United we stand: improving sentiment analysis by joining machine learning and rule based methods

In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction. 1

Ascending Combinatorial Auctions with Risk Averse Bidders

Ascending combinatorial auctions are being used in an increasing number of spectrum sales worldwide, as well as in other multi-item markets in procurement and logistics. Much research has focused on pricing and payment rules in such ascending auctions. However, recent game-theoretical research has shown that such auctions can even lead to inefficient perfect Bayesian equilibria with risk-neutral bidders. There is a fundamental free-rider problem without a simple solution, raising the question whether ascending combinatorial auctions can be expected to be efficient in the field. Risk aversion is arguably a significant driver of bidding behavior in high-stakes auctions. We analyze the impact of risk aversion on equilibrium bidding strategies and efficiency in a threshold problem with one global and several local bidders. Due to the underlying free-rider problem, the impact of risk-aversion on equilibrium bidding strategies of local bidders is not obvious. We characterize the necessary and sufficient conditions for the perfect Bayesian equilibria of the ascending auction mechanism to have the local bidders to drop at the reserve price. Interestingly, in spite of the free-riding opportunities of local bidders, risk-aversion reduces the scope of the non-bidding equilibrium. The results help explain the high efficiency of ascending combinatorial auctions observed in the lab. © 2015, Springer Science+Business Media Dordrecht

Preliminary needs assessment of mobile technology use for healthcare among homeless veterans

Background. Homeless veterans have complex healthcare needs, but experience many barriers to treatment engagement. While information technologies (IT), especially mobile phones, are used to engage patients in care, little is known about homeless veterans\u27 IT use. This study examines homeless veterans\u27 access to and use of IT, attitudes toward health-related IT use, and barriers to IT in the context of homelessness. Methods. Qualitative interviews were conducted with 30 homeless veterans in different housing programs in Boston, MA, ranging from emergency shelters to supportive transitional housing that allow stays of up to 2 years. Interviews were conducted in person, audio recorded and then transcribed. Three researchers coded transcripts. Inductive thematic analysis was used. Results. Most participants (90%) had a mobile phone and were receptive to IT use for health-related communications. A common difficulty communicating with providers was the lack of a stable mailing address. Some participants were using mobile phones to stay in touch with providers. Participants felt mobile-phone calls or text messages could be used to remind patients of appointments, prescription refills, medication taking, and returning for laboratory results. Mobile phone text messaging was seen as convenient, and helped participants stay organized because necessary information was saved in text messages. Some reported concerns about the costs associated with mobile phone use (calls and texting), the potential to be annoyed by too many text messages, and not knowing how to use text messaging. Conclusion. Homeless veterans use IT and welcome its use for health-related purposes. Technology-assisted outreach among this population may lead to improved engagement in care

An Operational System For Monitoring Oil Spills In The Mediterranean Sea: The PROMED System

The primary objective of this work was the development of an operational system for early detection of oil-spills, monitoring of their evolution, and provision of support to responsible Public Authorities during cleanup operations, based on Remote Sensing and GIS technologies. In case of emergency, the principal characteristics of the oil spill are defined with the aid of a space-borne synthetic aperture radar (SAR). The transport, spreading and dispersion of the oil spill is subsequently simulated on the basis of wind forecasts of the area. The use of thematic maps of protected, fishing and urban areas, and regions of high tourism allows the better assessment of the impact of an oil spill on the areas to be affected in terms of environmental sensitivity. Finally, reports are generated notifying port authorities, the media, and local organizations to be potentially affected by the presence of the oil spill. The pilot site for testing the PROMED System in Greece is the island of Crete

Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields

Spatially referenced data often have autocovariance functions with elliptical isolevel contours, a property known as geometric anisotropy. The anisotropy parameters include the tilt of the ellipse (orientation angle) with respect to a reference axis and the aspect ratio of the principal correlation lengths. Since these parameters are unknown a priori, sample estimates are needed to define suitable spatial models for the interpolation of incomplete data. The distribution of the anisotropy statistics is determined by a non-Gaussian sampling joint probability density. By means of analytical calculations, we derive an explicit expression for the joint probability density function of the anisotropy statistics for Gaussian, stationary and differentiable random fields. Based on this expression, we obtain an approximate joint density which we use to formulate a statistical test for isotropy. The approximate joint density is independent of the autocovariance function and provides conservative probability and confidence regions for the anisotropy parameters. We validate the theoretical analysis by means of simulations using synthetic data, and we illustrate the detection of anisotropy changes with a case study involving background radiation exposure data. The approximate joint density provides (i) a stand-alone approximate estimate of the anisotropy statistics distribution (ii) informed initial values for maximum likelihood estimation, and (iii) a useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure