International Business Machines Corporation

Ze Ming Zhao

Li Cao

Xiao Tian Xu

Cheng Fang Wang

Yi Shan Jiang

Peng Hui Jiang

Zhi Zhang

Hong Min

Jing James Xu

Jing Yan Ma

Xue Ying Zhang

Wu Song Fang

Zhan Wei Wang

Qing Li

Wenjing Liao

The present invention provides a computer-implemented method, a system, and a computer program product for path planning According to the computer-implemented method, a target discriminator is selected from a set of discriminators with different kernel sizes based on a target image obtained from an image capturing device. In this case, a confidence of the target image is determined using the target discriminator. The confidence indicates whether the target image contains a target object to be captured. Thereby, a movement indication for moving the image capturing device to capture the target object is determined based on the confidence of the target image.

Path planning

Embodiments of the present disclosure provide systems and methods for implementing self-bias detection based on performance and importance. A disclosed computer implemented method aggregates continuous input data through a K-means clustering algorithm to reduce the number of aggregated sub-group data pairs, enabling a reduced calculation time for computing bias and enhanced performance. The self-bias detection identifies a scale factor and a balance factor of aggregated sub-group data pairs, which indicate the importance of the detected bias.

Optimized bias self-detection based on performance and importance

An approach to optimize performance for large scale inference models. Data in the form of images is received from sensors such as cameras. The data is processed to generate data tags associated with the context of the image and portion the images. Model tags are generated based on data characteristics or user input. The tags and their associated data are added to a time-based queue for delivery to the appropriate inference models. Based on the embedded delivery time and frequency, the portioned images are delivered to the appropriate inference models.

Inference model optimization

Embodiments of the present disclosure relate to weight matrix prediction. In an embodiment, a computer-implemented method is disclosed. The method comprises sending a candidate weight matrix of a neural network to one of a plurality of computing nodes comprised in a computing system to perform a testing iteration. The method further comprises receiving a testing loss value from the one of the plurality of computing nodes based on the testing iteration. The method further comprises evaluating whether the testing loss value is applicable. The method further comprises determining that the candidate weight matrix is available to be employed in a new formal iteration in response to the testing loss value being applicable. In other embodiments, a system and a computer program product are disclosed.

Weight matrix prediction

In an approach, a processor extracts an audio signal from a video clip. A processor converts the audio signal into a text sequence. A processor selects a first set of keywords from the text sequence, the first set of keywords corresponding to a first audio segment of the audio signal. A processor tags a target video segment of the video clip with the first set of keywords, the target video segment corresponding to the first audio segment.

Automatic video tagging

Embodiments of the present invention relate to methods, systems, and computer program products for user behavior management. In embodiments, a group of states of a user of an application system within a previous time period may be obtained. A state in the group of states may be associated with a privilege of the user for accessing resources in the application system during the previous time period. A feature of the user may be generated based on the group of states. A privilege of the user at a current time may be managed in the application system based on the feature. With these embodiments, the user behavior may be managed according to various aspect of the user's historical states and thus the user may be managed in a more accurate and effective manner.

Behavior management

Techniques for multistage process model training are described herein. Another aspect includes determining a first gray level histogram corresponding to a first input image. Another aspect includes determining a second gray level histogram corresponding to a second input image. Another aspect includes determining a set of change values, each change value corresponding to a change in a respective gray level from the first gray level histogram to the second gray level histogram. Another aspect includes comparing each change value of the set of change values to a threshold. Another aspect includes, based on determining that a first change value of the set of change values is higher than the threshold, adding a first gray level corresponding to the first change value to a hot zone of the second input image. Another aspect includes training a model using the hot zone of the second input image.

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Beijing, China

Ze Ming Zhao