Au cours de la dernière décennie, nous avons assisté à une croissance exponentielle de documents numériques et de ressources multimédias, y compris une présence massive de ressources vidéo. Les vidéos sont devenu de plus en plus populaire grâce au contenue riche à l'audio riche qu'elles véhiculent (contenu audiovisuelle et textuelle). Les dernières avancées technologiques ont rendu disponibles aux utilisateurs cette grande quantité de ressources multimédias et cela dans une variété de domaines, y compris les domaines académiques et scientifiques. Toutefois, sans techniques adéquates se basant sur le contenu des multimédia, cette masse de donnée précieuse est difficilement accessible et demeure en vigueur inutilisable. Cette thèse explore les approches sémantiques pour la gestion ainsi que la navigation et la visualisation des ressources multimédias générées par les conférences scientifiques. Un écart, que l'on appelle sémantique, existe entre la représentation des connaissances explicites requis par les utilisateurs qui cherchent des ressources multimédias et la connaissance implicite véhiculée le long du cycle de vie d'une conférence. Le but de ce travail est de fournir aux utilisateurs une plateforme qui améliore la recherche de l'information multimédia des conférences en diminuant cette distance sémantique. L'objectif de cette thèse est de fournir une nouvelle approche pour le contenu multimédia basé sur la recherche d'information dans le domaine des conférences scientifiques.

In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, a limited number of studies have examined the impact of collaborative information search processes on novices’ query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users’ patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices. This thesis investigates the influence of collaboration between experts and novices as well as the use of a collaborative web search tool on novices’ query behavior. We used SearchTeam as our collaborative search tool. This empirical study involves four collaborative team conditions: SearchTeam and expert-novice team, SearchTeam and novice-novice team, traditional and expert-novice team, and traditional and novice-novice team. We analyzed participants’ query behavior in two dimensions: quantitatively (e.g. the query success rate), and qualitatively (e.g. the query reformulation patterns). The findings of this study reveal that the successful query rate is higher in expert-novice collaborative teams, who used the collaborative search tools. Participants in expert-novice collaborative teams who used the collaborative search tools, required less time to finalize all tasks compared to expert-novice collaborative teams, who used the traditional search tools. Self-issued queries and chat logs were major sources of terms that novice participants in expert-novice collaborative teams who used the collaborative search tools used. Novices as part of expert-novice pairs who used the collaborative search tools, employed New and Specialization more often as query reformulation patterns. The results of this study contribute to the literature by providing detailed investigation regarding the influence of utilizing collaborative search tool (SearchTeam) in the context of software troubleshooting and development. This study highlights the possible collaborative information seeking (CIS) activities that may occur among software developers’ interns and their mentors. Furthermore, our study reveals that there are specific features, such as awareness and built-in instant messaging (IM), offered by SearchTeam that can promote the CIS activities among participants and help increase novices’ query success rates. Finally, we believe the use of CIS tools, designed to support collaborative search actions in big software development companies, has the potential to improve the overall novices’ query behavior and search strategies.

Spatial-temporal distribution of soil moisture in the vadose zone is an important aspect of the hydrological cycle that plays a fundamental role in water resources management, including modeling of water flow and mass transport. The vadose zone is a critical transfer and storage compartment, which controls the partitioning of energy and mass linked to surface runoff, evapotranspiration and infiltration. This dissertation focuses on integrating hydraulic characterization methods with remote sensing technologies to estimate the soil moisture distribution by modeling the spatial coverage of soil moisture in the horizontal and vertical dimensions with high temporal resolution. The methodology consists of using satellite images with an ultrafine 3-m resolution to estimate soil surface moisture content that is used as a top boundary condition in the hydrologic model, SWAP, to simulate transport of water in the vadose zone. To demonstrate the methodology, herein developed, a number of model simulations were performed to forecast a range of possible moisture distributions in the Everglades National Park (ENP) vadose zone. Intensive field and laboratory experiments were necessary to prepare an area of interest (AOI) and characterize the soils, and a framework was developed on ArcGIS platform for organizing and processing of data applying a simple sequential data approach, in conjunction with SWAP. An error difference of 3.6% was achieved when comparing radar backscatter coefficient (σ0) to surface Volumetric Water Content (VWC); this result was superior to the 6.1% obtained by Piles during a 2009 NASA SPAM campaign. A registration error (RMSE) of 4% was obtained between model and observations. These results confirmed the potential use of SWAP to simulate transport of water in the vadose zone of the ENP. Future work in the ENP must incorporate the use of preferential flow given the great impact of macropore on water and solute transport through the vadose zone. Among other recommendations, there is a need to develop procedures for measuring the ENP peat shrinkage characteristics due to changes in moisture content in support of the enhanced modeling of soil moisture distribution.

Design and analysis of conceptually different cooling systems for the human heart preservation are numerically investigated. A heart cooling container with required connections was designed for a normal size human heart. A three-dimensional, high resolution human heart geometric model obtained from CT-angio data was used for simulations. Nine different cooling designs are introduced in this research. The first cooling design (Case 1) used a cooling gelatin only outside of the heart. In the second cooling design (Case 2), the internal parts of the heart were cooled via pumping a cooling liquid inside both the heart’s pulmonary and systemic circulation systems. An unsteady conjugate heat transfer analysis is performed to simulate the temperature field variations within the heart during the cooling process. Case 3 simulated the currently used cooling method in which the coolant is stagnant. Case 4 was a combination of Case 1 and Case 2. A linear thermoelasticity analysis was performed to assess the stresses applied on the heart during the cooling process. In Cases 5 through 9, the coolant solution was used for both internal and external cooling. For external circulation in Case 5 and Case 6, two inlets and two outlets were designed on the walls of the cooling container. Case 5 used laminar flows for coolant circulations inside and outside of the heart. Effects of turbulent flow on cooling of the heart were studied in Case 6. In Case 7, an additional inlet was designed on the cooling container wall to create a jet impinging the hot region of the heart’s wall. Unsteady periodic inlet velocities were applied in Case 8 and Case 9. The average temperature of the heart in Case 5 was +5.0oC after 1500 s of cooling. Multi-objective constrained optimization was performed for Case 5. Inlet velocities for two internal and one external coolant circulations were the three design variables for optimization. Minimizing the average temperature of the heart, wall shear stress and total volumetric flow rates were the three objectives. The only constraint was to keep von Mises stress below the ultimate tensile stress of the heart’s tissue.

Scientific exploration demands heavy usage of computational resources for large-scale and deep analysis in many different fields. The complexity or the sheer scale of the computational studies can sometimes be encapsulated in the form of a workflow that is made up of numerous dependent components. Due to its decomposable and parallelizable nature, different components of a scientific workflow may be mapped over a distributed resource infrastructure to reduce time to results. However, the resource infrastructure may be heterogeneous, dynamic, and under diverse administrative control. Workflow management tools are utilized to help manage and deal with various aspects in the lifecycle of such complex applications. One particular and fundamental aspect that has to be dealt with as smooth and efficient as possible is the run-time coordination of workflow activities (i.e. workflow orchestration). Our efforts in this study are focused on improving the workflow orchestration process in such dynamic and distributed resource environments. We tackle three main aspects of this process and provide contributions in each of them. Our first contribution involves increasing the scalability and site autonomy in situations where the mapped components of a workflow span across several heterogeneous administrative domains. We devise and implement a generic decentralization framework for orchestration of workflows under such conditions. Our second contribution is involved with addressing the issues that arise due to the dynamic nature of such environments. We provide generic adaptation mechanisms that are highly transparent and also substantially less intrusive with respect to the rest of the workflow in execution. Our third contribution is to improve the efficiency of orchestration of large-scale parameter-sweep workflows. By exploiting their specific characteristics, we provide generic optimization patterns that are applicable to most instances of such workflows. We also discuss implementation issues and details that arise as we provide our contributions in each situation.

Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying travelers’ behavior by providing rewards to enable shifts to more energy-efficient modes, (e.g., from auto to either bus or bicycles). Optimization conditions are formulated for the problem to understand solution properties, and numerical tests are carried out to study the effects of system parameters (e.g., token budget and coefficient of tokens) on the optimal solutions (i.e., energy savings). The multinomial logit model is used to formulate the full problem, comprised of an objective function and constraint of a token budget ranging from $5,000-$10,000. Comparably, the full problem is computationally reduced by various parameterization strategies, in that the number of tokens assigned to all travelers’ is parameterized and proportional to the expected energy savings. An optimization solution algorithm is applied with a global and local solver to solve a lagrangian sub-problem and a duo of heuristic solution algorithms of the original problem. These were determined necessary to attain an optimal and feasible solution. Input data necessary for this analysis is obtained for the Town of Amherst, MA from the Pioneer Valley Planning Commission (PVPC). The results demonstrated strong evidence to conclude a positive correlation between the system’s energy savings and the aforementioned system parameters. The local and global solvers solution algorithm reduced the average energy consumption by 11.48% - 19.91% and12.79% – 21.09% consecutively for the identified token budget range from a base case scenario with no tokens assigned. The duo of lagrangian heuristic algorithms improved the full problems solution i.e., higher energy savings, when optimized over a local solver, while the parameterized problem formulations resulted in higher energy savings when compared to the full problems’ formulation solution over local solver, but higher energy savings compared over the global solver. The Computational run-time for the global and local solvers solution algorithm for the full problem formulation required 43 hours and 24 minutes consecutively, while the local solver for the lagrangian heuristics and parameterized problem solution algorithm took 13 minutes and 7 minutes consecutively. Future research on this paper will be comprised of a bi-level optimization problem formulation where a high level optimization aims at maximizing system-wide energy savings, while a low-level consumer surplus maximization problem is solved for each system user.

This thesis is intended to provide fundamental information for the construction and analysis of rotordynamic theoretical models, and their comparison the experimental systems. Finite Element Method (FEM) is used to construct models using Timoshenko beam elements with viscous and hysteretic internal damping. Eigenvalues and eigenvectors of state space equations are used to perform stability analysis, produce critical speed maps, and visualize mode shapes. Frequency domain analysis of theoretical models is used to provide Bode diagrams and in experimental data full spectrum cascade plots. Experimental and theoretical model analyses are used to optimize the control algorithm for an Active Magnetic Bearing on an overhung rotor.

Modification of Hodgkin and Huxley’s experimentally derived set of nonlinear differential equations was implemented to accurately simulate the action potential of the Hirudo Medicinalis’s Retzius cell in MATLAB under analogous conditions to those found in the Retzius cell environment. The voltage-gated sodium and potassium channel responses to changes in membrane potential, as experimentally determined by Hodgkin and Huxley, were manipulated to suit simulation parameters established by electrophysiological Retzius cell recordings. Application of this methodology permitted additional accurate simulation of the Hirudo Medicinalis’s P cell under analogous conditions to those found in the P cell environment. Further refinement of this technique should allow for the voltage-gated behavioral based simulation of action potential waveforms found in variety of neurons under simulation conditions analogous to the nerve cell environment.

Procedural content generation for video games (PCGG) has seen a steep increase in the past decade, aiming to foster emergent gameplay as well as to address the challenge of producing large amounts of engaging content quickly. Most work in PCGG has been focused on generating art and assets such as levels, textures, and models, or on narrative design to generate storylines and progression paths. Given the difficulty of generating harmonically pleasing and interesting music, procedural music generation for games (PMGG) has not seen as much attention during this time. Music in video games is essential for establishing developers' intended mood and environment. Given the deficit of PMGG content, this paper aims to address the demand for high-quality PMGG. This paper describes the system developed to solve this problem, which generates thematic music for non-player characters (NPCs) based on developer-defined attributes in real time and responds to the dynamic relationship between the player and target NPC. The system was evaluated by means of user study: participants confront four NPC bosses each with their own uniquely generated dynamic track based on their varying attributes in relation to the player's. The survey gathered information on the perceived quality, dynamism, and helpfulness to gameplay of the generated music. Results showed that the generated music was generally pleasing and harmonious, and that while players could not detect the details of how, they were able to detect a general relationship between themselves and the NPCs as reflected by the music.

In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.