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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68658 条记录，以下是431-440 订阅

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Towards Unbiased Minimal Cluster Analysis of Categorical-and-Numerical Attribute Data 27th

Towards Unbiased Minimal Cluster Analysis of Categorical-an...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Zhang, Yunfan Luo, Xiaopeng Chen, Qingsheng Zou, Rong Zhang, Yiqun Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Computer Engineering Guangzhou Huali College Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031781650

Categorical and numerical attributes occur frequently in cluster analysis tasks. To bridge the information gap between the heterogeneous categorical and numerical attributes in cluster analysis, the existing approaches usually adopt prior assumptions to distance definition and cluster distribution, which unavoidably introduce bias to the clustering process. To address this issue, we propose to analyze mixed data comprising both categorical and numerical attributes by forming minimal clusters through neighborhood set theory. As the minimal clusters are the smallest cluster units that can be obtained without relying on prior assumptions, unbiased cluster analysis can be facilitated accordingly. To avoid information loss, distance and density metrics that are unified on both numerical and categorical attributes are also proposed and utilized to merge the minimal clusters hierarchically. It turns out that our proposed approach is highly interpretable, and is capable of accurately and robustly clustering data sets composed of any combination of numerical and categorical attributes. Extensive experimental evaluations demonstrate its efficacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Settling the Complexity of Testing Grainedness of Distributions, and Application to Uniformity Testing in the Huge Object Model 16

Settling the Complexity of Testing Grainedness of Distributi...

引用

16th Innovations in Theoretical computer science conference, ITCS 2025

作者： Canonne, Clément L. Sen, Sayantan Yang, Joy Qiping School of Computer Science University of Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore

ISBN: (纸本)9783959773614

In this work, we study the problem of testing m-grainedness of probability distributions over an n-element universe U, or, equivalently, of whether a probability distribution is induced by a multiset S ⊆ U of size |S| = m. Recently, Goldreich and Ron (Computational Complexity, 2023) proved that Ω(nc) samples are necessary for testing this property, for any c logmm) samples are necessary for testing this property when m = Θ(n). In this work, we positively settle this conjecture. Using a known connection to the Distribution over Huge objects (DoHo) model introduced by Goldreich and Ron (TheoretiCS, 2023), we leverage our results to provide improved bounds for uniformity testing in the DoHo model. © Clément L. Canonne, Sayantan Sen, and Joy Qiping Yang.

关键词： Probability distributions

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (纸本)9798350368741

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. © 2025 IEEE.

关键词： Information Extraction Speech Recognition Spoken Language Understanding

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A Data-Driven Adaptive Optimal Control with Input Saturation for Driverless Heavy-Duty Truck

A Data-Driven Adaptive Optimal Control with Input Saturation...

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international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ji, Honghai Yang, Jianfeng Liu, Shida Fan, Lingling Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9789819639687

This paper mainly studies the longitudinal speed tracking problem of driverless heavy-duty trucks based on data-driven methods. A data-driven adaptive optimal control algorithm with control input saturation constraints is proposed in this paper to solve the problems of control input saturation and difficulty in establishing an accurate model in driverless heavy-duty truck systems. First, control constraints are added to the control input saturation problem of driverless heavy-duty trucks. Then, the dynamic linearization technology is used to model the nonlinear system of driverless heavy-duty trucks. After that, an appropriate index function is selected and the Newton-type optimization method is used to obtain the optimal controller. The method proposed in this paper only uses a small amount of input and output data and does not need to establish an accurate mathematical model. The simulation results show that compared with the PID algorithm, the proposed algorithm has advantages in speed tracking when applied to driverless heavy-duty truck systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Automobiles

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Single-View Clothed Human Reconstruction using Symmetric Feature

Single-View Clothed Human Reconstruction using Symmetric Fea...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fu, Yanping Zhao, Zhuangzhuang Geng, Hao Zhao, Haifeng School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9798350368741

Single-view clothed human reconstruction has emerged as a prominent research focus, yet reconstructing the occluded backside of the human body remains a significant challenge due to viewpoint limitations and occlusion. In this paper, we propose a novel single-view clothed human reconstruction method that leverages non-rigid symmetric features guidance to effectively mitigate these challenges. Firstly, we utilize the natural symmetry of the human body to construct the non-rigid symmetry through the parametric human body model SMPL-X. Secondly, these symmetric-driven features are then used to enhance the pixel-aligned features, enabling a more precise and complete reconstruction. Finally, we introduce an innovative occupancy query selector that intelligently identifies and prioritizes the most relevant features during the query stage to further improve the quality of reconstruction. Experimental results demonstrate that the proposed method can achieve better reconstruction results, especially in occluded areas. © 2025 IEEE.

关键词： Pixel-aligned features Single-view human reconstruction Symmetric features

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Making Strides Security in Multimodal Fake News Detection Models: A Comprehensive Analysis of Adversarial Attacks 31st

Making Strides Security in Multimodal Fake News Detection M...

引用

31st international conference on Multimedia Modeling, MMM 2025

作者： Si, Jiahua Wang, Youze Hu, Wenbo Liu, Qiang Hong, Richang University of Science and Technology of China Hefei China Hefei University of Technology Hefei China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819620609

With the rise of social media as a crucial data source, fake news has proliferated, posing significant challenges to data accuracy and societal well-being. Our research investigates the impact of multimedia on the rapid dissemination of fake news, highlighting the need for effective detection models. We focus on developing algorithms to detect fake news, emphasizing a multimodal approach to accommodate the complexity of modern media formats. Our study underscores the urgent need for real-time detection models to control the swift spread of misinformation. We prioritize usability across various platforms, ensuring accessibility and efficiency for both large networks and small organizations. Additionally, we address the ethical implications of fake news detection, emphasizing adherence to social norms and legal frameworks to prevent abuse and ensure reliable information. A critical aspect of our research is examining adversarial attack techniques targeting multimodal fake news detection models. We analyze the vulnerability of current models to both unimodal and multimodal attacks and emphasize the necessity for advanced security measures to reliably counter substantial adversarial threats. Our study aims to advance the development of adaptable and robust systems to combat the spread of fake news by evaluating the resilience of multimodal detection models against various adversarial attack techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fake detection

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Research on visual odometry scheme based on semi-feature method

Research on visual odometry scheme based on semi-feature met...

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2024 international conference on Optical and Photonic Engineering, icOPEN 2024

作者： Peng, Hongxing Chen, Hu Chen, Shuji Zou, Xiangjun Wang, Hongjun Xiong, Juntao College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics Foshan China College of Intelligent Manufacturing and Modern Industry Xinjiang University Xinjiang China College of Engineering South China Agricultural University Guangzhou510642 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

This paper introduces a visual odometry scheme based on a semi-feature method aimed at enhancing the efficiency of front-end tracking in visual SLAM systems. By integrating direct and feature-based methods, our approach improves tracking speed while maintaining pose estimation accuracy. We replace traditional pose estimation with a direct method for regular frames and utilize feature-based methods for keyframes, optimizing computational resources without significant loss in performance. Experimental validation on the EuRoC dataset shows substantial improvements in real-time tracking efficiency compared to conventional feature-based methods. © 2025 SPIE.

关键词： computer vision

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An Analytical Perspective of Machine Learning Predictive Models to Diagnose Chronic Diseases 3rd

An Analytical Perspective of Machine Learning Predictive Mod...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Rana, Rattan Pal Singh Gupta, Sudhanshu Gupta, Umesh Haryana Ambala Mullana133207 India School of Computer Science Engineering and Technology Bennett University UP Greater Noida201310 India

ISBN: (纸本)9789819726707

Machine learning techniques have significantly impacted healthcare by enabling early detection of chronic diseases, leading to slightly reduced global mortality rates. However, the complexity of models and vast datasets requires careful selection and utilization of appropriate predictive models for specific diseases, leading to enhanced outcomes in healthcare systems. This article discusses recent research findings on the performance of various predictive models and their practical applications, providing insights for further advancements in healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Systems on Time Scales: Theory and Applications in Modeling 2nd

Systems on Time Scales: Theory and Applications in Modeli...

引用

2nd international conference on Mathematical Analysis and Applications in Modeling, ICMAAM 2023

作者： Bartosiewicz, Zbigniew Faculty of Computer Science Bialystok University of Technology Bialystok Poland

ISBN: (纸本)9789819791934

Systems on time scales incorporate continuous- and discrete-time dynamical systems. They can serve as models of real systems from various areas. Positive systems on time scales are studied here in more detail. Such systems appear in biology, medicine and chemistry. The language of time scales allows for natural description of time discretization in continuous-time models. During such discretization some properties of the system may be lost, while other may be preserved. Invariance under discretization of positivity and uniform exponential stability is investigated, both for linear and nonlinear systems. One of the results says that if discretization preserves positivity of the system, then it also preserves uniform exponential stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous time systems

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From Generalist to Specialist: A Survey of Large Language Models for Chemistry 31

From Generalist to Specialist: A Survey of Large Language Mo...

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31st international conference on Computational Linguistics, COLING 2025

作者： Han, Yang Wan, Ziping Chen, Lu Yu, Kai Chen, Xin X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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