16 results about "Task Performances" patented technology

Methods and systems are disclosed for selectively modifying the behavior of a pre-trained language model with respect to a designated task. A task-specific subspace is identified by training low-rank matrices for selected layers of the trained machine learning model, while freezing other parameters. The identified subspace is used to either attenuate or enhance task contributions by adjusting one or more model weight matrices. In some embodiments, overlapping subspaces are discriminated to preserve related task performance. These operations can be performed without access to original training data or full retraining. Some aspects of the disclosed techniques can allow efficient knowledge removal or addition in language models while minimizing adverse effects on unrelated tasks.

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

The invention discloses a generative agent cognitive diagnosis framework fusing teacher thinking, and the framework is used for modeling a cognitive judgment process of a teacher in a real teaching scene, systematically constructing task-aware thinking modeling, a behavior track-based observation reflection mechanism, and a causal-driven explanation generation module. Therefore, dynamic modeling and deviation diagnosis of the cognitive state of the student are realized. The method has the advantages that firstly, the method has good cold start capability, and reasonable judgment can be made even if complete historical behavior data is lacked; 2, the method has relatively high interpretability, can output a causal chain of'knowledge state-behavior process-task performance ', and supports accurate teaching intervention; and thirdly, the method has the ability of distinguishing the thinking levels of students, can assist in completing Bloom cognitive classification, and provides effective support for task layering and teaching design.

Techniques for performing tasks are provided. An example method includes receiving, via the one or more input devices, a natural-language speech input including a request to perform a task; providing, at a language model, a plan corresponding to the task; determining whether the plan satisfies a set of resolution criteria; in accordance with a determination that the plan satisfies the set of resolution criteria, initiating performance of the task according to the selected plan; and in accordance with a determination that the plan does not satisfy the set of resolution criteria: providing a query to an information retrieval service requesting a set of resolution data; receiving, from the information retrieval service, the set of resolution data; resolving the plan based on the set of resolution data; and initiating performance of the task according to the resolved plan.

This invention discloses a socialized horticulture intervention and assessment system based on VR technology, specifically relating to the field of auxiliary psychotherapy. The system includes: a label combination space construction module that establishes a three-dimensional coordinate space based on the patient's cognitive, emotional, and behavioral dimensions to form psychological label combination codes; a communication willingness assessment module that calculates the communication willingness score between two collaborating patients; a label graph construction module that constructs a label fit graph based on the communication willingness score to identify conflicting and complementary label relationships; a group reconstruction module that performs patient isolation and collaborative group reconstruction based on the spatial distribution of conflicting labels; a task performance assessment module that combines different collaborative structures and psychological label inputs to construct a task performance assessment model; and an intervention strategy configuration module that, based on the group structure and task node candidate allocation strategy, selects the optimal intervention strategy configuration scheme through the task response parameters output by the model, achieving optimal matching of task structures under individual label guidance.

Embodiments of the innovation relate to a method for generating a labeled object data set. The method comprises receiving a task performance video, playing the task performance video in reverse, and for each video frame of the task performance video, applying a mask to images of the objects within the object stream. The method further comprises identifying an object entering a first video frame of the task performance video along a direction perpendicular to a direction of the object stream, in response to detecting motion of the identified object from the first video frame of the task performance video to a second video frame of the task performance video along the perpendicular direction, designating the object as an object of interest, and storing mask segmentation data associated with the mask of the object of interest as part of the labeled object data set.

A method of adjusting mental state includes acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data, identifying a first set of features, predicting a baseline mental state, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The baseline mental state is predicted based on the first set of features. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. An indication of the one or more actions of the predicted path is output to the individual.

This application relates to a method, apparatus, computer device, and storage medium for adjusting a user's subjective willingness to accept a physical challenge task. The method includes: obtaining expected task performance data using average performance data of the current physical challenge task and the user's self-efficacy scale score; calculating a subjective estimate of the user's success rate in completing the current physical challenge task using the expected task performance data; calculating an overall estimate of the user's ability to perform the current physical challenge task using the subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; determining the user's subjective probability of accepting the current physical challenge task using the overall estimate; and adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task. This method can dynamically adjust a user's subjective willingness to accept a physical challenge task.

This disclosure provides a method and apparatus for task orchestration. The method includes: acquiring a task DAG configuration file; determining a task orchestration result based on first orchestration parameters, a first orchestration algorithm, and the task DAG configuration file; the first orchestration parameters and the first orchestration algorithm are determined in response to a user's selection operation; acquiring task performance data of each task in the task orchestration result through a preset port; and dynamically optimizing preset orchestration data based on the task performance data of each task to obtain an updated task orchestration result; the preset orchestration data includes at least one of the first orchestration parameters, the first orchestration algorithm, and the task DAG configuration file.

The invention discloses a stroke rehabilitation training method and a computer program product, a double-task training scene and a single-task training scene are provided, the double-task training scene comprises a motion task and a cognitive task which are executed at the same time, and the single-task training scene comprises a motion task and a cognitive task which are executed respectively; acquiring a first performance quantized value for executing the motion task and a second performance quantized value for executing the cognitive task in the single-task training scene by the user; acquiring a third performance quantized value for executing a motion task and a fourth performance quantized value for executing a cognitive task in the double-task training scene by the user; determining an athletic performance decrease quantized value based on the first and third performance quantized values; determining a cognitive performance decrease quantized value based on the second and fourth performance quantized values; a dual-task comprehensive performance decline quantized value is calculated based on the athletic performance decline quantized value and the cognitive performance decline quantized value to determine a rehabilitation performance of the user.

The invention provides a task optimization management method and system based on artificial intelligence, and relates to the technical field of task management, and the method comprises the steps: determining a plan start moment, a plan end moment, execution condition information and execution result information of a sub-task, determining a task graph structure, generating task execution information after a target task starts, and storing the task execution information in the task graph structure; and when the task execution cycle is finished, acquiring actual task execution condition information, and updating the task graph structure. And after the target task is completed, determining a target task performance management index. According to the method and the device, the task graph structure can be automatically obtained based on the Gantt chart of the target task, so that the optimal flow of task execution is obtained, the task execution efficiency is improved, the task execution information can be automatically generated, a clear task execution target can be formulated, and the execution efficiency is favorably improved. The task execution efficiency can be improved through continuous adjustment and optimization, and the target task performance management index can be determined, so that the task execution is effectively supervised.

To accurately estimate a work performance state of a user by using brain activity information of the user.SOLUTION: The method includes a step (ST1) of acquiring brain activity information of a user, and a step (ST2) of estimating a work performance state of the user based on a frequency of switching between DMN and CEN obtained based on the acquired brain activity information, wherein the step (ST2) of estimating the work performance state of the user includes a step (ST11) of acquiring a DMN activity value, a step (ST12) of acquiring a CEN activity value, a step (ST19) of calculating a time from when the DMN activity value matches the CEN activity value to when the DMN activity value matches the CEN activity value next time, a step (ST20) of comparing the time with a preset time-threshold, and a step (ST20) of determining that the work performance state of the user is good when the time is determined to be smaller than the time-threshold (: YES). ST21.SELECTED DRAWING: Figure 5

Techniques are described for performing automated operations that include analyzing computer-detected event activity to improve further computer processing, such as to determine tasks performed that cause the events, to use natural language processing (NLP) to generate textual descriptions of the tasks, and to use the generated descriptions to improve further processing related to the tasks.

A cognitive assessment system includes a computing device, a server coupled to the computing device and an eye tracking device coupled to the computing device. The computing device includes a cognitive assessment program with at least one cognitive assessment task for assessing at least one cognitive function of a user. The server includes a database. The database stores a historical task performance data of the user, a historical task performance model of the user, task performance data of a healthy population, and task performance data of a patient population. The eye tracking device is for capturing the eye movement data of the user.

Embodiments include a method for managing the execution of real-time tasks in a multi-core processor system. The method includes instantiating a plurality of task instances, each associated with a task-specific configuration. The method further includes executing one or more workers through each task instance according to a predefined execution phase. The method also includes calling a task profiling call associated with each task instance to record task-level performance data during execution, and calling an intra-task profiling call associated with each worker to record intra-task performance data within each execution phase.The procedure also includes synchronizing task-level performance data and intra-task performance data through a global configuration to provide unified profiling of the task instances running on the cores.

Methods and systems are disclosed for selectively modifying the behavior of a pre-trained language model with respect to a designated task. A task-specific subspace is identified by training low-rank matrices for selected layers of the trained machine learning model, while freezing other parameters. The identified subspace is used to either attenuate or enhance task contributions by adjusting one or more model weight matrices. In some embodiments, overlapping subspaces are discriminated to preserve related task performance. These operations can be performed without access to original training data or full retraining. Some aspects of the disclosed techniques can allow efficient knowledge removal or addition in language models while minimizing adverse effects on unrelated tasks.