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检索条件"任意字段=3rd International Conference on Computing and Data Science, CONF-CDS 2021"

共 907 条记录，以下是121-130 订阅

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The Implementation of Prioritized Task Scheduling for VOIP data Package Processing in Cloud computing 3

The Implementation of Prioritized Task Scheduling for VOIP D...

引用

3rd international conference on Electronics Representation and Algorithm, ICERA 2021

作者： Nurwarsito, Heru Aprilian, Kresna Indaryana University of Brawijaya Faculty of Computer Science Malang Indonesia

ISBN: (纸本)9781665434003

The rapid growth of computer technology makes researchers try to solve every problem faced. Cloud computing arises as one of the solutions for those problems. Currently, one of the problems available to Cloud computing is the limited resource. Considering its limited source, the VOIP data package processing can be one solution to utilize. Prioritized Task Scheduling, an algorithm that can prioritize data transmission on Cloud, is needed to create a reliable Cloud server. This research aims to develop Cloud computing technology with a concept of virtualization. The result shows that the implementation of the Prioritized Task Scheduling for Voice Over Internet Protocol (VOIP) data package processing on Cloud computing works as expected in the design of the system. The Quality of Service (QoS) performance value of the proposed method shows good and stable results in terms of delay, packet loss, and throughput. According to the standardization used on this paper, the packet loss result with 0% is categorized as "very good"and the biggest average delay with 12.11ms is also categorized as "very good". © 2021 IEEE.

关键词： Quality of service

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A Model of Resource Scheduling of Ideological and Political Education in Colleges and Universities Based on Cloud computing 1

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3rd international conference on Big data and Security, ICBDS 2021

作者： Rui, Ji School of Culture and Communication Shaanxi Radio and Television University Shaanxi Xi’an710119 China

ISBN: (数字)9789811908521

ISBN: (纸本)9789811908514

Cloud computing is the allocation and scheduling of Ideological and political education (IPE) resources in CAU(CAU) through certain algorithms in the virtual environment. Firstly, this paper introduces the resource scheduling problem of IPE in CAU, and introduces the research background and significance of the teaching resource scheduling model of Ideological and political theory course in CAU based on cloud computing. Then, the basic theoretical model based on cloud algorithm is proposed. Secondly, it analyzes and summarizes the corresponding countermeasures for the unfair class hour distribution, poor students’ autonomy and teachers’ one speech hall in the traditional classroom teaching methods. Then the performance of the resource scheduling model is tested. The test results show that the university IPE resource scheduling model based on cloud computing has high practicability and can meet the needs of users. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Inductive Network Representation based on Neighbor Aggregation 3

Inductive Network Representation based on Neighbor Aggregati...

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2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Xie, Jingsheng Chen, Xi Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China School of Computer Science and Technology Anhui University HefeiAH551 China

ISBN: (纸本)9783800758760

Network representation learning is to learn low dimensional vectors for nodes. It plays a critical role in network analysis. However, most existing network embedding methods focus on embedding the nodes that already exist in the network, and cannot be generalized to newly-added nodes without retraining the learning model. To deal with this problem, the most common idea is to directly aggregate neighbor embeddings as the representation of new nodes, but this may lead to ignoring the particularity of node neighbors themselves. To fill this gap, we present INRL-NA, an innovative method of Inductive Network Representation Learning based on Neighbor Aggregation, which uses the hierarchical information to selectively aggregate neighbor node embeddings, preserving both the structural information and feature information of network. First we build a hierarchical network, then train a refinement model on the hierarchical network, finally after a new node joins the network, we use the hierarchical information and feature information to selectively aggregate neighbor embeddings. The experimental results show that compared with direct aggregation of all neighbor representations, INRLNA has higher representation quality. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Aggregates

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Drought Level Prediction with Deep Learning 3rd

Drought Level Prediction with Deep Learning

引用

3rd international conference on computing and data science, conf-cds 2021

作者： Liu, Chenhao University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9789811688843

The forecast of drought levels is important for agricultural development and ecological protection. Some previous approaches include statistical methods, machine learning and so forth. To predict the drought level more accurately, we use the deep learning method, which avoids the addition of professional meteorological auxiliary information as in the previous research, so that the prediction system can automatically predict without the help of professionals. In this study, we have built a number of different deep learning models that combine RNN, CNN and attention mechanism, and use a public meteorological dataset named US Drought & Meteorological data. At the same time, we compared deep learning with other prediction methods. Our results show that deep learning network of Inception combined with LSTM and attention mechanism achieves the best F1 score of 0.728 and it performs better at distinguishing higher drought levels than other methods. Overall, we conclude that deep learning models have the best performance which exceeds machine learning, but under the current drought grade classification standards, the existing methods cannot distinguish the higher drought grades well. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Drought

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A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence 1

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3rd EAI international conference on Edge computing and IoT, EAI ICECI 2022

作者： Zhu, Zhirong Liu, Yiwen Gao, Yanxia Wen, Wenkan Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the rise of data storage computing and IoT technology. The increase in data volume and user demand, the accurate delivery of data and low latency during transmission become important factors that affect the end-user experience. To address this issue, previous authors have proposed the concept of edge computings. In the general environment of edge computing, reasonable scheduling of edge caches can largely achieve low latency and high efficiency, thus improving user experience. In this paper, based on existing research, we propose a combination of a joint learning framework for cache prediction based on region popularity and an edge collaborative cache value optimization method to further improve cache hit rate and cache utilization efficiency. The method obtains excellent expected results through simulation experiments. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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Construction of a smart governance system based on RFID technology 3

Construction of a smart governance system based on RFID tech...

引用

3rd international conference on Inventive Research in computing Applications, ICIRCA 2021

作者： Chen, Ling Taizhou Vocational Technical College Logistics Department Zhejiang Taizhou318000 China

ISBN: (纸本)9780738146270

Construction of a smart governance system based on RFID technology is studied in this paper. The purpose of using big data technology for urban governance is to analyze the large amount of urban data obtained so as to guide us to make correct decisions based on the analysis results. But as far as big data itself is concerned, the premise for obtaining correct analysis results is that there is a large amount of data for its analysis. Hence, this paper optimizes the RFID technology as the data collector to obtain the initial data and then, use the smart data mining technologies to validate the model. The systematic model is defined for the comprehensive analysis. © 2021 IEEE.

关键词： Big data

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REXIO: Indexing for Low Write Amplification by Reducing Extra I/Os in Key-Value Store Under Mixed Read/Write Workloads 25th

REXIO: Indexing for Low Write Amplification by Reducing Ex...

引用

25th international conference on Web Information Systems Engineering, WISE 2024

作者： Wang, Zizhao Qu, Qiang Han, Nan Deng, Zhelang Ma, Yizhuo Huang, Xiaowen Meng, Jintao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Shenzhen University Shenzhen China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819605781

In mixed read/write (r/w) workloads, Key-Value stores that utilize Log-Structured Merge trees face significant write amplification (WA) due to frequent writing, resulting in extensive data compaction. While current LSM-based KV stores effectively address WA in write-heavy workloads, they struggle with increased block cache invalidation and increased r/w I/O conflicts in mixed r/w workloads. This paper introduces REXIO, a novel indexing approach to reduce WA. It decouples RAM from Solid State Drives (SSDs) and stores addresses of KV pairs in the In-RAM hashing table, which reduces buffer invalidations and eliminates unnecessary data reorganization to lower extra I/Os, thereby reducing I/O conflicts. Additionally, REXIO stores keys and values in different blocks and employs In-blocking logging to optimize updating and deleting. The experimental results show that REXIO achieves a 68.3% reduction of WA and 3.4x throughput compared to state-of-the-art LSM-Tree approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Amplification

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Open-Source data Collection for Activity Studies at Scale 1

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3rd international conference on Activity and Behavior computing (ABC)

作者： Hoelzemann, Alexander Pithan, Jana Sabrina Van Laerhoven, Kristof Univ Siegen Ubiquitous Comp Hoelderlinstr 3 D-57076 Siegen Germany Univ Vechta Inst Sports Sci Driverstr 22 D-49377 Vechta Germany

ISBN: (数字)9789811903618

ISBN: (纸本)9789811903618;9789811903601

Activity studies range from detecting key indicators such as steps, active minutes, or sedentary bouts, to the recognition of physical activities such as specific fitness exercises. Such types of activity recognition rely on large amounts of data from multiple persons, especially with deep learning. However, current benchmark datasets rarely have more than a dozen participants. Once wearable devices are phased out, closed algorithms that operate on the sensor data are hard to reproduce and devices supply raw data. We present an open-source and cost-effective framework that is able to capture daily activities and routines and which uses publicly available algorithms, while avoiding any device-specific implementations. In a feasibility study, we were able to test our system in production mode. For this purpose, we distributed the *** smartwatch as well as our app to 12 study participants, who started the watches at a time of individual choice every day. The collected data was then transferred to the server at the end of each day.

关键词： Deep learning

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FE-YOLO: An Efficient and Lightweight Feature-Enhanced Fire Detection Method 3

FE-YOLO: An Efficient and Lightweight Feature-Enhanced Fire ...

引用

3rd international conference on Electronics, Communications and Information Technology, CECIT 2022

作者： Wang, Taofang Cao, Rui Wang, Li Taiyuan University of Technology College of Data Science Taiyuan China Taiyuan University of Technology College of Software Taiyuan China

ISBN: (纸本)9798350331974

The detection and location of fires are critical to forest safety. Timely detection of fires can effectively control natural disasters and maintain the stability of the ecosystem. The flame and smoke in the early stage of forest fires have the characteristics of small targets, and the transparent smoke makes the details of the marks unclear, which poses a significant challenge for the fire detection task. To solve the above problems, we propose a feature-enhanced network architecture based on the YOLO-v5s network for efficient multi-scale object detection, named FE-YOLO. Specifically, based on YOLO-v5s, we added the Mixup data enhancement strategy to solve the problem of blurred target boundaries. To further improve our proposed model, we offer a new feature pyramid module that can reuse features from previous layers to enhance image feature extraction. We evaluate the proposed method on homemade fire images, on which FEYOLO performs well. The mAP result of FE-YOLO is 72.53%. Compared with the baseline network YOLO-v5s, it has been improved by 3.42%, which can be used to detect forest fires quickly. © 2022 IEEE.

关键词： Smoke

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An Efficient Defect Detection System for Printed Circuit Boards with Edge-Cloud Fusion computing 3

An Efficient Defect Detection System for Printed Circuit Boa...

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3rd international conference on Industrial Artificial Intelligence, IAI 2021

作者： Wu, Yi Wang, Jing Chen, Yang-Quan Beijing University of Chemical Technology College of Information Science and Technology Beijing China University of Technology School of Electrical and Control Engineering Beijing China University of California Mechatronics Embedded Systems and Automation Lab MercedCA95343 United States

ISBN: (纸本)9781665435178

Many intelligent methods have been proposed and applied in the field of autonomous manufacturing inspection. These advanced algorithms with high requirements on computing power and network may lead to time delay, high cost and energy consumption in practical applications with massive data to be processed. We carry out an efficient defect detection system in an end-edge-cloud architecture with the concept of edge computing to process the big data quickly and effectively. A branchy deep learning model with early exit capability of inference is proposed to detect the category and location of the defect in printed circuit boards. We offload part of the computing tasks to the edge nodes by segmenting and deploying the DL model. Therefore, our system has high detection efficiency and makes real-time defect detection possible. © 2021 IEEE.

关键词： Printed circuit boards

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