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Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

In a partially ordered semigroup with the duality (or polarity) transform, it is possible to define a generalization of continued fractions. General sufficient conditions for convergence of continued fractions are provided. Two particular applications concern the cases of convex sets with the Minkowski addition and the polarity transform and the family of non-negative convex functions with the Legendre–Fenchel and Artstein-Avidan–Milman transforms.

We show how algebraic identities, inequalities and constructions, which hold for numbers or matrices, often have analogs in the geometric classes of convex bodies or convex functions. By letting the polar body $K^{\circ }$ or the dual function ${\phi }^{\ast }$ play the role of the inverses “$K^{-1}$” and “${\phi }^{-1}$”, we are able to conjecture many new results, which often turn out to be correct. As one example, we prove that for every convex function $\phi$ one has

We report detailed investigations of low-frequency excess noise in Ga-polarity GaN thin films deposited by RF-plasma assisted molecular beam epitaxy. The noise properties of the GaN thin films deposited with and without the intermediate-temperature buffer layers (ITBL) are studied in detailed to examine the effects of the ITBL on the noise. Substantial reduction in the flicker noise levels are observed for samples grown on ITBLs with a Hooge parameter of 3×10^-4, which is believed to be the lowest, to date, reported for GaN material. At low-temperatures, Lorentzian bumps originating from the generation-recombination processes are observed. Detailed studies of the temperature dependencies of the voltage noise power spectra have led to the formulation of a model for the observed low-frequency fluctuations. The model stipulates that the phenomenon arises from the thermally activated trapping and detrapping of carriers. The process results in the correlated fluctuations in the carrier number and the Coulombic scattering rate. Quantitative computation shows that number fluctuation dominates in our samples. Numerical evaluation of the deep-levels indicates substantial reduction in the trap density for the Ga-polarity GaN films.

Social Media (SM) has become the easiest, cheapest and fastest channel for companies to identify the events that affect their customers. The geo-location capabilities of the SM interactions enable Early Warning Systems to alert not only when the quality of service decays, but also where and how many customers are impacted. In this paper we present a system and a set of supporting metrics that exploit the geo-localized SM stream, quantify the perceived impact of events, incidents, etc. on a particular area over time. Industrial service providers can add this perceptional perspective to their standard monitoring tools to enable a prompt and appropriate reaction, the decision-making in marketing activities and to unveil customer acquisition opportunities applying the system to the competitors’ customers.

The production of miniature parts by the electrochemical discharge micromachining process ($\mu$-ECDM) draws the most of attractions into the industrial field. Parametric influences on machining depth (MD), material removal rate (MRR), and overcut (OC) have been propounded using a mixed electrolyte (NaOH:KOH- 1:1) varying concentrations (wt.%), applied voltage ($V$), pulse on time ($\mu$s), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO${}_2+{\mathrm{\text{NaSiO}}}_3$). Analysis of variances has been analyzed to test the adequacy of the developed mathematical model and multiresponse optimization has been performed to find out maximum MD with higher material removal at lower OC using desirability function analysis as well as neural network (NN)-based Particle Swarm Optimization (PSO). The SEM analysis has been done to find unexpected debris. MD has been improved with better surface quality using a mixed electrolyte at straight polarity using a tungsten carbide (WC) cylindrical tool along with $X$, $Y$, and $Z$ axis movement by computer-aided subsystem and combining with the automated spring feed mechanism. PSO-ANN provides better parametric optimization results for micromachining by the ECDM process.