Fingerprint recognition with embedded presentation attacks detection: are we ready?

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more “secure” and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance

A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this letter, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies

ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. The dataset is built by first optimizing a set of adversarial patches against an ensemble of models, using a state-of-the-art attack that creates transferable patches. The corresponding patches are then randomly rotated and translated, and finally applied to the ImageNet data. We use ImageNet-Patch to benchmark the robustness of 127 models against patch attacks, and also validate the effectiveness of the given patches in the physical domain (i.e., by printing and applying them to real-world objects). We conclude by discussing how our dataset could be used as a benchmark for robustness, and how our methodology can be generalized to other domains. We open source our dataset and evaluation code at https://github.com/pralab/ImageNet-Patch

The influence of environment on the sperm quality: a comprehensive, retrospective, cohort study.

influence of environment on the sperm quality: a comprehensive, retrospective, cohort stud

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

Innate immunity changes in soccer players after whole-body cryotherapy

Whole-body cryotherapy (WBC) consists of short exposure (up to 2–3 min) to dry air at cryogenic temperatures (up to -190 °C) and has recently been applied for muscle recovery after injury to reduce the inflammation process. We aimed to determine the impact of cryotherapy on immunological, hormonal, and metabolic responses in non-professional soccer players (NPSPs). Nine male NPSPs (age: 20 ± 2 years) who trained regularly over 5 consecutive days, immediately before and after each training session, were subjected to WBC treatment (WBC-t). Blood samples were collected for the evaluation of fifty analytes including hematologic parameters, serum chemistry, and hormone profiles. Monocytes phenotyping (Mo) was performed and plasmatic markers, usually increased during inflammation [CCL2, IL-18, free mitochondrial (mt)DNA] or with anti-inflammatory effects (IL2RA, IL1RN), were quantified. After WBC-t, we observed reduced levels of ferritin, mean corpuscular hemoglobin, mean platelet volume, testosterone, and estradiol, which however remain within the normal ranges. The percentage of the total, intermediates and non-classical Mo increased, while classical Mo decreased. CXCR4 expression decreased in each Mo subset. Plasma IL18 and IL2RA levels decreased, while IL1RN only exhibited a tendency to decrease and CCL2 showed a tendency to increase. Circulating mtDNA levels were not altered following WBC-t. The differences observed in monocyte subsets after WBC-t may be attributable to their redistribution into the surrounding tissue. Moreover, the decrease of CXCR4 in Mo subpopulations could be coherent with their differentiation process. Thus, WBC through yet unknown mechanisms could promote their differentiation having a role in tissue repair

PATIENT VOICES, a project for the integration of the systematic assessment of patient reported outcomes and experiences within a comprehensive cancer center: a protocol for a mixed method feasibility study

BACKGROUND: Listening to "patient voices" in terms of symptoms, emotional status and experiences with care, is crucial for patient empowerment in clinical practice. Despite convincing evidence that routine patient reported outcomes and experience measurements (PRMs) with rapid feed-back to oncologists can improve symptom control, patient well-being and cost effectiveness, PRMs are not commonly used in cancer care, due to barriers at various level. Part of these barriers may be overcome through electronic PRMs collection (ePRMs) integrated with the electronic medical record (EMR). The PATIENT VOICES initiative is aimed at achieving a stepwise integration of ePRMs assessment into routine cancer care. The feasibility project presented here is aimed at assessing the knowledge, use and attitudes toward PRMs in a comprehensive cancer centre; developing and assessing feasibility of a flexible system for ePRM assessment; identifying barriers to and developing strategies for implementation and integration of ePRMs clinical practice. METHODS: The project has been organized into four phases: a) pre-development; b) software development and piloting; c) feasibility assessment; d) post-development. A convergent mixed method design, based on concurrent quantitative and qualitative data collection will be applied. A web-survey on health care providers (HCPs), qualitative studies on patients and HCPs (semi-structured interviews and focus groups) as well as longitudinal and cross-sectional quantitative studies will be carried out. The quantitative studies will enroll 600 patients: 200 attending out-patient clinics (physical symptom assessement), 200 attending inpatient wards (psychological distress assessment) and 200 patients followed by multidisciplinary teams (patient experience with care assessment). The Edmonton symptom assessment scale, the Distress Thermometer, and a tool adapted from existing patient reported experience with cancer care questionnaires, will be used in quantitative studies. A multi-disciplinary stakeholder team including researchers, clinicians, health informatics professionals, health system administrators and patients will be involved in the development of potentially effective implementation strategies in the post development phase. DISCUSSION: The documentation of potential advantages and implementation barriers achieved within this feasibility project, will serve as a starting point for future and more focused interventions aimed at achieving effective ePRMs routine assessment in cancer care. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT03968718 ) May 30th, 2019

Nutrient Control of Yeast PKA Activity Involves Opposing Effects on Phosphorylation of the Bcy1 Regulatory Subunit

Kelch repeat proteins Gpb1 and Gpb2 control yeast PKA activity in response to nutrients by stimulating phosphorylation of the Bcy1 regulatory subunit. Gpb1 and Gpb2 function by blocking inhibition of Bcy1 phosphorylation by PKA catalytic subunits. Phosphorylated Bcy1 is more stable and is a more effective inhibitor of PKA activity