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: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Image analysis for user authentication

Abstract: A user can be authenticated to any of a number of computing devices using an authentication process that recognizes the user and verifies that an actual human being is attempting to be authenticated, in order to minimize the ability of another person to spoof the authentication process. A model of a user can be generated and stored in the cloud, enabling that model to be synchronized across various devices. A user accessing one of these devices can have image information captured, which can be used with a facial recognition process, to recognize the user, and with a human verification process, to verify that the facial information corresponds to a human user. Various approaches such as visual analysis, three-dimensional imaging, and thermal imaging can be used to verify that the human user being recognized is interactive with the device.

Context-sensitive learning for enhanced audiovisual emotion classification (Extended abstract)

Abstract: Human emotional expression tends to evolve in a structured manner in the sense that certain emotional evolution patterns, i.e., anger to anger, are more probable than others, e.g., anger to happiness. Furthermore the perception of an emotional display can be affected by recent emotional displays. Therefore, the emotional content of past and future observations could offer relevant temporal context when classifying the emotional content of an observation. In this work, we focus on audio-visual recognition of the emotional content of improvised emotional interactions at the utterance level. We examine context-sensitive schemes for emotion recognition within a multimodal, hierarchical approach: bidirectional Long Short-Term Memory (BLSTM) neural networks, hierarchical Hidden Markov Model classifiers (HMMs) and hybrid HMM/BLSTM classifiers are considered for modeling emotion evolution within an utterance and between utterances over the course of a dialog. Overall, our experimental results indicate that incorporating long-term temporal context is beneficial for emotion recognition systems that encounter a variety of emotional manifestations.

Monitoring domain allocation performance

Abstract: Systems and methods for monitoring the performance associated with fulfilling resource requests and determining optimizations for improving such performance are provided. A processing device obtains and processes performance metric information associated with processing a request corresponding to two or more embedded resources. The processing device uses the processed performance metric information to determine an allocation of one or more domains to be associated with the two or more embedded resources. In some embodiments, in making such a determination, the processing device assesses performance metric information collected and associated with subsequent requests for the two or more embedded resources using each of a variety of alternative allocations of domains associated with the two or more embedded resources. The processing device may also consider a number of factors, including domain selection criteria obtained from an original content provider. Aspects of systems and methods for generating recommendations to use a particular allocation of domains to process a subsequent request corresponding to the two or more embedded resources are also provided.

Network data transmission analysis

Abstract: Network computing systems may implement data loss prevention (DLP) techniques to reduce or prevent unauthorized use or transmission of confidential information or to implement information controls mandated by statute, regulation, or industry standard. Implementations of network data transmission analysis systems and methods are disclosed that can use contextual information in a DLP policy to monitor data transmitted via the network. The contextual information may include information based on a network user's organizational structure or services or network infrastructure. Some implementations may detect bank card information in network data transmissions. Some of the systems and methods may be implemented on a virtual network overlaid on one or more intermediate physical networks that are used as a substrate network.