University of Waterloo

About: University of Waterloo is a education organization based out in Waterloo, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 36093 authors who have published 93906 publications receiving 2948139 citations. The organization is also known as: UW & uwaterloo.

Using direct and indirect measures to study perception without awareness

Abstract: Many studies directed at demonstrating perception without awareness have relied on the dissociation paradigm. Although the logic underlying this paradigm is relatively straightforward, definitive results have been elusive in the absence of any general consensus as to what constitutes an adequate measure of awareness. We propose an alternative approach that involves comparisons of the relative sensitivity of comparable direct and indirect indexes of perception. The only assumption required by the proposed approach is that the sensitivity of direct discriminations to relevant conscious information is greater than or equal to the sensitivity of comparable indirect discriminations. The proposed approach is illustrated through an evaluation of Avant and Thieman’s (1985) recent claim that an indirect measure of perception based on judgments of apparent visual duration provides a more sensitive indicator of perception than does a direct measure based on forced-choice recognition. Contrary to this claim, when direct and indirect indexes are measured under comparable conditions, an indirect measure based on judgments of perceived duration provides a less sensitive index of perceptual processing than do comparable direct measures. The proposed approach provides a general conceptual/methodological framework for using the dissociation paradigm in studies directed at establishing unconscious processes.

SCUBA-2: the 10 000 pixel bolometer camera on the James Clerk Maxwell Telescope

Abstract: SCUBA-2 is an innovative 10000 pixel bolometer camera operating at submillimetre wavelengths on the James Clerk Maxwell Telescope (JCMT). The camera has the capability to carry out wide-field surveys to unprecedented depths, addressing key questions relating to the origins of galaxies, stars and planets. With two imaging arrays working simultaneously in the atmospheric windows at 450 and 850µm, the vast increase in pixel count means that SCUBA-2 maps the sky 100–150 times faster than the previous SCUBA instrument. In this paper we present an overview of the instrument, discuss the physical characteristics of the superconducting detector arrays, outline the observing modes and data acquisition, and present the early performance figures on the telescope. We also showcase the capabilities of the instrument via some early examples of the science SCUBA-2 has already undertaken. In February 2012, SCUBA-2 began a series of unique legacy surveys for the JCMT community. These surveys will take 2.5years and the results are already providing complementary data to the shorter wavelength, shallower, larger-area surveys from Herschel. The SCUBA-2 surveys will also provide a wealth of information for further study with new facilities such as ALMA, and future telescopes such as CCAT and SPICA.

Automatically learning semantic features for defect prediction

Abstract: Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models. To bridge the gap between programs' semantics and defect prediction features, this paper proposes to leverage a powerful representation-learning algorithm, deep learning, to learn semantic representation of programs automatically from source code. Specifically, we leverage Deep Belief Network (DBN) to automatically learn semantic features from token vectors extracted from programs' Abstract Syntax Trees (ASTs). Our evaluation on ten open source projects shows that our automatically learned semantic features significantly improve both within-project defect prediction (WPDP) and cross-project defect prediction (CPDP) compared to traditional features. Our semantic features improve WPDP on average by 14.7% in precision, 11.5% in recall, and 14.2% in F1. For CPDP, our semantic features based approach outperforms the state-of-the-art technique TCA+ with traditional features by 8.9% in F1.

Complex Wavelet Structural Similarity: A New Image Similarity Index

Abstract: We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.