Abstract: Techniques are described that include detecting customer personal information within any appropriate set of data, such as customer communications produced by customer-facing services offered by an organization. Once detected, the customer personal information may be tokenized within the customer communications, making the data appropriate for external systems, such as cloud-hosted applications. The disclosed techniques include a masking service that may be plugged into an on-premises pipeline of any customer-facing service that makes requests to an off-premises, cloud-hosted application. The masking service may apply rule-based detection and/or machine learning-based detection to detect both structured and unstructured customer personal information included in customer communications. The masking service may further tokenize or otherwise obfuscate or replace the detected customer personal information.

Abstract: A method and system for security protection for firmware of an accelerator by leveraging Virtualization-Based Security (VBS). A memory space is allocated for firmware of an accelerator from a Kernel Data Protection (KDP)-protected region of a system memory. The KDP-protected region is a specific area of the system memory that is protected by KDP. The firmware of the accelerator is placed in the KDP-protected region. A device memory management unit (MMU) page table corresponding to the memory space allocation for the firmware of the accelerator may be generated and placed in the KDP-protected region. A device driver of the accelerator sets attributes of page table entries of the device MMU page table appropriately, and accesses to the system memory may be controlled based on the attributes of the page table entries of the device MMU page table.

Abstract: There is disclosed in one example a ransomware mitigation engine, including: a processor; a convolutional neural network configured to provide file type identification (FTI) services including: identifying an access operation of a file as a write to the file or newly creating the file; computing a byte correlation factor for the file; classifying the file as belonging to a file type; determining with a screening confidence that the file type is correct for the file; determining that the screening confidence is below a screening confidence threshold; and circuitry and logic to provide heuristic analysis including: receiving notification that the confidence is below the confidence threshold; performing a statistical analysis of the file to determine a difference between an expected value and a computed value; determining from the difference, with a detection confidence, that the file has been compromised; and identifying the file as having been compromised by a ransomware attack.

Abstract: Systems and methods for obtaining passenger feedback during an autonomous vehicle (AV) ride are provided. Feedback can be obtained in different forms during the passenger's ride. For instance, the passenger may opt-in to shadow the AV as it performs various driving behaviors. Input devices such as a tablet or joystick may be activated in the autonomous vehicle. A heuristic scene is selected and presented on the tablet, and the passenger can use the joystick to perform the maneuver as the AV is performing the same. The passenger's driving behavior does not alter the operation of the AV. Alternatively, the scene is different from the AV's route and the driving behaviors are not synced. Further, the passenger can be prompted to provide a response after specific maneuvers are performed by the AV. Feedback data is obtained and recorded from each passenger and then evaluated and compared to the AV's driving behaviors.

Abstract: Various technologies described herein pertain to reserving seats in an autonomous vehicle. The autonomous vehicle includes a plurality of seats that can be occupied by passengers when riding in the autonomous vehicle. The autonomous vehicle is configured to assign a particular seat from the plurality of seats in the autonomous vehicle to a passenger based on information pertaining to the passenger and information pertaining to a ride-sharing trip. The autonomous vehicle is to transport at least the passenger and a differing passenger during at least a portion of the ride-sharing trip. Moreover, the passenger and the differing passenger independently request rides in the autonomous vehicle. The autonomous vehicle is further configured to output, to the passenger, an indication of the particular seat assigned to the passenger.

Abstract: There is disclosed in one example a ransomware mitigation engine, including: a processor; a convolutional neural network configured to provide file type identification (FTI) services including: identifying an access operation of a file as a write to the file or newly creating the file; computing a byte correlation factor for the file; classifying the file as belonging to a file type; determining with a screening confidence that the file type is correct for the file; determining that the screening confidence is below a screening confidence threshold; and circuitry and logic to provide heuristic analysis including: receiving notification that the confidence is below the confidence threshold; performing a statistical analysis of the file to determine a difference between an expected value and a computed value; determining from the difference, with a detection confidence, that the file has been compromised; and identifying the file as having been compromised by a ransomware attack.

Abstract: A method for halting malware includes: monitoring plural file system events with a system driver to detect an occurrence of a file system event having a predetermined file type and log event type; triggering a listening engine for file system event stream data of a file associated with the detection of the file system event, the file system event stream data indicating data manipulation associated with the file due to execution of a process; obtaining one or more feature values for each of plural different feature combinations of plural features of the file based on the file system event stream data; inputting one or more feature values into a data analytics model to predict a target label value based on the one or more feature values of the plural different feature combinations and agnostic to the process; and performing a predetermined operation based on the target label value.

Abstract: An autonomous vehicle (AV) described herein is configured to receive a pull over location specified by a passenger, and is further configured to refine the pull over location based upon one or more factors, where the factors include computer-readable content from a profile of the passenger, sensor data output by sensor systems of the AV, observed or predicted weather conditions, observed or predicted traffic, and/or observations recently generated by other AVs that belong to the same fleet as the AV.

Abstract: Methods and apparatus to set guest physical address mapping attributes for a trusted domain In one embodiment, the method includes executing a first one or more of instructions to establish a trusted domain and executing a second one or more of the instructions to add a first memory page to the trusted domain, where the first memory page is private to the trusted domain and a first set of page attributes is set for the first memory page based on the second one or more of the instructions, where the first set of page attributes indicates how the first memory page is mapped in a secure extended page table. The method further includes storing the first set of page attributes for the first memory page in the secure extended page table at a storage location responsive to executing the second one or more of the instructions.

Abstract: A method is described and includes acquiring sensor data produced by sensors of a plurality of vehicles traversing an area including a location of a user, wherein the vehicles traversing the area comprise a subset of a fleet of vehicles for providing rideshare services; processing the acquired sensor data to determine a category of the user; selecting a vehicle from the fleet of vehicles based on the category of the user, wherein the selected vehicle comprises at least one accommodation corresponding to the category of the user; and dispatching the selected vehicle to a pick-up location designated by the user.

Abstract: A method is described and includes acquiring sensor data produced by sensors of a plurality of vehicles traversing an area including a location of a user, wherein the vehicles traversing the area comprise a subset of a fleet of vehicles for providing rideshare services; processing the acquired sensor data to determine a category of the user; selecting a vehicle from the fleet of vehicles based on the category of the user, wherein the selected vehicle comprises at least one accommodation corresponding to the category of the user; and dispatching the selected vehicle to a pick-up location designated by the user.

Abstract: The present disclosure relates to compositions and methods of generating circular-permuted nucleic acids for direct use in homology directed genome editing. The method also allows the joining of a large number of DNA fragments, in a deterministic fashion. It can be used to rapidly generate nucleic acid libraries that can be directly used in a variety of applications without further cloning steps that include, for example, genome editing and pathway assembly. Kits for performing the method are also disclosed.

Abstract: An autonomous vehicle (AV) described herein is configured to receive a pull over location specified by a passenger, and is further configured to refine the pull over location based upon one or more factors, where the factors include computer-readable content from a profile of the passenger, sensor data output by sensor systems of the AV, observed or predicted weather conditions, observed or predicted traffic, and/or observations recently generated by other AVs that belong to the same fleet as the AV.

Abstract: Systems, methods, and apparatuses to support instructions for a hardware assisted heterogeneous instruction set architecture dispatcher are described.

Abstract: Described herein are various technologies pertaining to selecting an autonomous vehicle for a trip of a passenger based on a passenger cargo reservation request and real time load data from a fleet of autonomous vehicles. The passenger cargo reservation request specifies a request to store passenger cargo in a cargo storage area of an autonomous vehicle during a ride of a passenger in the autonomous vehicle. Further, an autonomous vehicle from the fleet of autonomous vehicles can be selected for the trip of the passenger based on the passenger cargo reservation request and the real time load data for the autonomous vehicles in the fleet of autonomous vehicles.

Abstract: There is disclosed in one example a ransomware mitigation engine, including: a processor; a convolutional neural network configured to provide file type identification (FTI) services including: identifying an access operation of a file as a write to the file or newly creating the file; computing a byte correlation factor for the file; classifying the file as belonging to a file type; determining with a screening confidence that the file type is correct for the file; determining that the screening confidence is below a screening confidence threshold; and circuitry and logic to provide heuristic analysis including: receiving notification that the confidence is below the confidence threshold; performing a statistical analysis of the file to determine a difference between an expected value and a computed value; determining from the difference, with a detection confidence, that the file has been compromised; and identifying the file as having been compromised by a ransomware attack.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support electronic mail (e-mail) messages that include image(s) having multiple distinct selectable regions configured to direct a browser to different respective links. To illustrate, a user device may obtain an image that includes multiple interactive regions and image information that is associated with the image. The image information may indicate layouts of the interactive regions and links that correspond to the interactive regions. The image may be divided into multiple sub-images based on the image information, such that the sub-images correspond to the interactive regions and, optionally, other regions. An e-mail message may be generated that includes a composite image of the multiple sub-images. The composite image may include multiple selectable regions that, upon selection at a receiving device, cause a browser to access a link associated with the selected region (e.g.

Abstract: Various technologies described herein pertain to reserving seats in an autonomous vehicle. The autonomous vehicle includes a plurality of seats that can be occupied by passengers when riding in the autonomous vehicle. The autonomous vehicle is configured to assign a particular seat from the plurality of seats in the autonomous vehicle to a passenger based on information pertaining to the passenger and information pertaining to a ride-sharing trip. The autonomous vehicle is to transport at least the passenger and a differing passenger during at least a portion of the ride-sharing trip. Moreover, the passenger and the differing passenger independently request rides in the autonomous vehicle. The autonomous vehicle is further configured to output, to the passenger, an indication of the particular seat assigned to the passenger.

Abstract: An autonomous vehicle (AV) described herein is configured to receive a pull over location specified by a passenger, and is further configured to refine the pull over location based upon one or more factors, where the factors include computer-readable content from a profile of the passenger, sensor data output by sensor systems of the AV, observed or predicted weather conditions, observed or predicted traffic, and/or observations recently generated by other AVs that belong to the same fleet as the AV.

Abstract: There is disclosed in one example a computer-implemented anti-ransomware method, including: selecting a file for inspection; assigning the file to a type class according to a file type identifier; receiving an expected byte correlation for the type class; computing, according to a byte distribution of the file, a byte correlation for the file; comparing, via statistical analysis, the byte correlation to the expected byte correlation; and determining that the file has been compromised, including determining that the file has a byte correlation that deviates from the expected byte correlation by more than a threshold, taking a ransomware remediation action for the file.