Abstract:
User authentication based on cognitive profiling. First, usage by a person of one or more computerized devices is automatically and continuously tracked. A cognitive profile of the person is automatically and continuously generated based on the tracked usage. Responsive to a request for authenticating an identity of the person, a cognitive question is automatically generated based on the cognitive profile, and is presented to a user professing to be the person. An answer to the cognitive question is received from the user. Finally, the identity of the person is automatically authenticated when the answer to the cognitive question is determined to be correct.
Abstract:
A method comprising: obtaining a first viewpoint representing a partial depiction of a system, wherein the first viewpoint comprises a property base, an instance base and a class base; obtaining a second viewpoint representing a partial depiction of the system, wherein the second viewpoint comprises a property base, an instance base and a class base, wherein the instance base of the second viewpoint is different than the instance base of the first viewpoint; creating a third viewpoint based on the first and second viewpoints, wherein the third viewpoint representing the system, wherein the third viewpoint comprises a property base, an instance base and a class base, wherein the instance base comprises instances defined by the instance base of the first viewpoint and instances defined by the instance base of the second viewpoint.
Abstract:
Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.
Abstract:
Embodiments of the present invention may provide the capability to detect complex events while providing improved detection and performance. In an embodiment of the present invention, a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions, wherein a desired output is defined as a sequence of states, and wherein a number of states of the hidden Markov model is less than or equal to the number of samples of the quantized data, adjusting the quantized data and the initial model values to improve accuracy of the model, determining a state sequence of the hidden Markov model, and outputting an indication of a detected event.
Abstract:
Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.
Abstract:
A computer-implemented method, computerized apparatus and computer program product, the method comprising: obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>1.
Abstract:
A system for project planning comprising: a processor; a graphical user interface which: receives a project plan having a plurality of project tasks hierarchically arranged in a plurality of tiers, associated by a plurality of parent-child relationships; enables a user to provide a plurality of project task characteristics for at least one of said plurality of project tasks; enables a user to alter a first at least one of a plurality of project task characteristics of a first project task; and a propagation software engine for adapting a second at least one of a plurality of project task characteristics of a second project task wherein a tier of said second project task is lower in a hierarchy arrangement with respect to a tier of first project task, using said processor.
Abstract:
A method of creating a system having pluggable analysis viewpoints over a design space model based on templates for analytical representation of different system aspects, comprising: a) Ontologically representing each of a plurality of system viewpoints with a subset of the components and classes using attributes and inter-attribute relationships. b) Automatically creating a unified design space model represented by the design space components according to a plurality of user defined pluggable analysis viewpoints and modeling viewpoints. c) Automatically generating a design space model derived from a plurality of analysis and modeling viewpoints. d) Receiving at least one change marked by a user with respect to a certain one of the plurality of analysis and modeling viewpoints. e) Automatically updating the design space model and the plurality of viewpoint models to reflect the at least one change. f) Outputting the updated design space model and the plurality of viewpoint models.