Active learning for automatic generation of narratives from numeric financial and supply chain data
Large enterprises compile and analyze large amounts of data on a daily basis. Typically the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by financial analysts can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand automating some of the manual processing requires more involved artificial intelligence techniques.
In our project we aim to solve one of these harder to automate tasks. In fact analyzing textual data using Natural Language Processing (NLP) techniques is one of the standardized methods of data processing in modern software tools. However the vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives.
Since the generation of text is a domain-dependent and non-trivial task, the automated generation of narratives requires novel research to be useful in an enterprise environment. In this project we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in the executive level of companies. Upon successful completion of the project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher-level data fusion skills.
An intelligent immersive content creation platform for the non-programmer for training, maintenance and assembly using AR and VR.
OVA’s StellarX is an uncompromisingly good virtual and augmented reality software. Purpose-built for teams that want to collaborate and create in those new paradigms.
COVID-19 AI based screening and monitoring of COVID-19 respiration patterns using acoustic sensors
This project addresses the pressing need for remote monitoring of long-term care homes to ensure potential cases of COVID-19 are identified early, isolated and treated.
Ryerson University’s Xiao-Ping Zhang, in concert with Altum View Systems Inc., will develop AI-based algorithms and systems to screen and monitor acoustic respiration patterns for COVID-19 in real-time, using customer mobile devices (e.g., mobile phones, wireless headphones, smart wrist-watches, etc.), low-cost electronic stethoscopes, and professional respiration monitor diagnostic devices. The COVID-19 acoustic respiration pattern screening and monitoring system will be incorporated into and complement Altum View Systems Inc.’s current camera-based health monitoring system for home care and long-term care facilities.
COVID-19: Agent-based framework for modelling pandemics in urban environment
The development of COVID-19 pandemic raises important questions on optimal policy design for managing and controlling the number of people affected. In order to answer these questions, one needs to better understand determinants of pandemic dynamics. Indeed, the development of epidemics depends on various factors including the intensity and frequency of social contacts and the amount of care and protection applied during those contacts. In particular, one area where the disease can be transmitted is the urban space of a large city such as Toronto.
The goal of the project is to create an agent-based framework for building virtual models of an urban area. This framework will be used as a virtual laboratory for testing various scenarios and their implications for the development of pandemics. In order for conclusions to be reliable, the models (known in the literature as synthetic population models or digital twins) have to be up to scale, with the number of agents comparable with the population of the city. This, in turn, requires implementations ready to be run in a large-scale distributed computing environment in the cloud as the algorithms behind the engine need high-performance computing power.
The framework will allow us to evaluate different COVID-19 mitigation policy designs. This includes possible decisions such as decreasing proneness to wearing masks, closing down some non-essential, high-contact, social network nodes (for example, hairdressers), limiting the number of people having simultaneous social gatherings or reducing the number of people on streets altogether via promoting actions such as #stayathome.
Creating a Predictive Financial Model to Optimize Digital Marketing Budget of SMEs Post COVID-19
In recent years, digital marketing has surpassed offline marketing and made marketing technology (Martech) the number one spending priority of businesses that didn’t exist ten years ago (Johnsen, 2017). Moreover, in the coming months, businesses are going to become more reliant than ever on their digital strategy. Without wanting to sound too alarmist, in many cases it will be the deciding factor in whether they make it through the tough times ahead. One thing is clear: marketers in the post-COVID-19 era will have to rethink what technologies they really need, which ones can help them save money, and which ones can help them transform their businesses that have been altered by this crisis. Data science can help companies acquire more customers, it can tell when, where and how to pitch target audience to maximize yields and minimize waste (it is estimated that on average 26% of marketing budgets are wasted on ineffective channels and strategies (Blake, Nosko, Tadelis, 2015)).
Using the right data sources, we can build simple (and more complex) models to predict impact on the customers’ behaviors and PPC (pay-per-click) conversion rates respectively, if you run a campaign at a certain point in time. These predictive analytics can estimate when the desired action will happen and what can impact it, and what would be the financial value.
The objective of this research is to introduce a predictive financial model to help Genius Camp and other SMEs in the Education industry, which are disrupted by COVID-19 situation, to allocate their digital marketing budget more efficiently. The data-backed solutions from this model will equip them with tools to make more informed decisions about going virtual and their resulting digital marketing initiatives. (i.e. how to structure their social media campaigns to achieve the highest ROI)
Detecting and Responding to Hostile Information Activities: unsupervised methods for measuring the quality of graph embeddings
The rise in online organized disinformation campaigns presents a significant challenge to Canadian national security. State and non-state hostile actors manipulate users on social media platforms to advance their interests. Patagona Technologies is a Toronto-based software development company started by two Ryerson alumni. The project with Ryerson University is a larger initiative with the Canadian Department of National Defense to address the challenges posed by online hostile actors by analyzing the structure and content of social networks.
Developing Efficient Machine Learning Models for Price Bidding
Curate Mobile operates a demand site platform (DSP), which is an advertising platform responsible forbidding in real time ad placements from various publishers. This process is a blind auction, happeningover 50,000 times a second, and during this bidding process we have less then 100ms to determinewhich of our clients should bid for this ad placement, how much it might be worth to them, and whatprice we believe we can win this auction for. During this project, we will add machine learning modelsto our DSP to provide fast decisions in real time to maximize the return on ad spend of our clients’campaigns. The main goal for this project is to add a proof-of-concept machine learning model to CurateMobile’sDSP, with a pipeline that will continually update the models with new data as it is ingested. Wewill also design a validation module to monitor and validate the performance of the developed models.
Development of a COVID-19 in pregnancy data respository and prognostication algorithm
Professors Dafna Sussman & Rasha Kashef of Ryerson University are teaming up with Mount Sinai Hospital to tackle the very difficult problem of achieving successful early intervention for pregnant women diagnosed with COVID-19.
The project aims to support medical professionals who are directly treating COVID-19 pregnancies. A comprehensive, anonymized data repository will be deployed in conjunction with dedicated prediction algorithms to score patients for their risk of severe deterioration. The repository together with the algorithm are expected to radically transform Canadian and, potentially, international healthcare providers’ ability to identify, manage and treat cases of COVID-19 in pregnant patients.
High-fidelity aerodynamic analysis of unmanned multirotor vehicles
The objective of the proposed research is to gain a better understanding of the complex aerodynamics of small multirotor vehicles, such as quadcopters. This will enable Aeryon Labs, a Canadian company and world leader in small unmanned systems, to improve the development cycles of their products and respond faster to customer needs. Multirotor vehicles are popular platforms for many remote sensing applications because of the relative ease to control them at hover. During fast flight, however, control becomes challenging, because of highly nonlinear aerodynamics. These nonlinearities are due to the small-scale aerodynamics typical for these vehicles, and the interaction of the flow fields of several rotors that operate in close proximity. The proposed research builds on existing research on multirotor-vehicle aerodynamics. In order to expand our understanding of the complex aerodynamics of multirotor vehicles, we propose to model a quadcopter using Computational Fluid Dynamics (CFD). The CFD results will be compared with the lower-fidelity predictions and experimental results. The investigation of the predictive method will benefit from experiments performed in the large low-speed wind tunnel at Ryerson University, flight tests and an existing collaboration with Aeryon Labs. As part of the proposed research project one postdoctoral fellow, one doctoral student and two MASc students will receive training in the area of applied aerodynamics. The results will benefit Aeryon Labs with superior design tools that will improve their product line. Small aerial systems represent a rapidly expanding market segment worldwide, in which Canadian companies, such as Aeryon, play an important role.
Mobilizing ‘Big energy data’ for building conservation to socialize sustainability
This project will establish the first customer-centric universal tool using mobile/cloud technology to deliver & share energy information electronically with energy customers to stimulate social change, encourage conservation & help manage/reduce GHG emissions.
A global desire to manage climate change is here. There needs to be a joint effort to help the energy user gain access to electronic information, so the data can be used to better manage conservation, sustainability and technology integration. Innovation in the energy sector continues to create data silos, as new technologies used don’t allow for the data to freely flow out of the systems to other solutions. Customers require easy access & a simplified understanding of electric information (not print or PDFs). For this to happen, the end customer requires data to be managed, simplified & shared, so information can be “open” to innovation and electronic data to be standardized.
The market needs Green practices and conservation to flourish in the private sector and public sector. Internationally and in Ontario, there are regulatory and political requirements to provide end-user access to electronic data, but without a simplified roadmap to interconnectivity how will it happen? The market needs linkages to the “Internet of Things” to learn, inform and conserve. In a few short years, there will be more than 25 billion devices generating data on every topic imaginable. The energy customer and building owner needs simplified data from multiple sources. Screaming has an opportunity to deliver this information cost-effectively to the utility, government & customer.
Multi-sensor miner data using smart computing platforms
The mining industry is a significant contributor to the Canadian economy, requiring close to 100,000 people to be hired by 2020. However, mining remains the second most dangerous job across the planet with 46.9 fatalities per 100,000 workers. Until the recent MINER act, there is little interest in providing communication services in mines. The MINER act made tracking all coal miners mandatory. In this project, we aim to develop a smart computing platform that combines data from digital wireless radio, thermal and video images in real time. This platform will incorporate tracking algorithms to aid in precisely locating miners, explosives and vehicles in mines continuously. This is very difficult due to the very nature of the mines. Extremely harsh, irregularly confined, and rough mine environments make radio wave propagation unpredictable. Furthermore, low visible light conditions prevent effective surveillance using cameras. Increasing number of sensors helps accurately locate targets, but will also increase the complexity of the algorithms. Using a variety of sensors will require several algorithms to run simultaneously to extract and fuse useful information from noisy data gathered from the mine in real-time. Especially identification of moving object in low-light video and thermal imaging is computationally very intensive. Hence, such a platform can only be implemented on a supercomputer, or a network of very fast computers. For this purpose, our work will rely on SOSCIP’s extensive computational capabilities. The result of this work will lead to significant improvements in safety of the mine personnel and better resource allocation and management in the mine
PBE Ryerson SOSCIP collaboration
The mining industry is a significant contributor to the Ontario economy. However, mining workers are exposed to five-fold higher occupational hazards than the industrial average. Accidents, collapse and fatalities are common in mines. Confined mine spaces can easily be filled with poisonous gas. Coal mines are highly inflammable. The mining sector also uses significant amount of energy and is a major source of pollution. Currently no reliable communication and control facilities are available in many mines. An efficient sensing, communications and, control infrastructure is necessary for the safe and efficient mining operations. That is the focus of this project.
There are many challenges in achieving this. Mine expand/change topology on a daily basis are often deep, huge and irregular in shape. Terrestrial communication technologies don’t work underground. Specialized technologies need to be deployed. Powering remote wireless sensor nodes is another issue that will require green communication technologies. Furthermore, localization and tracking of miners, vehicles and explosives using unreliable data in irregular mine cavities require advanced signal processing algorithms.
These challenges will be addressed in this project using wired, wireless and optical communication technologies. This will enable two way communications, access to the outside world. Specialized algorithms will be developed for localizing and controlling various units of the mine using the noisy data that will ensure the safety of miners while boosting productivity. This will also enable remote shut down of huge fans and water pumps when not necessary, saving energy and reducing carbon footprint. Overall, the outcome of the project will enable the realization of ‘Smart Mines’ that will function efficiently and safely.
Real-time food analysis using deep learning for Diabetes Self -Monitoring
Diabetes is the 6th leading cause of death in Canada, and affects over 3 million people; daily this number grows by 549 new cases. Diabetes patients often manage their condition via a combination of food intake and insulin. This method can cause blood glucose levels to fluctuate due to uncertainty in food portions consumed and can lead to severe illness. At Glucose Vision, our goal is to create a smartphone application capable of pre-evaluating diabetes patients’ meals before they consume themwith the snapof a picture. We are attempting to accomplish this goal by employing AI, machine learning as well as computer vision into our smartphone application. The first part of this project will be to assemble a dataset of foods tagged with nutritional informationthat can be used to train an image recognition algorithm. This will be the backend of the application allowing users to snap a photo of their meal and be displayed nutritional information such as a carbohydrate count based on their respective serving size. We will also require machine learning algorithms to make the meal information on specific foods such as homemade dishes more accurate and more personalized to the patient. By developing a model that uses these technologies, we believe we can create a smartphone application that will revolutionize how diabetes patients manage their condition and allow users to maintain consistent and healthier blood sugar levels.
Smart analytics for smart grid
How we use energy reveals vital information about our everyday behaviour – from our work and school schedules to when we are likely to be sleeping, eating and performing simple chores such as laundry or washing dishes. It even reveals when we are on vacation. While this information in the wrong hands could put us at risk, it also plays an important role in reducing our carbon footprint and ensuring the cost of energy is kept relatively low. Power generation companies use information derived from microclimate, small areas within general climate zones, to accurately predict power generation needs. Microclimate information is captured from smart meters, which wirelessly transmits data from our homes to utility companies. Considering that more than one million smart meters have been installed in Ontario homes since 2010 and that number continues to grow, it’s necessary to find a way to ensure the data captured by smart meters is protected and secure from threat.
Using advanced analytics to develop a multimodal signature of concussion and postconcussive syndrome
Concussion are extremely common in deployment and in military and civilian activities (i.e.) sports). Persisting symptoms that make up “post-concussive syndrome” (PCS) including headaches, balance difficulties , depression and anxiety can occur in 10-15% of cases. The diagnosis of concussion and PCS is currently based on a patient’s report of their symptoms and a physical exam. Research, including our own, has explored the value of specific tests including those that use eye movements, neuropsychological tests and MRI. Although useful in the research setting, we do not understand the value of these tests when used together and need to know what aspects of those tests are most valuable in developing future tools that distinguish those who are injured from those who are not. For this research we will utilize a dataset collected over the last four years, that contains MRI, neuropsychological, eye movement and other data from concussed, PCS and non-injured individuals. We will apply machine learning based methods (convolutional and long short term memory neural networks) to analyze the data to define more sensitive and specific tests. These tools may be used in both a military and civilian setting allowing for more personalized treatment and recovery programs thereby lessening the burden of concussion and PCS.
Social media analytics for early detection of foodborne disease
Foodborne disease has emerged as a serious and underreported public health problem with high health and financial costs. The World Health Organization (WHO) identifies foodborne illness outbreaks as a major global public health threat in the twenty-first century. Traditional surveillance systems such as Canadian Notifiable Disease Surveillance System capture only a fraction of the estimated 4 million annual cases of foodborne illness in Canada. They rely on the collection of numerous indicators including clinical symptoms, virology laboratory results, hospital admissions and mortality statistics resulting in a median delay of 6.5 days between case report from clinicians to the health departments. Public health decision-makers consider the delayed notification as a barrier to investigating foodborne disease, as it can potentially distribute geographically across great distances. Early detection of foodborne disease can reduce the number of exposed individuals by removing contaminated product from retail and foodservice outlets, increasing public awareness, and offering a more timely preventative and therapeutic measures to exposed individuals. We propose that social media data should be exploited as a complementary component of the traditional surveillance systems. The enormity and high variance of the information that propagates through large user communities presents the opportunity to mine the data for signals of foodborne disease activity; analyze illness patterns qualitatively and quantitatively; and to predict future outbreaks. We propose a host of social media-based predictive models to characterize and detect upcoming foodborne illness outbreaks through ambient tracking and monitoring over users’ conversations in social media. The objective is to advance research on foodborne disease detection from non-traditional sources to supply health decision makers with situational awareness.
Industry Partner(s): AdaptCore Technologies Inc.
Academic Institution: Ryerson University
Academic Researcher: Ebrahim Bagheri
Platform: Cloud
Focus Areas: Digital Media, Health