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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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FR2Seg: Continual Segmentation Across Multiple Sites via Fourier Style Replay and Adaptive Consistency Regularization 39

FR2Seg: Continual Segmentation Across Multiple Sites via Fou...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Cheng Zhang, Weiwen Zhang, Hongrui Xu, Xuemiao Zhang, Huaidong Zou, Jing Qin, Jing South China University of Technology China The Hong Kong Polytechnic University Hong Kong Guangdong Engineering Center for Large Model and GenAI Technology China State Key Laboratory of Subtropical Building Science China Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China

ISBN: (纸本)157735897X

In clinical imaging, medical segmentation networks typically require continually adapting to new data from multiple sites over time, as aggregating all data for learning at once can be impractical due to storage limitations and privacy concerns. However, existing methods basically overlook domain-specific characteristics and fall short of adequately capturing domain-invariant knowledge during continual learning, leading to undesired catastrophic forgetting of previous sites and inferior generalization to new sites. To tackle this issue, this paper introduces FR2Seg, to sufficiently exploit both domain-specific and domain-invariant knowledge for efficient continual learning with the aid of low-frequency cues. For the former aspect, we propose a Fourier style replay module to synthesize pseudo images with old-site styles for data augmentation during new-site training, effectively preventing catastrophic forgetting without sacrificing data privacy. For the latter, we present a Fourier adaptive consistency regularization to identify and constrain the optimization of domain-invariant parameters with explicit awareness of knowledge transferability across sites, ensuring excellent generalizability to new sites. Experimental results on two public datasets confirm our method's superiority over existing state-of-the-art continual learning methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Situation Awareness on Blockchain Based on Markov Game Model 4

Situation Awareness on Blockchain Based on Markov Game Model

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4th International Symposium on Smart and Healthy Cities, ISHC 2022

作者： Wang, Peng Qian, Baojian Yan, Yaoxi Sun, Jinyu Chen, Fubing Huaiyin Normal University Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis Jiangsu Huaian223300 China School of Computer Science and Technology Huaiyin Normal University Jiangsu Huaian223300 China

ISBN: (纸本)9781665491808

Aiming at the network security problem of the blockchain system, the game model based on Markov is proposed. The model perceives the network security situation of the blockchain system, accurately and comprehensively evaluates the current security status of the system through clustering screening and attack game, dynamically evaluates the security situation of the system, and establishes an attack transfer network and the best reinforcement scheme. The experiment shows that the Markov game model based on 51% double spend attack can evaluate and predict the current and future security status of the blockchain system, and can reduce the asset loss caused by blockchain attacks to a certain extent. © 2022 IEEE.

关键词： Blockchain

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A Self-supervised Joint Training Framework for Document Reranking

A Self-supervised Joint Training Framework for Document Rera...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Zhu, Xiaozhi Hao, Tianyong Cheng, Sijie Wang, Fu Lee Liu, Hai School of Computer Science South China Normal University China Guangzhou Key Laboratory of Big Data and Intelligent Education Guangzhou510000 China Fudan University China Hong Kong Metropolitan University China

ISBN: (纸本)9781955917766

Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks. Recent works indicate that further pretraining the language models on the task-specific datasets before fine-tuning helps improve reranking performance. However, the pre-training tasks like masked language model and next sentence prediction were based on the context of documents instead of encouraging the model to understand the content of queries in document reranking task. In this paper, we propose a new self-supervised joint training framework (SJTF) with a selfsupervised method called Masked Query Prediction (MQP) to establish semantic relations between given queries and positive documents. The framework randomly masks a token of query and encode the masked query paired with positive documents, and use a linear layer as a decoder to predict the masked token. In addition, the MQP is used to jointly optimize the models with supervised ranking objective during fine-tuning stage without an extra further pre-training stage. Extensive experiments on the MS MARCO passage ranking and TREC Robust datasets show that models trained with our framework obtain significant improvements compared to original models. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Forecasting

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UrbanWaste: In-the-Bin dataset for Waste Disposal Inspection with Multi-Granularity Hierarchical Labels 39

UrbanWaste: In-the-Bin Dataset for Waste Disposal Inspection...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Zhuoqi You, Zejun Dong, Yang Liu, Yukai Gao, Xiyue Miao, Qiguang Xi’an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi’an710126 China School of Computer Science and Technology Xidian University Shaanxi Xi’an710126 China

ISBN: (纸本)157735897X

Our world faces the challenge of efficiently and responsibly managing the ever-growing volume of urban waste. Many countries and regions have implemented categorized trash bins and require residents to sort their waste according to specified criteria. Proper waste classification by residents significantly reduces the workload in the waste disposal process. However, due to the lack of effective supervision during classification, the quality of waste sorting is often compromised. This misclassification can lead to higher pollution risks, lower recycling rates, and increased waste management costs and difficulties. To address this issue, we propose using images captured from within trash bins to supervise garbage delivery. We introduce UrbanWaste, an image dataset specifically designed for in-the-bin waste detection and segmentation. The dataset includes 25,254 RGB images and 140,008 annotated items, featuring dense annotations and multi-granularity labels across 193 distinct waste categories. We evaluated state-of-the-art segmentation models to understand their generalization and performance on UrbanWaste. Based on this dataset, we developed a comprehensive work-flow for waste classification inspection, which has been deployed in real-world districts to assess the system’s effectiveness. We hope UrbanWaste will inspire new directions in AI research for environmental sustainability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Wastewater disposal

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A New Model Based on Differential Evolutionary Algorithm and Markov Clustering for Identifying Protein Complexes

A New Model Based on Differential Evolutionary Algorithm and...

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Chinese Control Conference (CCC)

作者： Zengyu Feng Shouheng Tuo Tianrui Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology, ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database.

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Fast Minimum Spanning Tree Clustering Algorithm Based on Granular-Ball Computing 11th

Fast Minimum Spanning Tree Clustering Algorithm Based on Gr...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Jiang, Xiaocui Cheng, Dongdong Huang, Jinlong Zhang, Sulan Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing408100 China

ISBN: (纸本)9783031927461

Distance measure is a key issue in clustering analysis. Most existing clustering algorithms, like K-means and density peaks clustering, use Euclidean distance to measure the dissimilarity between objects, making it difficult for discovering irregular-shaped clusters. Some algorithms specially designed for recognizing irregular-shaped clusters have high time complexity. In this paper, we introduce granular-ball (GB) into clustering and use the neighbors of the boundary objects of granular-balls to quantitatively evaluate the distance between granular-balls. By combining minimum spanning tree (MST)-based clustering algorithm, we propose a GB-based MST clustering algorithm, called GB-MST. Since GB model is a multi-granularity data representation and the number of GBs is far less than that of objects in a dataset, the proposed algorithm greatly reduces the running time of MST-based clustering algorithms and the effective distance between GBs makes it suitable for discovering irregular-shaped clusters. The experiment results on synthetic and real datasets have shown that GB-MST outperforms the compared algorithms in terms of time and effectiveness in discovering irregular-shaped clusters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural Network

Drug-Protein Interaction Prediction by Fusion of Attention a...

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Chinese Control Conference (CCC)

作者： Tianrui Chen Shouheng Tuo Zengyu Feng School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

The identification of drug-protein interactions (DTIs) is a critical step in drug development and repositioning. However, detecting these interactions using scientific methods presents a formidable challenge. Existing models often struggle to extract relevant information from drug-protein sequences, and they fail to consider pre-existing interactions between pairs. Here, we introduce a deep learning model, DPAG, based on attention mechanisms and graph neural networks, which overcomes these limitations. DPAG leverages information from both the drug and protein sequences, as well as the association between drug-protein pairs, to predict DTIs. We conducted multiple experiments to evaluate the performance of the model in benchmark datasets, and the results showed that DPAG achieved excellent performance in terms of AUC, AUPR, precision, and recall. Our findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and repositioning. Code is available at https://***/veresse/DPAG.

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Ranking Neighborhood and Class Prototype Contrastive Learning for Time Series

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IEEE Transactions on big data 2024年

作者： Wei, Chixuan Yuan, Jidong Zhang, Yi Yu, Zhongyang Liu, Yanze Liu, Haiyang Communication University of China School of Data Science and Intelligent Media China Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing China Beijing Jiaotong University School of Computer Science and Technology China Ant Group China

Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. Existing contrastive learning methods conduct augmentations and maximize their similarity. However, they ignore the similarity of adjacent timestamps and suffer from the problem of sampling bias. In this paper, we propose a self-supervised framework for learning generalizable representations of time series, called R anking n E ighborhood and cla S s prototyp E contr A stive L earning (RESEAL). It exploits information about similarity ranking to learn an embedding space, ensuring that positive samples are ranked according to their temporal order. Additionally, RESEAL introduces a class prototype contrastive learning module. It contrasts time series representations and their corresponding centroids as positives against truly negative pairs from different clusters, mitigating the sampling bias issue. Extensive experiments conducted on several multivariate and univariate time series tasks (i.e., classification, anomaly detection, and forecasting) demonstrate that our representation framework achieves significant improvement over existing baselines of self-supervised time series representation © 2015 IEEE.

关键词： Contrastive Learning

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Research on the psychological classification of violent crime based on a convolutional neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16397-16408页

作者： Li, Hui Gao, Guandong Xiao, Ke Song, Shengzun College of Information Science and Technology Hebei Agricultural University Hebei Baoding071001 China Department of Information Management The National Police University for Criminal Justice Hebei Baoding071001 China The Centre of Data Science and Intelligent Correction Technology The National Police University for Criminal Justice Hebei Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Hebei Baoding071001 China Department of Penology The National Police University for Criminal Justice Hebei Baoding071001 China

This paper uses a text classification algorithm in the context of criminal psychology to scientifically evaluate the correctional effects among offenders in the hopes of preventing recidivism. The proposed algorithm uses an improved convolutional neural network (CNN) and feature fusion to carry out the psychological attribution of violent crime based on criminal fact texts, which are divided into three types: the premeditated type, the impulsive type, and the pathological type. First, a text set of criminal facts with labels was constructed by criminal psychology experts. Then, the numerical features are extracted by the word count and a keyword extraction method based on the criminal feature dictionaries, which are established by criminal psychology experts. The improved CNN model is used to extract the text features by replacing a fully connected layer with the global average pooling layer. Finally, after fusing the numerical features and the text features, softmax function is used to calculate the probability of each category and divide the attribution type. To verify the effect of the algorithm, the proposed model is evaluated and compared with the original CNN, the SVM based on TF-IDF and word count, random forest, and some integration methods. Experimental results show that the proposed algorithm is better in accuracy, recall, precision, and F1 in the micro and macro scopes, with the highest accuracy of 98%. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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