Towards Certification of Machine Learning-Based Distributed Systems

Machine Learning (ML) is increasingly used to drive the operation of complex distributed systems deployed on the cloud-edge continuum enabled by 5G. Correspondingly, distributed systems' behavior is becoming more non-deterministic in nature. This evolution of distributed systems requires the definition of new assurance approaches for the verification of non-functional properties. Certification, the most popular assurance technique for system and software verification, is not immediately applicable to systems whose behavior is determined by Machine Learning-based inference. However, there is an increasing push from policy makers, regulators, and industrial stakeholders towards the definition of techniques for the certification of non-functional properties (e.g., fairness, robustness, privacy) of ML. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues and proposes a first certification scheme for ML-based distributed systems.Comment: 5 pages, 1 figure, 1 tabl

On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach

Machine learning is becoming ubiquitous. From finance to medicine, machine learning models are boosting decision-making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy. Research on poisoning attacks and defenses received increasing attention in the last decade, leading to several promising solutions aiming to increase the robustness of machine learning. Among them, ensemble-based defenses, where different models are trained on portions of the training set and their predictions are then aggregated, provide strong theoretical guarantees at the price of a linear overhead. Surprisingly, ensemble-based defenses, which do not pose any restrictions on the base model, have not been applied to increase the robustness of random forest models. The work in this paper aims to fill in this gap by designing and implementing a novel hash-based ensemble approach that protects random forest against untargeted, random poisoning attacks. An extensive experimental evaluation measures the performance of our approach against a variety of attacks, as well as its sustainability in terms of resource consumption and performance, and compares it with a traditional monolithic model based on random forest. A final discussion presents our main findings and compares our approach with existing poisoning defenses targeting random forests.Comment: 15 pages, 8 figure

Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub

A Macroevolutionary: Study of the Southern Cone Native Flora

Las acciones eficientes de conservación requieren un profundo conocimiento de la biodiversidad en una región dada. Los análisis macro-evolutivos de floras integrales son innovadores y tienen la capacidad potencial de re-evaluar cuantitativamente el conocimiento tradicional de la biogeografía para una región. El presente proyecto plantea reconstruir la historia evolutiva de flora nativa del Cono Sur, evaluando aspectos de su antigüedad, diversidad, neo- y paleo- endemismos, con el fin de aportar información tanto para la toma de decisiones de conservación como para proyectos de ciencia básica. Analizar la distribución de la flora vascular nativa del Cono Sur en el marco de un contexto filogenético datado nos permitirá evaluar varias predicciones provenientes de la biogeografía (e.g., si las Yungas y la Selva Paranaense son las regiones más diversas dentro del Cono Sur, si los linajes más antiguos se concentran en los bosques sub-antárticos o si existen cunas de reciente especiación a lo largo de los altos Andes). Evaluar si las zonas identificadas como de alta diversidad, alto endemismo, refugios o cunas de especiación se encuentran abarcadas por el actual sistema de áreas protegidas.Fil: Aagesen, Lone. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Acosta, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Aliscioni, Sandra Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Belgrano, Manuel Joaquin. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Bena, María Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Brignone, Nicolás Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Denham, Silvia Suyai. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: de Tezanos Pinto, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Donadío, Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Freire, Susana Edith. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Giussani, Liliana Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Guerreiro, Carolina Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Ihasz, Fernanda Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Lizarazu, Mabel Angela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Moroni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Martinez, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Martinez, Leandro Carlos Alcides. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Nicola, Marcela Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: O'Leary, Nataly Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Ponce, Marta Monica. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Pozner, Raúl Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Salariato, Diego Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Abramo Barrera San Martin, Juca. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Santin, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Scataglini, María Amalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Sede, Silvana Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Suarez, Amalia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Urtubey, Estrella. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Zuloaga, Fernando Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaXXXVIII Jornadas Argentinas de BotánicaArgentinaSociedad Argentina de Botánic

Revisiting Trust Management in the Data Economy: A Roadmap

International audienceIn the last two decades, multiple ICT evolutions boosted the ability to collect and analyze vast amounts of data (on the order of Zettabytes). Collectively, they paved the way for the so-called data economy, revolutionizing most sectors of our society, including healthcare, transportation, and grids. At the core of this revolution, distributed data-intensive applications compose services operated by multiple parties in the cloud-edge continuum; they process, manage and exchange massive amounts of data at an unprecedented rate. However, data hold little value without adequate data protection. Traditional solutions, which aim to balance data quality and protection, are insufficient to address the peculiarities of the data economy, including trustworthy data sharing and management, composite service support, and multi-party data life cycle. This article analyzes how trust management systems can regain the lead in supporting trustworthy data-intensive applications, discussing current challenges and proposing a roadmap for new-generation trust management systems in the data economy

Explainable Data Poison Attacks on Human Emotion Evaluation Systems Based on EEG Signals

The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers’ perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. Besides, due to the instability and complexity of the EEG signals, it is challenging to explain and analyze how data poison attacks influence the decision process of EEG signal-based human emotion evaluation systems. In this paper, from the attackers’ side, data poison attacks occurring in the training phases of six different Machine Learning models including Random Forest, Adaptive Boosting (AdaBoost), Extra Trees, XGBoost, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN) intrude on the EEG signal-based human emotion evaluation systems using these Machine Learning models. This seeks to reduce the performance of the aforementioned Machine Learning models with regard to the classification task of 4 different human emotions using EEG signals. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub

Biomonitoring of the adult population in the area of turin waste incinerator: Baseline levels of polycyclic aromatic hydrocarbon metabolites

Exposure to polycyclic aromatic hydrocarbons (PAHs) was assessed in a cohort of 394 subjects, 198 residing in three small municipalities near a new waste-to-energy (WTE) incinerator located in the Turin area, and 196 residing in neighbouring control areas in the town (of Turin). The assessment of exposure to PAHs was part of a human biomonitoring study aimed at assessing potential incremental exposure to pollutants related to incineration activities through the analysis of such pollutants before the plant start-up, and after one and three years of operation. The exposure assessment described in this study was carried out before the start-up of the WTE incinerator. Ten monohydroxy-PAHs (OH-PAHs) were analyzed in urine samples, consisting in the principal metabolites of naphthalene (NAP), fluorene (FLU), phenanthrene (PHE), and pyrene (PYR). Concentrations of the sum of OH-PAHs (Σ10OH-PAHs) were in the range of 525–85200 ng/g creatinine, with P50 equal to 6770 ng/g creatinine. Metabolites of naphthalene were found at the highest concentrations (P50 values of 892 and 4300 ng/g creatinine for 1- and 2-OH-NAP, respectively) followed by the three OH-FLUs (P50 values of individual compounds in the range of 58.2–491 ng/g creatinine), the four OH-PHEs (P50 values in the range of 30.5–145 ng/g creatinine), and 1-OH-PYR (P50 value of 82.8 ng/g creatinine). Concentrations of 1-OH-NAP, 9-OH-FLU, 1-, 2-, 3, 4-OH-PHE, and 1-OH-PYR were significantly lower in subjects living near the WTE plant compared to those living in the town of Turin, with differences between the two groups in the range 14–31%. Smoking habits markedly influence the urinary concentrations OH-PAHs. Median concentrations of the single metabolites in smokers were from 1.4 fold (for 4-OH-PHE) to 14 fold higher (for 3-OH-FLU) than those observed in non-smokers. The heating system used also resulted to be a major contributor to PAH exposure. Concentrations of OH-PAHs were generally comparable with those observed in other industrialized countries. The profile pattern was consistent with those reported in the literature. Concentrations of OH-PAHs assessed in this study may be considered indicative of the background exposure to PAHs for adult population living in an urban and industrialized area

Prospects for fundamental physics with LISA

In this paper, which is of programmatic rather than quantitative nature, we aim to further delineate and sharpen the future potential of the LISA mission in the area of fundamental physics. Given the very broad range of topics that might be relevant to LISA, we present here a sample of what we view as particularly promising directions, based in part on the current research interests of the LISA scientific community in the area of fundamental physics. We organize these directions through a "science-first" approach that allows us to classify how LISA data can inform theoretical physics in a variety of areas. For each of these theoretical physics classes, we identify the sources that are currently expected to provide the principal contribution to our knowledge, and the areas that need further development. The classification presented here should not be thought of as cast in stone, but rather as a fluid framework that is amenable to change with the flow of new insights in theoretical physics