Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- ASETS (1)
- Anomaly Detection (1)
- Anomaly detection; Ensemble learning; Autoencoder; Support vector regression; Random forest; Building energy consumption (1)
- Cloud Simulators (1)
- Cloud computing (1)
-
- Component-based architecture (1)
- Comprehensive parametric CUDA kernel generation (1)
- Concurrency platforms (1)
- Confidence Sampling (1)
- Continuous max-flow (1)
- Convex optimization (1)
- Convolutional Network (1)
- Cryptography (1)
- Deep Learning (1)
- Dependability analysis (1)
- Distributed Systems (1)
- High availability (1)
- High-level parallel programming (1)
- Homomorphic Encryption Properties (1)
- Image segmentation (1)
- Internet of Things; Sensor Data; Gamification; Data Analytics; Machine Learning; Crowdsourcing (1)
- Neural Network (1)
- Pipelining (1)
- Prime Power Groups (1)
- Public Key Cryptosystem (1)
- Secure Integer Comparison (1)
- Semi-supervised Learning (1)
- Source-to-source compiler (1)
- Variational optimization (1)
- Publication
- Publication Type
Articles 1 - 7 of 7
Full-Text Articles in Engineering
Secure Integer Comparisons Using The Homomorphic Properties Of Prime Power Subgroups, Rhys A. Carlton
Secure Integer Comparisons Using The Homomorphic Properties Of Prime Power Subgroups, Rhys A. Carlton
Electronic Thesis and Dissertation Repository
Secure multi party computation allows two or more parties to jointly compute a function under encryption without leaking information about their private inputs. These secure computations are vital in many fields including law enforcement, secure voting and bioinformatics because the privacy of the information is of paramount importance.
One common reference problem for secure multi party computation is the Millionaires' problem which was first introduced by Turing Award winner Yao in his paper "Protocols for secure computation". The Millionaires' problem considers two millionaires who want to know who is richer without disclosing their actual worth.
There are public-key cryptosystems that …
Machs: Mitigating The Achilles Heel Of The Cloud Through High Availability And Performance-Aware Solutions, Manar Jammal
Machs: Mitigating The Achilles Heel Of The Cloud Through High Availability And Performance-Aware Solutions, Manar Jammal
Electronic Thesis and Dissertation Repository
Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their …
Metafork: A Compilation Framework For Concurrency Models Targeting Hardware Accelerators, Xiaohui Chen
Metafork: A Compilation Framework For Concurrency Models Targeting Hardware Accelerators, Xiaohui Chen
Electronic Thesis and Dissertation Repository
Parallel programming is gaining ground in various domains due to the tremendous computational power that it brings; however, it also requires a substantial code crafting effort to achieve performance improvement. Unfortunately, in most cases, performance tuning has to be accomplished manually by programmers. We argue that automated tuning is necessary due to the combination of the following factors. First, code optimization is machine-dependent. That is, optimization preferred on one machine may be not suitable for another machine. Second, as the possible optimization search space increases, manually finding an optimized configuration is hard. Therefore, developing new compiler techniques for optimizing applications …
Directed Acyclic Graph Continuous Max-Flow Image Segmentation For Unconstrained Label Orderings, John Sh Baxter, Martin Rajchl, A. Jonathan Mcleod, Jing Yuan, Terry M. Peters
Directed Acyclic Graph Continuous Max-Flow Image Segmentation For Unconstrained Label Orderings, John Sh Baxter, Martin Rajchl, A. Jonathan Mcleod, Jing Yuan, Terry M. Peters
Robarts Imaging Publications
Label ordering, the specification of subset–superset relationships for segmentation labels, has been of increasing interest in image segmentation as they allow for complex regions to be represented as a collection of simple parts. Recent advances in continuous max-flow segmentation have widely expanded the possible label orderings from binary background/foreground problems to extendable frameworks in which the label ordering can be specified. This article presents Directed Acyclic Graph Max-Flow image segmentation which is flexible enough to incorporate any label ordering without constraints. This framework uses augmented Lagrangian multipliers and primal–dual optimization to develop a highly parallelized solver implemented using GPGPU. This …
An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak
An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak
Electrical and Computer Engineering Publications
During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …
Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz
Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz
Electrical and Computer Engineering Publications
The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for …
A Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux, Katarina Grolinger, Wilson A. Higashino, Miriam A. M. Capretz
A Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux, Katarina Grolinger, Wilson A. Higashino, Miriam A. M. Capretz
Electrical and Computer Engineering Publications
The Internet of Things (IoT) enables connected objects to capture, communicate, and collect information over the network through a multitude of sensors, setting the foundation for applications such as smart grids, smart cars, and smart cities. In this context, large scale analytics is needed to extract knowledge and value from the data produced by these sensors. The ability to perform analytics on these data, however, is highly limited by the difficulties of collecting labels. Indeed, the machine learning techniques used to perform analytics rely upon data labels to learn and to validate results. Historically, crowdsourcing platforms have been used to …