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Using Word2Vec-LDA-Word Mover Distance for Comparing the Patterns of information Seeking and Sharing during the COVID-19 Pandemic 7

Using Word2Vec-LDA-Word Mover Distance for Comparing the Pat...

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Chan, Wei Wei Na Chua, Hui Sunway University Department of Computing and Information Systems Selangor Malaysia

ISBN: (纸本)9781665421683

During pandemics such as COVID-19, government announcements were sources to convey accurate and relevant information to the public in times of outbreak. Prior studies attempted to explore the public awareness and behavioral changes from various research disciplines in response to the COVID-19 pandemic. Literature has pointed out that the appropriate use of information sources significantly relates to public attitudes in battling the pandemic. Social media has been the widely used medium to express public interests in current events. Literature shows that social media use during a crisis effectively coordinates relevant information from different sources and promotes situational awareness. therefore, it is crucial to investigate scalable approaches to promptly gather insights into the public's interests and how governments responded to the interests relevant to the COVID-19 pandemic. However, there is little empirical research found that tackles these needs. therefore, we aim to close the research gap by examining the feasible approaches for (1) identifying if public information-seeking has similar patterns as information-sharing on social media during the COVID-19 pandemic, and (2) comparing the patterns with the government announcements to confirm if the announcements show aligned response to the public information-seeking and sharing during the COVID-19 pandemic. We applied text processing, LDA topic modeling, and Word Mover Distance techniques to realize our aim through a Malaysian case study. Our research work contributes to the application of the LDA-Word2Vec-Word Mover Distance architecture and algorithms that can be used for future investigation and comparison of information seeking and sharing patterns in different research subjects. © 2022 IEEE.

关键词： COVID-19

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Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired 7

Indian Currency Recognition:A Lightweight Transfer Learning ...

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7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Saini, Poonam Goyal, Ankit Pokhrel, Bishwash Singh, Jaspreet Kumar, Neetish Punjab Engineering College Dept. of Computer Science and Engg. Chandigarh India

ISBN: (纸本)9781665454018

the knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. the paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. the basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. the proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. the model undergoes fine tuning of essential hyperparameters. the proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application. © 2022 IEEE.

关键词： Network architecture

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A lightweight residual neural network for apple external quality grading 3

A lightweight residual neural network for apple external qua...

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3rd international conference on computer Vision and Data Mining, ICCVDM 2022

作者： Sun, Xiaojie Qin, Jian Yan, Long Liu, Xiaojia Shandong Technology and Business University 191 Binhai Middle Road YanTai264003 China Testing and Technology Center for Industrial Products of Shenzhen Customs District No.149 7th Industry Road District Nanshan Shenzhen518067 China

ISBN: (纸本)9781510661363

the current problem of relatively backward and inefficient apple fruit grading technology, computer vision-based classification methods are widely adopted, but traditional visual classification networks face the problems of many parameters, high computational effort and unsatisfactory classification accuracy. therefore, this paper proposes a lightweight residual network-based apple external quality grading method. Firstly, based on the traditional residual neural network, the network uses group convolution to replace the standard convolution in the original residual units1/4 the aims are to reduce the number of model parameters and computational effort;Secondly, to address the information non-circulation problem between group channels caused by group convolution, a Channel Shuffle operation is used to mix inter-group features to improve model performance;Finally, a parallel pooling structure is proposed to solve the problem of information loss of traditional pooling features. To build a dataset of apple images with extensive coverage of external quality information, and to perform data enhancement on a limited dataset, and to conduct experiments based on the augmented dataset using improved models in comparison with common neural network models. the experimental results show that the improved lightweight residual network model has only 2.97M parameters, the FLOPs are only 1/5 of the traditional model, and the classification accuracy is 96.5%, which is helpful for future implementation of apple grading in low performance mobile terminals. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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Using a Graph Transformer Network to Predict 3D Coordinates of Proteins via Geometric Algebra Modelling 7th

Using a Graph Transformer Network to Predict 3D Coordinate...

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7th international Workshop on Empowering Novel Geometric Algebra for Graphics and Engineering, ENGAGE 2022, held in conjunction with computer Graphics international conference, CGI 2022

作者： Pepe, Alberto Lasenby, Joan Chacón, Pablo Signal Processing and Communications Group Cambridge University Engineering Department Trumpington Street CambridgeCB2 1PZ United Kingdom Chacon Lab Rocasolano Physical Chemistry Institute Madrid28006 Spain

ISBN: (纸本)9783031309229

the state of the art in protein structure prediction (PSP) is currently achieved by complex deep learning pipelines that require several input features. In this paper, we demonstrate the relevance of Geometric Algebra (GA) for modelling protein features in PSP. We do so by proposing a novel GA metric based on the relative orientations of amino acid residues. We then employ this metric as an additional input feature to a Graph Transformer (GT) to aid the prediction of the 3D coordinates of a protein. Adding this GA-based orientational information improves the accuracy of the predicted coordinates even after few learning iterations and on a small dataset. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Proteins

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Design and Implementation of TLS Traffic Packet Filtering technology Based on Netfilter Framework 7

Design and Implementation of TLS Traffic Packet Filtering Te...

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7th international conference on Cyber Security and information Engineering, ICCSIE 2022

作者： Cheng, Jin Li, Chen College of Computer Engineering Anhui Sanlian University Hefei China

ISBN: (纸本)9781665455749

When using firewall technology to block network traffic, unexpected blocking often occurs. the main reason is that one IP will correspond to multiple domain names with the widespread use of virtual hosts. Considering this issue, this paper proposes traffic blocking technology based on SNI information. First, a Linux kernel module is developed based on the Netfilter/Iptables framework. Second, the unencrypted Server Name Indication (SNI) information in the TLS protocol is used in HTTPS communication to block traffic for domain names. Finally, specific iptables rules are configured to write into the Linux kernel and this rule supports batch writes of domain name sets. Experimental results show that this method can configure iptables traffic blocking rules for domain names and domain name sets. © 2022 IEEE.

关键词： HTTP

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Building Damage Assessment Using Deep Learning: Bibliometric Analysis 7

Building Damage Assessment Using Deep Learning: Bibliometric...

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7th IEEE international conference on information technology and Digital Applications, ICITDA 2022

作者： Sidharta, Sidharta Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Binus Graduated Program - Doctor of Computer Science Computer Science Department Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Natural catastrophes have become one of the hottest subjects as a result of the harm they have caused to human structures. In recent years, research pertaining to the discipline of building damage assessment (BDA), which seeks to study the effect of damage on structures, has grown substantially. this bibliometric study seeks to determine the state of the art in the subject of Building Damage Assessment Using Deep Learning by examining several papers. the method used (Donthu et al., 202) divides the steps of bibliometric analysis into four parts: identifying the field of research to be analyzed, identifying the methods used in bibliometric analysis, collecting research data with similar fields, and executing and analyzing the results of bibliometric analysis on the collected data. In the data collecting and analysis portion (Garza-Reyes, 2015), the identification of data search keywords, the execution of data searches, the enhancement of search results, statistical data processing, and data analysis are carried out. Several research questions pertaining to trends, influences, themes, authors, and other crucial components of doing research in the area of building damage assessment are answered using these techniques. the outcome is the mapping and resolution of all research problems, as well as the acquisition of bibliometric analysis-related data. the provided findings have been mapped into several types of visualization, including network % clustering graphs, overlay graphs, and density graphs, as well as other forms of visualization that will aid researchers in comprehending the study circumstances in the field. It is envisaged that this bibliometric study would make it simpler for scholars to do research in related topics. © 2022 IEEE.

关键词： Damage detection

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Prediction of Multi-Function Radar Signal Sequence Using Encoder-Decoder Structure 7

Prediction of Multi-Function Radar Signal Sequence Using Enc...

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7th international conference on Signal and Image processing, ICSIP 2022

作者： Feng, Kangan Chen, Hongyu Kong, Yukai Zhang, Lidong Yu, Xianxiang Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Air Force Academy Beijing100085 China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

the multi-function radar (MFR) can perform a variety of tasks and has complicated and adaptable waveform characteristics. Predicting MFR transmission signals is critical for electronic warfare (EW) devices to develop effective countermeasures. this paper therefore deals with the signal sequence prediction problem in the presence of multiple targets via using an encoder-decoder structure. Specifically, the gated recurrent units (GRU) network is developed as the encoder to extract the temporal features, while the transformer decoder layers are exploited as the decoder to output the prediction results. the experimental results demonstrate that the method proposed can achieve effective prediction even in non-ideal situations. © 2022 IEEE.

关键词： Forecasting

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Low power consumption agricultural greenhouse environmental monitoring system design and Implementation 7

Low power consumption agricultural greenhouse environmental ...

引用

2022 7th international conference on information Science, computer technology and Transportation, ISCTT 2022

作者： Zhou, Juncheng Liu, Yu Bian, Hanlin Sun, Jinkai School of Computer Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (纸本)9783800760565

To ensure the quality of agricultural products, it is essential to monitor crops during planting and growth. In this paper, we develop a Low power consumption agricultural greenhouse environment monitoring system to obtain accurate, real-time, and efficient information on crop growth. With STM32F407, ARM Cortex-M as the core of the acquisition and control unit, the system collects and monitors the temperature, humidity, atmospheric pressure, light intensity and harmful gas index in the growing environment of crops in real-time. the sensor data is transmitted to the server by Narrowband-Internet of things (NB-IoT) communication technology. We also built a website on the server side to display the corresponding data and control the thresholds remotely. In summary, A real-time crop information monitoring system with long-distance, low power consumption, intellectualization, multi-dimension, and multi-scale is implemented. Users can monitor and control the environment of crops more intuitively by viewing the web interface's text, curve, and picture information. the results show that the system can significantly reduce maintenance costs and simplify operations. the use of this system can effectively improve the economic benefits. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Crops

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ViT-ALZ: Vision Transformer with Deep Neural Network for Alzheimer’s Disease Detection 7th

ViT-ALZ: Vision Transformer with Deep Neural Network for ...

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7th international conference on Soft Computing: theories and Applications, SoCTA 2023

作者： Kumar, Hemant Agarwal, Rashi Department of Computer Science and Engineering Harcourt Butler Technical University UP Kanpur India Department of Information Technology Chhatrapati Shahu Ji Maharaj University UP Kanpur India

ISBN: (纸本)9789819720880

this study introduces the ViT-ALZ framework, which combines Vision Transformer (ViT) and deep neural networks (DNNs) to accurately detect Alzheimer’s disease (AD) using MRI scans. Our approach involves resizing images and applying augmentation techniques for dataset enhancement. the ViT-ALZ model integrates a ViT-based architecture that captures planar features from axial slices and utilizes weighted fusions to emphasize important visual token characteristics. DNN further enhances feature processing, leading to precise AD classification. Experimental results showcase significant performance metrics: accuracy, sensitivity, specificity, precision, F1-score, and Kappa values are 97.65%, 97.85%, 97.92%, 96.86%, 97.65%, and 95.19%, respectively, on the Kaggle AD dataset. this research underscores the value of deep learning and MRI images in effectively classifying AD, highlighting their potential for early diagnosis and care. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Directional Modulation Algorithm Based on Woodward-Lawson Method 7

Directional Modulation Algorithm Based on Woodward-Lawson Me...

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7th international conference on computer and Communication Systems, ICCCS 2022

作者： Wu, Luocheng Weng, Zhiyi Han, Wenjia Lu, Yin College of Automation Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Directional Modulation (DM) technique is one of the research hotspots for wireless network physical layer security communication. Due to the bad information beam-width and information disclosure of side lobe, DM signal has low safety performance and high complexity. the Woodward-Lawson method is applied to the directional modulation in this paper to reduce the bit error rate of the receiver and the complexity of the algorithm. Firstly, the desired target signal distribution function is set up to strengthen the distortion of the far-field distribution in the desired direction. then, Woodward-Lawson method is used to get the weighted value of the emitter array. Finally, the algorithm is simulated by Matlab and the simulation results show that the proposed algorithm provides a better information beam-width compared to the traditional wireless communication. It solves the problem of information disclosure of side lobe, and has a higher security performance. © 2022 IEEE.

关键词： Beamforming

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