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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Machine Learning-Based Crack Detection Methods in Ancient Buildings 24

Machine Learning-Based Crack Detection Methods in Ancient Bu...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P. Yang, Aihua Liu, Bin He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China College of Software Engineering Xiamen University of Technology Xiamen Fujian361024 China

ISBN: (纸本)9798400717147

This research paper introduces an innovative machine learning-based methodology for detecting cracks in ancient buildings, a vital aspect of architectural heritage conservation. Recognizing the limitations of conventional techniques in addressing the complex nature of historical structures, this study explores the integration of deep learning algorithms with enhanced image processing methods tailored for aged and varied materials typical of ancient architecture. The paper includes a comprehensive process involving meticulous data collection from diverse ancient structures, employing specialized preprocessing methods to handle the unique challenges of old surfaces. The development of a custom machine learning model, designed to adapt to the intricacies of historical construction, is thoroughly detailed. Experimental results demonstrate the model's superior performance in accurately identifying structural cracks compared to traditional methods. This research not only significantly contributes to the preservation of historical buildings but also exemplifies the versatile application of machine learning in the field of architectural conservation. The findings hold promise for revolutionizing the approach to maintaining and restoring our architectural heritage, ensuring longevity and integrity. © 2024 ACM.

关键词： Image enhancement

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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HFGS: 4D Gaussian Splatting with Emphasis on Spatial and Temporal High-Frequency Components for Endoscopic Scene Reconstruction

arXiv

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arXiv 2024年

作者： Zhao, Haoyu Zhao, Xingyue Zhu, Lingting Zheng, Weixi Xu, Yongchao School of Computer Science Wuhan University Hubei China School of Software Engineering Xi'an Jiaotong University Shaanxi China School of Computing and Data Science The University of Hong Kong Hong Kong

Robot-assisted minimally invasive surgery benefits from enhancing dynamic scene reconstruction, as it improves surgical outcomes. While Neural Radiance Fields (NeRF) have been effective in scene reconstruction, their slow inference speeds and lengthy training durations limit their applicability. To overcome these limitations, 3D Gaussian Splatting (3D-GS) based methods have emerged as a recent trend, offering rapid inference capabilities and superior 3D quality. However, these methods still struggle with under-reconstruction in both static and dynamic scenes. In this paper, we propose HFGS, a novel approach for deformable endoscopic reconstruction that addresses these challenges from spatial and temporal frequency perspectives. Our approach incorporates deformation fields to better handle dynamic scenes and introduces Spatial High-Frequency Emphasis Reconstruction (SHF) to minimize discrepancies in spatial frequency spectra between the rendered image and its ground truth. Additionally, we introduce Temporal High-Frequency Emphasis Reconstruction (THF) to enhance dynamic awareness in neural rendering by leveraging flow priors, focusing optimization on motion-intensive parts. Extensive experiments on two widely used benchmarks demonstrate that HFGS achieves superior rendering quality. Source code is available at https://***/zhaohaoyu376/HFGS. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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TROPICAL: Transformer-Based Hypergraph Learning for Camouflaged Fraudster Detection

TROPICAL: Transformer-Based Hypergraph Learning for Camoufla...

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IEEE International Conference on data Mining (ICDM)

作者： Venus Haghighi Behnaz Soltani Nasrin Shabani Jia Wu Yang Zhang Lina Yao Quan Z. Sheng Jian Yang School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales NSW Australia CSIRO's Data61 Australia

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Graph-based fraud detection has attracted increasing attention in recent years, reflecting its growing potential in mitigating sophisticated fraudulent activities. The main objective of graph-based fraud detection is to discern between fraud-sters and normal entities within graphs. As fraudsters adopt increasingly sophisticated camouflage tactics, combating them has become an urgent task. Despite the complex interactions within real-world networks involving high-order relations, ex-isting graph-based fraud detection methods often neglect non-pairwise relationships among entities in graphs. Thus, we empha-size the significance of investigating beyond pairwise relationships for building an effective fraud detection model. In this paper, we propose constructing a hypergraph from the original input graph to encapsulate comprehensive high-order relations and present TROPICAL, a novel TRansfOrmer-based hyPergraph LearnIng for detecting CAmouflaged maLicious actors in online social networks. TROPICAL learns representations by processing different hyperedge groups and incorporates positional encodings into the aggregated information to enhance their distinctiveness. Subsequently, the model feeds the learned aggregated sequential information into the transformer encoder, achieving rich rep-resentations for effective camouflaged fraudster detection. The superiority of TROPICAL is demonstrated through experiments conducted on two real-world datasets, compared against the state-of-the-art fraud detection models. The source codes and datasets of our work are available at https://***/TROPICAL.

关键词： Social networking (online) Source coding Buildings Transformers Fraud Feeds data mining

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

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IEEE Transactions on Multimedia 2025年 27卷 3343-3355页

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Zhang, Kaihao Chen, Ting Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China Australian National University School of Computing CanberraACT Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications. © 1999-2012 IEEE.

关键词： Restoration

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Decoding AI: Experimental Analysis of Artificial Intelligence based Wine Quality Prediction Logic using Convoluted Deep Classification Strategy 9

Decoding AI: Experimental Analysis of Artificial Intelligenc...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Geetha, C. Arunsundar, B. Vasumathi, G. Thamizhazhakan, K. Sudhakar, C. Venkata R.MK. Engineering College Department of Cse Chennai Kavarapettai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Department of Electronics And Communication Engineering Tirupati517102 India

ISBN: (纸本)9798350365092

Wine quality prediction is essential for optimizing production and ensuring consumer satisfaction. This study introduces a novel Convoluted Deep Classification (CDC) approach for accurate wine quality assessment. Leveraging convolutional neural networks (CNNs), our method effectively extracts hierarchical features from diverse wine data. The process begins with dataset preprocessing, encompassing chemical properties and sensory attributes. Subsequently, a hierarchical CNN architecture is tailored to capture intricate features such as acidity levels and volatile compounds. Techniques like data augmentation and transfer learning are employed to bolster model generalization. Evaluation on a benchmark dataset demonstrates outstanding performance, achieving a remarkable accuracy of 97%. This surpasses traditional methods and baseline CNN architectures, affirming the efficacy of our approach. Additionally, interpretability analyses unveil the key features influencing wine quality predictions, providing invaluable insights for producers and connoisseurs alike. Our CDC framework offers a robust and interpretable solution for wine quality prediction, empowering stakeholders with actionable insights for production optimization and quality assurance in the wine industry. © 2024 IEEE.

关键词： Quality assurance

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Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

arXiv

引用

arXiv 2024年

作者： Deng, Songhe Zhuo, Wei Xie, Jinheng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model’s ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://***/CVI-SZU/QA-CLIMS Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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ChatGPT: A Comprehensive Review of a Large Language Model 2

ChatGPT: A Comprehensive Review of a Large Language Model

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Batra, Reenu Yadav, Dileep Kumar School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Sgt University Department of Computer Science and Engineering Haryana Gurugram India Scset Bennett University UP Greater Noida India

ISBN: (纸本)9798350369960

In the evolving landscape of Natural Language Processing (NLP), the emergence of large language models has redefined the boundaries of human-computer interaction. This paper presents an in-depth review of ChatGPT, a pioneering exemplar in this domain. We commence with a thorough discussion on the evolution of NLP techniques and models, culminating in the inception of ChatGPT. The critical examination of pertinent research papers, projects, and benchmarks showcases the progression of large language models in the context of complex language understanding and generation tasks. The primary focus of this paper is to elucidate the intricate methodology underpinning ChatGPT's architecture and technology. We meticulously outline the comprehensive training process encompassing pretraining and fine-tuning phases, shedding light on the nuanced decisions that bolster model performance. The dataset employed for training and validation is delineated, contributing to an informed understanding of the model's capabilities. The user experience and feedback section encapsulates the empirical perspectives of interacting with ChatGPT, elucidating its strengths and limitations. The paper also prognosticates on the challenges and future directions for ChatGPT. The extant limitations are outlined, and plausible avenues for research and development are suggested to propel the model's potential. In conclusion, this review synthesizes the contributions of ChatGPT in the NLP landscape, underscoring its significance in reshaping the frontiers of language-based human-computer interaction. By amalgamating insights from methodology, applications, ethics, and performance, this paper offers a comprehensive compendium of the evolution and impact of ChatGPT in the realm of NLP research and applications. © 2023 IEEE.

关键词： ChatGPT Large Language Model Natural Language Processing OpenAI Reinforcement Learning Human Feedback

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Reinforcement Learning Based Control Domain Division in LEO Satellite Networks 25

Reinforcement Learning Based Control Domain Division in LEO ...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Tang, Feilong Li, Xue Chen, Long Liu, Jiacheng Gao, Ming Yang, Yanqin Xu, Wenchao Zhang, Heteng School of Data Science and Engineering East China Normal University Shanghai China School of Computing Science Simon Fraser University Vancouver Canada The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong School of Communication and Electric Engineering East China Normal University Shanghai China Suzhou All-Space-Time Information Technology Co. Ltd. Suzhou China

ISBN: (纸本)9798350330014

The performance of SDN-based LEO satellite networks significantly depends on the control domain division approaches. The dynamical topology in LEO networks results in the time-varying delay for network management. Therefore, the control domain division is very challenging and should be dynamical. In this paper, we propose a control domain division approach based on reinforcement learning (RL), which aims at reducing the average management delay of controllers during the control period, and as well as minimizing the number of control domain handovers. Firstly, we analyze the factors that need to be paid attention to in controller deployment, such as delay, load and controller switching cost, and formulate the controller deployment. By relaxing the constraints, we prove that the problem is NP-hard. Secondly, we propose an approach that considers future motion trajectory based on reinforcement learning (FMT _ RL) to deploy controllers. Since the dimension of the neural network in reinforcement learning is fixed, we use the improved K-Means algorithm to select the set of mechanisms to determine the dimension of the neural network. Particularly, in order to obtain the node composition control domain, the observation including both future motion trajectory and history controller selection information are input into the neural network to calculate the control domain division results. The experimental results demonstrate that our approach significantly outperforms related approaches, with better stability and average control cost. © 2023 IEEE.

关键词： Reinforcement learning

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