Abstract: Embodiments of the present disclosure relate to a computer-implemented method, device, and computer program product. The method includes: determining, based on a set of user behavior data over a first time period, a set of behavioral features for a user behavior over the first time period. The method further includes: determining, based on the set of behavioral features, a set of anomaly scores for the user behavior according to an anomaly detection model. The anomaly detection model is trained based on user behavior data over a second time period. The method further includes: updating a previously determined anomaly score threshold based on comparison of the determined set of anomaly scores with the anomaly score threshold. The anomaly score threshold is used for indicating whether the user behavior is anomalous. By continuously updating the anomaly score threshold, the solution uses the updated anomaly score threshold for anomaly detection of user behavior.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for allocating a workload. The method includes acquiring a first state graph of a plurality of devices that run a first workload at a first time point. The method further includes updating the first state graph based on a comparison between an active value of at least one node in the first state graph and a predetermined threshold. The method further includes determining a first load state of the plurality of devices at the first time point based on an updated first state graph. The method further includes allocating a second workload to the plurality of devices at a second time point based on the first load state, where active values of nodes in the updated first state graph are greater than the predetermined threshold.

Abstract: Embodiments of the present disclosure relate to a caching method, device, and computer program product for an edge server. The method includes acquiring a historical distribution of a set of variables associated with a network environment of the edge server, and generating one or more time series of the set of variables based on the historical distribution. The method further includes determining a caching strategy associated with the edge server based on the one or more time series of the set of variables. With the solution of the present disclosure, in an edge computing scenario, unnecessary overhead caused by ineffective caching can be reduced while ensuring smooth user experience.

Abstract: Embodiments of the disclosure provide systems and methods for estimating an amount of monetary loss to an accident damaged vehicle. An exemplary system includes a communication interface configured to receive one or more accident images taken of an accident damaged vehicle. It also includes a database for storing loss data of one or more past vehicles, and each past vehicle is associated with a historical accident. It further includes a processor coupled to the communication interface and the database. The processor is configured to detect the accident damaged vehicle in the one or more accident images, identify one or more most similar past vehicles, and estimate the amount of monetary loss to the accident damaged vehicle based on the loss data of the one or more past vehicles.

Abstract: Embodiments of the disclosure provide systems and methods for determining fault for a vehicle accident. An exemplary system includes a communication interface configured to receive a video signal from a camera. The video signal includes a sequence of image frames. The system further includes at least one processor coupled to the communication interface. The at least one processor detects one or more vehicles and one or more road identifiers in the image frames, transforms a perspective of each image frame from a camera view to a top view, determines a trajectory of each detected vehicle in the transformed image frames, identifies an accident based on the determined trajectory of each vehicle, and determines a type of the accident and a fault of each vehicle involved in the accident.