University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Remote bioenergetics measurements in wild fish: Opportunities and challenges ☆

Abstract: The generalized energy budget for fish (i.e., Energy Consumed = Metabolism + Waste + Growth) is as relevant today as when it was first proposed decades ago and serves as a foundational concept in fish biology. Yet, generating accurate measurements of components of the bioenergetics equation in wild fish is a major challenge. How often does a fish eat and what does it consume? How much energy is expended on locomotion? How do human-induced stressors influence energy acquisition and expenditure? Generating answers to these questions is important to fisheries management and to our understanding of adaptation and evolutionary processes. The advent of electronic tags (transmitters and data loggers) has provided biologists with improved opportunities to understand bioenergetics in wild fish. Here, we review the growing diversity of electronic tags with a focus on sensor-equipped devices that are commercially available (e.g., heart rate/electrocardiogram, electromyogram, acceleration, image capture). Next, we discuss each component of the bioenergetics model, recognizing that most research to date has focused on quantifying the activity component of metabolism, and identify ways in which the other, less studied components (e.g., consumption, specific dynamic action component of metabolism, somatic growth, reproductive investment, waste) could be estimated remotely. We conclude with a critical but forward-looking appraisal of the opportunities and challenges in using existing and emerging electronic sensor-tags for the study of fish energetics in the wild. Electronic tagging has become a central and widespread tool in fish ecology and fisheries management; the growing and increasingly affordable toolbox of sensor tags will ensure this trend continues, which will lead to major advances in our understanding of fish biology over the coming decades.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning

Abstract: The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

Evaluating Measures of Contemporary Sexism

Abstract: Two recently published measures of contemporary sexist attitudes were examined and compared with a sample of 106 Canadian college students. Swim, Aikin, Hall, and Hunter's (1995) Modern Sexism scale was found to be an acceptable measure of sexist attitudes in terms of its internal reliability and its ability to predict other gender-related political attitudes. Although the Modern Sexism scale and the Neosexism scale (Tougas, Brown, Beaton, & Joly, 1995) were equally good at predicting support for the feminist movement and attitudes toward lesbians and gay men, the Neosexism scale had better internal reliability and exhibited stronger gender differences. Moreover, the Neosexism scale was superior at predicting value orientations relevant to modern prejudices.

Coverless image steganography using partial-duplicate image retrieval

Abstract: Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. In this paper, we propose a novel coverless steganographic approach without any modification for transmitting secret color image. In our approach, instead of modifying a cover image to generate the stego-image, steganography is realized by using a set of proper partial duplicates of a given secret image as stego-images, which are retrieved from a natural image database. More specifically, after dividing each database image into a number of non-overlapping patches and indexing those images based on the features extracted from these patches, we search for the partial duplicates of the secret image in the database to obtain the stego-images, each of which shares one or several visually similar patches with the secret image. At the receiver end, by using the patches of the stego-images, our approach can approximately recover the secret image. Since the stego-images are natural ones without any modification traces, our approach can resist all of the existing steganalysis tools. Experimental results and analysis prove that our approach not only has strong resistance to steganalysis, but also has desirable security and high hiding capability.