30 accepted papers
Adaptive testing is widely adopted in AI-driven educational assessment systems (e.g., GRE), where the goal is to select an optimal subset of questions from a large question pool to accurately estimate an examinee's ability. A fundamental challenge is that: optimal question subsets are inherently per…
Arena-based evaluation has become a key method for assessing large language models (LLMs) through head-to-head model comparisons, closely reflecting human preferences. However, current arena rating systems (e.g., ELO rating system) often suffer from inefficiencies due to exhaustive or random model p…
Specializing Large Language Models for educational domains is a key frontier in creating personalized learning tools. The central challenge is not data scarcity but its abundance: efficiently selecting a curated data subset from vast corpora to enhance specialized skills and foster generalization, w
Underwater 3D scene reconstruction is critical for the operation of underwater robotics, yet remains highly challenging due to the semi-transparent water medium, which introduces optical distortions, light scattering, and severe visibility degradation. Therefore, effective underwater image enhanceme…
The advancements of Large Language Models (LLMs) are primarily attributed to massive pretraining data, which also introduces risks like privacy leakage and data contamination. Therefore, it is crucial to determine whether an LLM has been trained on a given target text. Existing detection methods pri…
Multimodal Sentiment Analysis (MSA) aims to infer human sentiment by integrating information from multiple modalities such as text, audio, and video. In real-world scenarios, however, the presence of missing modalities and noisy signals significantly hinders the robustness and accuracy of existing m
Ground and aerial robots, with distinct sensing perspectives, acquire heterogeneous point clouds that exhibit limited overlap, presenting significant challenges for collaborative mapping. To address these challenges, this article proposes a robust LiDAR-based aerial-ground collaborative mapping fram
This letter proposes a novel scan-to-neural model matching, tightly-coupled LiDAR-inertial Simultaneous Localization and Mapping (SLAM) system, which can achieve more accurate state estimation and incrementally reconstruct the dense map. Different from the existing methods, the key insight of the pr
Multimodal Sentiment Analysis (MSA) enhances emotion recognition by integrating information from multiple modalities. However, multimodal learning with missing modalities suffers from representation inconsistency and optimization instability, leading to suboptimal performance. In this paper, we intr…
Point cloud registration is the foundation of collaborative multi-robot mapping tasks in outdoor environments. Due to the dynamic changes in communication bandwidth, the density of point clouds transmitted from the robot to the server will also change simultaneously, which will significantly affect
Multimodal Sentiment Analysis (MSA) aims to infer human emotions by integrating complementary signals from diverse modalities. However, in real-world scenarios, missing modalities are common due to data corruption, sensor failure, or privacy concerns, which can significantly degrade model performanc…
Cognitive diagnosis, which assesses the learners' competence from learners' interaction logs, plays a vital role in education. It provides a crucial reference for gauging learners' proficiency levels and tailoring future learning activities accordingly. Researchers have proposed numerous cognitive d…
As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed various large-scale benchmarks to determine their capabilities, typically against a gold-standard test set and report metrics averaged across all items. Howe…
Recently, the research on mobile manipulators has attracted increasing attention. Ensuring that mobile manipulators can meet obstacle avoidance constraints and efficiently accomplish assigned tasks in dynamic environments remains a significant challenge. To address this issue, this paper proposes an
While LLM/VLM-powered AI agents have advanced rapidly in math, coding, and computer use, their applications in complex physical and social environments remain challenging. Building agents that can survive and thrive in the real world (e.g., by autonomously earning income) requires massive-scale inte…
Recent advances in foundation models have shown promising results in developing generalist robotics that can perform diverse tasks in open-ended scenarios given multimodal inputs. However, current work has been mainly focused on indoor, household scenarios. In this work, we present SimWorld-Robotics…
Accurately assessing internal human states is key to understanding preferences, offering personalized services, and identifying challenges in real-world applications. Originating from psychometrics, adaptive testing has become the mainstream method for human measurement and has now been widely appli…
Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models (LLMs). Existing framework based on ELO rating system suffers from the inevitable instability problem due to ranking inconsistency and the lack of attention to the varyi…
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…
As the deep integration of machine learning and intelligent education, Computerized Adaptive Testing (CAT) has received more and more research attention. Compared to traditional paper-and-pencil tests, CAT can deliver both personalized and interactive assessments by automatically adjusting testing q…
This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion di…
Expert-designed close-ended benchmarks are indispensable in assessing the knowledge capacity of large language models (LLMs). Despite their widespread use, concerns have mounted regarding their reliability due to limited test scenarios and an unavoidable risk of data contamination. To rectify this,…
As intelligent education evolves, it will provide students with multiple personalized learning services based on their individual abilities. Computerized adaptive testing (CAT) is designed to accurately measure a student’s ability using the least questions, providing an efficient and personalized te…
Computerized adaptive testing (CAT), as a tool that can efficiently measure student's ability, has been widely used in various standardized tests (e.g., GMAT and GRE). The adaptivity of CAT refers to the selection of the most informative questions for each student, reducing test length. Existing CAT…
User modeling techniques profile users' latent characteristics (e.g., preference) from their observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional user models may unconsciously capture biases related to sensitive attributes (e.g., gender) from behavior data, even…
The perception-aware motion planning method based on the localization uncertainty has the potential to improve the localization accuracy for robot navigation. How-ever, most of the existing perception-aware methods pre-build a global feature map and can not generate the perception- aware trajectory…
In this letter, we present VI-HSO, a hybrid sparse monocular visual-inertial odometry system based on two innovative techniques called adaptive interframe alignment (AIA) and dynamic inverse distance filter (DIDF). Although the sparse image alignment algorithm appears efficient for calculating frame
Computerized Adaptive Testing (CAT) refers to an efficient and personalized test mode in online education, aiming to accurately measure student proficiency level on the required subject/domain. The key component of CAT is the "adaptive" question selection algorithm, which automatically selects the b…
This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT). The proposed method exploits certain linearization properties of the SCDT to render the problem easier to solve in the SCDT space. The method uses the nearest subspace search technique…
Online 3D point cloud classification and scene understanding are crucial tasks for Unmanned Ground Vehicles (UGVs) equipped with multiple laser scanners. Due to the poor performance of traditional 2D image representation model for 3D point clouds, a novel Optimal Bearing Angle (OBA) model is therefo…