A method comprises computing position information from a global navigation satellite system (GNSS); computing an altitude measurement based on retrieved information from a vertical position sensor; determining a vertical protection level (VPL) associated with the position information; splitting the VPL into an upward VPL component and a downward VPL component; determining a vertical alert limit (VAL) associated with the altitude measurement; and splitting the VAL into an upward VAL component and a downward VAL component. The method optimizes an integrity budget allocation between the upward and downward VPL components. The method then recomputes the upward and downward VPL components given the optimized integrity budget allocation.

A method for calculating a total transmission probability within a social network based on timing includes a path probability calculating step, a first binary-addition tree searching step, a second binary-addition tree searching step and a transmission probability calculating step. The path probability calculating step is performed to calculate a plurality of time-path probability matrices from the social network. The first binary-addition tree searching step is performed to enumerate a plurality of feasible spread vectors and a plurality of 1-lag temporal vectors. The second binary-addition tree searching step is performed to enumerate a plurality of time-slot vectors of each of the 1-lag temporal vectors. The transmission probability calculating step is performed to calculate the total transmission probability of the social network. The time-path probability matrices are corresponding to a plurality of time values, and the time values are in the specific time and different from each other.

A method for calculating a total transmission probability within a social network based on timing includes a path probability calculating step, a first binary-addition tree searching step, a second binary-addition tree searching step and a transmission probability calculating step. The path probability calculating step is performed to calculate a plurality of time-path probability matrices from the social network. The first binary-addition tree searching step is performed to enumerate a plurality of feasible spread vectors and a plurality of 1-lag temporal vectors. The second binary-addition tree searching step is performed to enumerate a plurality of time-slot vectors of each of the 1-lag temporal vectors. The transmission probability calculating step is performed to calculate the total transmission probability of the social network. The time-path probability matrices are corresponding to a plurality of time values, and the time values are in the specific time and different from each other.

A method for automatic anonymous visitor identity resolution using machine learning, which includes generating a visitor histogram set from visitor events of a visitor event stream that include a visitor identifier and an internet protocol address, filtering a set of user identifiers into a candidate set of user identifiers based on the internet protocol address, obtaining one or more user histogram sets generated from user events that include user identifiers from the candidate set of user identifiers, and mapping the visitor identifier to a user identifier of the candidate set of user identifiers using a machine learning model and a histogram similarity matrix generated from the visitor histogram set, the one or more user histogram sets, and a set of histogram similarity functions. The method further includes presenting a response based on the mapping of the visitor identifier to the user identifier.

A method for automatic anonymous visitor identity resolution using machine learning, which includes generating a visitor histogram set from visitor events of a visitor event stream that include a visitor identifier and an internet protocol address, filtering a set of user identifiers into a candidate set of user identifiers based on the internet protocol address, obtaining one or more user histogram sets generated from user events that include user identifiers from the candidate set of user identifiers, and mapping the visitor identifier to a user identifier of the candidate set of user identifiers using a machine learning model and a histogram similarity matrix generated from the visitor histogram set, the one or more user histogram sets, and a set of histogram similarity functions. The method further includes presenting a response based on the mapping of the visitor identifier to the user identifier.

An example operation includes one or more of solving, by a scheduler node, integer programming problem of maximizing a sum of organizations' endorsing peers that run chaincodes from a plurality of chaincodes within a consortium, making, by the scheduler node, endorsement policies (EPs) for the chaincodes from the plurality of the chaincodes to be satisfiable at any time, applying administrator's constraint of available endorsing peers to the maximized sum of organizations' endorsing peers, and adding resulting endorsing peers to a maintenance list.

An example operation includes one or more of solving, by a scheduler node, integer programming problem of maximizing a sum of organizations' endorsing peers that run chaincodes from a plurality of chaincodes within a consortium, making, by the scheduler node, endorsement policies (EPs) for the chaincodes from the plurality of the chaincodes to be satisfiable at any time, applying administrator's constraint of available endorsing peers to the maximized sum of organizations' endorsing peers, and adding resulting endorsing peers to a maintenance list.

Disclosed is a box-regularized particle filtering process which includes an Epanechnikov kernel smoothing step. For this purpose, the process uses a special method for generating random numbers that follow an Epanechnikov probability density function. The process can be performed autonomously in a navigation system using correlation measurement, in particular on board an aircraft such as an aircraft, a flying drone or any self-propelled aerial carrier.

Disclosed is a box-regularized particle filtering process which includes an Epanechnikov kernel smoothing step. For this purpose, the process uses a special method for generating random numbers that follow an Epanechnikov probability density function. The process can be performed autonomously in a navigation system using correlation measurement, in particular on board an aircraft such as an aircraft, a flying drone or any self-propelled aerial carrier.

An embodiment of a spoken intent detection device includes technology to detect a phrase in an electronic representation of an audio stream based on a pre-defined vocabulary, associate a time stamp with the detected phrase, and classify a spoken intent based on a sequence of detected phrases and the respective associated time stamps. Other embodiments are disclosed and claimed.