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PNGOR: A Novel Guaranteed Outlier Removal Method Ensuring Robust Rotation Estimation From Planar Normals

IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7605-7612页

作者： Ma, Gang Wei, Hui Lin, Runfeng Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Lab Algorithms Cognit Models Shanghai 200437 Peoples R China

In this letter, we propose a guaranteed outlier removal method based on computational geometry consistency checks, named PNGOR, effectively leveraging planar normals from 3D scenes to estimate rotation. The challenge of estimating rotation can be conceptualized as a maximum consensus problem and we address it by approximating the computation through the resolution of subproblems associated with each plane correspondence. To efficiently remove outlier correspondences, our proposed method consists of three crucial modules: first, we present and validate rotation-invariant constraints to coarsely filter outlier correspondences;second, we propose a rotation-based guaranteed outlier removal method to further prune outlier correspondences, computing rotational upper and lower bounds for each inlier correspondence;finally, in the stage of computing maximum consensus, we employ an interval stabbing method to estimate the rotation. The efficacy and robustness of our proposed method were empirically validated across three extensive LiDAR point cloud datasets. Through the implementation of efficient outlier removal mechanisms and the effective suppression of rotational errors, our approach ultimately achieves state-of-the-art performance.

关键词： Point cloud compression Estimation Noise Three-dimensional displays Accuracy Robustness Learning systems computational geometry guaranteed outlier removal maximum consensus rotation decomposition rotation estimation

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Granular-Ball Fuzzy Set and Its Implement in SVM

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第11期36卷 6293-6304页

作者： Xia, Shuyin Lian, Xiaoyu Wang, Guoyin Gao, Xinbo Hu, Qinghua Shao, Yabin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Key Lab Big Data Intelligent Comp Key Lab Cyberspace Big Data Intelligent Secur Chongqing 400065 Peoples R China Tianjin Univ Sch Artificial Intelligence Tianjin 300072 Peoples R China

Traditional fuzzy set methods, designed around the finest granularity of inputs-individual points and their membership degrees-often struggle with inefficiencies and label noise. To overcome these challenges, we introduce granular-ball computing into the fuzzy set, creating the new granular-ball fuzzy set framework. This approach uses granular-ball inputs rather than single points, significantly reducing the number of entities and minimizing susceptibility to the noise affecting individual sample points. As a result, our framework enhances both efficiency and robustness compared to traditional methods and is applicable across various domains of fuzzy data processing. Furthermore, we apply this framework to fuzzy support vector machines (FSVMs), developing the Granular-ball Fuzzy Support Vector Machine (GBFSVM). Experimental tests on UCI benchmark datasets show that GBFSVM surpasses traditional models in efficiency and robustness.

关键词： Fuzzy set granular-ball SVM granular computing classification label noise robust Fuzzy set granular-ball SVM granular computing classification label noise robust

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Expressive Text-to-Speech with Contextual Background for ICAGC 2024 14

Expressive Text-to-Speech with Contextual Background for ICA...

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14th International Symposium on Chinese Spoken Language Processing

作者： Jiang, Yu Wang, Tianrui Wang, Haoyu Gong, Cheng Liu, Qiuyu Huang, Zikang Wang, Longbiao Dang, Jianwu Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China

ISBN: (纸本)9798331516833;9798331516826

In this paper, we describe a speech synthesis system that could generate speech with specified emotions and background sounds, and implement it in Track 2 of the ICAGC 2024 of ISC-SLP. This project innovatively combines two advanced audio generation models, GPT-SoVITS and AudioLDM 2, to generate emotional speech with a specific background. GPT-SoVITS is used to clone the timber and emotion from the target speaker's voice. AudioLDM 2 is employed to generate background audio according to the textual content. Ultimately, emotional speech with specific background audio can be generated by combining the outputs of the two models. Official evaluations of the generated results focused on 3 aspects: speaker similarity, audio and speech convincing matching degree, and emotional inspiration degree. Our method achieves a speaker similarity score of 3.33 and an emotional inspiration score of 3.33. In terms of convincing matching, our score is 2.66. In subjective MOS listening tests, the overall score averages 3.06 with a standard deviation of 0.41. Overall, our system secures the 2nd place.

关键词： TTS TTA Large Language Model Speech Fusion Generation ICAGC 2024

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Expressive Speech Synthesis with Theme-oriented Few-shot Learning in ICAGC 2024 14

Expressive Speech Synthesis with Theme-oriented Few-shot Lea...

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14th International Symposium on Chinese Spoken Language Processing

作者： Wang, Haoyu Wang, Tianrui Gong, Cheng Jiang, Yu Liu, Qiuyu Wang, Longbiao Dang, Jianwu Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China

ISBN: (纸本)9798331516833;9798331516826

This paper introduces an expressive speech synthesis system submitted to Track 1 of ICAGC 2024. The objective of this track is to clone the voices of the target speakers using provided speech data and modulate them to convey appropriate emotions for various themes, such as novel chapters and ancient Chinese poems. Our system primarily employs a pre-trained GPT-SoVITS, a two-stage large-scale speech synthesis system. In addition, we have developed a theme-oriented few-shot learning strategy tailored to specific themes. This strategy involves fine-tuning the pre-trained models with sentences spoken by different speakers but on the same theme. This approach aims to refine the models to focus on both the specific themes and individual speaker characteristics. The competition results underscore the efficacy of our approach, culminating in a fourth-place finish among all participating teams.

关键词： Speech Synthesis Few-shot Learning ICAGC 2024

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DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2025年第4期37卷 1714-1727页

作者： Sun, Xinjie Zhang, Kai Liu, Qi Shen, Shuanghong Wang, Fei Guo, Yuxiang Chen, Enhong Univ Sci & Technol China Sch Comp Sci & Technol State Key Lab Cognit Intelligence Hefei 230027 Peoples R China Liupanshui Normal Univ Liupanshui 553004 Guizhou Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol State Key Lab Cognit Intelligence Hefei 230027 Peoples R China

Knowledge Tracing (KT) predicts future performance by modeling students' historical interactions, and understanding students' affective states can enhance the effectiveness of KT, thereby improving the quality of education. Although traditional KT values students' cognition and learning behaviors, efficient evaluation of students' affective states and their application in KT still require further exploration due to the non-affect-oriented nature of the data and budget constraints. To address this issue, we propose a computation-driven approach, Dynamic Affect Simulation Knowledge Tracing (DASKT), to explore the impact of various student affective states (such as frustration, concentration, boredom, and confusion) on their knowledge states. In this model, we first extract affective factors from students' non-affect-oriented behavioral data, then use clustering and spatiotemporal sequence modeling to accurately simulate students' dynamic affect changes when dealing with different problems. Subsequently, we incorporate affect with time-series analysis to improve the model's ability to infer knowledge states over time and space. Extensive experimental results on two public real-world educational datasets show that DASKT can achieve more reasonable knowledge states under the effect of students' affective states. Moreover, DASKT outperforms the most advanced KT methods in predicting student performance. Our research highlights a promising avenue for future KT studies, focusing on achieving high interpretability and accuracy.

关键词： computational modeling Problem-solving Deep learning data models Accuracy Sun Predictive models Long short term memory Knowledge engineering Trajectory Knowledge tracing data mining user modeling affect simulation

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A Directional Connectivity Feature Enhancement Network for Pavement Crack Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 1039-1054页

作者： Qu, Zhong Wang, Jian-Dong Yin, Xue-Hui Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Key Lab Computat Intelligence Key Lab Cyberspace Big Data Intelligent SecurMini Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China

Crack detection is a fundamental effort to ensure road driving safety, aiming to detect potential safety hazards and avoid serious accidents. However, cracks can not be extracted completely and accurately due to problems such as low contrast, high noise, and complex topology of pavement cracks. To address these issues, we propose a directional connectivity feature enhancement network for pavement crack detection. In this network, we build multi-directional enhanced convolution to capture the complex topology of cracks, which is more sensitive to long cracks. To leverage both low-level detail information and high-level semantic information in the network, a novel multi-scale fusion attention is constructed to strengthen the mutual guidance of crack information between channels. The directional connectivity is introducted to establish loss module, which enhances the position and direction information between neighbouring pixels and further refines the crack edge features. To validate the effectiveness and accuracy of the proposed method, we experiment on six publicly available crack datasets, DeepCrack, Crack500, CFD, DCD, EdmCrack600 and DCCE. compared to other networks, our network achieves 2.2% improvement in ODS and 1.8% improvement in MIoU on the DeepCrack dataset, and 1.2% improvement in ODS and 0.9% improvement in MIoU on the Crack500 dataset. Sufficient experimental results show that our network has better crack detection performance.

关键词： Crack detection multi-directional enhanced convolution multi-scale fusion attention directional connectivity

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Multilevel perception boundary-guided network for breast lesion segmentation in ultrasound images

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MEDICAL PHYSICS 2025年第5期52卷 3117-3134页

作者： Yang, Xing Zhang, Jian Ou, Yingfeng Chen, Qijian Wang, Li Wang, Lihui Guizhou Univ Engn Res Ctr Text Comp & Cognit Intelligence Key Lab Intelligent Med Image Anal & Precise Diag Coll Comp Sci & TechnolMinist EducState Key Lab Guiyang Peoples R China

BackgroundAutomatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved considerable progress in automatic segmentation of breast tumors, their performance on tumors with similar intensity to the normal tissues is still not satisfactory, especially for the tumor *** accurately segment the non-enhanced lesions with more accurate boundaries, a novel multilevel perception boundary-guided network (PBNet) is proposed to segment breast tumors from ultrasound *** consists of a multilevel global perception module (MGPM) and a boundary guided module (BGM). MGPM models long-range spatial dependencies by fusing both intra- and inter-level semantic information to enhance tumor recognition. In BGM, the tumor boundaries are extracted from the high-level semantic maps using the dilation and erosion effects of max pooling;such boundaries are then used to guide the fusion of low- and high-level features. Additionally, a multi-level boundary-enhanced segmentation (BS) loss is introduced to improve boundary segmentation performance. To evaluate the effectiveness of the proposed method, we compared it with state-of-the-art methods on two datasets, one publicly available datasets BUSI containing 780 images and one in-house dataset containing 995 images. To verify the robustness of each method, a 5-fold cross-validation method was used to train and test the models. Dice score (Dice), Jaccard coefficients (Jac), Hausdorff Distance (HD), Sensitivity (Sen), and specificity(Spe) were used to evaluate the segmentation performance quantitatively. The Wilcoxon test and Benjamini-Hochberg false discovery rate (FDR) multi-comparison correction were then performed to assess whether the proposed method presents statistically significant performance (p <= 0.05$p\le 0.05$) difference comparing with existing methods. In addition, t

关键词： Ultrasonic imaging

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A homogeneous multimodality sentence representation for relation extraction

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INFORMATION FUSION 2025年 118卷

作者： Wang, Kai Chen, Yanping Yang, Weizhe Qin, Yongbin Guizhou Univ Coll Comp Sci & Technol Text Comp & Cognit Intelligence Engn Res Ctr Natl Educ Minist Guiyang 550025 Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China

Deep neural networks enable a sentence to be transformed into different multimodalities such as a token sequence representation (a one-dimensional semantic representation) or a semantic plane (a two-dimensional semantic representation). Sequence representation has the advantage of learning sequential dependencies of a sentence. Semantic plane is built by organizing all spans of a sentence, which is effective in resolving complicated sentence semantic structures. The two representations are derived from a homogeneous resource (the same sentence), but they are separately used in related works. In this paper, a homogeneous multimodality sentence representation is proposed to make full use of semantic information in a sentence. We construct a homomodality model, which is composed of three components: a sequential encoder to generate sequential modality, a plane encoder to build plane modality, and a multimodality fusion component aligning homogeneous multimodalities for learning a multi-granularity semantic representation. Our model is evaluated on four public datasets to support the relation extraction task. compared with related works, it achieves state-of-the-art performance on all datasets. Analytical experiments show that fusing homogeneous multimodalities is effective in making full use of sentence information for advancing the discriminability of a deep neural network.

关键词： Natural language processing Relation extraction Homogeneous multimodality Semantic structures

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Read, Eliminate, and Focus: A reading comprehension paradigm for distant supervised relation extraction

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NEURAL NETWORKS 2025年 185卷 107138页

作者： Meng, Zechen Zhao, Mankun Yu, Jian Xu, Tianyi Li, Xuewei Jin, Di Yu, Ruiguo Yu, Mei Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Key Lab Cognit Comp & Applicat Tianjin 300350 Peoples R China Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China

Distant supervision aligns the unstructured text to the knowledge base, thereby enabling automatic machine annotation. Nevertheless, this inevitably introduces a considerable amount of noise. Distant supervised relation extraction models aggregate all sentences sharing the same entity pairs into bags and employ various attention mechanisms to reduce the impact of noisy instances. However, this approach fails to preserve the contextual semantics associated with entity pair information and fails to leverage the strong semantics derived from labels. To address this issue, we explore a novel passage-level reading comprehension paradigm that simulates the human reading comprehension process by (1) eliminating incorrect options (reducing the influence of noisy labels) and (2) focusing on crucial clues (information about entity pairs). Specifically, we propose a RelFoRE model that first selects the top-k possible relations from the label set, and then obtains the most crucial information about the entity pair through bidirectional interactions between the passage, the question, and the options. Extensive experiments are conducted on three widely used datasets, demonstrating significant improvement of the RelFoRE over other competing methods.

关键词： Knowledge graph Relation extraction Distant supervision Machine reading comprehension

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Research on speech synthesis technology based on Tibetan rhythmic features

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Khysru, Kuntharrgyal Yangzom Tang, Wenjie Wei, Jianguo Qinghai Minzu Univ Key Lab Artificial Intelligence Applicat Technol Xining 810007 Peoples R China Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Tianjin 300072 Peoples R China

Text-to-speech(TTS) synthesis technology is one of the core technologies in the field of human-computer interaction, playing an important role in this area. This article starts from the theory of Tibetan grammar and the phonetic characteristics of Tibetan language, and designs a rhythm boundary automatic annotation method for Tibetan text and acoustic features based on the phonetic characteristics of Tibetan language. By predicting the rhythm structure hierarchy through a rhythm prediction model, and after modeling Tibetan rhythm on the acoustic model, the Tibetan synthesis model based on the improved Tacotron2 is constructed to obtain the final synthetic Tibetan speech. Experimental results show that the deep model for Tibetan speech synthesis, which utilizes Tibetan rhythm features, can further improve the naturalness and comprehensibility of the synthesized speech.

关键词： Tibetan dialect Speech synthesis Rhyme scheme Rhythmic modeling Tacotron2

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