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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Representation of Semantic Word Embeddings Based on SLDA and Word2vec Model

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Chinese Journal of Electronics 2023年第3期32卷 647-654页

作者： TANG Huanling ZHU Hui WEI Hongmin ZHENG Han MAO Xueli LU Mingyu GUO Jin School of Computer Science and Technology Shandong Technology and Business University Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing Key Laboratory of Intelligent Information Processing in Universities of Shandong Shandong Technology and Business University School of Information and Electronic Engineering Shandong Technology and Business University Information Science and Technology College Dalian Maritime University School of Computer and Information Technology Liaoning Normal University

To solve the problem of semantic loss in text representation, this paper proposes a new embedding method of word representation in semantic space called wt2svec based on supervised latent Dirichlet allocation(SLDA) and Word2vec. It generates the global topic embedding word vector utilizing SLDA which can discover the global semantic information through the latent topics on the whole document set. It gets the local semantic embedding word vector based on the Word2vec. The new semantic word vector is obtained by combining the global semantic information with the local semantic information. Additionally, the document semantic vector named doc2svec is generated. The experimental results on different datasets show that wt2svec model can obviously promote the accuracy of the semantic similarity of words,and improve the performance of text categorization compared with Word2vec.

关键词： Semantics Text categorization Resource management Task analysis

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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Cross-Domain Detection Transformer with Multi-view Adaptive Feature Alignment in Remote Sensing Imagery 2

Cross-Domain Detection Transformer with Multi-view Adaptive ...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Shu Han, Jianhong Wang, Ying Hao, Xinyuan Luo, Zhaoyi Wang, Yupei Chen, Liang Beijing Institute of Technology National Key Laboratory for Science and Technology on Space-Born Intelligent Information Processing Beijing China Shanghai Academy of Spaceflight Technology Shanghai China

ISBN: (纸本)9798331515669

Unsupervised Domain Adaptation (UDA) techniques are crucial for remote sensing object detection, designed to address performance degradation caused by the domain gap between training and test data. These methods leverage unlabeled target domain data, thus alleviating the high costs associated with data annotation. Recent developments in Detection Transformers (DETR) have simplified the detection pipeline and attracted significant research interest. Building on this architecture, we introduce an unsupervised domain adaptation detector for remote sensing object detection. Specifically, we introduce a multi-view adaptive feature alignment module that initially captures domain-specific features in complex backgrounds by leveraging a cross-attention mechanism. Subsequently, we employ contrastive learning to enforce the aggregation of domain-specific features from various perspectives, thereby improving the accuracy of feature alignment. Moreover, we demonstrate that integrating the self-training framework into DETR-based detectors can significantly mitigate the domain gap by further utilizing unlabeled data in the target domain. We validated the effectiveness and generalizability of our method across two remote sensing cross-domain detection scenarios using four public datasets. © 2024 IEEE.

关键词： object detection remote sensing imagery Unsupervised domain adaptation

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TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation 30

TransERR: Translation-based Knowledge Graph Embedding via Ef...

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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： Li, Jiang Su, Xiangdong Zhang, Fujun 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

This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at https://***/dellixx/TransERR. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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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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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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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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Exploring the Synergy of Dual-path Encoder and Alignment Module for Better Graph-to-Text Generation 30

Exploring the Synergy of Dual-path Encoder and Alignment Mod...

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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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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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