The malSource dataset: quantifying complexity and code reuse in malware development

During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today's cyberattacks and has consolidated as a commodity in the underground economy. In this paper, we analyze the evolution of malware from 1975 to date from a software engineering perspective. We analyze the source code of 456 samples from 428 unique families and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of benign software. We also study the extent to which code reuse is present in our dataset. We detect a significant number of code clones across malware families and report which features and functionalities are more commonly shared. Overall, our results support claims about the increasing complexity of malware and its production progressively becoming an industry.This work was supported in part by the Spanish Government through MINECO grants SMOG-DEV (TIN2016-79095-C2-2-R) and DEDETIS (TIN2015-7013-R), and in part by the Regional Government of Madrid through grantsCIBERDINE (S2013/ICE-3095) and N-GREENS (S2013/ICE-2731)

Discurso pronunciado por Juan Manuel Calleja Presbitero Director de la Casa de educacion establecida en la calle de Videvarrieta de la villa de Bilbao en los examenes publicos celebrados en los dias 28 y 29 de Julio de este presente año en las casas Consistoriales...

Título tomado del epígrafeFecha tomada del colofónSignaturizadoLa h. de lám. es grabado calc., Vista del Instituto de Primera clase y Colegio de Viscaya [sic] en Bilbao: Emile Lesaché sculp. a Pari

Pressure-dependent multiscale stochastic simulations using aMFH model constructed from full-field SVE realizations

n order to identify the behaviour of a UD-composite, a large amount of experimental tests are needed due to the non-determinisms inherent to this type of materials. One of these non-determinisms is the disposition of the fibres in the microstructure. This work addresses this problematic by building a pressure-dependent stochastic Mean-Field-Homogenization (MFH) ¿ based model capable of modelling the behaviour of the material up to its failure stage, bringing virtual testing a step closer to have a real engineering application. In order to develop the stochastic MFH model, Stochastic Volume Element (SVE) realisations of a UD composite material microstructure with RTM6 epoxy matrix modelled by a hyperelastic viscoelastic-viscoplastic constitutive model enhanced by a multi-mechanism nonlocal damage model (V.-D. Nguyen et al., ¿A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers¿, in Int. Journal of Solids and Structures, vol. 96, pp. 192-216, 2016) are performed, allowing to fully replicate its complex behaviour. These realizations are then used to obtain their apparent responses, being able to characterise the homogenised stochastic behaviour of the composite and allowing to construct a stochastic MFH model as developed in (L. Wu et al., ¿An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites¿, in Computer Methods in Applied Mechanics and Engineering, vol. 348, pp. 97-138, 2019). This work completes this model by introducing a pressure-dependency to the MFH model and the ability to account for the failure stage of the material, a phase in which a loss of size objectivity is encountered. In order to recover the size objectivity, the failure damage model parameters of each homogenised SVE model are identified to match the energy release rate of the full field simulations. These identified parameters are then used to generate proper random fields for SFEM. Finally, the built stochastic MFH model is used to perform stochastic analysis of a ply failure taking the geometrical uncertainties of the material microstructure into account

Prevalence estimation of significant fibrosis because of NASH in Spain combining transient elastography and histology

Hepatic fibrosis; Non-alcoholic steatohepatitis; Transient elastographyFibrosis hepática; Esteatohepatitis no alcohólica; Elastografía transitoriaFibrosi hepàtica; Esteatohepatitis no alcohòlica; Elastografia transitòriaBackground & Aims Non-alcoholic fatty liver disease (NAFLD) has become a major public health problem, but the prevalence of fibrosis associated with non-alcoholic steatohepatitis (NASH) is largely unknown in the general population. This study aimed to provide an updated estimation of the prevalence of NASH fibrosis in Spain. Methods This was an observational, retrospective, cross-sectional, population-based study with merged data from two Spanish datasets: a large (N = 12 246) population-based cohort (ETHON), including transient elastography (TE) data, and a contemporary multi-centric biopsy-proven NASH cohort with paired TE data from tertiary centres (N = 501). Prevalence for each NASH fibrosis stage was estimated by crossing TE data from ETHON dataset with histology data from the biopsy-proven cohort. Results From the patients with valid TE in ETHON dataset (N = 11 440), 5.61% (95% confidence interval [95% CI]: 2.53-11.97) had a liver stiffness measurement (LSM) ≥ 8 kPa. The proportion attributable to NAFLD (using clinical variables and Controlled Attenuation Parameter) was 57.3% and thus, the estimated prevalence of population with LSM ≥ 8 kPa because of NAFLD was 3.21% (95% CI 1.13–8.75). In the biopsy-proven NASH cohort, 389 patients had LSM ≥ 8 kPa. Among these, 37% did not have significant fibrosis (F2-4). The estimated prevalence of NASH F2-3 and cirrhosis in Spain's adult population were 1.33% (95% CI 0.29–5.98) and 0.70% (95% CI 0.10–4.95) respectively. Conclusions These estimations provide an accurate picture of the current prevalence of NASH-related fibrosis in Spain and can serve as reference point for dimensioning the therapeutic efforts that will be required as NASH therapies become available

Data-driven-based History-Dependent Surrogate Models in the context of stochastic multi-scale simulations for elasto-plastic composites

When developing stochastic models or performing uncertainty quantification in the context of multi-scale models, considering direct numerical simulations at the different scales is unreachable because of the overwhelming computational cost. Surrogate models of the micro-scale boundary value problems (BVP), typically Stochastic Volume Elements (SVE), are then developed and can be constructed or trained using off-line simulations. In such a data-driven approach, different kinds of surrogate models exist including in the context of non-linear behaviours, but difficulties arise when irreversible or history-dependent responses have to be accounted for as in the context of elasto-plastic composites. In this paper we investigate three kinds of surrogate models that can handle elasto-plasticity. Once trained using a synthetic database, neural-networks (NNWs) can substitute the micro-scale BVP resolution while reducing the computation time by more than 5 orders of magnitude. In the context of reversible behaviours or proportional loading, feed-forward NNWs can predict a homogenised response, possibly for different parametrised micro-structures. In order to introduce the history dependency, recurrent neural networks (RNNs) were shown to be efficient and accurate in approximating the history-dependent homogenised stress-strain relationships. The limitations of NNWs are mainly two-fold. On the one hand they are unable to extrapolate responses (they can only interpolate), and on the other hand they require a large synthetic database to be trained. A physics informed alternative is the deep material network (DMN) approach which consists in a network of mechanistic building blocks. During the training process, the DMN “learns” the weight ratio and interactions of the building blocks. Once trained, the DMN is able to predict nonlinear responses, including for unseen material responses and loading conditions, in a thermodynamically consistent way, although they are less computationally efficient than the NNWs in their online stage. A last approach is to identify the parameters of a semi-analytical mean-field-homogenization (MFH) model from the resolutions of different micro-scale BVP or SVEs: a set of MFH parameters is associated to each SVE. Since the surrogate is purely micro-mechanistic, it can handle damage-enhanced elasto-plasticity including strain-softening by considering objective quantities such as the critical energy release rate. The different surrogates are applied in two different contexts: On the one hand the Bayesian inference of multi-scale model parameters and on the other hand, the stochastic multi-scale simulation of composite coupons.Multiscale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM

Evaluation of Pulsed Electric Field-Assisted Extraction on the Microstructure and Recovery of Nutrients and Bioactive Compounds from Mushroom (Agaricus bisporus)

Pulsed electric field (PEF) is a sustainable innovative technology that allows for the recovery of nutrients and bioactive compounds from vegetable matrices. A. bisporus was chosen for its nutritional value and the effect of PEF pretreatment was evaluated using different conditions of electric field (2-3 kV/cm), specific energy (50-200 kJ/kg) and extraction time (0-6 h) to obtain the best conditions for nutrient and bioactive compound extraction. Spectrophotometric methods were used to evaluate the different compounds, along with an analysis of mineral content by inductively coupled plasma mass spectrometry (ICP-MS) and the surface was evaluated using scanning electron microscopy (SEM). In addition, the results were compared with those obtained by conventional extraction (under constant shaking without PEF pretreatment). After evaluating the extractions, the best extraction conditions were 2.5 kV/cm, 50 kJ/kg and 6 h which showed that PEF extraction increased the recovery of total phenolic compounds in 96.86%, carbohydrates in 105.28%, proteins in 11.29%, and minerals such as P, Mg, Fe and Se. These results indicate that PEF pretreatment is a promising sustainable technology to improve the extraction of compounds and minerals from mushrooms showing microporation on the surface, positioning them as a source of compounds of great nutritional interest

Stabilized micelles as delivery vehicles for paclitaxel

Paclitaxel is an antineoplastic drug used against a variety of tumors, but its low aqueous solubility and active removal caused by P-glycoprotein in the intestinal cells hinder its oral administration. In our study, new type of stabilized Pluronic micelles were developed and evaluated as carriers for paclitaxel delivery via oral or intravenous route. The pre-stabilized micelles were loaded with paclitaxel by simple solvent/evaporation technique achieving high encapsulation efficiency of approximately 70%. Gastrointestinal transit of the developed micelles was evaluated by oral administration of rhodamine-labeled micelles in rats. Our results showed prolonged gastrointestinal residence of the marker encapsulated into micelles, compared to a solution containing free marker. Further, the oral administration of micelles in mice showed high area under curve of micellar paclitaxel (similar to the area of i.v. Taxol®), longer mean residence time (9-times longer than i.v. Taxol®) and high distribution volume (2-fold higher than i.v. Taxol®) indicating an efficient oral absorption of paclitaxel delivered by micelles. Intravenous administration of micelles also showed a significant improvement of pharmacokinetic parameters of micellar paclitaxel vs. Taxol®, in particular higher area under curve (1.2-fold), 5-times longer mean residence time and lower clearance, indicating longer systemic circulation of the micelles

Construcción y validación del Cuestionario de Pica y Rumiación (CuPRu) en adolescentes y adultos jóvenes mexicanos

La investigación de los trastornos de Pica y Rumiación se ha centrado en niños, personas con discapacidad y mujeres embarazadas, lo anterior tiene importantes implicaciones debido a que se desconoce si estos se presentan en otras etapas del desarrollo y en otras poblaciones. Su padecimiento conduce a importantes problemas en la salud e incluso pueden llegar a derivar en la muerte, por lo que es indispensable contar con instrumentos de tamizaje válidos y confiables. Por tanto, el objetivo de este estudio fue desarrollar un instrumento para detectar los síntomas y conductas de trastornos de Pica y Rumiación, así como determinar sus propiedades psicométricas. Los reactivos fueron elaborados a partir de la revisión de la literatura y posteriormente fueron sometidos a revisión por siete expertos en el área, obteniendo coeficientes V de Aiken que indican un alto acuerdo entre jueces respecto al contenido del instrumento. Asimismo, se verificó su confiabilidad a partir de los coeficientes alfa 0.85 y omega 0.93. Posteriomente se llevó a cabo un Análisis Factorial Exploratorio y Confirmatorio, donde logró una estructura de cuatro factores que explican el 48.35% de la varianza, además los siguientes índices que corroboran un ajuste adecuado CMIN = 2.41, GFI = 0.92; AGFI = 0.89; CFI = 0.93; RMR=.063 y RMSEA = 0.064. Por último, se realizó la calibración de los reactivos donde se observó que todos discriminaban adecuadamente. En conclusión, las propiedades psicométricas obtenidas en este estudio demuestran que el Cuestionario de Pica y Rumiación, es válido y confiable.Construction and validation of the Pica and Rumination Questionnaire in Mexican adolescents and young adults. Research on Pica and Rumination Disorders has focused on children, people with disabilities, and pregnant women. This has important implications because it is unknown whether these occur at other stages of development and in other populations. These conditions leads to important health problems and can even lead to death, so it is essential to have valid and reliable screening instruments. Therefore, the objective of this study was to develop an instrument to detect the symptoms and behaviors of Pica and Rumination disorders, as well as to determine their psychometric properties in Mexican adolescents and young adults. The items were prepared from the review of the literature and were subsequently submitted for review by seven experts in the area, obtaining Aiken’s V coefficients that indicate a high agreement between judges regarding the content of the instrument. Likewise, the reliability of the scale was verified from the calculation of the alpha and omega coefficients, where the following indices were obtained: 0.85 and 0.93. Subsequently, an Exploratory and Confirmatory Factor Analysis was carried out, where a structure of four factors was achieved that explains 48.35% of the variance, in addition to the following indices that corroborate an adequate fit CMIN = 2.41, GFI = 0.92; AGFI = 0.89; CFI = 0.93; RMR =.063 and RMSEA = 0.064. Finally, the calibration of the reagents was carried out where it was observed that all discriminated adequately. In conclusion, the psychometric properties obtained in this study show that the Pica and Rumination Questionnaire is valid and reliable

Development of History-Dependent Surrogate Models in the context of stochastic multi-scale simulations for elasto-plastic composites

When developing stochastic models or performing uncertainty quantification in the context of multi-scale models, considering direct numerical simulations at the different scales is unreachable because of the overwhelming computational cost. Surrogate models of the micro-scale boundary value problems (BVP), typically Stochastic Volume Elements (SVE), are then developed and can be constructed or trained using off-line simulations. In such a data-driven approach, different kinds of surrogate models exist including in the context of non-linear behaviours, but difficulties arise when irreversible or history-dependent responses have to be accounted for as in the context of elasto-plastic composites. In this paper we investigate three kinds of surrogate models that can handle elasto-plasticity. Once trained using a synthetic database, neural-networks (NNWs) can substitute the micro-scale BVP resolution while reducing the computation time by more than 5 orders of magnitude. In the context of reversible behaviours or proportional loading, feed-forward NNWs can predict a homogenised response, possibly for different parametrised micro-structures. In order to introduce the history dependency, recurrent neural networks (RNNs) were shown to be efficient and accurate in approximating the history-dependent homogenised stress-strain relationships. The limitations of NNWs are mainly two-fold. On the one hand they are unable to extrapolate responses (they can only interpolate), and on the other hand they require a large synthetic database to be trained. A physics informed alternative is the deep material network (DMN) approach which consists in a network of mechanistic building blocks. During the training process, the DMN “learns” the weight ratio and interactions of the building blocks. Once trained, the DMN is able to predict nonlinear responses, including for unseen material responses and loading conditions, in a thermodynamically consistent way, although they are less computationally efficient than the NNWs in their online stage. A last approach is to identify the parameters of a semi-analytical mean-field-homogenization (MFH) model from the resolutions of different micro-scale BVP or SVEs: a set of MFH parameters is associated to each SVE. Since the surrogate is purely micro-mechanistic, it can handle damage-enhanced elasto-plasticity including strain-softening by considering objective quantities such as the critical energy release rate. The different surrogates are applied in two different contexts: On the one hand the Bayesian inference of multi-scale model parameters and on the other hand, the stochastic multi-scale simulation of composite coupons.Multiscale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM

A growth method to obtain flat and relaxed In0.2Ga0.8As on GaAs (0 0 1) developed through in situ monitoring of surface topography and stress evolution

13 páginas, 3 figuras.-- PACS: 81.15.Hi, 81.05.Ea, 78.35.+c, 68.35.Bs.-- Comunicación oral presentada en el XI Molecular Beam Epitaxy (MBE-XI), Pekín (11/09/2000).In this paper we develop a growth process for obtaining flat and relaxed In0.2Ga0.8As layers on GaAs (0 0 1). The process designed is based on the results obtained by in situ and real time characterization of surface morphology and layer relaxation. In particular, our results show that for growth temperatures Ts200°C the relaxation of In0.2Ga0.8As layers is inhibited and the morphology does not evolve to a crosshatched pattern. After growth thermal treatments of these low-temperature (LT) In0.2Ga0.8As layers induce the development of a very faint (rms=0.5 nm) crosshatched-like morphology. The relaxation process during the thermal annealing is strongly asymmetric and the layers present a high final strain state. By growing on top of the LT layer another In0.2Ga0.8As layer at higher temperature, relaxation is increased up to R≈70% and becomes symmetric. Depending on the growth process of the top layers morphology evolution differs, resulting in better morphologies for top layers grown by atomic layer molecular beam epitaxy (ALMBE) at Ts=400°C. We have obtained 400 nm In0.2Ga0.8As layers with a final degree of relaxation R=70% and very flat surfaces (rms=0.9 nm).The authors wish to acknowledge the Spanish “CICYT” for financial support under Project No. TIC99-1035-C02. M.U. González and M. Calleja thank the Consejería de Educación y Cultura de la Comunidad de Madrid for financial support.Peer reviewe