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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Fu, Honghao Zhang, Liang Fu, Biao Zhao, Rui Su, Jinsong Shi, Xiaodong Chen, Yidong Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China Ministry of Culture and Tourism China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

ISBN: (纸本)9798891761193

The primary objective of sign language translation (SLT) is to transform sign language videos into natural sentences. A crucial challenge in this field is developing signer-independent SLT systems which requires models to generalize effectively to signers not encountered during training. This challenge is exacerbated by the limited diversity of signers in existing SLT datasets, which often results in suboptimal generalization capabilities of current models. Achieving robustness to unseen signers is essential for signer-independent SLT. However, most existing method relies on signer identity labels, which is often impractical and costly in real-world applications. To address this issue, we propose the Signer Diversity-driven Data Augmentation (SDDA) method that can achieve good generalization without relying on signer identity labels. SDDA comprises two data augmentation schemes. The first is data augmentation based on adversarial training, which aims to utilize the gradients of the model to generate adversarial examples. The second is data augmentation based on diffusion model, which focuses on using the advanced diffusion-based text guided image editing method to modify the appearances of the signer in images. The combination of the two strategies significantly enriches the diversity of signers in the training process. Moreover, we introduce a consistency loss and a discrimination loss to enhance the learning of signer-independent features. Our experimental results demonstrate our model significantly enhances the performance of SLT in the signer-independent setting, achieving state-of-the-art results without relying on signer identity labels. © 2024 Association for Computational Linguistics.

关键词： Translation (languages)

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A Mutual Information-Based Assessment of Reverse Engineering on Rewards of Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1089-1100页

作者： Chen, Tong Liu, Jiqiang Baker, Thar Wu, Yalun Xiang, Yingxiao Li, Yike Niu, Wenjia Tong, Endong Zomaya, Albert Y. Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah27272 United Arab Emirates The University of Sydney School of Computer Science Sydney2006 Australia

Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference (EVD) and the mean reward loss (MRL). Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards, while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this article, based on the metric called normalized mutual information of reward clusters (C-NMI), we propose a novel IRL assessment;we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a fourth-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in object world, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59%, respectively, when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations. © 2020 IEEE.

关键词： Reverse engineering

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language Processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language Processing

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Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity

arXiv

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arXiv 2024年

作者： Wang, Zheng Peng, Zhaopeng Wang, Zihui Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities China School of Informatics Xiamen University China

Federated Learning (FL) stands to gain significant advantages from collaboratively training capacity-heterogeneous models, enabling the utilization of private data and computing power from low-capacity devices. However, the focus on personalizing capacity-heterogeneous models based on client-specific data has been limited, resulting in suboptimal local model utility, particularly for low-capacity clients. The heterogeneity in both data and device capacity poses two key challenges for model personalization: 1) accurately retaining necessary knowledge embedded within reduced submodels for each client, and 2) effectively sharing knowledge through aggregating size-varying parameters. To this end, we introduce Pa3dFL, a novel framework designed to enhance local model performance by decoupling and selectively sharing knowledge among capacity-heterogeneous models. First, we decompose each layer of the model into general and personal parameters. Then, we maintain uniform sizes for the general parameters across clients and aggregate them through direct averaging. Subsequently, we employ a hyper-network to generate size-varying personal parameters for clients using learnable embeddings. Finally, we facilitate the implicit aggregation of personal parameters by aggregating client embeddings through a self-attention module. We conducted extensive experiments on three datasets to evaluate the effectiveness of Pa3dFL. Our findings indicate that Pa3dFL consistently outperforms baseline methods across various heterogeneity settings. Moreover, Pa3dFL demonstrates competitive communication and computation efficiency compared to baseline approaches, highlighting its practicality and adaptability in adverse system conditions. © 2024, CC BY.

关键词： Embeddings

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Ciphertext-Independent and Unbounded Depth RSA-Based Updatable Encryption Scheme 1

Ciphertext-Independent and Unbounded Depth RSA-Based Updatab...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Kara, Mostefa Laouid, Abdelkader Bounceur, Ahcene Hammoudeh, Mohammad Nadhir, Abid Mohamed El Habib, Kahla Mohamed Dhahran31261 Saudi Arabia El Oued University Liap Laboratory PO Box 789 El Oued39000 Algeria College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom

ISBN: (纸本)9798331541064

We introduce a novel updatable encryption scheme based on the RSA cryptosystem to address the challenges of key rotation in secure communication systems. Updatable encryption allows ciphertexts encrypted under an old key to be transformed into ciphertexts under a new key without decrypting the plaintext, thereby enhancing security without compromising data integrity or confidentiality. Our RSA-based scheme leverages the mathematical properties of RSA to enable efficient and seamless key updates. We provide a security analysis demonstrating that our scheme is not vulnerable to information leaking. Experimental results indicate that our method offers a practical and efficient solution for large-scale systems that require regular key updates, with minimal impact on computational resources and performance. © 2024 IEEE.

关键词： Ciphertext

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Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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A New Adversarial Perspective for LiDAR-Based 3D Object Detection 39

A New Adversarial Perspective for LiDAR-Based 3D Object Dete...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Shijun Liu, Weiquan Guo, Yu Zang, Yu Shen, Siqi Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China College of Computer Engineering Jimei University China

ISBN: (纸本)157735897X

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Vehicle detection

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FedRME: Federated Road Markings Extraction from Mobile LiDAR Point Clouds 25

FedRME: Federated Road Markings Extraction from Mobile LiDAR...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chen, Liang Fan, Xiaoliang Jin, Haibing Sun, Xiaotian Cheng, Ming Wang, Cheng Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart Cities Computer Science Department School of Informatics Xiamen Fujian China

ISBN: (纸本)9781665405270

Road markings extraction (RME) from 3D point clouds acquired by mobile LiDAR systems has been widely used for road safety and autonomous driving. However, due to the increasing awareness of personal data protection and national informat ion security regulations, most autonomous driving companies are not willing to share their private point clouds data with the community. Therefore, such restriction of centralized training might inevitably inhibit the effectiveness of RME procedure. Federated learning (FL) is a distributed machine learning architecture that could address the aforementioned privacy-accuracy dilemma to collaboratively learn a global RME model from multiple clients without sharing raw data. In this paper, we propose a novel FedRME, a federated road markings extraction system to collaboratively learn a global RME model with multiple privacy-preserved local models from 3D mobile LiDAR point clouds. FedRME adopt the classical FedAvg model to construct a generalizable global feature embedding model without accessing local data. Moreover, to tackle data heterogeneity problem that local models vary in point clouds volumes and categories, we design a dynamic weighting mechanism to optimize the cooperative training effectiveness before server aggregation. Experimental results on three real-world mobil e LiDAR point clouds datasets with federated learning settings demonstrate that FedRME not only achieves superior performance but also reduces computation by up to 25%.The source code is available at https://***/WwZzz/easyFL#FedRME. © 2022 IEEE.

关键词： Extraction

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E2C: A Visual Simulator to Reinforce Education of Heterogeneous computing Systems

E2C: A Visual Simulator to Reinforce Education of Heterogene...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Ali Mokhtari Drake Rawls Tony Huynh Jeremiah Green Mohsen Amini Salehi High Performance Cloud Computing (HPCC) Laboratory School of Computing and Informatics University of Louisiana at Lafayette LA USA

Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. ln particular, with the increasing popularity of accelerator technologies (eg., GPUs and TPUs) and the emergence of domain-specific computing via ASlCs and FPGA, the matter of heterogeneity and understanding its ramifications on the system performance has become more critical than ever before. However, it is challenging to effectively educate students about the potential impacts of heterogeneity on: (a) the performance of distributed systems; and (b) the logic of resource allocation methods to efficiently utilize the resources. Making use of the real infrastructure (such as those offered by the public cloud providers) for benchmarking the performance of heterogeneous machines, for different applications, with respect to different obiectives, and under various workload intensities is cost- and time-prohibitive. Moreover, not all students (globally and nationally) have access or can afford such real infrastructure. To reinforce the quality of learning about various dimensions of heterogeneity, and to decrease the widening gap in education, we develop an open-source simulation tool, called E2C, that can help students researchers and practitioners to study any type of heterogeneous (or homogeneous) computing system and measure its performance under various system configurations. To make the learning curve shallow, E2C is equipped with an intuitive graphical user interface (GU1) that enables its users to easily examine system-level solutions (scheduling, load balancing, scalability, etc.) in a controlled environment within a short time and at no cost. In particular, E2C is a discrete event simulator that offers the following features: (i) simulating a heterogeneous computing system; (ii) implementing a newly developed scheduling method and plugging it into the system, (iii) measuring energy consumption and other output-related metrics; and (iv) powerful visual

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Automatic piano performance interaction system based on greedy algorithm for dexterous manipulator

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 473-485页

作者： Yufei WANG Junfeng YAO Yalan ZHOU Zefeng WANG Center for Digital Media Computing School of FilmXiamen UniversityXiamen 361005China Institute of Artificial Intelligence Xiamen UniversityXiamen 361005China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen 361005China

With continuous advancements in artificial intelligence(AI), automatic piano-playing robots have become subjects of cross-disciplinary interest. However, in most studies, these robots served merely as objects of observation with limited user engagement or interaction. To address this issue, we propose a user-friendly and innovative interaction system based on the principles of greedy algorithms. This system features three modules: score management, performance control, and keyboard interactions. Upon importing a custom score or playing a note via an external device, the system performs on a virtual piano in line with user inputs. This system has been successfully integrated into our dexterous manipulator-based piano-playing device, which significantly enhances user interactions.

关键词： Human-robot interaction Piano-playing robot Greedy algorithm Score parsing

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