Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Fatores críticos de sucesso como antecedentes da aceitação de um sistema de informação em uma universidade federal

In recent decades the public sector comes under pressure in order to improve its performance. The use of Information Technology (IT) has been a tool increasingly used in reaching that goal. Thus, it has become an important issue in public organizations, particularly in institutions of higher education, determine which factors influence the acceptance and use of technology, impacting on the success of its implementation and the desired organizational results. The Technology Acceptance Model - TAM was used as the basis for this study and is based on the constructs perceived usefulness and perceived ease of use. However, when it comes to integrated management systems due to the complexity of its implementation,organizational factors were added to thus seek further explanation of the acceptance of such systems. Thus, added to the model five TAM constructs related to critical success factors in implementing ERP systems, they are: support of top management, communication, training, cooperation, and technological complexity (BUENO and SALMERON, 2008). Based on the foregoing, launches the following research problem: What factors influence the acceptance and use of SIE / module academic at the Federal University of Para, from the users' perception of teachers and technicians? The purpose of this study was to identify the influence of organizational factors, and behavioral antecedents of behavioral intention to use the SIE / module academic UFPA in the perspective of teachers and technical users. This is applied research, exploratory and descriptive, quantitative with the implementation of a survey, and data collection occurred through a structured questionnaire applied to a sample of 229 teachers and 30 technical and administrative staff. Data analysis was carried out through descriptive statistics and structural equation modeling with the technique of partial least squares (PLS). Effected primarily to assess the measurement model, which were verified reliability, convergent and discriminant validity for all indicators and constructs. Then the structural model was analyzed using the bootstrap resampling technique like. In assessing statistical significance, all hypotheses were supported. The coefficient of determination (R ²) was high or average in five of the six endogenous variables, so the model explains 47.3% of the variation in behavioral intention. It is noteworthy that among the antecedents of behavioral intention (BI) analyzed in this study, perceived usefulness is the variable that has a greater effect on behavioral intention, followed by ease of use (PEU) and attitude (AT). Among the organizational aspects (critical success factors) studied technological complexity (TC) and training (ERT) were those with greatest effect on behavioral intention to use, although these effects were lower than those produced by behavioral factors (originating from TAM). It is pointed out further that the support of senior management (TMS) showed, among all variables, the least effect on the intention to use (BI) and was followed by communications (COM) and cooperation (CO), which exert a low effect on behavioral intention (BI). Therefore, as other studies on the TAM constructs were adequate for the present research. Thus, the study contributed towards proving evidence that the Technology Acceptance Model can be applied to predict the acceptance of integrated management systems, even in public. Keywords: Technology

Interdependencies in Corporate Development: Relationship between Strategic Alliance and Acquisition Activities

A substantial amount of work in the field of strategic management has attempted to explain the antecedents and outcomes of organizational learning. Though multinational corporations simultaneously engage in various types of tasks, activities, and strategies on a regular basis, the transfer of organizational learning in a multi-task context has largely remained under-explored in the literature. To inform our understanding in this area, this dissertation aimed at synthesizing findings from two parallel research streams of corporate development activities: strategic alliances and acquisitions. Structured in the form of two empirical studies, this dissertation examines: 1) the strategic outcomes of alliance experience of previously allying partners in terms of subsequent acquisition attempts, and 2) the performance implications of prior alliance experience for acquisitions. The first study draws on the relational view of inter-organizational governance to explain how various deal-specific and dyadic characteristics of a partnership relate to partnering firms’ post-alliance acquisition attempts. This model theorizes on a variety of relational mechanisms to build a cohesive theory of inter-organizational exchanges in a multi-task setting where strategic alliances ultimately lead to a firm’s decision to commit further resources. The second study applies organizational learning theory, and specifically examines whether frequency, recency, and relatedness of different dimensions of prior alliances, beyond the dyad-level experience, relate to an acquirer’s superior post-acquisition performance. The hypotheses of the studies are tested using logistic and ordinary least square regressions, respectively. Results analyzed from a sample of cross-border alliance and acquisition deals attempted (for study I) and/or completed (for study II) during the period of 1991 to 2011 generally support the theory that relational exchange determines acquiring firms’ post alliance acquisition behavior and that organizational routines and learning from prior alliances influence a future acquirer’s financial performance. Overall, the empirical findings support our overarching theory of interdependency, and confirm the transfer effect of learning across these alternate, yet related corporate strategies of alliance and acquisition.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Classification Of Frankfurters By Ft-raman Spectroscopy And Chemometric Methods.

Frankfurters are widely consumed all over the world, and the production requires a wide range of meat and non-meat ingredients. Due to these characteristics, frankfurters are products that can be easily adulterated with lower value meats, and the presence of undeclared species. Adulterations are often still difficult to detect, due the fact that the adulterant components are usually very similar to the authentic product. In this work, FT-Raman spectroscopy was employed as a rapid technique for assessing the quality of frankfurters. Based on information provided by the Raman spectra, a multivariate classification model was developed to identify the frankfurter type. The aim was to study three types of frankfurters (chicken, turkey and mixed meat) according to their Raman spectra, based on the fatty vibrational bands. Classification model was built using partial least square discriminant analysis (PLS-DA) and the performance model was evaluated in terms of sensitivity, specificity, accuracy, efficiency and Matthews's correlation coefficient. The PLS-DA models give sensitivity and specificity values on the test set in the ranges of 88%-100%, showing good performance of the classification models. The work shows the Raman spectroscopy with chemometric tools can be used as an analytical tool in quality control of frankfurters.

Comparing Near-infrared Conventional Diffuse Reflectance Spectroscopy And Hyperspectral Imaging For Determination Of The Bulk Properties Of Solid Samples By Multivariate Regression: Determination Of Mooney Viscosity And Plasticity Indices Of Natural Rubber.

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Metodo de reconstrução tridimensional para avaliação postural

Universidade Estadual de Campinas. Faculdade de Educação Física

Adsorption of amyloglucosidase from Aspergillus niger NRRL 3122 using ion exchange resin

Amyloglucosidase enzyme was produced by Aspergillus niger NRRL 3122 from solid-state fermentation, using deffated rice bran as substrate. The effects of process parameters (pH, temperature) in the equilibrium partition coefficient for the system amyloglucosidase - resin DEAE-cellulose were investigated, aiming at obtaining the optimum conditions for a subsequent purification process. The highest partition coefficients were obtained using 0.025M Tris-HCl buffer, pH 8.0 and 25ºC. The conditions that supplied the highest partition coefficient were specified, the isotherm that better described the amyloglucosidase process of adsorption obtained. It was observed that the adsorption could be well described by Langmuir equation and the values of Qm and Kd estimated at 133.0 U mL-1 and 15.4 U mL-1, respectively. From the adjustment of the kinetic curves using the fourth-order Runge-Kutta algorithm, the adsorption (k1) and desorption (k2) constants were obtained through optimization by the least square procedure, and the values calculated were 2.4x10-3 mL U-1 min-1 for k1 and 0.037 min-1 for k2 .

Hidrocarbonetos policíclicos aromáticos (HPAS) em cachaça, rum, uísque e álcool combustível

The concentration of 15 polycyclic aromatic hydrocarbons (PAHs) in 57 samples of distillates (cachaça, rum, whiskey, and alcohol fuel) has been determined by HPLC-Fluorescence detection. The quantitative analytical profile of PAHs treated by Partial Least Square - Discriminant Analysis (PLS-DA) provided a good classification of the studied spirits based on their PAHs content. Additionally, the classification of the sugar cane derivatives according to the harvest practice was obtained treating the analytical data by Linear Discriminant Analysis (LDA), using naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, benz[b]fluoranthene, and benz[g,h,i]perylene, as a chemical descriptors.

Distinção entre cachaças destiladas em alambiques e em colunas usando quimiometria

One hundred fifteen cachaça samples derived from distillation in copper stills (73) or in stainless steels (42) were analyzed for thirty five itens by chromatography and inductively coupled plasma optical emission spectrometry. The analytical data were treated through Factor Analysis (FA), Partial Least Square Discriminant Analysis (PLS-DA) and Quadratic Discriminant Analysis (QDA). The FA explained 66.0% of the database variance. PLS-DA showed that it is possible to distinguish between the two groups of cachaças with 52.8% of the database variance. QDA was used to build up a classification model using acetaldehyde, ethyl carbamate, isobutyl alcohol, benzaldehyde, acetic acid and formaldehyde as chemical descriptors. The model presented 91.7% of accuracy on predicting the apparatus in which unknown samples were distilled.

Bovine gene polymorphisms related to fat deposition and meat tenderness

Leptin, thyroglobulin and diacylglycerol O-acyltransferase play important roles in fat metabolism. Fat deposition has an influence on meat quality and consumers' choice. The aim of this study was to determine allele and genotype frequencies of polymorphisms of the bovine genes, which encode leptin (LEP), thyroglobulin (TG) and diacylglycerol O-acyltransferase (DGAT1). A further objective was to establish the effects of these polymorphisms on meat characteristics. We genotyped 147 animals belonging to the Nelore (Bos indicus), Canchim (5/8 Bos taurus + 3/8 Bos indicus), Rubia Gallega X Nelore (1/2 Bos taurus + 1/2 Bos indicus), Brangus Three-way cross (9/16 Bos taurus + 7/16 Bos indicus) and Braunvieh Three-way cross (3/4 Bos taurus + 1/4 Bos indicus) breeds. Backfat thickness, total lipids, marbling score, ribeye area and shear force were fitted, using the General Linear Model (GLM) procedure of the SAS software. The least square means of genotypes and genetic groups were compared using Tukey's test. Allele frequencies vary among the genetic groups, depending on Bos indicus versus Bos taurus influence. The LEP polymorphism segregates in pure Bos indicus Nelore animals, which is a new finding. The T allele of TG is fixed in Nelore, and DGAT1 segregates in all groups, but the frequency of allele A is lower in Nelore animals. The results showed no association between the genotypes and traits studied, but a genetic group effect on these traits was found. So, the genetic background remains relevant for fat deposition and meat tenderness, but the gene markers developed for Bos taurus may be insufficient for Bos indicus.

NEAR INFRARED SPECTROSCOPY FOR ESTIMATING SUGARCANE BAGASSE CONTENT IN MEDIUM DENSITY FIBERBOARD

Medium density fiberboard (MDF) is an engineered wood product formed by breaking down selected lignin-cellulosic material residuals into fibers, combining it with wax and a resin binder, and then forming panels by applying high temperature and pressure. Because the raw material in the industrial process is ever-changing, the panel industry requires methods for monitoring the composition of their products. The aim of this study was to estimate the ratio of sugarcane (SC) bagasse to Eucalyptus wood in MDF panels using near infrared (NIR) spectroscopy. Principal component analysis (PCA) and partial least square (PLS) regressions were performed. MDF panels having different bagasse contents were easily distinguished from each other by the PCA of their NIR spectra with clearly different patterns of response. The PLS-R models for SC content of these MDF samples presented a strong coefficient of determination (0.96) between the NIR-predicted and Lab-determined values and a low standard error of prediction (similar to 1.5%) in the cross-validations. A key role of resins (adhesives), cellulose, and lignin for such PLS-R calibrations was shown. PLS-DA model correctly classified ninety-four percent of MDF samples by cross-validations and ninety-eight percent of the panels by independent test set. These NIR-based models can be useful to quickly estimate sugarcane bagasse vs. Eucalyptus wood content ratio in unknown MDF samples and to verify the quality of these engineered wood products in an online process.

The B0.5IVe CoRoT target HD 49330 I. Photometric analysis from CoRoT data

Context. Be stars undergo outbursts producing a circumstellar disk from the ejected material. The beating of non-radial pulsations has been put forward as a possible mechanism of ejection. Aims. We analyze the pulsational behavior of the early B0.5IVe star HD 49330 observed during the first CoRoT long run towards the Galactical anticenter (LRA1). This Be star is located close to the lower edge of the beta Cephei instability strip in the HR diagram and showed a 0.03 mag outburst during the CoRoT observations. It is thus an ideal case for testing the aforementioned hypothesis. Methods. We analyze the CoRoT light curve of HD 49330 using Fourier methods and non-linear least square fitting. Results. In this star, we find pulsation modes typical of beta Cep stars (p modes) and SPB stars (g modes) with amplitude variations along the run directly correlated with the outburst. These results provide new clues about the origin of the Be phenomenon as well as strong constraints on the seismic modelling of Be stars.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.