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Understanding and improving fairness in cognitive diagnosis

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 proficiency.We 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

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Variational Bayesian Tensor Quantile Regression

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Acta Mathematica Sinica,English Series 2024年

作者： Yunzhi JIN Yanqing ZHANG Yunnan Key Laboratory of Statistical Modeling and data analysis Southwest United Graduate School Yunnan University Yunnan Key Laboratory of Statistical Modeling and data analysis Yunnan University

Quantile regression is widely used in variable relationship research for statistical learning.Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods. However, many modern applications involve tensor data with the intrinsic tensor structure. Traditional quantile regression can deal with tensor regression issues well. To this end, we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients. To incorporate the sparsity of tensor coefficients, we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients. The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation. The coordinate ascent algorithm is employed to optimize variational lower bound. Simulation studies and a real example show the numerical performances of the proposed method.

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big data analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China school of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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The“depict”strategy for discovering new compounds in complex matrices:Lycibarbarspermidines as a case

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Journal of Pharmaceutical analysis 2024年第3期14卷 416-426页

作者： Chen Han Zhixin Zhang Zhiyang Feng Chuanjia Zhai Xuejiao Li Yulian Shi Xiang Li Miao Li Ying Wang Gan Luo Xiaoyan Gao Department of Chinese Medicine analysis School of Chinese Materia MedicaBeijing University of Chinese MedicineBeijing102488China

The comprehensive detection and identification of active ingredients in complex matrices is a crucial challenge.Liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS)is the most prominent analytical platform for the exploration of novel active compounds from complex matrices.However,the LC-HRMS-based analysis workflow suffers from several bottleneck issues,such as trace content of target compounds,limited acquisition for fragment information,and uncertainty in interpreting relevant MS2 spectra.Lycibarbarspermidines are vital antioxidant active ingredients in Lycii Fructus,while the reported structures are merely focused on dicaffeoylspermidines due to their low content.To comprehensively detect the new structures of lycibarbarspermidine derivatives,a“depict”strategy was developed in this study.First,potential new lycibarbarspermidine derivatives were designed according to the biosynthetic pathway,and a comprehensive database was established,which enlarged the coverage of lycibarbarspermidine derivatives.Second,the polarity-oriented sample preparation of potential new compounds increased the concentration of the target compounds.Third,the construction of the molecular network based on the fragmentation pathway of lycibarbarspermidine derivatives broadened the comprehensiveness of identification.Finally,the weak response signals were captured by data-dependent scanning(DDA)followed by parallel reaction monitoring(PRM),and the efficiency of acquiring MS2 fragment ions of target compounds was significantly improved.Based on the integrated strategy above,210 lycibarbarspermidine derivatives were detected and identified from Lycii Fructus,and in particular,170 potential new compounds were structurally characterized.The integrated strategy improved the sensitivity of detection and the coverage of low-response components,and it is expected to be a promising pipeline for discovering new compounds.

关键词： Mass spectrometric detection Weak response signals Complex matrices Novel lycibarbarspermidines

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Mathematical analysis on a General Delayed HBV Model with Capsids and Two Infection Routes

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Acta Mathematicae Applicatae Sinica 2024年第2期40卷 508-525页

作者： Li-li LIU Hong-gang WANG Ya-zhi LI Shanxi Key Laboratory of Mathematical Techniques and Big data analysis on Disease Control and Prevention Complex Systems Research CenterShanxi UniversityTaiyuan 030006China school of Mathematics and Statistics Qiannan Normal University for NationalitiesDuyun 558000China

Considering that HBV belongs to the DNA virus family and is hepatotropic,we model the HBV DNA-containing capsids as a compartment.In this paper,a delayed HBV infection model is established,where the general incidence function and two infection routes including cell-virus infection and cell-cell infection are introduced.According to some preliminaries,including well-posedness,basic reproduction number and existence of two equilibria,we obtain the threshold dynamics for the model.We illustrate numerical simulations to verify the above theoretical results,and furthermore explore the impacts of intracellular delay and cell-cell infection on the global dynamics of the model.

关键词： delayed HBV model capsids general incidence function two infection routes global stability

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Weighted Probability Density Estimator with Updated Bandwidths

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Journal of Systems Science & Complexity 2024年第2期37卷 886-906页

作者： WU Yi YU Wei WANG Xuejun PRAKASA RAO B L S school of Big data and Artificial Intelligence Chizhou UniversityChizhou 247000China school of Big data and Statistics Anhui UniversityHefei 230601China CR Rao Advanced Institute of Mathematics Statistics and Computer ScienceHyderabad 500046India

In this paper,the authors study a class of weighted version of probability density estimator.It is shown that the weighted estimator contains some existing estimators of probability density,no matter they are recursive or non-recursive.Some statistical results including weak consistency,strong consistency,rate of strong consistency,and asymptotic normality are established under some mild conditions.Moreover,the random weighted estimator is also investigated.Some numerical simulations and a real data analysis are presented to study the numerical performances of the estimators.

关键词： Asymptotic normality consistency numerical simulation probability density real data analysis updated bandwidths weighted estimator

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Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

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Science China(Life Sciences) 2024年第7期67卷 1489-1501页

作者： Xinyu Yang Runhan Li Xindi Yang Yong Zhou Yi Liu Jing-Dong J.Han school of Life Sciences Peking UniversityBeijing 100871China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Beijing Key Lab of Traffic data analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China Clinical Research Institute Shanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China

The human face is a valuable biomarker of aging,but the collection and use of its image raise significant privacy concerns.Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations.We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability.We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations,indicating that the relative positioning of facial information is a low-level biomarker of facial aging.Through visual perception tests and computational3D face verification experiments,we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines,except when only the face shape information is accessible.Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

关键词： face point cloud age estimation face verification privacy coordinate-wise monotonic transformation

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Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

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Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang school of Big data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big data Intelligent Perception and Application Engineering Research Center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

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The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors

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Acta Mathematica Sinica,English Series 2024年第5期40卷 1244-1272页

作者： Yun Bao YAO Yu Tan LÜ Chao LU Wei WANG Xue Jun WANG school of Mathematical Sciences Anhui UniversityHefei 230601P.R.China school of Big data and Artificial Intelligence Chizhou UniversityChizhou 247000P.R.China school of Big data and Statistics Anhui UniversityHefei 230601P.R.China

In this paper,we consider the partial linear regression model y_(i)=x_(i)β^(*)+g(ti)+ε_(i),i=1,2,...,n,where(x_(i),ti)are known fixed design points,g(·)is an unknown function,andβ^(*)is an unknown parameter to be estimated,random errorsε_(i)are(α,β)-mix_(i)ng random variables.The p-th(p>1)mean consistency,strong consistency and complete consistency for least squares estimators ofβ^(*)and g(·)are investigated under some mild conditions.In addition,a numerical simulation is carried out to study the finite sample performance of the theoretical results.Finally,a real data analysis is provided to further verify the effect of the model.

关键词： (α,β)-mixing random variables partial linear regression model least squares estimator consistency

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Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance

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Quantitative Biology 2024年第1期12卷 85-99页

作者： Siyu Li Songming Tang Yunchang Wang Sijie Li Yuhang Jia Shengquan Chen school of Statistics and data Science Nankai UniversityTianjinChina school of Mathematical Sciences and LPMC Nankai UniversityTianjinChina

Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type annotation.However,existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types,which only exist in a test set.Here,we propose RAINBOW,a reference-guided automatic annotation method based on the contrastive learning framework,which is capable of effectively identifying novel cell types in a test set.By utilizing contrastive learning and incorporating reference data,RAINBOW can effectively characterize the heterogeneity of cell types,thereby facilitating more accurate annotation.With extensive experiments on multiple scCAS datasets,we show the advantages of RAINBOW over state-of-the-art methods in known and novel cell type annotation.We also verify the effectiveness of incorporating reference data during the training process.In addition,we demonstrate the robustness of RAINBOW to data sparsity and number of cell types.Furthermore,RAINBOW provides superior performance in newly sequenced data and can reveal biological implication in downstream analyses.All the results demonstrate the superior performance of RAINBOW in cell type annotation for scCAS data.We anticipate that RAINBOW will offer essential guidance and great assistance in scCAS data analysis.The source codes are available at the GitHub website(BioX-NKU/RAINBOW).

关键词： cell type annotation chromatin accessibility novel type reference-guided single-cell

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