Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: An in-line surface drain may comprise a cylindrical frame and a plurality of concentric rings coupled to the cylindrical frame. The plurality of concentric rings may be configured to protrude outward from a bottom external surface of the cylindrical frame, and the diameters of the plurality of concentric rings may be different. At least one of the plurality of concentric rings may be configured to detachably accommodate a riser pipe.

Abstract: A microelectronic chip device includes a semiconductor substrate and multiple on-chip strain sensors (OCSSs) constructed on the substrate at various locations of the substrate. The OCSSs may each include multiple piezoresistive devices configured to sense a strain at a location of the various locations and produce a strain signal representing the strain at that location. A strain measurement circuit may also be constructed on the semiconductor substrate and configured to measure strain parameters from the strain signals produced by the OCSSs. The strain parameters represent the strains at the various location. Values of the strain parameters can be used for analysis of mechanical stress on the chip device.

Abstract: A system which comprises a series of native applications, suited to run on mobile devices, and a series of web-based applications for which functionality and processing are optimized. The native applications and the web-based applications are coordinated to optimize processes of acquiring, storing and disseminating data for speed, integrity and security.

Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: Systems and methods for using single or multi-chain deposit tokens are disclosed. According to an embodiment, a method for deposit tokenization may include: (1) receiving, by a deposit tokenization service for a token issuer and from an authorized party, an instruction for tokenizing an amount of non-tokenized funds in a deposit account; (2) verifying, by the deposit tokenization service, an identity of the authorized party using a verifiable credential; (3) screening, by the deposit tokenization service and using an information oracle, the deposit account and/or the verifiable credential; (4) debiting, by the deposit tokenization service, the deposit account for the amount and crediting the amount to an omnibus account; (5) tokenizing, by the deposit tokenization service, the amount of the non-tokenized funds on a blockchain network as deposit tokens; and (6) crediting, by the deposit tokenization service, a wallet address on the blockchain network with the deposit tokens.

Abstract: An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

Abstract: Improved inspection techniques are described herein for checking for the presence of and identifying surface defects on a honeycomb body. The improved inspection utilizes measurement of travel of an outer surface of the honeycomb body to collect images of the outer surface. The images are combined into a composite image showing the outer surface of the honeycomb body. The composite image is analyzed to identify surface defects.

Abstract: A drug delivery device includes a housing, a container disposed in the housing, an activation mechanism, a needle insertion mechanism, and a fluid flow path. The container has an inner volume to contain a medicament which is urged out of the container by the activation mechanism. The needle insertion mechanism includes an actuation assembly adapted to insert a needle and a cannula to deliver the medicament from the container and a valve assembly. The fluid flow connection is coupled with the container and the needle insertion mechanism and is adapted to allow the medicament to flow from the container to the needle insertion mechanism. The valve assembly is repeatedly movable between at least a valve open position and a valve closed position to selectively allow and restrict the medicament to flow through the needle and/or the cannula.

Abstract: In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a structured file for which a machine learning model has made a malicious content classification. The code further includes code to remove a portion of the structured file to define a modified structured file that follows a format associated with a type of the structured file. The code further includes code to extract a set of features from the modified structured file. The code further includes code to provide the set of features as an input to the machine learning model to produce an output. The code further includes code to identify an impact of the portion of the structured file on the malicious content classification of the structured file based on the output.

Abstract: An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

Abstract: A method of assembling a pallet using a machine includes folding a first portion of a pallet support column to define a vertical support structure. The method further includes folding a second portion of the pallet support column to engage the vertical support structure. The method further includes gluing at least a portion of the second portion to the first portion. The method further includes coupling the pallet support column to at least one of a bottom member or a top member of the pallet.

Abstract: A foldably constructed pallet includes a top member forming a top surface of the foldably constructed pallet and a bottom member forming a bottom surface of the foldably constructed pallet. The bottom member includes, extending from the bottom surface, a plurality of outer sidewalls, and a plurality of internal sidewalls. An outer sidewall of the plurality of outer sidewalls and an internal sidewall of the plurality of internal sidewalls cooperate to define a cradle configured to hold a structural insert such that folded panels of the structural insert may be engaged with each of the outer sidewall and the internal sidewall. The top member may be connected to the bottom member and covering the cradle.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second sub score and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: Systems and methods are disclosed for monitoring models for bias. In one implementation, a system for automatically assessing a deployed model for selection of a cohort may include a processing device programmed to: apply the deployed model to data representing a first plurality of individuals, the data including at least one characteristic of the first plurality of individuals; based on the application, select a subset of the first plurality of individuals as a cohort; receive data representing a second plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the second plurality of individuals; compare the selected subset and the second plurality of individuals along the at least one characteristic; and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.

Abstract: A potentially malicious command including a plurality of features is received. Additionally, a plurality of nodes included in a decision tree are traversed, based on the plurality of features, to identify a leaf node included in the plurality of nodes. The leaf node is associated with (1) a first set of similar commands, each similar command from the first set of similar commands including the plurality of features, and (2) a second set of similar commands from the first set of similar commands and that were previously detected. Additionally, a probability that the potentially malicious command will be escalated as potentially malicious is determined based on the first set of similar commands and the second set of similar commands. Additionally, a first indication quantifying the first set of similar commands, a second indication quantifying the second set of similar commands, and the probability are caused to be displayed.

Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: Due to the threat of virus attacks and ransom ware, an apparatus and methods for protecting backup storage devices from malicious software virus attacks is explored. An independent backup storage system is connected to a primary storage server over an undiscoverable communications line. The backup storage system is a read-only backup storage system most of the time buffering the backup storage system from a virus or attack on the primary storage server. The backup storage system changes from a read-only backup storage system to a read/write backup storage system only during a backup window of time where data is backed up to the backup storage system. A snapshot of the backup data is maintained in the backup storage system and can be made available at numerous points of time in the past if the data primary storage server becomes corrupted.

Abstract: An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

Abstract: Systems and methods for interactive pathogen detection are described including receiving at least one target genome file and at least one near-neighbor genome file and analyzing the target genome file and the near-neighbor genome file to generate a plurality of raw e-probes unique to a target pathogen. Each raw e-probe includes a unique nucleic acid signature sequence selected from along a length of the pathogen genome of the target pathogen. The plurality of raw e-probes are curated to provide a curated e-probe set. The curated e-probe set can be in silico validated and/or in vitro validated. The resulting e-probe set can be used to determine presence of the target pathogen in a sample metagenome in an e-probe diagnostic system.