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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xinlei Feng, Maike Qiu, Jing Gu, Jinjin Zhao, Junhua School of Electrical and Computer Engineering The University of Sydney Australia School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium, our method adaptively integrates social events into forecasting models, aligning news content with time series fluctuations to provide richer insights. Specifically, we utilize LLM-based agents to iteratively filter out irrelevant news and employ human-like reasoning to evaluate predictions. This enables the model to analyze complex events, such as unexpected incidents and shifts in social behavior, and continuously refine the selection logic of news and the robustness of the agent's output. By integrating selected news events with time series data, we fine-tune a pre-trained LLM to predict sequences of digits in time series. The results demonstrate significant improvements in forecasting accuracy, suggesting a potential paradigm shift in time series forecasting through the effective utilization of unstructured news data. © 2024 Neural information processing systems foundation. All rights reserved.

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A Remotely Controlled Unmanned Surface Vehicle with Obstacle Avoidance for STEM Education

A Remotely Controlled Unmanned Surface Vehicle with Obstacle...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Lv, Minen Liang, Cheng Qian, Huihuan The Chinese University of HongKong School of Science and Engineering Shenzhen China The Chinese University of HongKong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798350327182

Nowadays, STEM (Science, Technology, Engineering, and Mathematics) education has been increasingly important. In some developed countries, STEM education plays a role in science integration with other fields of learning. With the recent development of robots, STEM education fields gradually set their sights on robots. Unmanned Surface Vehicles (USVs), which are significant areas in robotics research, should also be taken seriously. In this paper, we introduce a remotely controlled USV with obstacle avoidance for STEM Education. The mechanical and electrical design, the obstacle avoidance system, and the remote control platform of this USV are presented in the following parts. Testing results of the USV on the tranquil water surface and the wavy water surface are displayed as well. © 2023 IEEE.

关键词： Remote control

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A new Polynomial based Symmetric Key Algorithm using Polynomial Interpolation Methods 12

A new Polynomial based Symmetric Key Algorithm using Polynom...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Hasija, Taniya Ramkumar, K.R. Singh, Bhupendra Kaur, Amanpreet Mittal, Sudesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Defence Research and Development Organisation Centre for Artificial Intelligence & Robotics Bangalore India

ISBN: (纸本)9781665462617

The main attributes of a security framework are Confidentiality, Authentication, and Data integrity. There are two main standards of cryptography algorithms categorized as private or symmetric and public or asymmetric algorithms, symmetric algorithms use one secret key to encrypt and decrypt payloads, the list includes Data Encryption Standard (DES), Advanced Encryption Standard (AES), RC5, Blowfish, and others, and asymmetric algorithms use key pairs consists of public and private keys, RSA, and Elliptic Curve Cryptography are two best examples of the same. In spite of their successful implementations, these standards suffer from various attacks, and the beginning of large-scale quantum is the biggest threat to the commonly used cryptography algorithms. In general, Public-key cryptography relies upon mathematical problems that are hard to be broken with the available computational power but it is believed that quantum computers can break them at polynomial solvable time in future. Quantum-safe cryptography refers to a new generation of algorithms that are resilient against attacks launched by both classical and quantum computers. This work designs and implements a novel cryptography standard using polynomial interpolations to make it both supercomputer and quantum safe. © 2023 IEEE.

关键词： Polynomials

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Symmetric Key Cryptography: Review, Algorithmic Insights, and Challenges in the Era of Quantum Computers 14

Symmetric Key Cryptography: Review, Algorithmic Insights, an...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

ISBN: (纸本)9798350335095

In an increasingly digital world, security has become paramount. Whether it's personal information, financial transactions, or sensitive communications, the need to safeguard data from unauthorized access and malicious activities has never been more critical. Security plays a vital role in protecting individuals, organizations, and even nations from potential threats and risks. In this context, cryptography emerges as a fundamental tool for ensuring security in digital systems. Cryptography involves the use of mathematical algorithms and techniques to encode information, making it unintelligible to unauthorized parties. To secure the information symmetric key cryptography algorithms and asymmetric key cryptography algorithms are implemented from last five decades. This review examines symmetric key cryptography algorithms and the challenges they face in the era of quantum computing. Symmetric key cryptography relies on the computational complexity of mathematical problems to secure sensitive information. This review aims to provide valuable insights and awareness to researchers and practitioners in the field of cryptography, enabling them to address these challenges effectively and maintain the security of sensitive information in the quantum era. © 2023 IEEE.

关键词： Sensitive data

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Vision-Guided UAV Landing on a Swaying Ocean Platform in Simulation

Vision-Guided UAV Landing on a Swaying Ocean Platform in Sim...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Chen, Lingpeng Liu, Chongfeng Guo, Shuqi Qian, Huihuan The Chinese University of HongKong School of Science and Engineering Shenzhen China The Chinese University of HongKong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798350327182

This paper presents a new method for the autonomous landing of an unmanned aerial vehicle (UAV) on a moving ocean platform in simulation. The method uses GNSS data and a lightweight boat detection algorithm to guide the UAV toward the unmanned surface vehicle (USV). Once the USV is within the view of the down-looking camera, the UAV estimates its relative position by detecting a set of ArUco markers on the USV landing pad. During the descent process, to compensate for the heave motion of the USV caused by waves, the system utilizes data from a buoy equipped with a wave sensor. Simulation experiments demonstrate the reliability and effectiveness of the proposed approach. © 2023 IEEE.

关键词： Unmanned surface vehicles

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Parking Space Recognition Methods Based on Visual Image and Deep Learning

Parking Space Recognition Methods Based on Visual Image and ...

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2023 China Automation Congress, CAC 2023

作者： Deng, Siyi Xia, Yuchen Zuo, Weiliang Xin, Jingmin Zhou, Sanping Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an China

ISBN: (纸本)9798350303759

Parking space recognition technology is of great significance for intelligent parking lot management. To address the shortcomings of current parking lot management methods, we conducted research and practice on parking space recognition based on image processing and deep learning methods, resulting in a comprehensive and effective parking space recognition method. By preprocessing the parking lot images to divide the parking area and using the VGG16 neural network for parking space recognition training, we processed the parking lot video with the trained model. The experimental results show that most of the parking spaces in the video can be accurately identified, with an accuracy rate of 98.32%. The link to the parking space recognition video is: https://***/9xMph9USgW4. © 2023 IEEE.

关键词： Image processing

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Map Completion for SLAM Systems Based on Neural Radiance Field

Map Completion for SLAM Systems Based on Neural Radiance Fie...

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2023 China Automation Congress, CAC 2023

作者： Deng, Siyi Zuo, Weiliang Xia, Yuchen Xin, Jingmin Zhou, Sanping Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi' An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an China

ISBN: (纸本)9798350303759

Simultaneous localization and mapping (SLAM) systems aim to enhance the capabilities of robots in complex environments by building maps of unknown surroundings. Creating a dense and detailed map can significantly improve the robot's perception of the environment and enable it to perform more challenging tasks. However, traditional algorithms often struggle to balance localization, tracking, and mapping. Recently, neural radiance fields-based 3D reconstruction has emerged as an efficient and accurate method, offering a potential solution for map completion in SLAM systems. In our work, we integrate neural radiance fields with the SLAM system, providing a real-time solution for tracking, localization, and initial scene reconstruction, along with offline map completion. Experimental results demonstrate that our approach sacrifices some tracking accuracy to achieve complete map construction. © 2023 IEEE.

关键词： Mapping

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FISS+: Efficient and Focused Trajectory Generation and Refinement Using Fast Iterative Search and Sampling Strategy

FISS+: Efficient and Focused Trajectory Generation and Refin...

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2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023

作者： Sun, Shuo Chen, Jie Sun, Jiawei Yuan, Chengran Li, Yuanchen Zhang, Tangyike Ang, Marcelo H. National University of Singapore Department of Mechanical Engineering Singapore119077 Singapore Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9781665491907

Trajectory planning plays a crucial role in autonomous driving systems, as it is tasked to generate feasible trajectories under highly dynamic scenarios within the time constraint. This paper proposes a novel two-stage coarse-to-fine framework for efficient sampling-based trajectory planning. The proposed method is designed to iteratively generate new trajectory samples focused on the low-cost regions in the sampling space. Two trajectory exploration algorithms are well-designed for efficient search in discretized coarse global space and continuous fine local space, respectively. Experimental results on the first-of-its-kind planning benchmark tool CommonRoad show that our method significantly outperforms the baseline methods both in optimality and computational efficiency. Overall, our approach offers a promising solution for efficient and effective trajectory planning in more autonomous vehicle applications. © 2023 IEEE.

关键词： Trajectories

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SUNOD: Synthetic Underwater Non-Natural Object Detection Dataset

SUNOD: Synthetic Underwater Non-Natural Object Detection Dat...

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2023 China Automation Congress, CAC 2023

作者： Xia, Yuchen Deng, Siyi Li, Yuxi Zuo, Weiliang Xin, Jingmin Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an China

ISBN: (纸本)9798350303759

Deep-learning-based non-natural object detection in complex underwater environments is widely needed in many application scenarios. However, obtaining image data of underwater non-natural objects is difficult, so there is basically no relevant dataset. Besides, the underwater environments and lighting conditions are complex, which will lead to image quality degradation and increase the difficulty of detecting. For this purpose, we build a Synthetic Underwater Non-natural Object Detection (SUNOD) dataset. We first utilize a free software, Unreal Engine 5 (UE5), to create a realistic three-dimensional virtual underwater environment containing 12 kinds of non-natural objects. Then we take pictures of these non-natural objects and manually label 2,840 images. To make SUNOD more challenging and be able to create object detection models with more generalization capabilities and better robustness, we expand it to contain 72,943 images by 26 kinds of data augmentation methods. In addition, we report the performance of 5 YOLOv8 object detection models with different volumes on SUNOD. SUNOD can be used both to fine-tune pretrained models to accomplish the underwater non-natural object detection task and to be a benchmark to evaluate new models for this task. This study provides the first synthetic dataset for underwater non-natural object detection, which contributes to the research on the intelligence of submarines, exploration robots, and other underwater devices. © 2023 IEEE.

关键词： Object detection

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PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning

arXiv

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arXiv 2025年

作者： Villar-Corrales, Angel Behnke, Sven Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on video sequences and simulations with precise action annotations, limiting their ability to leverage the large amount of available unlabeled video data. To address this challenge, we propose PlaySlot, an object-centric video prediction model that infers object representations and latent actions from unlabeled video sequences. It then uses these representations to forecast future object states and video frames. PlaySlot allows to generate multiple possible futures conditioned on latent actions, which can be inferred from video dynamics, provided by a user, or generated by a learned action policy, thus enabling versatile and interpretable world modeling. Our results show that PlaySlot outperforms both stochastic and object-centric baselines for video prediction across different environments. Furthermore, we show that our inferred latent actions can be used to learn robot behaviors sample-efficiently from unlabeled video demonstrations. Videos and code are available at https://***/PlaySlot/. © 2025, CC BY.

关键词： Stochastic systems

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