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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Li, Jiaqi Du, Miaozeng Zhang, Chuanyi Chen, Yongrui Hu, Nan Qi, Guilin Jiang, Haiyun Cheng, Siyuan Tian, Bozhong School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Tencent AI Lab China Zhejiang University China

ISBN: (纸本)9798891760998

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks predominantly focus on coarse-grained knowledge, leaving the intricacies of fine-grained (FG) multimodal entity knowledge largely unexplored. This gap presents a notable challenge, as FG entity recognition is pivotal for the practical deployment and effectiveness of MLLMs in diverse real-world scenarios. To bridge this gap, we introduce MIKE, a comprehensive benchmark and dataset specifically designed for the FG multimodal entity knowledge editing. MIKE encompasses a suite of tasks tailored to assess different perspectives, including Vanilla Name Answering, Entity-Level Caption, and Complex-Scenario Recognition. In addition, a new form of knowledge editing, Multi-Step Editing, is introduced to evaluate the editing efficiency. Through our extensive evaluations, we demonstrate that the current state-of-the-art methods face significant challenges in tackling our proposed benchmark, underscoring the complexity of FG knowledge editing in MLLMs. Our findings spotlight the urgent need for novel approaches in this domain, setting a clear agenda for future research and development efforts within the community. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Factor Graph Neural Network Meets Max-Sum: A Real-Time Route Planning Algorithm for Massive-Scale Trips 23

Factor Graph Neural Network Meets Max-Sum: A Real-Time Route...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Li, Yixuan Wang, Wanyuan Xu, Weiyi Deng, Yanchen Wu, Weiwei Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Southeast University Nanjing China

Global route planning (GRP) is a typical combinatorial optimization problem that has been solved for a variety of industrial purposes, such as traffic flow management, network routing, and conflict prevention. The goal of the GRP is to find a route for each trip query such that all queries have a minimum global travel time. The GRP problem is NP-hard and computationally challenging, even for medium-sized instances. However, in real-world GRP applications, such as Google Maps-based vehicle route guidance systems, there are always massive-scale trips issued simultaneously, and real-time response is required. Existing mathematical programming-based exact methods and heuristics struggle to balance the extremes of optimality and scalability. Considering that many closed-related GRP instances must be solved repeatedly, this paper explores a deep learning approach to learn real-time and efficient solutions for GRP. This paper first proposes a novel route-query factor graph (RQ-FG) to model the GRP problem, where the message-passing damped Max-sum (DMS) algorithm can be exploited to generate high-quality approximate solutions. A hybrid pruning method is proposed to accelerate solving the DMS. We further devise a route-query factor graph neural network (RQ-FGNN) based on the RQ-FG, which has the ability to return solutions in milliseconds. Experiments demonstrate that our method can generate high-quality solutions in massive-scale GRP instances in real-time. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Message passing

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TabLog: test-time adaptation for tabular data using logic rules 24

TabLog: test-time adaptation for tabular data using logic ru...

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Proceedings of the 41st International Conference on Machine Learning

作者： Weijieying Ren Xiaoting Li Huiyuan Chen Vineeth Rakesh Zhuoyi Wang Mahashweta Das Vasant Honavar Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology The Pennsylvania State University PA Visa Research CA Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology and College of Information Sciences and Technology The Pennsylvania State University PA

We consider the problem of test-time adaptation of predictive models trained on tabular data. Effective solution of this problem requires adaptation of predictive models trained on the source domain to a target domain, using only unlabeled target domain data, without access to source domain data. Existing test-time adaptation methods for tabular data have difficulty coping with the heterogeneous features and their complex dependencies inherent in tabular data. To overcome these limitations, we consider test-time adaptation in the setting wherein the logical structure of the rules is assumed to remain invariant despite distribution shift between source and target domains whereas the numerical parameters associated with the rules and the weights assigned to them can vary to accommodate distribution shift. TabLog discretizes numerical features, models dependencies between heterogeneous features, introduces a novel contrastive loss for coping with distribution shift, and presents an end-to-end framework for efficient training and test-time adaptation by taking advantage of a logical neural network representation of a rule ensemble. We present results of experiments using several benchmark data sets that demonstrate TabLog is competitive with or improves upon the state-of-the-art methods for testtime adaptation of predictive models trained on tabular data. Our code is available at https://***/WeijieyingRen/TabLog.

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Improved Capon Estimator for High-Resolution DOA Estimation ...

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作者： Zuo, Weiliang Xin, Jingmin Liu, Changnong Zheng, Nanning Sano, Akira National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China Data Center China Construction Bank Beijing100033 China Keio University Department of System Design Engineering Yokohama223-8522 Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival (DOA) estimation performance of the standard Capon beamformer (SCB) in array processing, rigorous statistical performance analyses of these modified Capon estimators are still lacking. This paper studies an improved Capon estimator (ICE) for estimating the DOAs of multiple uncorrelated narrowband signals, where the higher-order inverse (sample) array covariance matrix is used in the Capon-like cost function. By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC (multiple signal classification), it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE, the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio (SNR). Furthermore the statistical performance of the ICE is analyzed, and the large-sample mean-squared-error (MSE) expression of the estimated DOA is derived. Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples, where the Cramer-Rao lower bound (CRB) is used to evaluate the validity of the derived asymptotic MSE expression. © 2014 Chinese Association of Automation.

关键词： Multiple signal classification

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Comparative analysis of Elman and Jordan Recurrent Neural Networks for Solar Radiation and Air Temperature Prediction Using Backpropagation Variants

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第18期49卷 87-98页

作者： Ed-Daoudi, Rachid Azhari, Mourad Ettaki, Badia Zerouaoui, Jamal LyRICA: Laboratory of Research in Informatics Data Sciences and Artificial Intelligence School of Information Sciences B.P. 604 Rabat-Instituts Rabat Morocco Center of Guidance and Educational Planning B.P. 6222 Rabat-Instituts Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire KenitraBP 133 Morocco

This study compares the effectiveness of Elman Recurrent Neural Networks (ERNN) and Jordan Recurrent Neural Networks (JRNN) for predicting solar radiation and ambient temperature in Ouarzazate, Morocco. data collected at 10-minute intervals from three meteorological stations (OUA_001, OUA_002, and OUA_003) over a 3–4-year period (2018-2022) were analyzed. The dataset was split into training (75%) and validation (25%) subsets to develop and test the models. The research systematically explored different network architectures with 1-2 hidden layers containing 4-12 neurons, applying three learning algorithms: BackPropagation (BP), BackPropagation with Momentum (BPM), and Resilient Backpropagation (Rprop). Key hyperparameters were optimized within specific ranges, including learning rates (0.00001-0.4) for BP/BPM and weight decay exponents (0.00001-4) for Rprop. Input variables included date, temperature, solar radiation, wind speed, relative humidity, precipitation, and atmospheric pressure in various combinations. Performance evaluation using Nash-Sutcliffe Efficiency (NSE) and Index of Agreement (d) revealed high prediction accuracy for both model types, with values exceeding 0.9 during validation. JRNN with BPM performed best at station OUA_001 (NSE: 0.909 for radiation, 0.971 for temperature), while ERNN with BPM demonstrated superior performance at station OUA_002 (NSE: 0.978 for radiation, 0.976 for temperature). At station OUA_003, both models showed comparable results when using BP. Despite the high overall accuracy, both models exhibited limitations in predicting extreme solar radiation values, particularly during nighttime hours. The study concludes that ERNN and JRNN are effective tools for short-term prediction of solar radiation and temperature in arid regions like Ouarzazate, though further refinement is needed to better capture extreme values and improve prediction accuracy during transitional periods. © 2025 Slovene Society Informatika. All rights reser

关键词： backpropagation prediction recurrent neural networks solar radiation temperature

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Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 196-208页

作者： Yufei Li Yufei Xin Xinni Li Yinrui Zhang Cheng Liu Zhengwen Cao Shaoyi Du Lin Wang School of Information Science and Technology Northwest UniversityXi’an 710127Shaanxi ProvinceChina Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong UniversityXi’anShaanxi Province 710004China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applicationsand Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anShaanxi Province 710049China

Pneumonia is a serious disease that can be fatal,particularly among children and the *** accuracy of pneumonia diagnosis can be improved by combining artificial-intelligence technology with X-ray *** study proposes X-ODFCANet,which addresses the issues of low accuracy and excessive parameters in existing deep-learningbased pneumonia-classification *** network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution(ODConv)module,leveraging the residual module for feature extraction from X-ray *** feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate feature information from different spatial ***,the ODConv module extracts and fuses feature information in four dimensions:the spatial dimension of the convolution kernel,input and output channel quantities,and convolution kernel *** experimental results demonstrate that the proposed method can effectively improve the accuracy of pneumonia classification,which is 3.77%higher than that of *** model parameters are 4.45M,which was reduced by approximately 2.5 *** code is available at https://***/limuni/X ODFCA NET.

关键词： Pneumonia Coordinate attention Dynamic convolution ResNet18 X-ODFCANet

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Return of Small-Scale Crowd Counting via Fast and Accurate Semi-Supervised Least Squares Model

Return of Small-Scale Crowd Counting via Fast and Accurate S...

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2023 IEEE Symposium Series on Computational intelligence, SSCI 2023

作者： Luo, Hao Du, Shaoyi Tian, Zhiqiang School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics National Engineering Research Center for Visual Information and Applications Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an China

ISBN: (纸本)9781665430654

Existing crowd counting techniques have achieved significant progress with the emergence of deep learning. During development, emerging crowd counting methods have generally become more and more complex and enormous, enabling them to understand and process more prior knowledge from input data. However, they suffer from two major drawbacks: 1) they generally require a significant amount of labeled training samples, which is labor-intensive, and 2) they require increasing computational hardware resources, making it luxurious and impractical to apply directly in small-scale scenes. To address these issues, we formulate crowd counting as a classification problem and leverage least squares model with a novel semi-supervised strategy. Technically, we construct the least squares model based on only two regularization terms: a regression term and a discriminative relaxation term. Moreover, we propose a semi-supervised soft label correcting strategy incorporated in the model. As a result, a fast and accurate crowd counting method is achieved. Experimental results on five small-scale benchmarks demonstrate the proposed method outperforms the other competitors in terms of both regression metrics and consumed time. © 2023 IEEE.

关键词： classification crowd counting least squares model semi-supervised

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Renewable Energy Short Circuit Ratio Prediction Based on Spiking Neural Networks

Renewable Energy Short Circuit Ratio Prediction Based on Spi...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Geng Yang, Shuangming Chen, Badong Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an China School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9798350303759

The integration of renewable energy power significantly influences the voltage support strength of the power system. With the rapid increase in renewable energy generation capacity in recent years, the power system faces mounting pressure. The short circuit ratio (SCR), a key indicator of voltage support capability, plays a crucial role in the regulation of power systems. Therefore, we propose a renewable energy SCR Prediction model based on spiking neural networks (SNNs). Moreover, this model takes into consideration the correlation between wind power generation and SCR, enabling accurate multivariate prediction. The prediction results on real-world power system data from renewable energy wind farms in north-ern China demonstrate the effectiveness of our proposed SNNs prediction model. And the SNNs model can be deployed on edge devices for low-power and efficient online prediction. © 2023 IEEE.

关键词： Forecasting

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LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Sun, Shangzhe Chen, Chi Yang, Bisheng Li, Liuchun Xu, Yuhang Jin, Ang State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application Ministry of Education of China China Institute of Artificial Intelligence in Geomatics Wuhan University Wuhan China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s. © Author(s) 2024.

关键词： Aircraft accidents

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A Crawler Detection Method for The College Entrance Examination Information Website 12

A Crawler Detection Method for The College Entrance Examinat...

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12th International Conference of Information and Communication Technology, ICTech 2023

作者： Chen, YaoYao Fan, Yue Li, Xin Zhang, YongHong Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Criminal Investigation Police University of China Shenyang China Shandong University Laboratory of Big Data and Artificial Intelligence Technology Jinan China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing China

ISBN: (纸本)9798350332919

The college entrance examination information website provides users with a variety of college entrance examination data that can be used for reference. Due to the public nature of the website and the large scale of the data, it is vulnerable to attacks by web crawlers, which can threaten the stability of the website and the security of the data, so it is especially important to effectively control the crawlers on the college entrance examination information website. In this paper, we propose a crawler detection method for the college entrance examination information website. Firstly, we set multi-level access frequency thresholds to screen out users who may have crawling behavior, and then we detect and identify suspicious users' access according to the characteristics of college entrance examination information website crawlers by SVM, and finally identify the real web crawlers and prohibit them from accessing the college entrance examination information website. Through experiments, it is proved that this method can effectively detect a large part of crawlers and ensure the data security of the college entrance examination information website. © 2023 IEEE.

关键词： Websites

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