We study the problem of knowledge tracing (KT) where the goal is to trace the students' knowledge mastery over time so as to make predictions on their future performance. Owing to the good representation capacity of deep neural networks (DNNs), recent advances on KT have increasingly concentrated on exploring DNNs to improve the performance of KT. However, we empirically reveal that the DNNs based KT models may run the risk of overfitting, especially on small datasets, leading to limited generalization. In this paper, by leveraging the current advances in adversarial training (AT), we propose an efficient AT based KT method (ATKT) to enhance KT model's generalization and thus push the limit of KT. Specifically, we first construct adversarial perturbations and add them on the original interaction embeddings as adversarial examples. The original and adversarial examples are further used to jointly train the KT model, forcing it is not only to be robust to the adversarial examples, but also to enhance the generalization over the original ones. To better implement AT, we then present an efficient attentive-LSTM model as KT backbone, where the key is a proposed knowledge hidden state attention module that adaptively aggregates information from previous knowledge hidden states while simultaneously highlighting the importance of current knowledge hidden state to make a more accurate prediction. Extensive experiments on four public benchmark datasets demonstrate that our ATKT achieves new state-of-the-art performance. Code is available at: \color{blue} {\url{https://github.com/xiaopengguo/ATKT}}.
MemLoss: Enhancing Adversarial Training with Recycling Adversarial Examples
Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic)（计算机工程系，阿米尔卡比尔技术大学（特拉布宗理工大学））；Department of Computer Engineering, Sharif University of Technology（计算机工程系，沙里夫技术大学）
S2O: Enhancing Adversarial Training with Second-Order Statistics of Weights
Department of Computer Science, University of Exeter（埃克塞特大学计算机科学系）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究实验室）；Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）
Adversarial-Ensemble Kolmogorov Arnold Networks for Enhancing Indoor Wi-Fi Positioning: A Defensive Approach Against Spoofing and Signal Manipulation Attacks
School of Computer Engineering, Kalinga Institute of Industrial Technology（卡林加工业技术学院计算机工程学院）；Meteorological Training Institute, India Meteorological Department（印度气象局气象培训学院）
针对 Wi-Fi 欺骗与信号强度操纵攻击，本文提出基于 KAN 的对抗训练与基线—鲁棒模型集成方法，显著降低室内定位误差并提升系统可靠性。
Enhancing robustness of data-driven SHM models: adversarial training with circle loss
School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院）；Institute of Intelligent Software（智能软件研究院）；Saarland University（萨尔大学）；University of Auckland（奥克兰大学）
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Center for Research and Formation in Artificial Intelligence, Universidad de los Andes（安第斯大学人工智能研究与培训中心）
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Center for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Bytedance（字节跳动）；Department of Electrical and Computer Engineering, University of British Columbia（不列颠哥伦比亚大学电气与计算机工程学院）
Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University（河南大学时空大数据工业技术研究院）
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
Huaiyin Institute of Technology, Huai'an, China（淮阴职业技术学院）；Chengdu Research Institute, City University of Hong Kong, Chengdu, China（香港城市大学成都研究院）；Saarland University, Saarbrücken, Germany（萨尔兰大学）；Alpha Benito Research, Los Angeles, USA（Alpha Benito研究公司）；City University of Hong Kong, Hong Kong, China（香港城市大学）
Enhancing Stock Movement Prediction with Adversarial Training
National University of Singapore（新加坡国立大学）；Tsinghua Unversity（清华大学）；University of Science and Technology of China（中国科学技术大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Enhancing NLP Robustness and Generalization through LLM-Generated Contrast Sets: A Scalable Framework for Systematic Evaluation and Adversarial Training
College of Natural Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校自然科学学院）
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
AI Research Lab, Department of Computer Science, University of South Dakota（人工智能研究实验室、计算机科学系、南达科他大学）；School of Information and Artificial Intelligence, Yangzhou University（信息与人工智能学院、扬州大学）；Department of Computer Science and Software Engineering, Auburn University（计算机科学与软件工程系、阿伯丁大学）
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
State Key Laboratory of Intelligent Transportation Systems（智能交通系统国家重点实验室）；School of Transportation Science and Technology, Beihang University（北京航空航天大学交通科学与工程学院）；Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute（伦斯勒理工学院土木与环境工程系）
Enhancing Adversarial Transferability by Balancing Exploration and Exploitation with Gradient-Guided Sampling
School of Computer Science & Software Engineering, Shenzhen University, China（深圳大学计算机科学与软件工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China（广东省人工智能与数字经济发展实验室（深圳））；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University, China（广东省智能信息处理省级重点实验室）；School of Computer Science, University of Exeter, U.K.（埃克塞特大学计算机科学学院）；Department of Computing and Information Technology, Great Bay University, China（大鹏大学计算与信息技术系）；Computer Vision Institute, School of Artificial Intelligence, Shenzhen University, China（人工智能学院计算机视觉研究所）
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Information Technology & Management, University of International Business and Economics（对外经济贸易大学信息技术与管理学院）
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
Jinan University（暨南大学）；Guangdong Institute of Smart Education（广东省智慧教育研究院）；TAL Education Group（好未来教育集团）；Think Academy International Education（学而思国际教育）
知识追踪（KT）是基于学生与智能教学系统的历史交互来预测学生未来表现的问题。最近的研究已经应用了多种类型的深度神经网络来解决KT问题。然而，现实世界的教育数据中有两个重要因素没有得到很好的体现。首先，大多数现有的作品增加输入表示的问题和知识成分的共现矩阵\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.}（KCs），但没有明确地将这种内在的关系到最终的响应预测任务。第二，学生的个性化历史表现没有得到很好的捕捉。在本文中，我们提出了\emph{AT-DKT}来提高原始深度知识追踪模型的预测性能，并使用两个辅助学习任务，即，\emph{question tagging (QT) prediction task}和\emph{individualized prior knowledge (IK) prediction task}。具体来说，QT任务通过预测问题是否包含特定的KC来帮助学习更好的问题表征。IK任务通过逐步预测隐藏在学生历史学习交互中的学生水平先验知识来捕获学生的全局历史表现。我们在三个真实世界的教育数据集上进行了全面的实验，并将所提出的方法与深度序列KT模型和非序列模型进行了比较。实验结果表明，\emph{AT-D…
