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A Neural Network and Bloom Filter-Based Name Search Method in Named Data networking 4

A Neural Network and Bloom Filter-Based Name Search Method i...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Liu, Yue Li, Xiaoyong Si, Xiaotian Zhang, Mengyang Yuan, Kaiguo Li, Pengcheng Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350394375

In a new network architecture like NDN, designing an efficient name lookup method has always been a valuable challenge. The paper studies the name lookup technology based on the Bloom filter and BPNN neural network mapping model in named data networking. In the part of the hybrid model based on the Bloom filter and BPNN, the paper proposes the fusion idea of the Bloom filter and BPNN, and provides a detailed introduction to the construction process of the hybrid model. At the same time, in order to improve the memory utilization of traditional Bloom filters, this article proposes a way to use neural networks to enhance it, and proved its effectiveness through experiments. © 2024 IEEE.

关键词： Network architecture

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Hex-Cell Clustering Based distributed Service Discovery Algorithm for MANETs 15

Hex-Cell Clustering Based Distributed Service Discovery Algo...

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15th international conference on computing Communication and networking Technologies, ICCCNT 2024

作者： Prabu, B. Jagatheeshkumar, G. Department of Computer Science Karuppann Mariappan College Tamilnadu Muthur India

ISBN: (纸本)9798350370249

Mobile Ad-Hoc Network (MANET) is a type of wireless network consisting of mobile devices (such as laptops, smartphones, tablets, etc.) that communicate with each other without the need for a centralized infrastructure like base stations or access points. MANETs are highly dynamic and adaptable, making them suitable for scenarios where traditional infrastructure-based networks are impractical or unavailable. Service discovery remains a formidable challenge in MANETs, with the primary concern being the efficient assignment of optimal services to service requesters. This study proposes a novel Hex-cell Clustering based distributed Service Discovery Algorithm (HC-DSDA) tailored specifically for MANETs. Utilizing a hexagonal grid structure for clustering, the algorithm appoints a Mobile Cluster Head (MCH) within each cell, tasked with coordinating with other MCHs in their respective cells. The Mscore (Mobile Score) concept were introduced to select MCH in an efficient manner which is calculated through the trust parameters of each mobile node. Extensive simulations validate the efficacy of the proposed algorithm, showcasing enhancements in precision, recall, F-measure, and time consumption. These results underscore the algorithm's potential in improving service discovery within MANETs, thereby addressing critical challenges in dynamic network environments. ©2024 IEEE.

关键词： Mobile ad hoc networks

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AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving 23

AutoFed: Heterogeneity-Aware Federated Multimodal Learning f...

引用

29th Annual international conference on Mobile computing and networking (ACM MobiCom)

作者： Zheng, Tianyue Li, Ang Chen, Zhe Wang, Hongbo Luo, Jun Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Fudan Univ Intelligent Networking & Comp Res Ctr Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781450399906

Object detection with on-board sensors (e.g., lidar, radar, and camera) is crucial to autonomous driving (AD), and these sensors complement each other in modalities. While crowdsensing may potentially exploit these sensors (of huge quantity) to derive more comprehensive knowledge, federated learning (FL) appears to be the necessary tool to reach this potential: it enables autonomous vehicles (AVs) to train machine learning models without explicitly sharing raw sensory data. However, the multimodal sensors introduce various data heterogeneity across distributed AVs (e.g., label quantity skews and varied modalities), posing critical challenges to effective FL. To this end, we present AutoFed as a heterogeneityaware FL framework to fully exploit multimodal sensory data on AVs and thus enable robust AD. Specifically, we first propose a novel model leveraging pseudo labeling to avoid mistakenly treating unlabeled objects as the background. We also propose an autoencoder-based data imputation method to fill missing data modality (of certain AVs) with the available ones. To further reconcile the heterogeneity, we finally present a client selection mechanism based on client model similarities to improve training stability and convergence rate. Our experiments confirm that AutoFed substantially improves over status quo in both precision and recall, while demonstrating strong robustness to adverse weather.

关键词： Autonomous driving autonomous vehicle object detection federated learning crowdsensing multimodal learning

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WholeGraph: A Fast Graph Neural Network Training Framework with Multi-GPU distributed Shared Memory Architecture

WholeGraph: A Fast Graph Neural Network Training Framework w...

引用

international conference for High Performance computing, networking, Storage and Analysis (HPC)

作者： Yang, Dongxu Liu, Junhong Qi, Jiaxing Lai, Junjie NVIDIA Beijing Peoples R China

ISBN: (纸本)9781665454445

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets, encoding graph data into low dimensional vectors. In this paper, we present a fast training graph neural network framework, i.e., WholeGraph, based on a multi-GPU distributed shared memory architecture. WholeGraph consists of partitioning the graph and corresponding node or edge features to multi-GPUs, eliminating the bottleneck of communication between CPU and GPUs during the training process. And the communication between different GPUs is implemented by GPUDirect Peer-to-Peer (P2P) memory access technology. Furthermore, WholeGraph provides several optimized computing operators. Our evaluations show that on large-scale graphs WholeGraph outperforms state-of-the-art GNN frameworks, such as Deep Graph Library (DGL) and Pytorch Geometric (PyG). The speedups of WholeGraph are up to 57.32x and 242.98x compared with DGL and PyG on a single machine multi-GPU node, respectively. The ratio of GPU utilization can sustain above 95% during GNN training process.

关键词： graph neural network GPU shared memory architecture

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Scalable distributed High-Order Stencil Computations

Scalable Distributed High-Order Stencil Computations

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international conference for High Performance computing, networking, Storage and Analysis (HPC)

作者： Jacquelin, Mathias Araya-Polo, Mauricio Meng, Jie Cerebras Syst Sunnyvale CA 94085 USA

ISBN: (纸本)9781665454445

Stencil computations lie at the heart of many scientific and industrial applications. Stencil algorithms pose several challenges on machines with cache based memory hierarchy, due to low re-use of memory accesses if special care is not taken to optimize them. This work shows that for stencil computation a novel algorithm that leverages a localized communication strategy effectively exploits the second generation Cerebras WaferScale Engine (WSE-2), which has no cache hierarchy. This study focuses on a 25-point stencil finite-difference method for the 3D wave equation, a kernel frequently used in earth modeling as numerical simulation. In essence, the algorithm trades memory accesses for data communication and takes advantage of the fast communication fabric provided by the architecture. The algorithm -historically memory-bound- becomes computebound. This allows the implementation to achieve near perfect weak scaling, reaching up to 503 TFLOPs on WSE-2, a figure that only full clusters can eventually yield.

关键词： Stencil computation high performance computing energy wafer-scale distributed memory multi-processor architecture and micro-architecture

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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RESEARCH ON distributed POWER QUALITY MONITORING DEVICE WITH 25.6K SAMPLING RATE 2

RESEARCH ON DISTRIBUTED POWER QUALITY MONITORING DEVICE WITH...

引用

2nd international conference on Power, Communication, computing and networking Technologies, PCCNT 2024

作者： Wu, Wenting Gao, Sen Wei, Ziwei Deng, Jindong NR Electric Co. Ltd Nanjing211102 China

ISBN: (纸本)9781837242672

Aiming at the problem that CVT increases C3 needs on-site sampling and the insufficient sampling rate of digital substations leads to insufficient accuracy of power quality monitoring. A distributed power quality monitoring device with a sampling rate of 25.6k is proposed. The device mainly realizes local sampling through the merging unit, and the merging unit sends the sample data to the power quality monitoring terminal through the multi-ASDU IEC61850-9-2 data frame format to complete the calculation and analysis of various indicators of power quality. The performance indicators of the distributed power quality monitoring device prototype have been verified by a third-party type test. As a stable and reliable power quality monitoring solution for substations, it has pretty promotion prospects. © The Institution of Engineering & Technology 2024.

关键词： 25.6K SAMPLING RATE distributed POWER QUALITY MONITORING

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Communication-Efficient -Stepping for distributed computing Systems 19

Communication-Efficient -Stepping for Distributed Computing ...

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19th IEEE international conference on Wireless and Mobile computing, networking and Communications, WiMob 2023

作者： Zhang, Haomeng Xie, Junfei Zhang, Xinyu Department of Electrical and Computer Engineering United States San DiegoCA92182 United States SDSU Department of Electrical and Computer Engineering San DiegoCA92182 United States UCSD Department of Electrical and Computer Engineering La Jolla CA92093 United States

ISBN: (纸本)9798350336672

This paper considers the single source shortest path (SSSP) problem, which is the key for many applications such as navigation, mapping, routing, and social networking. Existing SSSP algorithms are designed mostly for shared-memory systems. Nevertheless, with the prevalence of diverse smart devices like drones, there is a growing interest in deploying SSSP algorithms over distributed computing systems so that they can run efficiently onboard of smart devices via Mobile Ad Hoc computing or at the network edges via Mobile Edge computing. In this paper, we introduce a communication-efficient -stepping algorithm for distributed computing systems. The proposed algorithm is featured by 1) a message coordination architecture for reducing message exchanges between workers, 2) a pruning technique for reducing redundant computations, and 3) an aggregation technique for further reducing message exchanges when communication delay is significant. Theoretical analyses and experimental studies on real-world graph datasets demonstrate the promising performance of proposed algorithm. © 2023 IEEE.

关键词： Human resource management

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Detection of distributed Denial of Service in The Application Layer of IoT Using Machine Learning 15

Detection of Distributed Denial of Service in The Applicatio...

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15th international conference on computing Communication and networking Technologies, ICCCNT 2024

作者： Nanthiya, D. Gopal, S.B. Manjula Devi, R. Praveenkumar, R. Vijayakumar, S.D. Raja, T. Dharanish Sriram, L. Kongu Engineering college Department of Computer Technology -UG Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Perundurai India Kpr institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Kangeyam India Vivekanandha college of Technology for women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798350370249

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. The server typically takes requests and returns responses. There will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. The term 'machine learning' refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. The main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the Internet of Things (IoT) against distributed Denial of Service (DDoS) attacks. The lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： CIDAD Cyber security Cyber-attacks DDoS attacks

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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