Abstract: It is provided a method for controlling access to a physical space secured by an electronic lock. The method is performed in an access evaluator and comprises: obtaining one or more input parameters relating to a user requesting access to the restricted physical space; evaluating a first access condition based on a credential presented by the user; evaluating a second access condition using a machine-learning model, based on the one or more input parameters; and unlocking the electronic lock when both the first access condition and the second access condition are evaluated to be true.

Abstract: Methods and apparatus for illuminating scene areas to facilitate depth determination are described. A sequence of frames is displayed, e.g., projected, onto a scene area to illuminate the area for image capture. The sequence of frames includes a patterned frame followed by a concealing frame. The patterned frame and concealing frame are each displayed for no more than 1/60 of a second. The concealing frame maybe and sometimes does display a complementary pattern to the preceding patterned frame. When viewed sequentially by a human viewer the human sees a non-distracting uniformly illuminated scene area as a result of the effect of the concealing frame. One or more cameras are used to capture images of the illuminated area during a time in which the pattern is displayed and the scene area is effectively painted with a pattern that can facilitate depth determination based on one or more captured images.

Abstract: Methods and apparatus for illuminating scene areas to facilitate depth determination are described. A sequence of frames is displayed, e.g., projected, onto a scene area to illuminate the area for image capture. The sequence of frames includes a patterned frame followed by a concealing frame. The patterned frame and concealing frame are each displayed for no more than 1/60 of a second. The concealing frame maybe and sometimes does display a complementary pattern to the preceding patterned frame. When viewed sequentially by a human viewer the human sees a non-distracting uniformly illuminated scene area as a result of the effect of the concealing frame. One or more cameras are used to capture images of the illuminated area during a time in which the pattern is displayed and the scene area is effectively painted with a pattern that can facilitate depth determination based on one or more captured images.

Abstract: Methods and apparatus for illuminating scene areas to facilitate depth determination are described. A sequence of frames is displayed, e.g., projected, onto a scene area to illuminate the area for image capture. The sequence of frames includes a patterned frame followed by a concealing frame. The patterned frame and concealing frame are each displayed for no more than 1/60 of a second. The concealing frame maybe and sometimes does display a complementary pattern to the preceding patterned frame. When viewed sequentially by a human viewer the human sees a non-distracting uniformly illuminated scene area as a result of the effect of the concealing frame. One or more cameras are used to capture images of the illuminated area during a time in which the pattern is displayed and the scene area is effectively painted with a pattern that can facilitate depth determination based on one or more captured images.

Abstract: In one embodiment, an apparatus includes: a plurality of cores to execute instructions; a firmware agent to execute a first firmware; a Peripheral Component Interconnect Express (PCIe) interface to communicate with a device via a PCIe link; and a boot agent coupled to the PCIe interface to download the PCIe firmware from a non-volatile memory and provide the PCIe firmware to the PCIe interface. The PCIe interface may receive a PCIe firmware for the PCIe interface before the firmware agent is to receive the first firmware. Other embodiments are described and claimed.

Abstract: The present disclosure relates to methods and systems that preserve privacy in a secure multi-party computation (MPC) framework in multi-agent reinforcement learning (MARL). The methods and systems use a secure MPC framework that allows for direct computation on encrypted data and enables parties to learn from others while keeping their own information private. The methods and systems provide a learning mechanism that carries out floating point operations in a privacy-preserving manner.

Abstract: Examples described herein relate to a bootable processor that comprises circuitry to load boot firmware. The bootable processor can execute a firmware that is to collect an error log of an error during boot of the bootable processor and that occurred prior to enablement of an Out of Band (OOB) manageability port. The firmware can cause output of the error log to a second circuitry through an interface that is operational prior to enablement of the OOB manageability port.

Abstract: Examples include techniques to collecting and providing error related information for a multi-die system-on-a-chip (SOC) computing system following a critical or catastrophic error. Examples include circuitry on a first die that is configured to receive an indication of a critical or catastrophic error and cause error related information to be stored to a volatile memory at the first die that is arranged to continually maintain power during a global reset of the SOC. The circuitry can also be configured to provide the stored error related information to a requestor following the global reset of the SOC.

Abstract: It is provided a method for controlling access to a physical space secured by an electronic lock. The method is performed in an access evaluator and comprises: obtaining one or more input parameters relating to a user requesting access to the restricted physical space; evaluating a first access condition based on a credential presented by the user; evaluating a second access condition using a machine-learning model, based on the one or more input parameters; and unlocking the electronic lock when both the first access condition and the second access condition are evaluated to be true.

Abstract: A system detects a powerdown event, such as a power loss event, and performs a flush of volatile memory to persistent memory during a powerdown sequence. The system includes an energy backup device to power the system during the powerdown sequence. The system is configurable with optional settings that configure the powerdown sequence specific to a type of the energy backup device.

Abstract: A secure inference over Deep Neural Networks (DNNs) using secure two-party computation to perform privacy-preserving machine learning. The secure inference uses a particular type of comparison that can be used as a building block for various layers in the DNN including, for example, ReLU activations and divisions. The comparison securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by a provider of the DNN. Each party computing system parses their input into leaf strings of multiple bits. This is much more efficient than if the leaf strings were individual bits. Accordingly, the secure inference described herein is more readily adapted for using in complex DNNs.

Abstract: Systems and methods for selectively authenticating an untrusted device based on a trust level are disclosed. The system can include transmitting an authentication request from a first device seeking to authenticate a second device to access to an account. The first device receives an authentication token that can be used by the second device for authentication. The authentication token can be transmitted wirelessly to the second device or can be scanned by the second device in the case that the token is a scannable image displayed on the first device. The systems can determine a trust level for the second device based on an association between the first device and the second device. The system can provide the second device a degree of access to the account that relates to the trust level.

Abstract: A system includes a set of low resource devices, each configured to receive transactions to be added to an encrypted block chain ledger from a sample of untrusted high resource devices, prepare a proposed block of the received transactions, provide the proposed block to the sample of untrusted high resource devices, receive proposed blocks from the untrusted high resource devices originating from the set of low resource devices. The low resource devices run a consensus protocol to select one proposed block to add to the encrypted block chain ledger stored on the untrusted high resource devices.

Abstract: In one embodiment, an apparatus includes: a plurality of cores to execute instructions; a firmware agent to execute a first firmware; a Peripheral Component Interconnect Express (PCIe) interface to communicate with a device via a PCIe link; and a boot agent coupled to the PCIe interface to download the PCIe firmware from a non-volatile memory and provide the PCIe firmware to the PCIe interface. The PCIe interface may receive a PCIe firmware for the PCIe interface before the firmware agent is to receive the first firmware. Other embodiments are described and claimed.

Abstract: A system includes a set of low resource devices, each configured to receive transactions to be added to an encrypted block chain ledger from a sample of untrusted high resource devices, prepare a proposed block of the received transactions, provide the proposed block to the sample of untrusted high resource devices, receive proposed blocks from the untrusted high resource devices originating from the set of low resource devices. The low resource devices run a consensus protocol to select one proposed block to add to the encrypted block chain ledger stored on the untrusted high resource devices.

Abstract: In embodiments, a system may comprise a circuit component to perform a function; and a supervisor component (e.g., a safety component) coupled to the circuit component to monitor proper functioning of the circuit component. The circuit component may be configured to initiate provision of diagnostic information to the supervisor component, or to indicate to the safety component whether the circuit component will be responsive to a query from the safety component for diagnostic information.

Abstract: Systems and methods for private deep neural network training are disclosed. Method includes storing first private values at first machine and second private values at second machine; providing, to third machine, first share of first private values and first share of second private values; providing, to fourth machine, second share of first private values and second share of second private values; computing, at third machine, third machine-value based on first share of first private values and first share of second private values; computing, at fourth machine, fourth machine-value based on second share of first private values and second share of second private values; providing, to first machine and second machine, third machine-value and fourth machine-value; and computing, at first machine, a mathematical function of first private values and second private values, mathematical function being computed based on first private values stored at first machine, third machine-value, and fourth machine-value.

Abstract: A method for automated software testing may include mapping test cases to lines in files in a codebase. Each test case covers one or more lines in one or more files and has corresponding test results. The method may further include obtaining a change list including one or more changes. Each change specifies a changed line in a changed file. The method may further include determining impacted test cases, based on the mapping and the change list, prioritizing the impacted test cases based, in part, on test results corresponding to each impacted test case, executing, based on the prioritization, one or more impacted test cases to obtain executed test cases, and obtaining, for each executed test case, new test results.

Abstract: Systems and methods for private deep neural network training are disclosed. Method includes storing first private values at first machine and second private values at second machine; providing, to third machine, first share of first private values and first share of second private values; providing, to fourth machine, second share of first private values and second share of second private values; computing, at third machine, third machine-value based on first share of first private values and first share of second private values; computing, at fourth machine, fourth machine-value based on second share of first private values and second share of second private values; providing, to first machine and second machine, third machine-value and fourth machine-value; and computing, at first machine, a mathematical function of first private values and second private values, mathematical function being computed based on first private values stored at first machine, third machine-value, and fourth machine-value.

Abstract: In embodiments, a system may comprise a circuit component to perform a function; and a supervisor component (e.g., a safety component) coupled to the circuit component to monitor proper functioning of the circuit component. The circuit component may be configured to initiate provision of diagnostic information to the supervisor component, or to indicate to the safety component whether the circuit component will be responsive to a query from the safety component for diagnostic information.