Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain

Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area

UV-protective compounds in marine organisms from the Southern Ocean

Solar radiation represents a key abiotic factor in the evolution of life in the oceans. In general, marine, biota particularly in euphotic and dysphotic zones depends directly or indirectly on light, but ultraviolet radiation (UV-R) can damage vital molecular machineries. UV-R induces the formation of reactive oxygen species (ROS) and impairs intracellular structures and enzymatic reactions. It can also affect organismal physiologies and eventually alter trophic chains at the ecosystem level. In Antarctica, physical drivers, such as sunlight, sea-ice, seasonality and low temperature are particularly influencing as compared to other regions. The springtime ozone depletion over the Southern Ocean makes organisms be more vulnerable to UV-R. Nonetheless, Antarctic species seem to possess analogous UV photoprotection and repair mechanisms as those found in organisms from other latitudes. The lack of data on species-specific responses towards increased UV-B still limits the understanding about the ecological impact and the tolerance levels related to ozone depletion in this region. The photobiology of Antarctic biota is largely unknown, in spite of representing a highly promising reservoir in the discovery of novel cosmeceutical products. This review compiles the most relevant information on photoprotection and UV-repair processes described in organisms from the Southern Ocean, in the context of this unique marine polar environment

Assessment of serum allergen-specific IgE levels in horses with seasonal allergic dermatitis and recurrent airway obstruction in Spain

Allergic conditions are prevalent equine diseases that can be diagnosed by clinical examination alone, but definitive diagnosis is more likely with laboratory testing. The ELISA Allercept© test was used to analyse the serum samples of 73 horses with allergic diseases. Sixty-one horses (83.5%) had allergen-specific IgE levels ≥ 150 ELISA Units (EU), the cut-off defined by the assay. Fifty-four horses had allergic dermatitis (AD) with high IgE levels to Tyrophagus putrescentiae (51.9%), Rumex crispus (48.1%), Tabanus (46.3%) and Dermatophagoides farinae/ D. pteronyssinus (40.7%). Seven horses with recurrent airway obstruction (RAO) had a high prevalence of T. putrescentiae (85.7%), followed by that of Acarus siro (57.1%) and D. farinae/D. pteronyssinus (57.1%). Horses affected with RAO had more positive reactions to mites (2.22 ± 0.84) than did horses with AD (1.51 ± 0.61, P < 0.05). A strong correlation of serum allergen-specific IgE level was found between Culex tarsalis and Stomoxys (r = 0.943) and between Dactylis glomerata and both Secale cereale (r = 0.79) and R. crispus (r = 0.696). These results indicate that among horses with allergic diseases in Spain, ELISA tests demonstrated a high prevalence of serum allergen-specific IgE in response to mites. Our study emphasises the importance of laboratory testing and updating allergy panels to improve the likelihood of a definitive diagnosis and the identification of allergens that should be included in allergic disease treatment

Expressive partner homicides in spain

Introduction: Although the important concert about partner homicides, there are no scientific studies in Spain. The main aim of this study is to analyze differences in Spanish partner homicides between aggressor's sex with a) crime perpetration: attempted or completed and b) criminal process: pre-crime, offence and post-crime behavior. Method: We used Basa Data 'Protocolo de Análisis del Crimen Violento en Homicidios Familiares', composed by firmed sentences of family homicides in Spain. Specifically, we analyzed a sample of 323 partner homicide firmed sentences, mostly men. Results: The results show that attempted crimes are more associated with men, and completed homicides with women. Regarding pre-crime behaviors, the existence of previous conflict and previous break up are more associated with men. In criminal context, instrumental behaviors are more related with women while expressive behaviors don't show relation with aggressor's sex; to strangle the victim is more associated with women. Finally, none of post-criminal behaviors show relation with aggressor's sex. Discussion: The results regarding pre-crime and post-crime behaviors are consistent with precedents studies in other countries, also the predominance of expressive behaviors in both, men and women. Concerning to crime perpetration, it hasn't been found previous studies comparing it with offender's sex in partner homicides

A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features

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A new database of healthy and pathological voices☆ Ugo Cesari a, Giuseppe De Pietro b, Elio Marciano c, Ciro Niri d, Giovanna Sannino,b, Laura Verde e a Department of Otorhinolaryngology, University Hospital (Policlinico) Federico II of Naples, Via S.Pansini, 5 Naples, Italy b Institute of High Performance Computing and Networking (ICAR-CNR), Via Pietro Castellino, 111, Naples, Italy c Area of Audiology, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via S.Pansini, 5, Naples, Italy d Independent Doctor Surgeon Specialized in Audiology and Phoniatrics, Naples, Italy e Department of Engineering, University of Naples Parthenope, Centro Direzionale di Napoli, Isola C4, Naples, Italy

In the era of Edge-of-Things computing for the accomplishment of smart healthcare systems, the availability of accurate and reliable databases is important to provide the right tools for researchers and business companies to design, develop and test new techniques, methodologies and/or algorithms to monitor or detect the patient’s healthcare status. In this paper, the study and building of the VOice ICar fEDerico II (VOICED) database are presented, useful for anybody who needs voice signals in her/his research activities. It consists of 208 healthy and pathological voices collected during a clinical study performed following the guidelines of the medical SIFEL (Società Italiana di Foniatria e Logopedia) protocol and the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement. For each subject, the database contains a recording of the vowel /a/ of five seconds in length, lifestyle information, the medical diagnosis, and the results of two specific medical questionnaires

Engineers’ abilities influence spatial perspective changing

In this paper we studied the effect of engineering expertise in providing directional judgments. We asked two groups of people, engineers and non-engineers, to observe and memorize five maps, each including a four-point path, for 30 sec. The path was then removed and the participants had to provide two directional judgments: aligned (the imagined perspective on the task was the same as the one just learned), and counter-aligned (the imagined perspective on the task was rotated by 180°). Our results showed that engineers are equally able to perform aligned and counter-aligned directional judgments. The alignment effect due to the distance from the learning perspective was, in fact, shown only by non-engineers. Results are discussed considering engineering both learning expertise and specific predisposition

Charlas TED: Educación y Ciencia.

Este Trabajo Final de Grado (TFG) titulado Charlas TED. Educación y Ciencias tiene como objetivo principal analizar las diversas charlas relativas a la Educación publicadas por la organización TED en su página web (www.ted.com/talks) y, especialmente, aquellas que se relacionan con los distintos ámbitos científicos. TED, acrónimo de Tecnología, Entretenimiento y Diseño, es una entidad sin ánimo de lucro dedicada a difundir ideas, en forma de charlas, sobre casi todos los temas. Las charlas abarcan desde tecnología y ciencias hasta educación y problemas globales, se han traducido a más de 100 idiomas y están disponibles en vídeo de forma gratuita. Con este Proyecto de Investigación, pretendemos averiguar cuáles son los temas más relacionados con la Educación que se presentan con este innovador formato, a fin de esclarecer su importancia y repercusión en el tan preciado bien de la Educación. Asimismo, realizaremos un breve análisis de los vídeos que relacionan la Educación y la Ciencia a fin de destacar la importancia de la formación científica.The main objective of this final degree project entitled TED Talks, Education and Sciences is to analyze the various talks related to education published by the TED organization on its website and especially those that are related to different scientific fields. TED, an acronym for Technology, Entertainment and Design, is a non-profit organization dedicated to spreading ideas, in the form of talks, on almost every topic. The talks range from science and technology to education and global issues, have been translated into over 100 languages and are available on video for free. With this research project, we intend to find out which are the issues most related to education that are presented with this innovative format, in order to clarify its importance and impact on the precious asset of Education. We will also carry out a brief analysis of the videos that relate education and science in order to highlight the importance of scientific training

voice disorder identification by using machine learning techniques

Nowadays, the use of mobile devices in the healthcare sector is increasing significantly. Mobile technologies offer not only forms of communication for multimedia content (e.g. clinical audio-visual notes and medical records) but also promising solutions for people who desire the detection, monitoring, and treatment of their health conditions anywhere and at any time. Mobile health systems can contribute to make patient care faster, better, and cheaper. Several pathological conditions can benefit from the use of mobile technologies. In this paper we focus on dysphonia, an alteration of the voice quality that affects about one person in three at least once in his/her lifetime. Voice disorders are rapidly spreading, although they are often underestimated. Mobile health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is necessary to realize a valid and precise mobile health system. The key contribution of this paper is to investigate and compare the performance of several machine learning techniques useful for voice pathology detection. All analyses are performed on a dataset of voices selected from the Saarbruecken voice database. The results obtained are evaluated in terms of accuracy, sensitivity, specificity, and receiver operating characteristic area. They show that the best accuracy in voice diseases detection is achieved by the support vector machine algorithm or the decision tree one, depending on the features evaluated by using opportune feature selection methods