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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on Computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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Enhancing the Clarity of Surveillance Footage with Deep Residual Neural Networks

Enhancing the Clarity of Surveillance Footage with Deep Resi...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Prajwal Hegde N Jitendrasinh Jamadar Ravindra Changala Samrat Kumar Mukherjee Bijesh Dhyani B. Sachuthananthan Department of Artificial Intelligence and Data Science NMAM Institute of Technology Nitte Deemed to be University Karkala India G H Raisoni Institute of Engineering and Business Management Mohadi India Department of CSE Guru Nanak Institutions Technical Campus Hyderabad India Department of Management Studies Sikkim Manipal Institute of Technology Sikkim India Department of Mabagement Studies Grahic Era University (Deemed To Be University) Dehradun India Department of Mechanical Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Closed-circuit television (CCTV) camera pictures are often retained with low resolution as a result of technological and data storage constraints, which diminishes the usefulness of these images. This research suggests that by using intelligent design and training techniques, it is possible to boost the resolution of CCTV pictures using an enhanced deep residual network known as (EDSR). The EDSR approach has never before been used to improve the resolution of CCTV images before this study. In order to train and evaluate an EDSR that could generate high-resolution pictures from a low-resolution dataset, they used a dataset consisting of 986 CCTV images. Applying an EDSR method, they show that executing picture super-resolution with 60 layers is very effective, achieving a peak signal-to-noise ratio of 77.84 db. Additionally, this method has broader potential as a file compression approach that allows for the storing of low-resolution photographs with reduced file sizes and the capacity to recreate the same pictures in high resolution (HR) form

关键词： Training Image coding TV PSNR Surveillance Superresolution Memory Signal resolution Image reconstruction Residual neural networks

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Learning Optimal "Pigovian Tax" in Sequential Social Dilemmas

arXiv

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arXiv 2023年

作者： Hua, Yun Gao, Shang Li, Wenhao Jin, Bo Wang, Xiangfeng Zha, Hongyuan School of Computer Science and Technology East China Normal University Shanghai200092 China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen China School of Software Engineering Tongji University Shanghai201804 China School of Computer Science and Technology East China Normal University Shanghai200062 China

In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global performance. The externality theory, defined as "the activities of one economic actor affect the activities of another in ways that are not reflected in market transactions," is applicable to analyze the social dilemmas in MARL. One of its most profound non-market solutions, "Pigovian Tax", which internalizes externalities by taxing those who create negative externalities and subsidizing those who create positive externalities, could aid in developing a mechanism to resolve MARL's social dilemmas. The purpose of this paper is to apply externality theory to analyze social dilemmas in MARL. To internalize the externalities in MARL, the Learning Optimal Pigovian Tax method (LOPT), is proposed, where an additional agent is introduced to learn the tax/allowance allocation policy so as to approximate the optimal "Pigovian Tax" which accurately reflects the externalities for all agents. Furthermore, a reward shaping mechanism based on the approximated optimal "Pigovian Tax" is applied to reduce the social cost of each agent and tries to alleviate the social dilemmas. Compared with existing state-of-the-art methods, the proposed LOPT leads to higher collective social welfare in both the Escape Room and the Cleanup environments, which shows the superiority of our method in solving social dilemmas. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxation

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Theory and Analytical Solutions to Wellbore Problems with Hardening Drucker-Prager Models

SSRN

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SSRN 2024年

作者： Nguyen-Sy, Tuan Huang, Jian Gross, Herve Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam TotalEnergies E&P Research & Technology HoustonTX77002 United States

Recognizing and quantifying the elasto-plastic nature of underground formations is critical for various subsurface operations such as drilling, stimulation, production, injection, and storage. In the case of geological CO2 storage, for instance, it is key to identify storage sites characteristics and pumping parameters that lead to safe and perennial CO2 trapping. In this work, we investigate different rock hardening behaviors with the Drucker-Prager model: cohesion hardening, friction hardening, and their combination, jointed hardening. Our focus is to solve the elasto-plastic deformation analytically in the vicinity of a wellbore. The three hardening formulations predict different mechanical responses and stress-paths for solving the same wellbore problem with excavation. The analytical solutions with different hardening laws are provided in this study and verified with corresponding numerical results. This approach can be used to interpret field observations and calibrate experimental data with more comprehensive models. These new laws are implemented and benchmarked in GEOS, an open-source advanced numerical simulator of subsurface formations. © 2024, The Authors. All rights reserved.

关键词： Rock mechanics

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An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron

arXiv

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arXiv 2024年

作者： Alazab, Moutaz Khurma, Ruba Abu Castillo, Pedro A. Abu-Salih, Bilal Martı´n, Alejandro Camacho, David Faculty of Artificial Intelligence Al-Balqa Applied University Amman Jordan Computer Science Department Faculty of MEU Research Unit Middle East University Amman Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%. © 2024, CC BY.

关键词： Optimization

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Machine Learning based Intelligent Diagnosis of Orthodontics: a comprehensive review

Machine Learning based Intelligent Diagnosis of Orthodontics...

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International Conference on Computational intelligence and Knowledge Economy (ICCIKE)

作者： Iwin Thanakumar Joseph S Velliangiri Sarveshwaran Vijayakumar K S Benson Edwin Raj Ahmed A Elngar Anand Devadurai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhrapradesh India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Information Sciences Higher Colleges of Technology Fujairah United Arab Emirates Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City Egypt Department of Computer Science King Khalid University Gregar Abha

An effective prediction of orthodontics treatment outcome is highly essential for further scheme of clinical treatment for the patient with the simplified cost. An appropriate utilization of information technology simplifies the treatment for teeth irregularities and misaligned jaws in an effective manner. Lot of research works carried out in the area of dentistry to come out with an optimistic result. With the advent of artificial intelligence and machine learning methodologies in recent years, lot of clinical problems and difficulties can be efficiently managed and overcome by orthodontists. This research article provides the complete insight about the impact of intelligent technologies in the field of orthodontics. It provides a complete overview of futuristic applications in the field of orthodontic clinical diagnosis, planning of treatment for various cases and the correct prediction of outcome of clinical treatment which regularly faced by an orthodontist in the health care sector.

关键词： Deep learning Costs Biological system modeling System performance Teeth data models Planning

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Mdff: Multi-Domain Feature Fusion for Anomaly Recognition

SSRN

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SSRN 2024年

作者： Xu, Yuan Ye, Cheng-Shu Luo, Yi Ke, Wei Zhu, Qun-Xiong He, Yan-Lin Zhang, Yang Zhang, Ming-Qing College of Information Science & Technology Beijing University of Chemical Technology Beijing100029 China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing100029 China Research Institute of Mine Artificial Intelligence Chinese Institute of Coal Science Beijing10013 China Tiandi Science and Technology Co. Ltd. Beijing10013 China State Key Laboratory for Intelligent Coal Mining and Strata Control Beijing10013 China Faculty of Applied Science Macao Polytechnic University 999078 China

In the field of anomaly recognition in industrial processes, it has been observed that variations in the temporal frequency and spatial density of process data are a ubiquitous phenomenon, which lead to a reduction in the accuracy of anomaly detection. In response to these challenges, a novel anomaly recognition approach, termed Multi-Domain Feature Fusion (MDFF), is proposed. In this method, temporal features are initially captured using a Gated Recurrent Unit (GRU) network. Subsequently, the characteristics of the frequency domain are extracted through a combination facilitated by the Fast Fourier Transform (FFT) algorithm and a convolutional network, enabling the detailed analysis of frequency components within anomalous signals. Spatial features are extracted from the input dataset through a one-dimensional Convolutional Neural Network (1D-DCNN), which is augmented by data segmentation and cascade connections. Additionally, for the purpose of augmenting the effective fusion of features across diverse domains, a feature fusion technique, grounded on a channel attention mechanism, is implemented. The efficacy of the proposed method is assessed through simulation experiments conducted on BFIP and TE datasets. The results from these experiments demonstrate that the MDFF method is superior in the task of anomaly identification. © 2024, The Authors. All rights reserved.

关键词： Convolution

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UCPM: Uncertainty-Guided Cross-Modal Retrieval with Partially Mismatched Pairs

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IEEE Transactions on Image Processing 2025年 34卷 3622-3634页

作者： Zha, Quanxing Liu, Xin Cheung, Yiu-Ming Peng, Shu-Juan Xu, Xing Wang, Nannan Huaqiao University Department of Computer Science Xiamen 361021 China Key Laboratory of Pattern Recognition and Computer Vision Xiamen 361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021 China Hong Kong Baptist University Department of Computer Science Hong Kong Huaqiao University Department of Artificial Intelligence Xiamen 361021 China Fujian Province University Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Xiamen 361021 China University of Electronic Science and Technology of China Center for Future Multimedia School of Computer Science and Engineering Chengdu 610051 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an 710071 China

The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i

关键词： Cross-modal retrieval partially mismatched pairs trusted margin loss uncertainty guided division uncertainty self-correction

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