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检索条件"任意字段=21st International Conference on Architecture of Computing Systems"

共 4142 条记录，以下是41-50 订阅

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Demo: storyFrames: A Context-Aware Comic Generation System Based on Mobile Cloud computing 21

Demo: StoryFrames: A Context-Aware Comic Generation System B...

引用

21st IEEE international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Chen, Lien-Wu Lai, Chi Yang, Hao-Hsiang Feng Chia Univ Dept Informat Engn & Comp Sci Taichung 407 Taiwan

ISBN: (纸本)9798350363999;9798350364002

This paper designs and implements a deep learning based context-aware comic generation system, called storyFrames, to automatically generate the comic strips with dialogues based on mobile cloud computing. The input text from a user to generate comic strips is analyzed to extract key contextual words for achieving artistic style consistency, which is a challenge faced by existing generative models. Through in-depth analysis of the input text, storyFrames extracts visually and narratively rich scenes and ranks them based on their importance for arranging these scenes in the comic strips. In addition, deep sentiment analysis is explored to precisely reveal emotional fluctuations in the dialogues and select dialogue box styles that match the emotional states of the characters. To the best of our knowledge, this is the first system that explores deep learning, large language models, and mobile cloud computing to ensure coherence among elements within the comic strips and consistency in the overall story's logic, emotion, style, and contextual descriptions. In particular, an Android-based prototype is implemented to verify the feasibility and performance of storyFrames.

关键词： Computer Vision Deep Learning Large Language Model Mobile Cloud computing stylizing Layout

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HP-ICM: Curiosity-driven Offloading Based on Hypergraph Partitioning 21

HP-ICM: Curiosity-driven Offloading Based on Hypergraph Part...

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21st IEEE international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Wang, Chengwei Huang, Kang Hou, Chenxuan Qiu, Chao Wang, Xiaofei Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9798350363999;9798350364002

computing power network (CPN) is a distributed network system designed to connect and integrate computing resources globally, enabling efficient sharing and utilization of computing power. In dependent task offloading, the dependency relationship between tasks is generally used to determine the execution order. However, the assignment phase often overlooks the relevance and sharing between tasks, leading to a waste of system resources in CPNs. In the learning process, agents frequently encounter the issue of sparse rewards, which results in slow learning and makes it challenging to develop effective strategies. To address the aforementioned issues, we design a dependent task offloading method based on hypergraph partitioning and an intrinsic curiosity module, i.e., HP-ICM, which offloads tasks with similar resource requirements or dependencies into the same partition and utilizes the ICM to enhance the speed and quality of learning. Simulation results show that HP-ICM can reduce latency by 22.8% and energy consumption by 25.7% compared to the PPO baseline during task offloading.

关键词： Hypergraph partition intrinsic curiosity module dependent task offloading computing Power Networks

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An Ultra-Compact Calculation Unit with Temporal-Spatial Re-configurability 21

An Ultra-Compact Calculation Unit with Temporal-Spatial Re-c...

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21st IEEE Interregional NEWCAS conference (NEWCAS)

作者： Zhu, Guangxian Kan, Yirong Zhang, Renyuan Nakashima, Yasuhiko Nara Inst Sci & Technol Div Informat Sci Ikoma Japan

ISBN: (纸本)9798350300246

We develop an ultra-compact calculation unit with temporal-spatial re-configurability. On the basis of a novel bisection neural network (BNN) topology, a processing element array is flexibly partitioned into multiple calculation units on the hardware. Re-configurability in spatial (function) is achieved by adjusting the shape and location of each calculation unit. Meanwhile, we integrate stochastic computing (SC) logic on the proposed calculation unit to take advantages of the BNN topology. Re-configurability in temporal (accuracy) is achieved by adjusting the length of SC bit-stream. Experimental results show that our calculation unit outperforms some state-of-the-art approximate calculation units in terms of energy efficiency.

关键词： Reconfigurable architecture neural network stochastic computing

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2024 21st international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2024

2024 21st International Conference on Electrical Engineering...

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21st international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2024

ISBN: (纸本)9798350377545

The proceedings contain 112 papers. The topics discussed include: network connectivity measures to assess changes in brain activity of stroke patients;unraveling the complex interplay between socioeconomic status, air pollution, and heart disease hospitalizations in an urban population;design and implementation of a probe for 3D scanning with FPGA-based architecture;physics-informed machine learning for UAV control;optical and electrical properties of TiO2-GO nanostructures by ball-milling;neurofeedback training system to induce concentration states using virtual reality;hardware architecture for the SHA-3 family in CRYstALS-KYBER: post-quantum cryptography;and anemia severity detection in pediatric patients through REXlayer-integrated deep learning and eye conjunctival imaging.

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Architecting Digital Twin for Smart City systems: A Case study 21

Architecting Digital Twin for Smart City Systems: A Case Stu...

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21st international conference on Software architecture (ICSA)

作者： Kanigolla, Likhith Pal, Gaurav Vaidhyanathan, Karthik Gangadharan, Deepak Vattern, Anuradha Int Inst Informat Technol Hyderabad IIIT H Smart City Res Ctr Hyderabad India Int Inst Informat Technol Hyderabad IIIT H Software Engn Res Ctr Hyderabad India

ISBN: (纸本)9798350366266;9798350366259

Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident's quality of life. However, implementing smart city initiatives comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin (DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This concept offers a virtual replica of assets, processes, and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of smart city systems. Particularly in the context of managing water networks, DT technology holds significant promise. Visualization capabilities provide intuitive insights into the system's behavior, facilitating informed decision-making. This visualization, coupled with actuation capabilities, enables control actions based on predictive analytics and optimization algorithms, allowing for proactive management of water resources and infrastructure. To this end, in this paper, we present the architecture of WaterTwin, a DT developed for water quality networks in smart city systems. We demonstrate our approach through the use of a water quality network at the smart city living lab, IIIT Hyderabad campus.

关键词： Digital twin Smart City Software architecture IoT

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Collaborative User Mobility Prediction in Distributed Edge computing Framework 21

Collaborative User Mobility Prediction in Distributed Edge C...

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21st IEEE international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Ali, Sardar Jaffar Raza, Syed M. Choo, Hyunseung Sungkyunkwan Univ Dept AI Syst Engn Suwon South Korea Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9798350363999;9798350364002

User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cloud to facilitate DL model learning causes extensive overhead and privacy concerns. Distributed edge cloud-based federated learning solves these issues, but it faces challenges in handling out-of-distribution data from decentralized edges at the network periphery, and the model biases due to data heterogeneity. This paper addresses these limitations by proposing a fully distributed Collaborative User Mobility Prediction (CUMP) framework that mitigates the out-of-distribution data issue through collaboration among initial layers of DL models in edges that are selected using inter-edge mobility rates. The remaining part of each model only trains on local data, preserving biases towards their respective edges. This enhances the generalization, robustness, and predictive performance of the DL models. Results show that CUMP outperforms conventional global learning and state-of-the-art distributed personalized federated learning and cyclic incremental institutional learning by 63%, 12%, and 10% in predicting the next Point of Attachment (PoA) of a user and by 70%, 22%, and 28% in predicting user dwell time in current PoA, respectively. Thus, CUMP improves prediction performance while reducing network and storage overheads while preserving privacy.

关键词： Distributed edge architecture multi-access edge computing collaborative learning proactive mobility B5G/6G

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From the Physics Lab to the Computer Lab: Towards Flexible and Comprehensive DevOps for Quantum computing Invited Paper 21

From the Physics Lab to the Computer Lab: Towards Flexible a...

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21st ACM international conference on computing Frontiers (CF)

作者： Schulz, Martin Ahmed, Hossam Deng, Xiaolong Echavarria, Jorge Gammelmark, Mathias Karlsson, Sven Kaya, Ercuement Reznak, Michal Schulz, Laura Brandon Tovey, Matthew Tech Univ Munich Munich Germany Leibniz Supercomp Ctr Garching Germany Tech Univ Denmark Copenhagen Denmark

ISBN: (纸本)9798400704925

As quantum system mature and make their way out of physics laboratories into production facilities, we also need to push the development of the matching development and operational, in short DevOps, environments. While programming systems are often in forefront when talking about development environments for quantum systems, DevOps must cover many more aspects, which are often not covered. In this short paper, we will discuss what components are needed and describe our approaches to establish these components. The result is a first step towards an open, widely accessible and usable DevOps environment, which will form the basis to continue to drive the evolution of quantum systems.

关键词： Quantum computing Software Development Programming Environments Hybrid HPCQC

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AI Folk: Sharing Machine Learning Models in a Multi-Agent Community 21st

AI Folk: Sharing Machine Learning Models in a Multi-Agent Co...

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21st international Symposium on Distributed computing and Artificial Intelligence

作者： Olaru, Andrei Sorici, Alexandru Nan, Mihai Iancu, David-Traian Natl Univ Sci & Technol POLITEHN Bucharest 313 Splaiul Independentei Bucharest Romania

ISBN: (纸本)9783031820724;9783031820731

With the rapid growth in the number of available pre-trained machine learning (ML) models for common tasks, with different performance, focus, and capabilities, complex problems can increasingly be solved through adequate choice of model, more than through training or tuning new models. In this paper we introduce the AI Folk methodology to address the challenge of autonomously managing ML models in a community of agents which can use and exchange semantic information about the models that they are using. We present a proof-of-concept implementation in an autonomous driving setting tackling various practical challenges which arise when dealing with this goal.

关键词： machine learning multi-agent systems semantic description

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Maximizing Resource Utilization using Hybrid Cloud-based Task Allocation Algorithm 21

Maximizing Resource Utilization using Hybrid Cloud-based Tas...

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21st IEEE international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Mishra, Sambit Kumar Mohith, G. K. H. Ambati, Sai Teja Guduru, Krishna Koushik Senapati, Rajiv SRM Univ AP Dept Comp Sci & Engn Amaravati Andhra Pradesh India

ISBN: (纸本)9798350363999;9798350364002

Cloud computing operates similarly to a utility, providing users with on-demand access to various hardware and software resources, billed according to usage. These resources are primarily virtualized, with virtual machines (VMs) serving as critical components. However, task allocation within VMs presents significant challenges, as uneven distribution can lead to underloading or overloading, causing system inefficiencies and potential failures. This study addresses these issues by proposing a novel hybrid task allocation algorithm that combines the strengths of the Artificial Bee Colony (ABC) algorithm with Particle Swarm Optimization (PSO). Our approach aims to enhance resource utilization and reduce the risks of VM overload or underload. We conduct a comprehensive evaluation of the proposed hybrid algorithm against traditional ABC and PSO algorithms, focusing on their effectiveness in managing diverse task loads. The results of our empirical analysis indicate that our hybrid approach outperforms the conventional algorithms, leading to better resource utilization and more accurate task allocation. These findings have significant implications for optimizing task allocation in cloud computing environments, and we suggest potential avenues for future research to further refine these strategies.

关键词： Task Scheduling Virtual Machines Cloud computing Artificial Bee Colony Particle Swarm Optimization

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Full-Decentralized Federated Learning-Based Edge computing Peer Offloading Towards Industry 5.0 21

Full-Decentralized Federated Learning-Based Edge Computing P...

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IEEE 21st international conference on Industrial Informatics (INDIN)

作者： Chi, Hao Ran Radwan, Ayman Inst Telecomunicacoes Aveiro Portugal Univ Aveiro Aveiro Portugal

ISBN: (纸本)9781665493130

This paper gives a generic architecture and system modeling for full-decentralized on-demand edge computing-based peer offloading. Compared with the conventional centralized peer offloading strategies, the proposed full-decentralized peer offloading, based on federated learning, physically decentralizes peer offloading algorithm into edge computing, fully eliminating the rely on centralized servers (e.g., cloud). Meanwhile, compared with the other previous decentralized offloading schemes (blockchain-based, game theory-based, etc.), edge computing servers in this paper does not require global information to be shared, when they reach consensus of optimal peer offloading. In particular, the adjacent edge computing servers only share property-sensitive data (for the service providers of the edge computing servers) among each other, relying on which the whole edge computing network can reach global optimal peer offloading. In this paper, we consider energy efficiency as a use case to analyze the feasibility and efficiency of the proposed full-decentralized peer offloading architecture.

关键词： edge computing peer offloading decentralization

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