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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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Re-HGNM: a repeat aware hypergraph neural machine for session-based recommendation

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Neural Computing and Applications 2024年第17期36卷 9661-9674页

作者： Peng, Yuze Xu, Shengjun Huang, Yihua School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510006 China The Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Guangdong Zhuhai519080 China

Hypergraph neural network (HGNN) for session-based recommendation (SBR) is quite rare but has been rewarded with promising performance. However, under the hypergraph framework, no works have emphasized the importance of repeat consumption, which is an important consumption pattern in SBR (e.g., people watch movies or listen to music repeatedly over time) and limits the performance of SBR in a beyond graph topology manner. For filling this gap and better recommendation performance, this paper incorporates repeat consumption into HGNNs and proposes a new recommendation machine, named Re-HGNM, which leverages the powerful expressiveness of HGNNs in representation learning and takes repeat consumption into account by considering the relationship between repeat behaviors and modeling repeat recommendations. Specially, in the encoder of Re-HGNM, we process sessions as hyperedges, in which the repeated behaviors are merged, to constitute hypergraph that conveys not only the complex relations between unique items but the relationships between repeat behaviors. After learning item representation by hypergraph convolution, a repeat-explore module is employed to determine whether to make repeat recommendation. In repeat mode, the next click must be in old items;thereby, the shrunk prediction space makes it easier to find the target item. Therefore, in the decoding stage, two decoders correspondingly work under two separate modes. Finally, compared with the current best baseline, Re-HGNM relatively improved P@20 by 58.37%, 8.70%, 1.28%, and 1.16% and MRR@20 by 25.52%, 10.31%, 10.76%, and 2.76% on the Tmall, RetailRocket, Nowplaying, and Diginetica, to be the current state-of-the-art model for SBR. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： Topology

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Policy iteration-based adaptive optimal control for Markov jump systems: a transition-probability-free asynchronous approach

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Science China(information Sciences) 2024年第11期67卷 363-364页

作者： Weidi CHENG Chengcheng REN Shuping HE Changyin SUN Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education) School of Artificial Intelligence Anhui University

The controller and filter design problems of Markov jump systems(MJSs) have gained significant attention over the past few decades. These studies include various aspects,including stochastic stabilization [1], optimal tracking control [2], and dissipative filter design [3]. Although numerous publications address the optimal controller design for MJSs, the issue of hidden MJSs, particularly those with mismatched jumping modes between the system and the controller, has rarely been explored.

关键词： Markov processes

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Power Minimization for the IRS-Aided SWIPT Secure Communication System with Imperfect Cascaded Channel

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Journal of Communications and information Networks 2023年第2期8卷 133-140页

作者： Chunlong He Shuqiong Xu Gongbin Qian Xingquan Li Chiya Zhang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China School of Microelectronics Shenzhen Institute of Information TechnologyShenzhen 518000China School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhen 518055China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Peng Cheng Laboratory(PCL) Shenzhen 5180523China

intelligent reflecting surface(IRS)-assisted simultaneous wireless information and power transfer(SWIPT)is a promising technology for prolonging the lifetime of the users and improving system ***,it is challenging to obtain the perfect channel state information(CSI)at the base station(BS)due to the lack of radio frequency(RF)chains at the IRS,which becomes even harder when eavesdroppers(Eves)*** this paper,we study the power issues in the IRS-aided SWIPT system under the imperfect CSI of BS-IRS-Eves *** aim to minimize the transmitting power at the BS by jointly designing transmit beamforming and reflective beamforming with subject to the rate outage probability constraints of the Eves,the energy harvesting constraints of the energy receivers(ERs),and the minimum rate constraints of the information receivers(IRs).We use Bernstein-type inequality to solve the rate outage probability ***,the formulated nonconvex problem can be efficiently tackled by employing alternating optimization(AO)combined with semidefinite relaxation(SDR)and penalty convex-concave procedure(CCP).Numerical results demonstrate the effectiveness of the proposed scheme.

关键词： IRS SWIPT secure communication AO

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A Multi Target Drug Design Method Based on Target Protein Sequence and Feature Similarity

A Multi Target Drug Design Method Based on Target Protein Se...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Haoran Lin, Xiaoli Hu, Jing Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350386226

This paper describes a multi target drug design method based on features of the target proteins. The multi target drug which inhibit multiple proteins have prospective applications, but design is difficult. In this study, the target protein sequences are embedded to obtain target features. The features of each target are independently encoded as target latent vectors, and the features of multiple targets are jointly encoded as target similarity latent vectors. Based on the features of targets and the similarity features among the targets, multi target drugs are efficiently designed. In the experiments, the designed multi target drugs can be docked with target proteins with a proprietary molecular structure according to the requirements of different target pocket structures. The excellent fitness among the molecular structure of multi target drugs and the protein structure of multiple targets confirms the performance of the proposed method. In the molecular docking part of the experi¬ments, the binding affinity of designed multi target drugs is far better than that in the previous studies, which administrates the better performance of this work. © 2024 IEEE.

关键词： Molecular docking

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Singularity Analysis of Gradient Projection Method for Dynamic Polarization Control 16

Singularity Analysis of Gradient Projection Method for Dynam...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Xu, Yuxi Li, Zongkai Zhang, Bin Wang, Dawei School of Electrical and Information Technology Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China

We study two explicit singularities of the gradient projection method by analyzing and experimentally testing the Jacobian of the rotatable waveplate model for dynamic polarization control. © 2024 IEEE.

ISBN: (纸本)9798350372076

关键词： Polarization

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Improved YOLOv5s Tomato Leaf Small Disease Spot Detection Method for Embedded Devices

Improved YOLOv5s Tomato Leaf Small Disease Spot Detection Me...

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2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Li, Xiaojing Fang, Jiandong Zhao, Yvdong College of Information Engineering Inner Mongolia University of Technology China Inner Mongolia Key Laboratory of Perceptual Technology and Intelligent Systems China

ISBN: (纸本)9781510680012

In the field of tomato disease spot detection, despite significant advancements in convolutional neural network technology, challenges still persist in detecting small-sized tomato disease spots. This study aims to enhance the tomato disease spot detection model to improve the accuracy of detecting small disease spots on tomatoes. Firstly, the concept of DenseNet is introduced, and a Dense_C3 module is constructed, which incorporates two 3×3 convolutional layers within the Bottleneck of C3 to achieve dense feature propagation and reuse. Secondly, the Convolutional Block Attention Module (CBAM) is employed to weight the feature maps, enhancing the focus on local and spatial information to enhance model accuracy. To validate the effectiveness of the proposed improvements, experiments were conducted on tomato disease spots of different scales, including Septoria Leaf Spot, Early Blight, Late Blight, and Tomato Yellow Leaf Curl Virus. The experimental results demonstrate that the introduction of the Dense_C3 structure and CBAM improves the detection accuracy for small-scale targets. In a comprehensive evaluation, the improved YOLOv5s model shows increases of 2.4% in F1 score and 2.1% in mAP. © 2024 SPIE.

关键词： Viruses

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Dynamic Student Classification with Forgetting Mechanisms on Memory Networks for Knowledge Tracing 5

Dynamic Student Classification with Forgetting Mechanisms on...

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5th International Conference on intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Huang, Guimin Qin, Xiangqian School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350368284

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. Therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods. © 2024 IEEE.

关键词： Contrastive Learning

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Zhou, Ya Chen, Chen Huang, Guimin Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331506612

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%. © 2024 IEEE.

关键词： Semantics

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