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9th International Conference on Applications and Techniques in Information Security, ATIS 2018

作者： Xie, Xiaolan Qiu, Mengnan College of Information Science and Engineering Guilin University of Technology GuilinGuangxi Zhuang Autonomous Region China Guangxi Universities Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (纸本)9789811329067

In order to improve the utilization of cloud computing resources and maintain the load balance, this paper proposes a cloud computing resource scheduling optimization chaotic firefly algorithm based on the Tent mapping to solve the problem that the firefly algorithm has premature convergence and is easily trapped in the local optimum. In the firefly algorithm, a chaotic algorithm based on the Tent mapping is introduced. By perturbing individuals, the convergence speed is accelerated and the local most optimal probability is reduced. The Bernoulli shift transformation is introduced to improve the cloud computing model. The simulation results based on CloudSim show that the algorithm can shorten the task completion time and improve the overall processing capability of the system. © Springer Nature Singapore Pte Ltd 2018.

关键词： Cloud computing

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Improved Lightweight RFID Bidirectional Authentication Protocol LMAP++ 12th

Improved Lightweight RFID Bidirectional Authentication Proto...

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12th China Wireless Sensor Network Conference, CWSN 2018

作者： Cheng, Xiaohui Shen, Shuai Gui, Qiong Guangxi Key Laboratory of Embedded Technology and Intelligent System College of Information Science and Engineering Guilin University of Technology Jiangan road No. 12 Guilin541000 China

ISBN: (纸本)9789811368332

The security of the lightweight LMAP++ bidirectional authentication protocol commonly used in low-cost RFID systems is studied. The vulnerability of LMAP++ authentication protocol is analyzed, and the reason of LMAP++ authentication protocol vulnerabilities is studied. An improved lightweight LMAP++ bidirectional authentication protocol is proposed and a concrete formula is given to prove the protocol security. After comparison, the improved lightweight LMAP++ bidirectional authentication protocol can meet the security requirements such as confidentiality and traceability faced in RFID systems. Attacks such as tracking, counterfeit tags, replay, and positioning can be resisted, making it more adaptable to the security needs of low-cost RFID systems. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Radio frequency identification (RFID)

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Research on the security protection scheme for container-based cloud platform node based on blockchain technology 1

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4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Xie, Xiaolan Huang, Tao Guo, Zhihong College of Information Science and Engineering Guilin University of Technology GuilinGuangxi Zhuang Autonomous Region China Guangxi Universities Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (数字)9789811322037

ISBN: (纸本)9789811322020

With the development of science and technology, container-based cloud platforms have been widely used. For the security issues for container-based cloud platforms, this paper lists the current threats to container-based cloud platforms and demonstrates the principle of BlockChain technology. The aim of this paper is to demonstrate a security protection scheme for container-based cloud platform node based on BlockChain technology. With the characteristic of tamper-resistant and traceability of BlockChain, the Merkle tree is utilized in the scheme to protect vital documents in child nodes. The effectiveness of this technology for file security protection has been verified. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Containers

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Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering 5th

Collaboration Filtering Recommendation Algorithm Based on th...

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

ISBN: (纸本)9789811501173

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms. © Springer Nature Singapore Pte Ltd. 2019.

关键词： K-means clustering

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A Feedback Learning Segmentation Algorithm for Medical Images of Breast Cancer Incorporating Attention Mechanism 14

A Feedback Learning Segmentation Algorithm for Medical Image...

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14th International Conference on Signal Processing systems, ICSPS 2022

作者： Qiu, Lu Yunlei, Zhang Huaze, Shao Lin, Huang School of Information Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin541004 China School of Information Science and Engineering Guilin University of Technology Guangxi Guilin541004 China

ISBN: (纸本)9798350336313

The study of breast cancer image lesion segmentation algorithms is gradually playing an increasingly important role in the medical field to assist physicians in diagnosis. To further segment lesions precisely, a network model with UNet as a substrate and a fusion of attention mechanisms is proposed. The model is trained using breast cancer image data. An image chunking training strategy is used in the model training process. The maximum pooling is replaced by adaptive adjustment pooling in the downsampling process. The SE module is introduced at the bottom layer of the model. A five-in-one feature fusion strategy is added before convolution operation. A model feedback learning from deep to shallow layers is added to the bottom three layers of the model. The experimental results show that this algorithm improves the accuracy compared to the original UNet, residual ResUnet, and CBAM for the segmentation of breast cancer lesions. © 2022 IEEE.

关键词： Semantic Segmentation

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A Method of Multimodal Emotion Recognition in Video Learning Based on Knowledge Enhancement

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Computer systems Science & Engineering 2023年第11期47卷 1709-1732页

作者： Hanmin Ye Yinghui Zhou Xiaomei Tao School of Information Science and Engineering Guilin University of TechnologyGuilin541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin541004China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal UniversityGuilin541004China

With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online *** of the current research uses the comments of the learning platform or the learner’s expression for emotion *** research data on other modalities are *** of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on *** of the need for other modal research data,we construct a synchronous multimodal data set for analyzing learners’emotional states in online learning *** data set recorded the eye movement data and photoplethysmography(PPG)signals of 68 subjects and the instructional video they *** the problem of ignoring the instructional videos on learners and ignoring the knowledge,a multimodal emotion recognition method in video learning based on knowledge enhancement is *** method uses the knowledge-based features extracted from instructional videos,such as brightness,hue,saturation,the videos’clickthrough rate,and emotion generation time,to guide the emotion recognition process of physiological *** method uses Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks to extract deeper emotional representation and spatiotemporal information from shallow *** model uses multi-head attention(MHA)mechanism to obtain critical information in the extracted deep ***,Temporal Convolutional Network(TCN)is used to learn the information in the deep features and knowledge-based ***-based features are used to supplement and enhance the deep features of physiological ***,the fully connected layer is used for emotion recognition,and the recognition accuracy reaches 97.51%.Compared with two recent researches,the accuracy improved by 8.57%and 2.11%,*** the four public data sets,our proposed method also achieves bett

关键词： Emotion recognition video learning physiological signal knowledge enhancement deep learning CNN LSTM TCN

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An improved MTCNN face detection algorithm 5

An improved MTCNN face detection algorithm

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5th International Conference on E-Business, Information Management and Computer Science, EBIMCS 2022

作者： Wang, Yanjun Cheng, Xiaohui College of Information Science and Engineering Guilin University of Technology Guilin541004 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541004 China

ISBN: (纸本)9781450397834

Abstract: In view of the low accuracy of face detection by MTCNN, an improved MTCNN face detection algorithm is proposed. The detection accuracy is improved by the following factors: the migration learning to the original network model, the fine-tuned to the model key parameters, selected the best effect of the parameters, and determine the threshold of face confidence. Experimental results show that the improved MTCNN algorithm greatly improves the detection accuracy. © 2022 ACM.

关键词： Face recognition

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Analysis and research on the use situation of public bicycles based on spark machine learning 2019

Analysis and research on the use situation of public bicycle...

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2019 International Conference on Data Mining and Machine Learning, ICDMML 2019

作者： Li, Chengang Liu, Yu Li, Chengcheng College of Information Science and Engineering Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin China College of Information Science and Engineering Guilin University of Technology Guilin China

ISBN: (纸本)9781450360906

Public bicycles are a healthy and environmentally friendly means of transportation that facilitates people's travel. However, due to the uncertainty of urban travel, especially the tidal phenomenon, public bicycles often "difficult to borrow a car" and "return the car". This will result in unreasonable distribution of the site during the operation of the public bicycle system, unbalanced bicycle processes at various sites during peak hours, and unbalanced operation and management, which restricts the development of public bicycles. This paper uses the data of the San Francisco Bay Area as the experimental data of this paper, using Spark SQL and Spark Dataframe to analyze the use of public bicycle users and sites, according to the impact of different user types on the use of public bicycles, using K-means clustering algorithm Analyze the use of the site. Based on the Spark MLlib machine learning library, the gradient usage algorithm is used to predict daily usage. © 2019 ACM.

关键词： K-means clustering

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Hadoop + Spark Platform Based on Big Data system Design of Agricultural Product Price Analysis and Prediction by HoltWinters 5th

Hadoop + Spark Platform Based on Big Data System Design of A...

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Deng, Yun Zhu, Yan Zhang, Qingjun Cheng, Xiaohui Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541004 China Department of Information Science and Engineering Guilin University of Technology Guilin541004 China

ISBN: (纸本)9789811501173

In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Agricultural products

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CTO Recommendation of Electronic Products Based on Particle Swarm Optimization Hybrid Ant Colony Optimization Algorithm 5

CTO Recommendation of Electronic Products Based on Particle ...

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5th International Conference on E-Business, Information Management and Computer Science, EBIMCS 2022

作者： Yu, Youhua Xie, Xiaolan Cui, Yunlong School of Information Science and Engineering Guilin University of Technology Guangxi Guilin China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guangxi Guilin China Zhejiang Academy of Surveying and Mapping Zhejiang Hangzhou China

ISBN: (纸本)9781450397834

In recent years, with the continuous innovation of science and technology, users' demand for electronic products gradually presents a diversified trend. However, there are a wide variety of electronic products in the market and the products are updated quickly, which brings great difficulties for users to choose. To solve the above problems, this paper proposes a particle swarm optimization hybrid ant colony optimization algorithm to solve the CTO (Configure To Order) recommendation model of high-end electronic products, so as to provide customers with unique personalized customization services for electronic products. The simulation results show that the algorithm has good performance in solving the CTO recommendation model of electronic products. © 2022 ACM.

关键词： Ant colony optimization

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