Events

Speaker: Zhengkun Shang, Master's Candidate

Emotions are an essential part of human social interactions. By integrating an automatic affect recognizer into an artificial system, the system can detect humans’ emotions and provide personal responses. We aim to build a prompting system that uses a virtual human with emotional interaction capabilities to help persons with a cognitive disability to complete daily activities independently.

Speaker: Ali Wytsma, Master's Candidate

The naive Bayes model is a simple model that has been used for many decades, often as a baseline, for both supervised and unsupervised learning. With a latent class variable it is one of the simplest latent variable models, and is often used for clustering. The estimation of its parameters by maximum likelihood (e.g., gradient ascent, expectation maximization) is subject to local optima since the objective is non-concave. However, the conditions under which global optimality can be guaranteed are currently unknown.

Speaker: Areej Alhothali, PhD Candidate

Natural language texts are often meant to express or impact individuals emotions. Sentiment analysis researchers are increasingly interested in investigating natural language processing techniques as well as emotions theories to classify sentiments expressed in natural language text. Most sentiment analysis research effort focuses on classifying highly opinionated documents from the writer's perspectives and uses either count-based word representations that ignore sentence structure or a small set of lexicon resources that do not cover the wide range of words used on the Internet.

Speaker: Mariah Shein, Master's Candidate

The development of the field of reinforcement learning was based on psychological studies of the instrumental conditioning of humans and other animals. Recently, reinforcement learning algorithms have been applied to neuroscience to help characterize neural activity and animal behaviour in instrumental conditioning tasks. A specific example is the hybrid learner developed to match human behaviour on a two-stage decision task. This hybrid learner is composed of a model-free and a model-based system.

Speaker: Alan Tsang, PhD Candidate

With the ever increasing ubiquity of social networks in our everyday lives, comes an increasing urgency for us to understand their impact on human behavior. Social networks quantify the ways in which we communicate with each other, and therefore shape the flow of information through the community. It is this same flow of information that we utilize to make sound, strategic decisions.

Speaker: Deepak Rishi, Master's Candidate

Sentiment and emotional analysis on online collaborative software development forums can be very useful to gain important insights into the behaviours and personalities of the developers. Such information can later on be used to increase productivity of developers by making recommendations on how to behave best in order to get a task accomplished. However, due to the highly technical nature of the data present in online collaborative software development forums, mining sentiments and emotions becomes a very challenging task.

Speaker: Valerie Platsko, Master's Candidate

Smart meter technology allows frequent measurements of water consumption at a household level. This greater availability of data allows improved analysis of patterns of residential water consumption, which is important for demand management and targeting conservation efforts. The dataset in this thesis includes 8,000 single family residences in Abbotsford, British Columbia from 2012 to 2013, and contains hourly measurements of water consumption recorded by smart meters installed in 2010. This work focuses on identifying outdoor consumption due to its contribution to peak demand during the summer, which is important because of concerns about strain on infrastructure in Abbotsford.

Speaker: Marta Kryven, PhD Candidate

Human beings quickly and confidently attribute more or less intelligence to one another. What is meant by intelligence when they do so? And what are the surface features of human behavior that determine their judgments? Because the judges of success or failure in the quest for 'artificial intelligence' will be human, the answers to such questions are an essential part of cognitive science. This thesis studies such questions in the context of a maze world, complex enough to require non-trivial answers, and simple enough to analyze the answers in term of decision-making algorithms.

Speaker: Feng-Xuan Choo, PhD candidate

Building large-scale brain models is one method used by theoretical neuroscientists to understand the way the human brain functions. Researchers typically use either a bottom-up approach, which focuses on the detailed modelling of various biological properties of the brain and places less importance on reproducing functional behaviour, or a top-down approach, which generally aim to reproduce the behaviour observed in real cognitive agents, but typically sacrifices adherence to constraints imposed by the neuro-biology. 

The focus of this thesis is Spaun, a large-scale brain model constructed using a combination of the bottom-up and top-down approaches to brain modelling. Spaun is currently the world's largest functional brain model, capable of performing 8 distinct cognitive tasks ranging from digit recognition to inductive reasoning. The thesis is organized to discuss three aspects of the Spaun model.

Speaker: Zhucheng Tu, Master's Candidate

Modelling the similarity of two sentences is an important problem in natural language processing and information retrieval, with applications in tasks such as paraphrase identification and answer selection in question answering. The Multi-Perspective Convolutional Neural Network (MP-CNN) is a model that improved previous state-of-the-art models in 2015 and has remained a popular model for sentence similarity tasks. However, until now, there has not been a rigorous study of how the model actually achieves competitive accuracy.