Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Light emission guidance

Abstract: A visible-light based display system may be used to project visual guidance to picking and/or stowing agents in a materials handling facility dependent on their current location. The system may comprise a plurality of fixed-location display devices and/or mobile display devices coupled to a control system. The control system may send messages to particular ones of the display devices for projection of visual guidance usable to direct an agent to a particular inventory area in which an item is to be stowed or from which an item is to be picked, to identify a particular position within an inventory area, and/or to identify a particular item stored within an inventory area. The messages may include location, position, and/or descriptive information associated with an item to be stowed or picked. The projected visual guidance may include light or laser beams, text, graphics and/or images, and may be agent-specific, item-specific, and/or order-specific.

Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs

Abstract: Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost over time, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is $O(1)$-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) user-mobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.

Indicating irregularities in online financial transactions

Abstract: A system and method for preventing fraud in an online transaction is shown. Requests for financial transactions between on-line account holders are detected as well as relationships between the accounts. A fraud value related to a likelihood that a fraud is occurring in the transactions is determined based on the relationship. An indication of a likelihood of fraud is provided if the fraud value exceeds a predetermined amount. Thus the transaction is terminated and the appropriate parties are automatically notified.

Electronic communication with secure screen sharing of sensitive information

Abstract: A customer support application provides screen sharing of the user's computing device with a remote customer support agent, thereby enabling the customer support agent to view the content displayed on the user's device. Sensitive information that is displayed on a user's computing device is obfuscated from the computing device of the remote customer support agent, and a notification of that obfuscation is displayed on the user's computing device. Information can be determined to be sensitive based on a sensitive indicator tag or a heuristic.

Leveraging Context Information for Natural Question Generation

Abstract: The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.