This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Pr...
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This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.
With the completion of railroad line resurvey work, the measurement data are scattered in various units and lack unified storage and management, which brings many challenges and problems to railroad operation and main...
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ISBN:
(纸本)9781510668898
With the completion of railroad line resurvey work, the measurement data are scattered in various units and lack unified storage and management, which brings many challenges and problems to railroad operation and maintenance. To solve this problem, this study proposes a multi-version, multi-temporal spatial data storage technology, as well as a distributed fast storage technology and a spatial service engine by studying the data management work, aiming to realize unified and fast storage, management and sharing of railroad survey data to improve the reliability, consistency and validity of data. First, this study focuses on the multi-version and multi-temporal spatial data storage technology. By adopting this technology, a large amount of spatial data generated by railroad line resurvey can be effectively handled, and the storage and management of different versions and temporal phases of data can be realized. Meanwhile, distributed fast storage technology is introduced to provide high-speed data access and processing capability to cope with the storage and processing demand of massive data. In addition, using the spatial service engine, fast query and analysis of measurement data can be realized. Second, this study also conducts an in-depth research on the railroad line measurement data management system. First, the overall architecture of the system is designed to ensure the scalability and flexibility of the system. Then, the data structure of the system was optimized to accommodate the storage and query requirements of multi-version and multi-temporal data. In terms of technical architecture, distributed storage technology is adopted to realize fast storage and retrieval of remote sensing, 2D vector and 3D model data. In addition, the sharing and exchange of measurement data is realized through information sharing and information interface. The innovation of this study is to propose a multi-version and multi-temporal spatial data storage technology, and to reali
The development and use of connected vehicles are becoming increasingly important areas of study in the wireless networking and transportation research communities. These networks can provide various services such as ...
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The edge-cloud-HPC continuum is transformative for AI processing at the edge. With greater availability of both edge and cloud resources, AI inference, training, and optimization can be distributed across the continuu...
The edge-cloud-HPC continuum is transformative for AI processing at the edge. With greater availability of both edge and cloud resources, AI inference, training, and optimization can be distributed across the continuum. We target edge-cloud in particular where the workload at the Edge server can exhibit discrete changes, for instance, when motion is detected. We optimize for Quality of Experience (QoE) and utilize historical data from the Edge, graphs, and Deep Learning to infer the next action to take. Using a large synthetic workload and publicly profiled inference models, our results show that predictive guidance outperforms random choice or best guess in optimal QoE of the edge-cloud continuum.
With the advances in deep learning, speech enhancement systems benefited from large neural network architectures and achieved state-of-the-art quality. However, speaker-agnostic methods are not always desirable, both ...
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With the advances in deep learning, speech enhancement systems benefited from large neural network architectures and achieved state-of-the-art quality. However, speaker-agnostic methods are not always desirable, both in terms of quality and their complexity, when they are to be used in a resource-constrained environment. One promising way is personalized speech enhancement (PSE), which is a smaller and easier speech enhancement problem for small models to solve, because it focuses on a particular test-time user. To achieve the personalization goal, while dealing with the typical lack of personal data, we investigate the effect of data augmentation based on neural speech synthesis (NSS). In the proposed method, we show that the quality of the NSS system’s synthetic data matters, and if they are good enough the augmented dataset can be used to improve the PSE system that outperforms the speaker-agnostic baseline. The proposed PSE systems show significant complexity reduction while preserving the enhancement quality.
Traditional neural networks are simple to train but they typically produce overconfident predictions. In contrast, Bayesian neural networks provide good uncertainty quantification but optimizing them is time consuming...
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Robotic navigation in unknown, cluttered environ-ments with limited sensing capabilities poses significant chal-lenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI),...
Robotic navigation in unknown, cluttered environ-ments with limited sensing capabilities poses significant chal-lenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective navigation, especially when encountering challenging environmental conditions or navigating beyond the planning horizon. This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP). The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, which enables the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function to the local MPPI planner. Afterward, MPPI computes the optimal control sequence that satisfies the robot and collision avoidance constraints. Such an approach eliminates the necessity of a global map of the environment or an offline training process. We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks in complex unknown en-vironments, demonstrating its superiority in guiding the robot safely towards its desired goal while avoiding obstacles and escaping entrapment in local minima. The GPU implementation of GP-MPPI, including the supplementary video, is available at https://***/IhabMohamed/GP-MPPI.
We propose a new frontier concept called the Gaussian Process Frontier (GP-Frontier) that can be used to locally navigate a robot towards a goal without building a map. The GP-Frontier is built on the uncertainty asse...
We propose a new frontier concept called the Gaussian Process Frontier (GP-Frontier) that can be used to locally navigate a robot towards a goal without building a map. The GP-Frontier is built on the uncertainty assessment of an efficient variant of sparse Gaussian Process. Based only on local ranging sensing measurement, the GP-Frontier can be used for navigation in both known and unknown environments. The proposed method is validated through intensive evaluations, and the results show that the GP-Frontier can navigate the robot in a safe and persistent way, i.e., the robot moves in the most open space (thus reducing the risk of collision) without relying on a map or a path planner. A supplementary video that demonstrates the robot navigation behavior is available at https://***/ndpqTNYqGfw.
We revisit the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition by studying multi-agent real-time interactions. To date, all of th...
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作者:
Xinyao MaZaiqiao YeSameer PatilDepartment of Informatics
Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana University Bloomington United States of America Department of Informatics
Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana University Bloomington USA School of Computing
University of Utah United States of America
Systems worldwide deploy CAPTCHAs as a security mechanism to protect from unauthorized automated access. Typically, the effectiveness of CAPTCHAs is evaluated based on their resilience against bots. User perceptions o...
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ISBN:
(纸本)9781450394758
Systems worldwide deploy CAPTCHAs as a security mechanism to protect from unauthorized automated access. Typically, the effectiveness of CAPTCHAs is evaluated based on their resilience against bots. User perceptions of the interactive experience and effectiveness of CAPTCHAs have received less attention, especially for comparing the variations of CAPTCHAs presented in different regions across the world. As the first step toward filling this gap, we conducted semi-structured interviews with ten participants fluent in Chinese and English to investigate whether user perceptions are affected by variations in CAPTCHAs presented in China and the United States, respectively. We found notable differences in the perceived user experience and effectiveness across the different CAPTCHA types, but not across regional variations of the same type. Our findings point to a number of avenues for making the CAPTCHA user experience more universal and inclusive.
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