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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wu, Xin Bu, Yuqi Cai, Yi Wang, Tao School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Biostatistics and Health Informatics King's College London United Kingdom

ISBN: (纸本)9798891761681

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT's performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

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Journal of Systems Science & Complexity 2023年第1期36卷 433-456页

作者： ELSAWAH A M VISHWAKARMA G K MOHAMED H S FANG Kai-Tai Department of Statistics and Data Science Faculty of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 44519Egypt Department of Mathematics&Computing Indian Institute of Technology DhanbadDhanbad 826004India College of Transportation and Civil Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China The Key Lab of Random Complex Structures and Data Analysis The Chinese Academy of SciencesBeijing 100190China

A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are *** large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not *** paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation *** justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level *** results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.

关键词： Aberration adjusted Gray map technique Gray map technique Hamming distance moment aberration threshold accepting algorithm uniform design

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Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

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Computers, Materials & Continua 2024年第6期79卷 4741-4762页

作者： Zhenyu Qian Yizhang Jiang Zhou Hong Lijun Huang Fengda Li Khin Wee Lai Kaijian Xia School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Department of Medical Imaging The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Department of Neurosurgery The Changshu Affiliated Hospital of Soochow UniversityChangshu215500China Department of Scientific Research The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Changshu Key Laboratory of Medical Artificial Intelligence and Big Data Suzhou215500China Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

关键词： Deep subspace clustering multiscale network structure automatic hyperparameter tuning semi-supervised medical image clustering

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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Multi-UAV Logistics Planning Problem Based on Improved Genetic Simulated Annealing Algorithm

Multi-UAV Logistics Planning Problem Based on Improved Genet...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Xue, Zhentao Chen, Jian Cao, Yuanyang Zhang, Zichao Liu, Xuzan College of Engineering China Agricultural University Beijing China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijng China

ISBN: (纸本)9789811966125

Multi-drone logistics transportation and distribution have gradually become the focus of future logistics. So as to solve the multi-UAV logistics planning problem, the goal of this paper is to minimize the cost of take-off and variable-load flight, and builds a multi-UAV logistics planning model with constraints such as flight distance and logistics load. The Improved Genetic Simulated Annealing Algorithm(IGSAA) adds the SA mechanism on the basis of the classical GA, selectively adds non-optimal solutions and non-constrained solutions, and adopts the Improved Large Neighborhood Search Algorithm (ILNSA) to generate special algorithms with three strategies. By continuously updating the population iteration and selectively adding the probability according to the temperature change, the optimal UAV logistics planning is finally optimized. Finally, the simulation results close to reality show that compared with the initial random solution and the classical genetic algorithm, the improved genetic simulated annealing algorithm proposed in this paper reduces the flight cost of UAV by 16.78% and 8.49%, respectively, and can obtain lower transportation costs. Drone logistics solution. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Drones

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Intelligent Aquaculture Water Quality Detection Scheme Based on TurMass Technology 8

Intelligent Aquaculture Water Quality Detection Scheme Based...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhang, Yafang Huang, Yongxin Chen, Hongming Zhejiang Ocean University School of Information Engineering Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Nankai University College of Electronics Information and Optical Engineering Tianjin300350 China

ISBN: (纸本)9798350302455

To address the shortcomings of wireless data transmission in aquaculture water quality monitoring, we have developed an intelligent water quality detection device based on the STM32 microcontroller system as the processing unit and TurMass transmission technology. This device can detect water quality in real-time and transmit the data to the user end for real-time monitoring so that users can monitor water quality in real-time. Through rigorous and systematic experimental measurements, the accuracy of the detected data by the device was found to be extremely high, indicating that it can be used in practical applications. © 2023 IEEE.

关键词： Water quality

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Universality class of machine learning for critical phenomena

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Science China(Physics,Mechanics & Astronomy) 2023年第12期66卷 63-70页

作者： Gaoke Hu Yu Sun Teng Liu Yongwen Zhang Maoxin Liu Jingfang Fan Wei Chen Xiaosong Chen School of Systems Science and Institute of Nonequilibrium Systems Beijing Normal UniversityBeijing 100875China Data Science Research Center Faculty of ScienceKunming University of Science and TechnologyKunming 650500China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning *** reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks(NNs),whereas phase transitions of different universality classes require different *** on this finding,we proposed the universality class of machine learning for critical ***,we investigated and discussed the NNs of different universality *** research contributes to machine learning by relating the NNs with the universality class.

关键词： universality class machine learning percolation

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A Unified Loss for Handling Inter-Class and Intra-Class Imbalance in Medical Image Segmentation 39

A Unified Loss for Handling Inter-Class and Intra-Class Imba...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Feilong Yang, Feiyang Li, Xiongfei Zhang, Xiaoli Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University China

ISBN: (纸本)157735897X

In utilizing deep learning techniques for medical image segmentation, two types of imbalance issues are observed: inter-class imbalance between majority and minority classes and intra-class imbalance between easy and hard samples. However, existing loss functions typically confuse these issues, leading to enhancements that cater to only one aspect. Moreover, loss functions optimized for specific tasks often exhibit limited generalizability. To address these issues, we propose Inter-class and Intra-class Balance loss, as well as a unified loss termed Balance loss. The Inter-class Balance loss controls the extent of hard sample mining for majority class samples by considering the frequency of minority classes present in each input image. This approach requires no manual adjustment weights and adapts automatically to different datasets. The Intra-class Balance loss enhances the network's ability to learn from hard samples by performing mining on hard samples within each class. We evaluate our loss functions on five segmentation tasks with varying degrees of class imbalance. The experimental results show that our proposed Balance loss enhances segmentation performance compared with the current loss functions and exhibits superior robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Fast Spirulina detection with fixed-focus microscope and deep learning

Fast Spirulina detection with fixed-focus microscope and dee...

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2022 Applied Optics and Photonics China: AI in Optics and Photonics, AOPC 2022

作者： Liu, Hao Lu, Zhen Wang, Yinchu Chen, Ruiquan Cai, Yuwei Xu, Nanlin Peng, Zheng Peng, Xiao Wu, Huifeng College of Physics and Optoelectronic Engineering Shenzhen Key Laboratory of Photonics and Biophotonics Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province Shenzhen University Shenzhen518060 China Yantai264003 China National Basic Science Data Center Beijing100190 China Shandong Key Laboratory of Coastal Environmental Processes YICCAS Yantai264003 China

ISBN: (纸本)9781510662407

As a kind of microalgae, Spirulina plays an important role in fish culture, food processing industry, medical treatment and bioenergetic development due to its reasonable nutritional composition and high hydrogenase activity. However, the purity of Spirulina, which could be significantly affected by virus infection and miscellaneous algal issues, has great impact on the quality of the product. Thus, periodic Spirulina detection is necessary for quality control of Spirulina culture. Currently, there are two main methods of Spirulina detection: the optical microscopic method and the fluorescence detection method. The former has higher accuracy and a lower speed while the latter has a higher speed in a sample destructing mode. Deep learning-based method has the ability to accelerate data processing. Meanwhile, it can achieve high accuracy by model training and validation. In this work, we have applied deep learning to Spirulina detection to achieve a higher accuracy rate. The process was divided into four main steps: Spirulina culture, image acquisition, image preprocessing and YOLO-v3 model training. The hyperparametric modulation was carried out to determine the appropriate training parameters, providing a trained model with mAP of 0.839 at a detection speed of 20.53 fps. It has great application potential in quantity detection and size detection of cultured Spirulina. © 2023 SPIE.

关键词： data handling

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