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35,686 results found
Publication

Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning 

by Sameer Wagh; et. al.

We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol…

Publication

A Security Model and Fully Verified Implementation for the IETF QUIC Record Layer 

by Antoine Delignat-Lavaud; et. al.

We investigate the security of the QUIC record layer, as standardized by the IETF in draft version 30. This version features major differences compared to Google’s original protocol and prior IETF drafts. We model packet and header encryption, which uses a custom construction for privacy. To capture its goals, we propose a security definition for authenticated encryption with semi-implicit nonces. We show that QUIC uses an instance of a generic construction parameterized by a standard…

Publication

Hardware-Software Contracts for Secure Speculation 

by Marco Guarnieri; et. al.

Since the discovery of Spectre, a large number of hardware mechanisms for secure speculation has been proposed. Intuitively, more defensive mechanisms are less efficient but can securely execute a larger class of programs, while more permissive mechanisms may offer more performance but require more defensive programming. Unfortunately, there are no hardware-software contracts that would turn this intuition into a basis for principled co-design. In this paper, we put forward a framework for specifying such contracts,…

Publication

KARD: Lightweight Data Race Detection with Per-Thread Memory Protection 

by Adil Ahmad; et. al.

Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program’s execution for data races. However, despite extensive work on dynamic data race detection, most proposed systems for commodity hardware incur prohibitive overheads due to expensive compiler instrumentation of memory accesses; hence, they are not efficient enough to be used in all development and testing settings. KARD is a lightweight system that detects data…

Publication

SherLock: Unsupervised Synchronization-Operation Inference 

by Guangpu Li; et. al.

Synchronizations are fundamental to the correctness and performance of concurrent software. They determine which operations can execute concurrently and which can-not—the key to detecting and fixing concurrency bugs, as well as understanding and tuning performance. Unfortunately, correctly identifying all synchronizations has become extremely difficult in modern software systems due to the various forms of concurrency and various types of synchronizations. Previous work either only infers specific type of synchronization by code analysis or relies on…

Publication

Where there’s Smoke, there’s Fire: Wildfire Risk Predictive Modeling via Historical Climate Data 

by Shahrzad Gholami; et. al.

Wildfire is a growing global crisis with devastating consequences. Uncontrolled wildfires take away human lives, destroy millions of animals and trees, degrade the air quality, impact the biodiversity of the planet and cause substantial economic costs. It is incredibly challenging to predict the spatio-temporal likelihood of wildfires based on historical data, due to their stochastic nature. Crucially though, the accurate and reliable prediction of wildfires can help the stakeholders and decision-makers take timely, strategic and…

Publication

Exploring End-to-End Multi-channel ASR with Bias Information for Meeting Transcription 

by Xiaofei Wang; et. al.

Joint optimization of multi-channel front-end and automatic speech recognition (ASR) has attracted much interest. While promising results have been reported for various tasks, past studies on its meeting transcription application were limited to small scale experiments. It is still unclear whether such a joint framework can be beneficial for a more practical setup where a massive amount of single channel training data can be leveraged for building a strong ASR back-end. In this work, we…

Publication

Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings 

by Naoyuki Kanda; et. al.

Recently, an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech. It showed promising results for simulated speech mixtures consisting of various numbers of speakers. However, the model required prior knowledge of speaker profiles to perform speaker identification, which significantly limited the application of the model. In this paper, we extend the prior work by addressing the case…

Publication

Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis 

by Desh Raj; et. al.

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in systems dealing with speech separation, speaker diarization, and automatic speech recognition (ASR) in the last decade, it has become possible to build pipelines that achieve reasonable error rates on this task. In this paper, we propose an end-to-end modular system for the LibriCSS meeting data, which combines independently trained separation, diarization, and recognition components,…

Publication

Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition 

by Zhong Meng; et. al.

The external language models (LM) integration remains a challenging task for end-to-end (E2E) automatic speech recognition (ASR) which has no clear division between acoustic and language models. In this work, we propose an internal LM estimation (ILME) method to facilitate a more effective integration of the external LM with all pre-existing E2E models with no additional model training, including the most popular recurrent neural network transducer (RNN-T) and attention-based encoder-decoder (AED) models. Trained with audio-transcript…