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.

How Amazon web services uses formal methods

Abstract: Engineers use TLA+ to prevent serious but subtle bugs from reaching production.

A Robust and Efficient Video Representation for Action Recognition

Abstract: This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words (BOW) histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to BOW encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results.

Using virtual networking devices to manage external connections

Abstract: Techniques are described for providing managed virtual computer networks that may have a configured logical network topology with one or more virtual networking devices, with corresponding networking functionality provided for communications between multiple computing nodes of the virtual computer network by emulating functionality that would be provided by the networking devices if they were physically present. In some situations, the emulating of networking device functionality includes receiving routing communications directed to the networking devices and using included routing information to update the configured network topology for the managed computer network. In addition, the techniques may further include supporting interactions with devices that are external to the virtual computer network, including remote physical networking devices that are part of a remote computer network configured to interoperate with the virtual computer network, and/or specialized network devices that are accessible via a substrate network on which the virtual computer network is overlaid.

System and method for exposing popular nodes within a browse tree

Abstract: A computer-implemented system and method are provided for identifying popular nodes within a browse tree or other hierarchical browse structure based on historical actions of online users, and for calling such nodes to the attention of users during navigation of the browse tree. The system and method are particularly useful for assisting users in locating popular products and/or product categories within a catalog of an online merchant, but may be used in connection with browse structures used to locate other types of items. Node popularity levels are determined periodically (e.g., once per day) based on recent user activity data that represents users' affinities for such nodes. Such activity data may include, for example, the number of times each item was purchased, and/or the number of times each category was selected for display, within a selected period of time. Popular nodes are called to the attention of users by automatically “elevating” the nodes for display within the browse tree. For example, when a user selects a particular non-leaf category (a category that contains subcategories) for viewing, the most popular items corresponding to the selected category may be displayed (together with the immediate subcategories), allowing the user to view or directly access these items without having to navigate to lower levels of the browse tree (and particularly those associated with leaf categories). Subcategories may be elevated for display in a similar manner. The node elevation process may also be used to elevate items and/or categories that are predicted to be of interest to a user, regardless of popularity. In a preferred embodiment, both popular items are leaf categories are elevated on a user-specific basis using a combination of user-specific and non-user-specific activity data.

Computer-based funds transfer system

Abstract: A service configured to be accessible by two or more parties to a two-sided funds transfer transaction through a computer network (e.g., the Internet) provides functionality for sending a payment request to a target payer or payee who is not yet registered with the service. Depending upon who is initiating the transaction (i.e., a payer or a payee) the payment requests may be received as requests to send payments or requests to collect payments. In the latter case, the service may be further organized to solicit a payment, for example by transmitting an e-mail message to a second party to the funds transfer transaction. When used to collect payments, the service may be further organized to process one or more responses to the above-mentioned solicitations.