Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted across various fields, including education, healthcare, sports, sociology, and the evaluation of AI models. 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 paradigm. We delve into measurement models, question selection algorithm, 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.

Time series classification (TSC) is a critical area with broad applications. In the field of evidence theory, quantum evidence theory (QET) offers a promising framework for onedimensional TSC tasks, leveraging the capabilities of quantum basic probability amplitude (QBPA) to capture two-dimensional uncertainty. However, as the first step for the application of QET to TSC, how to construct QBPA still remains an open issue. In this paper, a novel approach to generate QBPA is devised. Specifically, we first apply the discrete Fourier transform (DFT) to the original data, extracting two-dimensional features embedded in the magnitude and phase from the frequency domain based on the front-few multi-frequency components, achieved by setting a threshold frequency index (TFI) to limit the frequencies considered. Next, we introduce the complex dual gaussian fuzzy number (CDGFN) as a carrier for QBPA, effectively representing two-dimensional uncertainty in the data. A CDGFN-based multisource information fusion (CDGFN-MSIF) algorithm for decision-making is proposed to combine information from different frequency components. Finally, the decisionmaking algorithm is validated on multiple time series datasets. Experimental results highlight the superior performance of the proposed approach over other state-of-the-art models, demonstrating its effectiveness and enhanced classification accuracy.

Evaluating AI systems, particularly large models, is an essential yet computationally expensive task. The use of extensive benchmarks often leads to substantial computational/human costs that may even exceed those of pretraining. The efficiency of AI model evaluation focuses on estimating the model's score on the full benchmark based on its responses to a smaller subset. Various empirical selection methods have been proposed to identify valuable subsets within these benchmarks. In this paper, we formally define and approximate the subset selection problem inherent in efficient evaluation. We prove that this problem actually optimizes a submodular function and that a unified subset can be identified using a simple greedy algorithm. Importantly, this approach is the first to provide theoretical guarantees of bias control and generalizability in score estimation. Using language models as a case study, experimental results across 11 different benchmarks validate its superiority in estimating model scores and maintaining ranking consistency. It can achieve accurate score estimation using no more than 30% of the full benchmark, thus facilitating efficient and sparse benchmark design.

Adaptive Testing System (ATS) is a promising testing mode, extensively utilized in standardized tests like the GRE. It offers personalized ability assessment by dynamically adjusting questions based on individual ability levels. Compared to traditional exams, ATS can improve the accuracy of ability estimates while simultaneously reducing the number of questions required. Despite the diverse testing formats of ATS, tailored to different adaptability requirements in various testing scenarios, there is a notable absence of a unified framework for modeling them. In this paper, we introduce a unified data-driven ATS framework that conceptualizes the various testing formats as a hierarchical test structure search problem. It can learn directly from data to solve for the optimal questions for each student, eliminating the need for manual test design. The proposed solution algorithm comes with theoretical guarantees for estimation error and convergence. Empirical results show that our framework maintains assessment accuracy while reducing question count by 20% on average and improving training stability.

The first-order parallel rotating double inverted pendulum (PRDIP) is a novel underdrive benchmark system. In this paper, a mathematical model of PRDIP system based on Lagrange equation is established, which is compared with the visual model (3D mechanical model) of PRDIP system based on the MATLAB/SIMSCAPE Multibody module. Through analyzing the established mathematical model of PRDIP, it can be concluded that the PRDIP system is controllable only when the length of the two pendulums is not equal and the coefficient of friction is small. The global control of the pendulum can be divided into two parts, including swing up control and stable control, according to the movement of the pendulum within the four quadrants of its motion plane. An energy feedback based control strategy and a state feedback H∞ based control strategy are designed for the swing up control and stable control of the pendulum, respectively. In addition, a dual-mode switching global control scheme based on EFC/H∞ is proposed. The simulations using MATLAB/SIMSCAPE are demonstrated to validate the effectiveness of the proposed dual-mode switching global control scheme.