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--- projects [2013/04/29 12:27] – mb
+++ projects [2013/09/03 15:14] – pd
@@ Line 1: / Line 1: @@
-====== Available Projects  ======
+====== Proposed Projects  ======
-Currently offered Projects, Summer 2013
+Current proposed Projects, Fall 2013
+\\
+====== Attentive Sensing for Better Two-Way Communication in Remote Learning Environments ======
+**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:
+  - Study the problem of detecting hand-raises in the preattentive sensor stream
+  - Implement algorithms for detecting hand-raises based upon this investigation
+  - Evaluate these algorithms in a real-classroom setting, using proprietary attentive sensing technology
+====== Attentive Sensing for Sport Video Recording Markets ======
+**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:
+.     Ground-truth available datasets
+.     Evaluate current attentive algorithms on these datasets
+.     Modify these algorithms to improve performance on these datasets
+------------
+\\
+======Hunting for Bugs in Logging: applying JPF to log4j======
+**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
+\\
+------------
+\\
+\\
+------------------
+\\
+Additional current possible  projects will be listed here by start of fall term.
+\\
+------------------
+\\
+\\
-(some projects still subject to confirmation)
+=====Previous Projects from Summer 2013=====
 ======Tracking and Activity Recognition Through Consensus in Distributed Camera Networks======
@@ Line 47: / Line 115: @@
 **Readings**: MacKenzie, I. S., & Teather, R. J. (2012). FittsTilt: The application of Fitts’ law to tilt-based interaction. Proceedings of the Seventh Nordic Conference on Human-Computer Interaction – NordiCHI 2012, pp. 568-577. New York: ACM.
-====== Attentive Sensing for Better Two-Way Communication in Remote Learning Environments ======
-**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:
-  - Study the problem of detecting hand-raises in the preattentive sensor stream
-  - Implement algorithms for detecting hand-raises based upon this investigation
-  - Evaluate these algorithms in a real-classroom setting, using proprietary attentive sensing technology
-====== Attentive Sensing for Sport Video Recording Markets ======
-**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:
-.     Ground-truth available datasets
-.     Evaluate current attentive algorithms on these datasets
-.     Modify these algorithms to improve performance on these datasets
 ====== Continuation of a Path Diagram to Syntax Application ======