an ultrasonic system. The probability distribution may assign reflection coefficients for radar and/or ultrasonic systems conditioned on values for modeling data. A mapping module may create a reflection model to overlay a virtual environment assembled from a second set of modeling data by applying the second set to the probability distribution to assign reflection values to surfaces within the virtual environment. Additionally, a test bench may evaluate an algorithm, for processing reflection data to generate control signals to an autonomous vehicle, with simulated reflection data from a virtual sensor engaging reflection values assigned within the virtual environment.