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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6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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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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Tile-size aware bitrate allocation for adaptive 360∘ video streaming

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Multimedia Tools and Applications 2025年第13期84卷 12615-12632页

作者： Huang, Jiawei Liu, Mingyue Liu, Jingling Gao, Feng Li, Weihe Wang, Jianxin School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Informatics The University of Edinburgh Edinburgh United Kingdom

360∘ videos have become increasingly popular recently, but consume much more bandwidth than non-360∘ videos. Usually, 360∘ video streaming partitions the video surface into multiple tiles and encodes the tiles independently to effectively and flexibly use limited link bandwidth. However, current bitrate adaptive algorithms generally aim to maximize the bitrate, rather than perceptual quality, resulting in degradation of user experience. More importantly, we reveal that the distribution of tile size is very skewed, that is, a small number of large tiles consumes more bandwidth than a large number of small tiles, further hurting the overall viewing quality. Therefore, in this paper, we propose a tile-size aware bitrate allocation scheme TSA for adaptive 360∘ video streaming to improve the viewing experience of users. Specifically, TSA cautiously decreases the quality of a few large tiles to allocate more bandwidth to large number of small tiles, thus improving the perceptual quality of overall video, without sacrificing large tiles excessively. Experiments over real-world datasets show that TSA effectively improves V-VMAF by up to 39% compared with several state-of-the-art adaptive bitrate algorithms. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Video streaming

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Prioritizing Test Gaps by Risk in Industrial Practice: An Automated Approach and Multi-Method Study

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IEEE Transactions on Software Engineering 2025年第05期51卷 1554-1568页

作者： Haas, Roman Sailer, Michael Joblin, Mitchell Juergens, Elmar Apel, Sven Saarbrücken Graduate School of Computer Science Germany CQSE GmbH Germany Saarland University Saarland Informatics Campus Germany

Context. Untested code changes, called test gaps, pose a significant risk for software projects. Since test gaps increase the probability of defects, managing test gaps and their individual risk is important, especially for rapidly changing software systems. Objective. This study aims at gaining an understanding of test gaps in industrial practice establishing criteria for precise prioritization of test gaps by their risk, informing practitioners that need to manage, review, and act on larger sets of test gaps. Method. We propose an automated approach for prioritizing test gaps based on key risk criteria. By means of an analysis of 31 historical test gap reviews from 8 industrial software systems of our industrial partners Munich Re and LV 1871, and by conducting semi-structured interviews with the 6 quality engineers that authored the historical test gap reviews, we validate the transferability of the identified risk criteria, such as code criticality and complexity metrics. Results. Our automated approach exhibits a ranking performance equivalent to expert assessments, in that test gaps labelled as risky in historical test gap reviews are prioritized correctly, on average, on the 30th percentile. In some scenarios, our automated ranking system even outpaces expert assessments, especially for test gaps in central code - for non-developers an opaque code property. Conclusion. This research underscores the industrial need of test gap risk estimation techniques to assist test management and quality assurance teams in identifying and addressing critical test gaps. Our multi-method study shows that even a lightweight prioritization approach helps practitioners to identify high-risk test gaps efficiently and to filter out low-risk test gaps. © 2025 IEEE.

关键词： Software testing

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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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