Abstract: Provided are methods, systems, and non-transitory computer-readable media for generating a feature vector for malware, including storing, in memory of a computing device, program code for a trained neural network that produces embedded representations for antivirus scan data; executing, by a processor of the computing device, the program code for the trained neural network to perform the operations of: (a) receiving an antivirus scan report (AVSR) for a malware file; (b) normalizing each label in the AVSR by separating the label into a sequence of tokens including a set of token strings; (c) embedding a first token and plural second tokens to generate an input sequence for the malware file; (d) inputting the input sequence into a neural model for producing antivirus scan data; and (e) outputting the antivirus scan data produced by the neural model as one or more feature vectors.

Abstract: Provided are systems, methods, and computer program products including at least one processor programmed or configured to perturb at least one training dataset based on mutual information extracted from an ensemble machine learning model to provide at least one adversarial training dataset, execute at least two machine learning models of an ensemble machine learning model, train at least two machine learning models with the at least one training dataset by feeding an input or output of one of the at least two machine learning models to the other of the at least two machine learning models, train the ensemble machine learning model with the at least one adversarial training dataset, receive a runtime input from a client device, and provide the runtime input to the trained ensemble machine learning model to generate a signal output indicating that the runtime input includes an out-of-distribution sample.

Abstract: Techniques are provided for configuration and storage of hierarchical data. One method comprises providing a user interface to configure different variants, wherein a configuration of a given variant comprises a specification of signals associated with the given variant, wherein a given signal comprises one or more rules; obtaining one or more representations of user interactions with the user interface to configure the given variant; initiating an update of at least one database table comprising one or more records associated with the given variant based on the one or more representations of the user interactions, wherein the at least one database table stores rule values specified for the one or more signals associated with the given variant as attributes of the given variant; and initiating an automated action using the at least one database table. A given variant may be, for example, a content-related variant or a product-related variant.

Abstract: Exemplary systems and methods are directed to training a machine learning model and for preventing leakage of training data by the machine learning model subsequent to training. A processor is configured to convert a sparse dataset into a matrix of plural data coordinates, generate a priority queue populated with the plural data coordinates, and iteratively select a data coordinate from the priority queue. Plural model values are calculated such that any zero value in the sparse dataset is avoided while maintaining a same result. A next feature is selected, and its weight is altered. Plural variables of the matrix are updated based on the altered weight value, and the priority queue is updated to adjust a priority of the data coordinates based on the update to the plural variables. The process is repeated for each next data coordinate until the model converges to a solution based on the model weights.

Abstract: A system and method for transferring an adversarial attack involving generating a surrogate model having an architecture and a dataset that mirrors at least one aspect of a target model of a target module, wherein the surrogate model includes a plurality of classes. The method involves generating a masked version of the surrogate model having fewer classes than the surrogate model by randomly selecting at least one class of the plurality of classes for removal. The method involves attacking the masked surrogate model to create a perturbed sample. The method involves generalizing the perturbed sample for use with the target module. The method involves transferring the perturbed sample to the target module to alter an operating parameter of the target model.

Abstract: A method, system, and computer program product for configuring a computer for data similarity determination using Bregman divergence may include storing a data set having plural data pairs with one or more data points corresponding to one or more features and generating a trained input convex neural network (ICNN) using the data set, the ICNN having one or more parameters. Training the ICNN may include extracting one or more features for each piece of data in the first data pair, generating an empirical Bregman divergence for the first data pair, and computing one or more gradients between the one or more features within the first data pair using known target distances and the computed empirical Bregman divergence.

Abstract: Embodiments relate to a system for modeling correlation in a sourcing model. The system can include a processor configured to collect voting output from plural voting sources and store the voting output in memory. The system can include a correlation modeling module configured to retrieve at least two voting outputs from memory. In some embodiments each voting output is from a different voting source. The correlation modeling module can determine correlation among at least two voting sources by measuring consensus among the at least two voting sources using an agreement metric. The correlation modeling module can determine a degree of a first-order interaction among the at least two voting sources. The correlation modeling module can determine a degree of correlation among the at least two voting sources having a degree of first-order interaction.

Abstract: A method, system, and computer program product for configuring a computer for data similarity determination using Bregman divergence may include storing a data set having plural data pairs with one or more data points corresponding to one or more features and generating a trained input convex neural network (ICNN) using the data set, the ICNN having one or more parameters. Training the ICNN may include extracting one or more features for each piece of data in the first data pair, generating an empirical Bregman divergence for the first data pair, and computing one or more gradients between the one or more features within the first data pair using known target distances and the computed empirical Bregman divergence.

Abstract: A method and system for configuring a computer for data classification using ordinal regression includes: receiving and storing a data set having data with a plurality of data features that have an ordinal relationship; generating a plurality of ordinal classification bins based on the relationship of the data features, at least one ordinal classification bin having walls defined by at least two hyperplanes; generating an ordinal regression model of the data set illustrating the data of the data set arranged into the plurality of ordinal classification bins; and tuning the slopes of the walls of the at least one ordinal classification bin based on the relationships between the plurality of data features of the data arranged within the at least one ordinal classification bin such that the slopes of the two hyperplanes defining the walls of the at least one ordinal classification bin are not parallel.

Abstract: A method and system for configuring a computer for data classification using ordinal regression includes: receiving and storing a data set having data with a plurality of data features that have an ordinal relationship; generating a plurality of ordinal classification bins based on the relationship of the data features, at least one ordinal classification bin having walls defined by at least two hyperplanes; generating an ordinal regression model of the data set illustrating the data of the data set arranged into the plurality of ordinal classification bins; and tuning the slopes of the walls of the at least one ordinal classification bin based on the relationships between the plurality of data features of the data arranged within the at least one ordinal classification bin such that the slopes of the two hyperplanes defining the walls of the at least one ordinal classification bin are not parallel.

Abstract: A system and method for transferring an adversarial attack involving generating a surrogate model having an architecture and a dataset that mirrors at least one aspect of a target model of a target module, wherein the surrogate model includes a plurality of classes. The method involves generating a masked version of the surrogate model having ewer classes than the surrogate model by randomly selecting at least one class of the plurality of classes for removal. The method involves attacking the masked surrogate model to create a perturbed sample. The method involves generalizing the perturbed sample for use with the target module. The method involves transferring the perturbed sample to the target module to alter an operating parameter of the target model.

Abstract: A method and system for cross-modal manifold alignment of different data domains includes determining for a shared embedding space a first embedding function for data of a first domain and a second embedding function for data of a second domain using a triplet loss, wherein triplets of the triplet loss include an anchor data point from the first, a positive and a negative data point from the second domain; creating a first mapping for the data of the first domain using the first embedding function in the shared embedding space; creating a second mapping for the data of the second domain using the second embedding function in the shared embedding space; and generating a cross-modal alignment for the data of the first domain and the data of the second domain.

Abstract: A joint assembly comprises an end portion of a first component that is accommodated within an end portion of a second component, with each respective first and second end portion having a corresponding first and second location feature, and an elongate resilient helical connection member. The second location feature comprises a proximal portion and a distal portion, with a clamping ring accommodated within the distal portion. When the joint is assembled, the first and second location features together define an annular cavity into which is accommodated the connection member to interlock the first and second components to one another. The clamping ring is adapted to exert a radially inwardly directed clamping force on the connection member.

Abstract: A method of performing statistical analysis, including outlier detection and anomalous behaviour identification, on large or complex datasets (including very large and massive datasets) is disclosed. The method allows large statistical datasets (which may be distributed) to be analysed, assessed, investigated and managed in an interactive fashion as a part of a production system or for ad-hoc analysis. The method involves first processing the data into histograms and storing them in a manner that is capable of rapid retrieval. Then these histograms can be manipulated to provide conventional statistical results in an interactive manner. It also provides a method whereby these histograms can be updated over time, rather than being re-processed each time they are to be used. It has particular benefit to two class probabilistic systems, where results need to be assessed on the basis of false-positives and false-negatives.

Abstract: A joint assembly includes an end portion of a first component that is accommodated within an end portion of a second component, with each respective first and second end portion having a corresponding first and second location feature, and an elongate resilient helical connection member. When the joint is assembled, the first and second location features together define an annular cavity into which is accommodated the connection member to interlock the first and second components to one another.

Abstract: A joint assembly comprises an end portion of a first component that is accommodated within an end portion of a second component, with each respective first and second end portion having a corresponding first and second location feature, and an elongate resilient helical connection member. The second location feature comprises a proximal portion and a distal portion, with a clamping ring accommodated within the distal portion. When the joint is assembled, the first and second location features together define an annular cavity into which is accommodated the connection member to interlock the first and second components to one another. The clamping ring is adapted to exert a radially inwardly directed clamping force on the connection member.

Abstract: A helmet comprises an outer shell, a visor movable between a retracted position and a deployed position and a movable bridge that is positionable against a user's face. The visor moves along an arc-shaped track and/or a pivot from a position stored within the helmet shell to a deployed position in front of the face and the bridge is moved against the user's face. The helmet and integrated bridge assembly provides a seal from the visor to the user's face, thus protecting the face against wind and other debris.

Abstract: A method of projected capacitance touch sensing on a display includes updating the display by applying a drive waveform to each pixel of the display over a plurality of frame periods to update a pixel state. The updating includes selecting each row of the display in turn and repeating a frame update to drive pixels with successive time slices of the drive waveforms. The method further includes sensing a signal from a projected capacitance touch sensing electrode of the display during a sensing interval to provide a touch sensing response; identifying when column drive levels of the drive waveforms for pixels of one selected row change by more than a threshold level between the selected row and a next selected row; and inhibiting the touch sensing responsive to the column drive row change signal indicating a greater than the threshold level change of the drive levels between the selected rows.

Abstract: A joint assembly includes an end portion of a first component that is accommodated within an end portion of a second component, with each respective first and second end portion having a corresponding first and second location feature, and an elongate resilient helical connection member. When the joint is assembled, the first and second location features together define an annular cavity into which is accommodated the connection member to interlock the first and second components to one another.

Abstract: An approach is provided for proxy-based content discovery and delivery. A proxy platform determines usage information for at least one user of a proxy server. The usage information includes, at least in part, browser history information, web application data usage information, or a combination thereof determined by the proxy server to be associated with the at least one user. The proxy platform processes and/or facilitates a processing of the usage information to determine at least one user profile for the at least one user. The proxy platform then determines one or more recommendations for the at least one user based, at least in part, on the at least one user profile.