Abstract: A method, system, or apparatus for mitigating computer and network security deficiencies is provided in which, the method, system, or apparatus scans computer system components for finding a vulnerability, generates a Vulnerability Priority Scoring System (VPSS) score for the vulnerability based on the vulnerability, develops a vulnerability mitigation policy based on a system state comprising the VPSS score, wherein the vulnerability mitigation policy provides a best action for mitigating the vulnerability selected among one or more trained possible actions by a deep neural network, and performs the vulnerability mitigation policy based on the best action. Other aspects, embodiments, and features are also claimed and described.

Abstract: The disclosed technology provides a method for detecting malicious activity in a network communication system. A first packet of a first flow is received from the network communication system. The first packet comprises a first sequence of data values. The first sequence of data values is converted to a first plurality of pixel image attribute values. A first portion of an image is generated based on the first plurality of pixel image attribute values. The image is processed using a trained neural network model to determine a likelihood of malicious activity in the first flow.

Abstract: Implementations disclose a handoff feature for a client device. A method includes receiving, from a first client device, an indication of a user selection of a handoff feature during a session associated with the first client device, identifying a second client device associated with the user, identifying a visit identifier (ID) corresponding to the session, identifying a navigation end-point of the session, generating a session continuation notification comprising a payload having the visit ID and the navigation endpoint, and transmitting the session continuation notification to the identified second client device.

Abstract: A method, system, or apparatus for mitigating computer and network security deficiencies is provided in which, the method, system, or apparatus scans computer system components for finding a vulnerability, generates a Vulnerability Priority Scoring System (VPSS) score for the vulnerability based on the vulnerability, develops a vulnerability mitigation policy based on a system state comprising the VPSS score, wherein the vulnerability mitigation policy provides a best action for mitigating the vulnerability selected among one or more trained possible actions by a deep neural network, and performs the vulnerability mitigation policy based on the best action. Other aspects, embodiments, and features are also claimed and described.

Abstract: A method for reducing a threat footprint for an area-of-interest by, determining a base risk score for an area-of-interest, deriving a graphical representation of the area-of-interest and one or more defense strategies for the area-of-interest, training an adversarial reinforcement learning agent to identify one or more successful attack paths on the graphical representation of the area-of-interest that are effective in bypassing the one or more defense strategies for the area-of-interest, determining an active risk score based upon the one or more successful attack paths identified by the adversarial reinforcement learning agent and determining one or more mitigation actions that will reduce the active risk score for the area-of-interest based upon the base risk score and one or more predetermined resource constraint.

Abstract: A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

Abstract: A modular system for hydrogen generation includes a plurality of cores electrically connected in series to a power supply, wherein each core includes an electrolyzer and a bypass circuit configured to electrically isolate the core from the power supply. The modular system also includes a hub including a water source and a controller, wherein the water source is in fluid communication with the electrolyzer of each of the plurality of cores, and the controller includes a switch activatable, in response to a triggering condition, to electrically isolate one or more of the plurality of cores from the power supply via a respective bypass circuit.

Abstract: Systems and methods for detecting a failure of a wheel speed sensor. One example system includes an encoder and an electronic processor. The electronic processor is configured to receive, from the wheel speed sensor, a wheel speed, receive, from the encoder, a signal, and determine, based on the signal from the encoder, a positional change of an electric motor shaft of the electric motor. The electronic processor is configured to determine, based on the wheel speed and the positional change of the electric motor, whether the wheel speed sensor is faulty.

Abstract: A method for generating or modifying movement signatures includes receiving a request to generate a movement signature, receiving first sensor data from one or more sensors of a first wearable device associated with a first user, the first sensor data being generated with one or more of an accelerometer, a gyroscope, or a magnetometer of the wearable device, and associating the first sensor data with the movement signature and storing the sensor data. The method also includes receiving second sensor data from one or more sensors of a second wearable device associated with a second user, the second sensor data being generated with one or more of an accelerometer, a gyroscope, or a magnetometer of the second wearable device, analyzing the second sensor data in comparison to the first sensor data, and based on the analysis: generating a notification, or modifying the movement signature.

Abstract: A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

Abstract: A system for controlling a vehicle using driving behavior involving automatic emergency braking (AEB) events. The system includes a plurality of sensors and an electronic processor. The system receives event data of an attribute of a driving behavior of a user related to handling an AEB event of a vehicle from the plurality of sensors. The system assigns a weighted value to the attribute based on a set of conditions for the attribute and the event data. The system determines an event score of the driving behavior related to the AEB event based on the weighted value of the attribute. The system determines a user driving behavior score based on the event score associated with one or more AEB events handled by the user. The system activates a vehicle countermeasure related to attributes responsive to determining the driving behavior score of the user is greater than a driving behavior threshold.

Abstract: A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

Abstract: A system and method for classifying an actuation of an electric parking brake of a vehicle is presented. In one example, the system includes a sensor configured to sense a vehicle parameter, an output device, a memory including an electric parking brake usage profile, and an electronic controller configured to receive the electric parking brake usage profile, receive the vehicle parameter, detect an actuation of the electric parking brake, and in response to detecting an actuation of the electric parking brake: determine a reason for the actuation of the electric parking brake, determine an attribute of the vehicle based on the vehicle parameter, classify the actuation of the electric parking brake based on a numerical value of the attribute and/or the reason for the actuation of the electric parking brake, update the electric parking brake usage profile based on the classification, and output the updated parking brake usage profile.

Abstract: An object storage system can receive chunks of an object. Each of the chunks includes data that is a subset of the object. Each subset has an arbitrary amount of data and at least two of the subsets include overlapping data. Each of the chunks is associated with a timestamp. Responsive to a request for the object the object storage system can reconstitute the object by including the subset of data from a most recent of the chunks based on the timestamps and including only nonoverlapping data from subsequent chunks in reverse chronological order based on the timestamps until the object is reconstituted. The object storage system can transmit the reconstituted object.

Abstract: Implementations disclose a handoff feature for a client device. A method includes receiving, from a first client device, an indication of a user selection of a handoff feature during a session associated with the first client device, identifying a second client device associated with the user, identifying a visit identifier (ID) corresponding to the session, identifying a navigation end-point of the session, generating a session continuation notification comprising a payload having the visit ID and the navigation endpoint, and transmitting the session continuation notification to the identified second client device.

Abstract: Implementations disclose a handoff feature for a content sharing platform. A method includes maintaining a session history of a session that occurred at a first client device, the session history identified by a visit identifier (ID) and comprising a set of recently consumed content items on a content sharing platform, determining that a user associated with the session is active on a second client device, transmitting, to the second client device, a session continuation notification associated with the visit ID and a navigation end-point of the session, receiving, from the second client device, a request for a content item corresponding to the navigation end-point of the session, and transmitting, to the second client device, instructions to load the content item and to request additional components of the content item using the visit ID.

Abstract: A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

Abstract: A system and method for classifying an actuation of an electric parking brake of a vehicle.

Abstract: A method, system, or apparatus for mitigating computer and network security deficiencies is provided in which, the method, system, or apparatus scans computer system components for finding a vulnerability, generates a Vulnerability Priority Scoring System (VPSS) score for the vulnerability based on the vulnerability, develops a vulnerability mitigation policy based on a system state comprising the VPSS score, wherein the vulnerability mitigation policy provides a best action for mitigating the vulnerability selected among one or more trained possible actions by a deep neural network, and performs the vulnerability mitigation policy based on the best action. Other aspects, embodiments, and features are also claimed and described.

Abstract: A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.