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arXiv 2023年

作者： Black, Mitchell Wan, Zhengchao Nayyeri, Amir Wang, Yusu School of Electrical Engineering and Computer Science Oregon State University CorvallisOR United States Halıcıoǧlu Data Science Institute University of California San Diego San DiegoCA United States

Message passing graph neural networks (GNNs) are a popular learning architectures for graph-structured data. However, one problem GNNs experience is oversquashing, where a GNN has difficulty sending information between distant nodes. Understanding and mitigating oversquashing has recently received significant attention from the research community. In this paper, we continue this line of work by analyzing oversquashing through the lens of the effective resistance between nodes in the input graph. Effective resistance intuitively captures the "strength" of connection between two nodes by paths in the graph, and has a rich literature spanning many areas of graph theory. We propose to use total effective resistance as a bound of the total amount of oversquashing in a graph and provide theoretical justification for its use. We further develop an algorithm to identify edges to be added to an input graph to minimize the total effective resistance, thereby alleviating oversquashing. We provide empirical evidence of the effectiveness of our total effective resistance based rewiring strategies for improving the performance of GNNs. © 2023, CC BY.

关键词： Graph neural networks

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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International Conference on data engineering

作者： Feng Cheng Jiang Xiao Cunyang Liu Shijie Zhang Yifan Zhou Bo Li Baochun Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics - silent assets - and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions.

关键词： Sharding Directed acyclic graph Distributed ledger Avatars Scalability Receivers Throughput

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High-Level Synthesis for Microfluidic Biochips Considering Actual Volume Management and Channel Storage

High-Level Synthesis for Microfluidic Biochips Considering A...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Chen Yuhan Zhu Zhen Chen Zhisheng Chen Genggeng Liu College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Fuzhou China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

In recent years, microfluidic biochips have been widely applied in various fields of human society. The emergence of distributed channel-storage architecture enables fluid to be directly cached within the flow channels without being transported to the dedicated storage. However, the volume of the flow channels is limited, it is important to carefully consider the fluid volume to be cached to ensure it can be accommodated within the appropriate flow channels. In this paper, we propose a high-level synthesis method considering actual volume management and channel storage through integer linear programming, providing a rational analysis of fluid caching and the temporal relationships between different fluid-handling tasks. Experimental results conducted on multiple benchmark tests demonstrate the effectiveness of the proposed method in reducing the completion time of bioassay, the number of caches, the maximum fluid caching volume, and the number of fluid-handling tasks.

关键词： Fluids Biochips Estimation Cells (biology) Integer linear programming Benchmark testing Task analysis

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GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

arXiv

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arXiv 2024年

作者： Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Mérouane Niyato, Dusit Poor, H. Vincent Yuen, Chau School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Center for 6G Technology Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Optimization is essential for the efficiency and reliability of multi-access edge computing (MEC) networks. Many of the optimization problems in this area are NP-hard and lack effective approximation algorithms. As a result, there is often a shortage of optimal (ground-truth) data, which limits the effectiveness of traditional deep learning approaches. Most existing learning-based methods require a large amount of optimal data and do not take into account the potential advantages of using suboptimal data, which can be obtained more efficiently. To illustrate this, we focus on the multi-server multi-user computation offloading (MSCO) problem, which is very common in systems like the Internet of Vehicles (IoV) and unmanned aerial vehicle (UAV) networks. To solve the MSCO problem, we introduce a Graph Diffusion-based Solution Generator (GDSG), designed to work with suboptimal datasets while still achieving convergence to the optimal solution with high probability. We reformulate the network optimization challenge as a distribution-learning problem and provide a clear explanation of how to learn from suboptimal training datasets. GDSG is developed as a multi-task diffusion generative model that utilizes a graph neural network (GNN) to capture the distribution of high-quality solutions. We employ a straightforward and efficient heuristic approach to generate a sufficient quantity of training data composed entirely of suboptimal solutions. In our implementation, we enhance the backbone GNN architecture to achieve better generalization. GDSG also attains nearly 100% task orthogonality, ensuring that there is no interference between the discrete and continuous solution generation training objectives. Additionally, we demonstrate that this orthogonality originates from the diffusion-related training loss in GDSG, rather than from the architecture of the neural network itself. The experiments demonstrate that the proposed GDSG outperforms other benchmark methods on both the o

关键词： Heuristic methods

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6D Movable Antenna Enhanced Multi-Access Point Coordination via Position and Orientation Optimization

arXiv

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arXiv 2024年

作者： Pi, Xiangyu Zhu, Lipeng Mao, Haobin Xiao, Zhenyu Xia, Xiang-Gen Zhang, Rui School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beihang University Beijing100191 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA sch

关键词： Convex optimization

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Federated Learning in Big data with IoT for Intrusion Detection

Federated Learning in Big Data with IoT for Intrusion Detect...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Anitha Govindaram J. S. Prasath A. Suganya K. Jayasakthi N. Rajkumar Jose Anand A. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Bengaluru Karnataka Department of Artificial Intelligence and Data Science Anand Institute of Higher Technology Chengalpattu Tamil Nadu Department of ECE KCG College of Technology Chennai Tamil Nadu Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Artificial Intelligence (AI) part of Federated Learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features.

关键词： Surveys Privacy data privacy Federated learning Reviews Intrusion detection Learning (artificial intelligence) Security Informatics Mobile computing

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Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-Network DRL Inference

Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Muhammad Irfan Hang Hu Myung J. Lee Arslan Qadeer Yang G. Kim Kazi Ahmed Daiki Nobayashi Department of Electrical Engineering City College of New York New York NY USA School of Computing & Data Science Wentworth Institute of Technology Boston MA USA Department of Computer Engineering Technology NYCCT Brooklyn NY USA Department of Electrical & Computer Engineering Technology NYIT New York NY USA Department of Electrical and Electronic Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

The Software-Defined Networking (SDN) paradigm has significantly improved network efficiency by integrating machine learning (ML) capabilities into the control plane (CP). This integration allows adaptive management of network resources in response to dynamic traffic conditions. However, the significant geographical distance between the CP and data plane (DP) causes considerable round-trip latency, measured in milliseconds, which poses a major challenge to time-sensitive traffic. To address this challenge, our research proposes a novel in-network Reinforcement Learning (RL) inference framework. This framework extends programmability from the CP to the DP, enabling timely and precise control of network resources to meet the stringent Quality of Service (QoS) requirements of mission-critical applications. The in-network RL inference is implemented using match-action tables within the Protocol Independent Switch Architecture (PISA) framework in the DP and validated through hardware deployment of protocol-independent packet processors (P4). To allocate bandwidth precisely based on QoS requirements, a P4 meter extern is used to differentiate the unique demands of individual traffic flows. Our enhanced deep-deterministic policy gradient (eDDPG)-based RL inference achieves superior performance with minimal processing overhead, reducing latency by 88.7% and jitter by 89.1 % compared to systems without RL inference.

关键词： Protocols Program processors Process control Switches Quality of service Reinforcement learning Channel allocation Jitter Network resource management Software defined networking

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Robust Decoding of the Auditory Attention from EEG Recordings Through Graph Convolutional Networks

Robust Decoding of the Auditory Attention from EEG Recording...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Siqi Cai Ran Zhang Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China

Auditory attention decoding (AAD) with electroencephalography (EEG) holds great promise in brain-computer interface (BCI). Despite much progress, it remains a research topic on how to effectively evaluate the performance of EEG-based AAD algorithms under an appropriate setting that reflects the use scenarios. It is desired that systems are evaluated under cross-subject and cross-trial settings. However, systems are often reported under same-subject, same-trial settings, where test data are not truly separated from the training data, thus potentially leading to model overfitting due to data leakage. In this paper, we study the robustness of graph convolutional network (GCN), a novel approach to learning the intricate spatial patterns in multi-channel EEG signals, by comparing GCN across cross-subject, cross-trial, and same-subject, same-trial settings. On two publicly available AAD datasets, it is found that GCN exhibits remarkable robustness, outperforming previous conventional convolutional neural network (CNN) solutions. We confirm the superiority of our GCN-based AAD model in terms of generalization and robustness.

关键词：

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Optimizing Business Performance with Cloud Computing: A Scalable and Efficient Approach

Optimizing Business Performance with Cloud Computing: A Scal...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Aravind Nuthalapati Arun M Prajitha C Rinesh S Betzy Babu Thoppil Abubeker K M Big Data and Cloud Architect Microsoft Charlotte Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam India Department of ECE Centre for Interdisciplinary Research Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India Department of Computer Science and Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India Department of Electronics and Communication Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

Cloud computing has emerged as a transformative force in modern business, offering unparalleled opportunities to enhance efficiency and scalability. This study investigates how unlocking the power of cloud computing can revolutionize business operations, enabling organizations to adapt to dynamic market demands. Despite their potential, existing methods often face challenges such as high implementation costs, security vulnerabilities, and limited integration with legacy systems, which hinder optimal utilization. This research proposes a novel Business Operations framework using Cloud Computing Technologies (BO-CCT) to address these issues. This framework integrates advanced cloud technologies with business processes to streamline operations, reduce costs, and improve scalability. The BO-CCT framework emphasizes secure data management, seamless integration, and real-time analytics to resolve existing limitations. The proposed method empowers businesses by leveraging cloud computing for enhanced decision-making, resource optimization, and operational agility. Findings indicate that the BO-CCT framework significantly improves business efficiency, reduces operational costs, and enables scalable growth. This study concludes that adopting cloud computing through the BO-CCT framework can revolutionize business operations, providing a competitive edge in today's digital economy.

关键词： Cloud computing Costs Scalability Decision making Companies Aerodynamics Real-time systems Security Optimization Business

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The Impact of the Energy Storage System Operation Strategy on Power Grid Reliability

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 1820-1826页

作者： Kim, Beopsoo Lee, Dayoung Rusetskii, Nikita Shusterzon, Konstantin Sidorov, Denis Kim, Insu Dept. of Electrical Engineering Inha University Korea Republic of Dept. of Electrical and Computer Engineering Inha University Korea Republic of School of Information Technology and Data Science Irkutsk National Research Technical University Russia Energy Systems Institute Siberian Branch Russian Academy of Sciences Russia

Based on the empirical data published by the Korea Electric Power Exchange and the Korea Meteorological Administration, we present a methodology that predicts the system marginal prices. For this purpose, this study uses the long-short term memory (LSTM) technique, which is commonly used in time-series prediction problems. As a result, we present a strategy for operating energy storage systems (ESSs) from the perspective of a prosumer rather than a system operator or conventional utilities. This study also examines the effects of the proposed prosumer actions on the system. The proposed operating strategy is established based on dynamic programming. In particular, this study takes the proposed operation strategy of ESSs the point of view of system reliability. © The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://***/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Reliability

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