Cloud providers offer their IaaS services based on virtualization to enable multi-tenant and isolated environments for cloud users. Currently, each provider has its own proprietary virtual machine (VM) manager, called...
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ISBN:
(纸本)9781509040537
Cloud providers offer their IaaS services based on virtualization to enable multi-tenant and isolated environments for cloud users. Currently, each provider has its own proprietary virtual machine (VM) manager, called the hypervisor. this has resulted in tight coupling of VMs to their underlying hardware hindering live migration of VMs to different providers. A number of user-centric approaches have been proposed from both academia and industry to solve this issue. However, these approaches suffer limitations in terms of performance (migration downtime), flexibility (decoupling VMs from underlying hardware) and security (secure live migration). this paper proposes LivCloud to overcome such limitations. An open-source cloud orchestrator, a developed transport protocol, overlay network and secured migration channel are crucial parts of LivCloud to achieve effective live cloud migration. Moreover, an initial evaluation of LAN live migration in nested virtualization environment and between different hypervisors has been considered to show the migration impact on network throughput, network latency and CPU utilization. the evaluation has demonstrated the need for optimization within the LAN environment.
Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. this study conducts an in-depth review of Machine Learning (ML) techniques applied to multivariate datasets for crop yield forecas...
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ISBN:
(数字)9798331518981
ISBN:
(纸本)9798331518998
Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. this study conducts an in-depth review of Machine Learning (ML) techniques applied to multivariate datasets for crop yield forecasting. We examine the performance of models including deep learning, ensemble classifiers, and hybrid approaches, emphasizing their ability to integrate diverse data sources and adapt to varying climatic conditions. Despite significant progress, challenges such as sparse data handling and generalization across regions persist. this paper not only identifies these challenges but also proposes advanced solutions for improving prediction accuracy, offering a roadmap for future research in agricultural ML frameworks.
A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a p...
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ISBN:
(纸本)9781467390422
A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a physical form (i.e., a robot). the robot may better engage students in the learning activity, and if students take embodied action in order to instruct the robot, they may develop deeper knowledge. In this paper, we investigate these two hypotheses using the rTAG system, a teachable robot for geometry learning. In a study with 37 4th-6th grade participants, we compare rTAG to two other conditions, one where students use embodied action to teach a virtual agent, and one where students teach a virtual agent on a personal computer. We find that while there are no significant learning differences between conditions, students' perceptions of the agent are influenced by condition and prior knowledge.
Internet of things was proposed about a decade ago, and since then has attracted many research interests in both academia and industry. In the last few years, the industry has picked up the steam and developed a numbe...
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Internet of things was proposed about a decade ago, and since then has attracted many research interests in both academia and industry. In the last few years, the industry has picked up the steam and developed a number of real-world applications with specialized hardware and system development. In this talk I will give a brief overview of recent progress on large scale sensor networking, passive and sensorless sensing, and battery-free communication and battery-free networking. We also review some of our effort in designing some basic theory related to Internet of things, our effort in designing large scale sensor networks, some progress in designing sensorless sensing, and vision about future research directions in this area. I will use sensorless sensing and high-accurate localization as running examples to illustrate our effort and endeavors in this fast-growing area.
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