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The thesis deals with the possibility of prediction of the exchange rate on forex. The combination of Elliott wave principle and Fibonacci numbers examines to what extent and in what time periods it is possible to predict exchange rate. The thesis use fundamental analysis and MACD oscillator to confirm the accuracy of this prediction.

abstract: The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation. This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

<p>Over the years, the Internet has become a platform where individuals share their thoughts and personal information. In some cases, these content contain some damaging or sensitive information, which a malicious data collector can leverage to exploit the individual. Nevertheless, what people consider to be sensitive is a relative matter: it not only varies from one person to another but also changes through time. Therefore, it is hard to identify what content is considered sensitive or damaging, from the viewpoint of a malicious entity that does not target specific individuals, rather scavenges the data-sharing platforms to identify sensitive information as a whole. However, the actions that users take to change their privacy preferences or hide their information assists these malicious entities in discovering the sensitive content. </p><p><br></p><p>This thesis offers Crowd Blending techniques to create privacy-preserving systems while maintaining platform utility. In particular, we focus on two privacy tasks for two different data-sharing platforms— i) concealing content deletion on social media platforms and ii) concealing censored information in cryptocurrency blockchains. For the concealment of the content deletion problem, first, we survey the users of social platforms to understand their deletion privacy expectations. Second, based on the users’ needs, we propose two new privacy-preserving deletion mechanisms for the next generation of social platforms. Finally, we compare the effectiveness and usefulness of the proposed mechanisms with the current deployed ones through a user study survey. For the second problem of concealing censored information in cryptocurrencies, we present a provably secure stenography scheme using cryptocurrencies. We show the possibility of hiding censored information among transactions of cryptocurrencies.</p>

Under the new Vision 2030 national transformation plan, the kingdom of Saudi Arabia seeks to increase number of annual pilgrims from eight million to thirty million. If oil has certain limits, then pilgrimage is framed as lasting “forever.” But this exuberant claim of “forever” belies a more subtle transformation unfolding at the level of knowledge, technology, and hospitality as Mecca and its crowds are made and re-made into a resource for a national economy. This dissertation examines the Saudi state’s efforts to manage, and ultimately intensify and optimize Mecca’s pilgrimage through new sciences and technologies of crowd management, logistics, and secular hospitality. I demonstrate how these new forms of knowledge production operate in tension with older and decidedly more Islamic ways of knowing, managing, and belonging in the holy city. Instead of approaching religious knowledge and secular knowledge as discrete spheres, my research explores their entanglements and aporias across a range of techno-political practices: navigation, hospitality, urban planning, systems thinking, crowd management, and optimization. Ultimately, I explore how in this moment of ritual intensity, the cosmopolitan logics of the holy city come to be blunted.

In this study, the relation between peer crowd identification and indoor tanning behavioral tendencies was examined. Participants were 174 undergraduate students at a large university in the USA. Results indicated peer crowd identification was significantly associated with indoor artificial UV tanning behavioral tendencies (attitudes, normative beliefs, past year use and intentions) independent of gender and skin type. Participants who identified with the popular peer crowd were at the greatest risk for indoor tanning UV exposure while identification with the brain crowd was protective against such behavior. The findings are discussed in terms of implications for future skin cancer intervention efforts.

We develop a computational framework for risk mitigation in high population density events. With increased global population, the frequency of high population density events is naturally increased. Therefore, risk-free crowd management plans are critical for efficient mobility, convenient daily life, resource management and most importantly mitigation of any inadvertent incidents and accidents such as stampedes. The status-quo for crowd management plans is the use of human experience/expert advice. However, most often such dependency on human experience is insufficient, flawed and results in inconvenience and tragic events. Motivated by these issues, we propose an agent-based mathematical model describing realistic human motion and simulating large dense crowds in a wide variety of events as a potential simulation testbed to trial crowd management plans. The developed model incorporates stylized mindset characteristics as an internal drive for physical behavior such as walking, running, and pushing. Furthermore, the model is combined with a visualisation of crowd movement. We develop analytic tools to quantify crowd dynamic features. The analytic tools will enable verification and validation to empirical evidence and surveillance video feed in both local and holistic representations of the crowd. This work addresses research problems in computational modeling of crowd dynamics, specifically: understanding and modeling the impact of a collective mindset on crowd dynamics versus mixtures of heterogeneous mindsets, the effect of social contagion of behaviors and decisions within the crowd, the competitive and aggressive pushing behaviors, and torso and steering dynamics.

Cette thèse porte sur la similitude entre un fluide et une foule et sur l'adaptation de l’algorithme de Particle Video pour le suivi et l'analyse de foule, ce qui aboutit à la conception d'un système complet pour l'analyse de la foule. Cette thèse en étudie trois aspects : la détection de la foule, l'estimation de sa densité et le tracking des flux afin d'obtenir des caractéristiques de comportement.L’algorithme de détection de la foule est une méthode totalement non supervisée pour la détection et la localisation des foules denses dans des images non-contextualisées. Après avoir calculé des vecteurs de features multi-échelles, une classification binaire est effectuée afin d'identifier la foule et l'arrière-plan.L'algorithme d'estimation de densité s'attaque au problème de l'apprentissage de modèles de régression dans le cas de larges foules denses. L'apprentissage est alors impossible sur données réelles car la vérité terrain est indisponible. Notre méthode repose donc sur l'utilisation de données synthétiques pour la phase d'apprentissage et prouve que le modèle de régression obtenu est valable sur données réelles.Pour notre adaptation de l’algorithme de Particle Video nous considérons le nuage de particules comme statistiquement représentatif de la foule. De ce fait, chaque particule possède des propriétés physiques qui nous permettent d'évaluer la validité de son comportement en fonction de celui attendu d'un piéton et d’optimiser son mouvement guidé par le flot optique. Trois applications en découlent : détection des zones d’entrée-sortie de la foule, détection des occlusions dynamiques et mise en relation des zones d'entrée et de sortie selon les flux de piétons. / This thesis focuses on the similarity between a fluid and a crowd and on the adaptation of the particle video algorithm for crowd tracking and analysis. This interrogation ended up with the design of a complete system for crowd analysis out of which, this thesis has addressed three main problems: the detection of the crowd, the estimation of its density and the tracking of the flow in order to derive some behavior features.The contribution to crowd detection introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. After retrieving multi-scale texture-related feature vectors from the image, a binary classification is conducted to identify the crowd and the background.The density estimation algorithm is tackling the problem of learning regression models when it comes to large dense crowds. In such cases, the learning is impossible on real data as the ground truth is not available. Our method relies on the use of synthetic data for the learning phase and proves that the regression model obtained is valid for a use on real data.Our adaptation of the particle video algorithm leads us to consider the cloud of particles as statistically representative of the crowd. Therefore, each particle has physical properties that enable us to assess the validity of its behavior according to the one expected from a pedestrian, and to optimize its motion guided by the optical flow. This leads us to three applications: the detection of the entry and exit areas of the crowd in the image, the detection of dynamic occlusions and the possibility to link entry areas with exit ones, according to the flow of the pedestrians.

Désormais, l'analyse des scènes denses s'impose incontestablement comme une tâche importante pour contrôler et gérer les foules. Notre recherche a pour objectifs d'apporter des solutions à l'estimation de la densité de la foule et de prouver l'utilité de cette estimation comme préalable pour d'autres applications. Concernant le premier objectif, afin de cerner les difficultés de la détection de personnes dans une foule, on se focalise sur l'estimation de la densité basée sur un niveau d'analyse bas. Dans un premier temps, on démontre que nos approches sont plus adéquates que les méthodes de l’état de l’art que ce soit pour compter les individus ou pour estimer le niveau de la foule. Dans un second temps, nous proposons une approche innovante dans laquelle une estimation locale au niveau des pixels remplace l'estimation au niveau global de la foule ou le nombre des personnes. Elle est basée sur l’utilisation des suivis de caractéristiques visuelles dans une fonction de densité. Notre recherche a également pour objectif d'utiliser la densité comme information supplémentaire pour affiner d'autres tâches. D'abord, nous avons utilisé la mesure de la densité qui comporte une description pertinente à la répartition spatiale des individus afin d'améliorer leur détection et leur suivi dans les foules. Ensuite, en prenant en compte la notion de la protection de la vie privée, nous ajustons le niveau de floutage en fonction de la densité de la foule. Enfin, nous nous appuyons sur l’estimation locale de la densité ainsi que sur le mouvement en tant qu'attributs pour des applications de haut niveau telles que la détection des évolutions et la reconnaissance des événements. / Crowd analysis has recently emerged as an increasingly important problem for crowd monitoring and management in the visual surveillance community. In this thesis, our objectives are to address the problems of crowd density estimation and to investigate the usefulness of such estimation as additional information to other applications. Towards the first goal, we focus on the problems related to the estimation of the crowd density using low level features in order to avert typical problems in detection of high density crowd. We demonstrate in this dissertation, that the proposed approaches perform better than the baseline methods, either for counting people, or alternatively for estimating the crowd level. Afterwards, we propose a novel approach, in which local information at the pixel level substitutes the overall crowd level or person count. It is based on modeling time-varying dynamics of the crowd density using sparse feature tracks as observations of a probabilistic density function. The second goal is to use crowd density as additional information to complement other tasks related to video surveillance in crowds. First, we use the proposed crowd density measure which conveys rich information about the local distributions of persons to improve human detection and tracking in videos of high density crowds. Second, we investigate the concept of crowd context-aware privacy protection by adjusting the obfuscation level according to the crowd density. Finally, we employ additional information about the local density together with regular motion patterns as crowd attributes for high level applications such as crowd change detection and event recognition.

In the simulation of human crowd behavior including evacuation planning, transportation management, and safety engineering in architecture design, the development of pedestrian model for higher behavior fidelity is an important task. To construct plausible facsimiles of real crowd movements, simulations should exhibit human behaviors for navigation, pedestrian decision-making, and social behaviors such as grouping and crowding. The research field is quite mature in some sense, with a large number of approaches that have been proposed to path finding, collision avoidance, and visually pleasing steering behaviors of virtual humans. However, there is still a clear disparity between the variety of approaches and the quality of crowd behaviors in simulations. Many social science field studies inform us that crowds are typically composed of multiple social groups (James, 1953; Coleman and James, 1961; Aveni, 1977). These observations indicate that one component of the complexity of crowd dynamics emerges from the presence of various patterns of social interactions within small groups that make up the crowd. Hence, realism in a crowd simulation may be enhanced when virtual characters are organized in multiple social groups, and exhibit human-like coordination behaviors. Motivated by the need for modeling groups in a crowd, we present a multi-agent model for large crowd simulations that incorporates socially plausible group behaviors. A computational model for multi-agent coordination and interaction informed by well- established Common Ground theory (Clark, 1996; Clark and Brennan, 1991) is proposed. In our approach, the task of navigation in a group is viewed as performing a joint activity which requires maintaining a state of common ground among group members regarding walking strategies and route choices. That is, group members communicate with, and adapt their behaviors to each other in order to maintain group cohesiveness while walking. In the course of interaction, an agent may present gestures or other behavioral cues according to its communicative purpose. It also considers the spatiotemporal conditions of the agent-group's environment in which the agent interacts when selecting a kind of motions. With the incorporation of our agent model, we provide a unified framework for crowd simulation and animation which accommodates high-level socially-aware behavioral realism of animated characters. The communicative purpose and motion selection of agents are consistently carried through from simulation to animation, and a resulted sequence of animated character behaviors forms not merely a chain of reactive or random gestures but a socially meaningful interactions. We conducted several experiments in order to investigate the impact of our social group interaction model in crowd simulation and animation. By showing that group communicative behaviors have a substantial influence on the overall distribution of a crowd, we demonstrate the importance of incorporating a model of social group interaction into multi-agent simulations of large crowd behaviors. With a series of perceptual user studies, we show that our model produces more believable behaviors of animated characters from the viewpoint of human observers. / Ph. D.