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arXiv 2022年

作者： Demir, Umut Ure, Nazim Kemal Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center Istanbul Turkey

In this work we propose a reinforcement learning (RL) framework that controls the density of a large-scale swarm for engaging with adversarial swarm attacks. Although there is a significant amount of existing work in applying artificial intelligence methods to swarm control, analysis of interactions between two adversarial swarms is a rather understudied area. Most of the existing work in this subject develop strategies by making hard assumptions regarding the strategy and dynamics of the adversarial swarm. Our main contribution is the formulation of the swarm to swarm engagement problem as a Markov Decision Process and development of RL algorithms that can compute engagement strategies without the knowledge of strategy/dynamics of the adversarial swarm. Simulation results show that the developed framework can handle a wide array of large-scale engagement scenarios in an efficient manner. © 2022, CC BY.

关键词： Markov processes

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Scalable Planning and Learning Framework Development for Swarm-to-Swarm Engagement Problems

arXiv

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arXiv 2022年

作者： Demir, Umut Satir, A. Sadik Sever, Gulay Goktas Yikilmaz, Cansu Ure, N. Kemal Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center Istanbul Turkey

Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations. © 2022, CC BY.

关键词： Reinforcement learning

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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

Chain of Condition: Construct, Verify and Solve Conditions f...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

ISBN: (纸本)9798891761681

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-Scale Feature Fusion and Advanced Representation Learning for Multi Label Image Classification

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Computers, Materials & Continua 2025年第3期82卷 5285-5306页

作者： Naikang Zhong Xiao Lin Wen Du Jin Shi Institute of Artificial Intelligence on Education Research College of InformationMechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234China Lab for Educational Big Data and Policymaking Ministry of EducationShanghai Normal UniversityShanghai200234China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal UniversityShanghai200234China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234China DS Information Technology Co. Ltd.Shanghai200032China Faculty of Innovation Engineering Macao university of Science and TechnologyMacao999078China

Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in *** class-specific precise representations at different scales is a key aspect of feature ***,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same *** some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different *** address these issues,we propose a two-stage,three-branch Transformer-based *** first stage incorporates multi-scale image feature extraction and hierarchical scale *** design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object *** second stage includes a global feature enhancement module and a region selection *** global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and ***,these components enable the efficient acquisition of class-specific precise feature *** experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed *** approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,*** results validate the reliability and superiority of our approach for multi-label image classification.

关键词： Image classification multi-label multi scale attention mechanisms feature fusion

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Assisting Crowdsourced data Collection by using data Mining Algorithms 10

Assisting Crowdsourced Data Collection by using Data Mining ...

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10th International Conference on Computer, Control, Informatics and its applications, IC3INA 2023

作者： Sihombing, Andre Indrawati, Ariani Akbar, Zaenal Manik, Lindung P. Yaman, Aris Albasri, Hatim Puspasari, Reny Radiarta, I Nyoman National Research and Innovation Agency Research Center for Data and Information Science Indonesia National Research and Innovation Agency Research Center for Artificial Intelligence and Cyber Security Indonesia Research Center for Fisheries National Research and Innovation Agency Indonesia Agency of Marine and Fisheries Extension and Human Resources Development Ministry of Marine Affairs and Fisheries Indonesia

ISBN: (纸本)9798350394870

Crowdsourcing has emerged as an alternative solution to utilize the collective knowledge and expertise of citizen science communities, and it is also considered a much cheaper solution. However, data quality becomes one of the challenges. Lack of motivation and unfamiliarity with labeled data will lead to poor quality of crowdsourced data. This research proposes data mining models to enhance user experience while filling questions in a crowdsourced-based application by building a recommendation system model. The system is implemented on Alboom, an application that provides early warning information regarding Harmful Algae Blooms. The model is built based on the Apriori and DBSCAN algorithms to provide recommendations on responses to the same questions. As a result, users do not need to submit a similar response repeatedly. The Silhouette Coefficient was used to evaluate the clustering model. In addition, we also evaluate the DBSCAN algorithm model by generating and predicting new random locations. The model successfully predicts which cluster it belongs to and outputs '-1' if the new points are outside the radius of every existing cluster. The recommendation system performance evaluation with live users will be our future research. © 2023 IEEE.

关键词： Recommender systems

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Developing a research center for artificial intelligence in Medicine

Mayo Clinic Proceedings: Digital Health

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Mayo Clinic Proceedings: Digital Health 2024年第4期2卷 677-686页

作者： Langlotz, Curtis P. Kim, Johanna Shah, Nigam Lungren, Matthew P. Larson, David B. Datta, Somalee Li, Fei Fei Montine, Thomas J. Harrington, Robert A. Gold, Garry E. Departments of Radiology Medicine and Biomedical Data Science Center for Artificial Intelligence in Medicine and Imaging Stanford University Stanford CA United States Center for Artificial Intelligence in Medicine and Imaging Stanford University Stanford CA United States Departments of Medicine and Biomedical Data Science Center for Artificial Intelligence in Medicine and Imaging Stanford Health Care Stanford University Stanford CA United States Department of Radiology Stanford University Stanford CA United States Department of Computer Science Institute for Human Centered Artificial Intelligence Stanford University Stanford CA United States Department of Pathology Stanford University Stanford CA United States Office of the Dean Weill Cornell Medicine New York NY United States Microsoft Redmond WA United States Research Information Technology Stanford University School of Medicine Stanford CA United States Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA United States

artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners. The center for artificial intelligence in Medicine and Imaging uses the following 4 key tactics to support AI/ML research: project-based learning opportunities that build interdisciplinary collaboration;internal grant programs that catalyze extramural funding;infrastructure that facilitates the rapid creation of large multimodal AI-ready clinical data sets;and educational and open data programs that engage the broader research community. The center is based on the premise that foundational and applied research are not in tension but instead are complementary. Solving important biomedical problems with AI/ML requires high-quality foundational team science that incorporates the knowledge and expertise of clinicians, clinician scientists, computer scientists, and data scientists. As AI/ML becomes an essential component of research and clinical care, multidisciplinary centers of excellence in AI/ML will become a key part of the scholarly portfolio of academic medical centers and will provide a foundation for the responsible, ethical, and fair implementation of AI/ML systems. © 2024 The Authors

关键词： AI artificial intelligence AIMI center Stanford center for artificial intelligence in Medicine and Imaging CT computed tomography EHR electronic health record FDA Food and Drug Administration HAI Stanford Institute for Human centered artificial intelligence ML machine learning STARR Stanford research Repository

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Video analysis for ergonomics assessment in the manufacturing industry: initial feedback on a case study 32nd

Video analysis for ergonomics assessment in the manufacturin...

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32nd European Safety and Reliability Conference, ESREL 2022

作者： Estrada-Lugo, Hector Diego Giuliani, Arianna Perez, Andres Alonso Leva, Maria Chiara St Ü Bl, Gernot P Ö Nitz, Thomas Tun Ç El, Mehmet Kemal Ure, Nazim School of Food Science and Environmental Health TU Dublin Ireland PROFACTOR GmbH Austria Artificial Intelligence and Data Science Research Center Istanbul Technical University Turkey

ISBN: (纸本)9789811851834

The manufacturing industry is being benefited from the new technologies developed in the field of artificial intelligence. However, as part of the European AI strategy, the role of workers in the industry must be protected by including human-centered ethical values. The *** project is developing a revolutionary human-AI teaming software platform comprised of interconnected utilities. This work reflects the preliminary results of some of the methodologies that are being developed within the project. An ergonomics assessment of manual activities performed by operators in a manufacturing workplace is carried out. The data for the assessment comes from video recordings obtained with cameras installed in strategic points of the shop floor. In this work, the assessment is done by manually selecting the images from the videos and scoring them based on the Rapid Upper-Limb Assessment and Rapid Entire Body Assessment methods. Once a scored is computed, an analysis of the activity is provided. The preliminary results show that distortion in the image from recording can affect the assessments. A method to enhance the video analysis in two major directions is proposed. The first direction focuses in the automatic operator detection. The second, on generating 3D information for ergonomic assessment with undistorted images. Some details related to the use case are omitted to preserve the anonymity of the operators in the company. © 2022 ESREL2022 Organizers. Published by research Publishing, Singapore.

关键词： Video analysis

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The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

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China Communications 2024年第3期21卷 283-295页

作者： Zheng Quan Yan Wenliang Wu Rong Tan Xiaobin Yang Jian Yuan Liu Xu Zhenghuan Laboratory for Future Networks Department of AutomationUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230088China Suzhou Nuclear Power Research Institute Suzhou 215004China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NEL-PSRPC) China Academy of Electronics and Information Technology of CETCBeijing 100041China

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’***,the Information-Centric Networking(ICN)came into *** a technical point of view,ICN is a promising future network *** and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of *** current research on ICN pricing mechanism is focused on paid ***,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to *** this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN *** solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each *** extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

关键词： advertisers cache free content Information-Centric Networking pricing strategy

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Observer-Based Control for a New Stochastic Maximum Power Point Tracking for Photovoltaic Systems With Networked Control System

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IEEE Transactions on Fuzzy Systems 2023年第6期31卷 1870-1884页

作者： Aslam, Muhammad Shamrooz Tiwari, Prayag Pandey, Hari Mohan Band, Shahab S. Guangxi University of Science and Technology School of Automation Liuzhou545006 China Halmstad University School of Information Technology Halmstad301 18 Sweden Bournemouth University Data Science and Artificial Intelligence Department of Information and Computing PooleBH12 United Kingdom National Yunlin University of Science and Technology Future Technology Research Center College of Future Douliou64002 Taiwan

This study discusses the new stochastic maximum power point tracking control approach toward the photovoltaic cells (PCs). A PC generator is isolated from the grid, resulting in a direct current microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed networked control systems beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi-Sugeno (T - S) fuzzy model is another option. The T - S fuzzy model is presented in a unified framework, for which 1) the fuzzy observer based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer-based controller can be created using the same premises being the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulation in the PC generator model for the real-time climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov-Krasovskii functional for combining with the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the tensor-product (T-P) transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 1993-2012 IEEE.

关键词： Linear matrix inequalities

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