Directed Research Project

Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of information for this task. The current deep learning models for relation extraction have mainly exploited this dependency information by guiding their computation along the structures of the dependency trees. One potential problem with this approach is it might prevent the models from capturing important context information beyond syntactic structures and cause the poor cross-domain generalization.

Towards Intelligent Defense against Application-Layer DDoS with Reinforcement Learning

Application-layer distributed denial-of-service (L7 DDoS) attacks, by exploiting application-layer requests to overwhelm functions or components of victim servers, has become a major rising threat to today’s Internet. However, because the traffic from an L7 DDoS attack appears totally legitimate in transport and network layers, it is difficult to detect and defend against an L7 DDoS attack with traditional DDoS solutions. 

Exploiting Domain Structure with Hybrid Generative-Discriminative Models

Machine learning methods often face a tradeoff between the accuracy of discriminative models and the lower sample complexity of their generative counterparts. This inspires a need for hybrid methods. We present the graphical ensemble classifier (GEC), a novel combination of logistic regression and naive Bayes. By partitioning the feature space based on known independence structure, GEC is able to handle datasets with a diverse set of features and achieve higher accuracy than a purely discriminative model from less training data.

Exploring Codata: The Relation to Object-Orientation

Functional languages are known to enjoy an elegant connection to logic: lambda-calculus corresponds to natural deduction. Unfortunately, the same cannot be said for object-oriented languages. Type systems have been designed to capture all the fancy features present in current object-oriented languages. We believe, however, that the logical foundation of object-orientation has not yet been fully explored. Our goal is to describe how objects arise naturally in logic.

Efficient Point Merging Using Data Parallel Techniques

We study the problem of merging three-dimensional points that are nearby or coincident.

We introduce a fast, efficient approach that uses data parallel techniques for execution in various shared-memory environments. Our technique incorporates a heuristic for efficiently clustering spatially close points together, which is one reason our method performs well against other methods.

Understanding the Impact of Dynamic Power Capping on Application Progress

Electrical power has become an important design constraint in high-performance computing (HPC) systems. On future HPC machines, power is likely to be a budgeted resource and thus managed dynamically. Power management software needs to reliably measure application performance at runtime in order to respond effectively to changes in application behavior. Execution time tells us little about how the science in the application is progressing toward an application-defined end goal.

Using Machine Learning to Explore Hidden Behavioral States Encoded in Facial Movement Videos of Mice

The era of big data and machine learning has rapidly produced techniques to process and extract patterns from large amounts of data. Videos are the epitome of big data by being inherently high dimensional both spatially and temporally. In Neuroscience, murine experiments often include a camera capturing the behavior of head-fixed mice and a camera capturing the activity of the brain while performing the task. Recent work has shown the high explanatory power of linear models using the mouse's facial movements to predict brain activity while preforming a task.

Scaling Collaborative Filtering with PETSc

Machine learning and recommendation systems have become extremely popular and widely used in recent years, with several major companies using recommenders to help their users sort through the vast array of products offered. Naturally, we want these recommender systems to give accurate predictions of products a user might like, but we also want these predictions quickly, based on the user's past preferences and newer preferences they may have just expressed.

Parallel Hypergraph Transversals

The DRP introduces parallel algorithms to compute all minimal transversals of a hypergraph. State of the art hypergraph transversal algorithms are used for design and analyzed for comparison. The critical vertex of hyperedges concept is used to conjunction with partitioning of hyperedges. Parallelism is achieved by duplicating an iterative state machine and splitting the workload. Multiple Hypergraphs are evaluated to demonstrate the various benefits and drawbacks to the algorithms.

A Flexible Approach to Relational Modeling of Social Network Spam

Social media websites face a constant barrage of spam, unwanted messages that distract, annoy, and even defraud honest users. These messages tend to be very short, making them difficult to identify in isolation. Furthermore, spammers disguise their messages to look legitimate, tricking users into clicking on links and tricking spam filters into tolerating their malicious behavior. Thus, some spam filters examine relational structure in the domain, such as connections among users and messages, to better identify deceptive content.

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