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Design and implementation of CNN custom processor based on risc-v architecture 21

Design and implementation of CNN custom processor based on r...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Li, Zhenhao Hu, Wei Chen, Shuang College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China

ISBN: (纸本)9781728120584

With the rapid development of CNN(convolutional neural networks), the traditional CPU platform can not make full use of the parallelism of CNN. We decide to adopt a new and popular processor architecture: the risc-v architecture for experimental design. In this paper, a new convolutional neural network processor is designed based on risc-v architecture. The processor can take advantage of the parallelism of CNN and is more flexible. This paper completely designed a CNN processor based on the risc-v architecture. The processor uses a classic five-stage pipeline structure, and implements instruction buffer memory and data buffer memory, and adds peripherals such as FLASH, SRAM, and SDRAM. And, this paper designed custom instructions. Given the convolution operation frequently occurring in CNN, vector store instruction, vector load instruction, vector addition instruction, and convolution operation instruction are designed to accelerate the execution of the convolution process. The design has passed the simulation experiment. It can not only complete the general instructions but also run the custom instructions. The final simulation test verified the correctness of the design. © 2019 IEEE.

关键词： Convolution

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Duration-Aware Task Assignment for Heterogeneous Mobility Users in Crowdsensing 25

Duration-Aware Task Assignment for Heterogeneous Mobility Us...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Zhiyuan Li, Peng Huang, Weiyi Wang, Jiahu Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms. © 2022 IEEE.

关键词： Combinatorial optimization

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Data Analysis and Planning Analysis Management system Based on Tensorflow Machine Learning Algorithms

Data Analysis and Planning Analysis Management System Based ...

引用

2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Hu, Shuang Huang, Li Bai, Feng Zhang, Jun School of Computer Science and Technology Wuhan University of Science and Technology China Intelligent Information Processing and Real-time Industrial System Hubei Key Laboratory Institute China

At present, machine learning is quite popular, and tensorflow framework is also very popular. This system takes the plan management system as the practice platform, realizes the data analysis function through the machine learning understanding and the key point. Most of the current market are planning management system, but there is no self-analysis ability, need to base on data, there are limitations. Firstly, the system collects training data such as weight, body fat rate and so on, then builds a deep learning neural network, and finally runs the model to realize plan analysis, so as to help users analyze the feasibility of the plan and provide suggestions for users, and the analysis results will be displayed through processing. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Learning algorithms

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Research on point aggregation algorithm based on WEBGL 3

Research on point aggregation algorithm based on WEBGL

引用

3rd IEEE International Conference on Electronic information Technology and Computer Engineering, EITCE 2019

作者： Chen, Jinghan Chen, Jianxun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135847

WebGL has the advantages of online interaction and hardware accelerated rendering to serve web maps. When there are a large number of spatial points in the visible area of the map that need to be visually expressed, the method of extracting and abstracting the distribution rules and intensiveness of the local spatial points can be achieved through technical synthesis of these spatial points, so as to achieve an objective and comprehensive display space by using limited areas. This paper focuses on the related technologies and principles of WebGL-based point aggregation visualization. The advantages and disadvantages of the algorithm are analyzed according to the aggregation process of the algorithm, and the algorithm is optimized. © 2019 IEEE.

关键词： Visualization

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Influence Maximization With Graph Neural Network in Multi-Feature Social Network 24

Influence Maximization With Graph Neural Network in Multi-Fe...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Yuhao Li, Peng Huang, Weiyi School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

The classical influence maximization (IM) prob-lem aims to select a set of nodes as seed nodes for information propagation. Most related studies focus on one-dimensional information dissemination. However, the diffusion of information should be a multi-dimensional (or multi-feature) diffusion process. The user's judgment of information often depends on the combined judgment of multiple-dimensional information. Based on this, we consider a multi-feature diffusion model. In-formation will be decomposed into multi-dimensional features for diffusion. The user will receive these features and integrate them into a complete message. In addition, we consider seed node selection as a network node classification task. Consid-ering that social networks satisfy a graph structure, we use the node classification technique of graph neural networks. This technique classifies the nodes by the network topology to find the set of influential nodes. We propose a multi-feature influence (MFI) algorithm to improve the performance of the prediction. The set of candidate nodes obtained by classification is used as a benchmark to find the set of nodes with maximum influence. Our experiment results on real datasets show that the proposed algorithm has better algorithmic efficiency than the traditional greedy algorithm and better effectiveness than similar heuristic algorithms. © 2022 IEEE.

关键词： information dissemination

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Two-sided Online Stable Task Assignment with Incomplete Lists and Ties in Spatial Crowdsourcing 19

Two-sided Online Stable Task Assignment with Incomplete List...

引用

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Huang, Weiyi Li, Peng Li, Bo Liu, Jing Nie, Lei Bao, Haizhou Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350346558

For the application of task assignment in crowdsourcing, previous research has focused on offline version or one-sided online version. Some of them draw extra attention to stability in task assignment. Our work extends a one-sided task assignment and its stable generalization. In particular, we present a two-sided online stable task assignment (TS-OSTA) problem in which any vertex from two sides can maintain an incomplete neighbor list and ties. We first give the fundamental definitions of task assignment in spatial crowdsourcing and stable matching. Then, we analyze and present the matching condition and formulate the TS-OSTA problem. As a warm-up, we investigate and analyze two classical baseline algorithms Greedy and Random Threshold. Then we present a bipartite ranking algorithm that maintains the efficiency of Greedy and effectiveness of Random Threshold. Through extensive experiments, we examine the performance of our algorithm in size, memory, and time. The results show that our algorithm performs better than the baselines. © 2022 IEEE.

关键词： Crowdsourcing

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CFinDEE: A Chinese Fine-Grained Financial Dataset for Document-Level Event Extraction 24

CFinDEE: A Chinese Fine-Grained Financial Dataset for Docume...

引用

33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Zhang, Tian Liu, Maofu Zhou, Bingying School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400701726

Document-level event extraction faces numerous challenges in accurately modeling real-world financial scenarios, particularly due to the inadequacies in existing datasets regarding data scale and fine-grained annotations. The development of datasets is a crucial factor in driving research progress;therefore, we present a high-quality Chinese document-level event extraction dataset, CFinDEE. This dataset, grounded in real-world financial news, defines 22 event types and 116 argument roles, annotating 26,483 events and 107,096 event arguments. CFinDEE aims to address these shortcomings by providing more comprehensive annotations and data augmentation, offering richer resources for document-level event extraction in the financial domain. CFinDEE extends data both horizontally and vertically, where horizontal expansion enriches the types of financial events, enhancing the diversity of the dataset;vertical expansion, by increasing the scale of the data, effectively boosts the practical value of the dataset. Experiments conducted on multiple advanced models have validated the high applicability and effectiveness of the CFinDEE dataset for document-level event extraction tasks in the financial field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Expansion

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Task Priority Aware Incentive Mechanism with Reward Privacy-Preservation in Mobile Crowdsensing 25

Task Priority Aware Incentive Mechanism with Reward Privacy-...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Jiahu Li, Peng Huang, Weiyi Chen, Zeqiang Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users' attention to their income. This algorithm protects users' income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions. © 2022 IEEE.

关键词： Budget control

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Optimized PCA based face recognition for mobile devices

Optimized PCA based face recognition for mobile devices

引用

2nd International Conference on Mechanical Engineering, industrial Electronics and Informatization, MEIEI 2013

作者： Zou, Dai Kun Hu, Wei Xia, Hong Wei Li, Wen Fei Yao, Zhuo Min Guo, Hong College of Computer Science Wuhan University of Computer Science and Technology Wuhan Hubei 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9783037858905

With the rapid development of embedded technology, mobile devices have been widely used than before. Face recognition has also been taken as a key application with PCA as the basic algorithm. Though PCA can provide basic information processing, it still has some problems to be used for mobile devices. The movement of the faces increases the difficulty of the recognition and the limited resources of mobile devices propose more constraints to traditional PCA algorithm. A novel approach is presented to optimize PCA based face recognition for better performance and faster recognition speed. The experiments show that the new approach can achieve its target though the optimization. © (2013) Trans Tech Publications, Switzerland.

关键词： Face recognition

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Analysis and Prediction of Commercial Big Data Based on WIFI Probe 4th

Analysis and Prediction of Commercial Big Data Based on WIFI...

引用

4th International Conference on Smart Computing and Communications, SmartCom 2019

作者： Zeng, Xiao Guo, Hong Liu, Zhe College of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9783030341381

With the development of the Internet economy, offline physical transactions are facing a huge test. The number of merchants in many shopping malls has decreased, and the volume of physical transactions has fallen sharply. As an important data of the operation status of the shopping mall, passenger traffic can grasp the best operating time, arrange employees reasonably, and judge the marketing volume of the shopping mall. Therefore, excellent mall operators need to know the status of the passenger flow in the market in a timely manner, and analyze the overall operation situation and make reasonable operational decisions. Based on the above problems, this paper designs and implements a commercial big data analysis and prediction system based on WIFI probe, obtains basic data through WIFI probe hardware sensor, and provides relatively accurate passenger flow information for commercial field, combined with big data analysis and machine learning prediction. The algorithm analyzes a series of information about passenger flow, store quantity, bounce rate, dwell time and other forecasting trend of passenger flow in the future, providing reliable data support for the commercial field and becoming the reference for various decisions. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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