According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

A system and method for a machine learning-based automated electronic time recording for personnel includes identifying, via a scene capturing device, a representation of a time recording space; identifying a body having a time recording pose within the time recording space based on an assessment of the representation of the time recording space; extracting a plurality of distinct features from the representation of the time recording space based on identifying the body having the time recording pose; executing automated user-recognition based on the extracting of the plurality of distinct features; executing automated time recording recognition based on the extracting of the plurality of distinct features; and executing automated electronic time recording, via a time recording application based on the automated user-recognition and the automated time recording recognition.

A method for generating an electronic signature for an electronic document is implemented by an electronic device that is in communication with a stylus pen and a handwriting input device that includes a writing area. The method includes: initiating a signature input procedure associated with the electronic document; obtaining a signature pattern and an experienced pressure dataset recorded by the handwriting input device during the signature input procedure, and obtaining a fingerprint dataset and an applied pressure dataset recorded by the stylus pen during the signature input procedure; determining whether the experienced pressure dataset corresponds with the applied pressure dataset; and in the case where the experienced pressure dataset corresponds with the applied pressure dataset, generating a signed electronic document by presenting the signature pattern on a part of the electronic document and embedding the fingerprint dataset in the signature pattern.

A method for generating an electronic signature for an electronic document is implemented by an electronic device that is in communication with a stylus pen and a handwriting input device that includes a writing area. The method includes: initiating a signature input procedure associated with the electronic document; obtaining a signature pattern and an experienced pressure dataset recorded by the handwriting input device during the signature input procedure, and obtaining a fingerprint dataset and an applied pressure dataset recorded by the stylus pen during the signature input procedure; determining whether the experienced pressure dataset corresponds with the applied pressure dataset; and in the case where the experienced pressure dataset corresponds with the applied pressure dataset, generating a signed electronic document by presenting the signature pattern on a part of the electronic document and embedding the fingerprint dataset in the signature pattern.

An information processing system includes a vehicle, and a server communicable with the vehicle. The vehicle includes a first acquisition unit configured to acquire first vital information on a user who boards the vehicle. The vehicle is configured to transmit to the server the first vital information on the user acquired with the first acquisition unit. The server is configured to prestore first vital information and settlement information on a registered user, determine whether or not the user is the registered user based on the first vital information on the user received from the vehicle and the first vital information on the registered user, and execute, when determining that the user is the registered user, a settlement process based on the settlement information on the registered user.

The embodiments disclose a method including providing an aerial drone coupled wirelessly to a social distancing application on a user digital device, wherein the drone is coupled to solar cell panels for recharging its batteries, providing a strobe light coupled to the drone for signaling an S.O.S. automatically in emergency situations, cellular communication device coupled to the drone for transmitting and receiving messages from the social distancing application, wherein the drone includes a cellular signal strength sensor to automatically move to a location to boost cellular signal strength, and providing at least one camera for capturing images and videos during user directed reconnaissance, drone sensors to detect and measure aerosols including biologics and DNA in an area, electromagnetic fields, barometric pressure, humidity, ambient temperature, wind speed and direction, detection and identification devices to detect unnatural sounds, to analyze and identify manmade, animal and environmental objects and conditions using computer vision.

The embodiments disclose a method including providing an aerial drone coupled wirelessly to a social distancing application on a user digital device, wherein the drone is coupled to solar cell panels for recharging its batteries, providing a strobe light coupled to the drone for signaling an S.O.S. automatically in emergency situations, cellular communication device coupled to the drone for transmitting and receiving messages from the social distancing application, wherein the drone includes a cellular signal strength sensor to automatically move to a location to boost cellular signal strength, and providing at least one camera for capturing images and videos during user directed reconnaissance, drone sensors to detect and measure aerosols including biologics and DNA in an area, electromagnetic fields, barometric pressure, humidity, ambient temperature, wind speed and direction, detection and identification devices to detect unnatural sounds, to analyze and identify manmade, animal and environmental objects and conditions using computer vision.

Methods, apparatus, and processor-readable storage media for artificial intelligence-driven avatar-based personalized learning techniques are provided herein. An example computer-implemented method includes obtaining multiple forms of input data from user devices associated with a user in a virtual learning environment; determining status information for user variables by processing at least a portion of the multiple forms of input data using a first set of artificial intelligence techniques; determining instruction-related modifications for the user by processing, using a second set of artificial intelligence techniques, at least a portion of the multiple forms of input data and at least a portion of the determined status information; implementing, based on the determined instruction-related modifications, modifications to an instructor avatar with respect to the user in the virtual learning environment; and performing one or more automated actions based on user response to the implemented modifications to the instructor avatar.

Methods, apparatus, and processor-readable storage media for artificial intelligence-driven avatar-based personalized learning techniques are provided herein. An example computer-implemented method includes obtaining multiple forms of input data from user devices associated with a user in a virtual learning environment; determining status information for user variables by processing at least a portion of the multiple forms of input data using a first set of artificial intelligence techniques; determining instruction-related modifications for the user by processing, using a second set of artificial intelligence techniques, at least a portion of the multiple forms of input data and at least a portion of the determined status information; implementing, based on the determined instruction-related modifications, modifications to an instructor avatar with respect to the user in the virtual learning environment; and performing one or more automated actions based on user response to the implemented modifications to the instructor avatar.

Data encryption and Human Pose Estimation based on imaging a body segment. A key for encrypting a data file is generated based on image data that represent a unique biometric feature of a body segment of a user or motion of the user. An image engine executes artificial intelligence to identify matching image data for decrypting the data file. The image engine is further trained to predict changes in image data due to aging, stress, and the like. An avatar associated with the user, which is generated based on a movement pattern of the user, is configurable for generating an encryption key and for use in an avatar-based language.