Abstract: A system employs Graph Prototypical Networks (GPN) for few-shot node classification on attributed networks, and a meta-learning framework trains the system by constructing a pool of semi-supervised node classification tasks to mimic the real test environment. The system is able to perform meta-learning on an attributed network and derive a highly generalizable model for handling the target classification task. The meta-learning framework addresses extraction of meta-knowledge from an attributed network for few-shot node classification, and identification of the informativeness of each labeled instance for building a robust and effective model.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: A tumor-targeting multi-mode imaging and analyzing method for living body based on gold nanoclusters can include incubating the relevant cells with chloroauric acid and the salt solution thereof with certain concentration under physiological conditions, thereby generating gold nanoclusters. After, real-time non-invasive fluorescence imaging, Raman imaging and/or ultrasonic imaging can be used to image the tumor tissue.

Abstract: A tumor-targeting multi-mode imaging and analyzing method for living body based on gold nanoclusters can include incubating the relevant cells with chloroauric acid and the salt solution thereof with certain concentration under physiological conditions, thereby generating gold nanoclusters. After, real-time non-invasive fluorescence imaging, Raman imaging and/or ultrasonic imaging can be used to image the tumor tissue.