In this project we study the problem of extracting semantic patterns from sentences to assist with several text analysis tasks, such as identifying checkworthy claims from text and parsing such claims into representations amenable to automatic fact-checking. We leverage NLP tools to parse each sentence into a tree presentation, and replace each specific token with appropriate, more general labels useful for identifying their semantic roles. Substructures in this parse tree serve as our patterns of interest.
Link to talk video: https://compsci.capture.duke.edu/Panopto/Pages/Viewer.aspx?id=f8b8abac-e471-4b79-826d-ab9001221efe
Advances in monitoring, tracing, and profiling large, complex datacenters produce rich datasets and establish a rigorous foundation for understanding datacenter performance. But the sheer volume and complexity of the data challenges existing techniques, which rely heavily on expert knowledge, human intervention, and simple statistics to gain performance insights.
Inspired by various applications including ad auctions, matching markets, and voting, mechanism design deals with the problem of designing algorithms that take inputs from strategic agents and return an outcome optimizing a given objective.
Entering the information age, the demands for online services increase dramatically. Such high demands are pushing the network systems to become more complex and making system availability a crucial requirement for both service providers and clients. The service providers are aiming to have an effective, efficient and stable service: the service should be failure resilient, be scalable to support a large group of clients and still keep acceptable performance. Whereas the clients need a “powerful” service – high performance without threats to their privacy or security.
Link to talk video: https://compsci.capture.duke.edu/Panopto/Pages/Viewer.aspx?id=a2282dc2-1e77-44d2-a1c0-ab88012e371e
Distributive Justice for Machine Learning: An Interdisciplinary Perspective on Defining, Measuring, and Mitigating Algorithmic Unfairness
DNA, the blueprint of life, is more than a carrier of genetic information. It offers a highly programmable substrate that can be used for computing, nanorobotics, and advanced imaging techniques. In this work, we use the programmable nature of synthetic DNA to engineer two novel applications. In the first part, DNA is programmed to improve the multiplexing capabilities of a fluorescence microscope while in the second part, we design a novel DNA computing architecture that using a strand displacing polymerase enzyme.
Modern machine learning, especially deep learning, faces a fundamental question: how to create models that efficiently deliver reliable predictions to meet the requirements of diverse applications running on various systems. This talk will introduce reuse-centric optimization, a novel direction for addressing the fundamental question. Reuse-centric optimization centers around harnessing reuse opportunities for enhancing computing efficiency. It generalizes the principle to a higher level and a larger scope through a synergy between programming systems and machine learning algorithms.
According to statistics, there are over 80,000 cyberattacks per day or over 30 million attacks per year. To make the Internet safe, both the industry and academia propose many solutions. However, these security solutions mainly concentrate on being effective, and ignore the other two features: deployment cost and usability. Therefore, though many works have been proposed to improve security, attacks still happen frequently.
Martin Rinard will present the stories of two research projects. The failure-oblivious computing project developed techniques for keeping systems running and delivering useful results to their users in the face of otherwise fatal errors or security vulnerabilities. The loop perforation project developed techniques for improving performance at the cost of small accuracy losses. Both projects provided new insights into the fundamental empirical resilience of existing software systems.