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Adaptive ensemble learning for efficient keyphrase extraction: Diagnosis, and distillation

EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Zhang, Kai Gang, Hongbo Hu, Feng Yu, Runlong Liu, Qi Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Sci & Technol China Sch Data Sci Hefei Peoples R China State Key Lab Cognit Intelligence Hefei Peoples R China Univ Pittsburgh Dept Comp Sci Pittsburgh PA USA

keyphrase extraction (KE) refers to the process of identifying words or phrases that signify the primary themes of a document. Although keyphrase extraction is important in many downstream applications, including scientific document indexing, search, and question answering, the challenge lies in executing this extraction both adaptively and effectively. To this end, we propose a novel Distillation-based Adaptive Ensemble Learning (DAEL) method specifically designed for efficient keyphrase extraction, encompassing diagnosis, aggregation, and distillation processes. Specifically, we initiate with a cognitive Diagnosis Module (CDM) to evaluate the diverse capabilities of individual KE models. Following this, an Adaptive Aggregation Module (AAM) is employed to create a weight distribution uniquely suited to each data instance. The process concludes with a Knowledge Distillation Module (KDM) to distill the superior performance of the ensemble model into a single model, thereby refining its efficiency and reducing computational cost. Extensive testing on real-world datasets highlights the superior performance of the proposed model. In comparison with leading-edge methods, our approach notably excels in processing text with complex structures or significant noise, marking a substantial advancement in KE effectiveness.

关键词： Natural language processing cognitive diagnosis Adaptive learning keyphrase extraction Knowledge distillation

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A discrete convolutional network for entity relation extraction

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NEURAL NETWORKS 2025年 184卷 107117页

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

Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations. In this paper, a discrete convolutional network is proposed to incorporate discrete linguistic interactions and deep feature weighting. This network applies a discretization strategy to fix parameters of convolutional kernels into ternary values. Then, these discretized kernels are used to learn discrete semantic structures from vectorized token representations. Our approach leverages the ability of discrete CNNs to capture discrete linguistic patterns of a sentence, thereby maintaining model expressiveness and improving performance in the relation extraction task. Furthermore, our method has the advantages of reducing the overfitting problem caused by depending on prior knowledge and decreasing the computational complexity by reducing the number of trainable parameters. Our model is evaluated on five widely used benchmark datasets. It achieves state-ofthe-art performance, outperforming all compared related works. Experimental results also demonstrate that, compared with traditional CNN networks, it achieves an average improvement of 14.66% in F1-score and accelerates training by an average of 17.46%, highlighting the efficiency and effectiveness of our model in the relation extraction task.

关键词： Relation extraction Discrete convolution Semantic structure Deep learning Natural language processing

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Target hierarchy-guided knowledge tracing : Fine-grained knowledge state modeling

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 251卷

作者： Sun, Xinjie Zhang, Kai Shen, Shuanghong Wang, Fei Guo, Yuxiang Liu, Qi Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Sci & Technol China Sch Data Sci Hefei Peoples R China State Key Lab Cognit Intelligence Hefei Peoples R China Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat Hefei Peoples R China

Knowledge Tracing (KT) focuses on modeling the exercise process of students, assessing their knowledge state changes during the exercise process, and further providing targeted guidance for teaching and learning. Current KT models have made significant progress by utilizing deep learning technologies to model relevant attributes of the questions. However, in current knowledge tracing strategies, we have yet to exhaustively explore how to precisely evaluate the hierarchies of students' knowledge mastery to better discern their current target hierarchy. To address the limitations of the KT model in accurately identifying and positioning target hierarchy, our research focuses on two key areas: first, we dynamically track students' target hierarchies by evaluating their feedback on questions at different hierarchies;second, we assess their actual questionsolving abilities within these target hierarchies, thereby optimizing our evaluation of their knowledge status. Based on these insights, we propose the T arget H ierarchy -guided K nowledge T racing ( THKT ) model. In this model, we first incorporate the identification of question hierarchy into the model representation. Then, to avoid homogenization of the target hierarchy, we dynamically track the appropriate hierarchy for students based on their varying feedback to questions of the same hierarchy, thereby pinpointing their learning target hierarchies. Simultaneously, we develop a KCs applied ability learning module that works in conjunction with the target hierarchies to generate interpretable prediction results. The proposed THKT model has been tested and evaluated using three public, real -world educational datasets. The findings clearly demonstrate that our approach shines in the realm of KT prediction tasks, providing significantly interpretability. For broader research, we plan to provide the source code at: https://***/xinjiesun-ustc/THKT to ensure accessibility and promote further innovation in this fie

关键词： Knowledge tracing Target hierarchy Fine-grained Applied ability Interpretability

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BIMODAL EMOTION DEPTH REcognitION METHOD OF FACIAL EXPRESSION AND POSTURE IN CYBER-PHYSICAL SYSTEMS

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MECHATRONIC SYSTEMS AND CONTROL 2024年第1期52卷 1-10页

作者： Ye, Haiyan Wu, Qilin Chaohu Univ Sch Comp & Artificial Intelligence Chaohu 238024 Peoples R China Chaohu Univ Key Lab Data Intelligence & Cyber Secur Chaohu 238024 Peoples R China

Due to the limitations of traditional face recognition methods, such as a single posture, numerous model parameters, and the impact of environmental factors, such as lighting, feature extraction speed can be slow. To effectively address these issues, a new bi-mo dal emotionbased deep learning face recognition method has been proposed that combines both emotional and postural information. This approach significantly improves feature extraction speed and provides a more efficient and effective solution for face recognition. By extracting facial expression and pose features, a multi-pose face data set is established, and the dual-mode emotion depth recognition method of expression and posture is studied. The convolution neural network algorithm based on multi-task cascade is used to collect facial expression and advanced pose features for face tracking, determine the key points of face pose, extract the arc surface of loss function, and compare with the corresponding pose features in the database, finally obtain the result of facial expression and pose bimodal emotion depth recognition. Experimental results show that the method can significantly improve the accuracy of face recognition and the speed of feature extraction.

关键词： Facial expression posture depth recognition cyber-physical systems

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Zero-shot 3D anomaly detection via online voter mechanism

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NEURAL NETWORKS 2025年 187卷 107398页

作者： Zheng, Wukun Ke, Xiao Guo, Wenzhong Fuzhou Univ Coll Comp & Data Sci Fujian Prov Key Lab Networking Comp & Intelligent Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Engn Res Ctr Big Data Intelligence Minist Educ Fuzhou 350116 Peoples R China

3D anomaly detection aims to solve the problem that image anomaly detection is greatly affected by lighting conditions. As commercial confidentiality and personal privacy become increasingly paramount, access to training samples is often restricted. To address these challenges, we propose a zero-shot 3D anomaly detection method. Unlike previous CLIP-based methods, the proposed method does not require any prompt and is capable of detecting anomalies on the depth modality. Furthermore, we also propose a pre-trained structural rerouting strategy, which modifies the transformer without retraining or fine-tuning for the anomaly detection task. Most importantly, this paper proposes an online voter mechanism that registers voters and performs majority voter scoring in a one-stage, zero-start and growth-oriented manner, enabling direct anomaly detection on unlabeled test sets. Finally, we also propose a confirmatory judge credibility assessment mechanism, which provides an efficient adaptation for possible few-shot conditions. Results on datasets such as MVTec3D-AD demonstrate that the proposed method can achieve superior zero-shot 3D anomaly detection performance, indicating its pioneering contributions within the pertinent domain.

关键词： Anomaly detection Zero-shot Multimodal Pretrained model Online voter mechanism

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A Self-Adaptive Strip Pooling Network for Segmenting the Kidney Glomerular Basement Membrane

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SENSORS 2025年第6期25卷 1829-1829页

作者： Song, Caifang Huang, Xiangsheng Lyu, Xiangyu Shanxi Normal Univ Sch Math & Comp Sci Taiyuan 030031 Peoples R China Xiongan Inst Innovat Hebei Key Lab Cognit Intelligence Xiongan 071700 Peoples R China

Accurate semantic segmentation and automatic thickness measurement of the glomerular basement membrane (GBM) can aid pathologists in carrying out subsequent pathological diagnoses. The GBM has a complex ultrastructure and irregular shape, which makes it difficult to segment accurately. We found that the shape of the GBM is striped, so we proposed an RSP model to extract both the strip and square features of the GBM. Additionally, grayscale images of the GBM are similar to those of surrounding tissues, and the contrast is low. We added an edge attention mechanism to further improve the quality of segmentation. Moreover, we revised the pixel-level loss function to consider the tissues around the GBM and locate the GBM as a doctor would, i.e., by using the tissues as the reference object. Ablation experiments with each module showed that SSPNet can better segment the GBM. The proposed method was also compared with the existing medical semantic segmentation model. The experimental results showed that the proposed method can obtain high-precision segmentation results for the GBM and completely segment the target. Finally, the thickness of the GBM was calculated using a skeleton extraction method to provide quantitative data for expert diagnosis.

关键词： self-adaptive strip pooling attention mechanism glomerular basement membrane (GBM) skeleton extraction

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GBCT: Efficient and Adaptive Clustering via Granular-Ball computing for complex data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第6期PP卷 PP页

作者： Xia, Shuyin Shi, Bolun Wang, Yifan Xie, Jiang Wang, Guoyin Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Dept Comp Sci Chongqing 400065 Peoples R China

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in the human brain, resulting in those methodsbad performance in efficiency, generalization ability, and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering via granular-ball computing. First, clustering algorithm based on granular-ball (GBCT) generates a smaller number of granular-balls to represent the original data and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust;besides, as granular-balls can fit various complex data, GBCT performs much better in nonspherical datasets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also be used to improve other traditional methods.

关键词： Clustering algorithms Partitioning algorithms computational modeling Robustness Noise Approximation algorithms Mathematical models Clustering methods Telecommunications Shape Clustering granular computing granular-ball multigranularity

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DSRSN algorithm for channel estimation in IRS assisted mmWave MIMO systems

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第6期19卷 1-7页

作者： Liu, Fulai Ge, Junjie Feng, Tian Du, Ruiyan Shi, Huiyang Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Lab Cognit Radio & Big Spectrum Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

The complexity of multi-dimensional channels makes accurate channel estimation difficult in intelligent reflecting surface (IRS) assisted mmWave MIMO systems. To improve the estimation accuracy, this paper presents a channel estimation algorithm based on a depthwise separable residual shrinkage network (DSRSN), named as DSRSN algorithm. Firstly, the channel matrix estimated by the least square method and the real channel matrix are given as the training input and the training label, separately. Then, a depthwise separable residual shrinkage frame is constructed to improve the accuracy of channel matrix estimation. Specially, to decrease the network parameters number, depthwise separable convolution is introduced. Next, the input features are processed by a soft threshold module to suppress channel noise and extract channel characteristics more accurately. Finally, the well-trained DSRSN model can exactly output the estimated channel matrix. According to the simulation results, the suggested DSRSN approach increases the estimation accuracy further while requiring less pilot overhead. For example, the simulation results verify that the normalized mean square error reduces by more than 2.94 times in comparision to the related methods, when the signal-to-noise ratio is 15dB.

关键词： Channel estimation Intelligent reflecting surface Depthwise separable convolution Residual shrinkage network

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Mc-music algorithm for fast DOA estimation

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WIRELESS NETWORKS 2025年第1期31卷 659-667页

作者： Liu, Fulai Li, Xubin Su, Zhibo Liu, Yubiao Zhang, Aiyi Northeastern Univ Qinhuangdao Lab Cognit Radio & Big Spectrum Data Proc Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

This paper presents a fast and compressible multiple signal classification algorithm (C-MUSIC) based on the manifold separation technique (MST) for a uniform circular array (UCA), named as MC-MUSIC. Making use of the MST, the steering vector (SV) of the UCA can be converted into a new SV, which has a Vandermonde structure similar to a uniform linear array. Then a noise-like subspace cluster (NLSC) can be generated by applying a new linear relation between the true direction-of-arrival (DOA) and virtual DOA to the new given SV. Finally, the angular field-of-view can be compressed to a small sector by the intersection of the NLSC so that the calculation times of the spatial spectrum can be reduced effectively. Simulations demonstrate the presented method can maintain the high precision of the DOA estimation with low computational complexity.

关键词： Uniform circular array Manifold separation technique Direction-of-arrival Multiple signal classification

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Dynamic cognitive Diagnosis: An Educational Priors-Enhanced Deep Knowledge Tracing Perspective

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2023年第3期16卷 306-323页

作者： Wang, Fei Huang, Zhenya Liu, Qi Chen, Enhong Yin, Yu Ma, Jianhui Wang, Shijin Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat Hefei 230026 Peoples R China State Key Lab Cognit Intelligence Wangjiang West Rd Hefei 230088 Peoples R China Inst Artificial Intelligence Hefei Comprehens Natl Sci Ctr State Key Lab Cognit Intelligence Hefei 230088 Peoples R China iFLYTEK AI Res Cent China Wuhan 430058 Peoples R China State Key Lab Cognit Intelligence Hefei 230088 Peoples R China

To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. compared to classical models, such as Bayesian knowledge tracing, which track students' knowledge proficiencies, deep learning-based knowledge tracing is usually modeled to predict students' performances on questions, while ignoring the interpretability of students' knowledge states. However, for many practical applications, such as learning resource recommendation, it would be more helpful if we could explicitly track students' abilities or knowledge proficiencies separately from performance prediction. Researchers in psychometric area already designed cognitive diagnosis solutions to quantify the knowledge states of students in static conditions (e.g., examination), where the educational priors (i.e., factors related to students' learning process) were proved beneficial for student modeling. Inspired by this, we propose dynamic cognitive diagnosis, which integrates the interpretability of educational priors from cognitive diagnosis into deep learning-based knowledge tracing methods. We first discuss and provide evidence of which educational priors can be integrated, including question attributes and interaction function. Then we show the effects of using the educational priors in deep learning-based knowledge tracing from two aspects, i.e., interpretability and accuracy. Through extensive experiments and analyses, we prove that properly chosen priors can enable deep learning-based methods to evaluate students' knowledge states in a manner that is consistent with domain knowledge or human experience. Moreover, educational priors also improve the accuracy of student performance prediction.

关键词： cognitive diagnosis deep learning intelligent tutoring systems knowledge tracing personalized e-learning

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