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A PREA and OODA Computing Framework of Unmanned systems to Perform complex Tasks

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IEEE Transactions on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles 2024年 1-18页

作者： Yang, Dongsheng Yu, Lei Zhang, Yi Zhu, Fenghua Li, Qiang Cui, Huachao Ma, Qiongmin Fan, Lili Tian, Yonglin Jinan University Guangzhou China Intelligent Science & Technology Academy Limited of CASIC Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 28th Research Institute of China Electronics Technology Group Corporation Nanjing China National Key Laboratory for Complex Systems Simulation National University of Defense Technology Changsha China School of Information and Electronics Beijing Institute of Technology Beijing China

A C2 computing framework for unmanned systems to perform complex tasks is proposed using methods of system of systems engineering, which is formed by combining the macro-scale command and control (C2) process mechanism model (PREA loop) and micro-scale C2 process mechanism model (OODA loop). Guided by PREA loop and OODA loop, the computing framework is divided into four steps and three kinds of transformations. The four steps are design, construction, operation monitoring, and assessment. The three transformations are tactical feedback, campaign feedback, and strategic feedback. A C2 organization model of joint landing combat force is established against the background of a typical complex task of joint landing operation, and the C2 computing framework based on the PREA &OODA is used to implement the C2 activities of the joint landing combat force. The influence of the computing framework on the performance of unmanned systems under different task conditions is verified, including task coordination load, task efficiency and sensitivity of unmanned systems response. Authors

关键词： Feedback

A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes

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arXiv 2024年

作者： Yu, Ting Lin, Xiaojun Wang, Shuhui Sheng, Weiguo Huang, Qingming Yu, Jun School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Three-Dimensional (3D) dense captioning is an emerging vision-language bridging task that aims to generate multiple detailed and accurate descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning, as well as complexities in data collection and processing of 3D point cloud sources. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field, which hinders its progress. In this paper, we provide a comprehensive review of 3D dense captioning, covering task definition, architecture classification, dataset analysis, evaluation metrics, and in-depth prosperity discussions. Based on a synthesis of previous literature, we refine a standard pipeline that serves as a common paradigm for existing methods. We also introduce a clear taxonomy of existing models, summarize technologies involved in different modules, and conduct detailed experiment analysis. Instead of a chronological order introduction, we categorize the methods into different classes to facilitate exploration and analysis of the differences and connections among existing techniques. We also provide a reading guideline to assist readers with different backgrounds and purposes in reading efficiently. Furthermore, we propose a series of promising future directions for 3D dense captioning by identifying challenges and aligning them with the development of related tasks, offering valuable insights and inspiring future research in this field. Our aim is to provide a comprehensive understanding of 3D dense captioning, foster further investigations, and contribute to the development of novel applications in multimedia and related domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Data handling

Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering

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arXiv 2024年

作者： Yu, Ting Fu, Kunhao Wang, Shuhui Huang, Qingming Yu, Jun School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Video Question Answering (VideoQA) represents a crucial intersection between video understanding and language processing, requiring both discriminative unimodal comprehension and sophisticated cross-modal interaction for accurate inference. Despite advancements in multi-modal pre-trained models and video-language foundation models, these systems often struggle with domain-specific VideoQA due to their generalized pre-training objectives. Addressing this gap necessitates bridging the divide between broad cross-modal knowledge and the specific inference demands of VideoQA tasks. To this end, we introduce HeurVidQA, a framework that leverages domain-specific entity-action heuristics to refine pre-trained video-language foundation models. Our approach treats these models as implicit knowledge engines, employing domain-specific entity-action prompters to direct the model’s focus toward precise cues that enhance reasoning. By delivering fine-grained heuristics, we improve the model’s ability to identify and interpret key entities and actions, thereby enhancing its reasoning capabilities. Extensive evaluations across multiple VideoQA datasets demonstrate that our method significantly outperforms existing models, underscoring the importance of integrating domain-specific knowledge into video-language models for more accurate and context-aware VideoQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

Mitigating Over-Trust in Chat Companion Robot Interaction Using Augmented Reality 4

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Mitigating Over-Trust in Chat Companion Robot Interaction Us...

4th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： Wang, Guobao Zhao, Changshun Liu, Ying He, Di Wang, Yuqing Zhang, Kaiyue Yanshan University Hebei Qinhuangdao China Jiangsu Xuzhou Construction Machinery Group Co. Ltd. Jiangsu Xuzhou221004 China Officers College of Pap Sichuan Chengdu China National Key Laboratory for Complex Systems Simulation Beijing China University of Electronic Science and Technology of China Sichuan Chengdu China Jiangsu Province Tongshan Secondary Vocational School Jiangsu Tongshan China

ISBN: (纸本)9798400708831

This study aims to explore the effects of different human-machine interfaces, system performance, and robot interaction modalities on over-trust in chat companion robot systems. We designed and implemented three types of interfaces: 2D Virtual Interface + Virtual Robot, 3D Augmented Reality Interface + AR-based Virtual Robot, and 3D Real World + NAO Physical Robot. We also manipulated the system performance (high or low) and the robot interaction modality (language, gesture, or both). We conducted a user study with 30 participants, who interacted with the chat companion robot under different conditions and rated their trust, distrust, and human-robot distance. We also measured the user experience in terms of usefulness and ease of use. The results showed that the type of interface significantly influenced the trust, distrust, and human-robot distance of the users, and that the 3D augmented reality interface was the most effective in enhancing trust and reducing distrust and distance. The performance of the system also affected the trust and distance of the users, and the gesture interface was the most sensitive to the performance variation. The modality of the interaction did not have a significant impact on the dependent variables. The user experience ratings also indicated that the 3D augmented reality interface was the most useful and easy to use. The implications and limitations of these findings are discussed in the paper. © 2023 ACM.

关键词： Augmented reality

Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

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arXiv 2023年

作者： Yu, Zhou Ouyang, Xuecheng Shao, Zhenwei Wang, Meng Yu, Jun The Key Laboratory of Complex Systems Modeling and Simulation the School of Computer Science Hangzhou Dianzi University China The HDU-ITMO Joint Institute Hangzhou Dianzi University China The School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of the blind LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet—a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the VQA model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM’s understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3 [1], Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. To demonstrate the generality of our approach, we instantiate Prophet with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). © 2023, CC BY.

关键词： Knowledge based systems

Architecture Transformation Research on Joint Border Defense systems-of-systems

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Architecture Transformation Research on Joint Border Defense...

作者： Mengmeng Zhang Shuanghui Yi Menglong Lin Honghui Chen Science and Technology on Information Systems Engineering Laboratory National Key Laboratory for Complex System Simulation

Currently,with the deepening of development and open of China's border,issues of border joint defense become increasingly *** means of cooperation and coordination are far from being able to align requirements;information systems of different joint defense units are difficult to interconnect and interoperate;the previous results of joint defense informatization are difficult to *** main reason is the lack of border joint defense information construction with a top-level design *** the lens of top-level design,this paper establishes a multi-viewpoint architecture framework and transformation process,develops the as-is and to-be joint defense architecture,and finally analyzes transformation principles from the as-is architecture to to-be *** paper mainly explores the architecture transformation mechanisms under the border joint defense scenario.

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A Distributed Approximation Algorithm for the Total Dominating Set Problem 1

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18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024

作者： Wang, Limin Zhang, Zhao Du, Donglei Mao, Yaping Zhang, Xiaoyan School of Mathematical Sciences Henan Institute of Science and Technology Xinxiang453003 China College of Mathematics and Computer Sciences Zhejiang Normal University Zhejiang321004 China Faculty of Management University of New Brunswick Fredericton E3B 5A3 New Brunswick Canada School of Mathematics and Statistics Qinghai Normal University Qinghai810008 China Academy of Plateau Science and Sustainability Xining Qinghai810008 China School of Mathematical Science and Institute of Mathematics Key Laboratory of Ministry of Education Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Jiangsu210023 China

ISBN: (数字)9789819777983

ISBN: (纸本)9789819777976

Given a network graph G=(V,E), a subset T⊆V is said to be a total dominating set (TDS) if every v∈V is adjacent to at least one node in T. In this paper, we present a distributed algorithm for the TDS problem via the LP relaxation techniques. For a positive integer k and maximum degree Δ, the proposed algorithm outputs a fractional total dominating set of expected size O(kΔ2k)|TDSOPT|. The distributed algorithm runs in O(k2) communication rounds, and the algorithm use messages of size O(logΔ). The fractional solution is then rounded to obtain an integer total dominating set for the original problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Consensus algorithm

Evaluation of Colchicine's interaction with the ATP-binding region of mice NLRP3-NACHT domain using molecular docking and dynamics simulation 46

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Evaluation of Colchicine's interaction with the ATP-binding ...

46th Vietnam Conference on Theoretical Physics, VCTP 2021

作者： Lai, Hien T. T. Do, Ha M. Nguyen, Toan T. Key Laboratory for Multiscale Simulation of Complex Systems Department of Theoretical Physics Faculty of Physics VNU University of Science Vietnam National University Ͽ Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi Viet Nam The Key Laboratory of Enzyme and Protein Technology VNU University of Science Vietnam National University Ͽ Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi Viet Nam

Since the discovery of the role of NLRP3 in microbial infection in 2001, many studies have shown that NLRP3 play a key role in causing many mammal acute and chronic diseases. However, a full understanding of the mechanism of NLRP3 activation is still lacking. Our previous theoretical work and experimental evidence show the role of ATP in interacting with and activating the NATCH region of NLRP3. In this study, we continue to use bioinformatics and molecular dynamic (MD) simulation to evaluate the competitive impact of the interaction the ligand ATP and colchicine (COL) with this NACHT protein. The later ligand is a medication to treat gout attacks. Our results show that COL bind stably to the ATP binding pocket of mice NACHT domain with high numbers of hydrophobic and van der Waals interactions, while hydrogen bond and electrostatic interactions are important types of contact for keeping ATP at its NACHT pocket. Our results assist in building in-silico screening model for natural compounds with pharmacological effects to NLRP3 similar to colchicine with few side effects. In addition, this work helps to better understand the balance between this inflammasome activation and inhibition, which will help in the improvement and development of new therapies for related diseases. © Published under licence by IOP Publishing Ltd.

关键词： Chemical activation

Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent systems with Gaussian Process Regression

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arXiv 2024年

作者： Dai, Xiaobing Yang, Zewen Xu, Mengtian Liu, Fangzhou Hattab, Georges Hirche, Sandra TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Robert Koch Institute Berlin Germany National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology 150001 China Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to system uncertainties and environmental disturbances. This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system. For continuous enhancement in predictive performance of Gaussian process model, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. Furthermore, the control performance of the proposed approach is ensured via the Lyapunov theory, based on a probabilistic guarantee for prediction error bounds. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems