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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Cong Lin, Zhonghao Zhang, Jinglin Chen, Shengyong College of Computer Science Zhejiang University of Technology China School of Control Science and Engineering Shangdong University China School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)157735897X

Accurate detection of dust storms is challenging due to complex meteorological interactions. With the development of deep learning, deep neural networks have been increasingly applied to dust storm detection, offering better learning and generalization capabilities compared to traditional physical modeling. However, existing methods face some limitations, leading to performance bottlenecks in dust storm detection. From the task perspective, existing research focuses on occurrence detection while neglecting intensity detection. From the data perspective, existing research fails to explore the utilization of multi-source data. From the model perspective, most models are built on convolutional neural networks, which have an inherent limitation in capturing long-range dependencies. To address the challenges mentioned, this study proposes Dust-Mamba. To the best of our knowledge, this study is the first attempt to accomplish both the occurrence and intensity detection of dust storms with advanced deep learning technology. In Dust-Mamba, multi-source data is introduced to provide a comprehensive perspective, Mamba and attention are applied to boost feature selection while maintaining long-range modeling capability. Additionally, this study proposes Structure Sharing Transfer Learning Strategies for intensity detection, which further enhances the performance of Dust-Mamba with minimal time cost. As shown by experiments, Dust-Mamba achieves Dice scores of 0.963 for occurrence detection and 0.560 for intensity detection, surpassing several baseline models. In conclusion, this study offers valuable baselines for dust storm detection, with significant reference value and promising application potential. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Seeking Nash Equilibrium for Linear Discrete-time Systems via Off-policy Q-learning

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IAENG International Journal of Applied Mathematics 2024年第11期54卷 2477-2483页

作者： Ni, Haohan Ji, Yuxiang Yang, Yuxiao Zhou, Jianping School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215000 China

This paper considers a non-zero-sum game for linear discrete-time systems involving two players. Based on a quadratic value function, we derive coupled algebraic Riccati equations. Then, we propose both on-policy and off-policy Qlearning algorithms, which operate without prior knowledge of the system dynamics, to achieve Nash equilibrium. These algorithms necessitate the inclusion of probing noise to ensure the persistence of excitation. We show that the on-policy Qlearning algorithm may introduce bias to the Nash equilibrium due to the probing noise, while the off-policy Q-learning algorithm maintains an unbiased property. Finally, we offer a numerical example to validate the effectiveness of the presented on-policy and off-policy Q-learning algorithms. © (2024), (International Association of Engineers). All rights reserved.

关键词： Riccati equations

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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

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Neural Computing and Applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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computer Modeling in engineering & sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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The Distributed Intelligent Collaboration to AAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C. S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS & AI ClaytonVIC3800 Australia Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Southeast University School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Southeast University Suzhou215123 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying autonomous aerial vehicles (AAVs) as aerial base stations enhances the coverage and performance of communication networks in vehicular edge computing scenarios. However, due to the limited communication range and energy capacity of AAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of AAVs and efficient task offloading remains a significant challenge. To address this problem, the AAV-assisted two-stage intelligent collaboration (UTIC) method is proposed in this article to tackle the joint position optimization and task scheduling issue. First, a AAV-assisted two-stage task scheduling system model is designed to optimize the allocation process. Second, an Enhanced Particle Swarm Optimization algorithm is designed to determine the optimal positions of AAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of AAVs. Third, deep deterministic policy gradient method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in AAV deployment and outperform three other classical reinforcement learning algorithms in terms of reducing system cost. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Constructing prediction intervals to explore uncertainty based on deep neural networks

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10441-10456页

作者： Yang, Jingling Chen, Liren Chen, Huayou Liu, Jinpei Han, Bing School of Big Data and Statistics Anhui University Hefei China School of Marine Science and Technology Tianjin University Tianjin China School of Business Anhui University Anhui Hefei China Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States School of Mathematical Science Anhui University Hefei China

The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of 'high coverage and narrow width'. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of 'high coverage, narrow width, and small bias' in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. © 2024 - IOS Press. All rights reserved.

关键词： Forecasting

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Explaining the better generalization of label distribution learning for classification

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science China(Information sciences) 2025年第5期68卷 348-361页

作者： Jing WANG Xin GENG School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Ministry of Education

Label distribution learning(LDL) has shown advantages over traditional single-label learning(SLL) in many realworld applications, but its superiority has not been theoretically understood. In this paper, we attempt to explain why LDL generalizes better than SLL. Label distribution has rich supervision information such that an LDL method can still choose the sub-optimal label from label distribution even if it neglects the optimal one. In comparison, an SLL method has no information to choose from when it fails to predict the optimal label. The better generalization of LDL can be credited to the rich information of label distribution. We further establish the label distribution margin theory to prove this explanation; inspired by the theory,we put forward a novel LDL approach called LDL-LDML. In the experiments, the LDL baselines outperform the SLL ones, and LDL-LDML achieves competitive performance against existing LDL methods, which support our explanation and theories in this paper.

关键词： label distribution learning classification generalization label distribution margin label ambiguity neural network

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DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

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SN computer science 2024年第7期5卷 801页

作者： Kaur, Navjeet Bhardwaj, Vivek Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University School of Computer Science and Engineering Manipal University Jaipur

The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing are the latest technologies for developing smart cities that are becoming popular to efficiently support latency-sensitive IoT application services. But these advanced technologies suffer from limited size and computation capabilities;therefore, optimal placement of services among available resources is an open issue. Therefore, the proposed work target the stated issue and presents a Dynamic and Distributed Service Placement Policy (DD-SPP) considering the edge and fog devices. The model is divided into three main phases, that is, the Service Type Estimator (STE), the Service Dependency Estimator & Resolution (SDER), and Resource Demand Estimator & Scheduling (RDES). STE and RDES modules depend on each other to perform optimal service placement. The results of the implementation showed satisfactory improvement with respect to present state-of-the-art policy where delay is improved by around 41%, energy is improved by 27%, response time by 28% and overall cost by 28%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing DD-SPP Edge computing Fog computing IoT Scheduling Service placement policy

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ReFF: Reinforcing Format Faithfulness in Language Models Across Varied Tasks 39

ReFF: Reinforcing Format Faithfulness in Language Models Acr...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yao, Jiashu Huang, Heyan Liu, Zeming Wen, Haoyu Su, Wei Qian, Boao Guo, Yuhang School of Computer Science and Technology Beijing Institute of Technology China School of Computer Science and Engineering Beihang University China

ISBN: (纸本)157735897X

Following formatting instructions to generate well-structured content is a fundamental yet often unmet capability for large language models (LLMs). To study this capability, which we refer to as format faithfulness, we present FORMATBENCH, a comprehensive format-related benchmark. Compared to previous format-related benchmarks, FORMATBENCH involves a greater variety of tasks in terms of application scenes (traditional NLP tasks, creative works, autonomous agency tasks), human-LLM interaction styles (single-turn instruction, multi-turn chat), and format types (inclusion, wrapping, length, coding). Moreover, each task in FORMATBENCH is attached with a format checker program. Extensive experiments on the benchmark reveal that state-of-the-art open- and closed-source LLMs still suffer from severe deficiency in format faithfulness. By virtue of the decidable nature of formats, we propose to Reinforce Format Faithfulness (REFF) to help LLMs generate formatted output as instructed without compromising general quality. Without any annotated data, REFF can substantially improve the format faithfulness rate (e.g., from 21.6% in original LLaMA3 to 95.0% on caption segmentation task), while keep the general quality comparable (e.g., from 47.3 to 46.4 in F1 scores). Combined with labeled training data, REFF can simultaneously improve both format faithfulness (e.g., from 21.6% in original LLaMA3 to 75.5%) and general quality (e.g., from 47.3 to 61.6 in F1 scores). We further offer an interpretability analysis to explain how REFF improves both format faithfulness and general quality. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Tunable topological interface states via a parametric system in composite lattices with/without symmetric elements

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2055-2074页

作者： Jianguo CUI Tianzhi YANG Wenju HAN Liang LI Muqing NIU Liqun CHEN School of Mechanics and Photoelectric Physics Anhui University of Science and TechnologyHuainan 232001Anhui ProvinceChina School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China School of Aerospace Engineering North University of ChinaTaiyuan 030051China School of Science Harbin Institute of TechnologyShenzhenShenzhen 518055Guangdong ProvinceChina Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Institute of Applied Mathematics and MechanicsSchool of Mechanics and Engineering ScienceShanghai UniversityShanghai 200444China Shanghai Institute of Aircraft Mechanics and Control Shanghai 200092China

Over the past decades, topological interface states have attracted significant attention in classical wave systems. Generally, research on the topological interface states of elastic waves is conducted in the lattices with symmetric elements. This paper proposes composite lattices with/without symmetric elements, and demonstrates the realization of tunable topological interface states of elastic waves via parametric *** quantize the topological characteristics of the bands, a modified Zak phase is defined to calculate the topological invariant by the eigenstates for the lattices with/without symmetric elements. The numerical results show that the tunable frequencies of topological interface states can be realized in composite lattices with/without symmetric elements through the modulation of the parametric excitation frequency. The tunable topological interface states can be introduced into the vibration energy harvesting to design efficient and steady energy harvesting systems.

关键词： metamaterial topological interface state parametric excitation asymmetric element modified Zak phase

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