Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Mahzabeen Emu

Thesis title: Artificial Intelligence Empowered Virtual Network Function Deployment and Service Function Chaining for Next-Generation Networks

Abstract: The entire Internet of Things (IoT) ecosystem is directing towards a high volume of diverse applications. From smart healthcare to smart cities, every ubiquitous digital sector provisions automation for an immersive experience. Augmented/Virtual reality, remote surgery, and autonomous driving expect high data rates and ultra-low latency. The Network Function Virtualization (NFV) based IoT infrastructure of decoupling software services from proprietary devices has been extremely popular due to cutting back significant deployment and maintenance expenditure in the telecommunication industry. Another substantially highlighted tech trend for delay-sensitive IoT applications has emerged as Multi-access edge computing (MEC). MEC brings NFV to the network edge (in closer proximity to users) for faster computation. Among the massive pool of IoT services in NFV context, the urgency for efficient edge service orchestration is constantly growing. The emerging challenges are addressed as collaborative optimization of resource utilities and ensuring Quality-of- Service (QoS) with prompt orchestration in dynamic, congested, and resource-hungry IoT networks. Traditional mathematical programming models are NP-hard, hence inappropriate for time-sensitive IoT environments. In this thesis, we promote the need to go beyond the realms and leverage artificial intelligence (AI) based decision-makers for \smart" service management. We oer different ways of integrating supervised and reinforcement learning techniques to support future-generation wireless network optimization problems. Due to the combinatorial explosion of some service orchestration problems, supervised learning is more superior to reinforcement learning performancewise. Unfortunately, open access and standardized datasets for this research area are still in their infancy. Thus, we utilize the optimal results retrieved by Integer Linear Programming (ILP) for building labeled datasets to train supervised models (e.g., artificial neural networks, convolutional neural networks). Furthermore, we discover that ensemble models are better than complex single networks for control layer intelligent service orchestration. Contrarily, we employ Deep Q-learning (DQL) for heavily constrained (reduced state-space) service function chaining optimization. We carefully address key performance indicators (e.g., optimality gap, service time, relocation and communication costs, resource utilization, scalability intelligence) to evaluate the viability of prospective orchestration schemes. We envision that AI-enabled network management can be regarded as a pioneering tread to scale down massive IoT resource fabrication costs, upgrade profit margin for providers, and sustain QoS mutually.

Committee Members:
Dr. Salimur Choudhury (supervisor, committee chair), Dr. Yimin Yang, Dr. Khaled Rabie (Manchester Metropolitan University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Sadman Sakib

Thesis title: Designing AI-aided Lightweight Solutions for Key Challenges in Sensing, Communication and Computing Layers of IoT: Smart Health Use-cases

Abstract: The advent of the 5G and Beyond 5G (B5G) communication system, along with the proliferation of the Internet of Things (IoT) and Artificial Intelligence (AI), have started to evolve the vision of the smart world into a reality. Similarly, the Internet of Medical Things (IoMT) and AI have introduced numerous new dimensions towards attaining intelligent and connected mobile health (mHealth). The demands of continuous remote health monitoring with automated, lightweight, and secure systems have massively escalated. The AI-driven IoT/IoMT can play an essential role in sufficing this demand, but there are several chal- lenges in attaining it. We can look into these emerging hurdles in IoT from three directions: the sensing layer, the communication layer, and the computing layer. Existing centralized remote cloud-based AI analytics is not adequate for solving these challenges, and we need to emphasize bringing the analytics into the ultra-edge IoT. Furthermore, from the communication perspective, the conventional techniques are not viable for the practical delivery of health data in dynamic network conditions in 5G and B5G network systems. Therefore, we need to go beyond the traditional realm and press the need to incorporate lightweight AI architecture to solve various challenges in the three mentioned IoT planes, enhancing the healthcare system in decision making and health data transmission. In this thesis, we present different AI-enabled techniques to provide practical and lightweight solutions to some selected challenges in the three IoT planes. Therefore, by exploring one vital use-cases from a diverse pool of available use-cases in each of the IoT planes, we summarize the contribution of the thesis as the following: • Focusing on the sensing plane, chapter 3 employs Reservoir Computing (RC) for noise-removal from the magnetocardiography (MCG) signal for continuous low-rate monitoring of cardiovascular activities. • In chapter 4, to tackle the challenging task of dynamic channel selection in the communication plane, a deep learning-based predictive channel selection method is lever- aged. The proposed AI-aided method will unravel the potential challenges associated with the dynamic channel conditions in the B5G networks while transmitting massive health data (big data) from countless IoT devices. • Finally, to facilitate the computing plane of IoT, in chapter 5, we selected one crucial use-case of cardiac arrhythmia monitoring and investigated how to embed lightweight AI analytics into the ultra-edge IoT sensors. We have employed publicly available data sources to gain insights and evaluated the proposed AI solutions by carefully identifying different performance indicators. Thus, we envision that, in the forthcoming future of B5G networks, we can achieve a smart and connected healthcare system in decision-making and efficient health data transmission by adopting lightweight computing and blending IoT and AI.


Committee Members:
Dr. Zubair Fadlullah (supervisor, committee chair), Dr. Quazi Rahman, Dr. Sameh Sorour (Queen's University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

Department Of Visual Arts Visiting Artist Lecture Series
Kathleen Nicholls Artist Talk

Friday March 26th at 1:30 p.m. on Zoom

Kathleen Nicholls has been a worker in the arts and creative industries. She currently provides strategic arts policy and program advice as a cultural industries advisor in the Government of Nunavut. She was previously the Director of Programming at the Nunavut Arts and Crafts Association in Iqaluit and the Programming Coordinator at Galerie SAW Gallery in Ottawa; in these roles, she coordinated over 800 arts and cultural events, workshops, exhibitions, and festivals. Her curatorial work has been featured in BLOUIN ARTINFO, BBC.com, Canadian Art, Le Devoir, Global National News, Nunatsiaq News, The Ottawa Citizen, Ottawa Magazine, The Royal Academy of the Arts, Voir and the Winnipeg Free Press. She has taught courses in visual arts and communications at the University of Windsor and Nunavut Arctic College, and holds a Master of Fine Arts degree from the University of Windsor and a Bachelor of Fine Arts degree from Lakehead University.

Join Zoom Meeting: https://lakeheadu.zoom.us/j/92427229967?pwd=d1ZYYy9uY2JsbmI2M3lTendtQWtp...
Meeting ID: 924 2722 9967
Passcode: 968278

In conjunction with the International Day for the Elimination of Racial Discrimination, the Office of Human Rights and Equity is hosting an event to facilitate a conversation on creating inclusive classrooms.

Click here to register for the Zoom registration link. 

THE DEPARTMENT OF COMPUTER SCIENCE GRADUATE SEMINAR 2021
Guest Speaker Series Presented By:

Dr. Sandra Zilles
"Optimal Machine Teaching Without Collusion"

Thursday, March 18, 2021
12:30 pm

Abstract:
In supervised machine learning, in an abstract sense, a concept in a given reference class has to be inferred from a small set of labeled examples. Machine teaching refers to the inverse problem, namely the problem to compress any concept in the reference class to a "teaching set" of labeled examples in a way that the concept can be reconstructed.

The goal is to minimize the worst-case teaching set size taken over all concepts in the reference class, while at the same time adhering to certain conditions that disallow unfair collusion between the teacher and the learner. In this presentation, it is shown how preference relations over concepts can be used in order to guarantee collusion-free teaching and learning. Intuitive examples are presented in which quite natural preference relations result in data-efficient collusion-free teaching of complex classes of concepts.

Further, it is demonstrated that optimal collusion-free teaching cannot always be attained by the preference-based approach. Finally, we will challenge the standard notion of collusion-freeness and show that a more stringent notion characterizes teaching with the preference-based approach. This presentation summarizes joint work with Shaun Fallat, Ziyuan Gao, David G. Kirkpatrick, Christoph Ries, Hans U. Simon, and Abolghasem Soltani.

Dr. Sandra Zilles is a Professor of Computer Science at the University of Regina, where she holds a Canada Research Chair in Computational Learning Theory. Her research on machine learning and artificial intelligence is funded by government agencies and industry partners and has led to over 100 research publications.

More than 45 highly qualified personnel have been trained under her (co-)supervision. Methods developed in Dr. Zilles’s lab have found applications in research on autonomous vehicles, in research on genetics, and in cancer research. Dr. Zilles has won several awards for her research and is a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada.

Her expertise is often called upon on program committees and organizational committees of leading conferences in her field, and she is an Associate Editor for the reputable Journal of Computer and System Sciences. She also serves on the Board of Directors for Innovation Saskatchewan and on the Board of Directors for the Pacific Institute for the Mathematical Sciences (PIMS).

To register for this virtual event, please email grad.compsci@lakeheadu.ca and a Zoom link will be shared.

Everyone is welcome.

Are you starting Year One of the Two-Year Faculty of Education Professional Program in September 2021? Please attend one of the mandatory information sessions on Tuesday, March 23 at 11:00 am, or Wednesday, March 24 at 2:30 pm.

Zoom Link: https://lakeheadu.zoom.us/j/99743008336?pwd=SHR5clQ1TVRMekN0NERJUUUrR0VD...

Meeting ID: 997 4300 8336

Password: 848141

You will be informed about your placement options, how to research the Board where you want to do your practice teaching, the required documentation (i.e. Police Record Check with VSS, and mandatory health and safety training), and important protocols.

If you are in classes during these presentations and cannot attend, the necessary information will be sent to you about placement forms, police checks and LU signed letter, health and safety required training, etc. later in March/early April.

If you have any questions, please contact Kathy Matic at kmatic@lakeheadu.ca

Zotero is free citation management software that allows you to collect, organize and cite your research. This session will cover the fundamentals of Zotero including installation, adding sources to Zotero, organizing sources, creating reference lists and in-text citations.

Zotero is a powerful tool to manage your research. This session covers tips to further leverage Zotero including deduping, tagging, managing files, working with PDFs, and more.

Wading through the vast amount of information available and that is shared online can be challenging, even for librarians. In this session we will examine "disinformation" and "fake news" so that you can spot it and avoid it. We will also look at criteria for evaluating both popular and scholarly information sources and explore sources for reliable information.

Student: Zixuan Hu

Title: Cultivation, Cell Development, Gene Analysis of Marine Microalgae Pleurochrysis carterae and Isochrysis zhangjiangensis

Supervisory Committee:

1. Dr. Wensheng Qin (Supervisor)
2. Dr. Kam Leung
3. Dr. Baoqiang Liao
4. Dr. Renhui Li (External examiner)

Live via Zoom. Please contact biology@lakeheadu.ca for meeting ID and password.