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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling Software Security Software development management

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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IEEE International Conference on robotics and Biomimetics

作者： Hang Zhou Haichao Liu Hongliang Lu Jun Ma Yiding Ji Robotics and Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering School of Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Intelligent Transportation Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

关键词： Training Trajectory planning Artificial neural networks Benchmark testing System recovery Safety Planning Autonomous vehicles Robots Predictive control

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AuDimo: A Musical Companion Robot to Switching Audio Tracks by Recognizing the Users Engagement 22nd

AuDimo: A Musical Companion Robot to Switching Audio Tracks ...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Hansika, W.K.N. Nanayakkara, Lakindu Yasassri Gammanpila, Adhisha de Silva, Ravindra Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka

ISBN: (纸本)9783030601164

Music has become an integral part that enriches human lives. It serves as a method of entertainment in social gatherings. Consequently Different playlists of music are played to energize, uplift, and comfort listeners in social gatherings. If most in that group of people don’t like the songs playing energy in between them can be lost. In this study, a musical companion robot was developed to interact with the audience through its rhythmic behaviors and through the song selections done by the robot based on the moods of the audience using Neural Networks and emotion recognition through facial recognition. The study demonstrated high levels of user acceptance based on the feedback received from a sample set of 15 volunteers within the age range of 21–25 years. Though The robot’s interaction through its rhythmic behaviors was a success, the song selection algorithm was below the level of acceptance at the startup of the robot. However, as the data demonstrates, the robot was able to accomplish the task of enhancing the audience through its appearance and behaviors. © 2020, Springer Nature Switzerland AG.

关键词： Music

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Md Abdur Rahman Hossain Shahriar Fan Wu Alfredo Cuzzocrea Dept. of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Center for Cybersecurity University of West Florida Pensacola FL USA Department of Computer Science Tuskegee University Tuskegee AL USA iDEA Lab University of Calabria Rende Italy

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

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.

关键词： Support vector machines Logistic regression Accuracy Large language models Bidirectional control Transformers Encoding Multilingual Tuning Random forests

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Deep Image Clustering by Spiking Neural Networks

Deep Image Clustering by Spiking Neural Networks

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Arash Mahyari Hadi AliAkbarpour Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Department of Computer Science Saint Louis University St. Louis MO USA

Neuromorphic cameras, or event cameras, are biologically-inspired sensors that detect changes in illumination at a pixel level, different from traditional cameras where each pixel independently and asynchronously outputs when it senses illumination changes. The neuromorphic cameras exhibit high-temporal resolution and dynamic range, useful in various applications. Spiking Neural Networks (SNNs), mimicking biological neurons, are efficient in processing neuromorphic images due to their event-driven, low-power nature. However, training SNNs is challenging because of the non-differentiability of neuron models which require different optimization techniques. Inspired by recent works on unsupervised deep clustering in conventional deep learning, we introduce a novel deep clustering algorithm that employs SNNs to extract and group visual features from neuromorphic images into clusters. It utilizes an SNN feature extractor for neuromorphic images, grouping the extracted features using K-means clustering. This algorithm, which doesn’t require pre-trained SNN weights, alternates between cluster updates and SNN weight adjustments. The extracted feature vectors in SNN are clustered and used as pseudo-labels for supervised training of the SNN extractor. The performance of the proposed algorithm is evaluated on two datasets of MNIST and NMNIST, and its effectiveness is assessed using the Normalized Mutual Information (NMI) metric. The performance of our SNN system with neuromorphic images, while marginally below its counterparts of ANNs with traditional images, demonstrates considerable potential.

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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Authentic Learning on DevOps Security with Labware: Git Hooks To Facilitate Automated Security Static Analysis

Authentic Learning on DevOps Security with Labware: Git Hook...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling Software

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Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach

Early Prediction of Cryptocurrency Price Decline: A Deep Lea...

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International Conference on computer and Information Technology (ICCIT)

作者： Mst Shapna Akter Hossain Shahriar Md Abdur Rahman Muhammad Rahman Alfredo Cuzzocrea Department of Intelligent Systems and Robotics University of West Florida USA Center for Cybersecurity University of West Florida USA Department of Mathematics Jahangirnagar University Savar Dhaka Bangladesh Department of Computer Science Clayton State University USA iDEA Lab University of Calabria Rende Italy

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market.

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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D2NT: A High-Performing Depth-to-Normal Translator

D2NT: A High-Performing Depth-to-Normal Translator

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yi Feng Bohuan Xue Ming Liu Qijun Chen Rui Fan Robotics & Artificial Intelligence Laboratory (RAIL) the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems the College of Electronic & Information Engineering Tongji University Shanghai P. R. China Department of Computer Science & Engineering the Hong Kong University of Science and Technology (Guangzhou) Hong Kong SAR P. R. China Robotics & Autonomous Systems Thrust of the Systems Hub the Hong Kong University of Science and Technol-ogy (Guangzhou) Nansha Guangzhou P. R. China

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy.

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