Abstract: A scrubbing medium, such as cooking oil, is trickle flowed through a packing material that includes woodchips. Gas, such as from a gasifier, is flowed through the packing material and the scrubbing medium to scrub the gas of tar and particulates. The gas may further flow through a polishing material, such as charcoal. The woodchips include a mixture of coarse and fine woodchips with at least about 25% fine woodchips. A flow of scrubbed gas with reduced tar and particulates in outputted.

Abstract: The present invention relates to RNAi agents, e.g., double-stranded RNAi agents, targeting the apolipoprotein C3 (APOC3) gene, and methods of using such RNAi agents to inhibit expression of APOC3 and methods of treating subjects having an APOC3 associated disorder, e.g., hypertriglyceridemia.

Abstract: A processor can be configured to receive data associated with, and/or access to, a computing system's file system structure. The processor can also be configured to determine file patterns, file path patterns and/or graph patterns associated with the computing system. The processor can also be configured to build a graph structure having nodes and edges, the graph structure representing the file patterns, file path patterns and graph patterns, wherein the nodes of the graph structure represent files and attributes of the files and the edges of the graph structure represent connectivity between the files. The processor can also be configured to train, based on the graph structure, a first machine learning model to learn a feature vector associated with a file. The processor can also be configured to train, based on the feature vector, a second machine learning model to identify a vulnerable ransomware target.

Abstract: A method is provided for identifying synthetic “deepfake” audio samples versus organic audio samples. Methods may include: receiving an audio sample comprising speech; converting the speech to text; aligning the text with phonemes identified within the audio sample; filtering the audio sample to only contain predetermined phonemes; obtaining, from the audio sample, a frequency response vector for each of the predetermined phonemes; transforming the frequency response vector for each of the predetermined phonemes to a classification space vector for each of the predetermined phonemes having a magnitude; normalizing the classification space vector for each of the predetermined phonemes; identifying each of the predetermined phonemes as one of synthetic or organic based on the classification space vector for each of the predetermined phonemes; and identifying the audio sample as synthetic or organic based on identification of each of the predetermined phonemes as one of synthetic or organic.

Abstract: Methods described herein relate to identifying an audio rogue base station intercept of a call using a modified distance bounding algorithm. Methods may include: establishing a shared resource for a session with a remote end device; selecting a nonce and provide the nonce to the remote end device; generating a sequence of bits; dividing the sequence of bits into a predetermined number of sub-sequences; performing a bit exchange with the remote end device using dual tone multi-frequency (DTMF) tones; determining, from the bit exchange, an average time of transmission; and identifying the presence or absence of an adversary intercepting the session.

Abstract: A method of using a computing device to compare performance of multiple algorithms. The method includes receiving, by a computing device, multiple algorithms to assess. The computing device further receives a total amount of resources to allocate to the multiple algorithms. The computing device additionally assigns a fair share of the total amount of resources to each of the multiple algorithms. The computing device still further executes each of the multiple algorithms using the assigned fair share of the total amount of resources. The computing device additionally compares the performance of each of the multiple based on at least one of multiple hardware relative utility metrics describing a hardware relative utility of any given resource allocation for each of the multiple algorithms.

Abstract: Techniques are disclosed, using one or more computers. for bidding on digital advertising space associated with media content on a digital platform. Popularity is determined of a plurality of electronic communication and associated clips on a digital platform. A clip is detected which meets a threshold for approaching an activity level of user activity indicating the popularity of the clip. The clip meeting the threshold is labeled as a non-fungible token (NFT) indicating available digital advertising space associated with the clip. In response to the labeling of the clip, monetary bidding is received, based on the NFT by entities buying digital advertising space for digital advertising content associated with the NFT.

Abstract: Rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information because images unwittingly modulate ambient sound as imperceptible distortions. Experiments have found that the side channel is further amplified by intrinsic behaviors of complementary metal-oxide-semiconductor (CMOS) rolling shutters and movable lenses such as in optical image stabilization (OIS) and auto focus (AF). This disclosure characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the point-of-view of smartphone cameras. In contrast with traditional optical-acoustic eavesdropping on vibrating objects, this side channel requires no line of sight and no object within the camera's field of view.

Abstract: A computer-implemented method for validating ownership of a digital asset is disclosed. The computer-implemented method includes transmitting a public key for encrypting a digital asset to an owner of the digital asset responsive to receiving a request for the public key. The computer-implemented method further includes receiving from the owner of the digital asset, an encrypted digital asset and a first liveness hash, wherein the digital asset is encrypted using the public key, and the first liveness hash is generated based, at least in part, on the digital asset in an unencrypted form and a first nonce. The computer-implemented method further includes determining whether the first liveness hash is valid. The computer-implemented method further includes generating in response to determining that the first liveness hash is valid, a digital asset record, wherein the digital asset record includes the encrypted digital asset and the first liveness hash.

Abstract: Embodiments of the invention are directed to a computer system that includes a memory electronically coupled to a processor system. The processor system is operable to perform processor system operations that include accessing a graph model representation of a computer network. The graph model is used to implement a resiliency-problem identification analysis that identifies a set of resiliency problems in the graph model. The graph model is used to apply a resiliency-problem solution analysis to a resiliency problem in the set of resiliency problems to generate a set of resiliency-problem solutions. Each resiliency-problem solution in the set of resiliency-problem solutions is ranked.

Abstract: A processor distributes, from a server, a trained supervised machine learning (ML) model and supervised and unsupervised feature information to a plurality of client devices; at each client device, trains the supervised ML model using local data to generate a local supervised ML model, constructs a local unsupervised ML model using the unsupervised feature information, and deploys the local supervised and unsupervised ML models; determining when a detection performance difference between the local supervised and unsupervised ML models reaches a threshold; identifies a proposed change to the supervised or unsupervised feature information; deploys the proposed change on one client device; responsive to determining the proposed change improves the detection performance of that client device, communicates the proposed change to a sampled set of client devices; and responsive to determining the proposed change improves the detection performance of a majority of the sampled set, communicates the proposed change to

Abstract: A processor distributes, from a server, a trained supervised machine learning (ML) model and supervised and unsupervised feature information to a plurality of client devices; at each client device, trains the supervised ML model using local data to generate a local supervised ML model, constructs a local unsupervised ML model using the unsupervised feature information, and deploys the local supervised and unsupervised ML models; determining when a detection performance difference between the local supervised and unsupervised ML models reaches a threshold; identifies a proposed change to the supervised or unsupervised feature information; deploys the proposed change on one client device; responsive to determining the proposed change improves the detection performance of that client device, communicates the proposed change to a sampled set of client devices; and responsive to determining the proposed change improves the detection performance of a majority of the sampled set, communicates the proposed change to

Abstract: Various embodiments of the present disclosure provide for detecting audio deepfakes through acoustic prosodic modeling. In one example, an embodiment provides for extracting one or more prosodic features from an audio sample and classifying the audio sample as a deepfake audio sample or an organic audio sample by applying a machine learning model to the one or more prosodic features. The one or more prosodic features can be indicative of one or more prosodic characteristics associated with human speech. Additionally, the machine learning model can be configured as a classification-based detector for audio deepfakes.

Abstract: A method, computer system, and a computer program for quick disaster recovery of cloud-native environments is provided. The present invention may include replicating at a secondary server site software executing in a cloud-native environment on a primary server site. The present invention may also include detecting a failure associated with the software executing in the cloud-native environment. The present invention may then include whether the detected failure is causing down time for the software executing in the cloud environment. The present invention may further include deploying the replicated software on the secondary server site in response to determining that the detected failure is causing down time.

Abstract: A method, computer system, and a computer program for quick disaster recovery of cloud-native environments is provided. The present invention may include replicating at a secondary server site software executing in a cloud-native environment on a primary server site. The present invention may also include detecting a failure associated with the software executing in the cloud-native environment. The present invention may then include whether the detected failure is causing down time for the software executing in the cloud environment. The present invention may further include deploying the replicated software on the secondary server site in response to determining that the detected failure is causing down time.

Abstract: A method is provided for identifying synthetic “deep-fake” audio samples versus organic audio samples. Methods may include: generating a model of a vocal tract using one or more organic audio samples from a user; identifying a set of bigram-feature pairs from the one or more audio samples; estimating the cross-sectional area of the vocal tract of the user when speaking the set of bigram-feature pairs; receiving a candidate audio sample; identifying bigram-feature pairs of the candidate audio sample that are in the set of bigram-feature pairs; calculating a cross-sectional area of a theoretical vocal tract of a user when speaking the identified bigram-feature pairs; and identifying the candidate audio sample as a deep-fake audio sample in response to the calculated cross-sectional area of the theoretical vocal tract of a user failing to correspond within a predetermined measure of the estimated cross sectional area of the vocal tract of the user.

Abstract: In a method for determining anomalous behavior of a candidate taking an exam, a processor receives first exam interface input values captured during an exam session on a candidate testing device. A processor generates a first interaction vector from the first exam interface input values. A processor generates a first interaction timeline from the first interaction vector. A processor determines an anomalous behavior based on a relationship between the first interaction timeline and a selected classification cluster.

Abstract: Example embodiments provide methods, apparatuses, systems, computing devices, and/or the like for vetting USB device firmware via a USB-specific firmware analysis framework. In one example, a method is provided for analyzing firmware of a Universal Serial Bus (USB) device. The example method includes steps of receiving a firmware image extracted from the USB device, identifying signatures from the firmware image relating to USB operation, and building an expected model of operation of the USB device using the identified signatures and a database of operational information regarding USB devices. The example method further includes the steps of generating a recovered model of operation of the USB device based on the firmware image and the identified signatures, and comparing the recovered model of operation with the expected model of operation to identify unexpected or unknown behaviors. The example method may further include generating a report comprising the identified unexpected or unknown behaviors.

Abstract: Systems and methods are described for a data breach detection based on snapshot analytics. The described systems and methods identify a plurality of snapshots of a data structure, identify a plurality of leaf nodes of the data structure for each of the snapshots, generate a vector of data attributes for each of the leaf nodes, assign a weight to each of the vectors to produce a set of weighted vectors for each of the snapshots, compute a distance metric between each pair of the snapshots based on the corresponding sets of weighted vectors, and detect an abnormal snapshot among the plurality of snapshots based on the distance metrics.

Abstract: A method of using a computing device to compare performance of multiple algorithms. The method includes receiving, by a computing device, multiple algorithms to assess. The computing device further receives a total amount of resources to allocate to the multiple algorithms. The computing device additionally assigns a fair share of the total amount of resources to each of the multiple algorithms. The computing device still further executes each of the multiple algorithms using the assigned fair share of the total amount of resources. The computing device additionally compares the performance of each of the multiple based on at least one of multiple hardware relative utility metrics describing a hardware relative utility of any given resource allocation for each of the multiple algorithms.