Thesis (M.A.)--York University, 2006. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 133-143). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR29628

Thesis (M.A.)--Rowan University, 2008. / Typescript. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

The skyrocketing amount of electricity consumed by many data centers around the globe has become a serious issue for the cloud computing and entire IT industry. The demand for data centers is rapidly increasing due to widespread usage of cloud services. It also leads to huge carbon emissions contributing to the global greenhouse effect. The US Environmental Protection Agency has declared that data centers represent a substantial portion of the energy consumption in the US and the whole world. Some of this energy consumption is caused by idle servers or servers running at higher-than-necessary frequencies. Due to the Dynamic Voltage and Frequency Scaling (DVFS) technology enabled in many CPUs, strategically reducing CPU frequency without affecting the Quality of Service (QoS) is desired. Our goal in this paper is to calculate and tune to the best CPU frequency for each running task combined with two commonly-used scheduling approaches, namely round robin and first fit algorithms, given the CPU configuration and the execution deadline. The effectiveness of our algorithms is evaluated under a CloudSim/CloudReport simulation environment as well as real hypervisor computer system with power gauge. The open source CloudReport, based on the CloudSim simulator, has been used to integrate our DVFS algorithm with the two scheduling algorithms to illustrate the efficiency of power saving in different scenarios. Furthermore, electricity consumption is measured and compared using power gauge of Watts Up meter.

abstract: In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients. / Dissertation/Thesis / Ph.D. Computer Science 2014

The Same-Different task is an experimental paradigm in which a stimulus pair is presented in succession to a participant whose task is to determine if the stimuli are Same or Different. Typical results show that participants tend to be quicker to respond Same then they are to respond Different. Since the 1960s, many models were proposed to explain this effect, but none has yielded conclusive evidence. The objective of this thesis is to test these models with three experiments by focusing on three research questions: 1) what is the source of the effect, the participant or the stimuli?; 2) what is the organization of the cognitive mechanisms underlying the task?; and 3) what is the effect of the number of attributes on the processing capacity? Results show that the fast-same effect stems from the characteristics of the stimuli rather than an inherent preference for sameness. They also show that the cognitive architecture underlying the task is serial, but that it does not seem to explain solely the fast-same effect. Indeed, the fast-same effect seems to be rather caused by a more efficient processing of Same stimuli in the first 500 ms of the treatment compared to Different stimuli.

abstract: The choices of an operator under heavy cognitive load are potentially critical to overall safety and performance. Such conditions are common when technological failures arise, and the operator is forced into multi-task situations. Task switching choice was examined in an effort to both validate previous work concerning a model of task overload management and address unresolved matters related to visual sampling. Using the Multi-Attribute Task Battery and eye tracking, the experiment studied any influence of task priority and difficulty. Continuous visual attention measurements captured attentional switches that do not manifest into behaviors but may provide insight into task switching choice. Difficulty was found to have an influence on task switching behavior; however, priority was not. Instead, priority may affect time spent on a task rather than strictly choice. Eye measures revealed some moderate connections between time spent dwelling on a task and subjective interest. The implication of this, as well as eye tracking used to validate a model of task overload management as a whole, is discussed. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2020

Thesis submitted to the Faculty of Education in fulfilment of the requirements for the Degree of Masters in the Department of Mathematics, Science and Technology Education at the University of Zululand, South Africa, 2013. / The purpose of this study was to examine the attitude and perception of grade 9 mathematics teachers to CTA (common task assessment) 2009 as well as their understanding of the role of CTA. Six secondary schools in Pietermaritzburg area in the province of KwaZulu-Natal were selected as cases for an in-depth qualitative study. Two schools were African schools and the other three were multiracial school and one private school. Fourteen (14) teachers were interviewed and were observed teaching mathematics. The study utilised participant observation, interviews and relevant documents as source of data collection. The main finding of the study was the frustrations expressed by teachers not clearly understanding what is expected of them and also the lack of official support for meaningful implementation and general lack of teachers understanding of the role of CTAs. Most teachers raised their concerns about the lack of mathematics content in grade 9 mathematics CTAs. Most teachers raised concerns about CTAs content favouring or geared towards mathematical literacy and leaving out pure mathematics and failing to prepare learners to be competent in mathematics and failing completely to prepare learners for grade 10 pure Mathematics.

No description available.

Multi-task learning (MTL) is a rapidly growing field in the world of autonomous vehicles, particularly in the area of computer vision. Autonomous vehicles are heavily reliant on computer vision technology for tasks such as object detection, object segmentation, and object tracking. The complexity of sensor data and the multiple tasks involved in autonomous driving can make it challenging to design effective systems. MTL addresses these challenges by training a single model to perform multiple tasks simultaneously, utilizing shared representations to learn common concepts between a group of related tasks, and improving data efficiency. In this thesis, we proposed a scalable MTL system for object detection that can be used to construct any MTL network with different scales and shapes. The proposed system is an extension to the state-of-art algorithm called Mask RCNN. It is designed to overcome the limitations of learning multiple objects in multi-label learning. To demonstrate the effectiveness of the proposed system, we built three different networks using it and evaluated their performance on the state-of-the-art BDD100k dataset. Our experimental results demonstrate that the proposed MTL networks outperform a base single-task network, Mask RCNN, in terms of mean average precision at 50 (mAP50). Specifically, the proposed MTL networks achieved a mAP50 of 66%, while the base network only achieved 53%. Furthermore, we also conducted comparisons between the proposed MTL networks to determine the most efficient way to group tasks together in order to create an optimal MTL network for object detection on the BDD100k dataset.