Abstract: Embodiments presented herein provide systems, methods and articles of manufacture for a computer-implemented method to monitor users interacting in an online multiuser environment. For each message sent by a user during a session, determining, via a first classifier, whether the message is either acceptable or unacceptable for the online multiuser environment. Upon satisfying a triggering condition for the session of the user, determining, via a second classifier, whether to escalate the session to a moderator for review.

Abstract: Methods, systems, computer-readable media, and apparatuses for providing dynamic incident response using advanced analytics are presented. In some embodiments, a computing device may determine that an incident has occurred. The computing device then may load a predefined response template that includes parameters for responding to the incident. Subsequently, the computing device may utilize a big data platform to identify one or more potential responders for the incident based on the predefined response template. In some additional embodiments, the computing device also may contact the identified potential responders and subsequently monitor communications by the identified potential responders that are responsive to the contact. The computing device may also update historical interaction data based on the monitoring, and this historical interaction data may be used to subsequently determine the likelihood that at least one potential responder will respond to a future incident.

Abstract: Apparatus and methods for partial evaluation of synaptic updates in neural networks. In one embodiment, a pre-synaptic unit is connected to a several post synaptic units via communication channels. Information related to a plurality of post-synaptic pulses generated by the post-synaptic units is stored by the network in response to a system event. Synaptic channel updates are performed by the network using the time intervals between a pre-synaptic pulse, which is being generated prior to the system event, and at least a portion of the plurality of the post synaptic pulses. The system event enables removal of the information related to the portion of the post-synaptic pulses from the storage device. A shared memory block within the storage device is used to store data related to post-synaptic pulses generated by different post-synaptic nodes. This configuration enables memory use optimization of post-synaptic units with different firing rates.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining geographic locations of devices. One of the methods includes obtaining an estimated user location associated with each respective IP address block based on observed events from the IP address block; obtaining an estimate of a probability model p(ev|loc), the probability model p(ev|loc) including a respective probability distribution of interest locations for each of multiple user locations; wherein obtaining the estimate of the probability model p(ev|loc) includes calculating p(ev|loc) from a p(zone|loc) matrix and a p(ev|zone) matrix; and using the estimate for the probability model p(ev|loc) and the observed events to calculate an estimate for multiple probability distributions X(loc) associated with a respective IP address block.

Abstract: Systems, methods, and media for updating a classifier are provided, in some embodiments, systems for updating a classifier are provided, the systems comprising: a hardware processor that is configured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an up-dated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update tire classifier based on the first weak learner.

Abstract: System and methods for providing a cognitive network (300). The methods involve generating Initialization Parameters (“IPs”) for a first Multi-Objective Optimization (“MOO”) algorithm based on project requirements; determining a first Pareto Front (“PF”) for a first Protocol Stack Layer (“PSL”) of a protocol stack by solving the first MOO algorithm using IPS (450, 550); initialize or constrain a second MOO algorithm using the first PF (100); determining a second PF for a second PSL succeeding the first PSL using the second MOO algorithm; analyzing the first and second PFs to develop Best Overall Network Solutions (“BONSs”); ranking the BONSs according to a pre-defined criteria; identifying a top ranked solution for BONSs that complies with current regulatory and project policies; computing configuration parameters for protocols of PSLs that enable implementation of the top ranked solution within the cognitive network; and dynamically re-configuring network resources of PSLs using the configuration parameters.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining geographic locations. One of the methods includes obtaining a sequence of events, each of the events including geographical location information, from a first device to be located; determining, for each event and each of a plurality of geographical locations, a probability that the respective event was obtained from a second device given that the second device is located at the respective geographical location; determining a probability that the sequence of events was obtained from the second device, including using a model representing how sequences of events are generated by network devices; and determining for each of the plurality of geographical locations a probability that the first device is located at the respective geographical location using the probability that the sequence of events was obtained.

Abstract: Generalized learning rules may be implemented. A framework may be used to enable adaptive spiking neuron signal processing system to flexibly combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized learning framework may employ time-averaged performance function as the learning measure thereby enabling modular architecture where learning tasks are separated from control tasks, so that changes in one of the modules do not necessitate changes within the other. Separation of learning tasks from the control tasks implementations may allow dynamic reconfiguration of the learning block in response to a task change or learning method change in real time. The generalized spiking neuron learning apparatus may be capable of implementing several learning rules concurrently based on the desired control application and without requiring users to explicitly identify the required learning rule composition for that task.

Abstract: Apparatus and methods for efficient synaptic update in a network such as a spiking neural network. In one embodiment, the post-synaptic updates, in response to generation of a post-synaptic pulse by a post-synaptic unit, are delayed until a subsequent pre-synaptic pulse is received by the unit. Pre-synaptic updates are performed first following by the post-synaptic update, thus ensuring synaptic connection status is up-to-date. The delay update mechanism is used in conjunction with system “flush” events in order to ensure accurate network operation, and prevent loss of information under a variety of pre-synaptic and post-synaptic unit firing rates. A large network partition mechanism is used in one variant with network processing apparatus in order to enable processing of network signals in a limited functionality embedded hardware environment.

Abstract: An exemplar dictionary is built from exemplars of digital content for determining predictor blocks for encoding and decoding digital content. The exemplar dictionary organizes the exemplars as clusters of similar exemplars. Each cluster is mapped to a label. Machine learning techniques are used to generate a prediction model for predicting a label for an exemplar. The prediction model can be a hashing function that generates a hash key corresponding to the label for an exemplar. The prediction model learns from a training set based on the mapping from clusters to labels. A new mapping is obtained that improves a measure of association between clusters and labels. The new mapping is used to generate a new prediction model. This process is repeated in order to iteratively refine the machine learning modes generated.

Abstract: System (300) and methods (400, 600) for providing a Cognitive Network (“CN”). The methods involve: partially solving Multi-Objective Optimization Algorithms (“MOOAs”) for Protocol Stack Layers (“PSLs”) using initialization parameters generated based on project requirements (572); and monitoring the convergence behaviors of MOOAs (584) to identify when solutions (106) thereof start to converge toward Pareto-Optimal solutions (104). In response to said identification, a convergence of a solution trajectory for at least one MOOA is “biased” so that compatible non-dominated solutions are generated at PSLs. A Pareto Front (100) for each PSL is determined by generating remaining solutions for MOOAs. The Pareto Fronts are analyzed in aggregate to develop Best Overall Network Solutions (“BONSs”). BONSs are ranked according to a pre-defined criteria. A Top Ranked Solution (“TRS”) is identified for BONSs that complies with current regulatory/project policies.

Abstract: The described implementations relate to data classification. One implementation includes identifying one or more likely classifications for an incoming data item using an algorithm. The implementation can also include providing the one or more identified classifications to a user. A selection of an individual identified classification for the incoming data item can be received from the user. The algorithm can be refined to reflect the selection by the user.

Abstract: In at least one embodiment, a trust rating system and method provide a precise and accurate, structured (yet adaptable and flexible), quantifying way of expressing historical trustworthiness so the user or decision maker can make more informed decisions on the data or information being evaluated.

Abstract: Apparatus and methods for high-level neuromorphic network description (HLND) framework that may be configured to enable users to define neuromorphic network architectures using a unified and unambiguous representation that is both human-readable and machine-interpretable. The framework may be used to define nodes types, node-to-node connection types, instantiate node instances for different node types, and to generate instances of connection types between these nodes. To facilitate framework usage, the HLND format may provide the flexibility required by computational neuroscientists and, at the same time, provides a user-friendly interface for users with limited experience in modeling neurons. The HLND kernel may comprise an interface to Elementary Network Description (END) that is optimized for efficient representation of neuronal systems in hardware-independent manner and enables seamless translation of HLND model description into hardware instructions for execution by various processing modules.

Abstract: A system configured to predict characteristics of an artificial heart is described. The system includes a processor and memory in electronic communication with the processor, and an artificial neural network configured to receive an input vector of a predetermined length to train the artificial neural network, produce an output vector based on the input vector, and compare the output vector with a target vector of the predetermined length. When the output vector does not match the target vector within a predetermined error rate, the network is configured to adjust at least one weight, and when the output vector matches the target vector within the predetermined error rate, the network is configured to execute the input vector to produce an estimate at least one characteristic of the artificial heart.

Abstract: A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The software and hardware engines are optimized to take into account short-term and long-term synaptic plasticity in the form of LTD, LTP, and STDP.

Abstract: A context aware apparatus is provided. The context aware apparatus includes an extracting unit configured to extract a terminological-box (T-box) from a semantic model, a first generating unit configured to generate a reasoning rule based on the extracted T-box, a second generating unit configured to generate a first assertion-box (A-box) based on sensing information, and a reasoning unit configured to infer a user context based on the reasoning rule and the first A-box.

Abstract: Sensory encoder may be implemented. Visual encoder apparatus may comprise spiking neuron network configured to receive photodetector input. Excitability of neurons may be adjusted and output spike may be generated based on the input. When neurons generate spiking response, spiking threshold may be dynamically adapted to produce desired output rate. The encoder may dynamically adapt its input range to match statistics of the input and to produce output spikes at an appropriate rate and/or latency. Adaptive input range adjustment and/or spiking threshold adjustment collaborate to enable recognition of features in sensory input of varying dynamic range.

Abstract: An interface facilitates user input of quantitatively weighted recommendations, including weighted factors in support of decision choices. A user input mechanism allows a user to specify a factor in support of a choice, and to specify values for quantitative parameters associated with the factor along two or more axes. An overall quantitative weight for the factor is generated based on the specified quantitative parameters. In one embodiment, a graphical user interface is presented, wherein the user specifies the values for the weighting parameters by dragging a movable indicator within an N-dimensional space. Each axis of the N-dimensional space corresponds to a weighting parameter. An overall quantitative weight for the factor is calculated, for example, as the product of the specified values along each of the axes. A visual indication of this calculation is presented, so as to provide an intuitive sense of the overall weight assigned to the factor.

Abstract: An approach is provided for contextual content suggestion. A recommendation platform processes and/or facilitates a processing of contextual information associated with at least one device to determine one or more locations, one or more contextual parameter values, or a combination thereof. The recommendation platform also determines popularity data associated with one or more content items with respect to the one or more locations, the one or more contextual parameter values, or a combination. The popularity data is determined from one or more other devices sharing at least substantially the one or more locations, the one or more contextual parameter values, or a combination thereof. The recommendation platform then causes, at least in part, a recommendation of the one or more content items to the at least one device based, at least in part, on the popularity information.