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indexPDT: A High Scalable distributed Classification Approach with Novel Cache Structure for Geo-location 25

indexPDT: A High Scalable Distributed Classification Approac...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Sun, Zhijie Li, Jing Xie, Jun Zheng, Binfan Zeng, Li Zhao, Rongqian Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measured by the mobile communication network. The geographic location information can help to support network performance monitoring and evaluation. Considering accuracy and cost, we mainly adopt a hybrid location scheme combined with feature matching location and Weighted Centroid Correction Location (WCCL). As for feature matching location, over 20 billion samples gathered from tens of thousands of cells daily updated. Due to the vast data scale, feature analysis encounters a severe performance bottleneck. To address this problem, we design the indexed parallel decision tree (indexPDT) operator and integrate it into WindTensor, a self-innovated distributed machine learning (ML) engine. indexPDT is a classifier unit of the random forest (RF) algorithm with a novel cache structure. It performs structured cache processing on the dataset's meta-information, which can accompany the splitting of nodes. The cache structure can be quickly converted into statistical information to help find the optimal splitting point, effectively reducing memory usage and improving performance. Under the public datasets testing on 5 nodes, the mean speedup ratios are 86x and 3x compared with SparkML and XGBoost, respectively. In the Geo-location scenario, for a single cell, the speedup ratios are 82x and 4x compared with SparkML and XG Boost, respectively. © 2023 IEEE.

关键词： Location

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A Bit Level Acceleration of Mixed Precision Neural Network 29

A Bit Level Acceleration of Mixed Precision Neural Network

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Qiu, Dehui Wang, Jing Hou, Yi Zhang, Weigong Gao, Lan Capital Normal University Information Engineering College Beijing China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350330717

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks. © 2023 IEEE.

关键词： bit level computing unit convolutional neural network FPGA hardware accelerator

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Design and Implementation of High-Speed Serial Transmission for ADC Based on JESD204B 3

Design and Implementation of High-Speed Serial Transmission ...

引用

3rd IEEE international conference on Intelligent Technology and Embedded systems, ICITES 2023

作者： Huang, Yawei Tian, Haishan Zhang, Mingru Sun, Yuxin Xie, Dan Tang, Chang School of Physics and Electronics Hunan Normal University Changsha China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

ISBN: (纸本)9798350343755

Currently, as the resolution of sensors such as radar continues to improve, the signal bandwidth is rapidly increasing. There is a growing demand for higher sampling rates and data transmission rates in Analog-to-Digital Converters (ADCs). However, the parallel transmission commonly used in ADCs faces issues such as high crosstalk and difficulties in transmission synchronization. In response to this problem, this paper proposes a method for the design and implementation of a high-speed serial data transmission interface based on JESD204B. Specifically, it utilizes Xilinx's ZYNQ-7 series FPGA and ADC as the core components and presents clock synchronization design, ADC circuit design, and FPGA 204B IP design methods. Experimental testing was conducted using selected link parameters (LMFS=4222) as an example. The experimental results demonstrate that correct JESD204B interface data transmission for four channels was achieved at a data rate of 10 Gbps, thus validating the feasibility of the proposed high-speed transmission link design based on JESD204B in this paper. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Integrating Blockchain for Supply Chain Management of Agricultural and Food Products 3rd

Integrating Blockchain for Supply Chain Management of Agricu...

引用

3rd international conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Cheripelli, Ramesh Prasannanjaneyulu, A.N.K. Department of Information Technology Vidya Jyothi Institue of Technology Hyderabad India Institute of Insurance and Risk Management Hyderabad India

ISBN: (纸本)9789819783540

Traceability is an essential part of agriculture supply chain management for guaranteeing the quality of food and boosting consumer happiness and loyalty. As a result of the problems associated with centralized data storage, we are transitioning to a system in which transparency and quality assurance are ensured at every stage of the supply chain, from framer to end user. The present supply chain model has some drawbacks, such as a lack of knowledge about the product’s travel history and origin and a lack of communication between the organizations of the supply chain. When different farmers and other interested parties use modern means of communication, everyone benefits. Blockchain technology improves supply chain security, transparency, and traceability by recording all supply chain transactions and making them permanently accessible to all parties. In this paper, we introduce a blockchain-based, distributed ledger system (DLT) for tracking assets that can guarantee the system’s honesty and openness. Most of the drawbacks of the old supply chain model were resolved by adopting this new one. We pre sented a decentralized supply chain concept with a smart contract for coordinating all supply chain transactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accountability Blockchain technology Credibility Food sustainability Reputation Security transparency Supply chain Traceability Trust

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Mutual information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling 23

Mutual Information as Intrinsic Reward of Reinforcement Lear...

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23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Zhang, Xianjie Sun, Jiahao Gong, Chen Wang, Kai Cao, Yifei Chen, Hao Liu, Yu Dalian University of Technology Dalian China University of Virginia Virginia United States Nanyang Technological University Singapore Institute of Automation Chinese Academy of Sciencec Beijing China

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services. Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), especially the regional dispatching of vehicles has a significant impact on matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get rides. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and request distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3% over the existing best on-demand ride pooling method. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Reinforcement learning

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Pawlowicz, Paulina Sunderam, Vaidy Hung, Che-Lun Kwiecien, Andrzej Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Emory University Department of Computer Science AtlantaGA United States National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei City Taiwan

ISBN: (纸本)9798350362480

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data. © 2024 IEEE.

关键词： information granules

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Research on the Model of Equipment Theory Learning based on Tabu Search-parallel Genetic Algorithm 3

Research on the Model of Equipment Theory Learning based on ...

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3rd international conference on Computers, information Processing and Advanced Education, CIPAE 2022

作者： Lin, Shicong Li, Fan Wu, Lili Army Aviation Institute of PLA Beijing China Unit 32380 of PLA Beijing China

ISBN: (纸本)9781665468121

In view of the characteristics of teaching and training tasks in military academies, a Tabu Search-parallel Genetic Algorithms is proposed in this paper, and the model of equipment theory learning is studied based on this algorithm. The algorithm can greatly improve the operation efficiency and convergence, and can realize fast retrieval and matching. It plays a key role in the establishment of learning model, and provides technical support for the development of equipment theory learning system in the next step. © 2022 IEEE.

关键词： Tabu search

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Research on distributed model forecasting parallel framework based on adaptive genetic algorithm

Research on distributed model forecasting parallel framework...

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Electronic Technology, Communication and information (ICETCI), IEEE international conference on

作者： Yuting Yang Xiaoxia Liu Shanghai Institute of Technology Shanghai China

With the progress of the Internet information age and the rapid development of Internet technology, adaptive genetic algorithms have been applied in various industries to process data in various fields. However, the traditional adaptive genetic algorithm has problems such as slow convergence speed and easy falling into local optimum. Based on the above background, this paper mainly studies an improved strategy of adaptive genetic algorithm based on a density parameter optimization algorithm. In addition, the proposed improved strategy mainly uses the nearest neighbor collaborative filtering algorithm to improve the average distributed parallel quality of the algorithm, and optimizes the distributed parallel mode and distributed parallel characteristics of the adaptive genetic algorithm respectively. Therefore, the improved strategy has the features of simplicity and high efficiency, fast convergence speed, and strong global search ability, having important research significance in the field of data analysis.

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Research on DDOS Attack Detection Method Based on Dynamic Threshold 3

Research on DDOS Attack Detection Method Based on Dynamic Th...

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3rd international conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Tan, Xiaobo Zhang, Zhongliang College of Information Science and Engineering Shenyang Ligong University Liaoning Shenyang China Department of Computer Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

distributed Denial of Service (DDOS) attack is a typical network attack. It paralyzes the target host through a large amount of resource occupation, which poses a great threat to Internet security. Since the birth of distributed denial of service (DDOS) attacks, a large number of them have been designed for DDOS detection every year. However, in the high-speed network era, they cannot control the complex and changing network environment. Therefore, this paper proposes a dynamic threshold detection method based on machine learning. This method processes network traffic in segments, analyzes the characteristics of a segment of network traffic and converts it into four traffic attributes, predicts the threshold of the next window by sliding window, and classifies data points in the window by using similarity. When all four attributes exceed the threshold range in a certain period of time, it is considered to be subject to DDOS attacks. At the same time, a freezing backtracking mechanism is proposed to prevent dynamic parameters from being polluted by attack traffic and speed up the generation of new thresholds after the attack. Compared with the traditional fixed threshold algorithm, this method has a very low false alarm rate for complex network environment, faster processing time, and significantly improved accuracy. © 2023 SPIE.

关键词： Network security

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Impedance and Admittance Specifications for Small-Signal Stability of Multi-bus DC distributed Power systems 3

Impedance and Admittance Specifications for Small-Signal Sta...

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3rd IEEE international Power Electronics and Application conference and Exposition, PEAC 2022

作者： Zhan, Ding Chen, Wu He, Bangbang Zhang, Chun School of Electrical Engineering Southeast University Nanjing China

ISBN: (数字)9781665491419

ISBN: (纸本)9781665491419

Currently, the scale of dc distributed power systems (DPSs) keeps growing, which makes the number of the buses larger and the stability analysis more complex. In this paper, impedance and admittance specifications of multi-voltage-level dc DPS that restricts impedance or admittance of the bus-connected converters or subsystems and the transfer function of intermediate bus converter (IBC) is proposed and studied. A two-voltage-level dc DPS experiment platform is constructed to verify the specifications. It is proved that the specifications are able to ensure the stability of the multi-voltage-level dc DPS, and thus guarantee the designing process of the parameters are decoupled and decentralized. It solves the problems of the cost and the timeliness of every converter, subsystem and IBC's information transmission and the curse of dimensionality that extensively exist in the stability analysis of multi-voltage-level dc DPSs. © 2022 IEEE.

关键词： Electric power transmission

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