Structure Query Language (SQL) is widely used to access data stored in relational database systems. Although a powerful and flexible language, SQL can also be complex and hard to learn. For most new SQL users, it's easy to write SQL statement by following SQL grammar and syntax rules, but it's...
Mutation testing is one of the effective approaches measuring test adequacy of test suites. It is widely used in both academia and industry. Unfortunately, the adoption and practical use of mutation testing for Python 2.x programs face three obstacles. First, limited useful mutation operators. Existing mutation testing tools support very...
Software testing is the process of evaluating the accuracy and performance of software, and automated software testing allows programmers to develop software more efficiently by decreasing testing costs. We compared two advanced random test generators, a Feedback-Directed Random Test Generator (FDR) and a Feedback-Controlled Random Test Generator (FCR), for an...
We model the popular board game of Clue as an MDP and evaluate Monte-Carlo policy rollout in a simulated environment pitting different agents and policies against each other. We describe the choices we made in the representation, along with some of the problems we encountered along the way. We find...
The Rust programming language is a systems programming language with a strong static type system. A central feature of Rust’s type system is its unique concept of “ownership”, which enables Rust to give a user safe, low-level control over resources without the overhead of garbage collection. In Haskell, most data...
Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman...
Constructing a panorama from a set of videos is a long-standing problem in computer vision. A panorama represents an enhanced still-image representation of an entire scene captured in a set of videos, where each video shows only a part of the scene. Importantly, a panorama shows only the scene background,...
Reasoning about any realistic domain always involves a degree of uncertainty.
Probabilistic inference in belief networks is one effective way of reasoning under
uncertainty. Efficiency is critical in applying this technique, and many researchers
have been working on this topic. This thesis is the report of our research in this...
The advent of deep learning models leads to a substantial improvement in a wide range of NLP tasks, achieving state-of-art performances without any hand-crafted features. However, training deep models requires a massive amount of labeled data. Labeling new data as a new task or domain emerges consumes time and efforts...
In real-time control systems, the value of a control decision depends not
only on the correctness of the decision but also on the time when that decision
is available. Recent work in real-time decision making has used machine learning
techniques to automatically construct reactive controllers, that is, controllers
with little...