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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Sun, Huaiyuan Zhang, Lei Dang, Lanxue Liu, Yang Xie, Yi Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan Universfiity Kaifeng475001 China Zhong Ke Langfang Institute of Spatial Information Applications Langfang065000 China

ISBN: (纸本)9781510664616

Flood control is one of the most important operations of Yellow River control. The dam bank danger in flood season every year has the characteristics of time-varying, multi-dimension and complex situation, which poses a certain threat to people's life and property safety. For timely, flood control engineering data quickly and implement management interface of information visualization, effectively reduce the labor intensity of artificial working risk, this paper puts forward a kind of based on three-dimensional library of Cesium Huang Heba shore the realization method of 3D visualization platform adopted the aerial drones to create dam shore entity 3D model, Combined with WebGIS technology and front-end technology, the system's 3D expression is realized. Through interface design and system function development, the system has three-dimensional model display,with the functions of dam bank cruise and measurement tools, the platform has strong sense of reality and good interaction effect, which realizes the three-dimensional visual display of the Madu control project in Zhengzhou, henan province. © 2023 SPIE.

关键词： Flood control

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment analysis 24

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

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

ISBN: (纸本)9798350319934

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. © 2022 IEEE.

关键词： Sentiment analysis

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Guided and Fused: Efficient Frozen CLIP-ViT with Feature Guidance and Multi-Stage Feature Fusion for Generalizable Deepfake Detection

arXiv

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arXiv 2024年

作者： Chen, Yingjian Zhang, Lei Niu, Yakun Chen, Pei Tan, Lei Zhou, Jing Henan Key Laboratory of Big Data Analysis and Processing Henan University China International Business School Henan University China

The rise of generative models has sparked concerns about image authenticity online, highlighting the urgent need for an effective and general detector. Recent methods leveraging the frozen pre-trained CLIP-ViT model have made great progress in deepfake detection. However, these models often rely on visual-general features directly extracted by the frozen network, which contain excessive information irrelevant to the task, resulting in limited detection performance. To address this limitation, in this paper, we propose an efficient Guided and Fused Frozen CLIP-ViT (GFF), which integrates two simple yet effective modules. The Deepfake-Specific Feature Guidance Module (DFGM) guides the frozen pre-trained model in extracting features specifically for deepfake detection, reducing irrelevant information while preserving its generalization capabilities. The Multi-Stage Fusion Module (FuseFormer) captures low-level and high-level information by fusing features extracted from each stage of the ViT. This dual-module approach significantly improves deepfake detection by fully leveraging CLIP-ViT’s inherent advantages. Extensive experiments demonstrate the effectiveness and generalization ability of GFF, which achieves state-of-the-art performance with optimal results in only 5 training epochs. Even when trained on only 4 classes of ProGAN, GFF achieves nearly 99% accuracy on unseen GANs and maintains an impressive 97% accuracy on unseen diffusion models. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking 12th

Temporal Global Re-detection Based on Interaction-Fusion At...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ma, Jingyuan Hou, Zhiqiang Han, Ruoxue Ma, Sugang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China

ISBN: (纸本)9783031463075

In long-term visual tracking, target occlusion and out-of-view are common problems that lead to target drift, adding re-detection to short-term tracking algorithms is a general solution. To better handle the problem of target disappearing and reappearing in long-term visual tracking, this paper proposes a temporal global re-detection method based on interaction-fusion attention. Firstly, ResNet50 is used as the feature extraction network to obtain the depth features of the template and the search region. Then, a new interaction-fusion attention is added to extract the connection of different dimensionality of features. Finally, Temporal ROI Align is introduced to select candidate boxes, increasing the use of historical information by re-detection method, and improving the accuracy of target localization. STMTrack algorithm is selected as the short-term tracking algorithm, which works with the proposed re-detection method to construct a long-term tracking algorithm, and experiments are conducted on the UAV123, LaSOT, UAV20L, and VOT2018-LT datasets, and the effectiveness of this re-detection method can be seen from the experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical detection

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Design and implementation of algorithm execution time predictor for remote sensing product workflow 3

Design and implementation of algorithm execution time predic...

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3rd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2023

作者： Zhou, Bing Wu, Qiong Li, Jiaguo Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668522

Remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-Task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. Remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Remote sensing

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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 433页

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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Research on resource scheduling method based on Kubernetes

Research on resource scheduling method based on Kubernetes

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Haoyang Song 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: (数字)9798331529482

ISBN: (纸本)9798331529499

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling.

关键词： Cloud computing Schedules Clustering algorithms Containers Fish Load management Scheduling Hardware Fish schools Genetic algorithms

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Mining Bytecode Features of Smart Contracts to Detect PonziScheme on Blockchain

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 1069-1085页

作者： Xiajiong Shen Shuaimin Jiang Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China

The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in untrustworthy ***,these features of this technology are also easily exploited by unscrupulous individuals,a typical example of which is the Ponzi scheme in *** negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been *** solve this problem,we propose a detection model for detecting Ponzi schemes in smart contracts using *** this model,our innovation is shown in two aspects:We first propose to use two bytes as one characteristic,which can quickly transform the bytecode into a high-dimensional matrix,and this matrix contains all the implied characteristics in the ***,We innovatively transformed the Ponzi schemes detection into an anomaly detection ***,an anomaly detection algorithm is used to identify Ponzi schemes in smart *** results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme ***,the F1-score of this model can reach 0.88,which is far better than those of other traditional detection models.

关键词： Ponzi scheme blockchain security smart contracts anomaly detection

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Estimation and spatiotemporal variations of black carbon aerosol over the North China Plain in 2018

Estimation and spatiotemporal variations of black carbon aer...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Cai, Kun Qi, Yongkai Li, Shenshen Zhang, Shuo College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668898

With the rapid economic development in the North China Plain, the significant emissions of black carbon (BC) aerosol have exerted notable impacts on climate, environment, and health, making it a hot topic in environmental studies in recent years. To compensate for the insufficient ground monitoring data, the PM2.5 satellite remote sensing inversion data and chemical model simulation data were utilized, applying the proportionality factor method to estimate the satellite concentration of BC. The estimation results were validated using BC monitoring data from seven stations in 2018, and based on this, the spatiotemporal variations of BC satellite concentration was obtained. The results showed that the estimated BC concentration using the proportionality factor method had good accuracy, with a correlation coefficient (R2) of 0.72. The spatial distribution of BC over the North China Plain in 2018 exhibited a decreasing trend from the inner region to the outer region, with high concentration areas mainly located in provinces with high straw burning and steel production. The seasonal variation of BC in the North China Plain in 2018 was pronounced, showing higher concentrations in autumn and winter and lower concentrations in spring and summer. © 2023 SPIE.

关键词： Satellites

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