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Exploring destination image of Huashan through semantic network: A perspective of crowd-source big data

Exploring destination image of Huashan through semantic netw...

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2022 international conference on image, signal Processing, and Pattern Recognition, ISPP 2022

作者： Mou, Jiarui College of Resources Shandong University of Science and Technology Taian271019 China

ISBN: (纸本)9781510654846

With the development of information and communication technology (ICT) and the rapid spread of smart mobile terminals, a large amount of crowd-sourced big data of user-generated content (UGC) is being produced in the cloud, providing us with new technical means and methods to analyze and calculate the image of tourism destinations. This paper proposes a technical framework for tourism destination image calculation based on cognitive theory, integrated with the application of content analysis and curve fitting methods, with the big data of Huashan travel blogs from *** as an example. It mainly analyzes the word frequency distribution pattern of blog big data, calculates the travel sentiment score, and constructs the travel sentiment word cloud map. The perception calculation of Huashan tourism image from three aspects of cognition, emotion, and overall is carried out, the semantic network map of Huashan tourist attractions is constructed, and the three-layer structure of Huashan tourism semantic network map with its internal connection characteristics, attractions relationship chain and resources is further analyzed. This research can provide an important technical framework and methodological basis for the extraction of tourism destination images, and provide valuable scientific references for the construction of tourism recommendation systems as well as the planning and management of the tourism department. © 2022 SPIE.

关键词： Blogs

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CLIP-Prefix for image Captioning and an Experiment on Blind image Guessing 10th

CLIP-Prefix for Image Captioning and an Experiment on Bl...

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10th EAI international conference on Industrial Networks and Intelligent systems, INISCOM 2024

作者： Huynh, Triet Minh Nguyen, Duy Linh Nguyen, Thanh Tri Vu, Thuy-Duong Thi Dang-Ngoc, Hanh Dang, Duc Ngoc Minh Computing Fundamental Department FPT University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031673566

image caption generation resides at the intersection of computer vision and natural language processing, with its primary goal being the creation of descriptive and coherent textual narratives that faithfully depict the content of an image. This paper presents two models that leverage CLIP as the image encoder and fine-tune GPT-2 for caption generation on the Flickr30k and Flickr8k datasets. The first model utilizes a straightforward mapping network and outperforms the original architecture with a BLEU-1 score of 0.700, BLEU-4 score of 0.257, and ROUGE score of 0.569 on the Flickr8k dataset. The second model constitutes a new architecture exploring the boundaries of minimal visual information required for captioning. It incorporates CLIP’s text encoder to produce input for the generator, while the image embedding serves solely as a validation mechanism. Despite its relatively lower performance, with a BLEU-1 score of 0.546, BLEU-4 score of 0.108, and ROUGE score of 0.444 on the Flickr8k dataset, this model demonstrates the decoder’s ability to create captions based on keyword descriptions alone, without direct access to the context vector. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： signal encoding

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ICMSSP 2022 - 2022 7th international conference on Multimedia systems and signal Processing

ICMSSP 2022 - 2022 7th International Conference on Multimedi...

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7th international conference on Multimedia systems and signal Processing, ICMSSP 2022

ISBN: (纸本)9781450396424

The proceedings contain 15 papers. The topics discussed include: application of color filtering and image filtering algorithms in circuit diagnosis of smart meter box;research on brick carving based on 3D digital modeling technology — take the example of Linxia brick sculpture art ‘River and Mountain’;improving identity-relevant deepfake video detection in real-world with adversarial data augmentation;land evolution analysis of Waisanding sandbar using Google Earth;research on course clustering based on semantic;application study of mobile technology assisted translation teaching mode in applied universities;on the influence and application of artificial intelligence on China’s commercial sports industry — empirical analysis based on artificial intelligence coupling model;efficiency enhancement and mode change of intelligent media communication under the interaction of artificial intelligence technology;and on the mechanism of artificial intelligence affecting sports tourism industry.

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Investigation of Vessel Segmentation by U-Net based on Numerous Datasets 1

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Fang, Zhe Jiang, Hao Zhang, Chao Yunnan Normal University Yunnan Kunming650000 China Yunnan Key Laboratory of Optoelectronic Information Technology Yunnan Normal University Yunnan Kunming650000 China

ISBN: (数字)9789819911455

ISBN: (纸本)9789819911448

The segmentation results of retinal vessels can be an essential foundation for diagnosing and detecting numerous clinical medical symptoms. The work in this paper is to first train on the public dataset DRIVE to obtain the U-Net model. Secondly, the model is applied to private databases to segment fundus vascular images. Finally, deep learning metrics are evaluated on the U-Net model and test results. The experiment results demonstrate the effectiveness of U-Net on vessel segmentation tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Enhancing Motor imagery EEG signal Classification with Simplified GoogLeNet 11

Enhancing Motor Imagery EEG Signal Classification with Simpl...

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11th international Winter conference on Brain-Computer Interface (BCI)

作者： Wang, Lu Wang, Junkongshuai Wen, Bo Mu, Wei Liu, Lusheng Han, Jiaguan Zhang, Lihua Jia, Jie Kang, Xiaoyang Fudan Univ Lab Neural Interface & Brain Comp InterfaceInst Engn Res Ctr AI & RobotState Key Lab Med Neurobi Minist EducShanghai Engn Res Ctr AI & RobotMOE Shanghai Peoples R China Ji Hua Lab Foshan Guangdong Peoples R China Fudan Univ Huashan Hosp Dept Rehabil Med Shanghai Peoples R China Fudan Univ Yiwu Res Inst Chengbei Rd Yiwu City 322000 Zhejiang Peoples R China Zhejiang Lab Res Ctr Intelligent Sensing Hangzhou 311100 Peoples R China

ISBN: (纸本)9781665464444

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in imageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： brain computer interface motor imagery EEG GoogLeNet

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SSIP 2022 - 2022 5th international conference on Sensors, signal and image Processing

SSIP 2022 - 2022 5th International Conference on Sensors, Si...

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5th international conference on Sensors, signal and image Processing, SSIP 2022

ISBN: (纸本)9781450397124

The proceedings contain 14 papers. The topics discussed include: low-cost video system for the monitoring process in the detection of sleep apnea;pneumonia image classification method based on improved convolutional neural network;research on the application of Unet with convolutional block attention module to semantic segmentation task;unsupervised transfer learning for generative image inpainting with adversarial edge learning;research on the identification method of dangerous goods in security inspection images based on deep learning;feature analysis and automatic extraction for the 3D point cloud of the sanitary wares body;judgment model of cock reproductive performance based on vison transformer;methodology for interoperability between health information systems, for information management and decision-making;and particle swarm optimization and differential evolution hybrid algorithm applied to calibration of triaxial accelerometer.

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Data Masking Analysis based on Masked Autoencoders Architecture for Leaf Diseases Classification 1

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18th international conference on Intelligent Information Hiding and Multimedia signal Processing, IIH-MSP 2022

作者： Li, Li-Hua Tanone, Radius Department of Information Management Chaoyang University of Technology Taichung City Taiwan Fakultas Teknologi Informasi Universitas Kristen Satya Wacana Salatiga Indonesia

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Agriculture is one of the sectors that drives the economy of a country, including Indonesia. However, the prevention of plant diseases that can be classified through leaves must also be considered by farmers to produce good yields. In this study, we conducted an experiment by implementing self-supervised learning on plant disease recognition. We built the Masked Autoencoders model from scratch on the leaf diseases dataset (strawberry, potato, and tomato) to perform the downstream task, namely classification. In this experiment, we masked the input image with portions of 50%, 75% and 90% to analyse how the model works in reconstructing the missing pixels to resemble the original image. To see the performance of our experiment, metrics such as accuracy, SSIM and PSNR are used to see the effectiveness of the algorithm in image reconstruction. The results obtained in this experiment are that the more classes in the dataset, the downstream accuracy using the pre-trained MAE model is relatively small. On the other hand, a small number of classes will result in linear probing accuracy above 75% when classifying leaf disease recognition. For the SSIM and PSNR results, we identified that the image similarity results depended on the shape of the image where a more varied leaf shape would help the reconstruction process to resemble the original image. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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11th international Symposium on signal, image, Video and Communications, ISIVC 2022 - conference Proceedings

11th International Symposium on Signal, Image, Video and Com...

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11th international Symposium on signal, image, Video and Communications, ISIVC 2022

ISBN: (纸本)9781665487245

The proceedings contain 43 papers. The topics discussed include: used car price prediction using machine learning: a case study;cloud computing for healthcare services: technology, application and architecture;adaptive region growing based on detecting the seed point in the central trachea to the pre-segment respiratory system;a stochastic 3d model for three-phase heterogeneous microstructures in SOFC-electrodes;partial finite-time stabilization for a mono-dimensional transport bilinear system;bilinear optimal stabilization of a non-homogeneous Fokker-Planck equation;stochastic modeling of hyperconnected mobile modular production problem;vehicle speed measurement, type, and color identification by video processing using machine learning algorithms;relevant pitch features selection for voice disorders families classification;stochastic volatility, a closed formula for futures contracts;deployment strategies of mobile networks for internet-of-things in smart maritime ports;and an image fusion method using shift-invariant adaptive selectivity representation and parameter-adaptive PCNN.

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Deep Learning Enhanced Channel Estimation for the internet of Vehicles OFDM systems

Deep Learning Enhanced Channel Estimation for the Internet o...

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IEEE international conference on Computational Electromagnetics (ICCEM)

作者： Zheng Liu Xiaomin Pan School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350383317

ISBN: (纸本)9798350383324

The deep learning enhanced channel estimation is presented for the OFDM communication in the internet of Vehicles (10V). Using the image enhancement and denoising, the channel responses at unknown positions are predicted under a specified signal-to-Noise Ratio (SNR). Simulation experiments were conducted in predefined urban road *** results show that this method shows significant improvement compared to traditional methods in the minimum mean square error (MMSE) of channel estimation, with an average decrease of 0.03 in a series of experiments with different SNRs.

关键词： Deep learning OFDM Roads Noise reduction Neural networks Channel estimation Mean square error methods

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Untargeted Backdoor Attack Against Object Detection 48

Untargeted Backdoor Attack Against Object Detection

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48th IEEE international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Luo, Chengxiao Li, Yiming Jiang, Yong Xia, Shu-Tao Tsinghua University Tsinghua Shenzhen International Graduate School China Research Center of Artificial Intelligence Peng Cheng Laboratory China

ISBN: (纸本)9781728163277

Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The back-doored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre-defined trigger patterns. Currently, most of the existing backdoor attacks were conducted on the image classification under the targeted manner. In this paper, we reveal that these threats could also happen in object detection, posing threatening risks to many mission-critical applications (e.g., pedestrian detection and intelligent surveillance systems). Specifically, we design a simple yet effective poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns. We conduct extensive experiments on the benchmark dataset, showing its effectiveness in both digital and physical-world settings and its resistance to potential defenses. © 2023 IEEE.

关键词： AI Security Backdoor Attack Object Detection Physical Attack Trustworthy ML

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