Abstract: Technologies for detecting minority events are disclosed. By performing a guided hierarchical classification algorithm with a decision tree structure and grouping the minority class(es) in with some of the majority classes, large majority classes may be separated from a minority class without requiring good detection of the minority events by themselves. The decision tree structure may be used only for the purpose of identifying if the data sample in question is a member of a minority class. If it is determined that it is not, a primary classification algorithm may be used. With this approach, the guided hierarchical classification algorithm need not perform as well as the primary classification algorithm for the majority events, but may provide improved detection for minority events.

Abstract: Methods, systems and computer program products memorize multiple inputs into an artificial neuron that includes multiple dendrites each having multiple dendrite compartments. Operations include computing coincidence detection as distal synapse activation that flows from more proximal ones of the dendrite compartments to a soma of the artificial neuron, generating a dendritic action potential responsive to the coincidence detection from a non-zero activation value input received at a corresponding one of the dendrite compartments that includes a non-zero receptivity, and responsive to generating the dendritic action potential, decrementing the activation value and the receptivity and passing the decremented activation value to a next one of the dendrite compartments.

Abstract: A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source, the public source comprising a public blockchain data source, the private data source comprising a private blockchain data source; a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources, the information comprising public blockchain information and private blockchain information; and, providing the information to a cognitive inference and learning system.

Abstract: One or more processors receive one or more variations to one or more first instruction elements in a first instruction set that indicate one or more second instruction elements of a second instruction set. One or more processors determine whether the one or more first instruction elements exceed a threshold of variability. One or more processors determine whether the one or more first instruction elements and the one or more second instruction elements are substantially equivalent. One or more processors determine whether a first outcome of the first instruction set is substantially similar to a second outcome of the second instruction set.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media to forecast demand by implementing an online demand prediction framework that includes a hierarchical temporal memory network (HTM) configured to learn temporal patterns representing sequences of states of time-series data collected from a set of one or more data sources representing demand and input to the HTM. In some embodiments, the HTM learns the temporal patterns using a Cortical Learning Algorithm.

Abstract: Systems and methods are disclosed to determine “don't care” variables in programming code. The method comprises obtaining an SMT formula having a plurality of SMT variables from programming code; obtaining a simplified SMT formula from the SMT formula, the simplified SMT formula includes a plurality of simplified SMT variables; obtaining an SAT formula from the simplified SMT formula, the SAT formula includes a plurality of SAT variables; determining which of the plurality of the SMT variables are “don't care” variables from the simplified SMT formula; and determining which of the plurality of the SMT variables are “don't care” variables from the simplified SAT formula.