L'université et le développement : analyse de deux institutions universitaires d'Afrique noire à la lumière du concept de développement : l'Université nationale du Rwanda et l'Université de Dar-es-Salaam en Tanzanie

Québec Université Laval, Bibliothèque 201

A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters

Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, handwritten character recognition is still an important research area. In this research, the Handwritten Ethiopian Character Recognition (HECR) dataset has been prepared to train the model. The images in the HECR dataset were organized with more than one color pen RGB main spaces that have been size normalized to 28 × 28 pixels. The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters. These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) is proposed for classification. In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification. The output error rates of the hybrid model and CNN with a fully connected layer are compared. A 0.4630 and 0.1612 error rates are achieved in classifying the handwritten testing dataset images, respectively. Thus XGBoost as a classifier performs a better result than the traditional fully connected layer

EXPLORATIONS IN WORKFLOW SCHEDULING FOR DYNAMIC CLOUD COMPUTING SYSTEMS

本文探讨云计算中使用的动态环境，解决了一些与在这种环境下工作流调度相关的主要问题。由于动态环境中的工作流调度是基于不同的标准完成的，因此不同的目标会造成多种不同的结果。针对我们的目标，本文专注于两个主要问题，文中的第三章和第四章致力于对这两个问题进行研究，并在每章节末尾对提出的启发式解决方法进行验证，实验结果表明我们能够获得预期目标。 第三章描述的问题是关于把工作流调度到一个异构资源的集合中，我们利用随机模型产生任务的执行时间、以及任务间的传输时间；利用这样的随机模型，确定性调度启发式可以偶尔表现良好。通过对一个众所周知的确定性启发式进行扩展，我们提出了一种新的随机调度方法。通过大量的仿真实...This thesis explores dynamic environments that cloud computing faces and addresses some of the main issues associated with workflow scheduling in these environments. Workflow scheduling in dynamic environments is done based on different criteria, this results in various objectives. In this thesis we focused on two main problems which related to our objectives, the two chapters (chapter 3 and chapt...学位：工学硕士院系专业：信息科学与技术学院_计算机科学与技术学号：2302014115464

Cost-effective and Low-complexity Non-constrained Workflow Scheduling for Cloud Computing Environment

Cloud computing possesses the merit of being a faster and cost-effective platform in terms of executing scientific workflow applications. Scientific workflow applications are found in different domains, such as security, astronomy, science, etc. They are represented by complex sizes, which makes them computationally intensive. The main key to the successful execution of scientific workflow applications lies in task resource mapping. However, task-resource mapping in a cloud environment is classified as NP-complete. Finding a good schedule that satisfies users' quality of service requirements is still complicated. Even if different studies have been carried out to propose different algorithms that address this issue, there is still a big room for improvement. Some proposed algorithms focused on optimizing different objectives such as makespan, cost, and energy. Some of those studies fail to produce low-time complexity and low-runtime scientific workflow scheduling algorithms. In this paper, we proposed a non-constrained, low-runtime, and low-time-complexity scientific workflow scheduling algorithm for cost minimization. Since the proposed algorithm is a list scheduling algorithm, its key success is properly selecting computing resources and its operating CPU frequency for each task using the maximum cost difference and minimum cost-execution difference from the mean. Our algorithm achieves almost the same cost reduction results as some of the current states of the arts while it is still low complex and uses less run-time