480 research outputs found
Is This a Joke? Detecting Humor in Spanish Tweets
While humor has been historically studied from a psychological, cognitive and
linguistic standpoint, its study from a computational perspective is an area
yet to be explored in Computational Linguistics. There exist some previous
works, but a characterization of humor that allows its automatic recognition
and generation is far from being specified. In this work we build a
crowdsourced corpus of labeled tweets, annotated according to its humor value,
letting the annotators subjectively decide which are humorous. A humor
classifier for Spanish tweets is assembled based on supervised learning,
reaching a precision of 84% and a recall of 69%.Comment: Preprint version, without referra
Operational systems used in hydrological forecasting activity. Case study: the upper part of Tutova river basin
Flash floods are among the most dangerous natural hazards. In order to issue flash flood warning messages are taken into account information provided by several sources. The analysis of an event that took place in the upper part of Tutova river basin, on the 13th of May 2017, was proposed in order to verify the accuracy of the products offered by the main systems used in flash floods forecasting activity. Following the analysis it can be observed that all the operational systems (ROFFG, SEEFFG, EFAS) forecasted a flash flood phenomena for Eastern part of the country, especially Tutova river basin, event that caused significant damages
Detecting Singleton Review Spammers Using Semantic Similarity
Online reviews have increasingly become a very important resource for
consumers when making purchases. Though it is becoming more and more difficult
for people to make well-informed buying decisions without being deceived by
fake reviews. Prior works on the opinion spam problem mostly considered
classifying fake reviews using behavioral user patterns. They focused on
prolific users who write more than a couple of reviews, discarding one-time
reviewers. The number of singleton reviewers however is expected to be high for
many review websites. While behavioral patterns are effective when dealing with
elite users, for one-time reviewers, the review text needs to be exploited. In
this paper we tackle the problem of detecting fake reviews written by the same
person using multiple names, posting each review under a different name. We
propose two methods to detect similar reviews and show the results generally
outperform the vectorial similarity measures used in prior works. The first
method extends the semantic similarity between words to the reviews level. The
second method is based on topic modeling and exploits the similarity of the
reviews topic distributions using two models: bag-of-words and
bag-of-opinion-phrases. The experiments were conducted on reviews from three
different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset
(800 reviews).Comment: 6 pages, WWW 201
Amp\`ere-Class Pulsed Field Emission from Carbon-Nanotube Cathodes in a Radiofrequency Resonator
Pulsed field emission from cold carbon-nanotube cathodes placed in a
radiofrequency resonant cavity was observed. The cathodes were located on the
backplate of a conventional -cell resonant cavity operating at
1.3-GHz and resulted in the production of bunch train with maximum average
current close to 0.7 Amp\`ere. The measured Fowler-Nordheim characteristic,
transverse emittance, and pulse duration are presented and, when possible,
compared to numerical simulations. The implications of our results to
high-average-current electron sources are briefly discussed.Comment: 5 pages, 6 figures; submitted to Applied Physics Letter
Exploratory Analysis of Highly Heterogeneous Document Collections
We present an effective multifaceted system for exploratory analysis of
highly heterogeneous document collections. Our system is based on intelligently
tagging individual documents in a purely automated fashion and exploiting these
tags in a powerful faceted browsing framework. Tagging strategies employed
include both unsupervised and supervised approaches based on machine learning
and natural language processing. As one of our key tagging strategies, we
introduce the KERA algorithm (Keyword Extraction for Reports and Articles).
KERA extracts topic-representative terms from individual documents in a purely
unsupervised fashion and is revealed to be significantly more effective than
state-of-the-art methods. Finally, we evaluate our system in its ability to
help users locate documents pertaining to military critical technologies buried
deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery
and Data Minin
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Every culture and language is unique. Our work expressly focuses on the
uniqueness of culture and language in relation to human affect, specifically
sentiment and emotion semantics, and how they manifest in social multimedia. We
develop sets of sentiment- and emotion-polarized visual concepts by adapting
semantic structures called adjective-noun pairs, originally introduced by Borth
et al. (2013), but in a multilingual context. We propose a new
language-dependent method for automatic discovery of these adjective-noun
constructs. We show how this pipeline can be applied on a social multimedia
platform for the creation of a large-scale multilingual visual sentiment
concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our
unified ontology is organized hierarchically by multilingual clusters of
visually detectable nouns and subclusters of emotionally biased versions of
these nouns. In addition, we present an image-based prediction task to show how
generalizable language-specific models are in a multilingual context. A new,
publicly available dataset of >15.6K sentiment-biased visual concepts across 12
languages with language-specific detector banks, >7.36M images and their
metadata is also released.Comment: 11 pages, to appear at ACM MM'1
Three-Dimensional Analysis of Wakefields Generated by Flat Electron Beams in Planar Dielectric-Loaded Structures
An electron bunch passing through dielectric-lined waveguide generates
erenkov radiation that can result in high-peak axial electric field
suitable for acceleration of a subsequent bunch. Axial field beyond
Gigavolt-per-meter are attainable in structures with sub-mm sizes depending on
the achievement of suitable electron bunch parameters. A promising
configuration consists of using planar dielectric structure driven by flat
electron bunches. In this paper we present a three-dimensional analysis of
wakefields produced by flat beams in planar dielectric structures thereby
extending the work of Reference [A. Tremaine, J. Rosenzweig, and P. Schoessow,
Phys. Rev. E 56, No. 6, 7204 (1997)] on the topic. We especially provide
closed-form expressions for the normal frequencies and field amplitudes of the
excited modes and benchmark these analytical results with finite-difference
time-domain particle-in-cell numerical simulations. Finally, we implement a
semi-analytical algorithm into a popular particle tracking program thereby
enabling start-to-end high-fidelity modeling of linear accelerators based on
dielectric-lined planar waveguides.Comment: 12 pages, 2 tables, 10 figure
Prediction of future hydrological regimes in poorly gauged high altitude basins: the case study of the upper Indus, Pakistan
In the mountain regions of the Hindu Kush, Karakoram and Himalaya (HKH) the "third polar ice cap" of our planet, glaciers play the role of "water towers" by providing significant amount of melt water, especially in the dry season, essential for agriculture, drinking purposes, and hydropower production. Recently, most glaciers in the HKH have been retreating and losing mass, mainly due to significant regional warming, thus calling for assessment of future water resources availability for populations down slope. However, hydrology of these high altitude catchments is poorly studied and little understood. Most such catchments are poorly gauged, thus posing major issues in flow prediction therein, and representing in fact typical grounds of application of PUB concepts, where simple and portable hydrological modeling based upon scarce data amount is necessary for water budget estimation, and prediction under climate change conditions. In this preliminarily study, future (2060) hydrological flows in a particular watershed (Shigar river at Shigar, ca. 7000 km<sup>2</sup>), nested within the upper Indus basin and fed by seasonal melt from major glaciers, are investigated. <br><br> The study is carried out under the umbrella of the SHARE-Paprika project, aiming at evaluating the impact of climate change upon hydrology of the upper Indus river. We set up a minimal hydrological model, tuned against a short series of observed ground climatic data from a number of stations in the area, in situ measured ice ablation data, and remotely sensed snow cover data. The future, locally adjusted, precipitation and temperature fields for the reference decade 2050–2059 from <i>CCSM3</i> model, available within the IPCC's panel, are then fed to the hydrological model. We adopt four different glaciers' cover scenarios, to test sensitivity to decreased glacierized areas. The projected flow duration curves, and some selected flow descriptors are evaluated. The uncertainty of the results is then addressed, and use of the model for nearby catchments discussed. The proposed approach is valuable as a tool to investigate the hydrology of poorly gauged high altitude areas, and to project forward their hydrological behavior pending climate change
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