Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201

Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions. Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models. SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value. The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis. The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies

A study of machine learning models application for porosity prediction using petrophysical well logs. Case Study: The Brent Group – Statfjord field

The use of machine learning algorithms for predictive analytics is making a growing impact in the field of petroleum geosciences. With the increasing cost and time-related factors for obtaining accurate porosity measurements from well logging and coring operations, machine learning (ML) provides a more economical and efficient solution to this challenge. In this thesis, various ML models are applied to predict porosity in a well penetrating the reservoir interval of the Brent Group to Top Cook formation. The study area is the Statfjord field, located in the Norwegian sector of the North Sea. Statfjord produces oil and associated gas from Jurassic sandstone in the Cook formation, Brent and Statfjord Group. Sixteen wells with several well logs serve as input features to predict the porosity in a blind well 33/9-4, all located in the field. The machine learning input features are the well logs, feature engineered logs, location points and the measured depth. The logs include: caliper, resistivity, gamma-ray, sonic, density; the engineered logs include: acoustic impedance and facies; the location: x,y,z; and the well’s measured depth. The input features are varied and ingested into the ML models to estimate the porosity in the predefined reservoir interval. The predicted porosity results for the blind well indicated an excellent performance demonstrated by the Bayesian ridge regression, linear regression and random forest models compared to the other ML models used in this study. These three algorithms are highly effective and accurate in predicting porosity with the limited range of the dataset and the results show they can be applied as a more general porosity estimation technique by varying the scale of the data samples and the number of wells

Detecting Hypoglycemia Incidents Reported in Patients\u27 Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety. OBJECTIVE: We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients\u27 secure messages. METHODS: An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical teams as containing patient-reported hypoglycemia incidents or not. A physician independently annotated 100 threads randomly selected from this dataset to determine interannotator agreement. We used this dataset to develop and evaluate HypoDetect. HypoDetect incorporates 3 machine learning algorithms widely used for text classification: linear support vector machines, random forest, and logistic regression. We explored different learning features, including new knowledge-driven features. Because only 114 (3.80%) messages were annotated as positive, we investigated cost-sensitive learning and oversampling methods to mitigate the challenge of imbalanced data. RESULTS: The interannotator agreement was Cohen kappa=.976. Using cross-validation, logistic regression with cost-sensitive learning achieved the best performance (area under the receiver operating characteristic curve=0.954, sensitivity=0.693, specificity 0.974, F1 score=0.590). Cost-sensitive learning and the ensembled synthetic minority oversampling technique improved the sensitivity of the baseline systems substantially (by 0.123 to 0.728 absolute gains). Our results show that a variety of features contributed to the best performance of HypoDetect. CONCLUSIONS: Despite the challenge of data imbalance, HypoDetect achieved promising results for the task of detecting hypoglycemia incidents from secure messages. The system has a great potential to facilitate early detection and treatment of hypoglycemia

DATA-DRIVEN ANALYSIS AND MAPPING OF THE POTENTIAL DISTRIBUTION OF MOUNTAIN PERMAFROST

In alpine environments, mountain permafrost is defined as a thermal state of the ground and it corresponds to any lithosphere material that is at or below 0°C for at least two years. Its degradation is potentially leading to an increasing rock fall activity and sediment transfer rates. During the last 20 years, knowledge on this phenomenon has significantly improved thanks to many studies and monitoring projects, revealing an extremely discontinuous and complex spatial distribution, especially at the micro scale (scale of a specific landform; tens to several hundreds of metres). The objective of this thesis was the systematic and detailed investigation of the potential of data-driven techniques for mountain permafrost distribution modelling. Machine learning (ML) algorithms are able to consider a greater number of pa- rameters compared to classic approaches. Not only can permafrost distribution be modelled by using topo-climatic parameters as a proxy, but also by taking into ac- count known field permafrost evidences. These latter were collected in a sector of the Western Swiss Alps and they were mapped from field data (thermal and geoelectrical data) and ortho-image interpretations (rock glacier inventorying). A permafrost dataset was built from these evidences and completed with environmental and mor- phological predictors. Data were firstly analysed with feature relevance techniques in order to identify the statistical contribution of each controlling factor and to exclude non-relevant or redundant predictors. Five classification algorithms, belonging to statistics and machine learning, were then applied to the dataset and tested: Logistic regression (LR), linear and non-linear Support Vector Machines (SVM), Multilayer perceptrons (MLP) and Random forests (RF). These techniques inferred a classifica- tion function from labelled training data (pixels of permafrost absence and presence) to predict the permafrost occurrence where this was unknown. Classification performances, assessed with AUROC curves, ranged between 0.75 (linear SVM) and 0.88 (RF). These values are generally indicative of good model performances. Besides these statistical measures, a qualitative evaluation was performed by using field expert knowledge. Both quantitative and qualitative evaluation approaches suggested to employ the RF algorithm to obtain the best model. As machine learning is a non-deterministic approach, an overview of the model uncertainties is also offered. It informs about the location of most uncertain sectors where further field investigations are required to be carried out to improve the reliability of permafrost maps. RF demonstrated to be efficient for permafrost distribution modelling thanks to consistent results that are comparable to the field observations. The employment of environmental variables illustrating the micro-topography and the ground charac- teristics (such as curvature indices, NDVI or grain size) favoured the prediction of the permafrost distribution at the micro scale. These maps presented variations of probability of permafrost occurrence within distances of few tens of metres. In some talus slopes, for example, a lower probability of occurrence in the mid-upper part of the slope was predicted. In addition, permafrost lower limits were automatically recognized from permafrost evidences. Lastly, the high resolution of the input dataset (10 metres) allowed elaborating maps at the micro scale with a modelled permafrost spatial distribution, which was less optimistic than traditional spatial models. The permafrost prediction was indeed computed without recurring to altitude thresh- olds (above which permafrost may be found) and the representation of the strong discontinuity of mountain permafrost at the micro scale was better respected. -- Dans les environnements alpins, le pergélisol de montagne est défini comme un état thermique du sol et correspond à tout matériau de la lithosphère qui maintient une température égale ou inférieure à 0°C pendant au moins deux ans. Sa dégradation peut conduire à une activité croissante de chutes de blocs et à une augmentation des taux de transfert de sédiments. Au cours des 20 dernières années, les connaissances sur ce phénomène ont considérablement augmenté grâce à de nombreuses études et projets de suivi, qui ont révélé une distribution spatiale extrêmement discontinue et complexe du phénomène, en particulier à la micro-échelle (échelle d’une forme géomorphologique; dizaines à plusieurs centaines de mètres). L’objectif de cette recherche était l’étude systématique et détaillée des potentialités offertes par une approche axée sur les données dans le cadre de la modélisation de la distribution du pergélisol de montagne. Les algorithmes d’apprentissage au- tomatique (machine learning) sont capables de considérer un plus grand nombre de variables que les approches classiques. La distribution du pergélisol peut être modélisée non seulement en utilisant des paramètres topo-climatiques (altitude, radiation solaire, etc.), mais aussi en tenant compte de la présence et de l’absence connues du pergélisol (observations de terrain). Collectées dans un secteur des Alpes occidentales suisses, ces dernières ont été cartographiées sur la base d’investigations de terrain (données thermiques et géoélectriques), d’interprétation d’orthophotos et d’inventaires de glaciers rocheux. Un jeu de données a été construit à partir de ces évidences de terrain et complété par des prédicteurs environnementaux et morphologiques. Les données ont d’abord été analysées avec des techniques mon- trant la pertinence des variables permettant d’identifier la contribution statistique de chaque facteur de contrôle et d’exclure les prédicteurs non pertinents ou redondants. Cinq algorithmes de classification appartenant aux domaines des statistiques et de l’apprentissage automatique ont ensuite été appliqués et testés : Logistic regression (LR), la version linéaire et non-linéaire de Support Vector Machines (SVM), Mul- tilayer perceptrons (MLP) et Random forests (RF). Ces techniques déduisent une fonction de classification à partir des données dites d’entraînement représentant l’absence et la présence certaine du pergélisol. Elles permettent ensuite de prédire l’occurrence du phénomène là où elle est inconnue. Les performances de classification, évaluées avec des courbes AUROC, variaient entre 0.75 (SVM linéaire) et 0.88 (RF). Ces valeurs sont généralement indicatives de bonnes performances. En plus de ces mesures statistiques, une évaluation qualitative a été réalisée et se base sur l’expertise géomorphologique. Les RF se sont révélées être la technique produisant le meilleur modèle. Comme l’apprentissage automatique est une approche non déterministe, il a également offert un aperçu des incertitudes de la modélisation, qui informent sur la localisation des secteurs les plus incertains dans lesquels des futures campagnes de terrain méritent d’être menées afin d’améliorer la fiabilité des cartes produites. Finalement, RF ont démontré leur efficacité dans le cadre de la modélisation de la distribution du pergélisol grâce à des résultats comparables aux observations de terrain. L’emploi de variables environnementales illustrant la micro-topographie du relief et les caractéristiques du sol (tels que les indices de courbure, le NDVI et la granulométrie) favorise la prédiction de la distribution du pergélisol à la micro- échelle, avec des cartes présentant des variations de la probabilité d’occurrence du pergélisol sur des distances de quelques dizaines de mètres. Par exemple, dans cer- tains éboulis, les cartes illustrent une probabilité plus faible dans la partie amont de la pente, ce qui s’avère cohérent avec les observations de terrain. La limite inférieure du pergélisol a ainsi été automatiquement reconnue à partir des évidences de terrain fournies à l’algorithme. Enfin, la haute résolution du jeu de données (10 mètres) a permis d’élaborer des cartes présentant une distribution spatiale du pergélisol moins optimiste que celle offerte par les modèles spatiaux classiques. La prédiction du pergélisol a en effet été calculée sans utiliser des seuils d’altitude (au-dessus desquels on peut trouver du pergélisol) et respecte ainsi mieux la représentation de la forte discontinuité du pergélisol de montagne à la micro-échelle. -- Negli ambienti alpini, il permafrost di montagna è definito come uno stato termico del suolo e corrisponde a qualsiasi materiale nella litosfera che mantiene una temper- atura uguale o inferiore a 0° C per almeno due anni. La sua degradazione può portare ad una crescente attività di caduta di blocchi e ad un aumento dei tassi di trasferi- mento dei sedimenti. Negli ultimi 20 anni, le conoscenze riguardanti il permafrost di montagna sono aumentate considerevolmente grazie ai numerosi studi e progetti di monitoraggio che hanno rivelato una distribuzione spaziale fortemente discontinua e complessa del fenomeno, in particolare alla scala della forma geomorfologica (definita come la micro scala, da decine a diverse centinaia di metri). L’obiettivo di questa ricerca é lo studio sistematico e dettagliato delle potenzialità offerte da un approccio basato sui dati, nell’ottica di una modellizzazione della distribuzione del permafrost di montagna. Gli algoritmi di apprendimento auto- matico (machine learning) sono in grado di considerare più variabili rispetto agli approcci classici. La distribuzione del permafrost può essere modellizzata non solo utilizzando i parametri topo-climatici classici (altitudine, radiazione solare, ecc.), ma anche considerando esempi di presenza e assenza del permafrost (osservazioni sul campo). Raccolti in un’area delle Alpi occidentali svizzere, questi ultimi sono stati mappati sulla base di indagini di terreno (dati termici e geoelettrici), interpretazione di ortofoto e inventari di ghiacciai rocciosi. A partire dalle evidenze di terreno, è stato creato un set di dati, al quale sono stati integrati diversi predittori ambien- tali e morfologici. I dati sono stati dapprima analizzati con tecniche di indagine della rilevanza delle variabili; tali tecniche sono capaci di identificare il contributo statistico di ciascun fattore di controllo del permafrost e sono in grado di escludere i predittori non pertinenti o ridondanti. Sono stati, quindi, applicati e testati cinque al- goritmi di classificazione appartenenti ai campi della statistica e dell’apprendimento automatico: Logistic regression (LR), la versione lineare e non lineare di Support Vector Machines (SVM), Multilayer Perceptron (MLP) e Random forest (RF). Queste tecniche deducono una funzione di classificazione dai cosiddetti dati di allenamento, che rappresentano l’assenza e la presenza certa del permafrost, e permettono in seguito di predire il fenomeno laddove è sconosciuto. Le prestazioni di classificazione, valutate con le curve AUROC, variavano da 0.75 (SVM lineare) a 0.88 (RF). Questi valori sono generalmente indicativi di buone prestazioni. Oltre a queste misure statistiche, è stata effettuata una valutazione qualitativa. RF si é rivelata essere la tecnica che produce il modello migliore. Poiché l’apprendimento automatico è un approccio non deterministico, é stato possibile ottenere informazioni sulle incertezze della modellizzazione. Quest’ultime indicano in quali aree il modello é più incerto e, dunque, dove occorre pianificare nuove campagne di terreno per migliorare l’affidabilità delle mappe prodotte. RF ha dimostrato la sua efficacia nella modellizzazione della distribuzione del per- mafrost con risultati paragonabili alle osservazioni sul campo. L’uso di variabili ambientali che illustrano la topografia e le caratteristiche del suolo (come indici di curvatura, NDVI e granulometria) aiuta a predire la distribuzione del permafrost alla micro scala, con mappe che mostrano variazioni spaziali importanti della probabilità del permafrost su distanze di poche decine di metri. In alcune falde di detrito le mappe mostrano una probabilità inferiore nella parte a monte, risultato coerente con le osservazioni sul campo. Il limite inferiore del permafrost è stato inoltre riconosci- uto automaticamente dagli esempi forniti all’algoritmo. Infine, l’alta risoluzione del set di dati (10 metri) ha permesso una simulazione della distribuzione spaziale del fenomeno meno ottimistica rispetto a quella fornita dai modelli classici. La previsione del permafrost è stata, infatti, calcolata senza utilizzare delle soglie di altitudine e quindi rispetta meglio la rappresentazione dell’alta discontinuità del permafrost di montagna alla micro scala

Raman spectroscopy for point of care urinary tract infection diagnosis

Urinary tract infections (UTIs) are one of the most common bacterial infections experience by humans, with 150 million people suffering one or more UTIs each year. The massive scale at which UTIs occurs translates to a tremendous health burden comprising of patient morbidity and mortality, massive societal costs and a recognised contribution to expanding antimicrobial resistance. The considerable disease burden caused by UTIs is severely exacerbated by an outdated diagnostic paradigm characterised by inaccuracy and delay. Poor accuracy of screening tests, such as urinalysis, lead to misdiagnosis which in turn result in delayed recognition or overtreatment. Additionally, these screening tests fail to identify the causative pathogen, causing an overreliance on broad-spectrum antimicrobials which exacerbate burgeoning antimicrobial resistance. While diagnosis may be accurately confirmed though culture and sensitivity testing, the prolonged delay incurred negates the value of the information provided doing so. A novel diagnostic paradigm is required that that targets rapid and accurate diagnosis of UTIs, while providing real-time identification of the causative pathogen. Achieving this precision management is contingent on the development of novel diagnostic technologies that bring accurate diagnosis and pathogen classification to the point of care. The purpose of this thesis is to develop a technology that may form the core of a point-of-care diagnostic capable of delivering rapid and accurate pathogen identification direct from urine sample. Raman spectroscopy is identified as a technology with the potential to fulfil this role, primarily mediated though its ability to provide rapid biochemical phenotyping without requiring prior biomass expansion. Raman spectroscopy has demonstrated an ability to achieve pathogen classification through the analysis of inelastically scattered light arising from pathogens. The central challenge to developing a Raman-based diagnostic for UTIs is enhancing the weak bacterial Raman signal while limiting the substantial background noise. Developing a technology using Raman spectroscopy able to provide UTI diagnosis with uropathogen classification is contingent on developing a robust experimental methodology that harnesses the multitude of experimental and analytical parameters. The refined methodology is applied in a series of experimental works that demonstrate the unique Raman spectra of pathogens has the potential for accurate classification. Achieving this at a clinically relevant pathogen load and in a clinically relevant timeframe is, however, dependent on overcoming weak bacterial signal to improve signal-to-noise ratio. Surface-enhanced Raman spectroscopy (SERS) provides massive Raman signal enhancement of pathogens held in close apposition to noble metal nanostructures. Additionally, vacuum filtration is identified as a means of rapidly capturing pathogens directly from urine. SERS-active filters are developed by applying a gold nanolayer to commercially available membrane filters through physical vapour deposition. These SERS-active membrane filter perform multiple roles of capturing pathogens, separating them from urine, while providing Raman signal enhancement through SERS. The diagnostic and classification performance of SERS-active filters for UTIs is demonstrated to achieve rapid and accurate diagnosis of infected samples, with real-time uropathogen classification, using phantom urine samples, before piloting the technology using clinical urine samples. The Raman technology developed in this thesis will be further developed toward a clinically implementable technology capable of ameliorating the substantial burden of disease caused by UTIs.Open Acces

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial