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arXiv 2023年

作者： Wu, Yanan Feng, Songhe Wang, Yang The Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The department of Computer Science University of Manitoba WinnipegMBR3T 2N2 Canada

Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem. To model such problem, we propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM), in which Graph Matching mechanism is introduced owing to its good performance of excavating the instance and label relationship. The framework explicitly establishes category correlations and instance-label correspondences by modeling the relation among content-aware (instance) and semantic-aware (label) category representations, to facilitate multi-label image understanding and reduce the dependency of large amounts of training samples for each category. Specifically, we first construct an instance spatial graph and a label semantic graph respectively and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Empirical results conducted on generic multi-label image recognition demonstrate the superiority of our proposed method. Moreover, the proposed method also shows advantages in multi-label recognition with partial labels and multi-label few-shot learning, as well as outperforms current state-of-the-art methods with a clear margin. Our code is available at https://***/yananwu0510/ML-SGM. © 2023, CC BY.

关键词： Image recognition

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Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

Deep Learning based Multilabel Hateful Speech Text Comments ...

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Current Development in Engineering and technology (CCET), IEEE International Conference on

作者： Naol Bakala Defersha Jemal Abawajy Kula Kekeba Software Engineering Department College of Electrical and Mechanical Engineering Centre of Excellence for HPC & Big Data Analytics Addis Ababa Science and Technology Ethiopia Faculty of Science Engineering and Built Environment School of Information Technology Deakin University Geelong VIC Australia Department of Software Engineering Technology Centre of Excellence for College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Ethiopia

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%.

关键词： Deep learning Voice activity detection Social networking (online) Text recognition Hate speech Speech recognition data models

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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DeEPCA: Decentralized exact PCa with linear convergence rate

arXiv

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arXiv 2021年

作者： Ye, Haishan Zhang, Tong Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen Hong Kong Hong Kong University of Science and Technology Hong Kong

Due to the rapid growth of smart agents such as weakly connected computational nodes and sensors, developing decentralized algorithms that can perform computations on local agents becomes a major research direction. This paper considers the problem of decentralized Principal components analysis (PCA), which is a statistical method widely used for data analysis. We introduce a technique called subspace tracking to reduce the communication cost, and apply it to power iterations. This leads to a decentralized PCA algorithm called DeEPCA, which has a convergence rate similar to that of the centralized PCA, while achieving the best communication complexity among existing decentralized PCA algorithms. DeEPCA is the first decentralized PCA algorithm with the number of communication rounds for each power iteration independent of target precision. Compared to existing algorithms, the proposed method is easier to tune in practice, with an improved overall communication cost. Our experiments validate the advantages of DeEPCA empirically. © 2021, CC BY.

关键词： Principal component analysis

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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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Heterogeneous Parallel Computing Based Real-Time Chaotic Video Encryption and its Application to Drone-Oriented Secure Communication

SSRN

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SSRN 2023年

作者： Jiang, Dong Shi, Fan-Feng Li, Tao Li, Yi-Fei Shan, Dan School of Information Engineering Yangzhou Polytechnic Institute Yangzhou225007 China School of Internet Anhui University Hefei230039 China College of Physical Science and Technology Yangzhou University Yangzhou225009 China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China

This paper proposes a real-time video encryption protocol based on multi-round confusion-diffusion architecture and heterogeneous parallel computing. It leverages the powerful computing capacity of Central Processing Unit (CPU) and the high parallel capability of Graphics Processing Unit (GPU) to perform byte generation, confusion and diffusion operations concurrently, thereby improving computational efficiency. Statistical and security analysis show that the proposed protocol exhibits exceptional statistical properties and provides resistance against different types of attacks. Encryption speed evaluation demonstrates that it can realize latency-free 640x640 30FPS video encryption using Intel Core i7-8700 and NVIDIA GeForce GTX 1060, with an average encryption time of 20.43ms, despite performing seven rounds of confusion and three rounds of diffusion operations on each frame. Additionally, the proposed protocol is adopted to implement a drone-oriented secure video communication system, with NVIDIA Jetson Xavier NX (NVIDIA Camel ARM CPU and Volta GPU), achieving latency-free 256x256 29FPS video encryption. © 2023, The Authors. All rights reserved.

关键词： Program processors

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An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Image segmentation

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An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation

arXiv

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arXiv 2022年

作者： Zhang, Jingyi Meng, Cheng Yu, Jun Zhang, Mengrui Zhong, Wenxuan Ma, Ping Center for Statistical Science Tsinghua University China Center for Applied Statistics Institute of Statistics and Big Data Renmin University of China China School of Mathematics and Statistics Beijing Institute of Technology China Department of Statistics University of Georgia United States

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this paper, we study model-free subsampling methods, which aim to identify a subsample that is not confined by model assumptions. Existing model-free subsampling methods are usually built upon clustering techniques or kernel tricks. Most of these methods suffer from either a large computational burden or a theoretical weakness. In particular, the theoretical weakness is that the empirical distribution of the selected subsample may not necessarily converge to the population distribution. Such computational and theoretical limitations hinder the broad applicability of model-free subsampling methods in practice. We propose a novel model-free subsampling method by utilizing optimal transport techniques. Moreover, we develop an efficient subsampling algorithm that is adaptive to the unknown probability density function. Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator. We also provide the optimal bandwidth for the proposed estimator. Numerical studies on synthetic and real-world datasets demonstrate the performance of the proposed method is superior. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Temporal Convolution Network Based on Attention Mechanism for Well Production Prediction

SSRN

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SSRN 2022年

作者： Zhen, Yan Fang, Junyi Zhao, Xiaoming Ge, Jiawang Xiao, Yifei School of Earth Science and Technology Southwest Petroleum University Sichuan Chengdu China Natural Gas Geology Key Laboratory of Sichuan Province Southwest Petroleum University Sichuan Chengdu China Spatial Information Technology and Big Data Mining Research Center Sichuan Chengdu China

Well production forecasting has a very important guiding significance for oilfield production and management. The traditional BP neural network is difficult to deal with the data with time continuity, and the recurrent neural network (RNN) has some shortcomings such as vanishing gradients and exploding gradients. In order to avoid these problems and further improve the accuracy of neural network models in well production prediction. In this study, a prediction model combining temporal convolutional network (TCN) and attention mechanism is proposed. TCN is able to obtain more sensory fields to solve longer time series with fewer network layers by causally expanding convolution. And the residual connectivity in the model structure enables the model to transfer information across layers to solve the problem of network degradation caused by overly deep networks. The attention mechanism can reinforce the influence of important features on the prediction results to improve the prediction accuracy of the model. To evaluate the effectiveness of the model, the TCN-attention model was used to forecast the oil production of two producing wells, and the prediction results were compared with those of LSTM, TCN, RNN and ARIMA. The results showed that TCN-Attention outperformed the other models in the prediction of the two wells in all evaluation metrics. The method proposed in this paper has excellent data fitting ability, can provide accurate prediction results, and have a certain application value in oilfield production. © 2022, The Authors. All rights reserved.

关键词： Time series

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Refining Groundwater Storage Loss to Support Irrigated Agriculture by Integrating Mt-Insar, Grace, and Hydraulic Head Measurements

SSRN

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SSRN 2024年

作者： Hu, Jiyuan Zhou, Zheng Wang, Jiabei Qin, Fen Wang, Jiayao Zhang, Rong Wu, Wenhao Wang, Lijun College of Geography and Environmental Science Henan University Kaifeng475004 China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Ministry of Education Kaifeng475004 China Henan Water Conservancy Survey. LTD Zhengzhou450008 China Henan Industrial Technology Academy of Spatial-Temporal Big Data Henan University Zhengzhou450046 China Hunan Province Key Laboratory of Coal Resources Clean-utilization and Mine Environment Protection Hunan University of Science and Technology Xiangtan411201 China

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to water scarcity and extensive. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed a refined methodology for estimating Groundwater Storage Anomalies (GWSA). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment/Global Land data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparison of wells and overall statistics verifies the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSA, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSA significantly refines the regular grid of ~27 km from GRACE to within 1 km in deep confined aquifers and effectively constrains uncertainties in the GRACE solution, probably caused by spatial leakages in the mass field. Furthermore, the gravity analysis of GWSA in HNP reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction. © 2024, The Authors. All rights reserved.

关键词： Aquifers

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