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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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Augmenting a Description Logic with Probability for Motion Patterns Within QSTR 6th

Augmenting a Description Logic with Probability for Motion ...

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6th International Conference on Advanced Computing, Networking, and Informatics, ICACNI 2018

作者： Talukdar, Upasana Barua, Rupam Hazarika, Shyamanta M. Biomimetic & Cognitive Robotics Lab Department of Computer Science & Engineering Tezpur University Tezpur India Department of Computer Science & Engineering Jorhat Engineering College Jorhat India Mechatronics and Robotics Lab Department of Mechanical Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9789811396793

Motion patterns are spatiotemporal. Human everyday spatiotemporal reasoning is predominantly qualitative;hence, a qualitative abstraction of motion patterns holds promise. Reasoning over qualitative models of spatiotemporal change is referred to as qualitative spatiotemporal reasoning (QSTR). Recognition of patterns within a QSTR framework involves reasoning over a multitude of entities. These entities interacting in numerous ways often lead to hierarchies and ample uncertainties. Herein lies the motivation of using a formal basis for the representation of large and diverse knowledge. Description logics (DLs), a family of knowledge representation formalism providing object-oriented representation with formal semantics, are a natural choice. However, traditional DLs delineate a delicate approach to handle uncertain features and relationships. A paradigm that combines a DL formalization with probability theory is required. Bayesian Networks (BNs) are the most widely used models to represent knowledge about an uncertain domain. This paper combines a DL with BN and proposes a formal, explicit qualitative knowledge representation formalism for recognition of motion patterns within an uncertain domain. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Bayesian networks

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ToP-ToM: Trust-aware Robot Policy with Theory of Mind

arXiv

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arXiv 2023年

作者： Yu, Chuang Serhan, Baris Cangelosi, Angelo UCL Interaction Centre Computer Science Department University College London LondonWC1E 6BT United Kingdom Cognitive Robotics Lab Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. Humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multiagent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human's trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human's trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings. © 2023, CC BY.

关键词： Multi agent systems

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Run and Catch: Dynamic Object-Catching of Quadrupedal Robots

Run and Catch: Dynamic Object-Catching of Quadrupedal Robots

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yangwei You Tianlin Liu Xiaowei Liang Zhe Xu Mingliang Zhou Zhibin Li Shiwu Zhang Xiaomi Robotics Lab Xiaomi Sci-Tech Park Haidian District Beijing P.R.China Department of Computer Science University College London London UK University of Science and Technology of China Hefei Anhui P.R.China

Quadrupedal robots are performing increasingly more real-world capabilities, but are primarily limited to locomotion tasks. To expand their task-level abilities of object acquisition, i.e., run-to-catch as frisbee catching for dogs, this paper developed a control pipeline using stereo vision for legged robots which allows for dynamic catching balls while the robot is in motion. To achieve high-frame-rate tracking, we designed a ball that can actively emit homogeneous infrared (IR) light and then located the flying ball based on binocular vision positioning using the onboard RealSense D450 camera with an additional IR bandpass filter. The camera was mounted on top of a 2-DoF head to gain a full view of the target ball. A state estimation module was developed to fuse the vision positioning, camera motor readings, localization result of RealSense T265 equipped on the back, and the legged odometry output altogether. With the use of a ballistic model, we achieved a robust estimation of both the ball and robot positions in an inertial coordinate. Additionally, we developed a close-loop catching strategy and employed trajectory prediction so that tracking and run-to-catch were performed simultaneously, which is critical for such drastically dynamic and precise tasks. The proposed approach was validated through both static testing and dynamic catch experiments conducted on the CyberDog robot with a high success rate.

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EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

arXiv

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arXiv 2023年

作者： Coelho, Andre Albu-Schaeffer, Alin Sachtler, Arne Mishra, Hrishik Bicego, Davide Ott, Christian Franchi, Antonio Oberpfaffenhofen Germany Dextrous Robotics Inc. MemphisTN United States Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands Department of Informatics Technical University of Munich Germany Technical University of Vienna Austria LAAS-CNRS University of Toulouse France

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Crop Type Classification using Multi-temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach

Crop Type Classification using Multi-temporal Sentinel-2 Sat...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Asim Hameed Khan Zuhair Zafar Muhammad Shahzad Karsten Berns Muhammad Moazam Fraz National University of Sciences and Technology (NUST) Islamabad Pakistan Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany Department of Computer Science University of Reading Berkshire United Kingdom Robotics Research Lab Technical University of Kaiserslautern Germany

Crop Type classification using Semantic Segmentation and remote sensing data is an important tool for decision-making related to precision agriculture. Such classification remains an unsolved challenge due to the choice of landscape, processing methodology and selected satellite imagery and its optical features, and most importantly the availability and usage of such datasets in a developing country like Pakistan. State-of-the-art semantic segmentation models lack in processing the temporal dimension of time series imagery and evident solution to process multi-spectral bands available in the satellite imagery. We propose a methodology to overcome these shortcomings by selecting appropriate band combinations for crop type classification and treating time series visual data as a single image. The proposed methodology is evaluated on the data set of six different crops collected from National Agriculture Research Center (NARC) Islamabad. The experimental results yield 85% accuracy for classifying various crop types based on the evaluation of five different semantic segmentation models. The code and the trained models are available at https://***/asimniazi63/crop-type-narc for other researchers working in the same domain.

关键词： Visualization Satellites Semantic segmentation Time series analysis Crops Optical imaging Agriculture

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Resilient and Adaptive Replanning for Multi-Robot Target Tracking with Sensing and Communication Danger Zones

arXiv

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arXiv 2024年

作者： Li, Peihan Wu, Yuwei Liu, Jiazhen Sukhatme, Gaurav S. Kumar, Vijay Zhou, Lifeng Department of Electrical and Computer Engineering Drexel University PhiladelphiaPA19104 United States GRASP Lab University of Pennsylvania PhiladelphiaPA19104 United States Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States

Multi-robot collaboration for target tracking presents significant challenges in hazardous environments, including addressing robot failures, dynamic priority changes, and other unpredictable factors. Moreover, these challenges are increased in adversarial settings if the environment is unknown. In this paper, we propose a resilient and adaptive framework for multi-robot, multi-target tracking in environments with unknown sensing and communication danger zones. The damages posed by these zones are temporary, allowing robots to track targets while accepting the risk of entering dangerous areas. We formulate the problem as an optimization with soft chance constraints, enabling real-time adjustments to robot behavior based on varying types of dangers and failures. An adaptive replanning strategy is introduced, featuring different triggers to improve group performance. This approach allows for dynamic prioritization of target tracking and risk aversion or resilience, depending on evolving resources and real-time conditions. To validate the effectiveness of the proposed method, we benchmark and evaluate it across multiple scenarios in simulation and conduct several real-world experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Excavation Reinforcement Learning Using Geometric Representation

arXiv

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arXiv 2022年

作者： Lu, Qingkai Zhu, Yifan Zhang, Liangjun The Robotics and Auto-Driving Lab Baidu Research SunnyvaleCA United States The Department of Computer Science University of Illinois at Urbana-Champaign IL United States

Excavation of irregular rigid objects in clutter, such as fragmented rocks and wood blocks, is very challenging due to their complex interaction dynamics and highly variable geometries. In this paper, we adopt reinforcement learning (RL) to tackle this challenge and learn policies to plan for a sequence of excavation trajectories for irregular rigid objects, given point clouds of excavation scenes. Moreover, we separately learn a compact representation of the point cloud on geometric tasks that do not require human labeling. We show that using the representation reduces training time for RL, while achieving similar asymptotic performance compare to an end-to-end RL algorithm. When using a policy trained in simulation directly on a real scene, we show that the policy trained with the representation outperforms end-to-end RL. To our best knowledge, this paper presents the first application of RL to plan a sequence of excavation trajectories of irregular rigid objects in clutter. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Personalized Prediction Models for Changes in Knee Pain among Patients with Osteoarthritis Participating in Supervised Exercise and Education

arXiv

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arXiv 2024年

作者： Rafiei, M. Das, S. Bakhtiari, M. Roos, E.M. Skou, S.T. Grønne, D.T. Baumbach, J. Baumbach, L. Institute for Computational Systems Biology University of Hamburg Hamburg Germany Department of Health Economics and Health Services Research University Medical Center Hamburg-Eppendorf Germany Center for Muscle and Joint Health Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark The Research and Implementation Unit PROgrez Department of Physiotherapy and Occupational Therapy Næstved-Slagelse-Ringsted Hospitals Slagelse Denmark Computational Biomedicine Lab Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark

Background: Knee osteoarthritis (OA) is a widespread chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing the OA symptoms pain and functional limitations, these strategies are often underutilized. To motivate and enhance patient engagement personalized outcome prediction models can be utilized. However, the accuracy of existing models in predicting changes in knee pain outcomes remains insufficiently examined. Objective: To validate existing models and introduce a concise personalized model predicting changes in knee pain from before to after participating in a supervised patient education and exercise therapy program (GLA:D®) among patients with knee OA. Method: Our prediction models leverage self-reported patient information and functional measures. To refine the number of variables, we evaluated the variable importance and applied clinical reasoning. We trained random forest regression models and compared the rate of true predictions of our models with those utilizing average values. In supplementary analyses, we additionally considered recently added variables to the GLA:D® registry. Result: We evaluated the performance of a full, continuous, and concise model including all 34, all eleven continuous, and the six most predictive variables respectively. All three models performed similarly and were comparable to the existing model, with R-squares of 0.31-0.32 and RMSEs of 18.65-18.85 – despite our increased sample size. Allowing a deviation of 15 VAS points from the true change in pain, our concise model and utilizing the average values estimated the change in pain at 58% and 51% correctly, respectively. Our supplementary analysis led to similar outcomes. Conclusion: Our concise personalized prediction model provides more often accurate predictions for changes in knee pain after the GLA:D® program than utilizing average pain improvement values. Neither the increase

关键词： Prediction models

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

引用

computer Systems science & Engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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