pwc-archive/papers-with-abstracts huggingface dataset:
Augmenting Knowledge Tracing by Considering Forgetting Behavior
Authors: Nagatani, Koki and Zhang, Qian and Sato, Masahiro and Chen, Yan-Ying and Chen, Francine and Ohkuma, Tomoko
Date: 2019-05-13 00:00:00
Proceeding: The World Wide Web Conference 2019 5
Tasks: Knowledge Tracing

Computer-aided education systems are now seeking to provide each student with personalized materials based on a student's individual knowledge. To provide suitable learning materials, tracing each student's knowledge over a period of time is important. However, predicting each student's knowledge is difficult because students tend to forget. The forgetting behavior is mainly because of two reasons: the lag time from the previous interaction, and the number of past trials on a question. Although there are a few studies that consider forgetting while modeling a student's knowledge, some models consider only partial information about forgetting, whereas others consider multiple features about forgetting, ignoring a student's learning sequence. In this paper, we focus on modeling and predicting a student's knowledge by considering their forgetting behavior. We extend the deep knowledge tracing model [17], which is a state-of-the-art sequential model for knowledge tracing, to consider forgetting by incorporating multiple types of information related to forgetting. Experiments on knowledge tracing datasets show that our proposed model improves the predictive performance as compared to baselines. Moreover, we also examine that the combination of multiple types of information that affect the behavior of forgetting results in performance improvement.
Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect
Authors: Shen, Shuanghong and Huang, Zhenya and Liu, Qi and Su, Yu and Wang, Shijin and Chen, Enhong
Date: 2022-07-11 00:00:00
Proceeding: International ACM SIGIR Conference on Research and Development in Information Retrieval 2022 7
Tasks: Knowledge Tracing

Knowledge Tracing (KT), which aims to assess students' dynamic knowledge states when practicing on various questions, is a fundamental research task for offering intelligent services in online learning systems. Researchers have devoted significant efforts to developing KT models with impressive performance. However, in existing KT methods, the related question difficulty level, which directly affects students' knowledge state in learning, has not been effectively explored and employed. In this paper, we focus on exploring the question difficulty effect on learning to improve student's knowledge state assessment and propose the DIfficulty Matching Knowledge Tracing (DIMKT) model. Specifically, we first explicitly incorporate the difficulty level into the question representation. Then, to establish the relation between students' knowledge state and the question difficulty level during the practice process, we accordingly design an adaptive sequential neural network in three stages: (1) measuring students' subjective feelings of the question difficulty before practice; (2) estimating students' personalized knowledge acquisition while answering questions of different difficulty levels; (3) updating students' knowledge state in varying degrees to match the question difficulty level after practice. Finally, we conduct extensive experiments on real-world datasets, and the results demonstrate that DIMKT outperforms state-of-the-art KT models. Moreover, DIMKT shows superior interpretability by exploring the question difficulty effect when making predictions. Our codes are available at https://github.com/shshen-closer/DIMKT.
Learning Behavior-oriented Knowledge Tracing
Authors: Xu, Bihan and Huang, Zhenya and Liu, Jiayu and Shen, Shuanghong and Liu, Qi and Chen, Enhong and Wu, Jinze and Wang, Shijin
Date: 2023-08-06 00:00:00
Proceeding: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023 8
Tasks: Knowledge Tracing

Exploring how learners' knowledge states evolve during the learning activities is a critical task in online learning systems, which can facilitate personalized services downstream, such as course recommendation. Most of existing methods have devoted great efforts to analyzing learners' knowledge states according to their responses (i.e., right or wrong) to different questions. However, the significant effect of learners' learning behaviors (e.g., answering speed, the number of attempts) is omitted, which can reflect their knowledge acquisition deeper and ensure the reliability of the response. In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners' knowledge states. Specifically, we first analyze and summarize several dominated learning behaviors including Speed, Attempts and Hints in the learning process. As the characteristics of different learning behaviors vary greatly, we separately estimate their various effects on learners' knowledge acquisition in a quantitative manner. Then, considering that different learning behaviors are closely dependent with each other, we assess the fused effect of multiple learning behaviors by capturing their complex dependent patterns. Finally, we integrate the forgetting factor with learners' knowledge acquisition to comprehensively update their changing knowledge states in learning. Extensive experimental results on several public datasets demonstrate that our model generates better performance prediction for learners against existing methods. Moreover, LBKT shows good interpretability in tracking learners' knowledge state by incorporating the learning behavior effects. Our codes are available at https://github.com/xbh0720/LBKT.
Question Difficulty Consistent Knowledge Tracing
Authors: Liu, Guimei and Zhan, Huijing and Kim, Jung-jae
Date: 2024-05-14 00:00:00
Proceeding: Proceedings of the ACM Web Conference 2024 5
Tasks: Knowledge Tracing

Knowledge tracing aims to estimate knowledge states of students over a set of skills based on students' past learning activities. Deep learning based knowledge tracing models show superior performance to traditional knowledge tracing approaches. Early works like DKT use skill IDs and student responses only. Recent works also incorporate questions IDs into their models and achieve much improved performance in the next question correctness prediction task. However, predictions made by these models are thus on specific questions, and it is not straightforward to translate them to estimation of students' knowledge states over skills. In this paper, we propose to replace question IDs with question difficulty levels in deep knowledge tracing models. The predictions made by our model can be more readily translated to students' knowledge states over skills. Furthermore, by using question difficulty levels to replace question IDs, we can also alleviate the cold-start problem in knowledge tracing as online learning platforms are updated frequently with new questions. We further use two techniques to smooth the predicted scores. One is to combine embeddings of nearby difficulty levels using the Hann function. The other is to constrain the predicted probabilities to be consistent with question difficulties by imposing a penalty if they are not consistent. We conduct extensive experiments to study the performance of the proposed model. Our experimental results show that our model outperforms the state-of-the-art knowledge tracing models in terms of both accuracy and consistency with question difficulty levels.
Interpretable Knowledge Tracing with Multiscale State Representation
Authors: Sun, Jianwen and Yu, Fenghua and Wan, Qian and Li, Qing and Liu, Sannyuya and Shen, Xiaoxuan
Date: 2024-05-14 00:00:00
Proceeding: The World Wide Web Conference 2024 5
Tasks: Knowledge Tracing

Knowledge Tracing (KT) is vital for education, continuously monitoring students' knowledge states (mastery of knowledge) as they interact with online education materials. Despite significant advancements in deep learning-based KT models, existing approaches often struggle to strike the right balance in granularity, leading to either overly coarse or excessively fine tracing and representation of students' knowledge states, thereby limiting their performance. Additionally, achieving a high-performing model while ensuring interpretability presents a challenge. Therefore, in this paper, we propose a novel approach called Multiscale-state-based Interpretable Knowledge Tracing (MIKT). Specifically, MIKT traces students' knowledge states on two scales: a coarse-grained representation to trace students' domain knowledge state, and a fine-grained representation to monitor their conceptual knowledge state. Furthermore, the classical psychological measurement model, IRT (Item Response Theory), is introduced to explain the prediction process of MIKT, enhancing its interpretability without sacrificing performance. Additionally, we extended the Rasch representation method to effectively handle scenarios where questions are associated with multiple concepts, making it more applicable to real-world situations. We extensively compared MIKT with 20 state-of-the-art KT models on four widely-used public datasets. Experimental results demonstrate that MIKT outperforms other models while maintaining its interpretability. Moreover, experimental observations have revealed that our proposed extended Rasch representation method not only benefits MIKT but also significantly improves the performance of other KT baseline models. The code can be found at https://github.com/lilstrawberry/MIKT.
Yesil o1 Pro: Evidence-Based AI Model for Health and Benchmarking in Clinical Decision Support
Authors: Yusuf Yesil
Date: 2025-02-15 00:00:00
Proceeding: JMIR Preprints 2025 2
Tasks: Benchmarking, Epidemiology, Large Language Model

Background:
Integrating evidence-based approaches in healthcare and artificial intelligence (AI) is crucial for enhancing clinical decision-making and patient safety. Yesil o1 Pro is a specialized large language model (LLM) designed to transform medical knowledge synthesis by leveraging a comprehensive, curated database of scientific literature, clinical guidelines, and medical textbooks.

Objective:
The system's innovative "AI Hospital" framework employs domain-specific expert agents coordinated by a central Master Agent, enabling tailored and precise medical responses across multiple disciplines.

Methods:
The model's advanced methodology incorporates sophisticated techniques including GraphRAG-based retrieval, extensive fine-tuning with 1.5 million question-answer pairs, and Chain of Thought (CoT) reasoning. Its robust training dataset comprises 100.5M words from high-impact journals, 96.8M words from core medical texts, and 74.4M words from international standards, ensuring a comprehensive and authoritative knowledge base.

Results:
Benchmark evaluations demonstrate Yesil o1 Pro's exceptional performance, achieving an overall accuracy of 89.1% and surpassing leading models like GPT-4o (83.9%) and Claude 3.5 Sonnet (83.0%). Domain-specific accuracies are particularly impressive, with 96.1% in Mental Health, 94.6% in Epidemiology, and 94.6% in Dentistry, highlighting the model's proficiency in handling complex, reasoning-intensive medical queries.

Conclusions:
The model shows promising applications in clinical decision support, interdisciplinary collaboration, professional education, and medical research. While challenges remain in real-world integration and maintaining alignment with evolving medical knowledge, Yesil o1 Pro represents a significant advancement in AI-driven healthcare support. Future research will focus on validating the model's utility in clinical environments and developing strategies for seamless healthcare system integration.
Capturing Homogeneous Influence among Students: Hypergraph Cognitive Diagnosis for Intelligent Education Systems
Authors: Shen, Junhao and Qian, Hong and Liu, Shuo and Zhang, Wei and Jiang, Bo and Zhou, Aimin
Date: 2024-08-25 00:00:00
Proceeding: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2024 8
Tasks: cognitive diagnosis

Cognitive diagnosis is a vital upstream task in intelligent education systems. It models the student-exercise interaction, aiming to infer the students' proficiency levels on each knowledge concept. This paper observes that most existing methods can hardly effectively capture the homogeneous influence due to its inherent complexity. That is to say, although students exhibit similar performance on given exercises, their proficiency levels inferred by these methods vary significantly, resulting in shortcomings in interpretability and efficacy. Given the complexity of homogeneous influence, a hypergraph could be a choice due to its flexibility and capability of modeling high-order similarity which aligns with the nature of homogeneous influence. However, before incorporating hypergraph, one at first needs to address the challenges of distorted homogeneous influence, sparsity of response logs, and over-smoothing. To this end, this paper proposes a hypergraph cognitive diagnosis model (HyperCDM) to address these challenges and effectively capture the homogeneous influence. Specifically, to avoid distortion, HyperCDM employs a divide-and-conquer strategy to learn student, exercise and knowledge representations in their own hypergraphs respectively, and interconnects them via a feature-based interaction function. To construct hypergraphs based on sparse response logs, the auto-encoder is utilized to preprocess response logs and K-means is applied to cluster students. To mitigate over-smoothing, momentum hypergraph convolution networks are designed to partially keep previous representations during the message propagation. Extensive experiments on both offline and online real-world datasets show that HyperCDM achieves state-of-the-art performance in terms of interpretability and capturing homogeneous influence effectively, and is competitive in generalization. The ablation study verifies the efficacy of each component, and the case study explicitly showcases the homogeneous influence captured by HyperCDM.
KG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation
Authors: Guan, Quanlong and Xiao, Fang and Cheng, Xinghe and Fang, Liangda and Chen, Ziliang and Chen, Guanliang and Luo, Weiqi
Date: 2023-10-21 00:00:00
Proceeding: ACM International Conference on Information and Knowledge Management 2023 10
Tasks: Collaborative Filtering

Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of individual students pose a significant challenge in selecting appropriate exercise questions. Collaborative filtering-based methods often struggle with recommending suitable exercises, while deep learning-based methods lack explanation, limiting their practical adoption. To address these limitations, this paper proposes KG4Ex, a knowledge graph-based exercise recommendation method. KG4Ex facilitates the matching of diverse students with suitable exercises while providing recommendation reasons. Specifically, we introduce a feature extraction module to represent students' learning states and construct a knowledge graph for exercise recommendation. This knowledge graph comprises three key entities (knowledge concepts, students, and exercises) and their interrelationships, and can be used to recommend suitable exercises. Extensive experiments on three real-world datasets and expert interviews demonstrate the superiority of KG4Ex over existing baseline methods and highlight its strong explainability.
CLRNetV2: A Faster and Stronger Lane Detector
Authors: Tu Zheng, Yifei HUANG, Yang Liu, Zheng Yang, Binbin Lin, Deng Cai, Xiaofei He
Date: 2025-03-18 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2025 3
Tasks: Lane Detection

Lane is critical in the vision navigation system of intelligent vehicles. Naturally, the lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored. On the other hand, current lane detection methods still struggle to detect complex dense lanes, such as Y-shape or fork-shape. In this work, we present Cross Layer Refinement Network aiming at fully utilizing both high-level and low-level features in lane detection. In particular, it first detects lanes with high-level semantic features and then performs refinement based on low-level features. In this way, we can exploit more contextual information to detect lanes while leveraging local-detailed features to improve localization accuracy. We present Fast-ROIGather to gather global context, which further enhances the representation of lane features. To detect dense lanes accurately, we propose Correlation Discrimination Module (CDM) to discriminate the correlation of dense lanes, enabling nearly cost-free high-quality dense lane prediction. In addition to our novel network design, we introduce LineIoU loss which regresses lanes as a whole unit to improve localization accuracy. Experiments demonstrate our approach significantly outperforms the state-of-the-art lane detection methods.
TerraTorch: The Geospatial Foundation Models Toolkit
Authors: Carlos Gomes, Benedikt Blumenstiel, Joao Lucas de Sousa Almeida, Pedro Henrique de Oliveira, Paolo Fraccaro, Francesc Marti Escofet, Daniela Szwarcman, Naomi Simumba, Romeo Kienzler, Bianca Zadrozny
Date: 2025-03-26 00:00:00
Tasks: Benchmarking, Decoder, Earth Observation, Hyperparameter Optimization

TerraTorch is a fine-tuning and benchmarking toolkit for Geospatial Foundation Models built on PyTorch Lightning and tailored for satellite, weather, and climate data. It integrates domain-specific data modules, pre-defined tasks, and a modular model factory that pairs any backbone with diverse decoder heads. These components allow researchers and practitioners to fine-tune supported models in a no-code fashion by simply editing a training configuration. By consolidating best practices for model development and incorporating the automated hyperparameter optimization extension Iterate, TerraTorch reduces the expertise and time required to fine-tune or benchmark models on new Earth Observation use cases. Furthermore, TerraTorch directly integrates with GEO-Bench, allowing for systematic and reproducible benchmarking of Geospatial Foundation Models. TerraTorch is open sourced under Apache 2.0, available at https://github.com/IBM/terratorch, and can be installed via pip install terratorch.
BugCraft: End-to-End Crash Bug Reproduction Using LLM Agents in Minecraft
Authors: Eray Yapağcı, Yavuz Alp Sencer Öztürk, Eray Tüzün
Date: 2025-03-25 00:00:00
Tasks: Minecraft

Reproducing game bugs, in our case crash bugs in continuously evolving games like Minecraft, is a notoriously manual, time-consuming, and challenging process to automate. Despite the success of LLM-driven bug reproduction in other software domains, games, with their complex interactive environments, remain largely unaddressed. This paper introduces BugCraft, a novel end-to-end framework designed to automate the reproduction of crash bugs in Minecraft directly from user-submitted bug reports, addressing the critical gap in automated game bug reproduction. BugCraft employs a two-stage approach: first, a Step Synthesizer leverages LLMs and Minecraft Wiki knowledge to transform bug reports into high-quality, structured steps to reproduce (S2R). Second, an Action Model, powered by a vision-based LLM agent (GPT-4o) and a custom macro API, executes these S2R steps within Minecraft to trigger the reported crash. To facilitate evaluation, we introduce BugCraft-Bench, a curated dataset of Minecraft crash bug reports. Evaluated on BugCraft-Bench, our framework successfully reproduced 30.23% of crash bugs end-to-end. The Step Synthesizer demonstrated a 66.28% accuracy in generating correct bug reproduction plans, highlighting its effectiveness in interpreting and structuring bug report information. BugCraft demonstrates the feasibility of automated reproduction of crash bugs in complex game environments using LLMs, opening promising avenues for game testing and development. The framework and the BugCraft-Bench dataset pave the way for future research in automated game bug analysis and hold potential for generalization to other interactive game platforms. Finally, we make our code open at https://bugcraft2025.github.io/
Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting
Authors: Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang
Date: 2025-03-21 00:00:00
Proceeding: CVPR 2025 1

Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. Second, we propose a Key-frame-guided Streaming Strategy that refines each key frame, enabling accurate reconstruction of temporally complex scenes while mitigating error accumulation. We conducted extensive in-domain and cross-domain evaluations, demonstrating that our approach can achieve streaming with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.
Poly-FEVER: A Multilingual Fact Verification Benchmark for Hallucination Detection in Large Language Models
Authors: Hanzhi Zhang, Sumera Anjum, Heng Fan, Weijian Zheng, Yan Huang, Yunhe Feng
Date: 2025-03-19 00:00:00
Tasks: Fact Checking, Fact Verification, Hallucination, Multilingual NLP

Hallucinations in generative AI, particularly in Large Language Models (LLMs), pose a significant challenge to the reliability of multilingual applications. Existing benchmarks for hallucination detection focus primarily on English and a few widely spoken languages, lacking the breadth to assess inconsistencies in model performance across diverse linguistic contexts. To address this gap, we introduce Poly-FEVER, a large-scale multilingual fact verification benchmark specifically designed for evaluating hallucination detection in LLMs. Poly-FEVER comprises 77,973 labeled factual claims spanning 11 languages, sourced from FEVER, Climate-FEVER, and SciFact. It provides the first large-scale dataset tailored for analyzing hallucination patterns across languages, enabling systematic evaluation of LLMs such as ChatGPT and the LLaMA series. Our analysis reveals how topic distribution and web resource availability influence hallucination frequency, uncovering language-specific biases that impact model accuracy. By offering a multilingual benchmark for fact verification, Poly-FEVER facilitates cross-linguistic comparisons of hallucination detection and contributes to the development of more reliable, language-inclusive AI systems. The dataset is publicly available to advance research in responsible AI, fact-checking methodologies, and multilingual NLP, promoting greater transparency and robustness in LLM performance. The proposed Poly-FEVER is available at: https://huggingface.co/datasets/HanzhiZhang/Poly-FEVER.