Improper driving results in fatalities, damages, increased energy consumptions, and depreciation of the vehicles. Analyzing driving behaviour could lead to optimize and avoid mentioned issues. By identifying the type of driving and mapping them to the consequences of that type of driving, we can get a model to prevent them. In this regard, we try to create a dynamic survey paper to review and present driving behaviour survey data for future researchers in our research. By analyzing 58 articles, we attempt to classify standard methods and provide a framework for future articles to be examined and studied in different dashboards and updated about trends.

CALGARY, Alberta, July 06, 2021 (GLOBE NEWSWIRE) -- Quorum Information Technologies Inc. (TSX Venture: QIS) (Quorum) announced today that it is receiving advisory services and funding of up to $724,746 from the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP) to support a research and development project to consolidate Quorum's dealership data and add machine learning capabilities to its Cloud-based applications. The NRC IRAP support is the next step in a process started in 2020 when Quorum launched QAnalytics – an enterprise reporting tool for the Quorum suite of products powered by Microsoft Power BI. QAnalytics is now utilized by 30% of Quorum's XSellerator Dealership Management System (DMS) customers. "QAnalytics has changed how we manage our 11 franchised dealerships in our auto group," stated Tim Davis, CEO of Davis Auto Group. "The real time metrics that QAnalytics provides for all aspects of our dealership's operations allow our management team to make confident, data-driven decisions." Quorum's next step is to strategically consolidate dealership data from its 1,025 customers on Microsoft Azure Synapse, enabling QAnalytics to deliver enhanced critical Business Intelligence insights into dealership operations and provide a consolidated dataset for Machine Learning projects.

The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm-based task scheduling method for crowdsourced software development. The proposed evolutionary scheduling method uses a multiobjective genetic algorithm to recommend an optimal task start date. The method uses three fitness functions, based on project duration, task similarity, and task failure prediction, respectively. The task failure fitness function uses a neural network to predict the probability of task failure with respect to a specific task start date. The proposed method then recommends the best tasks start dates for the project as a whole and each individual task so as to achieve the lowest project failure ratio. Experimental results on 4 projects demonstrate that the proposed method has the potential to reduce project duration by a factor of 33-78%.

New Collaboration Will Support the Growth of Calgary as an Innovation Hub for Emerging Technologies. Attabotics, the 3D robotics supply chain …

Objective: This paper presents an Alzheimer's disease (AD) detection method based on learning structural similarity between Magnetic Resonance Images (MRIs) and representing this similarity as a graph. Methods: We construct the similarity graph using embedded features of the input image (i.e., Non-Demented (ND), Very Mild Demented (VMD), Mild Demented (MD), and Moderated Demented (MDTD)). We experiment and compare different dimension-reduction and clustering algorithms to construct the best similarity graph to capture the similarity between the same class images using the cosine distance as a similarity measure. We utilize the similarity graph to present (sample) the training data to a convolutional neural network (CNN). We use the similarity graph as a regularizer in the loss function of a CNN model to minimize the distance between the input images and their k-nearest neighbours in the similarity graph while minimizing the categorical cross-entropy loss between the training image predictions and the actual image class labels. Results: We conduct extensive experiments with several pre-trained CNN models and compare the results to other recent methods. Conclusion: Our method achieves superior performance on the testing dataset (accuracy = 0.986, area under receiver operating characteristics curve = 0.998, F1 measure = 0.987). Significance: The classification results show an improvement in the prediction accuracy compared to the other methods. We release all the code used in our experiments to encourage reproducible research in this area

Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots.

SAS is the leader in analytics. Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS gives you THE POWER TO KNOW . The Canadian subsidiary of SAS has been in operation since 1988. Headquartered in Toronto, SAS employs more than 300 people across the country at its Vancouver, Calgary, Toronto, Ottawa, Quebec City and Montréal offices.

The AltaML Applied AI Lab launches today: https://lnkd.in/gbsggVF Each cohort of interns will work with industry partners Suncor, TransAlta, ATB Financial, Spartan Controls on applied #AI and #ML projects in #YYC. The AI Lab operates with support from Calgary Economic Development.

In a restaurant landscape where lean profit margins are getting even slimmer due to the necessary COVID-19 safety measures of distancing, staying afloat is an increasingly difficult challenge. Small wonder, then, that some operators are using whatever means they can to stand out from their competition. Robot waiters, although not a new phenomenon, are making headlines around the world again, but this time with a socially distanced twist. At Claypot Rice, a Chinese restaurant in Calgary, robot greeters and servers chat with guests, take orders and run food from the kitchen. These are typically three distinct roles performed by humans, a fact not lost on owner Alex Guo.

Inferring behavior model of a running software system is quite useful for several automated software engineering tasks, such as program comprehension, anomaly detection, and testing. Most existing dynamic model inference techniques are white-box, i.e., they require source code to be instrumented to get run-time traces. However, in many systems, instrumenting the entire source code is not possible (e.g., when using black-box third-party libraries) or might be very costly. Unfortunately, most black-box techniques that detect states over time are either univariate, or make assumptions on the data distribution, or have limited power for learning over a long period of past behavior. To overcome the above issues, in this paper, we propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system, and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns, over time. We have applied our approach on a real UAV auto-pilot solution from our industry partner with half a million lines of C code. We ran 888 random recent system-level test cases and inferred states, over time. Our comparison with several traditional time series change point detection techniques showed that our approach improves their performance by up to 102%, in terms of finding state change points, measured by F1 score. We also showed that our state classification algorithm provides on average 90.45% F1 score, which improves traditional classification algorithms by up to 17%.