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IAENG International Journal of computer science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Practical Parallel Algorithms for Non-Monotone Submodular Maximization

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Journal of Artificial Intelligence Research 2025年 82卷 39-75页

作者： Cui, Shuang Han, Kai Tang, Jing Li, Xueying Zhiyuli, Aakas Li, Hanxiao School of Computer Science and Technology Soochow University Jiangsu Suzhou China The Hong Kong University of Science and Technology China Alibaba Group Zhejiang Hangzhou China Qingdao University of Science and Technology Shandong Qingdao China

Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization. One measure of the parallelizability of a submodular maximization algorithm is its adaptive complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose a low-adaptivity algorithm achieving an (1/8 − ϵ)approximation with practical Õ(n) query complexity. Moreover, we also propose the first algorithm with both provable approximation ratio and sublinear adaptive complexity for the problem of non-monotone submodular maximization subject to a k-system constraint. As a by-product, we show that our two algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our algorithms is demonstrated by extensive experiments on real-world applications. ©2025 The Authors.

关键词： Consensus algorithm

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Deep Unfolding Network for Image Desnowing With Snow Shape Prior

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4740-4752页

作者： Guo, Xin Wang, Xi Fu, Xueyang Zha, Zheng-Jun University of Science and Technology of China School of Information Science and Technology Hefei230026 China

Effectively leveraging snow image formulation, which accounts for atmospheric light and snow masks, is crucial for enhancing image desnowing performance and improving interpretability. However, current direct-learning approaches often neglect this formulation, while model-based methods use it in overly simplistic ways. To address this, we propose a novel unfolding network that iteratively refines the desnowing process for more thorough optimization. Additionally, model-based techniques usually rely on real-world snow masks for supervision, a requirement that is impractical in many real-world applications. To overcome this limitation, we introduce a snow shape prior as a surrogate supervision signal. We further integrate the physical properties of atmospheric light and heavy snow by decomposing the optimization task into manageable sub-problems within our unfolding network. Extensive evaluations on multiple benchmark datasets confirm that our method outperforms current state-of-the-art techniques. © 1991-2012 IEEE.

关键词： Snow

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Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 559-573页

作者： Kong, Lingbai Yang, Hanchen Li, Wengen Zhang, Yichao Guan, Jihong Zhou, Shuigeng Tongji University Department of Computer Science and Technology Shanghai201804 China Fudan University School of Computer Science Shanghai200433 China

With the increasing traffic congestion problems in metropolises, traffic prediction plays an essential role in intelligent traffic systems. Notably, various deep learning models, especially graph neural networks (GNNs), achieve state-of-the-art performance in traffic prediction tasks but still lack interpretability. To interpret the critical information abstracted by traffic prediction models, we proposed a flexible framework termed Traffexplainer toward GNN-based interpretable traffic prediction. Traffexplainer is applicable to a wide range of GNNs without making any modifications to the original model structure. The framework consists of the GNN-based traffic prediction model and the perturbation-based hierarchical interpretation generator. Specifically, the hierarchical spatial mask and temporal mask are introduced to perturb the prediction model by modulating the values of input data. Then the prediction losses are backward propagated to the masks, which can identify the most critical features for traffic prediction, and further improve the prediction performance. We deploy the framework with five representative GNN-based traffic prediction models and analyze their prediction and interpretation performance on three real-world traffic flow datasets. The experiment results demonstrate that our framework can generate effective and faithful interpretations for GNN-based traffic prediction models, and also improve the prediction performance. The code will be publicly available at https://***/lingbai-kong/Traffexplainer. © 2024 The Authors.

关键词： Prediction models

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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Multi-view stereo algorithms based on deep learning: a survey

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Multimedia Tools and Applications 2025年第6期84卷 2877-2908页

作者： Huang, Hongbo Yan, Xiaoxu Zheng, Yaolin He, Jiayu Xu, Longfei Qin, Dechun Computer School Beijing Information Science & Technology University Beijing China Institute of Computing Intelligence Beijing Information Science & Technology University Beijing China Foreign Languages School Beijing Information Science & Technology University Beijing China

Multi-View Stereo (MVS) is a long-standing and fundamental task in computer vision, which aims to reconstruct the 3D geometry of a scene from a set of overlapping images. With known camera parameters, MVS matches pixels across images to compute dense correspondences and recover 3D points. Traditional MVS methods often encounter difficulties in addressing complex scenes due to limitations in robustness. However, recent advancements in deep-learning-based MVS have demonstrated remarkable performance gains, attributed to their enhanced feature extraction and cost volume processing capabilities. This comprehensive survey delves into deep-learning-based MVS algorithms, highlighting key challenges and avenues for future exploration. Our focus is on studies aimed at mitigating algorithmic memory requirements and elevating processing speeds. To better understand the developments in this field, we propose a new taxonomy. The deep-learning-based MVS algorithms are categorized into two categories: scene-oriented and view-oriented methods. Scene-oriented methods typically employ voxels or neural implicit representations as the representative form of the scene and infer the complete scene directly. View-oriented methods focus on estimating the depth map of a single view. Detailed classifications and reviews have been conducted for each category. Furthermore, we introduce several widely used datasets and metrics and empirical evaluation of representative algorithms across three popular datasets. This article concludes by discussing prevalent challenges in the MVS domain and promising research directions for the future. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Stereo image processing

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ReferSAM: Unleashing Segment Anything Model for Referring Image Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4910-4922页

作者： Liu, Sun-Ao Xie, Hongtao Ge, Jiannan Zhang, Yongdong University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The Segment Anything Model (SAM) has demonstrated remarkable capability as a general segmentation model given visual prompts such as points or boxes. While SAM is conceptually compatible with text prompts, it merely employs linguistic features from vision-language models as prompt embeddings and lacks fine-grained cross-modal interaction. This deficiency limits its application in referring image segmentation (RIS), where the targets are specified by free-form natural language expressions. In this paper, we introduce ReferSAM, a novel SAM-based framework that enhances cross-modal interaction and reformulates prompt encoding, thereby unleashing SAM’s segmentation capability for RIS. Specifically, ReferSAM incorporates the Vision-Language Interactor (VLI) to integrate linguistic features with visual features during the image encoding stage of SAM. This interactor introduces fine-grained alignment between linguistic features and multi-scale visual representations without altering the architecture of pre-trained models. Additionally, we present the Vision-Language Prompter (VLP) to generate dense and sparse prompt embeddings by aggregating the aligned linguistic and visual features. Consequently, the generated embeddings sufficiently prompt SAM’s mask decoder to provide precise segmentation results. Extensive experiments on five public benchmarks demonstrate that ReferSAM achieves state-of-the-art performance on both classic and generalized RIS tasks. © 1991-2012 IEEE.

关键词： Visual languages

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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DSCR: A Dynamic Secure Clustering Routing Scheme for UANETs Based on Reputation Mechanism

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IEEE Internet of Things Journal 2025年第10期12卷 14109-14123页

作者： Gong, Yinyan Chen, Yuxiang Li, Kuanching Liang, Wei Li, Xiong Wang, Jin Xiang, Yang Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Sanya Research Institute Hunan University of Science and Technology Sanya572024 China School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Swinburne University of Technology School of Software and Electrical Engineering HawthornVIC3122 Australia

In UAV ad hoc networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism dynamically secure cluster routing scheme (DSCR) for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra- and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs. © 2014 IEEE.

关键词： Drones

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Telegraph Flux Noise Induced Beating Ramsey Fringe in Transmon Qubits

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Chinese Physics Letters 2025年第4期42卷 23-37页

作者： Zhi-Hao Wu Ling-Xiao Lei Xin-Fang Zhang Shi-Chuan Xue Shun Hu Cong Li Xiang Fu Ping-Xing Chen Kai Lu Ming-Tang Deng Jun-Jie Wu College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China College of Science National University of Defense TechnologyChangsha 410073China

Ramsey oscillations typically exhibit an exponential decay envelope due to environmental noise. However,recent experiments have observed nonmonotonic Ramsey fringes characterized by beating patterns, which deviate from the standard behavior. These beating patterns have primarily been attributed to charge-noise *** this paper, we have experimentally observed Ramsey fringe with beating pattern for transmon qubits, and traced the origin to electric instruments induced flux noise.

关键词： ramsey fringes electric instruments induced flux noise ramsey oscillations ramsey fringe transmon qubits beating patterns beating pattern telegraph flux noise

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