检索结果-内蒙古大学图书馆

33rd ACM Web Conference, WWW 2024

作者： Wang, Fei Liu, Qi Chen, Enhong Liu, Chuanren Huang, Zhenya Wu, Jinze Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Business Analytics and Statistics The University of Tennessee Knoxville United States IFLYTEK Research State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis. © 2024 ACM.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index

Multi-label Feature Selection Based on Fuzzy Neighborhood Mu...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Chenxi Wang E Chen Mengli Ren Xiehua Yu Yaojin Lin Shaozi Li School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China School of Computer Science Lab of Data Science and Intelligence Application Minnan Science and Technology University Quanzhou China School of Computer Science Lab of Data Science and Intelligence Application Xiamen University Xiamen China

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance.

关键词： Training Rough sets Feature extraction Classification algorithms Indexes Information technology Information entropy

来源：评论

学校读者我要写书评

暂无评论

AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking 37

AdaptSSR: Pre-training User Model with Augmentation-Adaptive...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Yu, Yang Liu, Qi Zhang, Kai Zhang, Yuren Song, Chao Hou, Min Yuan, Yuqing Ye, Zhihao Zhang, Zaixi Yu, Sanshi Lei Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China OPPO Research Institute China Hefei University of Technology China

ISBN: (纸本)9781713899921

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Non-Regular Background Mesh based Hex Meshing

Non-Regular Background Mesh based Hex Meshing

引用

2024 International Meshing Roundtable, IMR 2024

作者： Zheng, Zhihao Dai, Liang Gao, Shuming State Key Lab of CAD & CG Zhejiang University China School of Data Science and Artficial Intelligence Wenzhou University of Technology China

ISBN: (纸本)9781713893462

Thanks to their superior numerical properties, conforming hex meshes have become a popular choice in the context of simulation. However, due to the global structural constraints, generating hex meshes has been a subject of extensive study, giving rise to numerous fascinating and challenging associated problems. In light of this, this paper introduces an innovative approach based on non-regular background mesh, aimed at automatically generating high-quality adaptive hex meshes for CAD models with arbitrary complexity. The algorithm initially generates a non-regular background mesh for the given solid model through several steps involving shape analysis, quad mesh sweeping and templates-based mesh refinement. Subsequently, to convert this background mesh into a conformal hex mesh, the algorithm identifies the globally optimized core hex mesh via a graph-cut algorithm. To further improve the quality of mesh, the algorithm conducts topology improvement, coupled with geometric optimization guided by quality metrics applied to boundary layers of core mesh. Experimental results showed the effectiveness of the proposed algorithm. © 2024 by SIAM.

关键词： Boundary layers

来源：评论

学校读者我要写书评

暂无评论

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

来源：评论

学校读者我要写书评

暂无评论

TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction 5th

TGNN: A GNN-Based Method with Multi-entity Node for Person...

引用

5th China Conference on Intelligent Networked Things, CINT 2022

作者： Mo, Zhikang Shao, Qixiang Wu, Likang Yu, Runlong Liu, Qi Xu, Jiexin Song, Hongmei Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Computer Science and Techonology University of Science and Technology of China Hefei China Department of Information Technology China Merchant Bank Shenzhen China

ISBN: (纸本)9789811989148

The offline business of banks is deeply involved in bank clients’ financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients’ banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Understanding and improving fairness in cognitive diagnosis

引用

science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

来源：评论

学校读者我要写书评

暂无评论

Attention-based unsupervised prompt learning for SAM in leaf disease segmentation

引用

Knowledge-Based Systems 2025年 320卷

作者： Luda Tian Yingchun Yuan Qing En Wei Ma Guanghui Zhang Fangfang Liang School of Information Science and Technology Hebei Agricultural University Baoding 071001 China Hebei Key Laboratory of Agricultural Big Data Baoding 071001 China Hebei Engineering Research Center for Agricultural Remote Sensing Application Baoding 071001 China Artificial Intelligence and Machine Learning (AIML) Lab School of Computer Science Carleton University Ottawa K1S 5B6 Canada Faculty of Information Technology Beijing University of Technology Beijing 100124 China

In modern agriculture, leaf disease segmentation is crucial for crop disease management and yield improvement. Since most deep learning-based segmentation models require extensive annotations, pursuing unsupervised methods becomes a practical solution. Although advanced models like the Segment Anything Model (SAM) can produce precise class-agnostic masks without annotations, their reliance on human-interactive prompts limits their utility to unsupervised disease segmentation tasks. To address this limitation, we present UPLS, a novel three-stage framework that leverages unsupervised prompt learning to enable SAM for leaf disease segmentation. This method employs unsupervised contrastive learning to progressively segment leaf and disease areas and automatically generate disease prompts for SAM. Concretely, for fine and intricate lesions, we utilize an improved high-frequency attention mechanism to extract high-frequency features from the leaf area, and construct contrastive losses between global priors and foreground/background features. Furthermore, we devise a strategy to automatically generate disease-related prompts from the segmented leaf and initial lesion regions, enabling SAM to refine disease boundaries without human intervention. Experiments on three public plant disease segmentation datasets show that UPLS outperforms existing non-fully supervised segmentation methods in accuracy and robustness. Source code is available at https://***/Tianluda/UPLS .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

引用

arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

来源：评论

学校读者我要写书评

暂无评论

Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm 24

Towards the Identifiability and Explainability for Personali...

引用

33rd ACM Web Conference, WWW 2024

作者： Li, Jiatong Liu, Qi Wang, Fei Liu, Jiayu Huang, Zhenya Yao, Fangzhou Zhu, Linbo Su, Yu University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Hefei Normal University Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9798400701719

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow theproficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novelresponse-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness. Our code is available at https://***/CSLiJT/ID-CDF. © 2024 ACM.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：