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6th Symposium on Fluid-Structure-Sound Interactions and Control, FSSIC 2023

作者： Baghlani, Hossein Norouzi, Mahmood Kayhani, Mohammad Hassan Ghatee, Mojtaba Kim, Mirae Kim, Kyung Chun Faculty of Mechanical Engineering Shahrood University of Technology Shahrood Iran Faculty of Materials Science and Engineering Shahrood University of Technology Shahrood Iran School of Mechanical Engineering Eco-friendly Smart Ship Parts Technology Innovation Center Pusan National University Busan46241 Korea Republic of

ISBN: (纸本)9789819762101

The present study investigates the impact of wormlike micellar droplets on dry solid horizontal surfaces experimentally. Here, the wormlike micellar sample is an aqueous solution of Cetyltrimethylammonium bromide (CTAB) and Potassium bromide (KBr). The effects of impact speed, surface roughness, and the viscosity and elasticity of the liquid phase on the spreading and receding of the droplet are studied in detail. The droplets are formed via 14G and 16G needles and the speed of impact is in the range of 2–3 m/s. The solid surfaces are plates of plexiglass and stainless steel. The results are compared with the impact of equivalent Newtonian droplets to investigate the effect of rheological properties. It is found that fluid elasticity causes a decrease in the spreading and an increase in the receding of droplets. The stress analysis suggests that this normal force is primarily a result of elongational viscosity, while the influence of the first normal stress difference on these dynamics is minimal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Stress analysis

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Machine Learning Based Multiple Impedance Control of a Space Free-Flying Robot 12

Machine Learning Based Multiple Impedance Control of a Space...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Safdari, Amirhossein Zarafshan, Payam Alipour, Khalil Tarvirdizadeh, Bahram Fang, Gu School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran Department of Mechatronics Engineering Tehran Iran School of Engineering Design and Built Environment Western Sydney University Sydney Australia

ISBN: (纸本)9798331529734

This study investigates the application of Machine Learning (ML) to reduce the inherent computational burden in Multiple Impedance Control (MIC) for robotic systems. Impedance Control (IC) is a powerful method for robots to interact with their environment by simultaneously controlling position and force. However, the increasing complexity of control methods such as MIC requires more and more complex computations, which can lead to problems such as increased response time and reduced efficiency in the control system, making implementation on the controller impractical. To address this challenge, this paper proposes a new ML-based method called ML-MIC. This method aims to reduce the computational complexity associated with MIC using ML techniques, thereby improving the efficiency and speed of robotic control systems. The development of ML-MIC not only leads to improved control system performance but also enables more effective implementation of robotic and mechatronic systems. © 2024 IEEE.

关键词： Robot learning

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Using Supervised Learning Models for Creating a New Fake News Analysis and Classification of a COVID-19 Dataset: A case study on Covid-19 in iran 8

Using Supervised Learning Models for Creating a New Fake New...

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8th International Conference on Web Research, ICWR 2022

作者： Parvizimosaed, Mohammadreza Esnaashari, Mehdi Damia, Amirhosein Bahmanyar, Razieh K. N. Toosi University of Technology Faculty of Computer Engineering Tehran Iran Iran University of Science and Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9781665466264

Today, the growth of the coronavirus as a pandemic and its global expansion is a significant concern in our society and the international community. However, in recent years, many individuals have shifted their major source of news and information to social networks. Consequently, the widespread dissemination of false and misleading information on social media is significant for most politicians. Our effort is not only against COVID-19 but against an 'infodemic' as well. To address this, on COVID-19, we have collected and released a labeled dataset of 7,000 social media postings Persian data, and articles of authentic and false news. Covid 19 fake news has been detected in other languages such as Arabic, English, Chinese, and Hindi. We execute a multi-label task (actual vs. fictitious) on the labeled dataset and compare it to six machine learning baselines: Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. On the test set, the support vector machine gives us the best results, with an 89 percent accuracy rate. © 2022 IEEE.

关键词： COVID-19

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Forward Legal Anonymous Group Pairing-Onion Routing for Mobile Opportunistic Networks

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6595-6612页

作者： Zhu, Xiuzhen Lin, Limei Huang, Yanze Wang, Xiaoding Hsieh, Sun-Yuan Wu, Jie Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan Temple University Department of Computer and Information Science 1925 N. 12th St PhiladelphiaPA19122 United States

Mobile Opportunistic Networks (MONs) often experience frequent interruptions in end-to-end connections, which increases the likelihood of message loss during delivery and makes users more susceptible to various cyber attacks. However, most currently proposed anonymous routing protocols are primarily designed for networks with stable connections, making it challenging to protect user identities in MONs. To address these challenges, we propose FLAG-POR (Forward Legal Anonymous Group Pairing-Onion Routing), a novel anonymous routing protocol specifically tailored to enhance message delivery anonymity and security in MONs. Specifically, we abstract the mobile opportunistic network as a contact graph. By introducing the concept of "groups» into the pairing-onion routing protocol, which encrypts messages and relay nodes layer by layer, we develop a novel group-based pairing-onion routing protocol. This protocol ensures message confidentiality and relay node anonymity, while also improving message forwarding rates, as any node within a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough theoretical analysis of the protocol's security and performance. The experimental results demonstrate that our FLAG-POR protocol outperforms baseline anonymous protocols in terms of delivery success rate, traceability rate, path anonymity, and node anonymity. Additionally, the FLAG-POR scheme effectively resists three potential threats to the routing system: collusion attack threat, node identification threat, and path identification threat, in any situation. © 2002-2012 IEEE.

关键词： Anonymity

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FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Privacy-Preserving Incentive Mechanism for Crowdsensing 5

FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Pri...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Cheng, Jiameng Zhang, Hongwei Tianjin University of Technology School of Computer Science and Engineering Tianjin China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350325294

Privacy-preserving incentive mechanism in crowd-sensing systems is an ongoing research challenge, partly due to the untrusted centralized servers and sensitive data leakage. In this paper, we focus on a secure incentive way with blockchain. First, we analyze the limitations in traditional crowdsensing systems with a trusted third party (TTP) and identify three ways privacy in the incentive mechanism (IM) of crowdsensing: identity, data, and revenues. Then, we propose a novel decentralized system for IM based on a full anonymous blockchain called FAB-D2PIM. In the proposed system, we use a one-way aggregate signature scheme (Boneh et al. EUROCRYPT 2003) for identity privacy and homomorphic Public Key Encryption (Boneh et al. TCC 2005) for sensing data and reward privacy. The proposed scheme not only protects the privacy of the workers but also decreases the operation rate of the task publishing center. © 2023 IEEE.

关键词： Blockchain

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Groundwater Level Forecasting using Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost 6

Groundwater Level Forecasting using Spatio-temporal Graph Co...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. Thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

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A Novel Sustainable Regression Modeling to Recognize the Likelihood of Customer Churn Management Forecasting 5th

A Novel Sustainable Regression Modeling to Recognize the Lik...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Agarwal, Eshika Gupta, Ritika Mishra, Sushruta Faris, Najlaa Nsrulaah School of Computer Engineering KIIT Deemed to be University Bhubaneswar India College of Technical Engineering National University of Science and Technology Dhi Qar64001 Iraq

ISBN: (纸本)9789819760350

Customer churn refers to a situation where customers end their association with a business, which poses a considerable challenge for various industries including telecommunication and e-commerce. Predictive modeling techniques offer a proactive approach to address this issue by distinguishing customers who are at a risk of churn, enabling businesses to deploy focused approaches to sustain the customers at the risk of churn. We have undertaken a comparable analysis of some machine learning techniques to predict the customer churn in this research paper. The study commences with an in-depth examination of several machine learning algorithms, such as decision trees, support vector machines, random forest, and logistic regression. Each algorithm’s suitability for customer churn prediction is evaluated based on its ability to handle imbalanced datasets, interpretability, computational efficiency, and predictive performance. Once the machine learning model is made (using the training data), we need an unseen dataset known as the testing data, to assess the model’s performance. The evaluation will be done by using a logistic regression model, which falls under supervised machine learning technique. Supervised technique uses a set of independent variables to predict the categorical dependent variable. Logistic regression model is adept at predicting categorical dependent variables using a set of independent variables. It offers simplicity and interpretability while effectively capturing the correlation between input features and the likelihood of the churn. Through comprehensive experimentation and evaluation on real-world datasets, we demonstrate the efficacy of the proposed approach in accurately predicting customer churn. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Dilated Convolutional Transformer for High-Quality Image Deraining

Dilated Convolutional Transformer for High-Quality Image Der...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yufeng Lu, Jiyang Chen, Hongming Wu, Xianhao Chen, Xiang Shenyang Aerospace University College of Electronic and Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350302493

Convolutional neural networks (CNNs) and Transformers have achieved significant success in image signal processing. However, little effort has been made to effectively combine the properties of these two architectures to satisfy image deraining. In this paper, we propose an effective de-raining method, dilated convolutional Transformer (DCT), which can enlarge the receptive fields of the network to aggregate global information. The fundamental building unit of our approach is the dilformer block containing multi-dilconv sparse attention (MDSA) and multi-dilconv feed-forward network (MDFN). The MDSA calculates the multi-scale query to generate accurate similarity map so that rich multi-scale information can be better utilized for the high-quality image reconstruction. In addition, we adopt ReLU to replace the original softmax to enforce sparsity in the Transformer for better feature aggregation. The MDFN is further established to better integrate the rain information of different scales in the feature transformation. Extensive experiments on the benchmarks show the favorable performance against state-of-the-art approaches. © 2023 IEEE.

关键词： Image reconstruction

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Enhancing Causal Discovery in Federated Settings with Limited Local Samples

Enhancing Causal Discovery in Federated Settings with Limi...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Guo, Xianjie Yi, Liping Wu, Xiaohu Yu, Kui Wang, Gang School of Computer Science and Information Engineering Hefei University of Technology Hefei China College of Computer Science Nankai University Tianjin China National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031822391

Causal discovery from observational data is crucial for understanding complex systems, but traditional methods often require centralized data, conflicting with growing privacy concerns. Although federated causal discovery (FCD) has emerged as a solution, existing methods struggle when individual clients possess limited local samples. This paper introduces FedECD, a novel approach addressing causal discovery in federated settings with limited local samples. FedECD comprises two phases: 1) Federated Causal Skeleton Optimization and 2) Federated Causal Structure Refinement, both leveraging Bootstrapping techniques to enhance robustness and accuracy across distributed clients. Both phases employ a two-layer aggregation strategy: client-layer aggregates results from Bootstrapped sub-datasets within each client, while server-layer aggregates across all clients. The first phase uses weighted aggregation to iteratively remove false causal edges based on conditional independence tests. In contrast, the second phase utilizes majority voting to determine edge directions, ensuring robust estimation of the true causal structure. Extensive experiments on eight benchmark Bayesian network datasets demonstrate the superiority of FedECD over existing FCD methods, particularly with limited sample sizes. FedECD achieves an average improvement of 7.53% in the Ar_F1 score compared to the best baseline, addressing a critical challenge in FCD. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Research on anime avatar generation technology based on wasserstein generative adversarial networks 4

Research on anime avatar generation technology based on wass...

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Wu, Houmin Lim, Sangguk Xiao, Bin School of Information Engineering Guangzhou Vocational College of Technology & Business Guangzhou511442 China Computer and Information Engineering Youngsan University Yangsan-si50510 Korea Republic of School of Computer Science Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781510686106

Currently, there is a scarcity of datasets for anime avatars, compounded by copyright concerns. To address this issue, this paper proposes a method for generating anime avatars. Firstly, a wasserstein generative adversarial networks(WGAN) network is employed to construct the model, with RMSprop chosen as the optimizer. Subsequently, publicly available anime avatar samples are randomly collected for model training, and model hyperparameters are determined through grid search and personal experience. Finally, during the experimental simulation phase, after 300 epochs of training, the training errors of the generator and discriminator decrease and stabilize, and the generated image samples closely resemble real images. Experimental results demonstrate that this method can generate realistic anime avatars with excellent performance. © 2024 SPIE.

关键词： Generative adversarial networks

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