Abstract:
The disclosure relates to machine-learning behavioral analysis to detect device theft and unauthorized device usage. In particular, during a training phase, an electronic device may generate a local user profile that represents observed user-specific behaviors according to a centroid sequence, wherein the local user profile may be classified into a baseline profile model that represents aggregate behaviors associated with various users over time. Accordingly, during an authentication phase, the electronic device may generate a current user profile model comprising a centroid sequence re-expressing user-specific behaviors observed over an authentication interval, wherein the current user profile model may be compared to plural baseline profile models to identify the baseline profile model closest to the current user profile model. As such, an operator change may be detected where the baseline profile model closest to the current user profile model differs from the baseline profile model in which the electronic device has membership.
Abstract:
Systems and methods are disclosed for determining a context-dependent virtual distance based on stigmergic interference. The method may include obtaining environmental status information relating to an environment in proximity to a client device, calculating, based on the obtained environmental status information, the context-dependent virtual distance between the client device and a user of the client device, and controlling a user signaling pattern of the client device based on the calculated context-dependent virtual distance.
Abstract:
The disclosure relates to various distance metrics that may quantify semantic and syntactic relationships between devices. More particularly, a first grammar associated with a first device and a second grammar associated with a second device may each comprise a symbol sequence that re-expresses one or more sequenced data items and one or more rules that represent a repeated pattern in the symbol sequence. Accordingly, one or more distance metrics that quantify a similarity between the first grammar and the second grammar may be calculated according to a comparison between the rules in the first grammar and the rules in the second grammar such that a relationship between the first device and the second device can be determined according to the one or more distance metrics.
Abstract:
Methods and apparatuses are disclosed for synchronizing data inputs generated at a plurality of frequencies by a plurality of data sources. A device receives a first set of data points from a first data source of the plurality of data sources generated at a first frequency of the plurality of frequencies, receives a second set of data points from a second data source of the plurality of data sources generated at a second frequency of the plurality of frequencies, selects a time window corresponding to a period of time during which at least a subset of the first set of data points and at least a subset of the second set of data points were generated, and generates a vector representing a first reduced form of the subset of the first set of data points and a second reduced form of the subset of the second set of data points.
Abstract:
The disclosure relates to creating a time-sensitive grammar. A device receives a plurality of data points, identifies a plurality of time gaps associated with the plurality of data points, each of the plurality of time gaps representing a dwell time or a frequency of occurrence of a data point of the plurality of data points, generates a generic time factor representing a multiple of the plurality of time gaps, and combines the generic time factor with the plurality of data points to create a time-sensitive sequence of data points. The generic time factor may be inserted into the time-sensitive sequence a number of times representing the dwell time or the frequency of occurrence of a corresponding data point of the plurality of data points.