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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Deng Yuandan Chen Bo Chen Weiqin Sun Bohang Zeng Juntao Mao Yanling Sichuan Provincial Key Laboratory of Intelligent Terminal jointly built by Hall and City Yi Bin China University of Electronic Science and Technology of China Yi Bin China School of Information and Software Engineering. University of Electronic Science and Technology of China Cheng Du China

The rapid expansion of multimedia data has made multimedia data mining a crucial area of research in the field of crime. In the realm of crime, the significance of multimedia data mining lies in its ability to reveal hidden patterns, correlations, and knowledge within criminal activities, providing powerful support for crime investigation and prevention. For instance, through similarity search, multimedia data relevant to criminal suspects can be swiftly identified. By identifying associations and connections between criminal organizations, criminal networks can be discovered. Through entity resolution and classification, individuals, locations, and items involved in criminal events can be automatically identified. The automatic feature learning and large-scale data processing capabilities of deep learning make it closely intertwined with data mining. Therefore, this paper summarizes the applications of deep learning-based multimedia data mining techniques in the field of crime since 2018, along with the compilation of relevant crime datasets and evaluation metrics. Additionally, it outlines the challenges present in this domain and provides an outlook on future developments.

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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Generative Adversarial Learning for intelligent Trust Management in 6G Wireless Networks

arXiv

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arXiv 2022年

作者： Yang, Liu Li, Yun Yang, Simon X. Lu, Yinzhi Guo, Tan Yu, Keping The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China The School of Software Engineering Chongqing University of Posts and Telecommunications China The Advanced Robotics and Intelligent Systems Laboratory University of Guelph Canada The Hosei University Japan

Emerging six generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high Quality-of-Trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. Then AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative adversarial learning-enabled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first reviewed, and then a potential heterogeneous and intelligent 6G architecture is introduced. Next, the integration of AI and trust management is developed to optimize the intelligence and security. Finally, the presented AI-based trust management method is applied to secure clustering to achieve reliable and real-time communications. Simulation results have demonstrated its excellent performance in guaranteeing network security and service quality. © 2022, CC BY.

关键词： Wireless networks

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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From Coarse to Fine: Learning Semantic Relations for Hyperspectral Image Classification

From Coarse to Fine: Learning Semantic Relations for Hypersp...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jinsheng Ji Xiankai Lu Tao Zhang Yiyou Guo Huan Xie School of Electrical and Electronic Engineering Nanyang Technological University Singaproe School of Software Shandong University Jinan China Shanghai Key Laboratory of Intelligent Sensing and Recognition Shanghai Jiao Tong University Shanghai China College of Surveying and Geo-Informatics Tongji University Shanghai China

Hyperspectral images (HSIs) is consisted of many narrow spectral bands which are capable of recording abundant features including both the spectral and spatial signatures information which have been widely used in various fields, such as urban planning, disaster monitoring. Due to the large number and high similarity of the collected spectral brands, many methods are developed to handle the problem of extracting effective features. Recently, many CNN-based methods have been proposed by exploiting the spectral-spatial signatures of the HSIs data and achieved promising results. Although many methods adopt patch-based input pattern to emphasize the importance of the spatial neighbor information of each pixel, the relations are still limited to a small area around the pixel and the latent relations among the pixels belonging to different semantic categories at the boundary are still not well exploited. To explore the relationships between pixels from a more global perspective, a neighbor-based relation mining framework is proposed to explore the long-range relations among different local regions. Experiments are conducted on two hyperspectral image classification datasets and the results demonstrate the effectiveness of the proposed long-range relations mining scheme by comparison with some state-of-the-art methods.

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Yangding Li Shaobin Fu Hao Feng Yangyang Zeng Jinghao Wang Zhihao Jiang Lvyun Zhang School of Information Science and Engineering Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China School of Big Data and Computer Science Hechi University Guangxi China

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.

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COMBINATORIAL PROPERTIES FOR A CLASS OF SIMPLICIAL COMPLEXES EXTENDED FROM PSEUDO-FRACTAL SCALE-FREE WEB

arXiv

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arXiv 2023年

作者： Xie, Zixuan Wang, Yucheng Xu, Wanyue Zhu, Liwang Li, Wei Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China School of Software Fudan University Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China Academy for Engineering and Technology Fudan University Shanghai200433 China

Simplicial complexes are a popular tool used to model higher-order interactions between elements of complex social and biological systems. In this paper, we study some combinatorial aspects of a class of simplicial complexes created by a graph product, which is an extension of the pseudo-fractal scale-free web. We determine explicitly the independence number, the domination number, and the chromatic number. Moreover, we derive closed-form expressions for the number of acyclic orientations, the number of root-connected acyclic orientations, the number of spanning trees, as well as the number of perfect matchings for some particular cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Fractals

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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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An Exact Potential Game-Based End-Edge-Cloud Collaborative Task Offloading Approach

An Exact Potential Game-Based End-Edge-Cloud Collaborative T...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yue Shen Bowen Liu Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

With the development of Industrial Internet of Things(IIoT), massive industrial devices produce a large number of delay-sensitive and computing-intensive tasks. Offloading tasks under the end-edge-cloud collaboration mode can improve the efficiency of completing tasks. However, on the one hand, tasks need to be finished before corresponding deadlines and pay for consuming resources, so delay and cost constitute the main factors affecting task offloading decisions. On the other hand, limited real-time resources in the system may not meet the needs of all devices, so we should ensure that as many devices can complete tasks on time as possible while optimizing task delay and cost of each device. To tackle the challenges, we propose an end-edge-cloud collaborative task offloading approach based on exact potential game. Aiming at optimizing delay-cost of each device and task completion rate of the whole system, the end-edge-cloud collaborative task offloading problem is modeled as an exact potential game and solved. Extensive experiments show that the approach can improve the task completion rate of the system while optimizing the delay-cost of each device.

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Circuit area optimization of multi-output MPRM based on ERWOA algorithm

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2023年第5期49卷 1193-1200页

作者： He, Juncai He, Zhenxue Wang, Fushun Huo, Zhisheng Xiao, Limin Intelligent Agricultural Equipment Research Institute Hebei Agricultural University Baoding071001 China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University Baoding071001 China High Performance Computing Platform Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

Mixed polarity Reed-Muller (MPRM) circuit area optimization has become a research hotspot in the field of integrated circuit design. It is a combinatorial optimization, aiming at finding the MPRM expression with the least number of terms among many MPRM expressions. A explosion strategy and restart strategy based whale optimization algorithm (ERWOA) is proposed. In addition, a multi-output MPRM circuit area optimization method is proposed, which uses the improved whale algorithm and the improved polarity conversion algorithm to search for the MPRM circuit with the least number of AND terms. Results on the MCNC Benchmark circuits show that the proposed algorithm increases the conversion efficiency by 99.93% and 99.96% at most, compared with the mixed polarity and inter-polarity conversion algorithms based on the list technology, respectively. Compared with the genetic algorithm and the artificial bee colony algorithm, the improved whale optimization algorithm saves the circuit area up to 18.32% with an average of 5.54%, and 14.41% with an average of 5.00%, respectively. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Genetic algorithms

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