Abstract: A method and system of selecting a software testing regimen for a software application. The method comprises receiving, at a security assessing server computing device, a Quality of Service (QoS) performance level in conjunction with a set of technical attributes of the software application, determining a security vulnerability diagnostic score for the software application based at least in part on the set of technical attributes and the QoS performance level, and selecting the software testing regimen in accordance with the QoS performance level and the security vulnerability diagnostic score.

Abstract: A method and system of applying a security vulnerability assessment of a software program. The method comprises directing, from a security assessing server, to a software program under execution, a plurality of attack vectors, diagnosing a set of results associated with the software program under execution as comprising a security vulnerability, the set of results produced based at least in part on the plurality of attack vectors, and assessing a monetary premium of a risk insurance policy merited by an enterprise based at least in part on a level of control ceded to an attacker in accordance with the set of results.

Abstract: Method and system of deploying a trained machine learning neural network in dynamic testing of security vulnerability in software applications. The method comprises directing, from a security assessing server computing device, to a software program under execution, a series of attack vectors, deploying a set of results produced in accordance with the software program under execution and the attack vectors to an input layer of the trained machine learning neural network, the trained machine learning neural network comprising an output layer that is interconnected with the input layer via a set of intermediate layers, and identifying, in accordance with a predetermined threshold percentage value of false positive software security vulnerability defects, one or more software security vulnerability defects associated with the results produced, the software security vulnerability defects being generated in accordance with the output layer of the trained machine learning neural network.

Abstract: A method and system of selecting a software testing regimen for a software application. The method comprises receiving, at a security assessing server computing device, a Quality of Service (QoS) performance level in conjunction with a set of technical attributes of the software application, determining a security vulnerability diagnostic score for the software application based at least in part on the set of technical attributes and the QoS performance level, and selecting the software testing regimen in accordance with the QoS performance level and the security vulnerability diagnostic score.

Abstract: A method and system for security vulnerability diagnostic assessment of an enterprise software application. The method comprises receiving, at a security assessing server, a set of technical attributes of the software application; receiving a set of execution context attributes of the software application; and determining a security vulnerability diagnostic score for the software application based at least in part on the set of technical attributes and the set of execution context attributes.

Abstract: A method and system of applying a security vulnerability assessment of a software program. The method comprises directing, from a security assessing server, to a software program under execution, a plurality of attack vectors, diagnosing a set of results associated with the software program under execution as comprising a security vulnerability, the set of results produced based at least in part on the plurality of attack vectors, and assessing a monetary premium of a risk insurance policy merited by an enterprise based at least in part on a level of control ceded to an attacker in accordance with the set of results.

Abstract: A method and system for dynamic testing and diagnostic assessment of security vulnerability of cloud-based enterprise software applications. The method comprises directing, to a software program under execution, a series of attack vectors; diagnosing a set of results associated with the software execution as comprising one of a security vulnerability and not a security vulnerability, the set of results produced based at least in part on the attack vectors; and assessing a dynamic security vulnerability score for the software program based at least in part on the diagnosing.

Abstract: A method and system of rendering security events in execution of a software application in a communication network. The method comprises receiving, at a memory of a server computing device, a waiver parameter specification identifying a waiver parameter in association with a recipient client device, the waiver parameter pertaining to a security violation event in accordance with a set of predetermined security policy violations, monitoring, via a processor of the server computing device, for the waiver parameter being generated, and generating, based on the monitoring, a waiver notification interface providing capability for including at least a set of successful pipeline execution results as evidence that the security violation event has been resolved.

Abstract: Method and system of deploying a trained machine learning neural network in dynamic testing of security vulnerability in software applications. The method comprises directing, from a security assessing server computing device, to a software program under execution, a series of attack vectors, deploying a set of results produced in accordance with the software program under execution and the attack vectors to an input layer of the trained machine learning neural network, the trained machine learning neural network comprising an output layer that is interconnected with the input layer via a set of intermediate layers, and identifying, in accordance with a predetermined threshold percentage value of false positive software security vulnerability defects, one or more software security vulnerability defects associated with the results produced, the software security vulnerability defects being generated in accordance with the output layer of the trained machine learning neural network.

Abstract: A method and system for implementing AI based neural networks for data analytics in dynamic testing of security vulnerability of cloud-based enterprise software applications. The method comprises directing, to a software program under execution, a series of attack vectors; diagnosing an at least a first set of results associated with the software program under execution as comprising one of a security vulnerability and not a security vulnerability, the at least a first set of results produced based at least in part on the attack vectors; and training a machine learning neural network classifier in accordance with a supervised classification that identifies false positive vulnerability defects of the at least a first set of results to produce a trained classifier, the neural network classifier including an input and an output layers connected via at least one intermediate layer that is configured in accordance with an initial matrix of weights.

Abstract: A method and system of rendering security events in execution of a software application in a communication network. The method comprises receiving, at a memory of the server computing device, a waiver parameter specification identifying at least one waiver parameter in association with at least one recipient client device of the plurality of client computing devices, the at least one waiver parameter based at least in part on an expected security event in the software application execution; during concurrent execution, in a processor of the server computing device, of object code of the software application, generating at least one waiver task automaton that monitors for the at least one waiver parameter; and generating, based on the monitoring, at a client interface of the at least one recipient communication device, a waiver notification interface in accordance with concurrent execution.

Abstract: A method and system of rendering security events in execution of a software application in a communication network. The method comprises receiving, at a memory of the server computing device, a waiver parameter specification identifying at least one waiver parameter in association with at least one recipient client device of the plurality of client computing devices, the at least one waiver parameter based at least in part on an expected security event in the software application execution; during concurrent execution, in a processor of the server computing device, of object code of the software application, generating at least one waiver task automaton that monitors for the at least one waiver parameter; and generating, based on the monitoring, at a client interface of the at least one recipient communication device, a waiver notification interface in accordance with concurrent execution.

Abstract: A method and system for dynamic testing and diagnostic assessment of security vulnerability of cloud-based enterprise software applications. The method comprises directing, to a software program under execution, a series of attack vectors; diagnosing a set of results associated with the software execution as comprising one of a security vulnerability and not a security vulnerability, the set of results produced based at least in part on the attack vectors; and assessing a dynamic security vulnerability score for the software program based at least in part on the diagnosing.

Abstract: A method and system for implementing AI based neural networks for data analytics in dynamic testing of security vulnerability of cloud-based enterprise software applications. The method comprises directing, to a software program under execution, a series of attack vectors; diagnosing an at least a first set of results associated with the software program under execution as comprising one of a security vulnerability and not a security vulnerability, the at least a first set of results produced based at least in part on the attack vectors; and training a machine learning neural network classifier in accordance with a supervised classification that identifies false positive vulnerability defects of the at least a first set of results to produce a trained classifier, the neural network classifier including an input and an output layers connected via at least one intermediate layer that is configured in accordance with an initial matrix of weights.

Abstract: A method and system of instantiating a user interface (UI) notification. The method comprises accessing a persona configuration, the persona configuration specified at least in part based on one or more script agents, the script agents including script code arranged in accordance with an execution state flow associated with execution of a software program; executing, in one or more processors of a server device, object code of the software program in accordance with the execution state flow in conjunction with the one or more script agents to generate a set of resultant parameters; and transmitting, to a UI display of a display device that is authorized in association with the persona configuration, information instantiated based on at least a subset of the resultant parameters.

Abstract: A method and system for security vulnerability diagnostic assessment of an enterprise software application. The method comprises receiving, at a security assessing server, a set of technical attributes of the software application; receiving a set of execution context attributes of the software application; and determining a security vulnerability diagnostic score for the software application based at least in part on the set of technical attributes and the set of execution context attributes.

Abstract: An adhesive copolymer comprising acrylate ester monomer units, poly(phenyleneoxide)-functional (meth)acryloyl monomer units, optional acid-functional monomer units, and optional non-acid functional polar monomer units is described.

Abstract: An adhesive composition is described comprising a tertiary amine- and hydroxyl-functional (meth)acrylate copolymer and a styrene/maleic anhydride crosslinking agent, which when crosslinked, provides a pressure-sensitive adhesive and pressure-sensitive adhesive articles.

Abstract: Pressure sensitive adhesives made from a polymodal asymmetric elastomeric block copolymer, a tackifying resin and liquid polyisoprene are provided. Articles containing these pressure sensitive adhesives including protective sheeting, labels, laminated adhesives, tapes and adhesive films are described. The pressure sensitive adhesive based on polymodal asymmetric block copolymer described herein exhibits good adhesion on various substrates such as stainless steel (SS), polypropylene (PP) and high density polyethylene (HDPE) and enhanced high temperature shear without the effect of electron beam or UV curing.

Abstract: A method of preparing a polycarbonate nanocomposite comprising forming a reactant mixture comprising a nanomaterial, a solvent, a dihydroxy compound and an activated carbonate; and polymerizing the dihydroxy compound and the activated carbonate in the presence of the solvent to form the polycarbonate nanocomposite is disclosed. Also disclosed are polycarbonate nanocomposites prepared in accordance with this method, and thermoplastic compositions comprising the polycarbonate nanocomposites. Also disclosed are polycarbonate compositions comprising the nanomaterial.