Survey of Computerized Adaptive Testing: A Machine Learning Perspective

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthcare, sports, and sociology, CAT has revolutionized testing practices. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing method. By examining the test question selection algorithm at the heart of CAT's adaptivity, we shed light on its functionality. Furthermore, we delve into cognitive diagnosis models, question bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing

Using Low-Rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing

International audienceTensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in the image. In this article, we extend the linear tensor method to the nonlinear tensor method and propose a nonlinear low-rank tensor unmixing algorithm to solve the generalized bilinear model (GBM). Specifically, the linear and nonlinear parts of the GBM can both be expressed as tensors. Furthermore, the low-rank structures of abundance maps and nonlinear interaction abundance maps are exploited by minimizing their nuclear norm, thus taking full advantage of the high spatial correlation in HSIs. Synthetic and real-data experiments show that the low rank of abundance maps and nonlinear interaction abundance maps exploited in our method can improve the performance of the nonlinear unmixing. A MATLAB demo of this work will be available at https://github.com/LinaZhuang for the sake of reproducibility

Using Low-Rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing

International audienceTensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in the image. In this article, we extend the linear tensor method to the nonlinear tensor method and propose a nonlinear low-rank tensor unmixing algorithm to solve the generalized bilinear model (GBM). Specifically, the linear and nonlinear parts of the GBM can both be expressed as tensors. Furthermore, the low-rank structures of abundance maps and nonlinear interaction abundance maps are exploited by minimizing their nuclear norm, thus taking full advantage of the high spatial correlation in HSIs. Synthetic and real-data experiments show that the low rank of abundance maps and nonlinear interaction abundance maps exploited in our method can improve the performance of the nonlinear unmixing. A MATLAB demo of this work will be available at https://github.com/LinaZhuang for the sake of reproducibility

Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification

International audienceSpectral-spatial framework has been widely applied for hyperspectral image classification task. Some well-established models, such as group sparse representation (GSR), have gained a certain advance but still mainly focus on the usage of local spatial similarity and neglect the nonlocal spatial information. Recently, nonlocal self-similarity (NLSS) has been exploited to support the spatial coherence tasks. However, current NLSS-based methods are biased toward the direct use of nonlocal spatial information as a whole, while the underlying spectral information is not well exploited. In this article, we proposed a novel method to exploit local spectral similarity through nonlocal spatial similarity, with the integration of local spatial consistency in a single framework. Specifically, the proposed approach first exploits the NLSS by searching the nonoverlapped similar patches in defined scopes. Then, spectral similarity is determined locally within the found patches. After that, the found similar data and the original data are fused in a designed pattern. Finally, the GSR-based classifier (GSRC) is applied to process the fused data characterized by the manifold learning algorithm. The experimental results based on three real hyperspectral data sets demonstrate the efficiency of the proposed method, with improvements over the other related nonlocal or local similarity-based methods

Liver X Receptor Agonist Therapy Prevents Diffuse Alveolar Hemorrhage in Murine Lupus by Repolarizing Macrophages

The generation of CD138+ phagocytic macrophages with an alternative (M2) phenotype that clear apoptotic cells from tissues is defective in lupus. Liver X receptor-alpha (LXRα) is an oxysterol-regulated transcription factor that promotes reverse cholesterol transport and alternative (M2) macrophage activation. Conversely, hypoxia-inducible factor 1-α (HIF1α) promotes classical (M1) macrophage activation. The objective of this study was to see if lupus can be treated by enhancing the generation of M2-like macrophages using LXR agonists. Peritoneal macrophages from pristane-treated mice had an M1 phenotype, high HIFα-regulated phosphofructokinase and TNFα expression (quantitative PCR, flow cytometry), and low expression of the LXRα-regulated gene ATP binding cassette subfamily A member 1 (Abca1) and Il10 vs. mice treated with mineral oil, a control inflammatory oil that does not cause lupus. Glycolytic metabolism (extracellular flux assays) and Hif1a expression were higher in pristane-treated mice (M1-like) whereas oxidative metabolism and LXRα expression were higher in mineral oil-treated mice (M2-like). Similarly, lupus patients’ monocytes exhibited low LXRα/ABCA1 and high HIF1α vs. controls. The LXR agonist T0901317 inhibited type I interferon and increased ABCA1 in lupus patients’ monocytes and in murine peritoneal macrophages. In vivo, T0901317 induced M2-like macrophage polarization and protected mice from diffuse alveolar hemorrhage (DAH), an often fatal complication of lupus. We conclude that end-organ damage (DAH) in murine lupus can be prevented using an LXR agonist to correct a macrophage differentiation abnormality characteristic of lupus. LXR agonists also decrease inflammatory cytokine production by human lupus monocytes, suggesting that these agents may be have a role in the pharmacotherapy of lupus

Enhanced electrochemical voltammetric fingerprints for plant taxonomic sensing

Graphene-embedded plant tissues show a high sensitivity to electrochemical signals, which enables a screen printed electrode to be used for electrochemical fingerprint recording. The electrochemical fingerprints obtained under different conditions can be transformed into multidimensional recognition modes for plant identification. These electrochemical fingerprints reflect the types and quantities of the electrochemically active substances in plant tissues such that the fingerprints can be used for chemotaxonomic investigations. In this paper, five species of Lycoris bulbs, including L. chinensis, L. radiate, L. aurea, L. sprengeri and L. straminea, were successfully recognized by electrochemical fingerprinting. The species's interspecific relationships were also investigated. L. chinensis and L. aurea show highly similar morphology but have a relatively distant relationship. Hybridized L. radiata shows a notably close relationship with L. straminea, suggesting that one of its parents may be L. radiata. In addition, L. chinensis also shows a close relationship with L. straminea, suggesting that the L. straminea may be produced by cross-breeding L. chinensis and L. radiate. The results mentioned above indicate that the proposed electro-chemotaxonomic methodology is an inexpensive and quick taxonomic method that can provide additional evidence for the existing taxonomy system