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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： Zhao, Tianxin Liu, Yingxin Su, Xiangdong Li, Jiang Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology China

ISBN: (纸本)9782493814104

The mainstream approaches view the knowledge graph-to-text (KG-to-text) generation as a sequence-to-sequence task and fine-tune the pre-trained model (PLM) to generate the target text from the linearized knowledge graph. However, the linearization of knowledge graphs and the structure of PLMs lead to the loss of a large amount of graph structure information. Moreover, PLMs lack an explicit graph-text alignment strategy because of the discrepancy between structural and textual information. To solve these two problems, we propose a synergetic KG-to-text model with a dual-path encoder, an alignment module, and a guidance module. The dual-path encoder consists of a graph structure encoder and a text encoder, which can better encode the structure and text information of the knowledge graph. The alignment module contains a two-layer Transformer block and an MLP block, which aligns and integrates the information from the dual encoder. The guidance module combines an improved pointer network and an MLP block to avoid error-generated entities and ensures the fluency and accuracy of the generated text. Our approach obtains very competitive performance on three benchmark datasets. Our code is available from https://***/IMU-MachineLearningSXD/G2T. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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EpLSA: Synergy of Expert-prefix Mixtures and Task-Oriented Latent Space Adaptation for Diverse Generative Reasoning 30

EpLSA: Synergy of Expert-prefix Mixtures and Task-Oriented L...

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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： Zhang, Fujun Su, Xiangdong Li, Jiang Yan, Rong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology China

ISBN: (纸本)9782493814104

Existing models for diverse generative reasoning still struggle to generate multiple unique and plausible results. Through an in-depth examination, we argue that it is critical to leverage a mixture of experts as prefixes to enhance the diversity of generated results and make task-oriented adaptation in the latent space of the generation models to improve the quality of the responses. At this point, we propose EpLSA, an innovative model based on the synergy of expert-prefix mixtures and task-oriented latent space adaptation for diverse generative reasoning. Specifically, we use expert-prefixes mixtures to encourage the model to create multiple responses with different semantics and design a loss function to address the problem that the semantics is interfered by the expert-prefixes. Meanwhile, we design a task-oriented adaptation block to make the pre-trained encoder within the generation model more effectively adapted to the pre-trained decoder in the latent space, thus further improving the quality of the generated text. Extensive experiments on three different types of generative reasoning tasks demonstrate that EpLSA outperforms existing baseline models in terms of both the quality and diversity of the generated outputs. Our code is publicly available at https://***/IMU-MachineLearningSXD/EpLSA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Hybrid Metric-Induced Nonnegative Matrix Factorization

Hybrid Metric-Induced Nonnegative Matrix Factorization

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2023 China Automation Congress, CAC 2023

作者： Yang, Sijun Chen, Wen-Sheng Pan, Binbin College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303759

NMF is a promising feature representation method for data analysis. Matrix decomposition losses can be described using different metrics, based on which different NMF algorithms can be developed. However, a small Euclidean distance metric is not guaranteed to have a small cosine similarity metric, and vice versa. To overcome the shortcomings of these metrics, we integrate these two metrics to present a novel hybrid metric-induced NMF (HMNMF) model. The update rules for the HMNMF algorithm are obtained using the gradient descent method to resolve the optimization problem. The proposed algorithm is proven to be convergent, which implies that it is reasonable and stable. The proposed HMNMF approach is compared with different metric-induced NMF algorithms for face recognition, the experimental results confirm the effectiveness and good performance of the proposed algorithm. © 2023 IEEE.

关键词： Matrix algebra

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Hyperbolic Representations for Prompt Learning 30

Hyperbolic Representations for Prompt Learning

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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： Chen, Nan Su, Xiangdong Bao, Feilong College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology China

ISBN: (纸本)9782493814104

Continuous prompt tuning has gained significant attention for its ability to train only continuous prompts while freezing the language model. This approach greatly reduces the training time and storage for downstream tasks. In this work, we delve into the hierarchical relationship between the prompts and downstream text inputs. In prompt learning, the prefix prompt acts as a module to guide the downstream language model, establishing a hierarchical relationship between the prefix prompt and subsequent inputs. Furthermore, we explore the benefits of leveraging hyperbolic space for modeling hierarchical structures. We project representations of pre-trained models from Euclidean space into hyperbolic space using the Poincaré disk which effectively captures the hierarchical relationship between the prompt and input text. The experiments on natural language understanding (NLU) tasks illustrate that hyperbolic space can model the hierarchical relationship between prompt and text input. We release our code at https://***/myaxxxxx/Hyperbolic-Prompt-Learning. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Knowledge Graph-Enhanced Recommendation with Box Embeddings 23rd

Knowledge Graph-Enhanced Recommendation with Box Embeddings

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Liang, Qiuyu Wang, Weihua Lv, Lei Bao, Feilong 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 Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9789819783663

Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most existing approaches ignore the hierarchical structure of the knowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the interaction between the user and the item, but also the head entity, the tail entity and the relation between them in the knowledge graph. Then the interaction between the item and the corresponding entity is calculated by the multi-task attention unit. Experimental results show that our method provides a large improvement over previous models in terms of Area Under Curve (AUC) and accuracy in publicly available recommendation datasets with three different domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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"study on the Grammatical Function of "la dhon"as Function Words in the Tibetan Language processing" 6

"study on the Grammatical Function of "la dhon"as Function W...

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6th International Conference on Machine Learning and Natural Language processing, MLNLP 2023

作者： Suonan, Cairang Gongque, Duojie Computer College of Qinghai Normal University Qinghai Xining810016 China Key Laboratory of Tibetan Information Processing Ministry of Education Qinghai Xining810008 China Tibetan Intelligent Information Processing and Machine Translation Key Laboratory Qinghai Xining810008 China

ISBN: (纸本)9798400709241

The study of Tibetan function words is an indispensable basic work in Tibetan natural language processing and has a wide range of practical application value. It is the core of Tibetan information processing and the basis of Tibetan natural language understanding, and has wide application prospects in Tibetan proofreading, information retrieval, bilingual translation, automatic classification and other technologies. On the basis of previous studies, this paper discusses the grammatical functions of Tibetan function words la dhon from the perspective of information processing, classification system and formal description of unfree function words, focuses on the grammatical functions of Tibetan function words la dhon, and puts forward the preliminary conception and processing methods of Tibetan function words. © 2023 ACM.

关键词： Natural language processing systems

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3D measurement of scattering media based on self-supervised fringe domain transformation

3D measurement of scattering media based on self-supervised ...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Tan, Ji Niu, Haipeng Luo, Suofan Liu, Jia He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration Ministry of Education Guangzhou510006 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

The scattering media measurement for structured light techniques remains challenging due to the subsurface scattering that destroys fringe patterns and introduces phase error. Recently, deep learning has been successfully applied to fringe analysis. Unfortunately, collecting a large scale of labeled data is difficult, especially in scattering media measurement scenarios. Inspired by image style transfer, we proposed a high-precision scattering medium measurement method based on self-supervised fringe domain transformation. Specifically, by treating degraded fringe and ideal fringe as two pattern styles, a cycle generation strategy is developed to achieve fringe enhancement by translating between two fringe domains. Two generators and two discriminators are used to form cycle-consistency constraints for self-supervised learning with unpaired dataset. Additionally, we combine numerical and physical constraints to jointly optimize network parameters, effectively suppressing phase-shift errors and improving phase quality. Since the proposed approach eliminates the need for label data, it exhibits great potential for applying deep learning to scattering medium measurement. Experiments verify that our method can obtain high-quality 3D morphology of translucent objects. © 2025 SPIE.

关键词： Velocity measurement

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Fusion Multi-scale Features and Attention Mechanism for Multi-view 3D Reconstruction 3

Fusion Multi-scale Features and Attention Mechanism for Mult...

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3rd IEEE International Conference on Electronic Technology, Communication and information, ICETCI 2023

作者： Xie, Qiqi Xin, Yuelan Sun, Kexin Zeng, Xi Qinghai Normal University Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9798350398410

In order to solve the problem that it is difficult to carry out the reconstruction accurately and completely in the process of multi-view 3D reconstruction, we puts forward to a 3D reconstruction method that combined with multi-scale features and attention mechanisms. To begin with, multi-scale features are extracted from the input source and reference images using a Feature Pyramid Network that adds a bottom-up structure to obtain the connections between the different scales of the feature map. What's more, the attention mechanism is used to optimize the cost volume regularization process and improve the accuracy of depth estimation. In the end, the improved network is trained. About the experimental results of DTU dataset, it can be seen that the 3D reconstruction results of this network reached 0.358mm and 0.366mm in Completeness (Comp) and Overall respectively, which are higher than traditional methods as well as some 3D reconstruction methods that are based on deep learning methods. At the same time, the results of visualization in the point cloud model have been significantly improved. In addition, experimental results on Tanks and Temples dataset show that this network also have a better generalization performance. © 2023 IEEE.

关键词： Deep learning

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Nucleation growth quenching for superior cluster catalysts

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Nano Research 2022年第9期15卷 7933-7939页

作者： Ruyue Wang Feng Cheng Yonggang Wang Dongyu Fan Bohan Deng Yuanzheng Long Haolin Tang Kai Huang Zhaowei Qu Ming Lei Binghui Ge Hui Wu State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China State Key Laboratory of Information Photonics and Optical Communications School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Space-ground Interconnection and Convergence Beijing University of Posts and Telecommunications(BUPT)Beijing 100876China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China

Cluster catalysts are rapidly growing into an important sub-field in heterogeneous catalysis,owing to their distinct geometric structure,neighboring metal sites,and unique electronic *** the thermodynamics and kinetics of the formation of nanoparticles have been largely investigated,the precise synthesis of clusters in wet chemical methods still faces great *** the study,a quenching strategy of asymmetric temperature in solution for the rapid generation of vacancy-defect rich clusters is *** quenching process can be used to synthesize multitudinous metal compound clusters,including metal oxides,fluorides,oxygen-sulfur compounds,and *** oxygen evolution reaction(OER),IrO_(2)clusters with abundant oxygen vacancies were obtained and uniformly dispersed in the *** to commercial IrO_(2),the prepared IrO_(2)cluster can be directly loaded on carbon paper and used as binder-free electrodes,which exhibit higher OER activity and long-term operational stability in alkaline *** quenching strategy provides a simple and efficient method for the synthesis of clusters,which has tremendous potential for industrial-scale preparation and application,especially can be further applied to flow electrochemical generators.

关键词： rapid-chilling cluster structure transition metal compounds binder-free electrode oxygen evolution reaction

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MTL-SRN: Multi-task Learning-based Signal recognition Network

MTL-SRN: Multi-task Learning-based Signal Recognition Networ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Pengxu Zhang, Xingjian Ma, Yuan Zhu, Hao Jiao, Jian Zhang, Qinyu Department of Electronics and Information Engineering Shenzhen518055 China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing The College of Electronic and Information Engineering Shenzhen518060 China

ISBN: (纸本)9798350351255

Wideband signal recognition is a crucial task in the cognitive wireless communication, involving accurate classification of different signal types, modulation types, center frequencies, etc. However, most conventional approaches treat the recognition of different parameters as multiple independent tasks, and often face performance bottlenecks due to the complexity and diversity of wideband signals. To overcome these challenges, we propose a multi-task learning (MTL) network that integrates multiple tasks of signal recognition into an end-to-end model to accomplish spectrum sensing, modulation recognition, and signal classification simultaneously. By employing a shared feature extraction network and a multi-task classification header, the proposed framework effectively captures the correlations and shared information among different tasks, thereby enhancing overall recognition performance. To validate the effectiveness of the proposed scheme, we compare its performance with other state-of-the-art recognition and classification networks. Experimental results demonstrate the significant performance of the proposed MTL network in spectrum sensing, modulation recognition, and signal classification tasks. © 2024 IEEE.

关键词： Signal modulation

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