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IEEE Symposium on Computational intelligence and Games, CIG

作者： Simão Reis Rita Novais Luís Paulo Reis Nuno Lau Artificial Intelligence and Computer Science Laboratory (LIACC) Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Aveiro Portugal

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

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Network Security Evaluation for Space-Ground Integrated Networks Based on Network Simulation 8

Network Security Evaluation for Space-Ground Integrated Netw...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Ye, Haibo Zhao, Lei Tao, Leiting Wang, Xiaofeng The 6th Research Institute of China Electronics Corporation Beijing China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350331035

Space-ground integrated networks (SGINs) have become an important direction of future network development. However, SGINs face many security threats because of the multidimensional heterogeneity of their structure. Therefore, effective network security evaluation is an indispensable part of SGIN security research. Most existing network security evaluations are oriented toward network environments with simple structures and small scales and cannot be applied to SGINs. This paper reports the simulation of realistic SGIN scenarios through network simulation. Based on such network simulation, this paper proposes a network security evaluation suitable for SGINs that realizes the evaluation of the security risks and threats to SGINs in order to further improve their security. First, a SGIN security evaluation architecture is constructed to hierarchically describe the SGIN security evaluation process. Then, considering the characteristics of SGINs and abnormal network behavior characteristics, a SGIN security evaluation index system is designed, laying a foundation for the security evaluation of SGINs. Second, in view of the requirements of security evaluation data collection, a data collection method with multiple categories of indices is proposed, which not only guarantees the effectiveness of data collection but also improves the collection efficiency. Finally, based on game theory, a network security evaluation model is designed for the systematic security evaluation requirements of SGINs. This model realizes the effective evaluation of the effects of abnormal network behavior while accounting for subjective intentions and objective patterns. Experimental results show that the proposed technique can effectively gather information about system data resources through data collection. At the same time, the proposed network security evaluation model conforms to the patterns of abnormal network behavior and effectively meets the needs of security evaluation for SGINs. © 202

关键词： data collection network security evaluation network simulation space-ground integrated network

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Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction 30

Take Care of Your Prompt Bias! Investigating and Mitigating ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Xu, Ziyang Peng, Keqin Ding, Liang Tao, Dacheng Lu, Xiliang Institute of Artificial Intelligence School of Computer Science Wuhan University China School of Mathematics and Statistics Wuhan University China Hubei Key Laboratory of Computational Science Wuhan University China Beihang University China The University of Sydney Australia Nanyang Technological University Singapore

ISBN: (纸本)9782493814104

Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labels. Prompt bias presents a significant challenge in assessing the factual knowledge within PLMs. Therefore, this paper aims to improve the reliability of existing benchmarks by thoroughly investigating and mitigating prompt bias. We show that: 1) all prompts in the experiments exhibit non-negligible bias, with gradient-based prompts like AutoPrompt and OptiPrompt displaying significantly higher levels of bias;2) prompt bias can amplify benchmark accuracy unreasonably by overfitting the test datasets, especially on imbalanced datasets like LAMA. Based on these findings, we propose a representation-based approach to mitigate the prompt bias during inference time. Specifically, we first estimate the biased representation using prompt-only querying, and then remove it from the model's internal representations to generate the debiased representations, which are used to produce the final debiased outputs. Experiments across various prompts, PLMs, and benchmarks show that our approach can not only correct the overfitted performance caused by prompt bias, but also significantly improve the prompt retrieval capability (up to 10% absolute performance gain). These results indicate that our approach effectively alleviates prompt bias in knowledge evaluation, thereby enhancing the reliability of benchmark assessments. Hopefully, our plug-and-play approach can be a golden standard to strengthen PLMs toward reliable knowledge bases. Code and data are released in https://***/FelliYang/PromptBias. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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ScholarGEC: Enhancing Controllability of Large Language Model for Chinese Academic Grammatical Error Correction 39

ScholarGEC: Enhancing Controllability of Large Language Mode...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Kong, Zixiao Wang, Xianquan Shen, Shuanghong Zhu, Keyu Xu, Huibo Su, Yu Institute of Advanced Technology State Key Laboratory of Cognitive Intelligence University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China School of Computer Science and Artificial Intelligence Hefei Normal University China

ISBN: (纸本)157735897X

Large language models (LLMs) have demonstrated exceptional error detection capabilities and can correct sentences with high fluency in grammatical error correction (GEC) tasks. However, when correcting Chinese academic papers, LLMs face significant challenges of over-correction. To delve deeper into this issue, we explore the underlying reasons. On one hand, each discipline has its unique vocabulary and expressions, and LLMs have insufficient and incomplete understanding of domain-specific sentences. On the other hand, the controllability of generative LLMs in GEC tasks is inherently poor, and the traditional sequence-to-sequence (Seq2Seq) correction structure exacerbates this issue. Considering the two aforementioned factors, we propose a new error correction framework for Chinese academic GEC tasks using LLMs, named ScholarGEC. To improve LLMs' understanding of domain-specific knowledge, we construct appropriate disciplinary knowledge prefixes for sentences and use this domain-specific knowledge data to fine-tune the LLM. To enhance the controllability of LLMs, we replace the traditional Seq2Seq structure with a Detection-Correction separated structure. We also introduce a special token during the process to improve the model's error detection stability. Additionally, we incorporate iterative self-reflection to enhance the stability of the generation, in the three parts of LLM generation. Extensive experiments demonstrate the effectiveness and robustness of our framework on a Chinese GEC dataset composed of academic papers, and further analysis reveals the capabilities of our framework in enhancing LLM performance in general GEC tasks. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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Progressive Self-supervised Representation Learning for 3D Facial Expression Recognition

Progressive Self-supervised Representation Learning for 3D F...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Hebeizi Li Hongyu Yang Di Huang School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Facial expression recognition (FER) is a critical area of research in face analysis. While 2D data has been extensively used, 3D data offers inherent advantages, such as increased resilience to illumination and pose variations. However, the limited size of current 3D FER datasets significantly constrains the performance of 3D FER methods. To overcome this challenge, we propose a novel self-supervised pre-training scheme by leveraging large-scale external 3D data, followed by fine-tuning on 3D FER datasets. Our approach starts with self-supervised learning on a large-scale 3D point cloud object dataset, specifically ShapeNet. We then move on to the FaceScape dataset, which is primarily used for morphable face prediction. To enhance robustness, we integrate synthetic data before fine-tuning on specific FER datasets. This multi-stage process allows the model to progressively learn 3D facial expression representations from coarse to fine. For this purpose, we utilize Point-MAE, a leading self-supervised model for representation learning. To enhance its ability for FER task, we further incorporate facial priors in the masking and point sampling steps, leveraging the distinctive characteristics of facial data. Our method achieves state-of-the-art performance on both BU-3DFE and Bosphorus datasets, matching or surpassing results achieved by other 2D+3D FER techniques.

关键词： Representation learning Training Point cloud compression Solid modeling Three-dimensional displays Face recognition Self-supervised learning Robustness Synthetic data Resilience

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ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling 41

ESM All-Atom: Multi-scale Protein Language Model for Unified...

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41st International Conference on Machine Learning, ICML 2024

作者： Zheng, Kangjie Long, Siyu Lu, Tianyu Yang, Junwei Dai, Xinyu Zhang, Ming Nie, Zaiqing Ma, Wei-Ying Zhou, Hao School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University Anker Embodied AI Lab Peking University Beijing100871 China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science Tsinghua University China Tsinghua University China PharMolix Inc. China

Protein language models have demonstrated significant potential in the field of protein ***, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom *** limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small *** this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular ***-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and *** results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language *** investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. Copyright 2024 by the author(s)

关键词： Molecular docking

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DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition

arXiv

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arXiv 2024年

作者： Li, Hebeizi Yang, Hongyu Huang, Di School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

Facial Expression Recognition (FER) has consistently been a focal point in the field of facial analysis. In the context of existing methodologies for 3D FER or 2D+3D FER, the extraction of expression features often gets entangled with identity information, compromising the distinctiveness of these features. To tackle this challenge, we introduce the innovative DrFER method, which brings the concept of disentangled representation learning to the field of 3D FER. DrFER employs a dual-branch framework to effectively disentangle expression information from identity information. Diverging from prior disentanglement endeavors in the 3D facial domain, we have carefully reconfigured both the loss functions and network structure to make the overall framework adaptable to point cloud data. This adaptation enhances the capability of the framework in recognizing facial expressions, even in cases involving varying head poses. Extensive evaluations conducted on the BU-3DFE and Bosphorus datasets substantiate that DrFER surpasses the performance of other 3D FER methods. © 2024, CC BY.

关键词： Face recognition

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Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第1期5卷 127-139页

作者： Tao, Xian Yan, Shaohua Gong, Xinyi Adak, Chandranath Institute of Automation Chinese Academy of Sciences Beijing100190 China Binzhou Institute of Technology Binzhou256606 China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing100190 China School of Artificial Intelligence University of ChineseAcademy of Sciences Beijing100049 China Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this article, a multiresolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pretrained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multiscale autoencoder framework. This hierarchical alignment strategy aids in receiving multilevel information for locating anomalies of various sizes. Moreover, a residual attention module is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques. © 2020 IEEE.

关键词： Textures

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