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Ieee International Conference on High Performance Computing and Communications (HPCC)

作者： Lanxue Dang Chunyu Wang Hongyu Han Yan-e Hou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset.

关键词： Deep learning Analytical models Sentiment analysis Semantics Feature extraction Motion pictures data models

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification

Point-Level and Set-Level Deep Representation Learning for C...

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International Conference on Computer and Communications (ICCC)

作者： Jihui Hu Pengfei Ye Danyang Li Lingyun Dong Xiaopan Chen Xiaoke Zhu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. experimental results demonstrate the effectiveness of the proposed approach.

关键词： Representation learning Training Image color analysis Computational modeling Task analysis

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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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Design of Safety Production Supervision and data Management System Based on Cloud Platform

Design of Safety Production Supervision and Data Management ...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Xu, Hongsheng Fan, Ganglong Luoyang Normal University Luoyang471934 China Henan Key Laboratory for Big Data Processing and Analytics of Electronic Commerce Luoyang471934 China

ISBN: (纸本)9783030251277

Safety production refers to the accident prevention and control measures in the production and operation activities, in order to avoid causing personnel injury and property damage accident. This paper firstly analyzes the performance and function of safety production supervision and management system, and points out the problems existing in the existing security supervision and management system. big data analysis technology is an effective way to solve these problems and can be timely and accurate detection of hidden dangers, improve the ability of the investigation and management. This paper presents design of safety production supervision and data management system based on cloud platform. The system is the unified support system platform based on cloud computing and intelligent terminal technology, and can improve the efficiency of data collection and realize the prediction and early warning of the real by big data technology. © 2020, Springer Nature Switzerland AG.

关键词： big data

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Construction of big data analysis Platform Based on Semantic Rough Concept Lattice Model

Construction of Big Data Analysis Platform Based on Semantic...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Xu, Hongsheng Kuang, Guofang Luoyang Normal University Luoyang471934 China Henan Key Laboratory for Big Data Processing and Analytics of Electronic Commerce Luoyang471934 China

ISBN: (纸本)9783030251277

This article first describes in detail big data mining technology based on semantic rough concept lattice model. data mining is facing practical application, even in the face of the same data set. SaaS service platform is mainly responsible for the realization of the function of the service layer, the layer of all business, application of public operation abstract. The external system of the platform is to store the data related to the platform and the data exchange. The paper presents construction of big data analysis platform based on semantic rough concept lattice model. Finally, the experimental results show that the proposed big data analysis method is effective. © 2020, Springer Nature Switzerland AG.

关键词： data mining

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Ieee International Conference on big data and Cloud Computing (BdCloud)

作者： Lei Zhang Xuefei Chen Yuxiang Ma School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mobile devices face the problem of handover signal sources in mobile communication networks. It is particularly difficult to address this problem in 5G networks, because the coverage area of 5G base stations is smaller than that of traditional mobile communication networks, e.g., 4G networks. Therefore, when a device is moving, it needs to frequently handover base stations to maintain the connection with the network in the 5G network. Given that 5G networks have high requirements for low latency and high reliability, new methods need to be proposed to optimize the selection of gNBs for devices. The prediction of the Ues’ trajectory can reduce redundant handovers between devices and base stations. However, the prediction of the Ues’ movement trajectory requires sufficient user movement data to achieve the desired prediction accuracy. For users with less trajectory data, inaccurate predictions may cause negative effects and even increase the number of handovers. In this paper, we propose a gNB handover model based on user similarity. The evaluation results show that our method can reduce the number of handovers by 50% compared with existing related solutions, which means that the proposed method will effectively reduce the transmission delay and enhance the robustness of the gNB.

关键词： Base stations 5G mobile communication Handover Predictive models Prediction algorithms data models Robustness

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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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Heterogeneous Software effort estimation via Cascaded Adversarial Auto-encoder 1

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In Software effort estimation (See) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous See data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct See, which is called as Heterogeneous Software effort estimation (HSee), has not been well studied. In this paper, we propose a HSee model, called Dynamic Heterogeneous Software effort estimation (i.e., DHSee), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSee with dynamically increasing effort data. experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSee method and other competing methods on both static and dynamic See scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

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Secrecy Performance analysis in Internet of Satellites: Physical Layer Security Perspective

Secrecy Performance Analysis in Internet of Satellites: Phys...

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Ieee International Conference on Communications in China (ICCC)

作者： Yongpeng Shi Ya Gao Yujie Xia Henan Key Laboratory of E-commerce Big Data Processing and Analysis School of Physics and Electronic Information Luoyang Normal College China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

As the latest evolving architecture of space networks, Internet of Satellites (IoSat) is regarded as a promising paradigm in the future beyond 5G and 6G wireless systems. However, due to the extremely large number of satellites and open links, it is challenging to ensure communication security in IoSat, especially for wiretap resisting. To the best of our knowledge, it is an entirely new problem to study the security issue in IoSat, since existing works concerning physical layer security (PLS) in satellite networks mainly focused on the space-to-terrestrial links. It is also noted that, we are the first to investigate PLS problem in IoSat. In light of this, we present in this paper an analytical model of PLS in IoSat where a terrestrial transmitter delivers its information to multi-satellite in the presence of eavesdroppers. By adopting the key parameters such as satellites' deployment density, minimum elevation angle, and orbit height, two major secrecy metric including average secrecy capacity and probability are derived and analyzed. As demonstrated by extensive numerical results, the presented theoretical framework can be utilized to efficiently evaluate the secrecy performance of IoSat, and guide the design and optimization for communication security in such systems.

关键词： Wireless communication Analytical models Satellites Physical layer security Orbits Internet Security

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analysis of Ontology Semantic Tagging Method for Semantic Web-Oriented big data

Analysis of Ontology Semantic Tagging Method for Semantic We...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongsheng Xu Shengli Jiang Cong Zheng Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang Normal University Luoyang China Conservatory of Music Luoyang Normal University Luoyang China

The transformation of formal representation based on ontology knowledge. The dimension is generated under the guidance of ontology, and the annotation result is a formal representation consistent with ontology, which ensures the consistency between dimension and ontology semantics. This paper introduces a semi-automatic text tagging tool, which can be divided into management tasks, information extraction, automatic learning and information marking. This paper proposes a semantic tagging method for big data ontology oriented to the semantic web.

关键词： Wireless communication Analytical models Semantics Web pages Tagging Ontologies big data

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