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University Calendar
Events for April
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PhD Defense - Rong Yang
Thu, Apr 03, 2014 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Rong Yang
Title: Addressing Human Decision Making in Security Games: Models and Algorithms
Committee:
Milind Tambe (chair)
Fernando Ordornez
Rajiv Maheswaran
Johnathan Gratch
Richard John (outside member)
Vincent Conitzer (Duke)
Abstract:
Security is a world-wide concern in a diverse set of settings, such as protecting ports, airport and other critical infrastructures, interdicting the illegal flow of drugs, weapons and money, preventing illegal poaching/hunting of endangered species and fish, suppressing crime in urban areas and securing cyberspace. Unfortunately, with limited security resources, not all the potential targets can be protected at all times. Game-theoretic approaches â in the form of ”security games” â have recently gained significant interest from researchers as a tool for analyzing real-world security resource allocation problems leading to multiple deployed systems in day-to-day use to enhance security of US ports, airports and transportation infrastructure. One of the key challenges that remains open in enhancing current security game applications and enabling new ones originates from the perfect rationality assumption of the adversaries â an assumption may not hold in the real world due to the bounded rationality of human adversaries and hence could potentially reduce the effectiveness of solutions offered.
My thesis focuses on addressing the human decision-making in security games. It seeks to bridge the gap between two important sub-fields in game theory: algorithmic game theory and behavioral game theory. The former focuses on efficient computation of equilibrium solution concepts, and the latter develops models to predict the behaviors of human players in various game settings. More specifically, I provide: (i) the answer to the question of which of the existing models best represents the salient features of the security problems, by empirically exploring different human behavioral models from the literature; (ii) algorithms to efficiently compute the resource allocation strategies for the security agencies considering these new models of the adversaries; (iii) real-world deployed systems that range from security of ports to wildlife security.
Location: Henry Salvatori Computer Science Center (SAL) - 126
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Zhenzhen Gao
Tue, Apr 08, 2014 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Zhenzhen Gao
Title: City-scale Aerial LiDAR Point Cloud Visualization
Committee:
Ulrich Neumann (chair)
Aiichiro Nakano
C.-C. Jay Kuo (outside member)
Abstract:
Aerial LiDAR (Light Detection and Ranging) is cost-effective in acquiring terrain and urban information by mounting a downward-scanning laser on a low-flying aircraft. It produces huge volumes of unconnected 3D points. This thesis focuses on the interactive visualization of aerial LiDAR point clouds of cities, which is applicable to a number of areas including virtual tourism, security, land management and urban planning.
A framework needs to address several challenges in order to deliver useful visualizations of aerial LiDAR cities. Firstly, the data is 2.5D, in that the sensor is only able to capture dense details of the surfaces facing it, leaving few samples on vertical building walls. Secondly, the data often suffers from noise and undersampling. Finally, the large size of the data can easily exceed the memory capacity of a computer system.
This thesis first introduces a visually-complete rendering framework for aerial LiDAR cities. By inferring classification information, building walls and occluded ground areas under tree canopies are completed either through pre-processing point cloud augmentation or through online procedural geometry generation. A multi-resolution out-of-core strategy and GPU-accelerated rendering enable interactive visualization of virtually unlimited size data. With adding only a slight overhead to existing point-based approaches, the framework provides comparable quality to visualizations of off-line pre-computation of 3D polygonal models.
The thesis then presents a scalable out-of-core algorithm for mapping colors from aerial oblique imagery to city-scale aerial LiDAR points. Without intensive processing of points, colors are mapped via a modified visibility pass of GPU splatting, and a weighting scheme leveraging image resolution and surface orientation.
To alleviate visual artifacts caused by noise and under-sampling, the thesis shows an off-line point cloud refinement algorithm. By explicitly regularizing building boundary points, the algorithm can effectively remove noise, fill gaps, and preserve and enhance both normal and position discontinuous features for piecewise smoothing buildings with arbitrary shape and complexity.
Finally, the thesis introduces a new multi-resolution rendering framework that supports real-time refinement of aerial LiDAR cities. Without complex computation and without user interference, simply based on curvature analysis of points of uniform sized spatial partitions, hierarchical hybrid structures are constructed indicating whether to represent a partition as point or polygon. With the help of such structures, both rendering and refinement are dynamically adaptive to views and curvatures. Compared to visually-complete rendering, the new framework is able to deliver comparable visual quality with less than 8% increase in pre-processing time and 2-5 times higher rendering frame-rates. Experiments on several cities show that the refinement improves rendering quality for large magnification under real-time constraint.
Location: Charles Lee Powell Hall (PHE) - 333
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Bin Liu
Tue, Apr 08, 2014 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Bin Liu
Title: Improving Efficiency, Privacy and Robustness for Crowd-Sensing Applications
Committee:
Ramesh Govindan (chair)
Leana Golubchik
Sandeep Gupta (outside member)
Abstract:
Every year, a wide variety of modern smart devices, such as smartphones and tablets, are released by big brands, like Apple, Samsung and HTC. Compared to previous generations, these smart devices are more sophisticated in two ways: (a) they run advanced operating systems which allow developers to create a large collection of complicated apps, and (b) they have more diverse sensors which can be used to perform various context-aware tasks. These two attributes, together, have conceived a new class of applications, crowd-sensing. Crowd-sensing is a capability by which a task requestor can recruit smartphone users to provide sensor data to be used towards a specific goal or as part of a social or technical experiment. For the purpose of supporting crowd-sensing tasks, professional apps are developed to provide specialized platforms, and high quality sensors are used to generate semantically rich data.
My dissertation focuses on possible ways to improve efficiency, privacy and robustness for crowd-sensing applications. First, targeting the general form of crowd-sensing, we design efficient algorithms to answer the following question: how to optimize the selection of crowd-sensing participants to deliver credible information about a task? Based on a model about credibility of information, we develop solutions for the discrete version and the time-averaged version of this problem.
Second, we consider a special crowd-sensing case in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. For this, we develop algorithms and an associated system design to perform collaborative learning task in a way that preserves user data privacy without significant loss of accuracy.
Finally, the technique of dynamic analysis can be employed to test many aspects of crowd-sensing apps, such as performance, security, and correctness properties. As an initial attempt, we show how to use dynamic analysis to detect placement ad fraud in which app developers manipulate visual layouts of ads in ways that result in invisible ad impressions and accidental clicks from real users. We demonstrate that the detection can be performed using optimized automated navigation methods in a large set of 1,150 tablet apps and 50,000 phone apps.
Location: Henry Salvatori Computer Science Center (SAL) - 222
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Joongheon Kim
Thu, Apr 10, 2014 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Joongheon Kim
Committee:
Andreas F. Molisch (Chair)
Ramesh Govindan (Co-chair)
Aiichrio Nakano
Antonio Ortega (Outside member)
Title: Elements of Next-Generation Wireless Video Systems: Millimeter-Wave and Device-to-Device Algorithms
Abstract:
This dissertation explores the possible issues and proposes promising solutions in next generation wireless and mobile systems.
For next generation wireless systems, one of the main research contributions is dedicated to multi-Gbps system design and implementation. To achieve multi-Gbps data rates, using millimeter-wave wireless channels is one of the most promising topics since the millimeter-wave systems can easily achieve multi-Gbps data rates according to ultra-wide bandwidth that is 2.16 Gbps in 60 GHz. Therefore, millimeter-wave technologies are actively discussing in next generation 5G cellular research in the bands of 28 GHz and 39 GHz as well. Even though the millimeter-wave wireless systems have this multi-Gbps benefit, research challenges also exist. According to the higher carrier frequencies, the attenuation of signals is a major factor that should be handled. To deal with this issue, relaying and beam training algorithms are mainly used and discussed.
For relaying in millimeter-wave wireless systems, we investigated a joint compression and relaying algorithm for outdoor video applications. Transmission of high-definition (HD) video is a promising application for millimeter-wave wireless links, since very high transmission rates are possible. In particular we consider a sports stadium broadcasting system where signals from multiple cameras are transmitted to a central location. Due to the high path-loss of 60 GHz radiation over the large distances encountered in this scenario, the use of relays might be required. The proposed algorithm analyzes the joint selection of the routes and the compression rates from the various sources for maximization of the overall video quality. We consider three different scenarios: (i) each source transmits only to one relay and the relay can receive only one data stream, and (ii) each source can transmit only to a single relay, but relays can aggregate streams from different sources and forward to the destination, and (iii) the source can split its data stream into parallel streams, which can be transmitted via different relays to the destination. For each scenario, we derive the mathematical formulations of the optimization problem and re-formulate them as convex mixed-integer programming, which can guarantee optimal solutions. Extensive simulations demonstrate that high-quality transmission is possible for at least ten cameras over distances of 300 m. Furthermore, optimization of the video quality gives results that can significantly outperform algorithms that maximize data rates.
For beam training in millimeter-wave wireless systems, we investigated a fast beam training algorithm with receive beamforming. Both IEEE standards and the academic literature have generally considered beam training protocols involving exhaustive search over all possible beam directions for both the beamforming initiator and responder. However, this operation requires a long time (and thus overhead) when the beamwidth is quite narrow such as for mm-wave beams (1 degree in the worst case). To alleviate this problem, we propose two types of adaptive beam training protocols for fixed and adaptive modulation, respectively, which take into account the unique propagation characteristics of millimeter waves. For fixed modulation, the proposed protocol allows for interactive beam training, stopping the search when a local maximum of the power angular spectrum is found that is sufficient to support the chosen modulation/coding scheme. We furthermore suggest approaches to prioritize certain directions determined from the propagation geometry, long-term statistics, etc. For adaptive modulation, the proposed protocol uses iterative multi-level beam training concepts for fast link configuration that provide an exhaustive search with significantly lower complexity. Our simulation results verify that the proposed protocol performs better than traditional exhaustive search in terms of the link configuration speed for mobile wireless service applications.
For next generation mobile systems, direct communication between mobile stations, i.e., called device-to-device communications, is actively discussed in next generation 3GPP cellular mobile systems. In addition, one of major applications of device-to-device mobile systems is adaptive video streaming. One of the most well-known device-to-device network algorithms, used in the FlashLinQ system, provides good performance in terms of the number of activated links. However, it is not optimized for transmission of video streams since it does not consider the quality, or the specific requirements of streaming. We propose an alternative algorithm that consists of a scheduling and a streaming component. The scheduling employs message-passing to determine max-independent sets. For designing the streaming component, a quality-aware stochastic algorithm is introduced that works based on the queue backlog sizes in each transmitter queue. The framework controls the quality of each chunk of video to maximize the qualities of streamed video subject to queue rate stability. The efficiencies of the proposed algorithm is verified by simulation studies in terms of (i) the number of video streaming stalls at receivers and (ii) the queue dynamics at transmitters. According to the simulation results, it is verified that the proposed algorithm presents desired performance in terms of user satisfaction and queue stability.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Ramin Moazeni
Mon, Apr 14, 2014 @ 08:00 AM - 10:00 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Incremental Development Productivity Decline
PhD Candidate: Ramin Moazeni
Defense Committee: Barry Boehm (Chair), Aiichiro Nakano and Stanley Settles (Outside Member)
Date: Monday, April 14, 2014
Time: 8:00 AM
Location: SAL 222
Abstract:
Software production is on the critical path of increasingly many program abilities to deliver effective operational capabilities. This is due to the number, complexity, independence, interdependence, and software‐intensiveness of their success‐critical components and interfaces. The estimation parameters and knowledge bases of current software estimation tools are generally good for stable, standalone, single increment development. However, they do not fully account for the degrees of program and software dynamism, incrementality, coordination, complexity, and integration. These phenomena tend to decrease software productivity relative to the cost model estimates made for the individual software components and for the overall systems, but it is difficult to estimate by how much.
Incremental software development generally involves either adding, modifying, or deleting parts of the code in the previous increments. This means that if a useful system is to be built, the maintenance that will have to go into previous increments will take away productivity from the later ones.
This research tests hypotheses about a phenomenon called Incremental Development Productivity Decline (IDPD) that may be more or less present in incremental software projects of various categories.
Incremental models are now being used by many organizations in order to reduce development risks while trying to deliver releases of the product on time. It has become the most common method of software development with characteristics that influence the productivity of projects.
Different ways of measuring productivity are presented and evaluated in order to come to a definition or set of definitions that is suitable to these categories of projects.
Data from several sources has been collected and analyzed, and hypotheses tested about the degree of IDPD and its variation by increment and category. The results indicated the existence of an IDPD phenomenon, that its magnitude varies by application category, but that it tended to vary from increment to increment.
Location: Henry Salvatori Computer Science Center (SAL) - 222
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Yili Zhao
Thu, Apr 17, 2014 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Yili Zhao
Comittee member:
Jernej Barbic (Chair)
Ulrich Neumann
Igor Kukavica (Math Department)
Location:
SAL 322
Time:
10:00AM - 12:00PM
Title:
Plant Substructuring and Real-time Simulation Using Model Reduction
Abstract:
This research is focusing on real-time, physically-based simulation of plants undergoing large deformations. To achieve this goal, we first propose a novel algorithm based on model
reduction and domain decomposition. It extends 3D nonlinear elasticity model reduction to open-loop multi-level reduced deformable structures. We decompose the input mesh into
several domains, build a reduced deformable model for every domain, simulate each one separately, and connect domains using proper inertia coupling. This makes model reduction deformable simulations much more versatile: localized deformations can be supported without prohibitive computational costs, parts can be re-used and precomputation time can be shortened. Our method does not use constraints, and can handle large domain rigid body
motion in addition to large deformations, due to our derivation of the gradient and Hessian of the rotation matrix in polar decomposition. We show real-time examples with multi-level
domain hierarchies and thousands of reduced degrees of freedom.
Then we design a pre-processor which takes a plant “polygon soup” triangle mesh as the only input and quickly pre-compute necessary data for the subsequent simulation. This tool breaks the ice for adoption of our multidomain dynamics simulator in practice. Our
pre-processor is robust to non-manifold input geometry, gaps between branches or leaves, free-flying leaves not connected to any branch, small unimportant geometry (“debris”) left in
the model, and plant self-collisions in the input configuration. Repeated copies (instances) of plant subparts such as leaves, petals or fruits can be automatically detected by our preprocessor. We enhanced our multidomain dynamics simulator to provide plant fracture, and inverse kinematics to easily pose plants. It can simulate complex plants at interactive rates, subjected to user forces, gravity or randomized wind. We simulated over 100 plants from diverse climates and geographic regions, including broadleaf (deciduous) trees and conifers,
bushes and flowers. Our largest simulations involve anatomically realistic adult trees with hundreds of branches and over 100,000 leaves.
Finally, we propose our future research in several directions including adding hierarchical instancing, collision detection and handling, etc.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
