Browsing UA Faculty Research by Publisher "ACM"
Now showing items 1-4 of 4
Predicting protein secondary structure by an ensemble through feature-based accuracy estimationProtein secondary structure prediction is a fundamental task in computational biology, basic to many bioinformatics workflows, with a diverse collection of tools currently available. An approach from machine learning with the potential to capitalize on such a collection is ensemble prediction, which runs multiple predictors and combines their predictions into one, output by the ensemble. We conduct a thorough study of seven different approaches to ensemble secondary structure prediction, several of which are novel, and show we can indeed obtain an ensemble method that significantly exceeds the accuracy of individual state-of-The-Art tools. The best approaches build on a recent technique known as feature-based accuracy estimation, which estimates the unknown true accuracy of a prediction, here using features of both the prediction output and the internal state of the prediction method. In particular, a hybrid approach to ensemble prediction that leverages accuracy estimation is now the most accurate method currently available: on average over standard CASP and PDB benchmarks, it exceeds the state-of-The-Art Q3 accuracy for 3-state prediction by nearly 4%, and exceeds the Q8 accuracy for 8-state prediction by more than 8%. A preliminary implementation of our approach to ensemble protein secondary structure prediction, in a new tool we call Ssylla, is available free for non-commercial use at ssylla.cs.arizona.edu. © 2020 ACM.
Representing and reasoning about dynamic codeDynamic code, i.e., code that is created or modified at runtime, is ubiquitous in today's world. The behavior of dynamic code can depend on the logic of the dynamic code generator in subtle and nonobvious ways, e.g., JIT compiler bugs can lead to exploitable vulnerabilities in the resulting JIT-compiled code. Existing approaches to program analysis do not provide adequate support for reasoning about such behavioral relationships. This paper takes a first step in addressing this problem by describing a program representation and a new notion of dependency that allows us to reason about dependency and information flow relationships between the dynamic code generator and the generated dynamic code. Experimental results show that analyses based on these concepts are able to capture properties of dynamic code that cannot be identified using traditional program analyses. © 2020 ACM.
Ripple Effect: Communicating Water Quality Data through Sonic VibrationsPollution in real time can be incredibly powerful, but is difficult to communicate. Persistent deterioration of land, air, and water are largely invisible to the eye and camera lens. What if water itself could visualize its quality and perform the level of contamination? Ripple Effect is an environmental art installation that reveals water contamination through sonic vibrations and light. Using software technology, water contamination levels are translated into sound waves. The installation consists of speakers that play gdata sound tracks', which vibrate water held in attached trays. Participants see and hear the water vibrate based on contaminant concentrations. This paper describes the concept, data-To-sound process, implementation, and participant evaluation surrounding the installation of Ripple Effect in communities neighboring resource extraction and other industrial activity. While there are many existing artworks that visualize environmental quality, Ripple Effect is novel in its use of local water quality data and interactive technology that allows the primary medium, water, to communicate directly with the participant. © 2021 Owner/Author.