This paper presents an overview of a human-centered initiative aimed at strengthening climate resilience along Nova Scotia's Eastern Shore. This region, a collection of rural villages with deep ties to the sea, faces existential threats from climate change that endanger its way of life. Our project moves beyond a purely technical response, weaving together expertise from Computer Science, Industrial Engineering, and Coastal Geography to co-create tools with the community. By integrating generational knowledge of residents, particularly elders, through the Eastern Shore Citizen Science Coastal Monitoring Network, this project aims to collaborate in building a living digital archive. This effort is hosted under Dalhousie University's Transforming Climate Action (TCA) initiative, specifically through its Transformative Adaptations to Social-Ecological Climate Change Trajectories (TranSECT) and TCA Artificial Intelligence (TCA-AI) projects. This work is driven by a collaboration model in which student teams work directly with residents. We present a detailed project timeline and a replicable model for how technology can support traditional communities, enabling them to navigate climate transformation more effectively.

A wall of high pressure stood in its westward path, poised to deflect the storm away from Florida and in a grand arc northeast. It was 10 days out from the earliest possible landfall--eons in weather forecasting--but meteorologists at the European Centre for Medium-Range Weather Forecasts, or ECMWF, were watching closely. The tiniest uncertainties could make the difference between a rainy day in Scotland or serious trouble for the US Northeast. Typically, weather forecasters would rely on models of atmospheric physics to make that call. This time, they had another tool: a new generation of AI-based weather models developed by chipmaker Nvidia, Chinese tech giant Huawei, and Google's AI unit DeepMind.

Objective: To develop machine learning models that can predict the number of COVID-19 cases per day given the last 14 days of environmental and mobility data. Approach: COVID-19 data from four counties around Toronto, Ontario, were used. Data were prepared into daily records containing the number of new COVID case counts, patient demographic data, outdoor weather variables, indoor environment factors, and human movement based on cell mobility and public health restrictions. This data was analyzed to determine the most important variables and their interactions. Predictive models were developed using CNN and LSTM deep neural network approaches. A 5-fold chronological cross-validation approach used these methods to develop predictive models using data from Mar 1 to Oct 14 2020, and test them on data covering Oct 15 to Dec 24 2020. Results: The best LSTM models forecasted tomorrow's daily COVID case counts with 90.7% accuracy, and the 7-day rolling average COVID case counts with 98.1% accuracy using independent test data. The best models to forecast the next 7 days of daily COVID case counts did so with 79.4% accuracy over all days. Models forecasting the 7-day rolling average case counts had a mean accuracy of 83.6% on the same test set. Conclusions: Our findings point to the importance of indoor humidity for the transmission of a virus such as COVID-19. During the coldest portions of the year, when humans spend greater amounts of time indoors or in vehicles, air quality drops within buildings, most significantly indoor relative humidity levels. Moderate to high indoor temperatures coupled with low IRH (below 20%) create conditions where viral transmission is more likely because water vapour ejected from an infected person's mouth can remain longer in the air because of evaporation and dry skin conditions, particularly in a recipient's airway, promotes transmission.

In a voyage lasting 40 days and conquering approximately 3 500 unmanned miles at sea, the Mayflower Autonomous Ship arrived in North America in Halifax, Nova Scotia on June 5, 2022. Following two years of design, construction and AI model training, the Mayflower Autonomous Ship (MAS) was officially launched in September 2020. Fast forward to 5 June 2022, and the ship completed an historic transatlantic voyage from Plymouth, UK to its North American arrival in Halifax, Nova Scotia. With no human captain or onboard crew, MAS is the first self-directed autonomous ship with technology that is scalable and extendible to traverse the Atlantic Ocean. MAS was designed and built by marine research non-profit ProMare with IBM acting as lead technology and science partner, with IBM automation, AI and edge computing technologies powering the ship's artificial intelligence (AI) captain to guide the vessel and make real-time decisions while at sea.

IBM's self-sailing Mayflower ship, tasked with making it across the Atlantic without any humans onboard to help, has suffered another mechanical glitch preventing it from continuing its intended journey. Named after the vessel that brought passengers from England to America in the 17th century, the Mayflower Autonomous Ship (MAS) was expected to retrace that historical voyage. But its attempts to cross the ocean, led by ProMare – a non-profit organization focused on marine research, with support from IBM – haven't exactly gone smoothly. We admire the tenacity and the project's aims but we're not going to pretend this has been perfect. The first attempt last year ended in failure.

We are currently looking to identify a Psychiatry Postdoctoral Fellow (PDF) to cover our Research Coordinator for the MIRA, Mental Health Virtual Assistant project for the period of 12 months (maternity leave) - starting July 1st, 2022 (somewhat negotiable). They will be working on a multi-disciplinary team, inclusive of 2 computing science Master students, 2 psychiatry Postdoctoral Fellows (one working hand-in-hand with this Fellow on the expansion of services to additional groups and provinces (supporting French language translation and service expansion to Quebec, among other responsibilities), and one supporting the expansion of services to children and youth), and 1 Indigenous studies PhD student, supporting the co-creation of MIRA with Indigenous communities. Funding is secured to fully support this position. The PDF would be first offered a 6-month contract, with the opportunity for another 6 month extension following a review. The PDF would be working under the supervision of Drs.

Sheema Khan is the author of Of Hockey and Hijab: Reflections of a Canadian Muslim Woman. The warning signs had been there all along. An assault on a 15-year-old boy; death threats against the man's own parents; a police safety bulletin warning of his gun stash and desire to kill a cop; violent attacks against his spouse; a weapons complaint to the RCMP; fear by neighbours and relatives of his sociopathic behaviour; rampant alcoholism. As an in-depth Globe feature reported, the nation's worst mass shooter "was the kind of man who made people nervous, bragged about knowing how to dispose of bodies and built miniature coffins as a hobby." As we wait for the launch of a public inquiry, there are so many questions about the horrible incident in Nova Scotia.

If a recent demonstration in Canada is any indication, the digital vineyard of the future might very well receive scientific data in real-time gathered by drones and transferred via a cell network. Global UAV Technologies Ltd., Jöst Vineyards, VineView (Scientific Aerial Imaging Inc.) and a major Canadian telecommunications company recently completed a 4G proof-of-concept mission in in Malagash, Nova Scotia, to demonstrate a real-word application of drone technology for a project called the "Digital Vineyard of the Future." "Fine wine making is in the growing of grapes with specific qualities, where many variables have to be taken into consideration," said Jonathan Rodwell, director of viticulture and winemaking for Jöst Vineyards. "We see these emerging technologies offering excellent opportunities for integrated measurement and management of our vineyards and focus on precision viticulture." Global UAV provided a 4G-enabled, Procyon 800E helicopter drone platform with a specialized multi-spectral imaging payload.

Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest. In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors. We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback. Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets.

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at causes of STEM attrition, let alone the use of machine learning to examine how well this phenomenon can be predicted. In this paper, we detail our efforts to model and predict dropout from STEM fields using one of the largest known datasets used for research on students at a traditional campus setting. Our results suggest that attrition from STEM fields can be accurately predicted with data that is routinely collected at universities using only information on students' first academic year. We also propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. We believe these results show great promise in using machine learning to improve STEM retention in traditional and non-traditional campus settings.