Supervisor: James Elder

Required Background: General CSE408x prerequisites, good programming skills, good math skills, knowledge of C and MATLAB programming languages

One of the challenges in remote learning is to allow students to communicate effectively with the lecturer. For example, when a student asks a question, communication will be more effective if the instructor has a zoomed view of the student’s face, so that s/he can interpret expressions etc.

The goal of this project is to apply attentive sensing technology (www.elderlab.yorku.ca) to this problem. This technology is able to monitor a large environment such as a classroom and direct a high-resolution ‘attentive’ sensor to events of interest.

In particular, working with a senior graduate student or postdoctoral fellow, the successful applicant will:

1. Study the problem of detecting hand-raises in the preattentive sensor stream
2. Implement algorithms for detecting hand-raises based upon this investigation
3. Evaluate these algorithms in a real-classroom setting, using proprietary attentive sensing technology

Supervisor: James Elder

Required Background: Good programming skills; Good math skills; Knowledge of C and MATLAB programming languages

The goal of this project is to modify York University’s patented attentive sensor technology to the sport video recording market. Specific application domains under investigation include skiing, indoor BMX parks, and horse tracks.

The general problem is to use attentive sensing technology (www.elderlab.yorku.ca) to visually detect and track multiple moving agents (e.g., skiers, riders, horses) and to select specific agents for active high-resolution smooth pursuit.

The student will work with senior graduate students, postdoctoral fellows and research scientists to help modify the attentive sensing technology to operate in these domains. Specific tasks include:

1. Ground-truth available datasets 2. Evaluate current attentive algorithms on these datasets 3. Modify these algorithms to improve performance on these datasets

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Supervisor: Franck van Breugel

Description: Java PathFinder (JPF) is a tool that can detect bugs in Java code. The Java library Apache log4j allows developers to control which log statements are output. In the past, Dickey et al. [1] have attempted to detect bugs in log4j by means of JPF with very limited succes.

Recently, in collaboration with Shafiei (NASA) we have developed an extension of JPF called jpf-nhandler. The aim of this project is to apply this extension to log4j.

[1] David A. Dickey, B. Sinem Dorter, J. Michael German, Benjamin D. Madore, Mark W. Piper, Gabriel L. Zenarosa. “Evaluating Java PathFinder on Log4J.” 2011.

Required Background: General CSE408x prerequisites

Supervisor: Wolfgang Stuerzlinger

Required Background: General 408X prerequisites, 3D Computer Graphics (3431) completed or equivalent, C/C++ coding experience or (if using Unity 4) Javascript C# coding experience

This project implements and evaluates a new method for 3D Rotation where the user uses both a 2D and 3D user interface to complete the task. The fundamental idea is to use the 3D interface for large-scale manipulation, but the 2D interface for precise adjustments. The project will use a Leap Motion or similar technology for 3D tracking.

Supervisor: Wolfgang Stuerzlinger

Required Background: General 408X prerequisites, 3D Computer Graphics (3431) completed or equivalent, 4431 desired, Javascript or C# coding experience

This project implements a kitchen planner application for an immersive virtual reality system. The implementation will be based on Unity 4.

Supervisor: Wolfgang Stuerzlinger

Required Background: General 408X prerequisites, 3D Computer Graphics (3431) completed or equivalent, 4431 desired, Javascript or C# coding experience

This project implements and tests various 3D Navigation and 3D Interaction methods in an immersive virtual reality system. The target is to enable the user to roam freely in a large environment while still being able to interact with the environment. The implementation will be based on Unity 4.

Supervisor:Zhen Ming (Jack) Jiang (zmjiang at cse dot yorku dot ca)

Required Background: Good programming skills (especially in Java); Good analytical and communication skills; Interested in large complex software systems and automated software analysis Short Description: Many large scale software systems ranging from e-commerce websites (e.g., eBay) to telecommunication infrastructures (e.g., AT&T) are required to be available and ready to service by millions of users all the time. It is essential to monitor the behavior of these systems in the field and troubleshoot problems whenever they arise. On one hand, many existing monitoring tools (e.g., PerfMon and pidstat) mainly focus on the high level resource usage data (e.g., CPU and memory). On other hand, although profilers (e.g., JProfiler and DTrace) can provide detailed information on the internal system behavior, it is not feasible to run them with the field systems due to their high overhead. Binary instrumentation is a program analysis technique, which can add additional monitoring points without modifying or restarting the system. This project aims to explore the feasibility of leveraging binary instrumentation to automatically monitor and debug the behavior of these field systems. The student(s) will first evaluate the pros and cons on various binary instrumentation libraries (e.g., ASM and PIN). Then he/she will implement a monitoring/debugging framework using the selected instrumentation library.

Supervisor: Zhen Ming (Jack) Jiang (zmjiang at cse dot yorku dot ca)

Required Background: Good programming skills in Java; Good analytical and communication skills; Knowledge in AI and statistics; Interested in large scale software analysis

Short Description: Software engineering data (e.g., source code repositories and bug databases) contains a wealth of information about a project's status and history. The research on Mining Software Repositories (MSR) aims to transform the data from static record-keeping repositories into knowledge, which can guide the software development process. For example, one can derive correct API usage patterns and flag anomalous (and potentially buggy) API usages by mining the source code across many projects in GitHub and Google Code. In this project, the student(s) will research and develop an efficient infrastructure, where MSR researchers and practitioners can share and analyze such data.

Additional current possible projects will be listed here by start of fall term.