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3rd International Conference on Artificial Intelligence and intelligent information processing, AIIIP 2024

作者： Zheng, Ziwei Geng, Shengling Zhou, Mingquan School of Computer Science Qinghai Normal University Qinghai Xining China Academy of Plateau Science and Sustainability People’s Government of Qinghai Province Beijing Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400707308

Feature selection is critical for enhancing data processing efficiency and decision accuracy. In multi-scale decision information systems, a novel hypergraph-based feature selection method is proposed to address the challenge of selecting the optimal scale combination from multi-scale features. An adjacency relationship matrix is defined to induce a hypergraph, which visualizes the relationships among multi-scale features and transforms the feature selection problem into a minimal vertex cover problem within the hypergraph. This approach facilitates the selection of the optimal scale feature subset. Additionally, the algorithm incorporates a priority queue to manage the sorting of features, significantly reducing time complexity and improving solution efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Data handling

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CRLNet: Cascaded Resolution Learning Network for Natural Scenes Segmentation

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IEEE intelligent Systems 2025年

作者： Li, Wei Tian, Shishun Hua, Guoguang Liao, Muxin Zhang, Yuhang Zou, Wenbin Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Electronics and Information Engineering Shenzhen518060 China Jiangxi Agricultural University School of Computer Science and Engineering Nanchang330045 China

The natural environment presents a multitude of scenes with diverse content, posing challenges for satisfactory segmentation results using existing segmentation networks. In response, we propose a Cascaded Resolution Learning Network (CRLNet) to enhance segmentation performance through global textual embedding and multi-resolution feature learning. The CRLNet constructs a multi-path segmentation system that integrates multi-resolution feature data from different paths, thereby progressively enhancing local feature learning. Two key modules, the Partition-Fusion Channel Attention Module (PFCAM) and the Features Learning Module (FLM), are pivotal components of CRLNet. The PFCAM serves as a computationally efficient channel attention module to mitigate segmentation confusion stemming from similar objects. Meanwhile, the FLM is tailored to learn resolution feature maps from different paths, facilitating the refinement of object representation and enhancing segmentation performance. Extensive experiments conducted on real natural scene datasets demonstrate the superiority of the proposed CRLNet over existing efficient segmentation methods in terms of accuracy. © 2025 IEEE.

关键词： Adversarial machine learning

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A High-Quality and Efficient Bus-Aware Global Router

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Chinese Journal of Electronics 2025年第2期34卷 444-456页

作者： Liu, Genggeng Wei, Ling Yu, Yantao Xu, Ning College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

As advanced technology nodes enter the nanometer era, the complexity of integrated circuit design is increasing, and the proportion of bus in the net is also increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between bus and non-bus in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-bus and the deviation value of bus;A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation;We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution;And a depth-first search (DFS)-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithms can effectively reduce bus deviation compared with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation. © 2015 Chinese Institute of Electronics.

关键词： Integrated circuit layout

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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Multi-scale CNN-Swin transformer network with boundary supervision for multiclass biomarker segmentation in retinal OCT images

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Biocybernetics and Biomedical Engineering 2025年第3期45卷 340-356页

作者： Zhanpeng Fan Xiaoming Liu Ying Zhang Jia Zhang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430070 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430070 China Wuhan Aier Eye Hospital of Wuhan University Wuhan 430064 China

Retinal biomarker morphology is closely associated with a variety of chronic ophthalmic diseases, in which biomarker localization and segmentation in optical coherence tomography (OCT) play a key role in the diagnosis of retina-related diseases. Although great progress has been made in deep learning based OCT biomarker segmentation, several challenges still exist. Due to issues such as image noise or class imbalance, retinal biomarkers affect the model’s recognition of other biomarkers. Moreover, small biomarkers are prone to lose accuracy during downsampling. And most existing methods rely on convolutional neural networks, which make it challenging to obtain the global context due to locality of convolution. Benefiting from the Swin Transformer with powerful modeling capabilities, we propose MSCS-Net (Multi-scale CNN-Swin Network), a network for OCT biomarker segmentation, which effectively combines CNN and Swin Transformer and integrates them in parallel into a dual-encoder structure. Specifically, an edge detection path is added alongside to enhance the localization of biomarkers at the edges. For the Swin Transformer branch, considering the irregular distribution of most OCT biomarkers, a new windowing partition is performed in the Swin Transformer to capture the features more efficiently. Meanwhile, we design a Feature Dimensionality Reduction Module to extensively collect the information of small-scale biomarkers. To effectively integrate information from two scales, we design a Transformer Cross Fusion Module to finely fuse the global and local feature information from the two-branch encoders. We validate the proposed approach on local and public datasets, and the experimental results demonstrate the effectiveness of the proposed framework.

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yangdong Chen Mohan Chen Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research.

关键词： Point cloud compression Three-dimensional displays Image synthesis Superresolution Contrastive learning Benchmark testing Signal processing Diffusion models Stability analysis Speech processing

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DiffuSyn: A Diffusion-Driven Framework With Syntactic Dependency for Aspect Sentiment Triplet Extraction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 842-854页

作者： Qiuhua Yi Xiangjie Kong Linan Zhu Chenwei Zhang Guojiang Shen College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou China School of Faculty of Education University of Hong Kong Hong Kong

Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained sentiment analysis task that involves identifying aspect and opinion terms and conducting sentiment analysis for each aspect-opinion pair. We categorize existing methods into tagging-based, span-based, and generation methods. However, despite notable achievements, these methods still face certain challenges: span-based methods struggle to distinguish similar spans, and generation methods are prone to slower decoding speeds. To address these challenges, we propose a novel ASTE framework, called DiffuSyn, which leverages diffusion models and syntactic dependency parsing to improve the performance of sentiment analysis. Specifically, we first add Gaussian noise to the indices at the beginning and end of aspect words and opinion words in sentences based on a non-autoregressive diffusion model. We obtain accurate boundaries of aspect and opinion terms through boundary denoising, and thus identify precise spans. Secondly, we introduce a syntactic dependency parser to capture the syntactic dependencies within review sentences, thereby providing effective syntactic information for sentiment analysis. Finally, we complete the matching of aspects and opinions by leveraging the extracted boundaries and syntactic semantic information, facilitating the prediction of sentiment relationships. We conduct experiments on four public datasets (ASTE-Data-V2), and the results indicate the effectiveness of our approach in the ASTE task. Furthermore, our method achieves state-of-the-art performance in both Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks.

关键词： Diffusion models Syntactics Sentiment analysis Fish Speech processing Data mining Transforms Noise reduction Decoding Tagging

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AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models

arXiv

引用

arXiv 2025年

作者： Zhu, Qin Huang, Fei Peng, Runyu Lu, Keming Yu, Bowen Cheng, Qinyuan Qiu, Xipeng Huang, Xuanjing Lin, Junyang Qwen Team Alibaba Group China School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are vulnerable to random guessing, leading to overestimated performance and substantial performance fluctuations. To obtain more accurate assessments of models’ reasoning capabilities, we propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi. Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models’ reasoning abilities. Extensive evaluation of eight modern LLMs shows that AutoLogi can better reflect true model capabilities, with performance scores spanning from 35% to 73% compared to the narrower range of 21% to 37% on the source multiple-choice dataset. Beyond benchmark creation, this synthesis method can generate high-quality training data by incorporating program verifiers into the rejection sampling process, enabling systematic enhancement of LLMs’ reasoning capabilities across diverse datasets. © 2025, CC BY-SA.

关键词： computer circuits

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Minimizing Disparities between Real and Pseudo Queries for Unsupervised Visual Grounding

Minimizing Disparities between Real and Pseudo Queries for U...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hui Jiang Changkai Ji Jilan Xu Yanhao Zhu Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Visual grounding involves the identification and localization of image regions given textual descriptions. To reduce the manual labeling effort on region-text pairs, unsupervised visual grounding aims to generate pseudo bounding box and query pairs for training grounding models. However, there exists significant disparities between real and pseudo queries in terms of object, attribute distributions, and textual formats, limiting the generalization performance of unsupervised grounding methods. To address this challenge, we propose a novel unsupervised visual grounding framework. During training, we prompt Multimodal Large Language Models to generate pseudo queries, in which the entities are beyond the object detector’s pre-defined limited categories, and are associated with richer attributes. We further devise a Modifier Tree structure to bridge the gap of textual format between real and pseudo queries. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art unsupervised approaches on public benchmark datasets, particularly when dealing with complex queries.

关键词： Training Location awareness Visualization Limiting Grounding Large language models Manuals Labeling Speech processing Image reconstruction

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