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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Hao Song Yuxia Lei Kangli Zi Fuyuan Quan Tianhang Zhang Qi Li School of Computer Science Qufu Normal University Rizhao Shandong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9 % over the best-performing baseline models.

关键词： Accuracy Semantics Knowledge graphs Contrastive learning Ontologies Data models Standards

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models

Leveraging Knowledge Distillation for Improved Event Extract...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Peiyao Zhou Zhikui Hu Kangli Zi Dawei Zhang School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents.

关键词： Accuracy Benchmark testing Information retrieval Question generation Question answering (information retrieval) Data mining Business

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Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration

arXiv

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arXiv 2024年

作者： Wen, Zhuofan Gui, Shangtong Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of Al Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base LLM verifies the draft for acceptance or rejection. In this framework, the final inference speed is decided by the decoding speed of the draft model and the acceptance rate of the draft provided by the draft model. Currently the widely used draft models usually generate draft tokens for the next several positions in a non-autoregressive way without considering the correlations between draft tokens. Therefore, it has a high decoding speed but an unsatisfactory acceptance rate. In this paper, we focus on how to improve the performance of the draft model and aim to accelerate inference via a high acceptance rate. To this end, we propose a CTC-based draft model which strengthens the correlations between draft tokens during the draft phase, thereby generating higher-quality draft candidate sequences. Experiment results show that compared to strong baselines, the proposed method can achieve a higher acceptance rate and hence a faster inference speed. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space 39

Learning with Open-world Noisy Data via Class-independent Ma...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

ISBN: (纸本)157735897X

关键词： Contrastive Learning

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Spatio-Temporal Coherent Snapshot Compressed Imaging with Time-Varying Composite Phase Masks

SSRN

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SSRN 2023年

作者： Zhang, Ru Zhang, Cheng Zhang, Liru Chen, Mingsheng Cheng, Hong Shen, Chuan Zhang, Quanbin Wei, Sui Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Province Hefei230601 China Advanced Laser Technology Laboratory of Anhui Province National University of Defense Technology Anhui Province Hefei230037 China

Snapshot compressed imaging (SCI) can encode an image sequence into a single-frame measurement and has the advantages of low requirements for physical sensors, low bandwidth/ memory requirements, and a fast acquisition speed. However, existing snapshot compressed imaging mainly focuses on incoherent imaging, such as video/hyperspectral imaging, and research on coherent imaging is rare. In this paper, snapshot compressed sensing is introduced for coherent imaging conditions, and a spatio-temporal coherent snapshot compressed imaging method with time-varying composite phase masks is proposed. Numerical experiments demonstrate the effectiveness and superiority of our proposed spatio-temporal coherent snapshot compressed imaging with time-varying composite phase masks method (CSCI-CPM) in terms of the reconstruction quality and robustness for both self-sparse and complex cases. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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Optimized Gradient Clipping for Noisy Label Learning

arXiv

引用

arXiv 2024年

作者： Ye, Xichen Wu, Yifan Zhang, Weizhong Li, Xiaoqiang Chen, Yifan Jin, Cheng Shanghai University China Fudan University China Hong Kong Baptist University Hong Kong Shanghai Key Laboratory of Intelligent Information Processing China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Previous research has shown that constraining the gradient of loss function w.r.t. model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples at different stages of training, significantly limiting the model capability to adapt to the variable nature of gradients throughout the training process. To address this issue, we propose a simple yet effective approach called Optimized Gradient Clipping (OGC), which dynamically adjusts the clipping threshold based on the ratio of noise gradients to clean gradients after clipping, estimated by modeling the distributions of clean and noisy samples. This approach allows us to modify the clipping threshold at each training step, effectively controlling the influence of noise gradients. Additionally, we provide statistical analysis to certify the noise-tolerance ability of OGC. Our extensive experiments across various types of label noise, including symmetric, asymmetric, instance-dependent, and real-world noise, demonstrate the effectiveness of our *** Codes 68T07, 68T10 © 2024, CC BY.

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Deep Reinforcement Learning based intelligent Routing for Multi-service Traffic SDN

Deep Reinforcement Learning based Intelligent Routing for Mu...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Lingjiao Wang Jiachen Li Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiang Tan University School of Automation and Electronic Information Xiang Tan University

The development of network has imposed higher requirements on QoS (Quality of Service) for routing, and the emergence of traffic classification techniques, software defined networking (SDN), and programmable network devices has made it possible to quickly identify user needs and route for service traffic requirements. Deep reinforcement learning routing algorithms for existing QoS compliant routing algorithms perform poorly in terms of generalizability. A deep graph reinforcement learning intelligent routing algorithm DDGL applied to SDN scenarios is proposed, which uses GNN to extract the link feature information of SDN data plane for deep reinforcement learning routing decision making, and comprehensively takes into account the performance metrics such as latency, packet loss, throughput, and bandwidth, to improve the algorithm generalization ability and enhance the SDN routing performance.

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A Hybrid Automatic Text Summarization Model for Judgment Documents 8th

A Hybrid Automatic Text Summarization Model for Judgment Doc...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Ouyang, Jianquan Huang, Wenpeng Liu, Tianming Key Laboratory of Intelligent Computing Information Processing Ministry of Education School of Computer Science and School of Cyberspace Science Xiangtan University Xiangtan China Department of Computer Science The University of Georgia AthensGA United States

ISBN: (纸本)9783031067662

Judgment documents are the final carrier of judicial trial activities, and they are an indispensable component for assisting sentencing decision-making and standardizing the scale of judgment. At present, the number of public judgment documents in China has reached 120 million, and it does not stop there, which brings a significant challenge for users to obtain useful information. To solve this problem, this paper proposes a hybrid automatic text summarization model for judgment documents. The method is divided into two stages. The first stage is, through extracting abstract technology, to extract key sentences from the original text to form a set of them. In the second stage, the set of key sentences extracted in the previous stage will be copied or rewritten to generate the final summary using the sequence generation model. The ROUGE indexes of this method in the automatic summarization experiment of judgment documents are 59.79, 37.71, and 52.67, which are 5.26, 7.25, and 10.41 higher than the benchmark model UniLM, respectively. The method proposed in this paper can effectively be applied in the automatic summarization service of judgment documents and solve the problem of information overload thereof and, finally, provide a new way for users to gain smooth access to judgment documents and information. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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