Abstract: A technique relates to structuring a semiconductor device. First empty cells are placed against hierarchical boundaries of a macro block. Functional cells are added in the macro block. Remaining areas are filled with second empty cells in the macro block. Shape requirements are determined for the first empty cells and the second empty cells. The first and second empty cells are replaced with determined shape requirements.

Abstract: A system and method of operating a system for controlling charging of a battery of a user's electric vehicle from a power source is based upon intervening in a controlled manner between the power source and the electric vehicle and dynamically controlling a level for charging of the vehicle's electric energy storage system. The level potentially being different from a maximum available charging power from the power source. The controlled intervention occurs in accordance with a charging schedule determined by the server and transmitted by the server to the charging cable. The charging cable then operates according to the charging schedule resulting in a corresponding controlled charging of the vehicle's electric energy storage system.

Abstract: The disclosure is directed to a smart charging cable that enables dynamically and remotely controlled charging of a battery such as may be found in an electric vehicle (EV). The EV charging cable is not specific to any particular manufacturer or brand of electric vehicle thereby facilitating unrestricted application and access to vehicular charging. The EV charging cable includes a processor arranged to execute instructions stored on a memory along with a communication module for wireless communication between the cable and a remote source. A car emulator may be used to simulate the presence of a vehicle to allow switched high power sources like standard installed EV chargers as well as a power meter for measuring the flow electricity passing through the cable. The aforementioned may be housed in a control box arranged between two portions of the cable to receive and deliver power from a source to a battery.

Abstract: Techniques facilitating using a blockchain system that integrates the trustworthiness of the blockchain concept with open scientific research by generating a blockchain of the experiments formed, data collected, analyses performed, and results achieved are provided herein. In an example, the blockchain system can form a blockchain representing a research project, wherein the blockchain comprises a first block of research data and a second block of analysis data representing a log of an analysis performed on the research data. Summary blocks and correction blocks can also be added to the blockchain representing the post analysis of the research results. One or more of the subsequent blocks can be linked to the preceding blocks using information in block headers that can also serve to determine whether modifications to the blocks have been performed.

Abstract: Systems, computer-implemented methods, and computer program products to generate virtual motion sensor data from computer animations are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a tracker component that can track virtual location data corresponding to a feature of a computer animated character in a virtual environment. The computer executable components can further comprise a virtual motion sensor component that, based on the virtual location data, can generate virtual motion sensor data.

Abstract: A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.

Abstract: Techniques for assessing the proficiency of artificial intelligence agents and users in a given knowledge domain are described. A plurality of proficiency agents can be initialized with a plurality of proficiency scores, by performing a plurality of assessments between pairs of proficiency agents selected from the plurality of proficiency agents. A first client device associated with a first user is matched with a first proficiency agent of the plurality of proficiency agents, based on a first proficiency score associated with the first user and a second proficiency score of the plurality of proficiency scores corresponding to the first proficiency agent. Assessments results of an assessment performed between the first client device and the first proficiency agent are received, and a rating system update function is used to update the first proficiency score and the second proficiency score, based on the assessment results.

Abstract: A method for authenticating a user is presented. Responsive to a request for access to a computer resource, a computer system prompts the user making the request to access the computer resource to perform a new motion in an environment in which the user is monitored by a sensor system. Detected biometric data in the new motion performed by the user is identified by the computer system. A determination is made as to whether the user performing the new motion is an authenticated user based on comparing the detected biometric data with stored biometric data for a prior motion performed by the authenticated user. The computer system provides access to the computer resource when the user is identified as the authenticated user.

Abstract: Systems, methods, and computer-readable media are described for determining a score for a target student answer using unlabeled data. The target answer is provided by a student to a question for which there is no ground-truth answer data. A set of student answers serves as a set of pseudo-reference answers and a classifier is used to score each answer based on each other answer. In this manner, each student answer serves as a pseudo-reference answer for each other student answer. A clustering approach can also be employed to cluster a set of student answers into clusters. The centroids of the clusters can then serve as the set of pseudo-reference answers. Clustering improves the robustness and efficiency of the score determined for a target student answer.

Abstract: Systems, computer-implemented methods, and computer program products to generate virtual motion sensor data from computer animations are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a tracker component that can track virtual location data corresponding to a feature of a computer animated character in a virtual environment. The computer executable components can further comprise a virtual motion sensor component that, based on the virtual location data, can generate virtual motion sensor data.

Abstract: Techniques facilitating using a blockchain system that integrates the trustworthiness of the blockchain concept with open scientific research by generating a blockchain of the experiments formed, data collected, analyses performed, and results achieved are provided herein. In an example, the blockchain system can form a blockchain representing a research project, wherein the blockchain comprises a first block of research data and a second block of analysis data representing a log of an analysis performed on the research data. Summary blocks and correction blocks can also be added to the blockchain representing the post analysis of the research results. One or more of the subsequent blocks can be linked to the preceding blocks using information in block headers that can also serve to determine whether modifications to the blocks have been performed.

Abstract: Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performance of one or more machine learning tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task.

Abstract: Methods and systems of navigating within a virtual environment are described. In an example, a processor may generate a portal that includes a set of portal boundaries. The processor may display the portal within a first scene of the virtual environment being displayed on a device. The processor may display a second scene of the virtual environment within the portal boundaries. The processor may receive sensor data indicating a movement of a motion controller. The processor may reposition the portal and the second scene in the first scene based on the sensor data, wherein the first scene remains stationary on the device during the reposition of the portal and the second scene. The processor may translate a location of the portal within the first scene to move the portal towards a user of the device until the second scene replaces the first scene being displayed on the device.

Abstract: Techniques that facilitate layered stochastics anonymization of data are provided. In one example, a system includes a machine learning component and an evaluation component. The machine learning component performs a machine learning process for first data associated with one or more features to generate second data indicative of one or more example datasets within a degree of similarity to the first data. The first data and the second data comprise a corresponding data format. The evaluation component evaluates the second data for a particular feature from the one or more features and generates third data indicative of a confidence score for the second data.

Abstract: A technique relates to structuring a semiconductor device. First empty cells are placed against hierarchical boundaries of a macro block. Functional cells are added in the macro block. Remaining areas are filled with second empty cells in the macro block. Shape requirements are determined for the first empty cells and the second empty cells. The first and second empty cells are replaced with determined shape requirements.