138 research outputs found
Detecting Sarcasm in Multimodal Social Platforms
Sarcasm is a peculiar form of sentiment expression, where the surface
sentiment differs from the implied sentiment. The detection of sarcasm in
social media platforms has been applied in the past mainly to textual
utterances where lexical indicators (such as interjections and intensifiers),
linguistic markers, and contextual information (such as user profiles, or past
conversations) were used to detect the sarcastic tone. However, modern social
media platforms allow to create multimodal messages where audiovisual content
is integrated with the text, making the analysis of a mode in isolation
partial. In our work, we first study the relationship between the textual and
visual aspects in multimodal posts from three major social media platforms,
i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to
quantify the extent to which images are perceived as necessary by human
annotators. Moreover, we propose two different computational frameworks to
detect sarcasm that integrate the textual and visual modalities. The first
approach exploits visual semantics trained on an external dataset, and
concatenates the semantics features with state-of-the-art textual features. The
second method adapts a visual neural network initialized with parameters
trained on ImageNet to multimodal sarcastic posts. Results show the positive
effect of combining modalities for the detection of sarcasm across platforms
and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of
ACM Multimedia 201
Using Weibull Distribution Analysis to Evaluate ALARA Performance
Abstract -As Low as Reasonably Achievable (ALARA) is the underlying principle for protecting nuclear workers from potential health outcomes related to occupational radiation exposure. Radiation protection performance is currently evaluated by measures such as collective dose and average measurable dose, which do not indicate ALARA performance. The purpose of this work is to show how statistical modeling of individual doses using the Weibull distribution can provide objective supplemental performance indicators for comparing ALARA implementation among sites and for insights into ALARA practices within a site. Maximum likelihood methods were employed to estimate the Weibull shape and scale parameters used for performance indicators. The shape parameter reflects the effectiveness of maximizing the number of workers receiving lower doses and is represented as the slope of the fitted line on a Weibull probability plot. Additional performance indicators derived from the model parameters include the 99 th percentile and the exceedance fraction. When grouping sites by collective total effective dose equivalent (TEDE) and ranking by 99 th percentile with confidence intervals, differences in performance among sites can be readily identified. Applying this methodology will enable more efficient and complete evaluation of the effectiveness of ALARA implementation
Recommended from our members
Using Weibull Distribution Analysis to Evaluate ALARA Performance
As Low as Reasonably Achievable (ALARA) is the underlying principle for protecting nuclear workers from potential health outcomes related to occupational radiation exposure. Radiation protection performance is currently evaluated by measures such as collective dose and average measurable dose, which do not indicate ALARA performance. The purpose of this work is to show how statistical modeling of individual doses using the Weibull distribution can provide objective supplemental performance indicators for comparing ALARA implementation among sites and for insights into ALARA practices within a site. Maximum likelihood methods were employed to estimate the Weibull shape and scale parameters used for performance indicators. The shape parameter reflects the effectiveness of maximizing the number of workers receiving lower doses and is represented as the slope of the fitted line on a Weibull probability plot. Additional performance indicators derived from the model parameters include the 99th percentile and the exceedance fraction. When grouping sites by collective total effective dose equivalent (TEDE) and ranking by 99th percentile with confidence intervals, differences in performance among sites can be readily identified. Applying this methodology will enable more efficient and complete evaluation of the effectiveness of ALARA implementation
Zero-Shot Hashing via Transferring Supervised Knowledge
Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.Comment: 11 page
Low-cost, Transportable Hydrogen Fueling Station for Early FCEV Adoption
Thousands of public hydrogen fueling stations are needed to support the early Fuel Cell Electric Vehicle (FCEV) market in the U.S.; there are currently 12. The California state government has been the largest investor of the hydrogen fueling infrastructure funding 9 permanent stations currently open to the public with 48 more in development costing anywhere from 5.5M each. To attract private investors and decrease dependence on government funding, a low-cost, mobile hydrogen dispensing system must be developed. This paper describes a transportable hydrogen fueling station that has been designed for 9.62/kg. This paper presents the mechanical design and operation of the fueling station. A complete report including an economic analysis and safety features is available at: http://hydrogencontest.org/pdf/2014/WSU_2014_HEF_CONTEST.pdf
OrChem - An open source chemistry search engine for Oracle®
<p>Abstract</p> <p>Background</p> <p>Registration, indexing and searching of chemical structures in relational databases is one of the core areas of cheminformatics. However, little detail has been published on the inner workings of search engines and their development has been mostly closed-source. We decided to develop an open source chemistry extension for Oracle, the de facto database platform in the commercial world.</p> <p>Results</p> <p>Here we present OrChem, an extension for the Oracle 11G database that adds registration and indexing of chemical structures to support fast substructure and similarity searching. The cheminformatics functionality is provided by the Chemistry Development Kit. OrChem provides similarity searching with response times in the order of seconds for databases with millions of compounds, depending on a given similarity cut-off. For substructure searching, it can make use of multiple processor cores on today's powerful database servers to provide fast response times in equally large data sets.</p> <p>Availability</p> <p>OrChem is free software and can be redistributed and/or modified under the terms of the GNU Lesser General Public License as published by the Free Software Foundation. All software is available via <url>http://orchem.sourceforge.net</url>.</p
Muertes por homicidio, consecuencia fatal de la violencia: el caso de Mexico, 1979-1992
Latina and European American Girls’ Experiences with Academic Sexism and their Self-Concepts in Mathematics and Science During Adolescence
The study investigated Latina and European American adolescent girls’ (N = 345, M = 15.2 years, range = 13 to 18) experiences with academic sexism in mathematics and science (M/S) and their M/S perceived competence and M/S value (liking and importance). M/S academic sexism was based on girls’ reported experiences hearing sexist comments about girls’ abilities in math and science. Older European American adolescents, and both younger and older Latina adolescents, who experienced several instances of academic sexism felt less competent in M/S than girls who experienced less sexism (controlling for M/S grades). In addition, among older girls (regardless of ethnicity), those who experienced several instances of academic sexism valued M/S less than girls who experienced less sexism
- …