Search results

Quivela2 is a tool to verify protocols modeled as object-oriented programs. Quivela is a prototype tool for constructing proofs of the security of cryptographic protocols.

The Alexa Auto SDK contains essential client-side software required to integrate Alexa into the automobile. The Auto SDK provides libraries that connect to Alexa and expose interfaces for your vehicle software to implement the platform-specific behavior for audio input, media streaming, calling through a connected phone, turn-by-turn navigation, controlling vehicle features such as heaters and lights, and

The Alexa APIs for Java consists of Java POJOS that represent the request and response JSON of Alexa services. These models act as a core dependency for the Alexa Skills Kit Java SDK These model classes are auto-generated using the JSON schemas in the developer documentation.

In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as SUPPORTED, REFUTED or NOTENOUGHINFO by annotators achieving 0.6841

The Alexa APIs for Python consists of python classes that represent the request and response JSON of Alexa services. These models act as a core dependency for the Alexa Skills Kit Python SDK. These model classes are auto-generated using the JSON schemas in the developer documentation.

This repository lets you train neural networks models for performing end-to-end full-page handwriting recognition using the Apache MXNet deep learning frameworks on the IAM Dataset.

Firectl is a basic command-line tool that lets you run arbitrary Firecracker MicroVMs via the command line. This lets you run a fully functional Firecracker MicroVM, including console access, read/write access to filesystems, and network connectivity.

This package is a Go library to interact with the Firecracker API. It is designed as an abstraction of the OpenAPI-generated client that allows for convenient manipulation of Firecracker VM from Go programs. There are some Firecracker features that are not yet supported by the SDK. These are tracked as GitHub issues with the firecracker-feature label. Contributions to address missing features are welcomed

This is the presentation website for Firecracker. We take pull request for content and FAQ improvements, as well as additons to the list of Firecracker integrations. When contributing to HTML pages in this repo, please format the entire file with the latest stable Prettier release, using the settings below for the HTML parser.

This repository provides resources for implementing a visual search engine. Visual search is the central component of an interface where instead of asking for something by voice or text, you show what you are looking for. When shown a real world, physical item, an AWS DeepLens device generates a feature vector representing that item. The feature vector generated by the AWS DeepLens device is sent to the

Secure Packager and Encoder Key Exchange (SPEKE) is part of the AWS Elemental content encryption protection strategy for media services customers. SPEKE defines the standard for communication between AWS Media Services and digital rights management (DRM) system key servers. SPEKE is used to supply keys to encrypt video on demand (VOD) content through AWS Elemental MediaConvert and for live content through

FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.

As Alexa-enabled devices continue to expand into new countries, we propose an approach for quickly bootstrapping machine-learning models in new languages, with the aim of more efficiently bringing Alexa to new customers around the world.

Amazon scientists are continuously expanding Alexa’s natural-language-understanding (NLU) capabilities to make Alexa smarter, more useful, and more engaging.

Smart speakers, such as the Amazon Echo family of products, are growing in popularity among consumer and business audiences. In order to improve the automatic speech recognition (ASR) and full-duplex voice communication (FDVC) performance of these smart speakers, acoustical echo cancellation (AEC) and noise reduction systems are required. These systems reduce the noises and echoes that can impact operation, such as an Echo device accurately hearing the wake word “Alexa.”

In recent years, the amount of textual information produced daily has increased exponentially. This information explosion has been accelerated by the ease with which data can be shared across the web. Most of the textual information is generated as free-form text, and only a small fraction is available in structured format (Wikidata, Freebase etc.) that can be processed and analyzed directly by machines.

This morning, I am delivering a keynote talk at the World Wide Web Conference in Lyon, France, with the title, Conversational AI for Interacting with the Digital and Physical World.

The Amazon Echo is a hands-free smart home speaker you control with your voice. The first important step in enabling a delightful customer experience with an Echo or other Alexa-enabled device is wake word detection, so accurate detection of “Alexa” or substitute wake words is critical. It is challenging to build a wake word system with low error rates when there are limited computation resources on the device and it's in the presence of background noise such as speech or music.

Just as Alexa can wake up without the need to press a button, she also automatically detects when a user finishes her query and expects a response. This task is often called “end-of-utterance detection,” “end-of-query detection,” “end-of-turn detection,” or simply “end-pointing.”

We are the team Alquist from CTU, Prague, Czech Republic.