Faculty members, please use the following form to submit project descriptions for this page: https://forms.office.com/r/QH4QnYr8Hq

Summer term: May 6-August 6, with final presentations during August 8-15 approximately

This listing is being updated until the start of the summer term.

[added 2024-04-15]

Course: EECS4080

Supervisor: Meiying Qin

Supervisor's email address: mqin@yorku.ca

Project Description: Debugging is one of the most important skills for computer science students. However, first-year students are usually not comfortable with working with a debugger. In order to help ease the process for first-year students, we plan to write an application that can visualize the process by animating the variable manipulated, either on a screen or using virtual reality/augmented reality. In this project, you will have the opportunity to gain hands-on experience in both designing and implementing a software application. You will gain experience in animation or virtual reality.

Required skills or prerequisites:

• Proficient in Python (as you will write a visualized debugger for students learning Python)
• Strong learning ability (You will be expected to learn VR/AR programming if we decide to use NR/AR)

Recommended skills or prerequisites: Experience with virtual reality/augmented reality or animation

Instructions: Please send your c.v. and transcript to the project supervisor. If available, please also send your e-portfolio (e.g., GitHub, or other links) of your previous projects.

[added 2024-04-15]

Course: EECS4080

Supervisor: Meiying Qin

Supervisor's email address: mqin@yorku.ca

Project Description: One of the biggest challenges that first-year students face is the transition from high school to university. This is expected to be more pronounced once the York Markham campus opens as all courses will use the flipped-class model. In this model, students are required to be more active in learning and preview the content before each class in order to stay on track. In order to assist first-year students in making a smoother transition even before school starts, we plan to release a game that simulates the life of a computer science student at the Markham campus to provide students with a preview of university life. In this project, you'll have the opportunity to gain hands-on experience in both designing and implementing a game.

Required skills or prerequisites:

• Strong software engineering skills;
• The game will either be on Android or a web-based game. So you will need to have some experience in either Java or web development;
• an interest in helping first-year students and suggesting game components based on your own experience.

Recommended skills or prerequisites: Experience with graphical user interface or game design.

Instructions: Please send your c.v. and transcript to the project supervisor. If available, please also send your e-portfolio (e.g., GitHub, or other links) of your previous projects.

[added (partially) 2024-04-15]

Course: EECS4080

Supervisor: Meiying Qin

Supervisor's email address: mqin@yorku.ca

Project Description:

project description is being updated

[added 2024-04-15]

Course: EECS4080 or EECS4480

Supervisor: Yan Shvartzshnaider

Supervisor's email address: rhythm.lab@yorku.ca

Project Description: Modern sociotechnical systems share and collect vast amounts of information. These systems violate users’ privacy by ignoring the context in which the information is shared and failing to incorporate contextual information norms.

Using techniques in natural language processing, machine learning, network, and data analysis, this project is set to explore the privacy implications of mobile apps, online platforms, and other systems in different social contexts/settings.

To tackle this challenge, the project will operationalize a cutting-edge privacy theory and methodologies to conduct an analysis of existing technologies and design privacy-enhancing tools.

Students will help analyze information handling practices of online services and design privacy-enhancing tools.

Specific tasks include: comprehensive literature review of existing methodologies and tools, analysis of privacy policies and regulations, visualization of information collection practices, and design of a web-based interface for analyzing extracted privacy statements to identify vague, misleading, or incomplete privacy statements.

For prior project, see this link

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 independently. Interest in usable privacy, critical analysis of privacy policies and privacy related regulation.

Recommended skills or prerequisites: Experience with Machine Learning, Natural Language Processing techniques, HCI design. Students with diverse backgrounds, including in technical fields, social sciences and humanities are encouraged to apply.

Instructions: Please fill in this form

[added 2024-04-15]

Course: EECS4080 or EECS4480

Supervisor: Yan Shvartzshnaider

Supervisor's email address: rhythm.lab@yorku.ca

Project Description: Designing a privacy-preserving VR experience requires adhering to contextual integrity of users’ data . This involves accounting for different modes of interaction and ensuring robustness to accommodate the evolving privacy norms associated with future VR adaptations.

To tackle this challenge, the project will operationalize a cutting-edge privacy theory and methodologies to develop mechanisms that ensure that information flows in accordance with users’ expectations and established societal norms in VR settings.

Students will help analyze information handling practices of VR applications and design tools to enhance privacy of VR users.

Specific tasks include conducting a comprehensive literature review of existing methodologies and tools, performing a dynamic analysis of data practices in VR applications, and checking for compliance with existing regulations and privacy policies.

For reference, see these papers:

• https://arxiv.org/pdf/2303.13684.pdf
• https://www.usenix.org/conference/usenixsecurity22/presentation/trimananda

Required skills or prerequisites:

• Good programming skills (experience with coding in Unity is a plus)
• Interest in usable privacy, critical analysis of privacy policies, and knowledge of privacy-related regulations
• Ability to work independently

Recommended skills or prerequisites:

• Experience with data analysis using Jupyter and/or R
• Familiarity with HCI design

[added 2024-04-15]

Course: EECS4080

Supervisor: Professor Kiemute Oyibo

Supervisor's email address: koyibo@yorku.ca

Project Description: Many courses in Social Science, Health Science, and Computer Science that require memorization are becoming more and more challenging for many college and university students, especially with the ever-increasing volume of textbooks and course materials due to the growing body of knowledge. In courses, such as psychology, biology, and Human-Computer Interaction (HCI) that are memorization intensive, students are often overwhelmed by the lengthy course materials, readings, and the uphill challenge to retain and recall most of the content taught in class. Given the many challenges that most students face in higher education, including having to work to help pay for their university education, they often lack sufficient time to read and properly understand the taught material. Hence, there is a need to find effective ways to support student learning. We argue that serious games can be utilized to support student learning in memory-intensive courses to increase content comprehension and retention. Serious games are interactive applications that use game elements for education purposes rather than entertainment. They are increasingly being used in the education domain to support student learning. In this project, we aim to design, implement, and evaluate a mnemonic-based serious game to help students learn and retain taught material easily in memorization-intensive courses individually and collaboratively.

Duties and Responsibilities: Requirements gathering and analysis, prototyping, application programming and evaluation, data analysis, report writing and presentation.

Required skills or prerequisites:

• Prototyping with tools such as Figma and programming on the mobile platform (e.g., Android, cross-platform).
• Software Engineering; LE/EECS 4441 3.00 – Human-Computer Interaction, LE/EECS 3461 3.00 – User Interfaces
• Ability to work independently as well as in a team.

Instructions:

Contact professor at email above.

[revised 2024-03-15, added 2024-04-11]

Course: EECS4480/EECS4080

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 either C, Python or Java 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:

• To work on autograder for Python code, you 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.
• To work on autograder for Java code, you must have completed EECS2030 (or a similar course) with an A/A+. Must have solid programming skills in Java, including the use of unit testing
• To work on autograder for C code, you must have completed EECS 2031 (or a similar course) with an A/A+. Must have solid programming skills in C, preferably including the use of unit testing (though experience with unit testing in other languages is acceptable).
• 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.

[added 2024-04-11]

Course: EECS4080

Supervisor: Pooja Vashisth

Supervisor's email address: vashistp@yorku.ca

Project Description: The aim of this project is to enhance the course navigation experience for students by providing them with an interactive and intuitive interface that can assist with accessing course materials, finding relevant information, and answering commonly asked questions.

The main objectives of this project are as follows:

1. Develop a chat bot capable of understanding natural language queries and responses.
2. Integrate the chat bot into the existing LMS platform used by our institution.
3. Implement functionality for students to interact with the chat bot to access course materials, find information about courses, assignments, deadlines, and grades.
4. Provide personalized recommendations to students based on their preferences, past interactions, and learning progress.
5. Ensure the chat bot is responsive, reliable, and efficient in handling concurrent user requests.
6. Design a user-friendly interface for both desktop and mobile devices.

Required skills or prerequisites:

1. Expertise in Development tools and IDEs (e.g., Python, NLP libraries, web development frameworks)
2. Understanding of existing LMS platform and its database structure
3. Knowledge of Hardware and software infrastructure for hosting and deploying the chat bot system
4. Knowledge of Web Development:

• HTML/CSS: Knowledge of HTML and CSS is essential for designing the user interface of the chatbot within the LMS.
• JavaScript: JavaScript can be used to add interactivity and dynamic features to the chatbot interface.

Recommended skills or prerequisites:

• Machine Learning (Optional): TensorFlow or PyTorch. These popular machine learning frameworks can be used for more advanced NLP tasks, such as intent recognition and sentiment analysis, if the project requires more sophisticated chatbot capabilities.
• Database: SQLite or PostgreSQL. These relational database management systems (RDBMS) can be used to store and manage data related to user interactions, course materials, FAQs, and personalized recommendations.
• User Authentication and Integration: LMS API. Depending on the LMS platform being used, you may need to utilize the LMS API to integrate the chatbot system with the existing LMS, allowing access to course materials, user data, and other relevant information. User Authentication Libraries: Libraries such as OAuth or JWT (JSON Web Tokens) can be used to handle user authentication and secure access to the chatbot system.

Instructions: Please send your c.v., transcript, and Statement of Interest in the project to the project supervisor.

[added 2024-04-11]

Course: EECS4080

Supervisor: Pooja Vashisth

Supervisor's email address: vashistp@yorku.ca

Project Description: The objective of this project is to streamline the process of evaluating programming assignments by automating the grading process, providing timely feedback to students, and reducing the workload of instructors. The main objectives of this project are as follows:

1. Develop an autograding system capable of executing and evaluating C and Linux programs.
2. Design a user-friendly interface for instructors to define test cases and grading criteria.
3. Implement a secure and scalable infrastructure to handle multiple submissions and concurrent grading tasks.
4. Provide detailed feedback and grading reports to students, highlighting areas of improvement and errors.
5. Support various programming concepts and features, including file handling, system calls, and command-line utilities.
6. Ensure the autograder system is reliable, efficient, and scalable to accommodate a large number of students and assignments.

Methodology: The project will follow these general steps.

1. Conduct a thorough analysis of the requirements and specifications for autograding C and Linux programs.
2. Research and select appropriate tools, frameworks, and libraries for building the autograding system.
3. Design and develop the autograder system, including the front-end interface for instructors and the back-end components for executing and evaluating programs.
4. Implement a secure sandbox environment to run student programs safely and prevent malicious activities.
5. Integrate tools and utilities for compiling, executing, and capturing program output and errors.
6. Develop a grading engine that compares student outputs with expected outputs, considering various edge cases.
7. Implement a user-friendly interface for instructors to define test cases, grading rubrics, and manage assignments.
8. Test and evaluate the autograder system's performance, accuracy, and scalability using representative test cases and a simulated workload.
9. Document the development process, including system architecture, algorithms used, and any challenges faced during implementation.

Required skills or prerequisites: Tech stack for this project:

1. Programming Languages:
   • C: As the project involves autograding C programs, a strong understanding of the C programming language is necessary.
   • Python: Python can be used for developing the autograder system, as it offers a wide range of libraries and frameworks for web development, automation, and scripting.
2. Web Development:
   • HTML/CSS: Knowledge of HTML and CSS is essential for designing the user interface of the autograder system.
   • JavaScript: JavaScript can be used to add interactivity and dynamic functionality to the web-based interface.
3. System Execution and Grading:
   • Linux Environment: Good knowledge of Linux is crucial for setting up the execution environment and running student programs in a secure sandbox.
   • Bash scripting: Bash scripting can be used to automate the execution of student programs, capture output, and evaluate correctness based on defined test cases.

Recommended skills or prerequisites:

1. Frameworks and Libraries:
   • Flask or Django: These Python web frameworks can be used to build the back-end of the autograder system, handle requests, and manage the database.
   • Bootstrap: Bootstrap is a popular front-end framework that provides pre-built UI components and responsive design features, making it easier to create a user-friendly interface.
2. Database:
   • SQLite or PostgreSQL: These relational database management systems (RDBMS) can be used to store and manage data related to students, assignments, test cases, and grading criteria.

Instructions: Please send your c.v., transcript, and Statement of Interest in the project to the project supervisor