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 ====== Proposed Projects   ======
-Below is a list of 4080 projects proposed by faculty members for Summer 2020. More project descriptions may be added once they come in. Please contact the supervisor directly you are in interested in a project. You can also contact faculty members individually to discuss possible projects. Click [[http://eecs.lassonde.yorku.ca/community/faculty-members/|here]] to see a list of faculty members, their research areas and their contact information.
+Below is a list of 4080/4088 projects proposed by faculty members. More projects may be added once they come in (normally in August). Please contact the supervisor directly you are in interested in a project. You can also contact faculty members individually to discuss possible projects. Click [[http://eecs.lassonde.yorku.ca/community/faculty-members/|here]] to see a list of faculty members, their research areas and their contact information.
+====== Characterizing, Understanding, and Detecting Software Bugs in Machine Learning Frameworks ======
+**Course:** EECS4080
+**Supervisor:** Song Wang
+**Contact:** <wangsong@yorku.ca>
+Today, machine learning and its applications has been integrated into every part of our society. With the development and evolution of information technologies such as smartphones, self-driving cars, and robotics, the dependence of our society on machine learning systems continues to grow, which makes the impact and cost of bugs in these intelligent systems increase dramatically. Building reliable and secure machine learning systems has become an increasingly critical challenge for software developers. Thus, characterizing and understanding the root cause of software bugs in ML frameworks is critical to detecting and repairing buggy behavior.
+This project will first conduct an empirical study to analyze and summarize unique characteristics of software bugs collected from four typical ML frameworks (PyTorch, TensorFlow, CNTK, and Theano). Based on the findings from the empirical study, we will further propose actionable guidelines for designing and exploring new software bug detection approaches to find new bugs on machine learning frameworks and their applications.
+**Required skills:** General EECS4313 and EECS3311 prerequisites;
+**Recommended skills:** Solid programming skills with Python and Java.
+====== Image Processing for Social Coding ======
+**Course:** EECS4088 (Capstone Project)
+**Supervisor:** Maleknaz Nayebi
+**Contact:** <mnayebi@yorku.ca>
+Software developers are increasingly sharing images in social coding environments such as stack overflow, GitHub, Bugzilla, and Slack. This growth is parallel to the general growth of in visual interactions in general purpose online social networks like Instagram, Facebook, and Pinterest. Developers' shared images are meaningful and provide complementary information compared to their associated text. These images extensively help in understanding the change requests, questions, or the responses submitted. Relying on these observations, we are working on automatically processing these images by first forming a database similar to ImageNet for software domain and then applying the text detection object detection and automated captioning techniques on these data. We will work with software teams and software developers to evaluate the usefulness of our methods and results. The project includes:
+(i) Forming and structuring a dataset of crowd labelled images for software related images,
+(ii) Mining content and objects within developers’ shared images,
+(iii) Automatic captioning the images to describe the content.
+**Required skills:** Good knowledge of Python, Good knowledge on Machine learning, Good knowledge of image processing, Ability to work independently, Ability to communicate clearly.
+====== Release Management for Mobile Apps ======
+**Course:** EECS4080
+**Supervisor:** Maleknaz Nayebi
+**Contact:** <mnayebi@yorku.ca>
+Release planning problem is addressing the question of what functionality should be included in any version and when that version should be released to different segments of users. Proper decision about the functionality of evolving software product releases is critical for the success or failure of a product.  The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. This led into the concept of user driven release management. This project includes:
+(i) Evaluating the applicability of four state of the art release management tools for mobile apps,
+(ii) Testing and enhancing the prototype tools for these four release management techniques.
+**Required skills:** Good knowledge of Python, Ability to manage Git and Jupiter Notebooks, Good knowledge on Machine learning, Ability to work independently, Ability to communicate clearly.
+====== Estimating emotional state from a speech audio signal ======
+**Course:** EECS4080/4088
+**Supervisor:** Michael Jenkin
+**Contact:** <jenkin@eecs.yorku.ca>
+When a robot interacts with a human it can be very helpful if the robot can estimate the emotional state of the person to whom it is talking. This estimation can be made in a number of ways, from the choice of words used, the visual cues obtained from a camera pointed at the person, and through an analysis if the speech signal obtained from a microphone listening to the speaker. This project is concerned with this last task. This project will apply modern recurrent neural networks, and in particular LSTM to the problem. The project will involve three major phases
+(i) Background work: Understanding the problem of estimating speech emotion from the audio signal and a review of approaches to date. Identifying and obtaining access to appropriate datasets for training and evaluations.
+(ii) Basic implementation: Based on the results of above, implement an existing LSTM-based approach to the problem and evaluate on the dataset identified above.
+(iii) Integration with a social robot system (SENTRYNet) so that the robot can utilize this information in the development of dialog when interacting with speakers.
+**Required skills:** General EECS408x prerequisites. Good knowledge of Python. Ability to work independently.
+====== Automatic classification of Eurasian Water-Milfoil from sonar and visual data ======
+**Course:** EECS4080/4088
+**Supervisor:** Michael Jenkin
+**Contact:** <jenkin@eecs.yorku.ca>
+Many lakes in Canada are infested with Eurasian Water-Milfoil (see Eurasian Water-Milfoil – Ontario's Invading Species ...www.invadingspecies.com › eurasian-water-milfoil). Eurasian Water-Milfoil (EWM) is an invasive water plant that is detrimental to the health of the lake which is infected. Treating such infections requires understanding where the infections are and the state of the infection. As part of a larger robot project, this project will develop a system for the automatic classification of sensor signals from an autonomous robot boat this is being deployed to monitor EWM infestation in a lake north of Toronto. This work will concentrate around the development of an Amazon Mechanical Turk to assist in labelling visual and sonar imagery to train a CNN to label infestation levels at different locations in the lake. The project will involve three major phases
+(i) Assist in data collection. For the first few weeks of term (before the weather changes) you will help in data acquisition, editing and organization while reviewing the process of Mechanical Turk systems (such as the Amazon toolkit).
+(ii) In consultation with domain experts, develop a model for infestation level and construct a Turk to apply this to a training dataset
+(iii) Develop a DNN to label the multi-modal dataset and evaluate it.
+**Required skills:** General EECS408x prerequisites. Good knowledge of Python. Ability to work independently. Ability to interact with subject matter experts. Willingness to participate in data collection sessions as scheduled in the early fall.
+====== A framework for VR-based vestibular assessment tools ======
+**Course:** EECS4080/4088
+**Supervisor:** Michael Jenkin
+**Contact:** <jenkin@eecs.yorku.ca>
+Commodity VR is already being used as part of ongoing assessment of a range of different conditions, including vestibular damage and its treatment. This project involves the development of a software library (written in Unity) to provide VR-based tools to support vestibular treatment both at the clinic and remotely. This work will build upon an existing code base but will (i) refactor the software so as to be useful as a more general library, and (ii) support a wide range of assessment and rehabilitation tools. This work will involve interaction with an off-site partner in their physiotherapy office. The project will involve
+(i) review of VR-based vestibular assessment and treatment and the existing system and treatment/assessment systems
+(ii) Understanding the existing code base and development of library structure to ensure a more device-agnostic input system
+(iii) Development of a small number of treatment modules (2 or 3) to demonstrate the effectiveness of the tools/libraries developed
+**Required skills:** General EECS408x prerequisites. Good knowledge of C#. Ability to work independently.
+/*
+**Recommended skills:** Good programming skills, good math skills, experience with Python
+*/
+/*
 ====== Serverless Computing Simulator ======
 **Supervisor:** Hamzeh Khazaei
@@ Line 57: / Line 161: @@
 **Recommended skills:** Basic knowledge in Linux containers and microservice architecture, blockchain systems, Python and serverless computing.
+====== The Platform for Modeling and Optimizing Serverless Applications ======
+**Supervisor:** Hamzeh Khazaei
+**Contact:** <hkh@eecs.yorku.ca>
+Coupled with the development of lightweight virtualization, specifically containers, is a growing trend in the paradigm of cloud computing shifting from Infrastructure-as-a-Service (IaaS) to Function-as-a-Service (FaaS). Many cloud service providers have launched their FaaS platforms where developers can host serverless functions with high-scalability, pay-as-you-go billing model, and reduced management overhead. However, such a new trend raises new important questions, including the predictability and optimization of the performance and cost of serverless applications.
+In PACS Lab, researchers have been working on addressing these urgent needs in the serverless computing area not yet addressed by industries and academics. This project is based on our published and submitted papers in which we give practical solutions to performance modeling and optimization problems for the serverless computing paradigm.
+This project will solve pain points for serverless application developers by providing an online ready-to-use platform for profiling, predicting, and optimizing serverless applications. The student will involve in developing a toolkit that integrates multiple APIs of major cloud service providers, automates the deployment, test, and performance profiling of serverless applications, and leverages analytical models to give developers informed performance and optimized application configurations. The project will involve studying the serverless architecture and developing a web application.
+**Required skills:** General EECS408x prerequisites; experience with major cloud computing platforms and APIs, experience with any web framework.
+**Recommended skills:**
+Experience with containers and serverless functions, solid programming skills.
+*/
 /*