Machine Learning Mitigants for Speech Based Cyber Risk

Statistical analysis of speech is an emerging area of machine learning. In this paper, we tackle the biometric challenge of Automatic Speaker Verification (ASV) of differentiating between samples generated by two distinct populations of utterances, those of an authentic human voice and those generated by a synthetic one. Solving such an issue through a statistical perspective foresees the definition of a decision rule function and a learning procedure to identify the optimal classifier. Classical state-of-the-art countermeasures rely on strong assumptions such as stationarity or local-stationarity of speech that may be atypical to encounter in practice. We explore in this regard a robust non-linear and non-stationary signal decomposition method known as the Empirical Mode Decomposition combined with the Mel-Frequency Cepstral Coefficients in a novel fashion with a refined classifier technique known as multi-kernel Support Vector machine. We undertake significant real data case studies covering multiple ASV systems using different datasets, including the ASVSpoof 2019 challenge database. The obtained results overwhelmingly demonstrate the significance of our feature extraction and classifier approach versus existing conventional methods in reducing the threat of cyber-attack perpetrated by synthetic voice replication seeking unauthorised access

A theoretical entropy score as a single value to express inhibitor selectivity

<p>Abstract</p> <p>Background</p> <p>Designing maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. Poor selectivity can underlie toxicity and side effects in the clinic, and for this reason compound selectivity is increasingly monitored from very early on in the drug discovery process. To make sense of large amounts of profiling data, and to determine when a compound is sufficiently selective, there is a need for a proper quantitative measure of selectivity.</p> <p>Results</p> <p>Here we propose a new theoretical entropy score that can be calculated from a set of IC₅₀data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC₅₀data, and is not dependent on a reference enzyme. In addition, the entropy score gives the most robust values with data from different sources, because it is less sensitive to errors. We apply the new score to kinase and nuclear receptor profiling data, and to high-throughput screening data. In addition, through analyzing profiles of clinical compounds, we show quantitatively that a more selective kinase inhibitor is not necessarily more drug-like.</p> <p>Conclusions</p> <p>For quantifying selectivity from panel profiling, a theoretical entropy score is the best method. It is valuable for studying the molecular mechanisms of selectivity, and to steer compound progression in drug discovery programs.</p

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches

Value of risk scores in the decision to palliate patients with ruptured abdominal aortic aneurysm

Background: The aim of this study was to develop a 48-h mortality risk score, which included morphology data, for patients with ruptured abdominal aortic aneurysm presenting to an emergency department, and to assess its predictive accuracy and clinical effectiveness in triaging patients to immediate aneurysm repair, transfer or palliative care. Methods: Data from patients in the IMPROVE (Immediate Management of the Patient With Ruptured Aneurysm: Open Versus Endovascular Repair) randomized trial were used to develop the risk score. Variables considered included age, sex, haemodynamic markers and aortic morphology. Backwards selection was used to identify relevant predictors. Predictive performance was assessed using calibration plots and the C-statistic. Validation of the newly developed and other previously published scores was conducted in four external populations. The net benefit of treating patients based on a risk threshold compared with treating none was quantified. Results: Data from 536 patients in the IMPROVE trial were included. The final variables retained were age, sex, haemoglobin level, serum creatinine level, systolic BP, aortic neck length and angle, and acute myocardial ischaemia. The discrimination of the score for 48-h mortality in the IMPROVE data was reasonable (C-statistic 0·710, 95 per cent c.i. 0·659 to 0·760), but varied in external populations (from 0·652 to 0·761). The new score outperformed other published risk scores in some, but not all, populations. An 8 (95 per cent c.i. 5 to 11) per cent improvement in the C-statistic was estimated compared with using age alone. Conclusion: The assessed risk scores did not have sufficient accuracy to enable potentially life-saving decisions to be made regarding intervention. Focus should therefore shift to offering repair to more patients and reducing non-intervention rates, while respecting the wishes of the patient and family

Influence of Doping Concentration on the Properties of Tin Doped Zinc Oxide Thin Films Prepared by Spray Pyrolysis for Photovoltaic Applications

This work reports on the preparation and characterization of zinc oxide (ZnO) thin films by spray pyrolysis on glass substrates. The effect of Sn doping with 1% Sn (TZO-1.00), 1.5% Sn (TZO-1.50), 2% Sn (TZO-2.00) on the structural, optical and electrical properties of the obtained films was studied. The obtained films are characterized by different techniques such as X-ray diffraction (XRD), UV-visible and electrical Hall Effect measurements. The results of the XRD characterization indicate that all the films have the polycrystalline hexagonal wurtzite structure with a preferred orientation (002). Spectroscopic measurements in the UV-VIS-IR wavelength range were found to give good average transmittance values of about 70%, with a high transmittance of 75% with 1.5% Sn doping. The optical gap value increases in the range of 3.23 to 3.29 eV with increasing tin content. The electrical analysis shows that the conductivity improves slightly with doping compared to the pure ZnO film

Skew-t copula for dependence modelling of impulsive (α-stable) interference

International audienceImpulsive interference is a strong limitation in ultra wide band systems or ad hoc networks. However, many work rely on the assumption of independent interference sam- ples which is in many situations an unrealistic assumption. We propose to model the dependence structure using natural extensions to existing interference models based on parameter copula models. We focus on a particular flexible class of models based on the skewed-t copula family. They allow one to capture interesting dependence features based on extremal concordance such as multivariate generalizations of joint extreme correlation known as tail dependence. In the skew-t copula family this can arise in both homogeneous and heterogeneous forms in the extreme quadrants of the multivariate distribution. Importantly, by considering the skew-t copula it is also amenable to efficient scalability to high dimensions. In a second step, we study the impact of these dependence in the receivers’ performance when they are designed assuming i.i.d. signals

Lattice dynamics of Co nanoparticles in Ag

A comparison is made between the lattice dynamics of cobalt embedded in silver, studied by Mossbauer spectroscopy and by classical molecular dynamics (MD). The MD is achieved in the NPT ensemble by means of the Rahman Parrinello technique which accounts for the temporal fluctuations of all the components of the stress tensor. Atomic interactions are described within an empirical embedded atom model. The mean square thermal vibration amplitude of substitutional Co is calculated and found to compare well with experimental value extracted from Mossbauer spectroscopy. Mossbauer spectroscopy shows the Debye temperature to be cluster size dependent and a comparison with MD suggests the possibility of quantitative cluster size estimates.info:eu-repo/semantics/publishe

Skew-t copula for dependence modelling of impulsive (α-stable) interference

International audienceImpulsive interference is a strong limitation in ultra wide band systems or ad hoc networks. However, many work rely on the assumption of independent interference sam- ples which is in many situations an unrealistic assumption. We propose to model the dependence structure using natural extensions to existing interference models based on parameter copula models. We focus on a particular flexible class of models based on the skewed-t copula family. They allow one to capture interesting dependence features based on extremal concordance such as multivariate generalizations of joint extreme correlation known as tail dependence. In the skew-t copula family this can arise in both homogeneous and heterogeneous forms in the extreme quadrants of the multivariate distribution. Importantly, by considering the skew-t copula it is also amenable to efficient scalability to high dimensions. In a second step, we study the impact of these dependence in the receivers’ performance when they are designed assuming i.i.d. signals