Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Robust rules for prediction and description

In this work, we attempt to answer the question: "How to learn robust and interpretable rule-based models from data for machine learning and data mining, and define their optimality?".Rules provide a simple form of storing and sharing information about the world. As humans, we use rules every day, such as the physician that diagnoses someone with flu, represented by "if a person has either a fever or sore throat (among others), then she has the flu.". Even though an individual rule can only describe simple events, several aggregated rules can represent more complex scenarios, such as the complete set of diagnostic rules employed by a physician.The use of rules spans many fields in computer science, and in this dissertation, we focus on rule-based models for machine learning and data mining. Machine learning focuses on learning the model that best predicts future (previously unseen) events from historical data. Data mining aims to find interesting patterns in the available data.To answer our question, we use the Minimum Description Length (MDL) principle, which allows us to define the statistical optimality of rule-based models. Furthermore, we empirically show that this formulation is highly competitive for real-world problems.NWO; GE Research BengaluruAlgorithms and the Foundations of Software technolog

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We propose featured team automata to support variability in the development and analysis of teams, which are systems of reactive components that communicate according to specified synchronisation types. A featured team automaton concisely describes a family of concrete product models for specific configurations determined by feature selection. We focus on the analysis of communication-safety properties, but doing so product-wise quickly becomes impractical. Therefore, we investigate how to lift notions of receptiveness (no message loss) to the level of family models. We show that featured (weak) receptiveness of featured team automata characterises (weak) receptiveness for all product instantiations. A prototypical tool supports the developed theory.Ter Beek received funding from the MIUR PRIN2017 FTXR7S project ITMaTTerS (Methods and Tools for Trust worthy Smart Systems). Cledou and Proença received funding from the ERDF_European Regiona lDevelopment Fund through the Operational Programme for Competitiveness and Internationalisation_ COMPETE 2020 Programme (project DaVinci, POCI-01-0145-FEDER-029946) and by National Funds through the Portuguese funding agency, FCT_Fundação para a Ciência e a Tecnologia. Proença also received National Funds through FCT/MCTES, within the CISTER Research Unit(UIDP/UIDB/04234/2020); by the Norte Portugal Regional OperationalProgramme_NORTE2020 (project REASSURE, NORTE-01- 0145-FEDER-028550) under the Portugal 2020 Partnership Agreement, through ERDF the FCT; and European Funds through the ECSEL Joint Undertaking(JU) under grant agreement No 876852 (project VALU3S).info:eu-repo/semantics/publishedVersio

Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners

Non-invasive acoustic analyses of voice disorders have been at the forefront of current biomedical research. Usual strategies, essentially based on machine learning (ML) algorithms, commonly classify a subject as being either healthy or pathologically-affected. Nevertheless, the latter state is not always a result of a sole laryngeal issue, i.e., multiple disorders might exist, demanding multi-label classification procedures for effective diagnoses. Consequently, the objective of this paper is to investigate the application of five multi-label classification methods based on problem transformation to play the role of base-learners, i.e., Label Powerset, Binary Relevance, Nested Stacking, Classifier Chains, and Dependent Binary Relevance with Random Forest (RF) and Support Vector Machine (SVM), in addition to a Deep Neural Network (DNN) from an algorithm adaptation method, to detect multiple voice disorders, i.e., Dysphonia, Laryngitis, Reinke's Edema, Vox Senilis, and Central Laryngeal Motion Disorder. Receiving as input three handcrafted features, i.e., signal energy (SE), zero-crossing rates (ZCRs), and signal entropy (SH), which allow for interpretable descriptors in terms of speech analysis, production, and perception, we observed that the DNN-based approach powered with SE-based feature vectors presented the best values of F1-score among the tested methods, i.e., 0.943, as the averaged value from all the balancing scenarios, under Saarbrücken Voice Database (SVD) and considering 20% of balancing rate with Synthetic Minority Over-sampling Technique (SMOTE). Finally, our findings of most false negatives for laryngitis may explain the reason why its detection is a serious issue in speech technology. The results we report provide an original contribution, allowing for the consistent detection of multiple speech pathologies and advancing the state-of-the-art in the field of handcrafted acoustic-based non-invasive diagnosis of voice disorders

Increasing Acid Concentration, Time and Using a Two-Part Silane Potentiates Bond Strength of Lithium Disilicate-Reinforced Glass Ceramic to Resin Composite:An Exploratory Laboratory Study

There is still a lack of consensus concerning the recommended etching concentration, application time and type of silane when bonding lithium disilicate-reinforced glass ceramics manufactured by CAD/CAM. The purpose of this study was thus to conduct an in vitro study which investigates the influence of hydrofluoric acid (HF) concentration, etching time and silane type on the microtensile bond strength (μTBS) of lithium disilicate to resin composites. Thirty-nine IPS e.max CAD blocks were randomly divided between thirteen groups (n = 3). The variables were HF concentration (9.5 or 4.9%), etching time (20 or 60 s) and silane type (Bis-Silane, Monobond Plus and ESPE Sil Silane). The blocks were cut into beams, aged for 10,000 cycles in a thermocycler and submitted to tensile stress to determine μTBS. A control group featuring the Monobond Etch & Prime (MEP) agent that combines etching/silanisation into a simultaneous process was also added. This group was discarded from the analysis due to only having pre-test failures. The data were analysed using a three-way ANOVA (α = 0.05). The HF concentration, etching time and silane type significantly influenced μTBS (p < 0.001). Significant interactions between time and silane type (p = 0.004), HF concentration and silane type (p < 0.001) and among the three factors (p < 0.001) were noted. Etching lithium disilicate with 9.5% HF (60 s), followed by the application of Bis-Silane, resulted in the highest μTBS (16.6 ± 9.0 MPa). The highest concentration and etching time under study, combined with a two-part silane, resulted in the highest bond strength, while the application of MEP showed a complete pre-test failure

Hypertrophic Adenoid Is A Major Infection Site Of Human Bocavirus 1.

Human bocavirus 1 (HBoV1) is associated with respiratory infections worldwide, mainly in children. Similar to other parvoviruses, it is believed that HBoV1 can persist for long periods of time in humans, probably through maintaining concatemers of the virus single-stranded DNA genome in the nuclei of infected cells. Recently, HBoV-1 was detected in high rates in adenoid and palatine tonsils samples from patients with chronic adenotonsillar diseases, but nothing is known about the virus replication levels in those tissues. A 3-year prospective hospital-based study was conducted to detect and quantify HBoV1 DNA and mRNAs in samples of the adenoids (AD), palatine tonsils (PT), nasopharyngeal secretions (NPS), and peripheral blood (PB) from patients undergoing tonsillectomy for tonsillar hypertrophy or recurrent tonsillitis. HBoV1 was detected in 25.3% of the AD samples, while the rates of detection in the PT, NPS, and PB samples were 7.2%, 10.5%, and 1.7%, respectively. The viral loads were higher in AD samples, and 27.3% of the patients with HBoV had mRNA detectable in this tissue. High viral loads and detectable mRNA in the AD were associated with HBoV1 detection in the other sample sites. The adenoids are an important site of HBoV1 replication and persistence in children with tonsillar hypertrophy. The adenoids contain high HBoV1 loads and are frequently positive for HBoV mRNA, and this is associated with the detection of HBoV1 in secretions.523030-

A versatile synthesis method of dendrites-free segmented nanowires with a precise size control

We report an innovative strategy to obtain cylindrical nanowires combining well established and low-cost bottom-up methods such as template-assisted nanowires synthesis and electrodeposition process. This approach allows the growth of single-layer or multi-segmented nanowires with precise control over their length (from few nanometers to several micrometers). The employed techniques give rise to branched pores at the bottom of the templates and consequently dendrites at the end of the nanowires. With our method, these undesired features are easily removed from the nanowires by a selective chemical etching. This is crucial for magnetic characterizations where such non-homogeneous branches may introduce undesired features into the final magnetic response. The obtained structures show extremely narrow distributions in diameter and length, improved robustness and high-yield, making this versatile approach strongly compatible with large scale production at an industrial level. Finally, we show the possibility to tune accurately the size of the nanostructures and consequently provide an easy control over the magnetic properties of these nanostructures

Insights from a national survey into why substance abuse treatment units add prevention and outreach services

BACKGROUND: Previous studies have found that even limited prevention-related interventions can affect health behaviors such as substance use and risky sex. Substance abuse treatment providers are ideal candidates to provide these services, but typically have little or no financial incentive to do so. The purpose of this study was therefore to explore why some substance abuse treatment units have added new prevention and outreach services. Based on an ecological framework of organizational strategy, three categories of predictors were tested: (1) environmental, (2) unit-level, and (3) unit leadership. RESULTS: A lagged cross-sectional logistic model of 450 outpatient substance abuse treatment units revealed that local per capita income, mental health center affiliation, and clinical supervisors' graduate degrees were positively associated with likelihood of adding prevention-related education and outreach services. Managed care contracts and methadone treatment were negatively associated with addition of these services. No hospital-affiliated agencies added prevention and outreach services during the study period. CONCLUSION: Findings supported the study's ecological perspective on organizational strategy, with factors at environmental, unit, and unit leadership levels associated with additions of prevention and outreach services. Among the significant predictors, ties to managed care payers and unit leadership graduate education emerge as potential leverage points for public policy. In the current sample, units with managed care contracts were less likely to add prevention and outreach services. This is not surprising, given managed care's emphasis on cost control. However, the association with this payment source suggests that public managed care programs might affects prevention and outreach differently through revised incentives. Specifically, government payers could explicitly compensate substance abuse treatment units in managed care contracts for prevention and outreach. The effects of supervisor graduate education on likelihood of adding new prevention and outreach programs suggests that leaders' education can affect organizational strategy. Foundation and government officials may encourage prevention and outreach by funding curricular enhancements to graduate degree programs demonstrating the importance of public goods. Overall, these findings suggest that both money and professional education affect substance abuse treatment unit additions of prevention and outreach services, as well as other factors less amenable to policy intervention