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FireExpert: Fire Event Identification and Assessment Leveraging Cross-Domain Knowledge and Large Language Model

IEEE Transactions on Mobile Computing 2025年第06期24卷 4794-4810页

作者： Luo, Guofeng Weng, Lijuan Li, Yunqian Sun, Yilu Hong, Yayao Wu, Yongyi Luo, Ruixiang Wang, Leye Wang, Cheng Chen, Longbiao School of Informatics China School of Computer Science Peking University China

Fire events threaten the safety of residents and the health of ecosystems in affected areas, and post-disaster recovery efforts also require a large investment of resources and time. In recent years, the rising frequency of fire events has motivated local governments to strengthen their monitoring and emergency response efforts. However, current fire event identification methods can only identify the presence of a fire, without the ability to distinguish its specific category. In addition, when a fire occurs, the lack of information about the affected areas makes it challenging for emergency management authorities to take timely and effective rescue measures. To address these issues, we propose a two-stage framework for fire event identification and assessment. Specifically, in the first stage, based on multi-band fused remote sensing images and heterogeneous environmental images, the proposed framework not only identifies various fire events but also accurately identifies the boundaries of the fire events. In the second stage, integrating the results of fire event identification with social media data and domain knowledge, we present a real-time assessment agent for fire events based on the large language model. This agent enables timely and accurate analysis of the impact of fires on the affected areas. We evaluate our method on a real-world authority dataset, and results show that our framework identifies fire events with an F1-score of 61.0% and a mAP of 57.7%, which outperforms state-of-the-art baseline methods. In addition, the assessment results of fire events in real cases indicate that the proposed fire event assessment agent can assist emergency responders in obtaining timely and accurate information © 2002-2012 IEEE.

关键词： Investments

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Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications

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Journal of Systems science and Systems Engineering 2025年第2期34卷 203-230页

作者： Likai Wang Qingyang Zhang Shengxiang Yang Yongquan Dong School of Computer Science and Technology Jiangsu Normal UniversityXuzhou 221000China School of Computer Science and Informatics De Montfort UniversityLeicesterLE19BHLeicesterUK

The grey wolf optimizer(GWO),a population-based meta-heuristic algorithm,mimics the predatory behavior of grey wolf *** exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO *** overcome the premature and stagnation of GWO,the paper proposes a multiple strategy grey wolf optimization algorithm(MSGWO).Firstly,an variable weights strategy is proposed to improve convergence rate by adjusting the weights ***,this paper proposes a reverse learning strategy,which randomly reverses some individuals to improve the global search ***,the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous ***,this paper proposes a rotation predation strategy,which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation *** verify the performance of the proposed technique,MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization *** results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.

关键词： Grey wolf optimizer variable weights reverse learning chain predation rotation predation

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LucIE: Language-guided local image editing for fashion images

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Computational Visual Media 2025年第1期11卷 179-194页

作者： Huanglu Wen Shaodi You Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Computer Vision Research Group in the Institute of Informatics University of AmsterdamAmsterdamthe Netherlands

Language-guided fashion image editing is challenging,as fashion image editing is local and requires high precision,while natural language cannot provide precise visual information for *** this paper,we propose LucIE,a novel unsupervised language-guided local image editing method for fashion *** adopts and modifies recent text-to-image synthesis network,DF-GAN,as its ***,the synthesis backbone often changes the global structure of the input image,making local image editing *** increase structural consistency between input and edited images,we propose Content-Preserving Fusion Module(CPFM).Different from existing fusion modules,CPFM prevents iterative refinement on visual feature maps and accumulates additive modifications on RGB *** achieves local image editing explicitly with language-guided image segmentation and maskguided image blending while only using image and text *** on the DeepFashion dataset shows that LucIE achieves state-of-the-art *** with previous methods,images generated by LucIE also exhibit fewer *** provide visualizations and perform ablation studies to validate LucIE and the *** also demonstrate and analyze limitations of LucIE,to provide a better understanding of LucIE.

关键词： deep learning language-guided image editing local image editing content preservation fashion images

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Does Adding a Modality Really Make a Positive Impacts in Incomplete Multi-modal Brain Tumor Segmentation?

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2194-2205页

作者： Qiu, Yansheng Jiang, Kui Yao, Hongdou Wang, Zheng Shin’ichi, Satoh the School of Computer Science Wuhan University Wuhan China China National Institute of Informatics The University of Tokyo Japan the National Institute of Informatics Japan

Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multimodal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion. Concretely, PNDC involves two elaborate pipelines, Reverse Audit and Forward Checksum. The former is to identify negative regions impacts of each modality. The latter calibrates whether the fusion prediction is reliable in these regions by integrating the positive impacts regions of each modality. Finally, the negative impacts region from each modality and miss-match reliable fusion predictions are utilized to enhance the learning of individual modalities and fusion process. It is noted that PNDC adopts the standard training strategy without specific architectural choices and does not introduce any learning parameters, and thus can be easily plugged into existing network training for incomplete multi-modal brain tumor segmentation. Extensive experiments confirm that our PNDC greatly alleviates the performance degradation of current state-of-the-art incomplete medical multi-modal methods, arising from overlooking the positive/negative impacts regions of the modality. The code is released at PNDC. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Multicarrier modulation

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Enhancing demand forecasting through combination of anomaly detection and continuous improvement

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Soft Computing 2025年第2期29卷 1243-1258页

作者： Jahani, Meysam Zojaji, Zahra Raji, Fatemeh Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Demand forecasting has emerged as a crucial element in supply chain management. It is essential to identify anomalous data and continuously improve the forecasting model with new data. However, existing literature fails to comprehensively cover both aspects of anomaly detection and continuous improvement in demand forecasting. This study proposes an enhanced model to improve accuracy in the demand forecasting. The proposed model introduces a novel data handling method that incorporates an anomaly detection autoencoder, improved with anomaly correction mechanisms. The data handling approach simultaneously detects data anomalies, distinguishes between expected and unexpected anomalies, and corrects anomalous data, ensuring cleaner input for demand forecasting. Then, the proposed model employs a long short-term memory architecture for demand forecasting, enhanced with a continuous improvement method. Thus, the model not only forecasts demand but also retrains the model when the anomaly data surpasses the predetermined threshold, thereby improving the accuracy of forecasting. The results show that the proposed model outperforms other models in detecting data anomalies, achieving an average precision-recall of 0.922, a receiver operating characteristic value of 0.739, and a significance level of less than 0.05. Finally, the model exhibits superior performance in demand forecasting, with average mean squared error, root mean squared error, and mean absolute error values of 33.167, 4.347, and 1.509, respectively, all with a significance level of less than 0.05. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Demand response

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COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection

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science China(Information sciences) 2025年第1期68卷 189-203页

作者： Xiaoqin ZHANG Zhenni YU Li ZHAO Deng-Ping FAN Guobao XIAO Zhejiang Province Key Laboratory of Intelligent Informatics for Safety and Emergency Wenzhou University Nankai International Advanced Research Institute (SHENZHEN FUTIAN) College of Computer Science Nankai University School of Computer Science and Technology Tongji University

We rethink the segment anything model(SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection(COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we propose an edge gradient extraction module, which generates a mask containing gradient information regarding the boundaries of camouflaged objects. This gradient mask is then used as a novel boundary prompt, enhancing the segmentation process. Thereafter, we design a box-boundary mutual guidance module, which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt. This collaboration enhances the model's ability to accurately detect camouflaged objects. Moreover, we employ the discrete wavelet transform to extract high-frequency features from image embeddings. The high-frequency features serve as a supplementary component to the multiprompt ***, our COMPrompter guides the network to achieve enhanced segmentation results, thereby advancing the development of SAM in terms of COD. Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance, surpassing the current leading model by an average positive metric of 2.2% in COD10K. In the specific application of COD, the experimental results in polyp segmentation show that our model is superior to top-tier methods as well. The code will be made available at https://***/guobaoxiao/COMPrompter.

关键词： segment anything model camouflaged object detection boundary prompt

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A Homogeneous Approach to Reasoning Over Global Geographic Data 44th

A Homogeneous Approach to Reasoning Over Global Geographic ...

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44th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Abdelmoty, Alia I. Satoti, Abdurauf School of Computer Science Informatics Cardiff University United Kingdom

ISBN: (纸本)9783031779145

Much of the information that we use is geospatially referenced. The need for homogeneous representation of global geographic themes is recognised as critical for sustainable development goals. The richness of local geographic data created and maintained by individual countries vary widely, creating what is known as a geospatial digital divide. Attempts to bridge this divide include the adoption of Discrete Global Grid Systems that provide an abstract and uniform method of partitioning space on Earth. This paper considers how the local methods of partitioning space adopted in individual countries and provided as open data can be integrated with this global grid system. The paper proposes a novel ontology design pattern for representing the integration of both grid systems, and evaluates it against existing methods. It is shown how a uniform treatment of spatial semantics is used to represent geographic places across grid systems. This proposal is a step towards the effective utilisation of these grid systems in building global geographic information systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ontology

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SCD: Statistical Color Distribution-Based Objective Image Colorization Quality Assessment 41st

SCD: Statistical Color Distribution-Based Objective Image Co...

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41st computer Graphics International Conference, CGI 2024

作者： Lyu, Hongjin Elangovan, Hareeharan Rosin, Paul L. Lai, Yu-Kun School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783031818059

Colorization research has long been a focal point in computer vision and image processing. However, due to its inherently ill-posed nature, a reasonable assessment of the quality of their outcomes remains a challenge. Subjective evaluations are often restricted to a limited number of participants due to the high costs. This along with the existence of individual differences and subjective biases makes it difficult to derive convincing conclusions. Despite no need for participants in objective evaluation metrics, the currently widely applied objective metrics fail to accurately reflect the quality of colorization results, thereby impeding the attainment of consistency with subjective user opinions. Facing the above problems, we propose a novel Statistical Color Distribution-based Objective Evaluation Metric (SCD) for better consistency with human opinions. We first segment images into semantic regions. For each semantic type, a novel two-dimensional natural color distribution w.r.t. hue and saturation is collected to better align with human perceptual observations during image assessment. An adjacency weighted matrix considering surrounding neighboring regions smooths the color distribution table, enabling a more reliable quality assessment. The application of probability density eliminates the issue of frequency anomalies caused by human visual insensitivity, ensuring more accurate *** extensive and comprehensive experiments involving two distinct datasets with the participation of 1321 volunteers, this paper demonstrates that the proposed SCD is more consistent with subjective user opinions compared with current objective metrics for evaluating colorization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Few-Shot Transfer Learning for Deep Reinforcement Learning on Robotic Manipulation Tasks 25th

Few-Shot Transfer Learning for Deep Reinforcement Learning ...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： He, Yuanzhi Wallbridge, Christopher D. Hernndez, Juan D. Colombo, Gualtiero B. School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031720611

Robot manipulation with simulation has become a mainstream approach in the robotics field recently. It entails lower risk and cost compared to direct training a real robot. Various physics engines, such as MuJoCo, offer simulated environments tailored for robot manipulation tasks. As the robotics field rapidly grows, model complexity and training times increase exponentially to meet the demands of diverse tasks. Solving this is challenging as it requires complex models and long training times. Deep Reinforcement Learning (DRL) is the current best-performing way to solve robot manipulation problems. However, although certain algorithms utilized automated curriculum learning to tackle multi-task robot manipulation problems, the models were still too complex to be solved with one training from scratch with acceptable accuracy and reasonable training time. To address this, we introduce a novel few-shot Transfer Learning (TL) technique for DRL that applies both Forward Transfer (FT) and Reverse Transfer (RT). TL facilitates breaking down a complex problem into easier-to-solve sub-problems and transferring the acquired knowledge to more complex ones. Our TL method appears able to accelerate the training process for all the MuJoCo Fetch tasks, while even improving performance by 20% and accelerating 85% for the most complex FetchSlide environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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The Score Reveal Problem: How do We Maximise Entertainment? 25th

The Score Reveal Problem: How do We Maximise Entertainment?

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25th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2024

作者： Fowler, Aric Booth, Richard School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031773662

In many elections or competitions, a set of voters assign points to the candidates in a way that indicates their preferences, with the winning candidate being the candidate with the highest total score. When it comes to revealing the result after all votes have been cast, some competitions proceed by having a roll call where each voter announces their vote in turn. This is often done for entertainment purposes, leading to the introduction of the score reveal problem: Which ordering of the voters should be chosen to maximise the entertainment value of the roll call? We define several entertainment measures and consider their properties, motivated by considerations such as avoiding early resolution of the outcome, focusing attention on the leading candidates, and catering towards preferences for surprise or suspense. We compare several approaches for finding optimal solutions, comparing the hardness of doing so with different entertainment measures and voting formats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Combinatorial optimization

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