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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce computing on Cloud

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Rong Gu Xu Huang Haipeng Dai Xiaoyu Geng Xiaofei Chen Yihua Huang Fu Xiao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.

关键词： Machine learning Parallel processing Big Data Data models Stability analysis Sparks Task analysis

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Energy Efficient Dynamic Load Balancing using Self-Organized Criticality in Grid computing

Energy Efficient Dynamic Load Balancing using Self-Organized...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Vivek Kumar Chinmaya Kumar Swain Aryabartta Sahu Dept. of CSE IIT Guwahati India Dept. of CSE SRM University India

Grid computing refers to a collection of compute resources from different geographic locations to achieve a common objective. It involves tremendous amounts of computational task which require reliable resource sharing across the grid for efficiently executing the tasks. Load disparity is a major issue in grid computing. So effectively and efficiently utilize the potentials of huge distributed resources, load balancing algorithms are of primary importance. In this paper, we proposed an elegant approach using Bak-Tang-Wiesenfeld sandpile model for dynamically load-balancing of tasks across the processing sites in an energy efficient manner. the proposed approach reduces redundant migrations between processing sites by transferring tasks in a breadth first manner. In this approach avalanche propagation condition is stricter and depends on the average load of two adjacent layers of neighbours and use of signalling mechanism rather than actual transfer of tasks during an avalanche, until a destination comes in equilibrium. We experimentally validated by a number of numerical experiments using Google traces. the results of the experiments show the effectiveness of the proposed approach which reduces the number of migrations and improves the energy efficiency without much increasing the total flowtime and degrading the load balancing aspects as compared to state-of-the-art approaches.

关键词： Heuristic algorithms Computational modeling Load management Grid computing Energy efficiency Internet Resource management

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Detecting Phishing Scams on Ethereum Using Graph Convolutional Networks with Conditional Random Field

Detecting Phishing Scams on Ethereum Using Graph Convolution...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Wenhan Hou Bo Cui Ru Li Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

As an innovative technology of great significance, blockchain has been widely used in various walks of life. Meanwhile, scams have experienced rapid growth on the blockchain, the phishing scam is a classic fraud among them. Ethereum is the largest blockchain platform that supports smart contracts, and its ecosystem has been under serious threat due to phishing scams. Consequently, phishing scam detection is particularly critical for Ethereum to avoid economic loss. However, accounts on Ethereum are represented as strings without features, which brings great difficulties for detection. To combat this challenge, we propose an effective method based on Graph Convolutional Network (GCN) with Conditional Random Field (CRF) to detect phishing scams. Firstly, we process accounts and their neighbors with transaction records to build transaction graphs. Due to lack of portrait information, we adopt DeepWalk to provide initial features for each node. then graph representations are learned through GCN with CRF. the extensive experiments show that the proposed model performs better than comparison methods on both precision and recall, which indicates that our method can effectively identify phishing scams on Ethereum.

关键词： Economics Phishing Smart contracts Ecosystems Feature extraction Blockchains Fraud

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Multi-Dimensional Intention Recognition for User-Generated Short Texts

Multi-Dimensional Intention Recognition for User-Generated S...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Ying Zhang Fan Zhang Xiao Yan Zhang Shandong University of Finance and Economics Jinan P. R. China

Online social media platforms are becoming increasingly popular.A huge number of people are using such platforms to share various information. Supply and demand constitute two important kinds of information in social media platforms. Examples are sell and buy, positions and job seeking, houses for rent and needs of accommodation,and so ***, it is relevant to match such supply and demand information automatically in order to facilitate the trades online. However, the problem is challenging due to the high volumes of information expressed in short and vague texts. In this paper, we propose a complete set of techniques to resolve the problem. First, we design an intention recognition model to recognize a user’ s complete intention from her/his original text posts,the recognized intention includes expected behaviors, intention objects, user sentiments and other relevant constraints. Second, we define the similarity for two intentions that are recognized. third, we devise an algorithm to match supply and demand intentions whose similarity is sufficiently high. We evaluate our proposal on real data. the results show that our approach returns accurate matching results efficiently.

关键词： Supply and demand Text recognition Social networking (online) Behavioral sciences Proposals Data mining

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Multi-level Federated Learning Mechanism with Reinforcement Learning Optimizing in Smart City 8th

Multi-level Federated Learning Mechanism with Reinforcement ...

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8th international conference on Artificial Intelligence and Security (ICAIS)

作者： Guo, Shaoyong Xiang, Baoyu Chen, Liandong Yang, Huifeng Yu, Dongxiao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Stat Grid HeBei Elect Power Co Co Ltd Shijiazhuang Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9783031067914;9783031067907

While taking account into data privacy protection, federated learning can mine local data knowledge and gather data value, which has been widely concerned by the Smart city and Internet of things. At present, a large amount of data is generated by the massive edge network in the smart city, but the resources of the edge side are limited. How to reduce the communication overhead between the edge and the centralized cloud server, improve the convergence speed of data model, and avoid resource waste caused by synchronized blocking of federated learning has become the core issue for the integration of federated learning and the Internet of things in the smart city. For this reason, this paper designs a multi-level federated learning mechanism in the smart city, and uses reinforcement learning agents to select nodes to offset the influence of the non-IID data that is not independent and identically distributed. At the same time, asynchronous non-blocking updating method is used to perform model aggregation and updating of federated learning to release the resources of faster devices and improving the efficiency and stability of federated learning. Finally, simulation results show that the proposed method can improve the efficiency of federated learning tasks in edge network scenarios with a lot of devices in the smart city.

关键词： Smart city Federated learning Reinforcement learning Edge network

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Construction of Social Community Knowledge Graph from Wikipedia

Construction of Social Community Knowledge Graph from Wikipe...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Shimei Li Tongge Xu Yueqi Liu Liqun Yang School of Cyber Science and Technology Beihang University China

Knowledge graphs have become a fundamental trend in the fields of healthcare, education, finance, ICT, science, engineering, society, politics, and tourism, etc. the community knowledge graph aims to explore the composition of communities and their relationships through visual social network analysis. However, the process of unstructured data like text raises challenges for knowledge extraction. To automatically build a knowledge graph, this study applies natural language processing and technology of knowledge graph creation. this paper proposes a novel method, Augmented Word Vectors (AWV), which is a word vector representation with lexical features and part-of-speech (POS) features. this work evaluates the performance of AWV on various deep learning models, and the experimental results show that AWV is effective for improving word embedding in the task of named entity recognition. Furthermore, an unsupervised approach based on rules and dependency semantics was applied to extract relations. Specifically, this method achieves high productivity by mapping sentences as dependency trees to extract relations mediated by verbs or nouns. We build a community knowledge graph for Hong Kong political groups by applying our method to the Chinese Wikipedia, which validates the effectiveness of the proposed method.

关键词： Deep learning Visualization Social networking (online) Semantics Knowledge graphs Encyclopedias Feature extraction

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FedACL: Federated Multi-Distillation with Auxiliary Classification Layers in IoT

FedACL: Federated Multi-Distillation with Auxiliary Classifi...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Zhipeng Gao Futeng Feng Zijia Mo Zijian Xiong Yang Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Federated learning is a popular privacy-preserving machine learning paradigm that can train a robust centralized model while guaranteeing the privacy of client data. However, traditional federated learning cannot cope with the problem of slow convergence and low accuracy due to model heterogeneity and statistical heterogeneity. To solve the problem, we propose a federated multi-distillation with auxiliary classification layers in IoT (FedACL). In FedACL, we add auxiliary classification layers after certain hidden layers in the local heterogeneous model, thus implementing a multi-distillation framework consisting of self-distillation in the local update and co-distillation in the communication. Besides, in order to improve the stability of model training, we propose a double-weighted method based on the test accuracy of the clients to measure the contribution of each client. A large number of experiments are conducted for different numbers of client and communication volumes under the Non-IID settings of CIFAR100 and FEMNIST datasets, and all of them prove that FedACL has fast convergence speed, high accuracy and good stability.

关键词： Training Data privacy Federated learning Stability analysis Data models Convergence

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A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation

A Joint Learning Sentiment Analysis Method Incorporating Emo...

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IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Jie Chen Luping Luo Bojing Ji Shu Zhao Yanping Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University China School of Big Data and Artificial Intelligence Chizhou University China

ISBN: (纸本)9781665456579

Social media is the platform for most people to share their opinions, emojis are also widely used to express moods, emotions, and feelings on social media. there have been many researched on emojis and sentiment analysis. However, existing methods mainly face two limitations. First, since deep learning relies on large amounts of labeled data, the training samples of emoji are not enough to achieve the training effect. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this paper, we propose a joint learning sentiment analysis method incorporating emoji-augmentation, and the method has two advantages compared with the existing work. First, We optimize the easy data augmentation method so that the newly generated sentences can also preserve the semantic information of emojis, which relieves the problem of insufficient training data with emojis. Second, it fuses emojis and text features to allow the model to better learn the mutual emotional semantics between text and emojis, jointly training emojis and words to obtain the sentence representations containing more semantic information of both emojis and text. Our experimental results show that the proposed method can significantly improve the performance compared with several baselines on two datasets.

关键词： Training Sentiment analysis Social networking (online) Fuses Text recognition Mood Semantics

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Inventory Management via Dynamic Ad-Hoc Network with Synchronized ESP-NOW Protocol

Inventory Management via Dynamic Ad-Hoc Network with Synchro...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： G Pratham S Shreyas B Sahana P.N. Jayanthi Rashtreeya Vidyalaya College of Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

In the context of modern inventory management, the ability to maintain accurate, real-time data across a distributed network is essential for efficient supply chain operations. this paper presents the design and implementation of an inventory management system utilizing a dynamic ad-hoc network based on the ESP-NOW protocol, a lightweight, peer-to-peer communication protocol that operates without the need for traditional Wi-Fi infrastructure. the system is built using ESP32 microcontrollers, which form a decentralized mesh network to monitor and manage inventory levels across various nodes within a warehouse or retail environment. A significant contribution of this paper is the introduction of a novel synchronization method tailored to the ESP-NOW protocol stack. Traditional synchronization mechanisms often face challenges in dynamic ad-hoc networks due to the lack of a central coordinator and the variability in node communication. To address this, the proposed method uses a time-stamped data packet exchange system, where nodes periodically broadcast their inventory data along with a synchronization token. Neighboring nodes receive these packets, adjust their internal clocks, and synchronize their inventory data based on the latest available information. this method ensures consistency across the network while minimizing the risk of data conflicts and reducing latency. the proposed system's architecture is designed to be highly scalable and flexible, accommodating the addition or removal of nodes without disrupting network functionality.

关键词： Mesh networks Protocols Microcontrollers Supply chains Inventory management Real-time systems Peer-to-peer computing Synchronization Wireless fidelity Monitoring

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Impact of Hardware Impairments on the Performance of Millimeter-Wave Massive MU-MIMO Systems with distributed Antennas 8

Impact of Hardware Impairments on the Performance of Millime...

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8th international conference on Communications and networking, ComNet2020

作者： Belaoura, Widad Ghanem, Khalida Shakir, Mohummed Zeeshan Qaraqe, Khalid Department of Electronics Algiers Algeria Algiers Algeria School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley Scotland United Kingdom Texas AM University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9781728153209

the impact of hardware impairments play an crucial role in any practical communication systems, yet they are generally omitted when investigating the performance of distributed massive multiple input multiple output (MIMO) systems. In particularly, for millimeter-Wave (mm-Wave) bands, no study has been addressed yet. To overcome this limitation, in this paper a mm-Wave distributed massive multi-user (MU) MIMO system based-hybrid beamforming structure, in which the residual hardware impairments have been incorporated in the transmitter processing, is presented. At each base station (BS), a zero-forcing (ZF) baseband processing is designed over the effective channel to efficiently remove the multi-user (MU) interference. the results show that, relative to the co-located mm-Wave massive MIMO with the same antenna configurations, mm-Wave distributed MIMO achieves a significantly higher performance while reducing the impingement of hardware practical defects. © 2020 IEEE.

关键词： Millimeter waves

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