the faculty specific ceremonies will include remarks from the faculty dean and the president & vice-chancellor, dr. moira mcpherson, a student speaker, and the conferral of graduates and medal recipients, followed by chancellor lyn mcleod’s remarks.

watch the ceremony here: https://www.youtube.com/channel/uclpdnqq41p2o_wo0o8j8c8w/featured

the faculty specific ceremonies will include remarks from the faculty dean and the president & vice-chancellor, dr. moira mcpherson, a student speaker, and the conferral of graduates and medal recipients, followed by chancellor lyn mcleod’s remarks.

visit this page to watch the ceremony: https://www.youtube.com/channel/uclpdnqq41p2o_wo0o8j8c8w/featured

the faculty specific ceremonies will include remarks from the faculty dean and the president & vice-chancellor, dr. moira mcpherson, a student speaker, and the conferral of graduates and medal recipients, followed by chancellor lyn mcleod’s remarks.

watch the ceremony here: https://www.youtube.com/channel/uclpdnqq41p2o_wo0o8j8c8w/featured

the faculty specific ceremonies will include remarks from the faculty dean and the president & vice-chancellor, dr. moira mcpherson, a student speaker, and the conferral of graduates and medal recipients, followed by chancellor lyn mcleod’s remarks.

to watch the ceremony, visit this page: www.youtube.com/channel/uclpdnqq41p2o_wo0o8j8c8w/featured

the faculty specific ceremonies will include remarks from the faculty dean and the president & vice-chancellor, dr. moira mcpherson, a student speaker, and the conferral of graduates and medal recipients, followed by chancellor lyn mcleod’s remarks.

watch the ceremony here: https://www.youtube.com/channel/uclpdnqq41p2o_wo0o8j8c8w/featured

title: “study of the activation of the inflammasome protein complex by haemophilus influenzae type a”

supervisory committee:
dr. marina ulanova (supervisor)
dr. neelam khaper
dr. kam leung
dr. aseem kumar (external examiner)

thursday, june 10th, 2021
10:00 am

all are welcome to attend. please contact biology@lakeheadu.ca for meeting id and password.

the department of history welcomes the 阿根廷vs墨西哥竞猜 community to an online presentation of the reel memories of the lakehead project.

television arrived in canada in 1952 and at the lakehead in 1954. early television was shot on 16mm film and this served the industry until the late 1970s. fortunately, local news footage from the first 20 years of broadcasting at the lakehead was donated by ckpr to the thunder bay museum several decades ago. approximately 250,000 feet or about 120 hours of footage from this period exists.

what makes this collection significant is the fact that the cities of port arthur and fort william were microcosms of the country as a whole. therefore, the news at the lakehead was a reflection of the news from across the country and the collection speaks to all aspects of life in canada at the time. this archival footage is also extremely rare because most television stations disposed of film when video technology replaced it. recent innovations in digital technology have finally provided a cost-effective way of freeing the film from its celluloid prison in order to make it available to researchers.

reel memories is a public history project dedicated to the preservation and exposition of the visual history of the lakehead region through this historical film footage.

join members of the reel memories team, dr. ron harpelle, along with lakehead alumni katie green, dr. tom peotto, for a presentation on their research into the first 20 years of the local television news at the lakehead. the presentation will take participants through the process involved in the preservation of the footage and provide glimpses into daily life at the lakehead in the 1960s and 70s. participants must register on the website below.

to learn more and for the link to watch the lecture virtually visit this webpage: https://www.thunderbaymuseum.com/reel-memories-of-the-lakehead/

this lecture session is part of the thunder bay historical museum society's long tradition of holding free public lectures.

please join the computer science department for the upcoming thesis defense:

presenter: kazi zainab khanam

thesis title: using homophily to analyze and develop link prediction model for healthcare professions with deep learning framework

abstract: twitter is a prominent social networking platform where users’ short messages or “tweets” are often used for analysis. however, there has not been much attention paid to mining the medical professions, such as detecting users’ occupations from their biographical content. mining such information can be useful to build recommender systems for cost-effective advertisements. conventional classifiers can be used to predict medical occupations, but they tend to perform poorly as there are a variety of occupations. as a result, the main focus of the research is to use various deep learning techniques to examine the textual properties of twitter users’ biographic contents, network properties, and the impact of homophily of twitter users employed in medical professional fields. in chapter 2, a survey is presented based on the concept of homophily as well as important social network topics that summarize the state of art methods that has been proposed in the past years to identify and measure the effect of homophily in multiple types of social networks. this enables us to find open challenges and directions for future research. in chapter 3, a model has been developed to identify twitter users working in medical professional fields by using textual properties of the twitter users’ bio contents. we have conducted our analysis by annotating the content of twitter users’ bios and propose a method of combining word embedding with state-of-art neural network models. finally, in chapter 4, the research introduces a link prediction model based on the homophily concept by using the twitter users’ followers and following ids identified from chapter 3. recent research has centered on analyzing rapidly2 evolving networks. while predicting links in dynamic networks is difficult, deep learning techniques and network representation learning algorithms, such as node2vec, have demonstrated significant improvements in prediction accuracy. however, node2vec’s stochastic gradient descent (sgd) approach is prone to falling into a local optimum, and as a consequence, node2vec fails to capture the network’s global structure. to address this problem, we propose noddle (integration of node2vec and deep learning method), a deep learning system in which we combine node2vec’s features and feed them into a four-layer hidden neural network. noodle takes advantage of adaptive learning optimizers for improving the performance of link prediction. on different social network datasets, experimental findings show that our approach outperforms conventional methods.

committee members:
dr. vijay mago (supervisor, committee chair), dr. yimin yang, dr. rajesh sharma (university of tartu)

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: mohiuddin md abdul qudar

thesis title: development of language model and opinion extraction for text analysis of online platforms

abstract: language models are one of the fundamental components in a wide variety of natural language processing tasks. the proliferation of text data over the last two decades and the developments in the field of deep learning have encouraged researchers to explore ways to build language models that have achieved results at par with human intelligence. an extensive survey is presented in chapter 2 exploring the types of language models, with a focus on transformer-based language models owing to the state-of-the-art results achieved and the popularity gained by these models. this survey helped to identify existing shortcomings and research needs. with the advancements of deep learning in the domain of natural language processing, extracting meaningful information from social media platforms, especially twitter, has become a growing interest among natural language researchers. however, applying existing language representation models to extract information from twitter does not often produce good results. to address this issue, chapter 3 introduces two tweetbert models which are domain specific language presentation models pre-trained on millions of tweets. tweetbert models significantly outperform the traditional bert models in twitter text mining tasks. moreover, a comprehensive analysis is presented by evaluating 12 bert models on 31 different datasets. the results validate our hypothesis that continuously training language models on twitter corpus helps to achieve better performance on twitter datasets. finally, in chapter 4, a novel opinion mining system called onset is presented. onset is mainly proposed to address the need for large amounts of quality data to fine-tune state-of-the-art pre-trained language models. fine-tuning language models can only produce good results if trained with a large amount of relevant data. onset is a technique that can fine-tune language models for opinion extractions using unlabelled training data. this system is developed through a fine-tuned language model using an unsupervised learning approach to label aspects using topic modeling and then using semi-supervised learning with data augmentation. with extensive experiments performed during this research, the proposed model can achieve similar results as some state-of-the-art models produce with a high quantity of labelled training data.

committee members:
dr. vijay mago (supervisor, committee chair), dr. gautam srivastva (adjunct professor), dr. karthik srinivasan (university of kansas)

please contact grad.compsci@lakeheadu.ca for the zoom link.
everyone is welcome.

may 20th, 2020 is global accessibility awareness day (gaad)!

global accessibility awareness day (gaad) is a global event that was established to get us talking, thinking, and learning about digital accessibility for users with different disabilities.

to celebrate gaad, student accessibility services is hosting an event to encourage the use of captioning in zoom across campus! join us on zoom at 11 am to learn everything about zoom captioning, then go to https://globalaccessibilityawarenessday.org/events/ to find out about all the events happening globally in support of accessibility.

https://lakeheadu.zoom.us/j/92979248907?pwd=nuowue5sqxjumunsvlfrtzz2tgkz...