The burgeoning realm of intelligent connected vehicle technology necessitates advanced data acquisition and management solutions to handle vast amounts of spatiotemporal data efficiently. This paper introduces a novel...
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In recent decades, vascular disease - which includes a variety of heart-related conditions - has emerged as the primary cause of death worldwide. There are numerous risks associated with heart disease, and it is impor...
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In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fi...
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In this work, we design a reconfigurable optical network and analyze its performance and energy consumption on a HPC system with scientific applications using the Structure Simulation Toolkit (SST). The results show a...
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ISBN:
(纸本)9798350364613;9798350364606
In this work, we design a reconfigurable optical network and analyze its performance and energy consumption on a HPC system with scientific applications using the Structure Simulation Toolkit (SST). The results show a reduction in execution time by 28.7% for HPCG. Additionally, the energy consumption reduces by about 25%.
Data poisoning and model manipulation represent significant threats to cybersecurity, where adversaries intentionally inject malicious data into training sets, leading to compromised machine learning models. This stud...
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As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most promi...
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ISBN:
(纸本)9798350395679;9798350395662
As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. This fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.
Beyond 5G and 6G networks are foreseen to be highly dynamic. These are expected to support and accommodate temporary activities and leverage continuously changing infrastructures from extreme edge to cloud. In additio...
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ISBN:
(纸本)9783031488023;9783031488030
Beyond 5G and 6G networks are foreseen to be highly dynamic. These are expected to support and accommodate temporary activities and leverage continuously changing infrastructures from extreme edge to cloud. In addition, the increasing demand for applications and data in these networks necessitates the use of geographically distributed Multi-access Edge Computing (MEC) to provide reliable services with low latency and energy consumption. Service management plays a crucial role in meeting this need. Research indicates widespread acceptance of Reinforcement Learning (RL) in this field due to its ability to model unforeseen scenarios. However, it is difficult for RL to handle exhaustive changes in the requirements, constraints and optimization objectives likely to occur in widely distributednetworks. Therefore, the main objective of this research is to design service management approaches to handle changing services and infrastructures in dynamic distributed MEC systems, utilizing advanced RL methods such as distributed Deep Reinforcement Learning (DDRL) and Meta Reinforcement Learning (MRL).
With the emergence of Information-Centric Networking as a potential paradigm shift in the computer networking field, the coupling between VANETS and Named-Data Networking, known as VNDN, has gained momentum as a promi...
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ISBN:
(纸本)9798350369458;9798350369441
With the emergence of Information-Centric Networking as a potential paradigm shift in the computer networking field, the coupling between VANETS and Named-Data Networking, known as VNDN, has gained momentum as a promising communication standard to foster the deployment of inter-vehicular communications in the real world, calling the attention of the research community. This work presents a concise VNDN review focusing on the background concepts. We also analyze the current status and open challenges, including the prominent data naming and caching approaches and the most efficient Interest and Data message forwarding and delivery strategies.
In recent years, blockchain networks have become a singular power in the financial sector. Proof of work (POW) networks are among the most significant and widespread kinds of blockchain networks. One of the main issue...
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The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions...
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The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a system, comparing its impact with that of the physical network. We contextualise this by defining a collective learning problem in which agents must reach a consensus about their environment in the presence of noisy information. We show that the physical connectivity of the agents plays a less important role than when an interaction network of limited connectivity is imposed on the system to constrain agent communication. Constraining agent interactions in this way drastically improves the performance of the system in a collective learning context. Additionally, we provide further evidence for the idea that 'less is more' when it comes to propagating information in distributed autonomous systems for the purpose of collective learning.
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