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--- ongoing [2011/05/30 17:17] – bil
+++ ongoing [2014/12/04 21:28] (current) – Added assigned projects stevenc
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-====== Ongoing projects ======
+====== Previous projects ======
+====== 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
+====== DDoS Attack using Google-bots ======
+**Supervisor**: Ntalija Vlajic
+**Recommended Background**: CSE 3213 or CSE 3214, CSE 3482
+Not long ago, botnets - networks of compromised computers - were seen as
+the most effective (if not the only) means of conducting Distributed Denial
+of Service (DDoS) attacks. However, with the growing popularity and prevalence
+of application-layer over other types of DDoS attacks, the DDoS execution
+landscape is becoming increasingly more diverse. An especially interesting
+new trend is the execution of application-layer DDoS attacks by means of
+skillfully manipulated Web-crawlers, such as Google-bots.
+The goal of this project is to design, implement and test a real-world
+framework consisting of the following: a) the attacker's web-accessible
+domain specially designed to attract Google-bots and then manipulate them
+into generating attack traffic towards the target/victim site; b) the
+victim's Web site set up in Amazon S3 cloud. In addition to the hands-on
+component, the project will also look into the statistical/numerical
+estimation of the framework's anticipated 'attack potential' relative
+to an actual (real-world) target/victim site.
+====== Comparison of Finger Tracking systems ======
+**Student**:
+**Supervisor**: Wolfgang Stuerzlinger
+**Required Background**: C/C++ coding
+__Description__
+This project compares the Leap Motion and the 3Gear System against each other in a comparative Fitts' law study
+__Completed__
+Winter, 2013
 ====== Imputation of missing values in microarray data ======
@@ Line 9: / Line 69: @@
 __Description__
-    Microarrays are a relatively new technology that have had tremendous impact
+Microarrays are a relatively new technology that have had tremendous
-    on many areas within biology and bioinformatics.  Microarray technology
+impact on many areas within biology and bioinformatics.  Microarray
-    enables researchers to study the behaviour of many genes and/or conditions
+technology enables researchers to study the behaviour of many genes
-    in a single experiment.
+and/or conditions in a single experiment.
-    Due to technological limitations and experiment design issues, microarray
-    data sets typically have several missing values.  It has been shown [3] that
-    imputation of these values improves the accuracy of different processing
-    tasks, including clustering, that are typically done on these data sets.
-    Therefore, good imputation algorithms are required.
-    In this project, we will explore fast and accurate imputation algorithms for
+Due to technological limitations and experiment design issues,
-    microarray data.  The student will first read the papers assigned and write
+microarray data sets typically have several missing values.  It has been
-    a short summary of them.  Then, he will study the performance a few
+shown that imputation of these values improves the accuracy of
-    algorithms from the literature (many algorithms are already implemented but
+different processing tasks, including clustering, that are typically
-- 2 may need to be implemented).  Finally, he will work with the
+done on these data sets.  Therefore, good imputation algorithms are
-    supervisor on the design of better algorithms for the problem being
+required.
-    studied.  He will use publicly available data sets to compare the
-    performance (accuracy and speed) of the new algorithm(s) to the GMCImpute
-    algorithm and several other existing ones.
-    Throughout the course, the student is required to maintain a course website
+In this project, we will explore fast and accurate imputation algorithms
-    to report any progress and details about the project.
+for microarray data.  The student will first read the papers assigned
+and write a short summary of them.  Then, he will study the performance
+a few algorithms from the literature (many algorithms are already
+implemented but 1 - 2 may need to be implemented).  Finally, he will
+work with the supervisor on the design of better algorithms for the
+problem being studied.  He will use publicly available data sets to
+compare the performance (accuracy and speed) of the new algorithm(s) to
+the GMCImpute algorithm and several other existing ones.
+Throughout the course, the student is required to maintain a course
+website to report any progress and details about the project.
 ====== An Open Source Structural Equation Modeling Graph Drawing Application ======
@@ Line 42: / Line 102: @@
 __Description__
-Structural equation modeling (SEM) is a statistical technique that is becoming increasingly popular in the sciences. SEM allows researchers to test the validity of hypothesized models involving complex relationships among multiple variables. These models can include latent variables, which are not measured directly but are constructs inferred by observed variables. Structural equation models can be represented visually by graphs (Figure 1 - attached).  To generate figure 1 currently in R would require over 80 lines of code which has no reusability and has to be re written each time a new graph has to be developed or analyzed (R is a UNIX based command line only program, however it is a very powerful analytic research tool).
+Structural equation modeling (SEM) is a statistical technique that is becoming increasingly popular in the sciences. SEM allows researchers to test the validity of hypothesized models involving complex relationships among multiple variables. These models can include latent variables, which are not measured directly but are constructs inferred by observed variables. Structural equation models can be represented visually by graphs. To generate such graphs currently in R would require over 80 lines of code which has no reusability and has to be re written each time a new graph has to be developed or analyzed (R is a UNIX based command line only program, however it is a very powerful analytic research tool).
 Collected data is used to estimate the parameters of the equations and assessing the fit of the model. There are several SEM software options available to researchers, however all have serious limitations (Windows only, Unix only, expensive licensing fees, text based or command line only, no GUI, etc).