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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and data Analysis

作者： Hong He Tianding Chen Hui Wang Ao Chen Xin Yu Yimei Fan School of Physics and Information Engineering Minnan Normal University Zhangzhou Fujian China School of Data and Computer Science Xiamen Institute of Technology Xiamen Fujian China School of Computing and Information Science Fuzhou Institute of Technology Fuzhou Fujian China

ISBN: (纸本)9798400711831

This paper designs a bionic sea turtle fin, which mimics the shape of the sea turtle's forelimbs and maintains an overall streamlined shape to reduce the resistance received by the outer side of the fin. This is of great significance and practical value for exploring new underwater propulsion methods and enriching and improving underwater propulsion technology. In terms of shape design, this paper uses Solid Works software to design the shape according to the contour of the sea turtle's forelimbs, and based on bionic principles, analyzes the flapping motion rules and propulsion mechanism of the sea turtle. To verify that the efficiency of the biomimetic turtle hydrofoil traction machine is higher than that of traditional traction machines, this study focuses on the optimization of the external shape configuration. The Flow Simulation plugin within the Solid Works software is applied to perform computational fluid dynamics CFD) simulations on the biomimetic turtle hydrofoil traction machine. Analyze the impact of different distances and different pitch angles between the fins on the motion performance; set the calculation domain, boundary conditions, residuals, inlet velocity, and sea conditions, etc., and perform simulation experiments on the bionic sea turtle fin tractors with different distances and different angles; analyze the obtained velocity cloud diagram and pressure cloud diagram. The results show that the ocean current resistance received by the bionic sea turtle fin tractor is significantly less than that of the traditional tractor, which verifies the feasibility of the bionic fin and its good application prospects.

关键词： Bionic Design

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A Novel Masked Facial Recognition System Using CNN 2

A Novel Masked Facial Recognition System Using CNN

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Parvathi, R. Murugan, Chitra Sohag, Md. Sabbir Hasan Krishnaprasath, V.T. Arun, M.R. Vellore Institute of Technology Department of Smart Computing Katpadi Vellore632014 India Rajalakshmi Institute of Technology Department of Electronics and Communication Tamilnadu Chennai India Bangladesh University of Business and Technology Department of Electrical and Electronic Engineering Bangladesh Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore641105 India Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai600062 India

ISBN: (纸本)9798350347579

The present work consists of the elaboration of models for facial recognition of individuals with a mask and to present suggestions of different methodologies that can be applied to adapt existing models. There is a need to evaluate the accuracy of these architectures in real scenarios, especially when it comes to applications that help prevent COVID-19. In this work convolutional neural network is applied to facial recognition. In addition, the ability of transfer learning to assist in the creation of new models will be evaluated. So, the steps include pre-processing of the chosen database, Model development, Training of models with hyperparameter variation, and Validation of models. In a problem of deep convolutional networks with multiclasses, the obtained results were able to classify the vast majority of images and be consistent with the current state of the art. © 2023 IEEE.

关键词： Face recognition

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification 31

AGCL: Aspect Graph Construction and Learning for Aspect-leve...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jian, Zhongquan Wu, Daihang Wang, Shaopan Wang, Yancheng Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China College of Management Mahidol University Thailand School of Computing and Data Science Xiamen University Malaysia Malaysia Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9798891761964

Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed at furnishing the model with finely tuned aspect information to bolster its task-understanding ability. AGCL's pivotal innovations reside in Aspect Graph Construction (AGC) and Aspect Graph Learning (AGL), where AGC harnesses intrinsic aspect connections to construct the domain aspect graph, and then AGL iteratively updates the introduced aspect graph to enhance its domain expertise, making it more suitable for the ALSC task. Hence, this domain aspect graph can serve as a bridge connecting unseen aspects with seen aspects, thereby enhancing the model's generalization capability. Experiment results on three widely used datasets demonstrate the significance of aspect information for ALSC and highlight AGL's superiority in aspect learning, surpassing state-of-the-art baselines greatly. Code is available at https://***/jian-projects/agcl. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction 2

Impurity explicit optimal Machine learning and statistical p...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Machine Unlearning Through Fine-Grained Model Parameters Perturbation

arXiv

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arXiv 2024年

作者： Zuo, Zhiwei Tang, Zhuo Li, Kenli Datta, Anwitaman College of Computer Science and Electronic Engineering Hunan University China College of Computing and Data Science Nanyang Technological University Singapore Shenzhen Research Institute Hunan University China Faculty of Computing Engineering & Media De Montfort University Leicester United Kingdom

Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree. © 2024, CC BY.

关键词： Differential privacy

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Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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Interpretable Deep Learning for Solar Flare Prediction

Interpretable Deep Learning for Solar Flare Prediction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Vinay Ram Gazula Katherine G. Herbert Yasser Abduallah Jason T. L. Wang Department of Data Science New Jersey Institute of Technology Newark NJ USA School of Computing Montclair State University Montclair NJ USA Department of Computer Science New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

We propose to incorporate three interpretable methods, namely SHAP (SHapley Additive exPlanations), PDP (partial dependence plots) and Anchors, into a deep learning-based model, called SolarFlareNet, for operational flare forecasting. SolarFlareNet takes as input a sample of SHARP (Space-weather HMI Active Region Patches) magnetic parameters and predicts as output whether a solar flare would occur within the next 24 hours. We analyze flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite's X-ray flare catalogs and construct a database of flares with identified active regions in the catalogs. This database, together with the SHARP magnetic parameters, is used to train and test the SolarFlareNet model. Our experimental results describe the use of the three proposed methods (SHAP, PDP, and Anchors) to interpret the SolarFlareNet model and demonstrate the effectiveness of the methods.

关键词： Deep learning Additives Accuracy databases Explainable AI Predictive models Forecasting

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Inverse Multiobjective Optimization by Generative Model Prompting

Inverse Multiobjective Optimization by Generative Model Prom...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jiao Liu Abhishek Gupta Yew-Soon Ong Puay Siew Tan College of Computing & Data Science Nanyang Technological University (NTU) Singapore School of Mechanical Sciences Indian Institute of Technology Goa India A*STAR Singapore Institute of Manufacturing Technology (SIMTech) Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The integration of multiobjective optimizers with inverse models—that map points on the Pareto front to corresponding nondominated solutions—has drawn attention. These inverse models serve a dual purpose, not only facilitating the generation of candidate solutions during the optimization process, but also offering insights for multiobjective decision-making upon completion of optimization. However, today’s inverse models mainly serve to capture one-to-one mapping relations, restricting them to learn only from nondominated solution samples. As a result, the information embedded in dominated samples is not fully utilized. In this paper, we introduce a novel approach of building conditional inverse generative models (invGMs) from optimization data, making the most of both nondominated and dominated solution samples during training. Different from standard inverse models, decision-makers can query such invGMs with prompts expressed in the form of any desired objective function values, leading them to produce a corresponding solution. Through iterative prompting, invGMs are shown to accelerate the creation of diverse sets of high-quality solutions even during the course of multiobjective optimization runs. Empirical studies on three industrial optimization problems highlight the proposed method’s faster convergence rate and improved inverse modeling accuracy.

关键词： Training Accuracy Inverse problems Decision making Linear programming data models Iterative methods

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A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective

arXiv

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arXiv 2024年

作者： Liu, Chenyu Zhou, Xinliang Wu, Yihao Ding, Yi Zhai, Liming Wang, Kun Jia, Ziyu Liu, Yang College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Central China Normal University China University of Science and Technology of China China Institute of Automation Chinese Academy of Sciences China

Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature of emotions is a physiological and psychological state change in response to brain region connectivity, making emotion recognition focus more on the dependency between brain regions instead of specific brain regions. A significant trend is the application of graphs to encapsulate such dependency as dynamic functional connections between nodes across temporal and spatial dimensions. Concurrently, the neuroscientific underpinnings behind this dependency endow the application of graphs in this field with a distinctive significance. However, there is neither a comprehensive review nor a tutorial for constructing emotion-relevant graphs in EEG-based emotion recognition. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of graph-related methods in this field from a methodological perspective. We propose a unified framework for graph applications in this field and categorize these methods on this basis. Finally, based on previous studies, we also present several open challenges and future directions in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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