Abstract: Embodiments disclosed herein relate to user-centric security classifications of cryptographic token sets that represent a prediction of whether a token set is malicious. A user-centric security classification of a token set is generated according to user behaviors that characterize users involved with the token set, including users holding tokens of the token set and users creating tokens of the token set. A model is trained to recognize user behaviors of certain users whose involvement with the token set increase the likelihood that the token set is non-malicious and secure. Such user behaviors that the model is trained to recognize include high time-rank behaviors, or holding onto cryptographic tokens for extended lengths of time. Upon generation of a user-centric security classification for a token set, graphical displays of tokens or of the token set are updated to reflect the user-centric security classification.

Abstract: Disclosed herein is a time-based leaderboard that ranks users based on a length of time each user has controlled or possessed a given digital object. The leaderboard includes customization options for purposes of user identification and identity connected to social network objects. The leaderboard further uses a staking feature where users provide their digital objects to universal wallets to hold for a predetermined period based on smart contract limitations. Staking improves leaderboard position. The leaderboard further enables expression and displayed of staked digital objects despite the user no longer having actual possession of the digital object. A digital object generator builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users.

Abstract: Disclosed herein is digital object generator that builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users. The user specific input is positioned on pre-configured regions of a 3D object such as a polygon. Examples of the pre-configured regions include faces of the 3D object, orbits around the 3D object, or identifiable regions associated with the 3D object. The 3D object is rendered as a part of a social media page and enables social interactions between users. In the social media page, the 3D object rotates displaying regions/faces to page visitors. In some embodiments, the 3D object is implemented as a pet or companion of a user avatar in a virtual, augmented, or extended reality space.

Abstract: Disclosed herein is digital object generator that builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users. The user specific input is positioned on pre-configured regions of a 3D object such as a polygon. Examples of the pre-configured regions include faces of the 3D object, orbits around the 3D object, or identifiable regions associated with the 3D object. The 3D object is rendered as a part of a social media page and enables social interactions between users. In the social media page, the 3D object rotates displaying regions/faces to page visitors. In some embodiments, the 3D object is implemented as a pet or companion of a user avatar in a virtual, augmented, or extended reality space.

Abstract: Disclosed herein is a social media platform profile identification and social discovery feature. Disclosed social media networks enable introduction of users that may not otherwise know one another based on commonality between those users. Social media profiles are identified by digital objects instead of or in addition to more traditional indexing methods such as real names or screen names. Social discovery on a social network is performed via matching to similar behavior profiles in activity monitored by a block explorer. Machine learning models categorize behavior patterns observed by the block explorer into a machine recognized glossary. Social networks further recommend actions by users based on the monitored online behaviors of social connections.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Exemplary aspects for a specific example concern a radar system having sensor circuitry including multiple radar sensors to provide sensor data via multiple virtual channels and multiple data types, a memory circuit with memory buffers, and a bus-interface circuit to control bus interconnects for bus communications involving a radar signal transmitter and the memory circuit. Radar signals are received and processed, via data acquisition path circuitry in multiple circuit paths and via streams of data in response to and to accommodate the operations of the sensor circuitry. A master controller conveys data, via the bus-interface circuit, to the buffers for the sensor data, and generates selectable-type transactions to be linked in selected ones of the buffers, in response to the data provided from the sensor circuitry and based on the sensor data being provided via different ones of the multiple virtual channels and of the multiple data types.

Abstract: A method of acquiring data, a computer program product for implementing the method, a system for acquiring data, and a vehicle including the system. The method includes determining one or more data types and virtual channels required for one or more applications. The method also includes allocating a plurality of circular buffers in memory according to the determined data type(s) and virtual channel(s). One or more of the circular buffers are allocated to safety data lines. The remaining circular buffers are allocated to functional data lines. The method further includes storing at least one functional data line in a circular buffer allocated to functional data lines according to a data type and virtual channel of the functional data line. The method also includes storing at least one safety data line in a circular buffer allocated to safety data lines.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is digital object generator that builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users. The user specific input is positioned on pre-configured regions of a 3D object such as a polygon. Examples of the pre-configured regions include faces of the 3D object, orbits around the 3D object, or identifiable regions associated with the 3D object. The 3D object is rendered as a part of a social media page and enables social interactions between users. In the social media page, the 3D object rotates displaying regions/faces to page visitors. In some embodiments, the 3D object is implemented as a pet or companion of a user avatar in a virtual, augmented, or extended reality space.

Abstract: Disclosed herein is a social media platform profile identification and social discovery feature. Disclosed social media networks enable introduction of users that may not otherwise know one another based on commonality between those users. Social media profiles are identified by digital objects instead of or in addition to more traditional indexing methods such as real names or screen names. Social discovery on a social network is performed via matching to similar behavior profiles in activity monitored by a block explorer. Machine learning models categorize behavior patterns observed by the block explorer into a machine recognized glossary. Social networks further recommend actions by users based on the monitored online behaviors of social connections.

Abstract: Disclosed herein is a time-based leaderboard that ranks users based on a length of time each user has controlled or possessed a given digital object. The leaderboard includes customization options for purposes of user identification and identity connected to social network objects. The leaderboard further uses a staking feature where users provide their digital objects to universal wallets to hold for a predetermined period based on smart contract limitations. Staking improves leaderboard position. The leaderboard further enables expression and displayed of staked digital objects despite the user no longer having actual possession of the digital object. A digital object generator builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users.

Abstract: A MIPI CSI-2/D-PHY receiving device is configured to handle being hot plugged to MIPI CSI-2/D-PHY transmitting device. During a hot plugging event, the MIPI CSI-2/D-PHY receiving device has not been initialized by receipt from the MIPI CSI-2/D-PHY transmitting device of a Stop State signal of duration TINIT. Though the MIPI CSI-2/D-PHY transmitting device is already transmitting data associated with a partial frame, the MIPI CSI-2/D-PHY receiving device will not enter into an error or unknown state, and will ignore line start/end and frame end events and drop the data packets associated with the partial frame until a frame start event corresponding to a full frame is received from the MIPI CSI-2/D-PHY transmitting device.

Abstract: Exemplary aspects for a specific example concern a radar system having sensor circuitry including multiple radar sensors to provide sensor data via multiple virtual channels and multiple data types, a memory circuit with memory buffers, and a bus-interface circuit to control bus interconnects for bus communications involving a radar signal transmitter and the memory circuit. Radar signals are received and processed, via data acquisition path circuitry in multiple circuit paths and via streams of data in response to and to accommodate the operations of the sensor circuitry. A master controller conveys data, via the bus-interface circuit, to the buffers for the sensor data, and generates selectable-type transactions to be linked in selected ones of the buffers, in response to the data provided from the sensor circuitry and based on the sensor data being provided via different ones of the multiple virtual channels and of the multiple data types.

Abstract: Accordingly, systems and methods for purchasing, securing, and maintaining emoji sequences is presented herein. In one or more examples, an enterprise or user can generate or purchase an emoji sequence, which can include a sequence of emojis that can be used to uniquely identify an enterprise. Once the emoji sequence has been acquired, in one or more examples, the emoji sequence can have a digital token associated with it, wherein the digital token can be derived from the emoji sequence string, in a manner that is unique and deterministic.

Abstract: A MIPI CSI-2/D-PHY receiving device is configured to handle being hot plugged to MIPI CSI-2/D-PHY transmitting device. During a hot plugging event, the MIPI CSI-2/D-PHY receiving device has not been initialized by receipt from the MIPI CSI-2/D-PHY transmitting device of a Stop State signal of duration TINIT. Though the MIPI CSI-2/D-PHY transmitting device is already transmitting data associated with a partial frame, the MIPI CSI-2/D-PHY receiving device will not enter into an error or unknown state, and will ignore line start/end and frame end events and drop the data packets associated with the partial frame until a frame start event corresponding to a full frame is received from the MIPI CSI-2/D-PHY transmitting device.