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SSRN 2022年

作者： Lu, Zhengcai He, Jialin Tian, Zhengwei School of Artificial Intelligence & Big Data Luzhou Vocational and Technical College Sichuan 646000 China Luzhou Key Laboratory of Intelligent Data Analysis and Processing Sichuan 646000 China

Neighborhood rough set based attribute reduction, as a powerful tool to deal with numerical data, is widely used in areas such as machine learning, pattern recognition, and decision support. A neighborhood actually contains rich potential knowledge, which deserves in-depth study for more optimized reduction and better classification quality. However, few literatures care for this work. This study introduces the neighborhood partition technique for original exploration and obtain a remarkable achievement supporting a novel attribute reduction. We firstly construct the partitioned neighborhood rough set model (PNRS), which brings a significant revolution in the rules for determining which region an object belongs to. Then, the neighborhood positive region certainty (NPRC) is proposed for attribute *** NPRC expands the contribution of a single object to the positive region from {0,1} to [0,1] so as to describe classification abilities of attributes more concretely and profoundly. Finally, a new attribute reduction algorithm is designed. It employs the NPRC to evaluate attributes and guide greedy search to find an optimal subset of features. Experimental results show that the proposed algorithm is capable of finding a smaller number of attributes and has higher classification performance than other available algorithms. © 2022, The Authors. All rights reserved.

关键词： Pattern recognition

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Deep Hashing with Triplet Loss of Hash Centers and Dissimilar Pairs for Image Retrieval

Deep Hashing with Triplet Loss of Hash Centers and Dissimila...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ye Liu Yan Pan Jian Yin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Artificial Intelligence Department Lizhi Inc. Beijing China Big Data Department Lizhi Inc. Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image hash coding learning based on deep neural network have a wide range of application scenarios and become a research hotspot in recent years. In the framework of most existing deep hashing methods, it is necessary to first design a loss function that can converge quickly, and then select an appropriate pre-training deep network and tune it. In order to balance global and local constraints, this paper combines the design of loss functions required by the processes of image feature learning and image hash coding, and adds the constraints of triple loss of hash centers and dissimilar pairs. The proposed novel loss function can give consideration to the characteristics of global hash centers, local dissimilarity of images and image classification labels in the training process. The pre-trained large models have been incorporated into the framework, which can extract image embeddings to assist in the generation of hash codes. The comparative experiments on several image datasets show that the DTLH method proposed in this paper can achieve better results than the traditional hashing methods and the deep hashing methods under different coding lengths.

关键词： Training Representation learning Image coding Codes Federated learning Computational modeling Image retrieval

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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An Efficient Attribute Reduction Approach Based on Partitioned Neighborhood Rough Sets

SSRN

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SSRN 2022年

作者： Lu, Zhengcai He, Jialin Tian, Zhengwei School of Artificial Intelligence and Big Data Luzhou Vocational and Technical College Luzhou646000 China Luzhou Key Laboratory of Intelligent Data Analysis and Processing Luzhou646000 China

关键词： Pattern recognition

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Efficient Attribute Reduction Based On Partitioned Neighborhood Rough Sets

SSRN

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SSRN 2022年

作者： Lu, Zhengcai He, Jialin Tian, Zhengwei School of Artificial Intelligence & Big Data Luzhou Vocational and Technical College Sichuan646000 China Luzhou Key Laboratory of Intelligent Data Analysis and Processing Sichuan646000 China

关键词： Pattern recognition

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Diffusion Model-based Contrastive Learning for Human Activity Recognition

arXiv

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arXiv 2024年

作者： Xiao, Chunjing Han, Yanhui Yang, Wei Hou, Yane Shi, Fangzhan Chetty, Kevin The School of Computer and Information Engineering Henan University Kaifeng475004 China University College London LondonWC1E 6BT United Kingdom The School of Computer and Information Engineering Henan University China The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The Department of Security and Crime Science University College London LondonWC1E 6BT United Kingdom

WiFi Channel State Information (CSI)-based activity recognition has sparked numerous studies due to its widespread availability and privacy protection. However, when applied in practical applications, general CSI-based recognition models may face challenges related to the limited generalization capability, since individuals with different behavior habits will cause various fluctuations in CSI data and it is difficult to gather enough training data to cover all kinds of motion habits. To tackle this problem, we design a diffusion model-based Contrastive Learning framework for human Activity Recognition (CLAR) using WiFi CSI. On the basis of the contrastive learning framework, we primarily introduce two components for CLAR to enhance CSI-based activity recognition. To generate diverse augmented data and complement limited training data, we propose a diffusion model-based time series-specific augmentation model. In contrast to typical diffusion models that directly apply conditions to the generative process, potentially resulting in distorted CSI data, our tailored model dissects these condition into the high-frequency and low-frequency components, and then applies these conditions to the generative process with varying weights. This can alleviate data distortion and yield high-quality augmented data. To efficiently capture the difference of the sample importance, we present an adaptive weight algorithm. Different from typical contrastive learning methods which equally consider all the training samples, this algorithm adaptively adjusts the weights of positive sample pairs for learning better data representations. The experiments suggest that CLAR achieves significant gains compared to state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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CLSDRL:A routing optimization method for traffic feature extraction

CLSDRL:A routing optimization method for traffic feature ext...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Xia, Hong Li, Jianguo Chen, Yanping Lv, Ning Wang, ZhongMin Dong, Qingyi Xi'an University of Posts and Telecommunications School of Computer Science and Technology Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665441582

In order to solve the impact of the temporal and spatial characteristics of traffic on network routing optimization, this paper proposes convolution long-short memory neural network deep reinforcement learning (CLSDRL) model for routing optimization. The CLSDRL model consists of deep deterministic policy gradients (DDPG) deep couple with convolution neural network (CNN) and long-short memory neural network (LSTM). After extracting the spatial and temporal characteristics of network traffic with CNN and LSTM, routing decisions are made with DDPG algorithm. Experiments are conducted under different load intensities, and the network performance is evaluated by the average network delay and packet loss rate, experimental results show that this method can improve significantly network performance. © 2021 IEEE.

关键词： Reinforcement learning

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM

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Tsinghua Science and Technology 2020年第6期25卷 712-720页

作者： Lei Zhang Chenbo Xu Yihua Gao Yi Han Xiaojiang Du Zhihong Tian Henan Key Laboratory of Big Data Analysis and Processing Kaifeng 457004China Institute of Data and Knowledge Engineering Henan UniversityKaifeng 475004.China National Internet Emergency Center Zhengzhou 450000China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6008USA Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 510006China

In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be *** to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win *** one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to *** solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup *** model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero *** CBOW model can predict the context of a word in a ***,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.

关键词： Word2vec mutiplayer online battle arena games Continuous Bag Of Words(CBOW)model Long Short-Term Memory(LSTM)

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Prediction model of physical activity level and hypertension based on artificial neural network 4

Prediction model of physical activity level and hypertension...

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2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Hua, Liang Yu, Wang Jinying, Li Wanjun, Zhang Yiting, Wang School of Physical Education Henan University Kaifeng Henan475001 China Department of Health Management People's Hospital of Henan Province People's Hospital of Henan University People's Hospital of Zhengzhou University Zhengzhou450003 China Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng475004 China School of Foreign Language Henan University Kaifeng Henan475001 China

Purpose: Taking the population of henan Provincial People's Hospital Chronic Disease Control Center from July 1, 2020 to December 31, 2020 who came to the hospital for physical examination as the research object, analyze and explore the role and contribution of physical activity level in the prediction of hypertension by using the LSTM network model, which can provide references for the clinical diagnosis of hypertension. Methods: Randomly select 2000 physical examination data, remove missing and invalid data, and preprocess them. Finally, select factors such as gender, age, body mass index, weight grade, waist-to-hip ratio, physical activity level, body fat percentage and other factors to establish a neural network prediction model. Then test and study the model, focusing on exploring the contribution of physical activity level to prediction. Result: The level of physical activity has certain advantages in predicting the prevalence of hypertension, but the predictive ability in the later stage is insufficient © Published under licence by IOP Publishing Ltd.

关键词： Disease control

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