This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major North American shelter containing 12 years of service interactions with over 40,000 individuals, the optimized pruning for unordered search (OPUS) algorithm is used to develop rules that are both intuitive and effective. The rules are evaluated within a framework compatible with the real-time delivery of a housing program meant to transition high risk clients to supportive housing. Results demonstrate that the median time to identification of clients at risk of chronic shelter use drops from 297 days to 162 days when the methods in this paper are applied.

We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data, modeled as members of a metric space. Our key step is to build universal kernels (cc-universal) on the metric spaces, which results in reproducing kernel Hilbert spaces for the predictor and response that are rich enough to characterize the conditional independence that determines sufficient dimension reduction. For univariate distributions, we construct the universal kernel using the Wasserstein distance, while for multivariate distributions, we resort to the sliced Wasserstein distance. The sliced Wasserstein distance ensures that the metric space possesses similar topological properties to the Wasserstein space while also offering significant computation benefits. Numerical results based on synthetic data show that our method outperforms possible competing methods. The method is also applied to several data sets, including fertility and mortality data and Calgary temperature data.

The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

The Department of Civil Engineering in the Schulich School of Engineering and the Department of Community Health Sciences in the Cumming School of Medicine at the University of Calgary are accepting applications for a Postdoctoral Scholar in the development of a novel agent-based granular model of the COVID-19 pandemic in a heterogenous population. A multi-scale mechanistic approach is used that embeds detailed characteristics of viral spread and individual interactions. The research project will focus on calibrating the model that is currently being developed, while also interfacing with a highly interdisciplinary team across the fields of epidemiology, virology, bioinformatics, sociology, economy, psychology, and community health. The work will involve gathering and analyzing data pertaining to social demographics and socio-cultural factors, including viral and epidemiological aspects. A main source of this data will be compiled from the Centre for Health Informatics (CHI) at the University of Calgary, medical and social psychology journals as well as France's Disease Institute (INSERM).

This paper introduces Teachable Reality, an augmented reality (AR) prototyping tool for creating interactive tangible AR applications with arbitrary everyday objects. Teachable Reality leverages vision-based interactive machine teaching (e.g., Teachable Machine), which captures real-world interactions for AR prototyping. It identifies the user-defined tangible and gestural interactions using an on-demand computer vision model. Based on this, the user can easily create functional AR prototypes without programming, enabled by a trigger-action authoring interface. Therefore, our approach allows the flexibility, customizability, and generalizability of tangible AR applications that can address the limitation of current marker-based approaches. We explore the design space and demonstrate various AR prototypes, which include tangible and deformable interfaces, context-aware assistants, and body-driven AR applications. The results of our user study and expert interviews confirm that our approach can lower the barrier to creating functional AR prototypes while also allowing flexible and general-purpose prototyping experiences.

Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other designated tasks. In this paper, we propose FedLE, an energy-efficient client selection framework that enables lifespan extension of edge IoT networks. In FedLE, the clients first run for a minimum epoch to generate their local model update. The models are partially uploaded to the server for calculating similarities between each pair of clients. Clustering is performed against these client pairs to identify those with similar model distributions. In each round, low-powered clients have a lower probability of being selected, delaying the draining of their batteries. Empirical studies show that FedLE outperforms baselines on benchmark datasets and lasts more training rounds than FedAvg with battery power constraints.

The AI can write essays and poems and answer questions about many topics. "If I thought about using ChatGPT for creating an essay, the answer could be ok – I might use it, but I will also be using these other resources because my interest as a student is to create something that can be for the benefit of others," said Moya who is a research assistant with a new University of Calgary research project investigating the ethical use of AI in post-secondary learning and teaching. "I want to be creative and create opportunities for others." Known as ChatGPT and created by a company called OpenAI, the software is designed to generate human-like responses to a wide range of inputs by using algorithms. "It was interesting to see that this tool could provide some particular insights that could become the starting point of something," Moya said.

Artificial intelligence apps, such as ChatGPT, can be part of our educational toolbox just as dictionaries, calculators, and web searches are. If we think of artificial intelligence apps as another tool that students can use to ethically demonstrate their knowledge and learning, then we can emphasize learning as a process not a product. Have open and honest conversations with your students about your expectations regarding artificial intelligence apps and their use in your courses. Start with a discussion on artificial intelligence literary and what that means in your course. Think about your current assessments through the lens of artificial intelligence.

Global AI-powered Order-to-Cash platform, Sidetrade, announced an acceleration to its North America offensive strategy with plans to invest $24 million and add 110 full-time jobs in Calgary over the next three years. Just one year since the launch of its North America operations, Sidetrade has exceeded expectations with 58% of its new bookings now from the North America market. The SaaS provider has been recognized by Gartner as one of just three Leaders in the 2022 Magic Quadrant for Invoice to Cash applications. Sidetrade is now accelerating its expansion into North America by investing $24 million in the next three years and hiring in the region. Brad Parry, President and CEO of Calgary Economic Development, said: "Sidetrade's expansion in Calgary as its North American headquarters speaks to the city's leading business environment and the exciting momentum in our tech and innovation ecosystem. Alberta and Calgary are centres for AI excellence with highly skilled talent, and as a global leader in AI, Sidetrade joins a growing roster of multinational companies that call Calgary home, where bright minds with big ideas are solving global challenges."

Fotech has launched two next-generation Helios DAS systems at the International Pipeline Expo in Calgary, Canada, between 27 and 29 September 2022. The new Helios DAS TL4 (single-channel) and the Helios DAS TX4 (dual-channel) interrogators deliver lower false alarm rates and enhanced monitoring and incident detection. They incorporate new machine learning capabilities, which allows a faster, cost effective and more systematic deployment of solutions in long linear assets such, as pipelines and perimeters. Pedro Barbosa, Senior Product Manager at Fotech, says, "The new Helios DAS TL4 and Helios DAS TX4 interrogators take monitoring of pipelines, critical infrastructure and perimeters to the next level. The machine learning that is built into them means they deliver exceptional accuracy with a much-reduced false alarm rate. As a result, users have extremely high confidence in alarms, and don't waste precious time or resource investigating false alarms."