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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Kehang Liu, Qi Zhang, Kai Liu, Ye Tao, Hanqing Huang, Zhenya Chen, Enhong Auhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9783031306716

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge based systems

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A Fair Scheduling Algorithm and Schedulability Analysis for Performance Asymmetric Multiprocessors

A Fair Scheduling Algorithm and Schedulability Analysis for ...

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IEEE International Conference on Industrial technology (ICIT)

作者： Peng Wu Feihang Jin Tao Yan Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Institute of Big Data Science and Industry School of Computer and Information Technology Taiyuan China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

We propose a new scheduling policy for performance asymmetric multiprocessors that have identical instruction sets but different processing speeds. The difference between it and the original Pfair scheduling is that when selecting scheduling parameters such as the pseudo deadline of the subtask and selecting the processor when running the highest priority subtask, it can be aware of the performance asymmetry of the underlying processor. In addition, we also propose a sufficient schedulability test for Pfair scheduling on performance asymmetric multiprocessors.

关键词： Schedules Job shop scheduling Processor scheduling Scheduling algorithms Instruction sets Partitioning algorithms Time factors

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RAGRAPH: A General Retrieval-Augmented Graph Learning Framework 38

RAGRAPH: A General Retrieval-Augmented Graph Learning Framew...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jiang, Xinke Qiu, Rihong Xu, Yongxin Zhang, Wentao Zhu, Yichen Zhang, Ruizhe Fang, Yuchen Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China University of Electronic Science and Technology of China China Center on Frontiers of Computing Studies Peking University Beijing China Big Data Technology Research Center Nanhu Laboratory Jiaxing China Peking University Information Technology Institute Binhai Tianjin China

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability. © 2024 Neural information processing systems foundation. All rights reserved.

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Homomorphic Encryption-Based Privacy Protection data Processing Strategies in Fog Computing

Homomorphic Encryption-Based Privacy Protection Data Process...

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big data and Privacy Computing (BDPC), International Conference on

作者： Shiyang Song Jinhai Tang Haozhe Wang Dongxu Yuan Zhiyuan Zhang Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

With the development of Intelligent Transportation Systems (ITS), real-time processing and privacy protection for traffic data become particularly important. In this research, we explore how to process traffic data efficiently and securely in fog computing environments, especially in wireless in-vehicle networks. First, we propose a homomorphic encryption-based strategy that performs computation on ciphertexts, eliminating the need for frequent decryption and thus achieving higher data processing efficiency. Then, we provide an in-depth discussion on data integrity and real-time performance, and propose a novel data validation method to ensure data integrity and real-time updates in fog computing environments. In addition, we designed a series of simulation experiments using virtual data to verify the effectiveness of our approach. The experimental results show that our strategy not only greatly improves the data processing speed, but also effectively guarantees the privacy and integrity of the data. Overall, this study provides a new and efficient method for traffic data processing in fog computing environments, which is an important reference value for future traffic data processing.

关键词： Wireless communication data privacy data integrity data processing Real-time systems Computational efficiency Protection

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation 29

A Drug Box Recognition System Based on Deep Learning and Clo...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Honglei Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor's prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it's hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement. © 2023 IEEE.

关键词： Cloud-Edge Collaboration Image Recognition Pharmacy Automation Text Recognition

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SemFlow: binding semantic segmentation and image synthesis via rectified flow 24

SemFlow: binding semantic segmentation and image synthesis v...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chaoyang Wang Xiangtai Li Lu Qi Henghui Ding Yunhai Tong Ming-Hsuan Yang School of Intelligence Science and Technology Peking University UC Merced Institute of Big Data Fudan University

ISBN: (纸本)9798331314385

Semantic segmentation and semantic image synthesis are two representative tasks in visual perception and generation. While existing methods consider them as two distinct tasks, we propose a unified framework (SemFlow) and model them as a pair of reverse problems. Specifically, motivated by rectified flow theory, we train an ordinary differential equation (ODE) model to transport between the distributions of real images and semantic masks. As the training object is symmetric, samples belonging to the two distributions, images and semantic masks, can be effortlessly transferred reversibly. For semantic segmentation, our approach solves the contradiction between the randomness of diffusion outputs and the uniqueness of segmentation results. For image synthesis, we propose a finite perturbation approach to enhance the diversity of generated results without changing the semantic categories. Experiments show that our SemFlow achieves competitive results on semantic segmentation and semantic image synthesis tasks. We hope this simple framework will motivate people to rethink the unification of low-level and high-level vision.

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Channel Attention-Based Adaptive Near Infrared Spectral Deep Residual Network for Palmprint Image Recognition 16

Channel Attention-Based Adaptive Near Infrared Spectral Deep...

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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Xu, Xuebin Xing, Xiaomin Meng, Kan Xi'an University of Posts and Telecommunications The Department of Data Science and Big Data Technology Xi'an China

ISBN: (纸本)9781665405539

The palmprint images contains a multitude of unique characteristics for human identity recognition, making it the most potential and influential research content in biometric projects and some palmprint recognition algorithms have been exploited in recent years. Most of the existing methods are mainly aimed at some specific scenarios such as high-resolution palmprint image capture device. However, these methods are not universal in realistic scenarios and these algorithms cannot meet the constantly changing requirement of diversity applications. So as to fully address these issues, this paper presents a concrete framework for Low-resolution palmprint image recognition named Improved Residual Channel Attention Network(IRCANet).Experiments are carried out on datasets Hong Kong PolyU, our method achieves rank-1 accuracy of 99.63% in 15dpi. © 2021 IEEE.

关键词： Palmprint recognition

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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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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian 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 China

ISBN: (纸本)9798350337020

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. © 2023 IEEE.

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A nearly optimal distributed algorithm for computing the weighted girth

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science China(Information sciences) 2021年第11期64卷 80-94页

作者： Qiang-Sheng HUA Lixiang QIAN Dongxiao YU Xuanhua SHI Hai JIN 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 TechnologyHuazhong University of Science and Technology School of Computer Science and Technology Shandong University

Computing the weighted girth, which is the sum of weights of edges in the minimum weight cycle,is an important problem in network analysis. The problem for distributively computing girth in unweighted graphs has garnered lots of attention, but there are few studies in weighted graphs. In this paper, we propose a distributed randomized algorithm for computing the weighted girth in weighted graphs with integral edge weights in the range [1, nc], where n is the number of vertices and c is a constant. The algorithm is devised under the standard synchronous CON GE S T model, which limits each vertex can only transfer O（log n） bits information along each incident edge in a round. The upper bound of the algorithm is O（n log2n） rounds. We also prove the lower bound for computing the weighted girth is ?（D + n/log n） where D is the hop diameter of the weighted graph. This means our distributed algorithm is optimal within a factor of O（log3n）.

关键词： distributed algorithms weighted girth $\mathcal{CONGEST}$ model communication complexity round complexity

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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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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