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A Vision-Based Neural Networks Model for Turbine Trench-Filler Diagnosis 1

7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Isaza, Cesar Guerrero-Garcia, Fernando Anaya, Karina Jenab, Kouroush Ortega-Moody, Jorge Networking and Telecommunication Academic Program Polytechnic University of Queretaro Queretaro Mexico Productive System Master Academic Program Polytechnic University of Queretaro Queretaro Mexico School of Engineering and Computer Science Morehead State University MoreheadKY40351 United States

ISBN: (数字)9783031396199

ISBN: (纸本)9783031396182

Vision-based neural networks as artificial intelligence models have been critical in many manufacturing industries, including automotive, food, and aerospace. Machine vision and deep learning have provided practical, promising, and accessible innovations to address many problems during manufacturing and diagnostics. Recent studies offer beneficial results in implementing industrial artificial intelligence systems that require determining, comparing, and evaluating optimal technological solutions. The aerospace industry has to deal with the problem of diagnosing critical components that continually need the expertise of a trained human being. On the other hand, automatic diagnostics are becoming a critical technology to deal with this problem. However, these systems require a particular configuration of computer vision algorithms which, in the case of turbine trench-filling components, have yet to be added to the literature. Considering the above, we report in this paper a new methodology that uses a pre-trained deep neural network framework available online to automatically diagnose geometric nonconformities in aeronautical components that protect the turbines from vibrations and reduce noise from the engines to the aircraft deck. The method is based on the following stages: a computer vision system with a monochrome camera to acquire images, training of deep neural networks with transfer learning, and a stage to analyze nonconformities automatically. In addition, a benchmark of several pre-trained deep neural networks is presented to address the problem. According to the experimental results, the YOLO deep neural network topology significantly contributes to automatic diagnosis with high precision and exact rate. Finally, we show that integrating deep neural networks with control graphs is a promising strategy to optimize online diagnostics in the production lines of the aeronautical industry. © The Author(s), under exclusive license to Springer Nature Switzerland

关键词： Deep neural networks

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Evaluating the Privacy and Security Implications of Mobile Apps

Evaluating the Privacy and Security Implications of Mobile A...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Shafiul Islam Mazumder Sazzadul Islam Shovon Md Tanvir Chowdhury Minhajul Islam Habiba Azad Raisa Habibur Rahman Monjurul Islam Sumon Md. Khalid Mahbub Khan Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Information System Central Michigan University Michigan United States Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering University of Information Technology & Sciences (UITS) Dhaka Bangladesh

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The rapid growth of mobile applications, combined with an increasing reliance on these apps for a variety of purposes, has prompted serious concerns about user privacy and data security. This study aims to assess the privacy practices, data handling methods, and security vulnerabilities of mobile apps in a variety of categories, including social media, e-commerce, health and fitness, and financial management. This study identifies major privacy and security issues by examining privacy policies and terms of service, as well as how these apps handle sensitive data. The study also examines free and premium apps' privacy procedures, taking into account the implications for user data protection. The findings illustrate differences in data handling and security standards across different app categories, highlighting the importance of transparent privacy policies, strong security mechanisms, and limited data sharing to protect user information. The research helps to define standards and best practices for promoting secure app activity, increasing user confidence, and guaranteeing a safer digital environment.

关键词： Privacy Social networking (online) Data security Ecosystems Data protection Mobile applications Electronic commerce Protection Standards Best practices

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The Application of Regression Models to Market Rate of Return Prediction

The Application of Regression Models to Market Rate of Retur...

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2022 International Conference on Statistics, Data science, and Computational Intelligence, CSDSCI 2022

作者： Chen, Weizhi Fu, Jiawei Yang, Yu Yang, Zhenbang College of Computer Science and Technology Zhejiang University Hangzhou China College of Engineering and Applied Science University of Cincinnati 2901 Woodside Drive Cincinnati United States School of information and Electrical engineering Zhejiang University City College Hangzhou China School of Intelligent System engineering Sun Yat-sen University Shen Zhen China

ISBN: (纸本)9781510661325

Quant investing is very popular nowadays. To predict returns, there are lots of algorithm and models for financial market trading. In this case, with some new methods, quantitative researchers' ability to forecast an investment's return. In this paper, a new dataset obtained from a Kaggle competition called Ubiquant Market Prediction will be introduced and some classical method and model will be tested on it. This dataset has following characteristics. Firstly, its' target is return rate instead of the return, which means it could adapt to different scales of investment. Secondly, 300 dimensional anonymous features extracted are used in prediction so that this method could be applied in the prediction of different kinds of investments which have different kinds of features. Thirdly, this dataset considers the types and time periods of investment behavior. For the reasons above, the dataset is quite valuable. The classical methods tested in the experiment include Support Vector Regression (SVR), ensemble learning, k-Nearest Neighbor (KNN), Deep Neural Network (DNN), random forest, Stochastic Gradient Descent (SGD) regressor, passive aggressive regressor and Multilayer Perceptron (MLP) regressor. These methods are tested on this dataset and their performance are compared in the result and discussion section, aiming to figure out whether these classical approaches could have a good result in the dataset. © 2023 SPIE.

关键词： Investments

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Unlearnable 3D point clouds: class-wise transformation is all you need 24

Unlearnable 3D point clouds: class-wise transformation is al...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xianlong Wang Minghui Li Wei Liu Hangtao Zhang Shengshan Hu Yechao Zhang Ziqi Zhou Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9798331314385

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC.

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HLMEA: Unsupervised Entity Alignment Based on Hybrid Language Models 39

HLMEA: Unsupervised Entity Alignment Based on Hybrid Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Xiongnan Wang, Zhilin Chen, Jinpeng Yang, Liu Oh, Byungkook Hwang, Seung-Won Li, Jianqiang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Alibaba Group China School of Computer Science Beijing University of Posts and Telecommunications China Xiangjiang Laboratory China School of Computer Science and Engineering Central South University China Computer Science & Engineering Konkuk University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Entity alignment (EA) is crucial for integrating knowledge graphs (KGs) constructed from diverse sources. Conventional unsupervised EA approaches attempt to eliminate human intervention but often suffer from accuracy limitations. With the rise of large language models (LLMs), leveraging their capabilities for EA presents a promising direction. However, it introduces new challenges: formulating the LLM-based EA problem and extracting the background knowledge in LLMs to realize EA without human intervention. This paper proposes HLMEA, a novel hybrid language model-based unsupervised EA method. HLMEA formulates the EA task into a filtering and single-choice problem and synergistically integrates small language models (SLMs) and LLMs. Specifically, SLMs filter candidate entities based on textual representations generated from KG triples. Then, LLMs refine this selection to identify the most semantically aligned entities. An iterative self-training mechanism allows SLMs to distill knowledge from LLM outputs, enhancing the EA ability of hybrid language models in subsequent rounds cooperatively. We also conducted extensive experiments on benchmark datasets to evaluate HLMEA's performance. The results demonstrate that HLMEA significantly outperforms unsupervised and even supervised EA baselines, proving its potential for scalable and effective EA across large KGs. The code and data are available at https://***/xnjin-ai/HLMEA. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed system Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed system Security Symposium, NDSS 2023. All Rights Reserved.

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging Control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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NASTCNN: a spatio-temporal convolutional neural network with non-local attention mechanism for image-induced brain activity pattern recognition

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Optoelectronics Letters 2025年

作者： TAN Xiangxin SHAO Mingxiong ZENG Ke GU Yue School of Computer Science and Engineering， Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， Tianjin University of Technology Krembil Research Institute， University Health Network

Deciphering the brain’s visual processing mechanisms has always been a hot topic. Using neural signal patterns to predict the corresponding external visual stimuli is crucial in understanding the interaction of real visual scenes. There are some challenges in the study of image-induced brain activity pattern recognition based on electroencephalogram (EEG)， e.g. how to fully utilize the effective features of EEG signals to improve the classification， and how the frequency and the length of EEG effect the model performance. We design an experimental paradigm that minimizes the interaction between visual stimuli， and propose a spatio-temporal convolutional neural network with non-local attention mechanism (NASTCNN) for image-induced brain activity pattern recognition. The framework utilizes non-local feature extraction operations to realize global information interaction between EEG signals over long distances and enhance the feature expression of some important information. Our model achieves the best performance on both MNIST-EEG and ImageNet-EEG datasets. The classification results are 24.00% and 50.10% respectively， higher than the random probability. We also analyze the effects of different EEG frequency bands and time subsequences of different lengths on the classification performance and which brain regions are activated over time. The results show that when full frequency band and entire length sequence are selected， the classification result is optimal. The occipital region is essential for processing visual tasks， and the temporal region and some frontal regions also play important role in the later stage of visual decoding. This research can help us better understand the visual processing mechanism of the brain.

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Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

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computer systems science & engineering 2023年第5期45卷 1129-1143页

作者： Anwer Mustafa Hilal Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Mohamed I.Eldesouki Ishfaq Yaseen Abdelwahed Motwakel Radwa Marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Biomedical image processing is widely utilized for disease detection and classification of biomedical *** color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and *** removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is *** article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)*** presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the *** attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image *** by,Inception with ResNet-v2 model is employed for feature ***,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the *** design of PO algorithm assists in the optimal parameter selection of the TSVM *** ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

关键词： Tongue color image analysis political optimizer twin support vector machine inception model deep learning

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Video image mosaic via multi-module cooperation

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Optoelectronics Letters 2021年第11期17卷 688-692页

作者： ZHANG Zhe XUE Wanli Engineering Research Center of Learning-Based Intelligent System Ministry of EducationSchool of Computer Science and EngineeringTianjin University of TechnologyTianjin 500384China

We propose a video image mosaic method based on multi-module cooperation. This method stitches the video into a panorama with a large field of view, divided into three modules: the key frame selection module, the image mosaic module, and the optimization module. The key frame selection module obtains key frames by comprehensively evaluating the overlap rate and image quality. The image mosaic module stitches the key frames into a panoramic image to generate an initial mosaic result. The optimization module makes the mosaic result more natural and eliminates ghosts by using object detection advantages. Our method is tested on videos taken in real scenes, and the results have a more comprehensive and natural description.

关键词： frame image Video image mosaic via multi-module cooperation

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