Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Civic Hackathons: Innovation, Procurement, or Civic Engagement?

Abstract: At all levels, governments around the world are moving toward the provision of open data, that is, the direct provision to citizens, the private sector, and other third parties, of raw government datasets, controlled by a relatively permissible license. In tandem with this distribution of open data is the promotion of civic hackathons, or “app contests” by government. The civic hackathon is designed to offer prize money to developers as a way to spur innovative use of open data, more specifically the creation of commercial software applications that deliver services to citizens. Within this context, we propose that the civic hackathon has the potential to act in multiple ways, possibly as a backdoor to the traditional government procurement process, and as a form of civic engagement. We move beyond much of the hype of civic hackathons, critically framing an approach to understanding civic hackathons through these two lenses. Key questions for future research emphasize the emerging, and important, nature of this research path.

Spaces of Neoliberalism: Urban Restructuring in North America and Western Europe

Abstract: Description: This is the first volume to analyse systematically the role of neoliberalism in contemporary processes of urban restructuring. Drawing upon cutting–edge theoretical work within radical geography, critical urban studies, neo–marxian state theory and critical social theory, contributions by leading scholars map the spaces of neoliberalism that have been forged and contested within contemporary North American and Western European cities.

A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids

Abstract: Microgrid equipped with heterogenous energy resources and a bank of energy storage devices presents the idea of small scale distributed energy management (DEM). DEM facilitates the minimization of the transmission and operation costs, peak load and environmental pollution. Microgrid also enables active customer participation by giving them the access to the real time information and control. The capability of fast restoration against physical/cyber attack, integration of renewable energy resources and information and communication technologies (ICT) make microgrid as an ideal candidate for distributed power systems. The energy management system of microgrid can perform real time energy forecasting of renewable resources, energy storage elements and controllable loads in making proper short term scheduling to minimize total operating costs. Cost benefit analysis of microgrid reveals that cooperation among different microgrids can play an important role in the reduction of import energy cost from the utility grid. Cooperation among microgrids in smart microgrid network (SMN) brings the energy sharing and management issues. In this paper we present a review of existing optimization objectives, constraints, solution approaches and tools used in microgrid energy management. This review paper can provide a foundation to embark on an in depth study in the area of energy management for smart microgrid network.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Abstract: Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

Recognizing Human Emotional State From Audiovisual Signals

Abstract: Machine recognition of human emotional state is an important component for efficient human-computer interaction. The majority of existing works address this problem by utilizing audio signals alone, or visual information only. In this paper, we explore a systematic approach for recognition of human emotional state from audiovisual signals. The audio characteristics of emotional speech are represented by the extracted prosodic, Mel-frequency Cepstral Coefficient (MFCC), and formant frequency features. A face detection scheme based on HSV color model is used to detect the face from the background. The visual information is represented by Gabor wavelet features. We perform feature selection by using a stepwise method based on Mahalanobis distance. The selected audiovisual features are used to classify the data into their corresponding emotions. Based on a comparative study of different classification algorithms and specific characteristics of individual emotion, a novel multiclassifier scheme is proposed to boost the recognition performance. The feasibility of the proposed system is tested over a database that incorporates human subjects from different languages and cultural backgrounds. Experimental results demonstrate the effectiveness of the proposed system. The multiclassifier scheme achieves the best overall recognition rate of 82.14%.