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.

Managing communications between computing nodes

Abstract: Techniques are described for managing communications between multiple intercommunicating computing nodes, such as multiple virtual machine nodes hosted on one or more physical computing machines or systems. In some situations, users may specify groups of computing nodes and optionally associated access policies for use in the managing of the communications for those groups, such as by specifying which source nodes are allowed to transmit data to particular destinations nodes. In addition, determinations of whether initiated data transmissions from source nodes to destination nodes are authorized may be dynamically negotiated for and recorded for later use in automatically authorizing future such data transmissions without negotiation. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Computer services for assisting users in locating and evaluating items in an electronic catalog based on actions performed by members of specific user communities

Abstract: A Web based system provides informational services for assisting customers in selecting products or other types of items from an electronic catalog of a merchant. Users of the system can create and join user communities, such as communities based on user hobbies, localities, professions, and organizations. The system also supports implicit membership communities that are based on email addresses (e.g., all users having a “nasa.com” email address), shipping/billing addresses, and other known user information. Using purchase history data collected for online users, the system automatically identifies and generates lists of the most popular items (and/or items that are becoming popular) within particular communities, and makes such information available to users for viewing. For example, in the context of an online book store users of the nasa.com community may automatically be presented a Web page which lists the bestselling book titles among nasa.com users, or may be sent email notifications of purchase events or hotselling books within the community. Another feature involves automatically notifying users interested in particular products of other users (preferably other members of the same community) that have purchased the same or similar products. For example, in one embodiment, when a user accesses a book detail page, the detail page is customized to include the names and email addresses of other members of the user's community that recently purchased the same book.

Heuristics-Based Query Processing for Large RDF Graphs Using Cloud Computing

Abstract: Semantic web is an emerging area to augment human reasoning. Various technologies are being developed in this arena which have been standardized by the World Wide Web Consortium (W3C). One such standard is the Resource Description Framework (RDF). Semantic web technologies can be utilized to build efficient and scalable systems for Cloud Computing. With the explosion of semantic web technologies, large RDF graphs are common place. This poses significant challenges for the storage and retrieval of RDF graphs. Current frameworks do not scale for large RDF graphs and as a result do not address these challenges. In this paper, we describe a framework that we built using Hadoop to store and retrieve large numbers of RDF triples by exploiting the cloud computing paradigm. We describe a scheme to store RDF data in Hadoop Distributed File System. More than one Hadoop job (the smallest unit of execution in Hadoop) may be needed to answer a query because a single triple pattern in a query cannot simultaneously take part in more than one join in a single Hadoop job. To determine the jobs, we present an algorithm to generate query plan, whose worst case cost is bounded, based on a greedy approach to answer a SPARQL Protocol and RDF Query Language (SPARQL) query. We use Hadoop's MapReduce framework to answer the queries. Our results show that we can store large RDF graphs in Hadoop clusters built with cheap commodity class hardware. Furthermore, we show that our framework is scalable and efficient and can handle large amounts of RDF data, unlike traditional approaches.

BAE: BERT-based Adversarial Examples for Text Classification

Abstract: Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work.

Successor Features for Transfer in Reinforcement Learning

Abstract: Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.