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EVOH in situ fibrillation and its effect of strengthening, toughening and hydrophilic modification on PVDF hollow fiber microfiltration membrane via TIPS process

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Journal of Materials Science 2019年第7期54卷 5971-5987页

作者： Cui, Zhenyu Tang, Xiuxiu Li, Wei Liu, Haonan Zhang, Jing Wang, Hong Li, Jianxin State Key Laboratory of Separation Membranes and Membrane Processes National Center for International Joint Research on Separation Membranes School of Material Science and Engineering Tianjin Polytechnic University Tianjin 300387 China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 China

In order to enhance the strength and overcome the poor antifouling capacity of poly(vinylidene fluoride) (PVDF) membrane used in water treatment, herein, poly(ethylene-co-vinyl alcohol) (EVOH) was selected and the PVDF/EVOH blend hollow fiber microfiltration membrane was prepared via the thermally induced phase separation (TIPS) technique. The morphology for the pristine and blend membrane was compared, and the distribution of EVOH on outer surface and within matrix was explored. The fibrous-shaped EVOH enhanced the breaking strength of the membrane markedly (up to 13.63 MPa) due to the in situ fibrillation;especially, when the blend membrane was immersed into water, the internal plasticization of water molecular enhanced the toughness of the membrane and the elongation at break increased up to 86.39% compared with 27.63% for the corresponding dry membrane and 36.62% for the pristine membrane, respectively. The addition of EVOH introduced hydroxyl group into the bulk and thus endowed the membrane with a better hydrophilicity (the contact angle is as low as 43°) and higher pure water flux (up to 449.11 L m⁻² h⁻¹) compared with pristine PVDF membrane. Moreover, the blend membrane showed a better rejection of carbonic particle (nearly 100%) and higher flux recovery rate (up to 87.30%). The present investigation offers an effective and simple pattern to regulate microstructure and enhance mechanical strength, flux and hydrophilicity of the polymeric microfiltration membrane via the TIPS process for water treatment. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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Project development promoting strategy in open source community based on social network centrality analysis

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Journal of Computational Information Systems 2013年第2期9卷 721-728页

作者： Huang, Yuan Li, Bing He, Peng State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Research Center of Complex Network Wuhan University Wuhan 430072 China School of Computer Wuhan University Wuhan 430072 China

With the increasing popularity and widely used of public-oriented Internet, the open source software development has become a prevalent development pattern. The perspective of this paper is focused on the developers in Source Forge community, where it is not only a software network viewed software as unit, but also a social network as developers. At first, developer networks were built and the degree centrality, betweenness centrality and closeness centrality were calculated, and then the developers with highest centrality were recommended. After that, the number of downloads, views and developments are evaluation indicators of the success of open source software projects. The relations between centrality measures and evaluation indicators of the success of open source software project are analyzed to measure the consistency of the measure centrality indicators with successful community development of a single open-source software projects so to achieve successful potential projects development. Copyright © 2013 Binary Information Press.

关键词： Open source software

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Sparsity-based occlusion handling method for person re-identification 21

Sparsity-based occlusion handling method for person re-ident...

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Huang, Bingyue Chen, Jun Wang, Yimin Liang, Chao Wang, Zheng Sun, Kaimin National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan430072 China Research Institute of Wuhan University in Shenzhen China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

ISBN: (纸本)9783319144412

Person re-identification has recently attracted a lot of research interests, it refers to recognizing people across non-overlapping surveillance cameras. However, person re-identification is essentially a very challenging task due to variations in illumination, viewpoints and occlusions. Existing methods address these difficulties through designing robust feature representation or learning proper distance metric. Although these methods have achieved satisfactory performance in the case of illumination and viewpoint changes, seldom of they can genuinely handle the occlusion problem that frequently happens in the real scene. This paper proposes a sparsity-based patch matching method to handle the occlusion problem in the person re-identification. Its core idea is using a sparse representation model to determine the occlusion state of each image patch, which is further utilized to adjust the weight of patch pairs in the feature matching process. Extensive comparative experiments conducted on two widely used datasets have shown the effectiveness of the proposed method. © Springer International Publishing Switzerland 2015.

关键词： Cameras

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Unequal Error Protection Based on Expanding Window Fountain for Object-Based 3D Audio

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Wuhan University Journal of Natural Sciences 2017年第4期22卷 323-328页

作者： YANG Cheng HU Ruimin SONG Yucheng SU Liuyue WANG Xiaochen CHEN Wei National Engineering Research Center for Multimedia Software/School of Computer Science Wuhan UniversityWuhan 430072HubeiChina Hubei Key Laboratory of Multimedia and Network Communication Engineering(Wuhan University) Wuhan 430072HubeiChina Research Institute of Wuhan University in Shenzhen Shenzhen 518000GuangdongChina School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001GuizhouChina

This paper proposes an unequal error protection（UEP）coding method to improve the transmission performance of three-dimensional（3D）audio based on expanding window fountain（EWF）.Different from other transmissions with equal error protection（EEP）when transmitting the 3D audio *** approach of extracting the important audio object is presented,and more protection is given to more important audio object and comparatively less protection is given to the normal audio *** and subjective experiments have shown that the proposed UEP method achieves better performance than equal error protection method,while the bits error rates（BER）of the important audio object can decrease from 10^（–3） to 10^（–4）,and the subjective quality of UEP is better than that of EEP by 14%.

关键词： object-based 3D audio unequal error protection equal error protection

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Knowledge Graph Augmented network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis

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IEEE Transactions on Knowledge and Data engineering 2023年第10期35卷 10098-10111页

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Jin, Hua Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Wuhan430072 China Jd Explore Academy at *** Beijing100101 China Wuhan University Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Hubei Wuhan430072 China Hospital of Kunming University of Science and Technology Kunming650032 China

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets. © 1989-2012 IEEE.

关键词： Semantics

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

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2023 China Automation Congress, CAC 2023

作者： Yang, Ming Zhang, Kaiyang Mu, Chao Wang, Xin Liu, Yuanlong Zhang, Heng Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

ISBN: (纸本)9798350303759

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data. © 2023 IEEE.

关键词： Cryptography

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Multiple deception resources deployment strategy based on reinforcement learning for network threat mitigation

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Scientific Reports 2025年第1期15卷 1-15页

作者： Li, Changsong Zhao, Ning Wu, Hao State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing 100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing 100044 China Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing 100044 China National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC) Beijing 100044 China

The Advanced Persistent Threat (APT) poses significant security challenges to the availability and reliability of government and enterprise information systems. Due to the high concealment and long duration characteristic of APT, industry and academia typically adopt active defense methods to combat APT. By deploying deception defense (DD) methods such as honeypots, the attacker’s target can be effectively confused. However, in reality, honeypots are usually deployed in resource-constrained systems. Therefore, how to effectively deploy deception resources is an urgent problem that needs to be addressed. This paper proposes a multi-type deception resources deployment strategy generation algorithm based on reinforcement learning for the network reconnaissance stage of APT. Through the analysis of network assets and attack process, the algorithm balances the two dimensions of defense effectiveness and defense cost, generating the deployment strategy for deception resources. The experimental results show that compared to other baselines, the proposed algorithm achieves defensive success probability of 97.09% while reducing the attack probability of the target asset by at least 10.34%. This effectively reduces defense costs while ensuring defense efficiency. In addition, the algorithm demonstrates good convergence and stability. © The Author(s) 2025.

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Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models

Improving Sharpness-Aware Minimization with Fisher Mask for ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhong, Qihuang Ding, Liang Shen, Li Mi, Peng Liu, Juhua Du, Bo Tao, Dacheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China JD Explore Academy China School of Informatics Xiamen University China Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University China

Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization. Prior works show that the recently-proposed sharpness-aware minimization (SAM) optimization method can improve the model generalization. However, SAM adds a perturbation to each model parameter equally (but not all parameters contribute equally to the optimization of training), which we argue is sub-optimal and will lead to excessive computation. In this paper, we propose a novel optimization procedure, namely FSAM, which introduces a Fisher mask to improve the efficiency and performance of SAM. In short, instead of adding perturbation to all parameters, FSAM uses the Fisher information to identity the important parameters and formulates a Fisher mask to obtain the sparse perturbation, i.e., making the optimizer focus on these important parameters. Experiments on various tasks in GLUE and SuperGLUE benchmarks show that FSAM consistently outperforms the vanilla SAM by 0.67∼1.98 average score among four different pretrained models. We also empirically show that FSAM works well in other complex scenarios, e.g., fine-tuning on generation tasks or limited training data. Encouragingly, when training data is limited, FSAM improves the SAM by a large margin, i.e., up to 15.1. © 2022 Association for Computational Linguistics.

关键词： Fisher information matrix

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