State monitoring is a fundamental building block for Cloud services. The demand for providing state monitoring as services (MaaS) continues to grow and is evidenced by CloudWatch from Amazon EC2, which allows cloud consumers to pay for monitoring a selection of performance metrics with coarse-grained periodical sampling of runtime states. One of the key challenges for wide deployment of MaaS is to provide better balance among a set of critical quality and performance parameters, such as accuracy, cost, scalability and customizability. This dissertation research is dedicated to innovative research and development of an elastic framework for providing state monitoring as a service (MaaS). We analyze limitations of existing techniques, systematically identify the need and the challenges at different layers of a Cloud monitoring service platform, and develop a suite of distributed monitoring techniques to support for flexible monitoring infrastructure, cost-effective state monitoring and monitoring-enhanced Cloud management. At the monitoring infrastructure layer, we develop techniques to support multi-tenancy of monitoring services by exploring cost sharing between monitoring tasks and safeguarding monitoring resource usage. To provide elasticity in monitoring, we propose techniques to allow the monitoring infrastructure to self-scale with monitoring demand. At the cost-effective state monitoring layer, we devise several new state monitoring functionalities to meet unique functional requirements in Cloud monitoring. Violation likelihood state monitoring explores the benefits of consolidating monitoring workloads by allowing utility-driven monitoring intensity tuning on individual monitoring tasks and identifying correlations between monitoring tasks. Window based state monitoring leverages distributed windows for the best monitoring accuracy and communication efficiency. Reliable state monitoring is robust to both transient and long-lasting communication issues caused by component failures or cross-VM performance interferences. At the monitoring-enhanced Cloud management layer, we devise a novel technique to learn about the performance characteristics of both Cloud infrastructure and Cloud applications from cumulative performance monitoring data to increase the cloud deployment efficiency.

The aromatic amino acids tryptophan, tyrosine, and phenylalanine have been extensively used for different label-free protein studies. These investigations extract information on protein conformations and interactions from the emitted fluorescence's intensity, wavelength, and/or polarization. Like most fluorescent organic compounds, these amino acids also undergo transitions into dark meta-stable states, including triplet and photo-radical states. These transitions are notably sensitive to the surrounding environment, offering an additional set of parameters that reflect the protein's interactions, folding states, and immediate surroundings. Transient State (TRAST) monitoring has been developed to quantify fluorophore transition dynamics by recording the average fluorescence intensity in response to a modulated excitation. In this work, we performed TRAST experiments to investigate tyrosine autofluorescence and used it to detect conformational changes in calmodulin, a calcium-binding protein containing two tyrosine residues. A photophysical model for tyrosine was established, and it was revealed how tyrosine's dark state transitions changed with excitation intensity, solvent pH, and redox conditions. The TRAST experiments demonstrated that tyrosine's dark state transitions could serve as valuable information sources for label-free analyses of protein conformations and interactions. / De aromatiska aminosyrorna tryptofan, tyrosin och fenylalanin har använts i stor utsträckning för olika inmärkningsfria proteinstudier. Dessa undersökningar extraherar information om proteinkonformationer och interaktioner från den emitterade fluorescens intensiteten, dess våglängd och/eller polarisering. Liksom de flesta fluorescerande organiska föreningar genomgår dessa aminosyror också övergångar till mörka metastabila tillstånd, inklusive triplett- och fotoradikaltillstånd. Dessa övergångar är särskilt känsliga för den omgivande miljön, och erbjuder en extra uppsättning parametrar som återspeglar proteinets interaktioner, vikningstillstånd och omedelbara omgivningar. Transient State (TRAST) monitorering har utvecklats för att kvantifiera fluoroforövergångsdynamik genom att registrera den genomsnittliga fluorescensintensiteten som svar på en modulerad excitation. I detta arbete utförde vi TRAST-experiment för att undersöka tyrosinautofluorescens och använde den för att detektera konformationsförändringar i calmodulin, ett kalciumbindande protein som innehåller två tyrosiner. En fotofysikalisk modell för tyrosin etablerades, och hur tyrosins mörka tillståndsövergångar förändrades med excitationsintensitet, lösningsmedels pH och redoxförhållanden kunde faststållas. TRAST- experimenten visade att tyrosins mörka tillståndsövergångar kan fungera som värdefulla informationskällor för inmärkningsfria analyser av proteinkonformationer och interaktioner.

Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Software obfuscation is a protection technique for making code unintelligible to automated program comprehension and analysis tools. It works by performing semantic preserving transformations such that the difficulty of automatically extracting the computational logic out of code is increased. Obfuscating transforms in existing literature have been designed with the ambitious goal of being resilient against all possible reverse engineering attacks. Even though some of the constructions are based on intractable computational problems, we do not know, in practice, how to generate hard instances of obfuscated problems such that all forms of program analyses would fail. In this thesis, we address the problem of software protection by developing a weaker notion of obfuscation under which it is not required to guarantee an absolute blackbox security. Using this notion, we develop provably-correct obfuscating transforms using dependencies existing within program structures and indeterminacies in communication characteristics between programs in a distributed computing environment. We show how several well known static analysis tools can be used for reverse engineering obfuscating transforms that derive resilience from computationally hard problems. In particular, we restrict ourselves to one common and potent static analysis tool, the static slicer, and use it as our attack tool. We show the use of derived software engineering metrics to indicate the degree of success or failure of a slicer attack on a piece of obfuscated code. We address the issue of proving correctness of obfuscating transforms by adapting existing proof techniques for functional program refinement and communicating sequential processes. The results of this thesis could be used for future work in two ways: first, future researchers may extend our proposed techniques to design obfuscations using a wider range of dependencies that exist between dynamic program structures. Our restricted attack model using one static analysis tool can also be relaxed and obfuscations capable of withstanding a broader class of static and dynamic analysis attacks could be developed based on the same principles. Secondly, our obfuscatory strength evaluation techniques could guide anti-malware researchers in the development of tools to detect obfuscated strains of polymorphic viruses. / Whole document restricted, but available by request, use the feedback form to request access.

Software obfuscation is a protection technique for making code unintelligible to automated program comprehension and analysis tools. It works by performing semantic preserving transformations such that the difficulty of automatically extracting the computational logic out of code is increased. Obfuscating transforms in existing literature have been designed with the ambitious goal of being resilient against all possible reverse engineering attacks. Even though some of the constructions are based on intractable computational problems, we do not know, in practice, how to generate hard instances of obfuscated problems such that all forms of program analyses would fail. In this thesis, we address the problem of software protection by developing a weaker notion of obfuscation under which it is not required to guarantee an absolute blackbox security. Using this notion, we develop provably-correct obfuscating transforms using dependencies existing within program structures and indeterminacies in communication characteristics between programs in a distributed computing environment. We show how several well known static analysis tools can be used for reverse engineering obfuscating transforms that derive resilience from computationally hard problems. In particular, we restrict ourselves to one common and potent static analysis tool, the static slicer, and use it as our attack tool. We show the use of derived software engineering metrics to indicate the degree of success or failure of a slicer attack on a piece of obfuscated code. We address the issue of proving correctness of obfuscating transforms by adapting existing proof techniques for functional program refinement and communicating sequential processes. The results of this thesis could be used for future work in two ways: first, future researchers may extend our proposed techniques to design obfuscations using a wider range of dependencies that exist between dynamic program structures. Our restricted attack model using one static analysis tool can also be relaxed and obfuscations capable of withstanding a broader class of static and dynamic analysis attacks could be developed based on the same principles. Secondly, our obfuscatory strength evaluation techniques could guide anti-malware researchers in the development of tools to detect obfuscated strains of polymorphic viruses. / Whole document restricted, but available by request, use the feedback form to request access.

Software obfuscation is a protection technique for making code unintelligible to automated program comprehension and analysis tools. It works by performing semantic preserving transformations such that the difficulty of automatically extracting the computational logic out of code is increased. Obfuscating transforms in existing literature have been designed with the ambitious goal of being resilient against all possible reverse engineering attacks. Even though some of the constructions are based on intractable computational problems, we do not know, in practice, how to generate hard instances of obfuscated problems such that all forms of program analyses would fail. In this thesis, we address the problem of software protection by developing a weaker notion of obfuscation under which it is not required to guarantee an absolute blackbox security. Using this notion, we develop provably-correct obfuscating transforms using dependencies existing within program structures and indeterminacies in communication characteristics between programs in a distributed computing environment. We show how several well known static analysis tools can be used for reverse engineering obfuscating transforms that derive resilience from computationally hard problems. In particular, we restrict ourselves to one common and potent static analysis tool, the static slicer, and use it as our attack tool. We show the use of derived software engineering metrics to indicate the degree of success or failure of a slicer attack on a piece of obfuscated code. We address the issue of proving correctness of obfuscating transforms by adapting existing proof techniques for functional program refinement and communicating sequential processes. The results of this thesis could be used for future work in two ways: first, future researchers may extend our proposed techniques to design obfuscations using a wider range of dependencies that exist between dynamic program structures. Our restricted attack model using one static analysis tool can also be relaxed and obfuscations capable of withstanding a broader class of static and dynamic analysis attacks could be developed based on the same principles. Secondly, our obfuscatory strength evaluation techniques could guide anti-malware researchers in the development of tools to detect obfuscated strains of polymorphic viruses. / Whole document restricted, but available by request, use the feedback form to request access.

Software obfuscation is a protection technique for making code unintelligible to automated program comprehension and analysis tools. It works by performing semantic preserving transformations such that the difficulty of automatically extracting the computational logic out of code is increased. Obfuscating transforms in existing literature have been designed with the ambitious goal of being resilient against all possible reverse engineering attacks. Even though some of the constructions are based on intractable computational problems, we do not know, in practice, how to generate hard instances of obfuscated problems such that all forms of program analyses would fail. In this thesis, we address the problem of software protection by developing a weaker notion of obfuscation under which it is not required to guarantee an absolute blackbox security. Using this notion, we develop provably-correct obfuscating transforms using dependencies existing within program structures and indeterminacies in communication characteristics between programs in a distributed computing environment. We show how several well known static analysis tools can be used for reverse engineering obfuscating transforms that derive resilience from computationally hard problems. In particular, we restrict ourselves to one common and potent static analysis tool, the static slicer, and use it as our attack tool. We show the use of derived software engineering metrics to indicate the degree of success or failure of a slicer attack on a piece of obfuscated code. We address the issue of proving correctness of obfuscating transforms by adapting existing proof techniques for functional program refinement and communicating sequential processes. The results of this thesis could be used for future work in two ways: first, future researchers may extend our proposed techniques to design obfuscations using a wider range of dependencies that exist between dynamic program structures. Our restricted attack model using one static analysis tool can also be relaxed and obfuscations capable of withstanding a broader class of static and dynamic analysis attacks could be developed based on the same principles. Secondly, our obfuscatory strength evaluation techniques could guide anti-malware researchers in the development of tools to detect obfuscated strains of polymorphic viruses. / Whole document restricted, but available by request, use the feedback form to request access.

Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Ce travail de thèse traite du développement d'une méthode de télédiagnostique de l'état de santé des suspensions des trains à grande vitesse à partir de mesures de la réponse dynamique du train en circulation par des accéléromètres embarqués. Un train en circulation est un système dynamique dont l'excitation provient des irrégularités de la géométrie de la voie ferrée. Ses éléments de suspension jouent un rôle fondamental de sécurité et de confort. La réponse dynamique du train étant dépendante des caractéristiques mécaniques des éléments de suspension, il est possible d'obtenir en inverse des informations sur l'état de ces éléments à partir de mesures accélérométriques embarquées. Connaître l'état de santé réel des suspensions permettrait d'améliorer la maintenance des trains. D’un point de vue mathématique, la méthode de télédiagnostique proposée consiste à résoudre un problème statistique inverse. Elle s'appuie sur un modèle numérique de dynamique ferroviaire et prend en compte l'incertitude de modèle ainsi que les erreurs de mesures. Les paramètres mécaniques associés aux éléments de suspension sont identifiés par calibration Bayésienne à partir de mesures simultanées des entrées (les irrégularités de la géométrie de la voie) et sorties (la réponse dynamique du train) du système. La calibration Bayésienne classique implique le calcul de la fonction de vraisemblance à partir du modèle stochastique de réponse et des données expérimentales. Le modèle numérique étant numériquement coûteux d'une part, ses entrées et sorties étant fonctionnelles d'autre part, une méthode de calibration Bayésienne originale est proposée. Elle utilise un métamodèle par processus Gaussien de la fonction de vraisemblance. Cette thèse présente comment un métamodèle aléatoire peut être utilisé pour estimer la loi de probabilité des paramètres du modèle. La méthode proposée permet la prise en compte du nouveau type d'incertitude induit par l'utilisation d'un métamodèle. Cette prise en compte est nécessaire pour une estimation correcte de la précision de la calibration. La nouvelle méthode de calibration Bayésienne a été testée sur le cas applicatif ferroviaire, et a produit des résultats concluants. La validation a été faite par expériences numériques. Par ailleurs, l'évolution à long terme des paramètres mécaniques de suspensions a été étudiée à partir de mesures réelles de la réponse dynamique du train / The work presented here deals with the development of a health-state monitoring method for high-speed train suspensions using in-service measurements of the train dynamical response by embedded acceleration sensors. A rolling train is a dynamical system excited by the track-geometry irregularities. The suspension elements play a key role for the ride safety and comfort. The train dynamical response being dependent on the suspensions mechanical characteristics, information about the suspensions state can be inferred from acceleration measurements in the train by embedded sensors. This information about the actual suspensions state would allow for providing a more efficient train maintenance. Mathematically, the proposed monitoring solution consists in solving a statistical inverse problem. It is based on a train-dynamics computational model, and takes into account the model uncertainty and the measurement errors. A Bayesian calibration approach is adopted to identify the probability distribution of the mechanical parameters of the suspension elements from joint measurements of the system input (the track-geometry irregularities) and output (the train dynamical response).Classical Bayesian calibration implies the computation of the likelihood function using the stochastic model of the system output and experimental data. To cope with the fact that each run of the computational model is numerically expensive, and because of the functional nature of the system input and output, a novel Bayesian calibration method using a Gaussian-process surrogate model of the likelihood function is proposed. This thesis presents how such a random surrogate model can be used to estimate the probability distribution of the model parameters. The proposed method allows for taking into account the new type of uncertainty induced by the use of a surrogate model, which is necessary to correctly assess the calibration accuracy. The novel Bayesian calibration method has been tested on the railway application and has achieved conclusive results. Numerical experiments were used for validation. The long-term evolution of the suspension mechanical parameters has been studied using actual measurements of the train dynamical response

Driving a heavy-duty distribution truck in an urban environment is an intricate task that requires atremendous effort from the driver. Even a minor error such as briefly looking away from the road for asecond could have fatal consequences. The automotive industry is heavily investing into making roads saferand innovations such as the seat belt has been able to save millions of lives. With the introduction of new lawsand regulation there is an emerging adaptation of driver monitoring systems in vehicles which aims to savemore lives and improve the driving experience. This master thesis presents the exploration and design of a holistic concept for how driver state monitoringcould be implemented in a manual heavy-duty distribution truck in the year of 2035 at Scania. The mainpurpose is to add value to drivers by finding solutions that bridge driver needs and monitoring technology.Driver state monitoring is a field that introduces abilities for the vehicle to connect and understand the driverin order to provide more efficient assists for a safer and more pleasurable driving experience. An iterative usercentered design process was initialized with user interviews, observations and literature reviews that resulted inan in-depth view of the driver’s needs and a summary of the latest research and innovations. A creative phasegenerated multiple ideas that where evaluated using qualitative feedback from drivers and experts. A final setof features was selected and by using 3D modelling and video editing, an eight minute long animated film wascreated that showcases the possibilities of driver state monitoring in a heavy-duty distribution truck in 2035. The final concept is a unified vision of 22 features in a heavy-duty electric distribution truck, utilizing hybriddriver state monitoring techniques in order to provide a safer and more pleasurable driving experience. It isinspired and created around the latest research and innovations in the automotive industry and aims to inspireScania in their future work to implement and innovate new solutions based on driver state monitoring. This isa thesis work by two Master of Science student within Industrial Design Engineering from Luleå University ofTechnology at Scania in the spring of 2020. / Att köra en distributionslastbil i stadsmiljö är en komplicerad uppgift som kräver en stor insats från föraren. Till och med ett litet misstag som att titta bort från vägen under en kort period kan få katastrofala följder.Fordonsindustrin investerar mycket inom säkerhet och innovationer som säkerhetsbältet har väldigtframgångsrikt bidragit till att rädda miljontals liv på vägarna. I och med nya lagar implementeras system ifordon som bevakar föraren, med målet att rädda fler liv och skapa en bättre körupplevelse. Det här examensarbetet syftar till att designa ett helhetskoncept som visar hur bevakning av föraren kanimplementeras i en manuell distributionslastbil på Scania år 2035. Det huvudsakliga målet är att skapa värdeför förarna genom att hitta lösningar som bryggar gapet mellan förarnas behov och tekniken som möjliggörbevakning av föraren. Genom att introducera denna typ av förarbevakningsteknik ges möjligheten förfordonet att bättre förstå sig på föraren, vilket i sin tur möjliggör ökad säkerhet och bättre körupplevelser. Eniterativ och användarcentrerad designprocess användes och inleddes med intervjuer, observationer och enlitteraturstudie, vilket resulterade i en djup förståelse för förarnas behov samt en summering av den senasteforskningen och innovationerna. En kreativ fas genererade en mängd idéer som sedan utvärderas med hjälp avkvalitativ feedback från både förare och experter. Ett slutligt urval av funktioner togs fram och visualiseradesgenom att skapa en animerad film. Den färdiga åtta minuter långa filmen visar på möjligheterna med tekniksom bevakar föraren i en distributionslastbil år 2035. Det slutliga konceptet förenar 22 funktioner i en elektrisk distributionslastbil. Ett flertal tekniker utnyttjas föratt bevaka föraren och tillsammans bidrar de till en säkrare och mer njutbar körupplevelse. Konceptet ärinspirerat av- och bygger på den senaste forskningen och de senaste innovationerna inom fordonsindustrin,med målet att inspirera Scania i framtida projekt där förarbevakningssystem utvecklas och implementeras.Detta arbete är ett examensarbete gjort av två civilingenjörsstudenter inom tekniks design från Luleå tekniskauniversitet på Scania under våren 2020.