Eighth Annual Student Research Forum

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1 Eighth Annual Student Research Forum February 18, 2011 COMPUTER SCIENCE AND COMPUTATIONAL SCIENCE

2 PRESENTATION SCHEDULE Session Chair: Dr. George Miminis Head, Computer Science: Dr. Edward Brown Director, Computational Science: Dr. Martin Plumer FRIDAY, FEBRUARY 18, 2011, EN Opening Remarks 0930 Proactive Source Routing, Zehua Wang 0950 Evolution of Globular Clusters in the Milky Way Galaxy, Adam Royle 1010 Fall Detection for the Elderly, Biru Cui BREAK 1100 Mathematical Modelling of Thrombin Generation in Children, Steven Dlamini 1120 Approximation Complexity of the Knapsack Problem, Renesa Nizamee 1140 Monte Carlo Simulations to Investigate the Magnetic Properties of Anti-ferromagnetic IrMn3, Vahid Hemmati LUNCH 1400 Positioning Using Motion Information in RF Reference Frames, Wasiq Waqar 1420 Integrating Human Decision-Making within an Ontology Alignment Process, Muhammad Nasir 1440 Data Organization and Visualization Using the Self-Sorting Map, Grant Strong 1500 Closing Remarks

3 FRIDAY, FEBRUARY 18, Zehua Wang, MEng, Faculty of Engineering Proactive Source Routing Routing is a fundamental operation on mobile ad hoc networks. In this work, we propose a Proactive Source Routing protocol (PSR) that is as lightweight as distance vectors and nearly as powerful as link states. PSR maintains full source routing information at all times in order to support opportunistic data forwarding in a dynamically changing multi-hop wireless networks. PSR is shown robust against both high-rate node mobility and fast link quality variation Adam Royle, M.Sc. Computational Science Evolution of Globular Clusters in the Milky Way Galaxy Globular clusters are very old objects that have been orbiting the Galaxy since just after the Galaxy formed, over ten billion years ago. Due to this large time scale, computer simulations provide the best tool to study their evolution. During their orbits, they experience external forces from the Galaxy which may perturb the cluster. A description of the problem, the methods used to simulate these orbits and some preliminary results will be presented Biru Cui, Ph.D. Computer Science Fall Detection for the Elderly Falling is one of the dangerous accidents for elderly people, especially in the case when they become unconscious after the fall. This project proposes a fall detector to alert emergency medical care personnel when this happens Steven C. Dlamini, M.Sc. Computational Science Mathematical Modelling of Thrombin Generation in Children Much research work has been done in modeling thrombin generation for vascular systems. Traditionally thrombin generation problems were not a major concern for children and infants hence most research in this area is largely confined to characterizing the thrombin generation system in adults. However, with the advancement of surgery procedures, there have been increases in incidents of infant

4 mortality due to thrombotic complications during or after surgery. In addition thrombin generation in adults is significantly different from thrombin generation in children hence the current practice of referring to adult values when trying to treat thrombotic complications in children is not accurate. In this light, it is therefore of paramount importance to pursue a study of thrombin generation in children Renesa Nizamee, M.Sc. Computer Science Approximation Complexity of the Knapsack Problem Since NP-complete problems seem to be hard to solve optimally, it is natural to look for approximate solutions. Some NP-complete problems are as hard to approximate as it is to solve optimally. However, there are NP-complete problems that are easy to approximate well; an example of such a problem is the Knapsack problem. We can define the problem as follows: given a set of items, each with a weight and a value, and a weight limit, determine which items to include in the collection so that the total weight is at most the limit and the total value is as large as possible. For example, a humanitarian mission might need to find the best set of donated items to load to a helicopter going to a disaster area. Here we will talk about the algorithms and complexity of approximating Knapsack, both in the traditional and structure-approximation setting Vahid Hemmati, M.Sc. Computational Science Monte Carlo Simulations to Investigate the Magnetic Properties of Anti-ferromagnetic IrMn3. The anti-ferromagnetic IrMn3 is the main material used in stabilizing the magnetization of the soft ferromagnetic layer, employed in rewritable memories. The theories explaining the Exchange Bias phenomena which provides the magnetic field to stabilize a soft ferromagnetic layer, significantly confirms the role of the spin-configuration of magnetic atoms in the ferromagnet. The Metropolis Monte Carlo simulation method is used to determine thermal equilibrium properties and spin configurations on thin films of IrMn3, which are formed from ABC stacked layers of the Kagome lattice. The method, its use in an HPC environment, and some preliminary results will be reviewed.

5 1400 Wasiq Waqar, M.Sc., Computer Science Positioning Using Motion Information in RF Reference Frames Due to an increase in the demand of context-aware applications, there is a greater demand for accurate user location information. Today, the location for outdoor environments is mostly provided by the Global Positioning System. However current indoor positioning and tracking technologies for general objects are not as reliable or practical. There is an imminent need for an indoor localization technology which is not only accurate but also responsive. In the past, numerous Inertial Measurement Unit (IMU) based localization techniques have been proposed, but most of them use customized hardware. Now, a new generation of smart phone devices equipped with numerous sensors, supplemented by infrastructure-based radio frequency (RF) capabilities, allow new opportunities for accurate and fast positioning schemes. In this talk, I introduce my research of using smart phone for sensor driven indoor mobile positioning. Today s mobile devices contain embedded sensors like gyroscope, accelerometer, electronic compass, etc. We can use these sensors supplemented by sporadic Wi-Fi signals, to transcribe user location. As IMU-based positioning schemes suffer from integration drift, we can use existing Wi-Fi signals and other techniques to assist in keeping the location estimation error to a minimum. I will conduct research on how we can use the user motion information and Wi-Fi signals in collaboration to develop a near real-time indoor positioning scheme Muhammad Nasir, M.Sc. Computer Science Integrating Human Decision-Making within an Ontology Alignment Process Ontology alignment is of key importance within the information integration domain. Primarily, ontology alignment research focuses on creating state-of-the-art alignment algorithms. Recently, researchers have started to analyse and give importance to various aspects of user involvement within ontology mapping processes. Currently, the so-called user-guided interactive systems experience difficulty in using the feedback in a generalized and effective manner. Several validations of mappings produced by the system are required to be performed by the user. This feedback is then used for different purposes; to remove the rejected mappings from the final mapping set, to discover the order for validations, exploring which mapping approach is best (among the many applied) for the given set of ontologies and others. This results into too many validations at once, specific-to-ontology improvements, and others, which can be improved. Since ontologies are commonly used to represent complex domain knowledge, their size can grow easily. As a consequence, the alignment algorithm produces large number of mappings which ultimately the user has to process and manipulate. The visualization of mappings is supposed to help the users in understanding the

6 underlying features, but representing a large number of mappings usually results in a visual clutter. Therefore we argue that there are three aspects to the solution for an ontology alignment problem that we want to explore in this research. First is the underlying alignment algorithm which uses state-of-the-art techniques using linguistic, semantic, structural similarity and other types of analysis to generate approximate alignment suggestions. The second is the integration of human decision-making within the ontology alignment process. The final is the enhancements to the interface with visual and interaction features to support decision-making process. First instead of working on the entire set of ontologies, we will make use of user s decision-making capabilities in the process. The user will select the relevant subjects based on their judgement regarding the given ontologies upon which the alignment algorithm will attempt to generate mappings. After producing these mappings from each group of selected subsets, the process of integration and conflict resolution will be carried out, using the user s judgement to produce a global mapping set. Secondly, an edge-bundling algorithm with interactive features will be applied for mapping representation and manipulation. The edge-bundling approach is quite successful in representing an organized view of large graphs. Similarly, we have huge ontologies which when aligned results in large number of alignment edges. These edges can be grouped using the bundling algorithm, producing well-structured overview of the mappings. After discovering the interested mapping set (i.e. a bundle), the user can further delve into individual mappings inside the set by using interactive features such as zooming, filtering and focusing. The user will also get immediate feedback regarding the quality of alignment process as a result of attempting to perform a particular mapping manipulation action. The feedback may be used to guide the user in making an informed decision about a mapping by showing that consequent effects of performing that action. We believe that our proposed features would help the user not just performing better in various tasks related to ontology mappings but also to effectively deal with problem of large ontologies and mapping sets Grant Strong, Ph.D. Computer Science Data Organization and Visualization using the Self-Sorting Map Data comes in many forms. High dimensional data in particular is hard for human beings to make sense of in its raw form. Our visual and analytical systems are brilliantly engineered to make sense of and extract patterns from complex pictures yet they are ill-equipped to deal with tables of numbers directly. For this reason people have been trying for years to come up with ways to reduce the dimensionality of their data so it can be displayed in picture form so we can make sense of it easily if there is sense to be made. Of the methods that have been developed, most are limited to dealing with data as vectors and do not put any constraints on the range in which it is output. This lack of constraints means that occlusion can be a real problem and dealing with it is a whole field onto itself. In a lot of cases we are not as concerned with data being perfectly placed in the output space, but rather that the relative relationships between the data are modeled

7 intuitively. We present a general, parallelizable algorithm for organizing data that can work on vector data as well as or better than the predecessors from which it was inspired in terms of speed and final presentation, with the added bonus of it being adaptable to run on non-vector nominal data. The fundamental difference in this algorithm is that it seeks to order data items within the confines of a celled structure so that the correlation between the similarity of the items and the relative positions of them in the structure is maximized.

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