Opinion Mining on Non-English Short Text

As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining

Determination of contents based on learning styles through artificial intelligence

The study presents the development of a platform for structuring adaptive courses based on active, reflexive, theoretical and pragmatic learning styles using artificial intelligence techniques. To this end, the following phases were followed: search, analysis and classification of information about the process of generating content for courses; analysis and coding of the software component for generating content according to learning styles; and application of tests for validation and acceptance. The main contribution of the paper is the development of a model using neural networks and its integration in an application server to determine the contents that correspond to the active, reflexive, theoretical and pragmatic learning styles

Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments

Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features.Comment: 10 pages, 1 figure, 3 table

Gauge Theories with Ultracold Atoms

We discuss and review in this chapter the developing field of research of quantum simulation of gauge theories with ultracold atoms.Comment: Contribution for the proceedings of the Advanced School and Workshop on "Strongly Coupled Field Theories for Condensed Matter and Quantum Information Theory" held in Natal from 2-21/8 of 2015. Published in "Springer Proceedings in Physics" (ISBN 978-3-030-35473-2), with material from the PhD Thesis of Jo\~ao C. Pinto Barros, available at https://iris.sissa.it/handle/20.500.11767/57731#.XcQtPk6YWh

Investigation of periodic resonators as wave barriers for mitigating surface seismic waves using Bloch-Floquet theory

Every year around the world, earthquakes and other seismic waves cause damage to civil infrastructures. The most harmful waves for civil infrastructure are surface waves, as this study focused on it. Therefore, this study aims to investigate the behavior of resonators as an approach to reducing surface seismic waves based on both infinite and finite lattices for the proposed resonator. To this end, first, an infinite lattice is evaluated using the Bloch-Floquet theory by modeling the smallest repetition of the considering lattice. The dispersion relation of the considered resonator is obtained by an eigenfrequency analysis for each wave vector in the first irreducible Brillouin zone. Then, the bandgap for surface waves is defined using the sound line concept, a common approach in solid-state physics to find the pure surface modes of the dispersion relation for resonators. The sound line concept is used to distinguish between the pure surface and other waves, such as body waves. In Bloch-Floquet theory, the lattice is assumed to have an infinite number of unit cells; however, in real applications, the lattice needs to have a finite number of unit cells. Therefore, the accuracy of the bandgap obtained for the infinite lattice is evaluated by considering a finite lattice model in both frequency and time domains to consider a more realistic case. The results show that the considered resonator has a notable surface wave bandgap. Moreover, the results of the finite lattice conform well to the results of the infinite lattice in both frequency and time domains. The proposed resonator is made of concrete and has a height of six meters, and the unit cell constant is considered two meters. The obtained bandgap is between 14 and 21 Hz, confirmed by a finite model in both frequency and time domains. As a result, the proposed resonator can reduce surface seismic waves efficiently

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection

Ingestible Osmotic Pill for in vivo sampling of gut microbiomes

Technologies capable of noninvasively sampling different locations in the gut upstream of the colon enable new insights into the role of organ-specific microbiota in human health. Herein, an ingestible, biocompatible, battery-less, 3D-printed microengineered pill with an integrated osmotic sampler and microfluidic channels for in vivo sampling of the gut lumen and its microbiome upstream of the colon is discussed. The pill’s sampling performance is characterized using realistic invitro models and validated in vivo in pigs and primates. Herein, the results show that the bacterial populations recovered from the pill’s microfluidic channels closely resemble the bacterial population demographics of the microenvironment to which the pill is exposed. Herein ,it is believed that such lab-on-a-pill devices revolutionize the understanding of the spatial diversity of the gut microbiome and its response to medical conditions and treatments

Ingestible Osmotic Pill for in vivo sampling of gut microbiomes

Technologies capable of noninvasively sampling different locations in the gut upstream of the colon enable new insights into the role of organ-specific microbiota in human health. Herein, an ingestible, biocompatible, battery-less, 3D-printed microengineered pill with an integrated osmotic sampler and microfluidic channels for in vivo sampling of the gut lumen and its microbiome upstream of the colon is discussed. The pill’s sampling performance is characterized using realistic invitro models and validated in vivo in pigs and primates. Herein, the results show that the bacterial populations recovered from the pill’s microfluidic channels closely resemble the bacterial population demographics of the microenvironment to which the pill is exposed. Herein ,it is believed that such lab-on-a-pill devices revolutionize the understanding of the spatial diversity of the gut microbiome and its response to medical conditions and treatments