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International conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

International Conference on Artificial Intelligence and Its ...

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International conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

ISBN: (纸本)9783031714252

The proceedings contain 21 papers. The special focus in this conference is on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation. The topics include: Enhancing Advanced Time-Series Forecasting of Electric Energy Consumption Based on RNN Augmented with LSTM Techniques;collective Intelligence for Model Transformation by Example;artificial Intelligence in Tourism Sector: Analysis Study;initial Steps Required for Archaeological image processing;digital Transformation Based on Cutting-Edge Technologies Case Study: Mauritania 4.0;hybrid Approach to Define Axioms of the Multimedia Ontology of the Archaeological Field;benchmarking Outlier Detection: Integrating Classical methods and Deep Learning Techniques for Advanced Fault Analysis;real-Time Torque and Drag Prediction in Oilwell Drilling: A Comparative Study of Machine Learning Models;maximizing Total Net Revenue for the Identical parallel Machines Order Acceptance and Scheduling Problem with Sequence-Dependent Setup Times;robust and Intelligent Fuzzy Logic Controllers for a Differential Mobile Robot Trajectory Tracking;face Detection Based on Deep Learning Approaches: A Comparative Study;A Novel Artificial Intelligence-Based Intrusion Detection System—NAI2DS;deep and Wide Neural Networks for Distinguisher Attacks;using Machine Learning Techniques for Multi-agent Systems Testing;a Smart City Model Based on Artificial Intelligence for Disaster Risk Management in the Arabic Countries;evaluating Naive Bayes Classifiers for Traffic Crash Prediction in Rome, Italy: A Comparative Examination;internet of Things and the Smart Building Sector;securing Smart Homes: A Cloud-Based IoT Approach to Intelligent Home Security;adaptive Model Predictive Control for Achieving Lane Tracking.

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International conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

International Conference on Artificial Intelligence and Its ...

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International conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

ISBN: (纸本)9783031714283

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Implementation and Optimization of 8×8 Block Discrete Cosine Transform on MGPUSim

Implementation and Optimization of 8×8 Block Discrete Cosin...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Shuang Yang Yaobin Wang Ling Li Jiawei Qin Guotang Bi School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Discrete cosine transform for 8×8 block(DCT8×8) is widely used in image compression due to its high signal decorrelation rate. Current research for DCT is mainly focused on CPU and single GPU platforms, and the exploration of multi-GPU architectures is still insufficient. With the rapid increase of image data volume, the DCT8×8 algorithm on CPU and single GPU architectures can no longer meet the demand of efficient computation. Therefore, optimizing DCT8×8 algorithms on multi-GPU architectures becomes particularly important. To address this challenge, this paper explores the porting and optimization strategies of DCT algorithms on the new MGPUSim ***, we extend the instruction implementation of MG-PUSim to accomplish the porting of the original DCT8×8 algorithm (O-DCT), and then propose a kernel implementation strategy for optimizing the DCT8×8 algorithm by setting the size of the workgroup appropriately so that the kernel function can take advantage of the symmetry of the transformation matrix to reduce the redundant computation, which we refer to as the M-DCT. We conducted experiments on multiple grayscale images with different resolutions. The experimental results show that O-DCT performs well on MGPUSim, achieving an average 1.93× speedup ratio compared to the original DCT8×8 kernel (kernel1) in the CUDA SDK. Further experimental results show that M-DCT improves the execution efficiency on MGPUSim by 1.76× relative to O-DCT, and also achieves an average of 1.3× acceleration ratio compared to the optimized DCT8×8 kernel (kernel2) in the CUDA SDK, which improves the computational performance of the DCT8×8 algorithm on multi-GPU architectures. It provides new ideas and methods for future research on image compression on multi-GPU architectures.

关键词： image resolution image coding Instruction sets Graphics processing units Computer architecture Gray-scale Computational efficiency Discrete cosine transforms Kernel Optimization

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WAIR: Watermark Attack on image Retrieval Systems

WAIR: Watermark Attack on Image Retrieval Systems

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International conference on parallel and distributed Systems (ICPADS)

作者： Zhu Duan Xiaolong Zheng Peilun Du Liang Liu Huadong Ma School of Computer Science Beijing University of Posts and Telecommunications

Recent studies show that image retrieval systems are vulnerable to adversarial attacks that adds imperceptible noise to the images. But the imperceptibility of noise limits the attack performance. As a commonly acceptable interference, watermark often appears in images, which can be treated as ‘‘imperceptible’’ but is unexploited. In this paper, we propose a Watermark Attack method on image Retrieval systems (WAIR). Attacking retrieval systems is challenging due to black-box model, the absence of confidence guide, and attack failures and low efficiency caused by the randomness of traditional evolutionary algorithms. To solve these challenges, we propose a new evolutionary algorithm called Gene Joint Selecting Algorithm (GJSA) that jointly optimize the watermark parameters. We also design the Fitness Record Table (FRT), a new data structure that records the historical attack effect to guide the following evolution and avoid local optimal solutions. Thanks to FRT, WAIR can also reduce the duplicate searching caused by algorithm randomness. The extensive experiments show that WAIR can attack the black-box image retrieval system with a successful rate of 0.806, which is 12.4% higher than the traditional evolutionary algorithm. Moreover, for attacking commercial image retrieval system, we achieve 2$\times$ higher attack success rate on Baidu image Retrieval API than the existing methods.

关键词： Electronic equipment Flight recording image retrieval Web and internet services Closed box Watermarking Evolutionary computation

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PB-UAP: Hybride Universal Adversarial Attack for image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation

A Lightweight Chinese Summarization Generation Method Based ...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Shiyue Liu Yannan Xiao Xueyong Wang Wenming Huang Zhenrong Deng Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models.

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Cuckoo Search Optimized Histogram Equalization for Low Contrast image Enhancement

Cuckoo Search Optimized Histogram Equalization for Low Contr...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Niveditta Thakur Nafis Uddin Khan Sunil Datt Sharma Jaypee University of Information Technology Solan India

ISBN: (纸本)9781665454025

image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretation by different automated image processing system and human beings. Although, for low-contrast images (with lower dynamic range) traditional histogram equalization deteriorate quality of the output image by introducing washed-out form caused by too much brightness change. This paper presents histogram equalization based image enhancement method which is optimized using cuckoo search algorithm. Foremost purpose of the proposed method is to control over and under enhancement by calculating clip limits of the image histogram before performing histogram equalization. This method uses cuckoo search algorithm to automatically control the clip limit according to the image nature using improved fitness function. Effectiveness of the proposed method in comparison to other methods is analyzed using well known image quality metrics.

关键词： image quality Histograms Visualization Heuristic algorithms Feature extraction Noise measurement Indexes

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Tamper-forgery image Detection in Security Digital image: A Review

Tamper-forgery Image Detection in Security Digital Image: A ...

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Blockchain and distributed Systems Security (ICBDS), IEEE International conference on

作者： Nilesh N. Thorat Prashant Dhotre Nilesh Kulal Aarti P Pimpalkar Vijaya S. Patil Sachin M. Kolekar Computer Science & Engineering Department MIT School of Computing MIT Art Design and Technology University Pune Information Technology Department MIT School of Computing MIT Art Design and Technology University Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Concerns with image security exist in every sector that uses digital photographs. Historically, public safety and forensics have depended on images from the crime scene, biometric photos, suspect photos, and other sources. With the development of digital imaging, there has been a significant increase in the use of digital images in this field. Although the use of digital image processing has led to the creation of several new forensic methods of inquiry, it has also simplified picture modification. Digital picture validity is an issue since several snipping and image alteration programs are freely available to the public. It serves as solid proof in many different types of crimes and is utilized as documentation for many purposes. Creating and modifying photos has become easier and more accessible with the development of image manipulation and editing tools. The two most common methods of photo counterfeiting are copy-and-move forgery and image splicing. An image can be spliced, or combined from two photos, to hide or show an error scenario. A section of a picture can be copied and pasted into another image. The many kinds of digital image frauds are examined in this study along with software that detects them. An analysis was done on the methods currently in use for spotting phony photos.

关键词： Reviews Digital images Forensics Splicing Training data Solids Forgery Software Fraud Security

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Lightweight Edge Detection Model with parallel Pixel Difference

Lightweight Edge Detection Model with Parallel Pixel Differe...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Wenhao Zheng Ling Yao Bo Liu Mingwu Ren Zhiyuan Ma School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Shoufa Highway Maintenance And Construction Co Ltd Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Although existing learning-based edge detection methods have made significant progress, they typically require large network parameters and substantial computational resources, which limits their practical applicability. Instead of focusing on larger deep models to improve detection performance, we propose a lightweight deep edge detection method that reduces computational costs while achieving comparable detection performance. This approach is built on a residual framework network architecture. Specifically, we introduce an efficient parallel Pixel Difference Convolution Block (PPDB) as the core building block of our method to reduce model complexity. Additionally, to better leverage features from different scales, we incorporate a lightweight dynamic upsampling technique and develop a Refined Feature Fusion Block (RFFB) to achieve high-quality results. Experimental results on benchmark datasets show that our method requires about 200K fewer parameters and FLOPs compared to state-of-the-art methods such as LDC, while maintaining competitive performance.

关键词： Deep learning Computer vision Convolution image edge detection Computational modeling Focusing Network architecture Feature extraction Computational efficiency Complexity theory

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Guided Spatio-Temporal Learning Method for 4K Video Super-Resolution 5

Guided Spatio-Temporal Learning Method for 4K Video Super-Re...

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5th ACM International conference on Multimedia in Asia, MMAsia 2023

作者： Jiang, Qin Wang, Qinglin Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology China

ISBN: (纸本)9798400702051

4K Video Super-Resolution (VSR) presents a challenging task in video processing, as most existing VSR models have high computational complexity, limiting their application to high-resolution videos, particularly for 4K resolution videos. To address this issue, we propose a novel Guided Spatio-Temporal Video Super-Resolution network (GST-VSR) designed to perform 4K VSR on a single GPU. The proposed method comprises two key components: the Spatio-Temporal Alignment Network (STAN) and the Super-resolution Reconstruction Network (SRN), which work together to enhance the quality of the output frames. The STAN is responsible for extracting highly relevant features in frames and aligning the reference frame with the neighboring frames at the feature level to maintain temporal consistency. The SRN fuses high-quality features into the final high-resolution frames. Unlike existing methods, our proposed approach does not require explicit optical flow estimation, making it more efficient and less computationally demanding. To facilitate the training and testing of the compared models, we have established a new dataset, Pixabay-Set, consisting of 145 videos suitable for the 4K VSR task. Experimental results on the test dataset show that the proposed method achieves competitive performance compared to state-of-the-art models. In summary, our proposed GST-VSR network provides an effective solution to the challenging task of 4K VSR. © 2023 Copyright held by the owner/author(s).

关键词： Statistical tests

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