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 **Required skills or prerequisites:**
-Good programming and data analysis skills overall, and experience in using Jupyter and/or R for data analysis.  Ability to work indecently.
+Good programming and data analysis skills overall, and experience in using Jupyter and/or R for data analysis.  Ability to work independently.
 Interest in usable privacy, critical analysis of privacy policies and privacy related regulation.
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 **Instructions:**
 Please fill in [[https://forms.gle/oVVg6hEConSNf9p28|this form]]
+==== Strengthening the Security of a Python Autograder ====
+**Course:**  EECS4480/EECS4080/EECS4088
+**Supervisor:**  Jonatan Schroeder
+**Supervisor's email address:** jonatan@yorku.ca
+**Project Description:**
+Unit testing platforms like Java's JUnit and Python's unittest provide a simple interface for evaluating the correctness of individual functions in a large project. These platforms can also be used in an academic environment to automatically test student-submitted code in programming assignments and generate a grade based on if these tests pass or fail. However, given that these platforms were originally developed for running code that is expected to be trusted, this practice can lead to a potential risk if students are able to provide code that causes the test to pass without resulting in the expected value (see https://www.seas.upenn.edu/~hanbangw/blog/hack-gs/). While most modern autograding platforms introduce security practices to avoid this kind of code from receiving a valid grade, some vulnerabilities still exist.
+For this project you will strengthen the security of an autograder process for Python code for the PrairieLearn platform. You will start by creating possible attack vectors in the form of code that is expected to cause the autograder to pass a test without actually returning the expected results. Examples of attack vectors include code that saves or outputs well-formatted values that are interpreted by the autograder as a success, code that is able to identify secret information from the autograder code, and/or code that crashes the original autograder process. Then you will implement safeguards that ensure student-submitted code is unable to bypass container sandbox limitations, and that ensure that malicious student code does not result in a successful grade.
+You will work in coordination with the supervisor and the PrairieLearn developer community to brainstorm possible strategies and guidelines. Your final deliverable will be a pull request to the PrairieLearn codebase with the proposed fix.
+**Required skills or prerequisites:**
+Must have completed EECS 1015 (or a similar course) with an A/A+. Must have solid programming skills in Python, including the use of unit testing. Must be able to work independently and have good communication skills.
+**Recommended skills or prerequisites:**
+EECS 3221 is highly recommended. Experience with Docker containers is helpful but can be obtained during the project. Git experience is helpful. Experience with open source software development is an asset.
+**Instructions:**
+Additional information about PrairieLearn can be found here: https://prairielearn.readthedocs.io/en/latest/. A sample PrairieLearn assessment that includes Python autograded questions can be found here: https://us.prairielearn.com/pl/course_instance/136606/assessment/2351069. Please submit a brief description of your experience with the skills listed above.
+==== CTF for Applied Cryptography course ====
+**Course:**  EECS4480/EECS4090/EECS4080/EECS4088
+**Supervisor:**  Ruba Al Omari
+**Supervisor's email address:** alomari@yorku.ca
+**Project Description:**
+Create a CTF for the applied cryptography course that includes tasks that cover classical, symmetric, and asymmetric techniques. Something similar to https://cryptohack.org/ but on a smaller scale. The idea is for this CTF to be created and maintained locally the flags can be changed regularly. With publicly available CTFs students can easily google the flags or read the writeups.
+**Required skills or prerequisites:**
+Programming skills, and an understanding of cryptography.
+**Recommended skills or prerequisites:**
+It is recommended that students have taken EECS3481 before.
+**Instructions:**
+Please send your CV and transcript and specify whether you are a computer security major. Optional: Link to any previous projects that you have worked on.
+==== Computer Science Education Research - Robots Tutors in First Year Programming Courses ====
+**Course:**  EECS4080
+**Supervisor:**  Meiying Qin
+**Supervisor's email address:** mqin@yorku.ca
+**Project Description:**
+The goal of this project is to implement a robotic tutor to assist first-year programming students in Computer Science Education (CSE) research, while also delving into the realm of Human-Robot Interactions (HRI). Students enrolled in the first-year programming course will go to a designated location to interact with a robot to do exercises together. While robot tutors have been utilized in pre-university education, few studies explored their effectiveness within post-secondary education settings. By participating in the 4080 project, students will gain immersive exposure to the entire research project lifecycle, spanning from initial project planning and design to the contemplation of ethical considerations in real-world applications. The roles and responsibilities of students involved in this project include drafting protocol and consent forms, project design, and implementation of the project.
+**Required skills or prerequisites:**
+Python
+**Recommended skills or prerequisites:**
+Data analysis and
+writing skills
+**Instructions:**
+Please send your c.v. and transcript
+==== C++ in Embedded Systems: A Reality Check ====
+**Course:**  EECS4070/4080/4088/4090
+**Supervisor:**  James Smith
+**Supervisor's email address:** drsmith@yorku.ca
+**Project Description:**
+While C++ is one of the most common general purpose programming languages, it is not commonly used in resource-poor (“bare metal”) embedded devices. Issues related to complexity, efficiency, memory requirements, determinism and real time constraints are often cited as reasons for this reluctance. With the advent of less restrictive 32-bit embedded devices, updates to the C++ language and availability of C++ compilers alongside C in many embedded device IDEs we wish to examine whether the assumptions that are commonly held about C++ in the embedded systems space still hold true.
+The learning outcomes will be as follows.  By the end of this course, the student will be able to
+  - Articulate how they have applied the knowledge they have gained in other software engineering courses to a real-world system
+  - Implement schedulers (Cooperative, with and without ISRs, as well as with and without finite state machine) for a baseline embedded system problem in both contemporary procedural C (C11+ or greater and C++17 or greater), as well as contemporary object oriented C++ (C++17 or greater)
+  - Illustrate the performance differences between contemporary procedural (C11 or greater and C++17 or greater) and contemporary object oriented (C++17 or greater) programming solutions for baseline, resource-poor bare metal embedded devices.
+  - Articulate the questions that a particular area of research in embedded systems and programming languages attempts to address.
+  - Prepare a professional presentation that outlines the contributions they made to the project and the knowledge they acquired.
+**Required skills or prerequisites:**
+General knowledge of procedural and object-oriented programming languages.  Previous experience with Arduinos or other embedded devices like Raspberry Pis.
+**Recommended skills or prerequisites:**
+Previous C++ experience.
+==== CiteFair: an online tool to detect and mitigate unfairness citation patterns in scientific articles ====
+**Course:**  EECS4080/4088/4090
+**Supervisor:**  Alvine Belle
+**Supervisor's email address:** alvine.belle@lassonde.yorku.ca
+**Project Description:**
+The number of citations of scientific articles has a huge impact on recommendations for funding allocations, recruitment decisions, and rewards, just to name a few. However, some researchers belonging to some socio-cultural groups (e.g., women) are usually less cited than other researchers coming from dominating groups. This may be due to the presence of some unfairness citation patterns in some scientific articles. These citation patterns are tangible examples of biases against researchers from some socio-cultural groups and may inevitably cause unfairness and inaccuracy in the assessment of articles impact. These citations patterns may therefore translate to significant disparities in promotion, retention, grant funding, awards, collaborative opportunities, and publications.
+The project will first start by analyzing the existing scientific literature to find out the various unfairness citations patterns that may be present in some scientific articles. Then, the project will focus on the exploration of existing mitigation solutions and their limitations.
+The project will then aim at developing an online tool called CiteFair that will be able to:
+  - Automatically analyze scientific articles to detect the potential presence of unfairness citation patterns
+  -Rely on existing bibliometric tools to provide some suggestions to articles authors to mitigate these citations patterns and increase the fairness citation score of their articles.
+The project will also consist in validating the accuracy of the CiteFair tool by making experiments on a sample of the scientific articles published within the last decade in a wide range of venues. Experiments will also focus on evaluating the usability and performance of the CiteFair tool.
+**Required skills or prerequisites:**
+Solid experience with JavaScript, HTML, and CSS
+**Recommended skills or prerequisites:**
+Experience with web-development frameworks (e.g., React JS, Spring Boot) and good oral and written skills in English
+==== Large Language Models based Test Case Generation ====
+**Course:**  EECS4080
+**Supervisor:**  Song Wang
+**Supervisor's email address:** wangsong@yorku.ca
+**Project Description:**
+Recently, pre-trained large language models (LLMs) have emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring the quality and reliability of software products. As the scope and complexity of software systems continue to grow, the need for more effective software testing techniques becomes increasingly urgent and making it an area ripe for innovative approaches such as the use of LLMs. Our recent collaboration with Meta also confirms the limitations of existing widely used testing techniques in test input generations, test oracle generation, and test scenario generation. This project takes a solid initial step towards exploring the next-generation software testing techniques powered by LLMs.
+**Required skills or prerequisites:**
+Be familiar with DL libraries such as Tensorflow and Pytorch;
+**Instructions:**
+Send your c.v. and transcript to supervisor