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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Prediction of Wave Forces on Floating Body using Gated Recurrent Unit Network

Prediction of Wave Forces on Floating Body using Gated Recur...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Wang, Kai Ma, Xin Yan, Mingwei Li, Zhi Li, Yibin Shandong University Center for Robotics School of Control Science and Engineering Jinan250061 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Wave-body interaction is an important problem for offshore crane lifting or lowering the payload in waves. Computational Fluid Dynamics (CFD) require high computational and time costs to deal with wave-body interaction problems, which cannot provide real-Time data for the deep-sea crane control systems in practical engineering. This paper proposes an efficient method for predicting wave forces on the moving floating bodies using Gated Recurrent Unit (GRU) networks. To obtain the accurate training dataset, the numerical wave tank (NWT) for simulating wave-body interactions is proposed by integrating OpenFOAM software and the waves2Foam library. The test cases of wave interaction with the floating body is first carried out to verify the NWT by comparing the numerical results against experimental data. The dataset is established by CFD simulation for training, testing, and validating of the GRU network. The procedures of training the neural network for predicting the wave force on the payload submerged in waves are presented in detail. The results show that the prediction network can accurately predict the dynamic response of wave-body interactions in a shorter time than CFD methods. © 2022 IEEE.

关键词： Forecasting

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Cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Rajiv Rai, Vipin Joshi, Shivani Raj, Daksh Amrita Galgotias University Department of Computer Science & Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350378092

Cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing. © 2024 IEEE.

关键词： Phishing

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Research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

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High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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Grassland Mouse Hole Recognition Model Based on UAV Remote Sensing and Improved YOLOv7

Grassland Mouse Hole Recognition Model Based on UAV Remote S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Han, Yu Bai, Xiangyu Gao, Xiaochen Cheng, Haoran Zhou, Kexin College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China School of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (纸本)9781665410205

As the most widely distributed vegetation type on Earth, grasslands are the second largest ecosystem after forests, and are known as the 'skin of the earth'. However, the frequency of rodent infestation has been increasing year by year in recent years due to climate change, loose soil and other factors. As the current traditional monitoring methods are time-consuming and laborious, while the deep learning method has the advantages of low cost, high efficiency and high accuracy. Therefore, this paper proposes a combination of UAV remote sensing and deep learning methods to monitor grassland mouse hole. First, because there is no publicly available grassland mouse hole dataset, this study used a UAV to capture images to create a grassland mouse hole dataset. Second, an improved YOLOv7 model, YOLOAPM, is proposed with the goal of improving the accuracy of small target recognition. Through ablation and comparison experiments, the validity of the improved model as well as the method of this paper are verified to be improved compared to other models. Finally, in order to facilitate the monitoring task of the researchers, a monitoring system is established and the mouse hole identification of this paper is embedded into the system. © 2024 IEEE.

关键词： Abiotic

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Self-powered tactile sensor for real-time recognition of Morse code based on machine learning

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Nano Research 2025年第2期18卷 760-768页

作者： Shenxing Tan Yang Jiang Xujiang Chao Fei Liang Ripeng Li Tao Jiang Hai-Dong Yu Zhong Lin Wang Guangzhou Institute of Blue Energy Guangzhou 510555China Frontiers Science Center for Flexible Electronics Xi'an Institute of Flexible Electronics(IFE)and Xi'an Institute of Biomedical Materials&EngineeringNorthwestern Polytechnical UniversityXi'an 710072China Beijing Key Laboratory of Micro-Nano Energy and Sensor Center for High-Entropy Energy and SystemsBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Engineering University of Chinese Academy of SciencesBeijing 100049China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Developing lightweight,green,and flexible wearable electronics with high sensitivity and multifunctional sensing capabilities is of important significance in the field of outdoor sports,such as mountaineering,animal tracking and *** work proposes a silk fibroin fibers-based triboelectric nanogenerator(SF TENG)to harvest tiny energy from human fingertip tapping and act as a self-powered tactile *** SF-TENG adopts a green,efficient,and low-cost fabrication strategy,in which a breathable and electropositive silk fibroin fiber membrane and a silver conductive layer are prepared by electrostatic spinning and magnetron sputtering,and combined with a conductive cloth and a breathable tape to form a flexible sensor that can be attached to a human *** thin and soft portable TENG device,having a thickness of only 0.3 mm and a mass of 354 mg at the dimension of 4.5 cm×4.5 cm,can generate a maximum power density of 1.0 mW·m^(–2).Furthermore,the SF-TENG has excellent sensitivity of 1.767 mV·Pa^(–1) with good cyclic *** superior sensing characteristics provide new avenues for Morse code applications toward outdoor wearable autonomous *** proposed SF-TENG offers promising solutions in multi-scenario outdoor sport,human-machine interface interaction,and security systems.

关键词： flexible electronics triboelectric nanogenerator silk fibroin Morse code translation

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Unsupervised feature selection via multiple graph fusion and feature weight learning

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science China(Information sciences) 2023年第5期66卷 56-72页

作者： Chang TANG Xiao ZHENG Wei ZHANG Xinwang LIU Xinzhong ZHU En ZHU School of Computer Science China University of Geosciences State Key Laboratory for Novel Software Technology Nanjing University School of Computer National University of Defense Technology National Supercomputing Center in Jinan Qilu University of Technology (Shandong Academy of Sciences) College of Mathematics Physics and Information Engineering Zhejiang Normal University

Unsupervised feature selection attempts to select a small number of discriminative features from original high-dimensional data and preserve the intrinsic data structure without using data labels. As an unsupervised learning task, most previous methods often use a coefficient matrix for feature reconstruction or feature projection, and a certain similarity graph is widely utilized to regularize the intrinsic structure preservation of original data in a new feature space. However, a similarity graph with poor quality could inevitably afect the final results. In addition, designing a rational and efective feature reconstruction/projection model is not easy. In this paper, we introduce a novel and efective unsupervised feature selection method via multiple graph fusion and feature weight learning（MGF2WL） to address these issues. Instead of learning the feature coefficient matrix, we directly learn the weights of diferent feature dimensions by introducing a feature weight matrix, and the weighted features are projected into the label space. Aiming to exploit sufficient relation of data samples, we develop a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph, which is then deployed to regularize the local data structure of original data in a projected label space. Finally, we design a block coordinate descent algorithm with a convergence guarantee to solve the resulting optimization problem. Extensive experiments with sufficient analyses on various datasets are conducted to validate the efficacy of our proposed MGF2WL.

关键词： unsupervised feature selection graph fusion feature weight learning local structure preservation

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Prioritized Random Access Based on Compute-and-Forward

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China Communications 2025年第4期22卷 254-267页

作者： Xu Yu Cui Chen Guo Qing School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China National Engineering Research Center of Visual Technology School of Computer SciencePeking UniversityBeijing 100871China

In wireless networks,the prioritized transmission scheme is essential for accommodating different priority classes of users sharing a common *** this paper,we propose a prioritized random access scheme based on compute-and-forward,referred to as expanding window sign-compute diversity slotted ALOHA(EW-SCDSA).We improve the expanding window technique and apply it to a high-throughput random access scheme,i.e.,the signcompute diversity slotted ALOHA(SCDSA)scheme,to implement prioritized random *** analyze the probability of user resolution in each priority class utilizing a bipartite graph and derive the corresponding lower bounds,the effectiveness of which is validated through simulation *** results demonstrate that the EW-SCDSA scheme can provide heterogeneous reliability performance for various user priority classes and significantly outperforms the existing advanced prioritized random access scheme.

关键词： compute-and-forward diversity transmission prioritized transmission random access slotted ALOHA

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Behavioral Dynamics-based Impedance Control for Collision Avoidance of Human-Following Robots

Behavioral Dynamics-based Impedance Control for Collision Av...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Tian, Haoyu Ma, Xin Shandong University Center of Robotics School of Control Science and Engineering Jinan250061 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Human-following is an important part of man-machine interaction and has wide application value. In Human-following, the following stability, human comfort and obstacle avoidance are considered comprehensively. To solve the above problems, we propose a human behavioral dynamics-based impedance control for collision avoidance of human-following robot, which take the comfort and the following stability into consideration. Specifically, an impedance control method combined with virtual interaction force is introduced to simulate the human social dynamic characteristics to improve the comfort of human-following. The uniformly ultimately bounded property of the impedance control is proved by using the Lyapunov stability theorem. Moreover, an obstacle component constructed by behavioral dynamics is proposed to extend the impedance controller for collision avoidance. Behavioral dynamics compared with an artificial potential field method possess better collision avoidance performance during humanfollowing. The simulation results validate that the human-following system integrated with collision avoidance is effective. © 2022 IEEE.

关键词： Collision avoidance

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