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Accelerated Selective Algebraic Multigrid Method for Fully-Coupled Incompressible Flow Solver

INTERNATIONAL JOURNAL OF computational FLUID DYNAMICS 2025年

作者： Liang, Yuechao Guo, Xiao-Wei Zhang, Qingyang Li, Chao Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China

The fully coupled pressure-based algorithm is widely recognised for its superior convergence and robustness in solving incompressible flow problems. However, the increased scale of equations and the difficulty in solving linear systems have limited the widespread use of this algorithm in large-scale simulations. This paper presents an optimised block selective algebraic multigrid method significantly reducing computational complexity. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. Furthermore, an aggressive coarsening strategy is introduced to reduce complexity and enable the solution of larger-scale problems. Numerical experiments demonstrate that the solution time is shortened by 14% to 49% compared to the latest existing methods and outperforms the segregated algorithm. By addressing the computational challenges associated with the selective algebraic multigrid solver, this work unleashes the superior convergence properties of the fully coupled method.

关键词： Fully coupled method velocity-pressure coupling selective algebraic multigrid multigrid parallelisation incompressible flow simulation

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A Single Source Generalization Model via Spatial Amplitude Perturbation and Sensitivity Guidance for Colored Medical Image Segmentation 27th

A Single Source Generalization Model via Spatial Amplitude ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zeyuan Yu, Chunyan College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031782008

For medical images, domain shift is a very common phenomenon. To address this issue, researchers have proposed unsupervised domain adaptation and multi-source domain generalization. However, these methods are sometimes impractical for clinical applications since they need multi-domain data. To this end, single-source domain generalization has been further proposed. However, most single-source domain generalization methods are designed for grayscale medical images, making them unsuitable for color images such as fundus images. In this paper, we first propose a novel and effective Fourier transform-based data augmentation method for single-source domain color medical images, named spatial amplitude perturbation module (SAPM). The SAPM uses different Gaussian distributions to perturb different regions of the amplitude map obtained by FFT decomposition, thereby avoiding the need for information from other domains and ensuring the diversity of the augmented images. Then, we use feature sensitivity to guide the network to learn domain-invariant features, which can suppress feature channels sensitive to domain shift and emphasize feature channels insensitive to domain shift. We evaluate our method on a multi-domain fundus segmentation benchmark, and the results demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Multi-Granularity Feature Interaction and Multi-Region Selection Based Triplet Visual Question Answering

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IEEE Transactions on Big Data 2025年第3期11卷 1346-1356页

作者： Liu, Heng Wang, Boyue Sun, Yanfeng Gao, Junbin Li, Xiaoyan Hu, Yongli Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing100124 China The University of Sydney Discipline of Business Analytics The University of Sydney Business School CamperdownNSW2006 Australia

Accurately locating the question-related regions in one given image is crucial for visual question answering (VQA). The current approaches suffer two limitations: (1) Dividing one image into multiple regions may lose parts of semantic information and original relationships between regions;(2) Choosing only one or all image regions to predict the answer may correspondingly result in the insufficiency or redundancy of information. Therefore, how to effectively mine the relationship between image regions and choose the relevant image regions are vital. In this paper, we propose a novel Multi-granularity feature interaction and Multi-region selection-based triplet VQA model (M2TVQA). To tackle the first limitation, we propose the multi-granularity feature interaction strategy that adaptively supplements the global coarse-granularity features with the regional fine-granularity features. To overcome the second limitation, we design the Top-K learning strategy to adaptively select K most relevant image regions to the question, even if the selected regions are far away in space. Such a strategy can select as many relevant image regions as possible and reduce introducing noise. Finally, we construct the multi-modality triplet to predict the answer of VQA. Extended experiments on two public outside knowledge datasets OK-VQA and KRVQA verify the effectiveness of the proposed model. © 2015 IEEE.

关键词： Question answering

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DCI-Net: Remote Sensing Image-Based Object Detector 27th

DCI-Net: Remote Sensing Image-Based Object Detector

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cui, Quanyue Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031781124

Recently the analysis of remotely sensed images has played a vital role in various aspects of research. The current researches ignore the unique prior knowledge in remote sensing images and do not consider exploring the contextual information of the object, while the existence of multi-scale and high image resolution of objects in remote sensing images also affects the accuracy of the object detection task. Based on the above problems, this paper proposes a object detector DCI-Net (Dynamic Context-Aware IoU Network) based on remote sensing images, in which the proposed CASK (Context-Aware Selective Kernel) module can explicitly model the interdependence between the convolutional feature channels. A loss function Pi_IoU is proposed, which adaptively adjusts the penalty factor in combination with the size of the detected object. A DySample module is introduced, which is able to effectively extract and utilize the spatial structure features. The model in this paper improves the detection accuracy of complex objects in remote sensing images. On the DIOR dataset, compared with the baseline model YOLOV9, the accuracy is improved by 0.6%, the number of parameters is decreased by 4%, and the floating point operation speed is improved by 27.8%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical remote sensing

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Ethical Aspects of ChatGPT in software Engineering Research

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

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SparseTrack: Multi-Object Tracking by Performing Scene Decomposition Based on Pseudo-Depth

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4870-4882页

作者： Liu, Zelin Wang, Xinggang Wang, Cheng Liu, Wenyu Bai, Xiang Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence Wuhan430074 China Huazhong University of Science and Technology School of Software Engineering Wuhan430074 China

Exploring robust and efficient association methods has always been an important issue in multi-object tracking (MOT). Although existing tracking methods have achieved impressive performance, congestion and frequent occlusions still pose challenging problems in multi-object tracking. We reveal that performing sparse decomposition on dense scenes is a crucial step to enhance the performance of associating occluded targets. To this end, we propose a pseudo-depth estimation method for obtaining the relative depth of targets from 2D images. Secondly, we design a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets and perform data association on these sparse target subsets in order from near to far. By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack. SparseTrack provides a new perspective for solving the challenging crowded scene MOT problem. Only using IoU matching, SparseTrack achieves comparable performance with the state-of-the-art (SOTA) methods on the MOT17 and MOT20 benchmarks. © 1991-2012 IEEE.

关键词： Object detection

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Lightweight LUW-DETR for efficient underwater benthic organism detection

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Visual Computer 2025年 1-16页

作者： Li, Ning Ding, Bocheng Yang, Gaochao Ni, Shiyun Wang, Mingliang School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Changzhou213164 China

With the advancement of marine science research, the study and monitoring of benthic organisms have gained prominence. These organisms, inhabiting the seafloor, play crucial roles in marine ecosystems. However, traditional methods for benthic organism surveys rely heavily on manual operations, which are labor-intensive and prone to errors. Moreover, the complexity of underwater environments poses significant challenges for automated detection algorithms. Current models face issues such as large parameter sizes, high computational demands, and difficulties in detecting small targets and overlapping objects. To address these challenges, we propose LUW-DETR, a lightweight algorithm based on the transformer architecture. LUW-DETR incorporates a high–low-frequency attention mechanism and dynamic group convolution module to significantly improve feature extraction and computational efficiency. Experimental results demonstrate that LUW-DETR achieves competitive performance with an mAP of 83.1% on the URPC 2020 dataset and 63.7% on the Pascal VOC 2007 dataset, while reducing parameters by 28.3% and computational cost by 29.6% compared to baseline models. These results highlight LUW-DETR’s balance between accuracy and efficiency, making it suitable for resource-constrained underwater environments. Our source code is available at:https://***/DingBC/LUWDETR. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Marine biology

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Enhancing Large Language Model Inference Efficiency via Lookahead Cache Filtering

Enhancing Large Language Model Inference Efficiency via Look...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ou, Jie Chen, Yueming Jiang, Shuaihong Tian, Wenhong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

The large Key-Value (KV) cache is a significant challenge in deploying Large Language Models (LLMs). Current research addressing these issues employs cache compression techniques, which we find suffer from information loss and the "lost-in-the-middle" problem. We propose the Lookahead Cache Filtering (LCF), which retains the full cache in host memory to keep complete details, using the Important Key-Value Lookahead Prediction by Approximate Sorting and the Gather-based Matrix Multiplication on CPU, to filter important KV cache to reduce the overhead of loading cache to GPU and improve the throughput of inference. Finally, we use the Multi-Scale Pyramid Information Fusion mechanism to enhance information fusion to further improve the effectiveness of inference results. Experiments demonstrate that LCF effectively maintains accuracy without introducing additional latency while reducing memory requirements. Our code will be available on GitHub. © 2025 IEEE.

关键词： Approximate Sorting Important Key-Value Lookahead Information Fusion Lost-in-the-Middle Operator Fusion on CPU

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When Crypto Fails: Demystifying Cryptographic Defects in Ethereum Smart Contracts

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IEEE Transactions on software Engineering 2025年第5期51卷 1381-1398页

作者： Zhang, Jiashuo Chen, Jiachi Shen, Yiming Zhang, Tao Wang, Yanlin Chen, Ting Gao, Jianbo Chen, Zhong Peking University School of Computer Science Beijing100871 China Sun Yat-sen University School of Software Engineering Zhuhai510275 China Macau University of Science and Technology School of Computer Science and Engineering 999078 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Kashi Institute of Electronics and Information Industry Kashi844000 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China

Ethereum has officially provided a set of system-level cryptographic APIs to enhance smart contracts with cryptographic capabilities. These APIs have been utilized in over 13.8% of Ethereum transactions, motivating developers to implement various on-chain cryptographic tasks, such as digital signatures. However, since developers may not always be cryptographic experts, their ad-hoc and potentially defective implementations could compromise the theoretical guarantees of cryptography, leading to real-world security issues. To mitigate this threat, we conducted a comprehensive study aimed at demystifying and detecting cryptographic defects in smart contracts. Through the analysis of 3,762 real-world security reports, we defined 12 types of cryptographic defects in smart contracts with detailed descriptions and practical detection patterns. Based on this categorization, we proposed CryptoScan, the first static analyzer to automate the pre-deployment detection of cryptographic defects in smart contracts. CryptoScan utilizes cross-contract and inter-procedure static analysis to identify crypto-related execution paths and employs taint analysis to extract fine-grained crypto-specific semantics for defect detection. Furthermore, we collected a large-scale dataset containing 79,598 real-world crypto-related smart contracts and evaluated CryptoScan's effectiveness on it. The results demonstrated that CryptoScan achieves an overall precision of 96.1% and a recall of 93.3%. Notably, CryptoScan revealed that 19,707 (24.8%) out of 79,598 smart contracts contain at least one cryptographic defect. Although not all defects directly cause financial losses, they indicate prevalent non-standard cryptographic implementations that should be addressed in real-world practices. © 1976-2012 IEEE.

关键词： Smart contract

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Evaluating the Suitability of Inception Score and Fréchet Inception Distance as Metrics for Quality and Diversity in Image Generation 24

Evaluating the Suitability of Inception Score and Fréchet I...

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7th International Conference on computational Intelligence and Intelligent Systems, CIIS 2024

作者： Chan, Derrick Adrian Sithungu, Siphesihle Philezwini Academy of Computer Science and Software Engineering University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400717437

Variational Autoencoders (VAEs) have gained popularity as one of the main approaches for generating diverse and high-quality synthetic images. This study examines the suitability of evaluation metrics, specifically Inception Score and Fréchet Inception Distance (FID), for assessing these images. Particularly, the study focuses on the generation of synthetic images based on the MNIST handwritten digits dataset. Through the use of VAE-generated MNIST image samples, the study analyses the abovementioned metrics alongside alternative methods that can be used to assess image quality and diversity. The findings made from the study reveal the strengths and limitations of each metric in evaluating image quality and diversity. This paper underscores the need for tailored metrics to enhance the evaluation of generative models, while specifically using the performance of a VAE as the domain of investigation. © 2024 Copyright held by the owner/author(s).

关键词： Fréchet Inception Distance

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