<p>Information fusion is to combine information from a variety of sources, or sensors. When the sources are uncertain or contradicting conflict can arise. To deal with such uncertainty and conflict a trust model can be used. The most common ones in information fusion currently is bayesian theory and Dempster-Shafer theory. Bayesian theory does not explicitly handle ignorance, and thus predetermined values has to be hard coded into the system. This is solved in Dempster-Shafer theory by the introduction of ignorance. Even though Dempster-Shafer theory is widely used in information fusion when there is a need for ignorance to be modelled, there has been serious critique presented towards the theory. Thus this work aims at examining another trust models utility in information fusion namely subjective logic. The examination is executed by studying subjective logic using two scenarios from the literature. The results from the scenarios points to subjective logic being a reasonable approach for modelling trust in information fusion.</p>

Anonymity metrics have been proposed to evaluate anonymity preserving systems by estimating the amount of information displayed by these systems due to vulnerabilities. A general metric for anonymity that assess the latter systems according to the mass and quality of information learned by an attacker or a collaboration of attackers is proposed here. The proposed metric is based on subjective logic, a generalization of evidence and probability theory. As a consequence, we proved based on defined scenarios that our metric provide a better interpretation of uncertainty in the measure and it is extended to combine various sources of information using subjective logic operators. Also, we demonstrate that two factors: trust between collaborating attackers and time can influence significantly the metric result when taking them into consideration.

Information fusion is to combine information from a variety of sources, or sensors. When the sources are uncertain or contradicting conflict can arise. To deal with such uncertainty and conflict a trust model can be used. The most common ones in information fusion currently is bayesian theory and Dempster-Shafer theory. Bayesian theory does not explicitly handle ignorance, and thus predetermined values has to be hard coded into the system. This is solved in Dempster-Shafer theory by the introduction of ignorance. Even though Dempster-Shafer theory is widely used in information fusion when there is a need for ignorance to be modelled, there has been serious critique presented towards the theory. Thus this work aims at examining another trust models utility in information fusion namely subjective logic. The examination is executed by studying subjective logic using two scenarios from the literature. The results from the scenarios points to subjective logic being a reasonable approach for modelling trust in information fusion.

Indiana University-Purdue University Indianapolis (IUPUI) / With the large number of Android apps available in app stores such as Google Play, it has become increasingly challenging to choose among the apps. The users generally select the apps based on the ratings and reviews of other users, or the recommendations from the app store. But it is very important to take the security into consideration while choosing an app with the increasing security and privacy concerns with mobile apps. This thesis proposes different ranking schemes for Android apps based on security apps evaluated from the static code analysis tools that are available. It proposes the ranking schemes based on the categories of evidences reported by the tools, based on the frequency of each category, and based on the severity of each evidence. The evidences are gathered, and rankings are generated based on the theory of Subjective Logic. In addition to these ranking schemes, the tools are themselves evaluated against the Ghera benchmark. Finally, this work proposes two additional schemes to combine the evidences from difference tools to provide a combined ranking.

L'efficacité caractérise tous les aspects du droit des contrats. De plus, elle semble avoir été toujours immanente à l'instrument contractuel, pouvant s'appréhender comme la véritable logique traversant toute l'évolution juridique du contrat pendant les siècles. Et notamment, d'après l'analyse des règles du Code Napoléon en matière de contrats, de la relative jurisprudence et également de la Réforme du droit des contrats du 2016, nous avons découvert deux formes de logique sous-entendues à l'efficacité, l'une subjective et l'autre objective. La théorie classique du contrat est dominée par une logique subjective de l'efficacité résultant de la volonté des parties. En effet, la magnificence de cette volonté règne presque sans partage, consacrée, d'ailleurs, par les principes fondamentaux d'intangibilité et de stabilité du contrat et glorifiée par le rôle très restreint du juge, qui est un simple et scrupuleux exécuteur de la volonté des parties. En revanche, l'affaiblissement du rôle de la volonté subjective a déterminé une recrudescence de l'efficacité objective, ouvrant la voie à une analyse plus globale du contrat qui intègre la subjectivité de la volonté dans l'objectivité de l'environnement socio-économique. Cette lecture nouvelle révèle une compréhension plus complexe et objective du contrat qui nous amène à l'étudier et à l'intégrer dans le milieu où il opère. Le contrat n'est donc plus tourné uniquement vers la subjectivité engendrée par les volontés puisqu'il considère également l'objectivité issue des enjeux extérieurs. / The effectiveness characterizes any and alI aspects of the law of contracts. Moreover, it seems to have been always so immanent in the contractual instrument that can be grasped as the real logic through all the legal evolution of the contract over many centuries. ln particular, from the analysis of the rules of the Code Napoléon on contracts, the related case law as welI as the Reforrn of the law of contracts enacted in 2016, we inferred two forms of logic underlying the contractual effectiveness, one subjective and the other objective. The classical theory of contract is dominated by a subjective logic of effectiveness resulting from the will of the parties. Indeed, the magnificence of this will reigns almost unfettered and, moreover, is sealed on the other band by the fundamental principles of the contract sanctity and stability and glorified by the very limited role of the judge, as a simple and scrupulous executor of the parties' will. Conversely, the weakening of the role of the subjective will has determined an increase of the objective effectiveness, paying the way for a more comprehensive analysis of the contract that incorporates the subjectivity of the parties' will into the objectivity of the socio-economic environrnent. This new line of interprelation reveals a more complex and objective understanding of the contract leading us to study and integrate it in the context where it operates. The con tract is then no longer turned only to subjectivity generated by the parties' will, since it also considers the objectivity deriving from environrnental eternal challenges.

Presented here is a novel tool to extract and track believable vortex core lines from unsteady Computational Fluid Dynamics data sets using multiple feature extraction algorithms. Existing work explored the possibility of extracting features concurrent with a running simulation using intelligent software agents, combining multiple algorithms' capabilities using subjective logic. This work modifies the steady-state approach to work with unsteady fluid dynamics and is designed to work within the Concurrent Agent-enabled Feature Extraction concept. Each agent's belief tuple is quantified using a predefined set of information. The information and functions necessary to set each component in each agent's belief tuple is given along with an explanation of the methods for setting the components. This method is applied to the analyses of flow in a lid-driven cavity and flow around a cylinder, which highlight strengths and weaknesses of the chosen algorithms and the potential for subjective logic to aid in understanding the resulting features. Feature tracking is successfully applied and is observed to have a significant impact on the opinion of the vortex core lines. In the lid-driven cavity data set, unsteady feature extraction modifications are shown to impact feature extraction results with moving vortex core lines. The Sujudi-Haimes algorithm is shown to be more believable when extracting the main vortex core lines of the cavity simulation while the Roth-Peikert algorithm succeeding in extracting the weaker vortex cores in the same simulation. Mesh type and time step is shown to have a significant effect on the method. In the curved wake of the cylinder data set, the Roth-Peikert algorithm more reliably detects vortex core lines which exist for a significant amount of time. the method was finally applied to a massive wind turbine simulation, where the importance of performing feature extraction in parallel is shown. The use of multiple extraction algorithms with subjective logic and feature tracking helps determine the expected probability that an extracted vortex core is believable. This approach may be applied to massive data sets which will greatly reduce analysis time and data size and will aid in a greater understanding of complex fluid flows.

The advancement of computational fluid dynamics to simulate highly complex fluid flow situations have allowed for simulations that require weeks of computation using expensive high performance clusters. These simulations often generate terabytes of data and hinder the design process by greatly increasing the post-processing time. This research discusses a method to extract shock waves and separation and attachment lines as the simulation is calculating and as a post-processing step. Software agents governed by subjective logic were used to make decisions about extracted features in converging and converged data sets. Two different extraction algorithms were incorporated for shock waves and separation and attachment lines and were tested on four different simulations. A supersonic ramp simulation showed two shock waves at 10% of convergence, but did not reach their final spatial locations until 85% convergence. A similar separation and attachment line analysis was performed on a cylinder in a cross flow simulation. The cylinder separation and attachment lines were within 5% of their final spatial locations at 10% convergence, and at 85% convergence, much of the cylinder and trailing separation and attachment lines showed probability expectation values of approximately 0.90 - 1.00. An Onera M6 wing simulation was used to investigate the belief tuples of the two separate shock waves at full convergence. Probability expectation values of approximately 0.90 - 1.00 were displayed within the two shock waves because they are strong shock waves and because they met the physical requirements of shock waves. A separation and attachment line belief tuple analysis was also performed on a delta wing simulation. The forward portions of these lines showed probability expectation values of approximately 0.90 - 1.00, but dropped to approximately 0.60 - 0.75 as a consequence of their respective vortices breaking down and losing their strength. Similar to shock waves, high probability expectation values meant the separation and attachment lines were strong and physically met separation and attachment line physics. The subjective logic process presented in this research was able to determine which shock waves and separation and attachment lines were most probable, making it easier to view and further investigate these important features.

<p>With the large number of Android apps available in app stores such as Google Play, it has become increasingly challenging to choose among the apps. The users generally select the apps based on the ratings and reviews of other users, or the recommendations from the app store. But it is very important to take the security into consideration while choosing an app with the increasing security and privacy concerns with mobile apps. This thesis proposes different ranking schemes for Android apps based on security apps evaluated from the static code analysis tools that are available. It proposes the ranking schemes based on the categories of evidences reported by the tools, based on the frequency of each category, and based on the severity of each evidence. The evidences are gathered, and rankings are generated based on the theory of Subjective Logic. In addition to these ranking schemes, the tools are themselves evaluated against the Ghera benchmark. Finally, this work proposes two additional schemes to combine the evidences from difference tools to provide a combined ranking.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / The number of smart mobile devices has grown at a significant rate in recent years. This growth has resulted in an exponential number of publicly available mobile Apps. To help the selection of suitable Apps, from various offered choices, the App distribution platforms generally rank/recommend Apps based on average star ratings, the number of installs, and associated reviews ― all the external factors of an App. However, these ranking schemes typically tend to ignore critical internal factors (e.g., bugs, security vulnerabilities, and data leaks) of the Apps. The AppStores need to incorporate a holistic methodology that includes internal and external factors to assign a level of trust to Apps. The inclusion of the internal factors will describe associated potential security risks. This issue is even more crucial with newly available Apps, for which either user reviews are sparse, or the number of installs is still insignificant. In such a scenario, users may fail to estimate the potential risks associated with installing Apps that exist in an AppStore. This dissertation proposes a security-related and evidence-based ranking framework, called SERS (Security-related and Evidence-based Ranking Scheme) to compare similar Apps. The trust associated with an App is calculated using both internal and external factors (i.e., security flaws and user reviews) following an evidence-based approach and applying subjective logic principles. The SERS is formalized and further enhanced in the second part of this dissertation, resulting in its enhanced version, called as E-SERS (Enhanced SERS). These enhancements include an ability to integrate any number of sources that can generate evidence for an App and consider the temporal aspect and reputation of evidence sources. Both SERS and E-SERS are evaluated using publicly accessible Apps from the Google PlayStore and the rankings generated by them are compared with prevalent ranking techniques such as the average star ratings and the Google PlayStore Rankings. The experimental results indicate that E-SERS provides a comprehensive and holistic view of an App when compared with prevalent alternatives. E-SERS is also successful in identifying malicious Apps where other ranking schemes failed to address such vulnerabilities. In the third part of this dissertation, the E-SERS framework is used to propose a trust-aware composition model at two different granularities. This model uses the trust score computed by E-SERS, along with the probability of an App belonging to the malicious category, as the desired attributes for selecting a composition as the two granularities. Finally, the trust-aware composition model is evaluated with the average star rating parameter and the trust score. A holistic approach, as proposed by E-SERS, to computer a trust score will benefit all kinds of Apps including newly published Apps that follow proper security measures but initially struggle in the AppStore rankings due to a lack of a large number of reviews and ratings. Hence, E-SERS will be helpful both to the developers and users. In addition, the composition model that uses such a holistic trust score will enable system integrators to create trust-aware distributed systems for their specific needs.

Computational fluid dynamics simulations are advancing to correctly simulate highly complex fluid flow problems that can require weeks of computation on expensive high performance clusters. These simulations can generate terabytes of data and pose a severe challenge to a researcher analyzing the data. Presented in this document is a general method to extract computational fluid dynamics flow features concurrent with a simulation and as a post-processing step to drastically reduce researcher post-processing time. This general method uses software agents governed by subjective logic to make decisions about extracted features in converging and converged data sets. The software agents are designed to work inside the Concurrent Agent-enabled Feature Extraction concept and operate efficiently on massively parallel high performance computing clusters. Also presented is a specific application of the general feature extraction method to vortex core lines. Each agent's belief tuple is quantified using a pre-defined set of information. The information and functions necessary to set each component in each agent's belief tuple is given along with an explanation of the methods for setting the components. A simulation of a blunt fin is run showing convergence of the horseshoe vortex core to its final spatial location at 60% of the converged solution. Agents correctly select between two vortex core extraction algorithms and correctly identify the expected probabilities of vortex cores as the solution converges. A simulation of a delta wing is run showing coherently extracted primary vortex cores as early as 16% of the converged solution. Agents select primary vortex cores extracted by the Sujudi-Haimes algorithm as the most probable primary cores. These simulations show concurrent feature extraction is possible and that intelligent agents following the general feature extraction method are able to make appropriate decisions about converging and converged features based on pre-defined information.