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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhou, Yang Shan, Shimin Wei, Hongkui Zhao, Zhehuan Feng, Wenshuo Software School Dalian University of Technology Dalian116620 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9780000000002

Relation Extraction aims at recognizing the relation between pairs of entities mentioned in a text. Advances in LLMs have had a tremendous impact on NLP tasks. In this work, we propose a textual data augmentation framework called PGA for improving the performance of models for relation extraction task in the scientific domain. The framework introduces two ways of data augmentation, utilizing a LLM to obtain pseudo-samples with the same sentence meaning but with different representations and forms by paraphrasing the original training set samples. As well as instructing LLM to generate sentences that implicitly contain information about the corresponding labels based on the relation and entity of the original training set samples. These two kinds of pseudo-samples participate in the training of the relation extraction model together with the original dataset, respectively. The PGA framework in the experiment improves the Fl scores of the three mainstream models for relation extraction within the scientific domain. Also, using a large language model to obtain samples can effectively reduce the cost of manually labeling data. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Simulation Design ofWarehouse Business Process Based on Activity and WMS 1

Simulation Design ofWarehouse Business Process Based on Acti...

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1st International Conference on Disruptive Human Resource Management: People, Technology and Strategy, ICDHRM 2024

作者： Qiu, Xiaoping Wang, Ning Li, Pengfei Liu, Haixiang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Tangshan Institute of Southwest Jiaotong University Hebei Tangshan063000 China School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China Sichuan Institute of Industrial Software Technology Sichuan Chengdu611756 China

ISBN: (纸本)9781643685236

With the annual growth of social logistics volume, the business volume at the warehouse end continues to increase and management becomes more complex. In order to measure and provide feedback on the effectiveness of the warehouse management system to optimize the warehousing business process, it is necessary to simulate it. The article develops a warehouse business process simulation system that integrates workflow engines based on process interaction strategy. An experiment is designed using the goods warehousing process as an example, and the results verify the effectiveness of the simulation system. The system simulates time through a random number generator. Each link of the process assigns tasks to different workstations for concurrent execution through the listener. The simulation process is created in the central warehouse and uniformly scheduled by the scheduler. The system also has the function of equipment random failure to simulate its impact on warehouse efficiency. Finally, the simulation results can be output for statistical analysis of the data. The results show that the system can calculate and analyze the entire process of warehouse business processes, provide useful guidance for the work of shipping personnel and warehouse management personnel, and achieve refined management of warehouse resources. © 2024 The Authors.

关键词： Warehouses

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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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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China 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 Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

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. © 2023 IEEE.

关键词： Smart contract

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Hybrid Feature Measurement based on Linear and Nonlinear Nonnegative Matrix Factorization 22

Hybrid Feature Measurement based on Linear and Nonlinear Non...

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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Sicong Ye Yang Zhao Jihong Pei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering in Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450398336

The nonnegative matrix factorization algorithm is an effective data dimensionality reduction method. The principle is to convert the image into a nonnegative linear combination of low dimensional basis images. Nonnegative matrix factorization can be divided into linear algorithm and nonlinear algorithm. Because of different decomposition theory, linear NMF algorithms mainly extract first-order features of data, while nonlinear NMF algorithms mainly extract high-order features. Most of the current studies only focus on one of the models without combining the two together, which leads to the lack of data features. Therefore it is necessary to integrate the two types of algorithms for research. The paper proposes hybrid feature measurement based on linear and nonlinear nonnegative matrix factorization. The algorithm utilizes the idea of feature fusion. The basis image features of the two algorithms are mixed in the model. Finally a feature similarity measurement is obtained as the measure method. The proposed algorithm has good performance on the public datasets and effectively improves the recognition.

关键词： Hybrid feature measurement Nonnegative matrix factorization Basis image

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Relation Extraction Model Based on Semantic Enhancement Mechanism

arXiv

引用

arXiv 2023年

作者： Liu, Peiyu Du, Junping Shao, Yingxia Guan, Zeli School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications 100876 China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

Relational extraction is one of the basic tasks related to information extraction in the field of natural language processing, and is an important link and core task in the fields of information extraction, natural language understanding, and information retrieval. None of the existing relation extraction methods can effectively solve the problem of triple overlap. The CasAug model proposed in this paper based on the CasRel framework combined with the semantic enhancement mechanism can solve this problem to a certain extent. The CasAug model enhances the semantics of the identified possible subjects by adding a semantic enhancement mechanism, First, based on the semantic coding of possible subjects, pre-classify the possible subjects, and then combine the subject lexicon to calculate the semantic similarity to obtain the similar vocabulary of possible subjects. According to the similar vocabulary obtained, each word in different relations is calculated through the attention mechanism. For the contribution of the possible subject, finally combine the relationship pre-classification results to weight the enhanced semantics of each relationship to find the enhanced semantics of the possible subject, and send the enhanced semantics combined with the possible subject to the object and relationship extraction module. Complete the final relation triplet extraction. The experimental results show that, compared with the baseline model, the CasAug model proposed in this paper has improved the effect of relation extraction, and CasAug's ability to deal with overlapping problems and extract multiple relations is also better than the baseline model, indicating that the semantic enhancement mechanism proposed in this paper It can further reduce the judgment of redundant relations and alleviate the problem of triple overlap. © 2023, CC BY.

关键词： Semantics

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PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction

arXiv

引用

arXiv 2024年

作者： Zhou, Yang Shan, Shimin Wei, Hongkui Zhao, Zhehuan Feng, Wenshuo Software school Dalian University of Technology Dalian116620 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China

Relation Extraction (RE) aims at recognizing the relation between pairs of entities mentioned in a text. Advances in LLMs have had a tremendous impact on NLP. In this work, we propose a textual data augmentation framework called PGA for improving the performance of models for RE in the scientific domain. The framework introduces two ways of data augmentation, utilizing a LLM to obtain pseudo-samples with the same sentence meaning but with different representations and forms by paraphrasing the original training set samples. As well as instructing LLM to generate sentences that implicitly contain information about the corresponding labels based on the relation and entity of the original training set samples. These two kinds of pseudo-samples participate in the training of the RE model together with the original dataset, respectively. The PGA framework in the experiment improves the F1 scores of the three mainstream models for RE within the scientific domain. Also, using a LLM to obtain samples can effectively reduce the cost of manually labeling data. Copyright © 2024, The Authors. All rights reserved.

关键词： Character recognition

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Quantum verification of NP problems with single photons and linear optics

引用

Light(Science & Applications) 2021年第9期10卷 1750-1760页

作者： Aonan Zhang Hao Zhan Junjie Liao Kaimin Zheng Tao Jiang Minghao Mi Penghui Yao Lijian Zhang National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation(Ministry of Education)and College of Engineering and Applied SciencesNanjing University210093NanjingChina Collaborative Innovation Center of Advanced Microstructures Nanjing University210093NanjingChina State Key Laboratory for Novel Software Technology Nanjing University210093NanjingChina

Quantum computing is seeking to realize hardware-optimized algorithms for application-related computational ***(nondeterministic-polynomial-time)is a complexity class containing many important but intractable problems like the satisfiability of potentially conflict constraints(SAT).According to the well-founded exponential time hypothesis,verifying an SAT instance of size n requires generally the complete solution in an O(n)-bit *** contrast,quantum verification algorithms,which encode the solution into quantum bits rather than classical bit strings,can perform the verification task with quadratically reduced information about the solution in O~(Õ−−√n)*** we realize the quantum verification machine of SAT with single photons and linear *** using tunable optical setups,we efficiently verify satisfiable and unsatisfiable SAT instances and achieve a clear completeness-soundness gap even in the presence of experimental *** protocol requires only unentangled photons,linear operations on multiple modes and at most two-photon joint *** features make the protocol suitable for photonic realization and scalable to large problem sizes with the advances in high-dimensional quantum information manipulation and large scale linear-optical *** results open an essentially new route toward quantum advantages and extend the computational capability of optical quantum computing.

关键词： quantum optics verify

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Uncertainty-Aware Modeling for Improving Prediction of Toxicity 23

Uncertainty-Aware Modeling for Improving Prediction of Toxic...

引用

23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zhou, Xinlei Wang, Xizhao Zhou, Xinpeng Tu, Siwu Ranwang School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China College of Electronic Information Guangxi Minzu University 530000 China Jiangxi Drug Inspection Center Nanchang330001 China

ISBN: (纸本)9798331528041

Accurately predicting molecular toxicity is challenging in drug discovery and chemical safety assessment. Except for the representation of molecular structure, the inherent class imbalance and uncertainty often complicate the task. In this work, we present UnBGMT (Uncertainty-Aware Balanced Graph Multi-set Transformer), which integrates uncertainty modeling into the Graph Multiset Transformer (GMT) framework to improve the reliability and accuracy of molecular toxicity predictions. Our method employs Monte Carlo dropout (MC dropout) to evaluate uncertainty, which is then used to guide an adaptive loss function that adjusts weights in training. This approach guides the model to focus on the minority and samples with middle-level uncer-tainty, mitigating issues related to class imbalance and improving model stability. We conducted sufficient experiments on the Tox21 dataset, demonstrating the superiority of UnBGMT by comparing performance with baseline models and conducting ablation studies. Our results highlight the benefits of incorporating uncertainty in toxicity prediction, which provides a deeper insight into the im-pact of uncertainty on model performance. © 2024 IEEE.

关键词： Prediction models

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