Method and system for determining a status of a mobile device of a user are disclosed. For example, the method includes receiving first sensor data at a first time from an application installed on a mobile device of a user, determining a first location of the mobile device, immediately subsequent to receiving the first sensor data at the first time, receiving second sensor data at a second time from the application, the second time following the first time by a time interval, determining a second location of the mobile device, determining a distance between the first location and the second location, determining whether the distance corresponding to the time interval exceeds a predetermined threshold, and determining whether a trip log indicative of at least one trip during the time interval is received from the application.

Disclosed herein is a shipping container that includes numerous multi-factor authentication (MFA) tie ins to provide added security to the container. In some embodiments, the shipping container is associated with a mobile user profiles within a mobile application. A retailer makes a sale, then, using a mobile application, associates the container with the destination address and the MFA details associated with the buyer's mobile application account. Once the container is loaded up, the container will not open again except for the buyer using the buyer's MFA authentication credentials.

Methods for modifying a patrol route of a safety officer include monitoring persons within geographic areas, identifying a safety officer, tracking an elapsed time value for each of the persons indicating a respective quantity of time since a person has been within a threshold proximity to the safety officer, determining a total elapsed time value for each of the geographic areas based on the respective elapsed time values for each of the persons currently located in the geographic area, determining that a patrol route of the safety officer should be modified based on the total elapsed time value for each of the geographic areas, modifying the patrol route of the safety officer, and transmitting the modified patrol route to the safety officer. The modified patrol route indicates that the safety officer is to patrol a given geographic area of the geographic areas.

A trustability analyzing system (100) includes: a friend list acquisition unit (111) configured to acquire, via a network, person attribute information of a holder of an account that is subject to determination; a friend information acquisition unit (112) configured to acquire, via the network, a list of friend accounts of the account that is subject to determination; an attribute information acquisition unit (113) configured to acquire, based on the list of friend accounts, information about the friend via the network; a person attribute estimation unit (121) configured to estimate, based on the information about the friends, person attributes of the holder of the account that is subject to determination; a distance calculation unit (122) configured to calculate a distance between the person attribute information acquired by the person attribute acquisition means and the person attributes estimated by the person attribute estimation means; and a trust level calculation unit (123) configured to calculate, based on the distance calculated by the distance calculation means, a trust level of the account that is subject to determination.

Techniques for risk evaluation include receiving, from a requesting entity, a request for monitoring target entities specifying a first identifier associated with each target entity and target entity information. The system generates a second identifier and a third identifier for each target entity and stores a mapping of the second identifiers to the first identifiers and the third identifiers, preventing the second identifiers from being provided to the requesting entity. The system monitors a periodically updated data set and determines risk metrics for the target entities, comparing each risk metric to a threshold value to identify target entities whose risk data indicates an insider threat. The system generates a third identifier for the identified target entities and provides the third identifiers to the requesting entity. Responsive to a request for a corresponding first identifier, the system identifies and provides the first and third identifiers to the requesting entity.

A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

A system includes an autonomous vehicle (AV) comprising a sensor, a control subsystem, and an operation server. The control subsystem receives sensor data comprising location coordinates of the AV from the sensor. The operation server detects an unexpected event from the sensor data, comprising at least one of an accident, an inspection, and a report request. The operation server receives a message from a user comprising a request to access particular information regarding the AV and location data. The operation server associates the AV with the user if the location coordinates of the AV match location data of the user. The operation server establishes a communication path between the user and a remote operator for further communications.

Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

The present disclosure relates to a safety management system. The safety management system calculates a real-time data risk score and an incident data risk score based on real-time data received from a wearable device and incident data selected from big data, calculates a total risk score by summing all values obtained by multiplying calculated risk for respective data by weights for respective data, compares the total risk store with a preset threshold score, and transmits a dangerous situation message to a risk recognition subject when it is determined that a user is at risk. The safety management system of the present disclosure may transmit the real-time data, the incident data, and the dangerous situation message using a 5G communication system, and a safety management server for determining whether or not the user is at risk may be implemented using an artificial neural network.

A computer-implemented method executed by at least one processor for performing mini-batching in deep learning by improving cache utilization is presented. The method includes temporally localizing a candidate clip in a video stream based on a natural language query, encoding a state, via a state processing module, into a joint visual and linguistic representation, feeding the joint visual and linguistic representation into a policy learning module, wherein the policy learning module employs a deep learning network to selectively extract features for select frames for video-text analysis and includes a fully connected linear layer and a long short-term memory (LSTM), outputting a value function from the LSTM, generating an action policy based on the encoded state, wherein the action policy is a probabilistic distribution over a plurality of possible actions given the encoded state, and rewarding policy actions that return clips matching the natural language query.