116,847 research outputs found
Carting Away the Oceans 9
The Carting Away the Oceans report, released annually since 2008, identifies which major grocery chains are leaders in sustainable seafood and which are falling behind.The findings are telling.In the latest update, Whole Foods, Wegmans, Hy-Vee, and Safeway topped the list for their sustainable seafood practices. Roundy's, Publix, A&P, and Save Mart were the worst ranked companies. Publix and Kroger, both top ten supermarkets based on their annual sales, sell more Red List species than any other U.S. grocery chain.Applauding industry leaders and exposing those lagging behind is key to getting supermarkets to take responsibility and play their part in protecting our oceans and the people who depend on the
Using Nuances of Emotion to Identify Personality
Past work on personality detection has shown that frequency of lexical
categories such as first person pronouns, past tense verbs, and sentiment words
have significant correlations with personality traits. In this paper, for the
first time, we show that fine affect (emotion) categories such as that of
excitement, guilt, yearning, and admiration are significant indicators of
personality. Additionally, we perform experiments to show that the gains
provided by the fine affect categories are not obtained by using coarse affect
categories alone or with specificity features alone. We employ these features
in five SVM classifiers for detecting five personality traits through essays.
We find that the use of fine emotion features leads to statistically
significant improvement over a competitive baseline, whereas the use of coarse
affect and specificity features does not.Comment: In Proceedings of the ICWSM Workshop on Computational Personality
Recognition, July 2013, Boston, US
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Big Data in the Oil and Gas Industry: A Promising Courtship
The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fast computational model providing fuzzy logic and supervised learning is one
of the main challenges in the machine learning. In this research paper, we
present FSL-BM algorithm as an efficient solution of supervised learning with
fuzzy logic processing using binary meta-feature representation using Hamming
Distance and Hash function to relax assumptions. While many studies focused on
reducing time complexity and increasing accuracy during the last decade, the
novel contribution of this proposed solution comes through integration of
Hamming Distance, Hash function, binary meta-features, binary classification to
provide real time supervised method. Hash Tables (HT) component gives a fast
access to existing indices; and therefore, the generation of new indices in a
constant time complexity, which supersedes existing fuzzy supervised algorithms
with better or comparable results. To summarize, the main contribution of this
technique for real-time Fuzzy Supervised Learning is to represent hypothesis
through binary input as meta-feature space and creating the Fuzzy Supervised
Hash table to train and validate model.Comment: FICC201
Learning Generative Models with Visual Attention
Attention has long been proposed by psychologists as important for
effectively dealing with the enormous sensory stimulus available in the
neocortex. Inspired by the visual attention models in computational
neuroscience and the need of object-centric data for generative models, we
describe for generative learning framework using attentional mechanisms.
Attentional mechanisms can propagate signals from region of interest in a scene
to an aligned canonical representation, where generative modeling takes place.
By ignoring background clutter, generative models can concentrate their
resources on the object of interest. Our model is a proper graphical model
where the 2D Similarity transformation is a part of the top-down process. A
ConvNet is employed to provide good initializations during posterior inference
which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our
model can robustly attend to face regions of novel test subjects. More
importantly, our model can learn generative models of new faces from a novel
dataset of large images where the face locations are not known.Comment: In the proceedings of Neural Information Processing Systems, 201
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