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arXiv 2024年

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo Dept. of Intelligent Systems and Robotics University of West Florida PensacolaFL United States Center for Cybersecurity University of West Florida PensacolaFL United States Department of Computer Science Tuskegee University TuskegeeAL United States iDEA Lab University of Calabria Rende Italy

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024, CC BY.

关键词： Embeddings

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Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

arXiv

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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Few-shot learning based histopathological image classification of colorectal cancer

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intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Transmission Control Protocol (TCP) delay analysis in real time network

Transmission Control Protocol (TCP) delay analysis in real t...

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2020 Advances in Science and Engineering Technology International Conferences, ASET 2020

作者： D'Souza, Joanita Kaur, Maninder Jeet Mohamad, Hassan Anwar Maheshwari, Piyush Amity University Dubai Department of Computer Science Engineering Dubai United Arab Emirates Institute of Robotics and Intelligent Systems Toronto Canada

ISBN: (纸本)9781728146409

The aim of this paper is to evaluate the Transmission control protocol (TCP) delay performance of IEEE802.11 WLANs using Wireshark which is a network performance monitoring tool. It also shows a detailed study of sniffing tools, logging tools, pre filtering and post sniffing. This paper also provides a way to find the maximum TCP delays over a time period and to plot a graph using Microsoft excel in order to perform further analysis. The study was carried out in a real time WLAN environment. The output that is obtained shows the maximum delay over time along with certain filters that have been applied. This is only possible for the active nodes present in the network. © 2020 IEEE.

关键词： Transmission control protocol

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Bifurcations and Application of the Vallis - Model 11

Bifurcations and Application of the Vallis - Model

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11th IEEE International Conference on Computational Cybernetics and Cyber-Medical systems, ICCC 2024

作者： Nagy, Balint Molnar, Gyorgy Joos, Antal Nagy, Eniko Institute of Computer Engineering University of Dunaújváros Department of Mathematics Dunaújváros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Institute of Electrophysics Óbuda University Kandó Kálmán Faculty of Electrical Engineering Department of Natural Sciences Budapest Hungary Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University János Csere Apáczai Faculty Győr Hungary Antal Bejczy Center for Intelligent Robotics Institute of Cyberphysical Systems Óbuda University John von Neumann Faculty Budapest Hungary

ISBN: (纸本)9798350350142

Investigating chaotic systems is a fertile field considering that chaos is an exotic chapter in the theory of differential equations, and it has useful applications in the theory of communications. In this work a well-known chaotic system is investigated from the bifurcation point of view and some applications are also introduced. The data studied are also visualized for better clarity. The results observed can be helpful in the encryption methods of the implementation of different communication channels. © 2024 IEEE.

关键词： Chaotic systems

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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application

Modern Manufacturing for Alloy Wheel Defect Detection using ...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels systems architecture User interfaces Mobile applications

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

arXiv

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arXiv 2023年

作者： Shen, You Zhang, Yunzhou Wu, Yanmin Wang, Zhenyu Yang, Linghao Coleman, Sonya Kerr, Dermot College of Information Science and Engineering Northeastern University Shenyang110819 China School of Electronic and Computer Engineering Peking University Shenzhen China Faculty of Robotics and Engineering Northeastern University Shenyang China The Department of Electronic and Computer Engineering Technical University in Munich Germany School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Signal propagation in transformers: theoretical perspectives and the role of rank collapse 22

Signal propagation in transformers: theoretical perspectives...

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Lorenzo Noci Sotiris Anagnostidis Luca Biggio Antonio Orvieto Sidak Pal Singh Aurelien Lucchi Dept of Computer Science ETH Zürich Dept of Computer Science ETH Zürich and Robotics & ML CSEM SA Alpnach Switzerland Dept of Computer Science ETH Zürich and MPI for Intelligent Systems Tübingen Department of Mathematics and Computer Science University of Basel

ISBN: (纸本)9781713871088

Transformers have achieved remarkable success in several domains, ranging from natural language processing to computer vision. Nevertheless, it has been recently shown that stacking self-attention layers — the distinctive architectural component of Transformers — can result in rank collapse of the tokens' representations at initialization. The question of if and how rank collapse affects training is still largely unanswered, and its investigation is necessary for a more comprehensive understanding of this architecture. In this work, we shed new light on the causes and the effects of this phenomenon. First, we show that rank collapse of the tokens' representations hinders training by causing the gradients of the queries and keys to vanish at initialization. Furthermore, we provide a thorough description of the origin of rank collapse and discuss how to prevent it via an appropriate depth-dependent scaling of the residual branches. Finally, our analysis unveils that specific architectural hyperparameters affect the gradients of queries and values differently, leading to disproportionate gradient norms. This suggests an explanation for the widespread use of adaptive methods for Transformers' optimization.

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heat...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： You Shen Yunzhou Zhang Yanmin Wu Zhenyu Wang Linghao Yang Sonya Coleman Dermot Kerr College of Information Science and Engineering Northeastern University Shenyang China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Faculty of Robotics and Engineering North-eastern University Technical University in Munich Germany Shenyang China Department of Electronic and Computer Engineering Technical University Munich Germany School of Computing Engineering and Intelligent Systems Ulster University N. Ireland UK

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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