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AlphaSOC: Reinforcement Learning-based Cybersecurity automation for Cyber-Physical Systems 13

AlphaSOC: Reinforcement Learning-based Cybersecurity Automat...

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13th ACM/IEEE international conference on Cyber-Physical Systems (ICCPS )

作者： Silva, Ryan Hickert, Cameron Sarfaraz, Nicolas Brush, Jeff Silbermann, Josh Sookoor, Tamim Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA

ISBN: (纸本)9781665409674

Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordinate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.

关键词： Reinforcement Learning Cybersecurity Cyber-Physical System

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Adaptive Admittance Control with Dynamical Systems for Optimized Motion Adaptation

Adaptive Admittance Control with Dynamical Systems for Optim...

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international conference on Advanced robotics and Mechatronics (ICARM)

作者： Yuting Guo Guangzhu Peng Chenguang Yang School of Automation Nanjing University of Information Science and Technology Nanjing China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Trajectory adaptation is a crucial ability for robots to perform interactive tasks with the environment. This paper develops a motion adaptation framework for robots that learns and adapts the task trajectory in unknown environments. Firstly, we utilize dynamical system (DS) to learn the task trajectory of robots online through demonstration. When the robot is performing interactive tasks with the environment, a force observer based admittance control method is designed so that the robot can adaptively optimize and adjust reference trajectory. Moreover, we design a robot position controller based on neural learning to provide a precise trajectory tracking performance. Finally, the global stability of the system is analyzed by the Lyapunov theory, and a set of simulations are conducted to evaluate the motion adaptability of the proposed framework.

关键词： Adaptation models Trajectory tracking Heuristic algorithms Force Aerospace electronics Stability analysis Trajectory Dynamical systems Robots Admittance control

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Intelligent automation in Stock Market Prediction: A Survey of Automated models 4

Intelligent Automation in Stock Market Prediction: A Survey ...

引用

4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Anirudhan Adukkathayar, C. Hulipalled, Vishwanath R. Simha, Jay Bharthish Chundi, Ramesh Reva University Bangalore India Reva University School Of Computing And Information Technology Bangalore India Reva University Abiba Systems Cto And Race Labs Bangalore India Reva University School Of Computer Science And Applications Bangalore India

ISBN: (纸本)9798350384628

In an era marked by global digitization, the landscape of stock market prediction has undergone a transformative shift, ushering in a new era of technologically-driven trading. With soaring market capitalization, stock trading has emerged as a focal point for financial investment. Analysts and researchers have pioneered tools and methodologies to anticipate stock price movements, empowering investors in making informed decisions. Advanced trading models now harness unconventional textual data from social platforms, leveraging cutting-edge machine learning techniques like text data analytics and ensemble methods to significantly enhance prediction accuracies. Despite these advancements, the dynamic, erratic, and often chaotic nature of stock market data continues to present formidable challenges. This study elucidates the systematic application of machine learning-based approaches and sentimental analysis approaches for stock market prediction, grounded in a versatile framework. A critical analysis of findings from the reputable digital libraries and databases including ACM digital library and Scopus, forms the cornerstone of this inquiry. Moreover, an expansive comparative analysis was conducted to delineate key trends and significant directions. This comprehensive exploration aims to furnish budding researchers with a foundational understanding of this burgeoning domain and pave the way for further inquiry into promising avenues. © 2023 IEEE.

关键词： Sentiment analysis

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Traffic Police Gesture Command Recognition Based on Convolutional Neural Network AlexNet

Traffic Police Gesture Command Recognition Based on Convolut...

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Signal Processing and Intelligent Computing (SPIC), international conference on

作者： Lijuan Qin Shiyi Wang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Traffic police gesture signal recognition is of great significance in traffic management and urban intelligent transportation system, this paper researches the automatic recognition technology of traffic police command gesture signal based on the deep learning method, in the process of model training, the training set is used to train AlexNet, and the model is evaluated by the test set. The experimental results show that the model achieves high accuracy in the recognition of traffic police gesture signals. Finally, based on the trained model, a traffic police gesture signal recognition system is designed in MATLAB platform to realize the gesture signal recognition function on local images. The system can be used as a practical application tool for gesture signal recognition in traffic management, providing support for improving the efficiency and safety of urban traffic command.

关键词： Training Image recognition Accuracy Law enforcement Signal processing Mathematical models Intelligent transportation systems System analysis and design Testing Matlab

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Time-Aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering 48

Time-Aware Multiway Adaptive Fusion Network for Temporal Kno...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Yonghao Liang, Di Fang, Fang Wang, Sirui Wu, Wei Jiang, Rui Centre for Natural Language Processing Meituan Inc Beijing China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9781728163277

Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be capable to learn temporal-specific presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel Time-aware Multiway Adaptive (TMA) fusion network. Inspired by the step-by-step reasoning behavior of humans. For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a temporal-specific representation of the question. This representation can be incorporated with the pre-trained KG embedding to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of TMA on the CronQuestions dataset's complex questions are absolutely improved by 24% and 10% compared to the best-performing baseline. Furthermore, we also show that TMA employing an adaptive fusion mechanism can provide interpretability by analyzing the proportion of information in question representations. © 2023 IEEE.

关键词： knowledge graph question answering neural language processing Temporal knowledge graph question answering

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Research on the Planning Method of unmanned underwater vehicle Cluster Encirclement under Weak Connectivity Conditions

Research on the Planning Method of unmanned underwater vehic...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Jingwei Dong Yazhe Li Jingxiang Feng Yao Yao Fuming Wang Underwater Information Systems Research Department Jiangsu Automation Research Institute Lianyungang China Operations Department Three Jiangsu Nuclear Power Co. Ltd. Lianyungang China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

In underwater environments, due to the limitation of exploration distance, UUV (unmanned underwater vehicle) clusters face challenges in coordination and efficiency when performing capture tasks. In order to effectively capture dynamic targets, this paper proposes a capture strategy for UUV clusters, aiming to achieve effective monitoring and capture of dynamic targets. Firstly, establish a mathematical model for dynamic target UUV cluster capture decision-making and the collaborative action mechanism of UUV clusters during the capture process. Ensure efficient collaboration of UUV clusters in capturing dynamic targets and improve the success rate of capture. Secondly, a target motion trajectory prediction method based on timestamps and dynamic models is proposed, which aims to reduce the adverse effects of communication delays. By predicting the target’s motion trajectory, more accurate target position information is provided for UUV clusters, thereby optimizing the capture strategy. In addition, this article designs a target tracking method based on angle measurement information. After the calculation is completed, the artificial potential field method is used to plan the trajectory of the UUV, in order to achieve effective encirclement of the target.

关键词： Target tracking Accuracy Dynamics Decision making Predictive models Real-time systems Mathematical models Trajectory Delays Vehicle dynamics

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Detection of Potholes using Convolutional Neural Network models: A Transfer Learning Approach

Detection of Potholes using Convolutional Neural Network Mod...

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2021 IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2021

作者： Pramanik, Anik Bijoy, Md. Hasan Imam Rahman, Md. Sadekur Daffodil International University Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9781665478694

The most serious issue with the road and highway transportation system is accidental deaths. A pothole is the most prominent cause of poor road conditions, and as a result, road damage is booming in Bangladesh and throughout the world. Using image processing techniques to identify and classify potholes is a very operative term for preventing road accidents and providing a safe journey. As a result, the objective of this study is to identify the pothole using image processing techniques, which is the Transfer Learning methodology. For identifying potholes, two conventional neural network models, Visual Geometry Group (VGG16) and Residual Networks (ResNet50), were used. The major goal of this paper is to recognize potholes and furnish the Road and Highway Department (RHD) with a Computer Vision based solution. For model feeding, several image preprocessing approaches are utilized, and our suggested models achieve a considerable boost in performance, with VGG16 gaining 96.31% and ResNet50 acquiring 98.66% accuracy, respectively. As a consequence, the high accuracy of the proposed models implies that the research study is indeed successful. © 2021 IEEE.

关键词： Computer vision

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Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers

Reshaping Robot Trajectories Using Natural Language Commands...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Bucker, Arthur Figueredo, Luis Haddadin, Sami Kapoor, Ashish Ma, Shuang Bonatti, Rogerio Tech Univ Munich Munich Germany Microsoft Redmond WA USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Natural language is the most intuitive medium for us to interact with other people when expressing commands and instructions. However, using language is seldom an easy task when humans need to express their intent towards robots, since most of the current language interfaces require rigid templates with a static set of action targets and commands. In this work, we provide a flexible language-based interface for human-robot collaboration, which allows a user to reshape existing trajectories for an autonomous agent. We take advantage of recent advancements in the field of large language models (BERT and CLIP) to encode the user command, and then combine these features with trajectory information using multi-modal attention transformers. We train the model using imitation learning over a dataset containing robot trajectories modified by language commands, and treat the trajectory generation process as a sequence prediction problem, analogously to how language generation architectures operate. We evaluate the system in multiple simulated trajectory scenarios, and show a significant performance increase of our model over baseline approaches. In addition, our real-world experiments with a robot arm show that users significantly prefer our natural language interface over traditional methods such as kinesthetic teaching or cost-function programming. Our study shows how the field of robotics can take advantage of large pre-trained language models towards creating more intuitive interfaces between robots and machines. Project webpage: https://arthurfenderbucker. ***/NL_trajectory_reshaper/

关键词： Natural languages Education Programming Predictive models Transformers Manipulators Trajectory

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Development of a Series Elastic Elbow Neurological Exam Training Simulator for Lead-pipe Rigidity

Development of a Series Elastic Elbow Neurological Exam Trai...

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IEEE international conference on robotics and automation (ICRA)

作者： Gim, Kevin G. He, Maxine Mansouri, Mahshid Pei, Yinan Ripperger, Evan Zallek, Christopher M. Hsiao-Wecksler, Elizabeth T. Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Neurosci Program Urbana IL 61801 USA OSF HealthCare Illinois Neurol Inst Dept Neurol Peoria IL 61603 USA

ISBN: (纸本)9781728190778

This paper describes the development of a 1-DOF kinesthetic force display device in the form of an arm training simulator that replicates the haptic feeling of lead-pipe rigidity in the elbow joint. Patients with lead-pipe rigidity have uniformly elevated muscle tone throughout the range of motion, which is an important clinical sign for diagnosing Parkinson's disease during a neurological examination. The simulator could provide training opportunities for healthcare trainees to learn and practice the assessment technique for lead-pipe rigidity. The simulator was driven by a series elastic actuator in order to have more accurate joint torque control in a safe and cost-effective manner for rendering abnormal muscle resistance. A mathematical model of lead-pipe rigidity based on hyperbolic tangent was proposed to recreate the elevated muscle resistance at different Unified Parkinson's Disease Rating Scale (UPDRS) 0-3. Performance of the simulator was evaluated through benchtop tests and rigidity simulation tests. Preliminary results suggested the simulator had good torque control accuracy (an average RMSE < 0.27 Km) and good fidelity in mimicking clinically-measured lead-pipe rigidity at UPDRS 0-3.

关键词： Training Resistance Torque control Medical services Muscles Lead Mathematical models

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Patrol Agent: An Autonomous UAV Framework for Urban Patrol Using on Board Vision Language Model and on Cloud Large Language Model

Patrol Agent: An Autonomous UAV Framework for Urban Patrol U...

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robotics and Computer Vision (ICRCV), international conference on

作者： Zihao Yuan Fangfang Xie Tingwei Ji School of Aeronautics and Astronautics Zhejiang University Hangzhou China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

Unmanned Aerial Vehicles (UAVs) used for urban patrols typically require human control or supervision. To enhance the automation of UAV s in this context, we propose the Patrol Agent, which is able to patrol, identify and track a target in a fixed area autonomously without any human intervention. The Patrol Agent employs Vision Language Model (VLM) for accurate visual information, object detection model for rough detection about the target, and Large Language Model (LLM) deployed on cloud for analysis and action-deciding. During patrols, the agent uses a lightweight VLM to generate captions of the scenes it observes. These captions are then sent to the LLM on cloud for further analysis which provides responses regarding the danger level of the scene, appropriate actions to take, and the detailed reasons behind these actions. When the agent identifies and tracks a target, it activates the VLM only when the object detection model detects an object corresponding to the target. This approach conserves computing resources and enhances onboard operational speed. The proposed agent can identify and track targets without requiring fine-tuning data or human intervention. It outperforms Visual Question Answering (VQA) models in patrol and uses fewer computing resources compared to agents that solely rely on VLM for tracking.

关键词： Visualization Analytical models Target tracking Computational modeling Large language models Roads Object detection Autonomous aerial vehicles Question answering (information retrieval) Object recognition

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