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arXiv 2024年

作者： Chen, Haonan Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China

Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train different models on large-scale search log data to identify the relevance between search contexts and candidate documents. The common training paradigm is to pair the search context with different candidate documents and train the model to rank the clicked documents higher than the unclicked ones. However, this paradigm neglects the symmetric nature of the relevance between the session context and document, i.e., the clicked documents can also be paired with different search contexts when training. In this work, we propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context, i.e., the current query, and train our model to rank the generated sequence along with the original sequence. This approach enables models to learn that the relevance of a document may vary as the session context changes, leading to a better understanding of users’ search patterns. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty. Through experimentation on two extensive public search logs, we have successfully demonstrated the effectiveness of our model. © 2024, CC BY.

关键词： Search engines

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A Clustering-Based Color Reordering Method for Reversible data Hiding in Palette Images

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IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Embeddings

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PTCR-PJF: A Person-Job Fit Model for Structured Resumes

PTCR-PJF: A Person-Job Fit Model for Structured Resumes

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiaming Li Jingfa Yao Guifa Teng Mingyue Yan School of Information Science and Technology Hebei Agricultural University Baoding China Hebei Software Institute Baoding China Intelligent Interconnection Equipment and Multi-Modal Big Data Application Technology Research and Development Center Baoding China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the declining efficiency of online recruitment and the increasing trend of structured job seeker information, existing Person-job Fit (PJF) methods are challenged by insufficient matching accuracy and the complexity of data structures. Traditional models often fail to utilize information in structured resumes fully and are inefficient in processing large volumes of heterogeneous data. To address these issues, we propose a novel Person-Job Fit model (PTCR-PJF) based on an improved Text Convolutional Neural Network (TextCNN) and Roformer joint modeling. PTCR-PJF employs two position-aware strategies: sine-cosine absolute position encoding (SPE) and rotary relative position encoding (RoPE), effectively enhancing the recognition of specific positional information in job postings. Additionally, the model employs a dual-layer text convolution module and applies chunk max pooling and adaptive max pooling to extend its perception of positional information further. It also integrates a Roformer based on RoPE to capture long-distance dependencies. Extensive experiments conducted on the large-scale real-world dataset CareerBuilder2012 and comparisons with 12 baseline models show that PTCR-PJF achieves state-of-the-art (SOTA) performance. The results indicate a significant advantage of the PTCR-PJF model in processing structured resume data, offering a new technological path for online intelligent recruitment.

关键词： Adaptation models Resumes Neural networks Predictive models Market research Transformers data models

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Liu Min Li Chu He Xu Tian Junbin Mao Min Zeng Jin Liu Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications.

关键词： Performance evaluation Sensitivity Spaceborne radar Millimeter wave radar Depression data models Reliability Monitoring Signal resolution Diseases

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Quantum Entanglement in Dirac Dynamics via Continuous-Time Quantum Walks in a Quantum Circuit Framework

arXiv

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arXiv 2024年

作者： Wang, Wei-Ting Chang, Yen-Jui Chang, Ching Ray Department of Physics National Taiwan University Taipei106216 Taiwan Department of Physics and Quantum Information Center Chung Yuan Christian University Taoyuan320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

We propose a Continuous-Time Quantum Walks (CTQW) model for one-dimensional Dirac dynamics simulation with higher-order approximation. Our model bridges CTQW with a discrete-time model called Dirac Cellular Automata (DCA) via Quantum Fourier Transformation (QFT). From our continuous-time model, we demonstrate how varying time intervals and position space sizes affect both quantum entanglement between the internal space and external (position) space of the quantum state and the relativistic effect called Zitterbewegung. We find that the time interval changes the transition range for each site, and the position space sizes affect the value of transition amplitude. Therefore, it shows that the size of spacetime plays a crucial role in the observed quantum entanglement and relativistic phenomena in quantum computers. These results enhance the understanding of the interplay between internal and external spaces in Dirac dynamics through the insights of quantum information theory and enrich the application of the quantum walks-based algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Towards Efficient data-Centric Robust Machine Learning with Noise-based Augmentation

arXiv

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arXiv 2022年

作者： Liu, Xiaogeng Wang, Haoyu Zhang, Yechao Wu, Fangzhou Hu, Shengshan School Of Cyber Science And Engineering National Engineering Research Center For Big Data Technology And System Services Computing Technology And System Lab Hubei Engineering Research Center On Big Data Security Huazhong University Of Science And Technology Wuhan430074 China

The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noisedbased data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale for Diagnosing Alzheimer’s Disease

Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale fo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaolu Lin Pinya Lu Jie Pan Hongqin Yang Xuemei Ding Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Provincial University Engineering Research Center for Big Data Analysis and Application Fujian Normal University Fuzhou China Xiamen Jingyi Zhikang Technology Co. Ltd Xiamen China Fujian Provincial Key Laboratory for Photonics Technology Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computing Engineering and Intelligent Systems Ulster University Derry-Londonderry NI UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Alzheimer's disease (AD) is a neurodegenerative disease and mild cognitive impairment (MCI) is the early stage of AD. Previous studies have predominantly focused on binary classification using 3 dimensional - convolutional neural network (3D-CNN) for AD diagnosis, with limited progress in multi-classification. Moreover, the current 3D-CNNs often adopt a single-scale architecture with massive parameters growth. Additionally, obtaining precise location information of brain imaging data is crucial for improving the classification accuracy with 3D-CNN. Hence, we propose a multi-scale 3D-CNN based on coordinate attention mechanism to marvelously capture and integrate 3D features with fewer parameters, improving the accuracy of AD diagnosis. A total of 447 cognitively normal (CN), 512 MCI, and 358 AD sMRI images from the Alzheimer's Disease Neuroimaging Initiative datasets are used for multi-class classification task, yielding a classification accuracy of 92.8%. The model merely involves 2.41 M parameters and achieves the best classification results with the least number of parameters when compared to other representative CNN architectures including ResNet 18, ResNet 34, ConvNeXt tiny, and VGG 11. Through the ablation experiment, the addition of attention mechanism and the multi-scale classification enhances the classification performance by 4.5% and 1.5%, respectively. Furthermore, our model outperforms the other six existing studies in terms of accuracy for classifying AD vs. MCI vs. CN. Overall, this study underscores the efficacy of our approach for AD diagnosis, showcasing its utility in diagnosing AD patients and providing novel insights for diagnosing other neurological disorder diseases.

关键词： Neuroimaging Accuracy Attention mechanisms Three-dimensional displays Resonance Convolutional neural networks Alzheimer's disease

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FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices

arXiv

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arXiv 2025年

作者： Yao, Dezhong Shi, Yuexin Liu, Tongtong Xu, Zhiqiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process in conventional FL introduces significant computation and communication overhead, which is unfriendly for resource-constrained edge devices. One-shot FL has emerged as a promising approach to mitigate communication overhead, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups. Our code is available at https://***/YXShi2000/FedMHO. © 2025, CC BY.

关键词： Federated learning

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Heterogeneous differential privacy for vertically partitioned databases

Heterogeneous differential privacy for vertically partitione...

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作者： Xia, Yang Zhu, Tianqing Ding, Xiaofeng Jin, Hai Zou, Deqing National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Deakin University Victoria Australia

Existing privacy-preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information-based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility. © 2019 John Wiley & Sons, Ltd.

关键词： database systems

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Application of Convolutional Neural Network in Livestock Target Detection of IoT Images

Application of Convolutional Neural Network in Livestock Tar...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Maohong Tian Feiyang Zhao Nengmei Chen Dequan Zhang Jie Li Hualin Li School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Shu Yi Xin Credit Management Co. LTD Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges and Universities Engineering Research Center Chongqing China Chongqing Da Tong Experimental School Chongqing China Shu Yi Xin Credit Management Co. LTD Chongqing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

The national rural revitalization strategy has advanced, and new requirements for modern internet-of-things (IoT)-based farm supervision tools have been proposed. This study proposes a “you-only-look-once” (YOLO) v5 convolutional neural network to optimize the candidate frame's center point algorithm. Five YOLOv5 models are used to identify and classify captive pigs. According to the experimental results, the K-median algorithm has a better effect than using the K-means algorithm, and its average intersection over union is approximately 1-2% higher. In our scenario, the commercial application value of the YOLOv5s model is higher, and the corresponding performance results of F1-score, mAP0.5, mAP0.5:0.95, FPS, and R 2 are 92.21%, 95.18%, 64.85%, 75 FPS, and 85.54%, respectively.

关键词： YOLO Seminars Image recognition Target recognition Approximation algorithms Agriculture Classification algorithms

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