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SSRN 2022年

作者： Fan, Chao Yang, Litao Lin, Hao Qiu, Yingying School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China

Skin lesion segmentation is a critical method for extracting pathological information from dermoscopy images, which is of great significance for lesion location, recognition, monitoring and treatment. Due to the varied sizes of lesions, different forms and colors, and blurring boundaries, the present detection methods are unable to accurately anticipate local features, which has an impact on the segmentation accuracy. Therefore, the Dual-branch Feature Extraction Network (DFE-Net) is suggested in this paper. We designed two types of encoders based on the Transformer and Efficient Channel Attention (ECA) module, one for extracting the global feature and another for the local feature, before fusing and decoding. Simultaneously, the skip connections from the Enhanced ECA Feature Extraction Modules to decoders are designed to reduce feature loss during decoding, this will restore the boundaries and local features to the greatest extent possible. We test the model on three publicly available skin lesion datasets: PH2, ISIC-2016, and ISIC-2018. The results show that our model is superior to other previous methods, and the segmentation characteristics are more accurate representations of the actual lesion. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Blockchain Security Research Progress and Hotspots

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第2期49卷

作者： Yuan, Ke Yan, Yingjie Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin300350 China

Blockchain is poised to advance the cause of product innovation and technology evolution as a novel decentralized infrastructure and distributed computing model. However, there are serious security risks in the blockchain itself, which will restrict the development of blockchain and related applications. To better grasp the current status of blockchain security research and explore the research hotspots and frontier, this study used Citespace as a bibliometric analysis tool to collect 3993 research papers between 2014 and 2020 from the WoS Core Collection as a data sample. We conduct a comprehensive overview of blockchain security research in terms of authors, research institutions, publications, countries, citations and keywords in a visual graph. This paper shows that the literature record continues to be refreshed, and it peaked in 2019. China and the USA are the main regions for collaborative research. They have established partnerships with many countries whose research institutions and authors play an important role in the collaborative network. Blockchain security research hotspots are mainly related to "cryptocurrency", "supply chain", "smart contract", "healthcare", "consensus mechanism", "fog computing", "cyber attack" and "Internet of Thing (IoT)". The lineage of blockchain development has evolved from an early focus on cryptocurrency and blockchain security risk analysis to securing other areas, such as financial systems, the IoT and healthcare through blockchain. In the future, blockchain security research will be combined with much newer technologies to form diverse developments © 2022. IAENG International Journal of Computer *** Rights Reserved.

关键词： Blockchain

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Location Rating Based on Graph Neural Network 2020

Location Rating Based on Graph Neural Network

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Fengbin Zheng Li Ding Lanxue Dang Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

With the rapid development of social networks, people are increasingly interested in sharing their location. Location recommendation has become an important personalization service for location-based social networks (LBSN). Location rating is one of the important tools. However, location-based social networks contain multidimensional network structures and node information. Existing approaches mostly focus on network structures that utilize one of these dimensions, which makes it difficult to efficiently aggregate information from multiple dimensions simultaneously. To overcome these difficulties, one of the recent approaches is the social recommendation based on graph neural networks (GNNs). Based on this, this paper proposes a graph neural network framework for location rating. Graph neural networks are highly inductive and can efficiently aggregate network structure and node information. In particular, the method not only aggregates homogeneous social networks composed of user and heterogeneous bipartite graphs composed of user-location, but also constructs location networks using location sequences to propose an aggregation model for location networks. Experiments are conducted on two datasets, and the results show that the method improves on the root mean square error (RMSE) and mean absolute error (MAE) metrics.

关键词： Graph neural network Location rating Location based social networks

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Design of Offline analysis System for Remote Sensing data Service Based on Hive

Design of Offline Analysis System for Remote Sensing Data Se...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Hongkuo Zhang Jiaxiang Zhang Meng Tian Baojun Qiao School of Computer and Information Engineering Heanan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

With the wide application of remote sensing data service platform, the platform accumulates more and more data, and the remote sensing users' demand for remote sensing data service becomes more and more diverse. Therefore, how to deal with remote sensing service data, make statistics and analyze its potential value, and meet the diverse needs of remote sensing users becomes particularly important. Based on big data technology, this paper firstly collects and stores remote sensing user behavior data and remote sensing service platform business data, then uses Hive to build offline data warehouse, performs dimensional modeling and hierarchical design of the data, and finally obtains relevant index data values by statistical analysis. Through the statistical analysis of remote sensing service data can not only provide data support for active service mode, but also provide support for the decision of remote sensing application department managers.

关键词： Process design Analytical models Statistical analysis data warehouses data models Indexes Intelligent systems

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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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An Algorithm of data Classification Based on PCA and K-Means++

An Algorithm of Data Classification Based on PCA and K-Means...

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Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Ruirui Huang Qianshuai Cheng Zhengquan Chen Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665424998

Principal component analysis (PCA) and K-means++ algorithm are used to classify and analyze eight main variables of the average annual consumption expenditure of urban households in 31 provinces and cities in China in a given year, so as to better build the reasonable consumption level of each area. In this paper, a data classification method based on PCA and improved K-means++ is proposed. In this method, PCA was used to extract eight variable characteristics of consumer expenditure, and K-means++ algorithm was used to classify the data. The experimental results show that this method reduces the dependence of the characteristic dimension of variable data and the k-means++ algorithm on the initial classification center of gravity, and improves the accuracy and stability of data classification results.

关键词： Dimensionality reduction Conferences Urban areas Optics Stability analysis Classification algorithms Telecommunications

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Graph Structure-Aware Bi-directional Graph Convolution Model for Semantic Role Labeling

Graph Structure-Aware Bi-directional Graph Convolution Model...

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IEEE International Conference on big data

作者： Wenke Ding Congcong Zhang Gaofei Xie Xiaojie Hu Xiajiong Shen Yatian Shen School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665445993

Dependency tree contains rich syntactic structure information, which is very effective for semantic role labeling. Semantic role labeling is a task with strong syntactic dependence. How to effectively use subgraphs to predict surrounding graph structures for context of dependency tree and encode the dependency relationships between predicates and arguments in the sentence which to be annotated is still a challenging problem. In this paper, we propose a bidirectional graph convolution model based on distribution of graph structure and the attention mechanism, which is used to make better use of all syntactic information in the sentence dependency tree and the dependencies between predicates and arguments, so as to improve the accuracy of semantic role labeling. Without heavy feature engineering and extra knowledge of a particular language, our model obtained 88.9% F1 value on the WSJ test set of public dataset CoNLL2005, and 0.7% improvement compared with the existing best model.

关键词： Knowledge engineering Convolution Conferences Semantics Bidirectional control Syntactics big data

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Base Station handover Based on User Trajectory Prediction in 5G Networks

Base Station handover Based on User Trajectory Prediction in...

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

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

In the 5G era, user equipment connected to 5G base stations can obtain better communication services. However, due to the limited coverage of base stations, the movement of users may cause frequent handover of base stations. With the widespread deployment of 5G base stations, how to reduce unnecessary handover times when users connect to the base station becomes particularly important. In recent years, user trajectory data has been mined and applied to many scenarios. In the 5G network, by judging the user’s movement trajectory, the number of handovers required for the user to connect to the 5G base station can be effectively reduced. In this paper, we propose a 5G base station handover method based on trajectory prediction. A CNN-LSTM neural network, which combines a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has been proposed to predict the user’s trajectory. The evaluation results show that our mechanism can effectively reduce the number of base station handovers and improve the efficiency of users using the network. In addition, the stability of 5G networks can be improved by reducing inefficient base station handover.

关键词： Base stations 5G mobile communication Time series analysis Handover Predictive models Prediction algorithms Feature extraction

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key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing Images

Key-Point Based Distribution Probability Optimization for Ob...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yufei Wang Xiaopan Chen Danyang Li Jitong Zhang Yulong Cai School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665409513

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors.

关键词： Location awareness Heating systems Computer vision Estimation Detectors Object detection Prediction algorithms

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Design and Research of Remote Sensing Process Language based on XML

Design and Research of Remote Sensing Process Language based...

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Information Technology,big data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianyu Zuo Heng Li Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665428781

In the era of rapid development of remote sensing technology, in order to improve the production efficiency of remote sensing data processing flow and further optimize the editing of remote sensing processes in the visual interface. In this paper we have analyzed the characteristics and attributes of remote sensing processes and algorithms. We made the production of processes and algorithms into scripts, and realizes to create remote sensing processes by inputting scripting language in the visual interface. A remote sensing language based on XML is designed to read and store key information. This language will use XML to drive the production of remote sensing process. With the language we design, a remote sensing process customization system has been implemented. It can improve the editing and production efficiency of remote sensing data processing flow.

关键词： Visualization Flowcharts XML Production Programming data processing Sensors

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