Abstract: Methods, systems, and computer program products are provided for improving LLMs which include: receiving a prompt from a user device of a user; in response to receiving the prompt, automatically generating a response to the prompt by: inputting user data associated with the user to a reinforcement learning model; determining a first decoding method from a plurality of decoding methods based at least partially on the user data; inputting the prompt to an LLM; and generating a first token of the response based at least partially on the prompt and using the first decoding method; and communicating the response to the user device.

Abstract: Described herein is a method performed by a user device that interacts with an access device. The user device determines input data at a plurality of sensors included in the user device in response to a movement of the user device by a user. Using a trained machine learning model, the user device classifies the input data as being a specific movement pattern of a plurality of specific movement patterns. The user device identifies an access data instance associated with the specific movement pattern. Further, the user device transmits, using a first RF antenna, the access data instance to an access device comprising a second RF antenna. The access device in turn generates an authorization request message comprising the access data instance.

Abstract: The present disclosure relates to method and apparatus for removing shadows from a High Resolution (HR) image. The HR image including shadows is received. The HR image converted into Low Resolution (LR) image. LR shadow-free image is generated from the LR image using first Artificial Intelligence (AI) model. A HR shadow-free image is generated based on combination of the LR shadow-free image and the HR image using second AI model. The present disclosure provides an efficient framework and lightweight network architectures which are used to remove the shadows based on corresponding shadow characteristics. The framework can be deployed in resource-constrained platforms. The present disclosure facilitates processing selected areas of the HR image and retaining the unselected area. Thus, less computational resources are required.

Abstract: There is provided a method for segmenting a point cloud by an electronic device. The method includes receiving the point cloud including colorless data and/or featureless data. Further, the method includes determining a normal vector for the received point cloud and/or a spatial feature for the received point cloud. Further, the method includes segmenting the point cloud based on the at least one of one or more normal vectors and one or more spatial features.

Abstract: The present disclosure relates to method and apparatus for removing shadows from a High Resolution (HR) image. The HR image including shadows is received. The HR image converted into Low Resolution (LR) image. LR shadow-free image is generated from the LR image using first Artificial Intelligence (AI) model. A HR shadow-free image is generated based on combination of the LR shadow-free image and the HR image using second AI model. The present disclosure provides an efficient framework and lightweight network architectures which are used to remove the shadows based on corresponding shadow characteristics. The framework can be deployed in resource-constrained platforms. The present disclosure facilitates processing selected areas of the HR image and retaining the unselected area. Thus, less computational resources are required.