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IEEE International Conference on Systems, Man and Cybernetics

作者： Ye Liu Yan Pan Jian Yin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Artificial Intelligence and Big Data Department Lizhi Inc. Beijing & Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets.

关键词： Training Computer vision image coding Codes Large language models image retrieval Transformers Encoding Vectors Spectrogram

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Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang Transform and Feature Fusion Dimensionality Reduction 22

Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang T...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Lin Fan Ruiling Yao Zhongmin Wang School of Computer Science and Technology Xi'an University of Posts and Telecommunications China and Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time.

关键词： Hilbert-Huang Transform Hypertension Principal Component analysis Pulse Diagnosis

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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A Robust Feature Extraction Method for Heterogenous Remote Sensing images Based on Feature Transfer 5

A Robust Feature Extraction Method for Heterogenous Remote S...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Qiao, Pingan Hua, Zhifan Yang, Dongfang Miao, Xin Xiao, Zhao Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710121 China Graduate School Rocket Force University of Engineering Xi'an710025 China School of Automation Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

A robust feature extraction method based on feature transfer for heterogeneous remote sensing images is proposed to address the problem of insufficient generalization ability of traditional edge feature extraction methods applied to heterogeneous remote sensing images. Firstly, the heterogeneous remote sensing images are transferred based on CycleGAN to reduce the impact of heterogeneous remote sensing images on the edge feature extraction results due to the cross-data domain. Secondly, dilated convolution is introduced in the edge feature extraction model DexiNed, which makes the optimized network model pay more attention to the structured features of the image. The experimental results show that the proposed method improves the structured feature extraction accuracy of heterogenous remote sensing images, which has better robust feature extraction effect compared with DexiNed. © 2022 ACM.

关键词： Feature extraction

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A New Visual & Inertial Based Satellite Quality Evaluation Method 5

A New Visual & Inertial Based Satellite Quality Evaluation M...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Qiao, Pingan Wu, Ruichen Sun, Lei Yang, Dongfang Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710121 China School of Automation Xi'an University of Posts and Telecommunications Xi'an710121 China The Rocket Force University of Engineering Graduate School Xi'an710025 China

ISBN: (纸本)9781450396899

Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite quality evaluation method is proposed. This method can judge whether the GPS (Global Positioning System) is interfered by comparing the visual-inertial navigation information with the GPS data information in the geographic coordinate system. This method enables the UAV to quickly switch the navigation mode under the condition of being disturbed, which ensures that the navigation function is accurate and free from interference, and provides a more solid foundation for the navigation of the UAV and other platforms. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks

arXiv

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arXiv 2022年

作者： Lu, Ziyi Yu, Na Wang, Xuehe The School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Low-resource generation of multi-hop reasoning questions 58

Low-resource generation of multi-hop reasoning questions

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Yu, Jianxing Liu, Wei Qiu, Shuang Su, Qinliang Wang, Kai Quan, Xiaojun Yin, Jian School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9781952148255

This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance. Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text. Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text. Since the labeled data is limited and insufficient for training, we propose to learn the model with the help of a large scale of unlabeled data that is much easier to obtain. Such data contains rich expressive forms of the questions with structural patterns on syntax and semantics. These patterns can be estimated by the neural hidden semi-Markov model using latent variables. With latent patterns as a prior, we can regularize the generation model and produce the optimal results. Experimental results on the HotpotQA data set demonstrate the effectiveness of our model. Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements. © 2020 Association for Computational Linguistics

关键词： Semantics

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Leveraging Contaminated datasets to Learn Clean-data Distribution with Purified Generative Adversarial Networks

arXiv

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arXiv 2023年

作者： Tian, Bowen Su, Qinliang Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangdong China

Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many variants of GAN have been proposed. However, existing GANs are almost established on the assumption that the training dataset is clean. But in many real-world applications, this may not hold, that is, the training dataset may be contaminated by a proportion of undesired instances. When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution). To learn the target distribution from contaminated datasets, two purified generative adversarial networks (PuriGAN) are developed, in which the discriminators are augmented with the capability to distinguish between target and contaminated instances by leveraging an extra dataset solely composed of contamination instances. We prove that under some mild conditions, the proposed PuriGANs are guaranteed to converge to the distribution of desired instances. Experimental results on several datasets demonstrate that the proposed PuriGANs are able to generate much better images from the desired distribution than comparable baselines when trained on contaminated datasets. In addition, we also demonstrate the usefulness of PuriGAN on downstream applications by applying it to the tasks of semi-supervised anomaly detection on contaminated datasets and PU-learning. Experimental results show that PuriGAN is able to deliver the best performance over comparable baselines on both tasks1 Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment analysis

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Lanxue Dang Chunyu Wang Hongyu Han Yan-E Hou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset.

关键词： Deep learning Analytical models Sentiment analysis Semantics Feature extraction Motion pictures data models

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