Abstract: An Incident-centric Mass Notification System (“Incident-centric MNS system”) for notifying a plurality of recipients of an Emergency Event is disclosed. The Incident-centric MNS system may include a Mass Notification System (“MNS”) Notification Engine, modality corridor (“MoCo”) dispatcher, and a plurality of modality corridors, wherein each modality corridor of the plurality of modality corridors establishes, for signal communication, a signal path from a plurality of signal paths, between the MNS Notification Engine and a recipient of the plurality of recipients. The MNS Notification Engine is configured to manage MNS Incidents and Notifications and the MoCo dispatcher is configured to select a first modality corridor from the plurality of modality corridors to establish a first signal path from the MNS Notification Engine.

Abstract: An Incident-centric Mass Notification System (“Incident-centric MNS system”) for notifying a plurality of recipients of an Emergency Event is disclosed. The Incident-centric MNS system may include a Mass Notification System (“MNS”) Notification Engine, modality corridor (“MoCo”) dispatcher, and a plurality of modality corridors, wherein each modality corridor of the plurality of modality corridors establishes, for signal communication, a signal path from a plurality of signal paths, between the MNS Notification Engine and a recipient of the plurality of recipients. The MNS Notification Engine is configured to manage MNS Incidents and Notifications and the MoCo dispatcher is configured to select a first modality corridor from the plurality of modality corridors to establish a first signal path from the MNS Notification Engine.

Abstract: First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.

Abstract: First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.