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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Trend analysis in competitive programming using topic modeling

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Procedia computer science 2024年 245卷 591-600页

作者： Albert Yulius Ramahalim Christoffer Edbert Karuniawan Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora.

关键词： competitive programming topic modeling trend analysis

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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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A fine-tuned vision transformer-based on limited dataset for facial expression recognition

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Procedia computer science 2024年 245卷 574-582页

作者： Rio Febrian Ronald Richie Huang Nicholas Setiono Dimas Ramdhan Andry Chowanda Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With the increase of interest in Facial Expression Recognition (FER) in the past few decades. Several challenges surfaced with the invention of many different FER models which are often based on Convolution Neural Network (CNN) architectures. Recently, an attention-based transformer model has been presented to address FER. One of the major issues with Transformers is the need for a large data quantity for training. Therefore, in this paper, we propose to learn how to fine-tune a vision transformer-based (ViT) model using a limited dataset. We will be using the JAFFE Dataset, which consists of only 213 images containing seven different emotions. The proposed method is evaluated using several fine-tuning methods, such as adding dropout, data augmentation, and layer freezing. We compared the models implemented with 5% dropout regularization, augmented dataset (up to 5000 images), and freezing the initial model's layers, fine-tuning around a fourth of the last layers. The best model was achieved by fine-tuning ViT L-16 with 96.06% accuracy, trained with 5% dropout in the augmented dataset, and freezing the initial 21st layers. We also compared our model to the other previous work model and the results showed that our model reached the state-of-the art for the JAFFE dataset.

关键词： Facial Expression Recognition Vision Transformer Attention Deep Learning

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LoGra: An LSTM-DDPG Integrated MPQUIC Scheduler for Mobile Video Streaming 100

LoGra: An LSTM-DDPG Integrated MPQUIC Scheduler for Mobile V...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Vu, Minh Hai Trung Nguyen, Thanh Ly Dinh, Thi Ha Le Nguyen, Phi Nguyen, Kien Hanoi University of Science and Technology School of Information and Communication Technology Hanoi Viet Nam Phenikaa University Faculty of Computer Science Hanoi Viet Nam Institute for Advanced Academic Research Chiba University Japan Graduate School of Informatics Chiba University Japan

ISBN: (纸本)9798331517786

With the increasing demand for video streaming services, efficient video streaming with MPQUIC in mobile wireless networks is gaining interest. Although MPQUIC can leverage multiple network connections (e.g., Wi-Fi, 5G) for simultaneous transfer, improving bandwidth and resilience, its performance is heavily dependent on the MPQUIC scheduler. In mobile scenarios, network fluctuations challenge the scheduler to adapt to dynamic conditions while maintaining good video streaming quality. Addressing this issue, this paper proposes a novel MPQUIC scheduler named LoGra (i.e., LSTM-DDPG integrated MPQUIC Scheduler) with two advanced features. First, LoGra utilizes Long Short-Term Memory (LSTM) to model the temporal correlation of network conditions over time and handle the challenges posed by mobility patterns. Second, it leverages the Deep Deterministic Policy Gradient (DDPG), with its self-learning capabilities and the strength of deep neural networks, to analyze the informative temporal feature vector to influence scheduling decisions. We have implemented the proposed scheduler and compared it to existing ones. The results show that the LoGra scheduler effectively manages multipath communication in heterogeneous wireless networks under mobility scenarios. Moreover, compared to existing schedulers, LoGra achieves significant improvements in data transmission, both in general metrics (loss, goodput) and streaming-related metrics (bitrate, freezing time). © 2024 IEEE.

关键词： Video streaming

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Mental illness detection using sentiment analysis in social media

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Procedia computer science 2024年 245卷 971-978页

作者： Kasimirus Derryl Odja Jasson Widiarta Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts.

关键词： Sentiment Analysis Mental Illness k-NN Random Forest Neural Network

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Enhancing Waste Classification with YOLOv8 Models for Efficient and Accurate Sorting

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Procedia computer science 2024年 245卷 889-895页

作者： Deverel Vieri Rendy Susanto Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods, such as manual sorting, have been widely used to segregate waste but suffer from inefficiencies and inaccuracies. In contrast, deep learning models offer an alternative solution for waste classification, overcoming the limitations of traditional methods. A deep learning approach using YOLOv8 was proposed to classify waste into six distinct categories. Three different YOLOv8 variants: nano, small, and medium, are trained after the dataset has been augmented into 3,500 labeled images. The results indicate that these models were able to achieve high accuracy in classifying images, with the nano variant having the least training time and an accuracy of approximately 89%.

关键词： Waste Classification Waste Management Artificial Intelligence Machine Learning Deep Learning Convolution Neural Network YOLOv8

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Sound field interpolation with unsupervised calibration for freely spaced circular microphone array in rotation-robust beamforming 31

Sound field interpolation with unsupervised calibration for ...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Luan, Shuming Wakabayashi, Yukoh Toda, Tomoki Graduate School of Information Science Nagoya University Nagoya Japan Department of Computer Science Toyohashi University of Technology Toyohashi Japan Information Technology Center Nagoya University Nagoya Japan

ISBN: (纸本)9789464593600

This paper introduces a new approach to perform unequally spaced sound field interpolation (SFI) for beamforming using a freely spaced circular microphone array (CMA) that is robust to rotation. Unlike previous methods that required a known microphone distribution, the proposed approach uses unsupervised calibration to estimate the error angle of each microphone. This is accomplished through an innovative optimization problem, making it practical for CMAs with freely spaced microphones. By using only the number of channels and rotation angle, the proposed method enables accurate unequally spaced SFI. Experimental results show that the proposed method outperforms previous methods on freely spaced CMAs. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Calibration

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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Tactile Sensing of Extensor Tendons and Near-Sensor Gesture Recognition for Control of Robotic Hand 38

Tactile Sensing of Extensor Tendons and Near-Sensor Gesture ...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Hu, Yushen Zhang, Zhejun Lei, Tengteng Wong, Man The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Guangzhou Hkust Fok Ying Tung Graduate School Guangzhou511458 China

ISBN: (纸本)9798331508890

A flexible active-matrix piezoelectric tactile sensor array is fabricated and affixed on the back of a hand to monitor the location and deformation of the extensor-tendons associated with a hand gesture. Based on the same thin-film transistor technology, an artificial neural network (ANN) allowing real-time and near-sensor classification of sensor signals is implemented. The integration of the tactile sensor array and an ANN facilitates the development of a compact human-machine interface system for recognition of hand gestures and their mimicry by a robotic hand. © 2025 IEEE.

关键词： Thin film transistors

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