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Reverse extraction of early-age hydration kinetic equation from observed data of Portland cement

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science china(Technological sciences) 2010年第6期53卷 1540-1553页

作者： WANG lin1,YANG Bo2,ZHAO XiuYang1,2,chen YueHui2 & CHANG Jun3 1 school of computer science and technology,shandong university,jinan 250101,china 2 Provincial Key Laboratory for Network-based Intelligent Computing,university of jinan,jinan 250022,china 3 school of Material science and Engineering,university of jinan,jinan 250022,china 1. school of computer science and technology Shandong University Jinan 250101 China2. Provincial Key Laboratory for Network-based Intelligent Computing University of Jinan Jinan 250022 China3. School of Material Science and Engineering University of Jinan Jinan 250022 China

The early-age hydration of Portland cement paste has an important impact on the formation of microstructure and development of ***,manual derivation of hydration kinetic equation is very difficult because there are multi-phased,multi-sized and interrelated complex chemical and physical reactions during cement *** this paper,early-age hydration kinetic equation is reversely extracted automatically from the observed time series of hydration degree of Portland cement using evolutionary computation method that combines gene expression programming and particle swarm optimization *** order to reduce the computing time,GPUs are used for acceleration in *** have shown that according to the extracted kinetic equation,simulation curve of early-age hydration is in good accordance with the observed experimental ***,this equation still has a good generalization ability even changing chemical composition,particle size and curing conditions.

关键词： Portland cement hydration kinetic equation reverse extraction gene expression programming particle swarm optimization

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SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene

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Journal of computer science & technology 2024年第2期39卷 305-319页

作者：王玉洁陈学霖陈宝权 school of computer science and technology Shandong UniversityQingdao 266237China State Key Laboratory of General Artificial Intelligence Beijing 100871China school of Intelligence science and technology Peking UniversityBeijing 100871China Tencent AI Lab Tencent Holdings LimitedShenzhen 518057China

We present SinGRAV, an attempt to learn a generative radiance volume from multi-view observations of a single natural scene, in stark contrast to existing category-level 3D generative models that learn from images of many object-centric scenes. Inspired by SinGAN, we also learn the internal distribution of the input scene, which necessitates our key designs w.r.t. the scene representation and network architecture. Unlike popular multi-layer perceptrons (MLP)-based architectures, we particularly employ convolutional generators and discriminators, which inherently possess spatial locality bias, to operate over voxelized volumes for learning the internal distribution over a plethora of overlapping regions. On the other hand, localizing the adversarial generators and discriminators over confined areas with limited receptive fields easily leads to highly implausible geometric structures in the spatial. Our remedy is to use spatial inductive bias and joint discrimination on geometric clues in the form of 2D depth maps. This strategy is effective in improving spatial arrangement while incurring negligible additional computational cost. Experimental results demonstrate the ability of SinGRAV in generating plausible and diverse variations from a single scene, the merits of SinGRAV over state-of-the-art generative neural scene models, and the versatility of SinGRAV by its use in a variety of applications. Code and data will be released to facilitate further research.

关键词： generative model neural radiance field 3D scene generation

Weighted Co-Training for Cross-Domain Image SentimentClassification

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Journal of computer science & technology 2017年第4期32卷 714-725页

作者： meng chen lin-lin Zhang Xiaohui Yu Yang Liu school of computer science and technology Shandong University Jinan 250101 China school of Information technology York University Toronto M3J 1P3 Canada

Image sentiment classification, which aims to predict the polarities of sentiments conveyed by the images, has gained a lot of attention. Most existing methods address this problem by training a general classifier with certain visual features, ignoring the discrepancies across domains. In this paper, we propose a novel weighted co-training method for cross-domain image sentiment classification, which iteratively enlarges the labeled set by introducing new high-confidence classified samples to reduce the gap between the two domains. We train two sentiment classifiers with both the images and the corresponding textual comments separately, and set the similarity between the source domain and the target domain as the weight of a classifier. We perform extensive experiments on a real Flickr dataset to evaluate the proposed method, and the empirical study reveals that the weighted co-training method significantly outperforms some baseline solutions.

关键词： sentiment classification cross-domain weighted co-training

实现记忆检索和回忆中漫游的一种计算AM模型

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吉首大学学报(自然科学版) 2020年第1期41卷 9-16页

作者：谭成兵詹林晏立陈波亳州职业技术学院智能工程系安徽理工大学计算机科学与工程学院红河学院工学院山东理工大学计算机科学与技术学院

为了捕捉自传体记忆,提出了一种针对自传体记忆的计算AM模型.模型为3层网络结构.底层由5W1H构成的特定事件知识进行编码,提供检索线索;中间层通过关联特定事件知识对事件进行编码;顶层通过关联相关事件对事件集进行编码.按照自下而上的... 详细信息

为了捕捉自传体记忆,提出了一种针对自传体记忆的计算AM模型.模型为3层网络结构.底层由5W1H构成的特定事件知识进行编码,提供检索线索;中间层通过关联特定事件知识对事件进行编码;顶层通过关联相关事件对事件集进行编码.按照自下而上的记忆搜索过程,AM模型可以分别在中间层和顶层识别相应的事件和事件集,同时,还能通过规则记忆检索过程模仿人在回忆中漫游的现象.基于一组数据集的实验结果表明,模型不仅具有鲁棒的、灵活的记忆检索,而且相较于普遍采用的基于关键字的查询方法记忆检索模型,对含噪记忆检索线索有更好的响应性能,还能够模仿回忆中的漫游现象.

关键词：人工智能认知模型自传体记忆回忆中漫游

一种基于Bayesian网络的网页推荐算法

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山东大学学报（工学版） 2011年第4期41卷 137-142页

作者：王爱国李廉杨静陈桂林合肥工业大学计算机与信息学院安徽合肥230009

为改善用户的Web页面访问行为、提高访问效率,设计了一种基于贝叶斯网络的网页推荐模型及推荐算法。通过收集和分析服务器中的描述文件和日志文件,利用Bayesian网络分析页面间的依赖关系,构建了基于贝叶斯网络的网页推荐模型并产生推荐... 详细信息

为改善用户的Web页面访问行为、提高访问效率,设计了一种基于贝叶斯网络的网页推荐模型及推荐算法。通过收集和分析服务器中的描述文件和日志文件,利用Bayesian网络分析页面间的依赖关系,构建了基于贝叶斯网络的网页推荐模型并产生推荐集。通过在Microsoft公司提供的网络日志数据集上做的实验,可以获得超过80%的准确率和覆盖率。理论分析和实验结果表明:算法能够在线实时向用户做出个性化的推荐,与已有的推荐算法相比,算法能较快地给出推荐集,并且可以获得更高的准确率和覆盖率。

关键词：数据挖掘个性化推荐协同过滤贝叶斯网络

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MPEG-4流的多分形小波模型仿真及性能分析

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计算机工程与应用 2006年第14期42卷 16-21页

作者：林青家陈涤刘允才山东大学信息科学与工程学院上海交通大学电子信息与电气学院上海200030 济南250100

论文采用多分形小波模型(MWM)对MPEG—4视频流进行仿真,其母小波采用Haar小波,并利用一阶统计、二阶统计和多分形分析的方法,对模型在刻画流量的主要统计特性(如概率密度分布、LRD特性、突发性和多分形特征)方面的性能进行了评估,其结果... 详细信息

论文采用多分形小波模型(MWM)对MPEG—4视频流进行仿真,其母小波采用Haar小波,并利用一阶统计、二阶统计和多分形分析的方法,对模型在刻画流量的主要统计特性(如概率密度分布、LRD特性、突发性和多分形特征)方面的性能进行了评估,其结果为:MWM模型是一个简单的和有实用价值的MPEG—4视频流的仿真模型,它可以很好地刻画出流量的全局统计特性和多分形特征,但此模型也有缺陷,对于具有高突发性的视频流量的刻画能力较弱,需要我们在以后的研究工作中加以完善。

关键词：多分形小波模型突发性 MPEG—4视频流 QQ图

Order-Revealing Encryption:File-Injection Attack and Forward Security

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Journal of computer science & technology 2021年第4期36卷 877-895页

作者： Yuan Li Xing-chen Wang lin Huang Yun-Lei Zhao school of computer science Fudan UniversityShanghai 201203China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China State Key Laboratory of Cryptology Beijing 100878China

Order-preserving encryption(OPE)and order-revealing encryption(ORE)are among the core ingredients for encrypted databases(EDBs).In this work,we study the leakage of OPE and ORE and their forward *** propose generic yet powerful file-inject ion attacks(FI As)on OPE/ORE,aimed at the situations of possessing order by and range *** FI As only exploit the ideal leakage of OPE/ORE(in particular,no need of data denseness or frequency).We also improve their efficiency with the frequency statistics using a hierarchical idea such that the high-frequency values will be recovered more *** conduct some experiments on real datasets to test the performance,and the results show that our FI As can cause an extreme hazard on most of the existing OPEs and OREs with high efficiency and 100%recovery *** then formulate forward security of ORE,and propose a practical compilation framework for achieving forward secure ORE to resist the perniciousness of *** compilation framework can transform most of the existing OPEs/OREs into forward secure OREs,with the goal of minimizing the extra burden incurred on computation and *** also present its security proof,and execute some experiments to analyze its *** proposed compilation is highly efficient and forward secure.

关键词： order-revealing encryption order-preserving encryption file-injection attack forward security

Evaluating and improving the interpretability of item embeddings using item-tag relevance information

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Frontiers of computer science 2020年第3期14卷 143-158页

作者： Tao LIAN lin DU Mingfu ZHAO Chaoran CUI Zhumin chen Jun MA College of Data science Taiyuan University of TechnologyJinzhong 030600China Software College Shandong UniversityJinan 250101China school of computer and Control Engineering University of Chinese Academy of SciencesBeijing 101408China school of computer science and technology Shandong University of Finance and EconomicsJinan 250014China school of computer science and technology Shandong UniversityQingdao 266237China

Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent ***,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation ***,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item *** this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance ***,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent *** also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item *** on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes *** leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.

关键词： recommender system matrix factorization item embedding item-tag relevance interpretability

Poisson disk sampling through disk packing

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Computational Visual Media 2015年第1期1卷 17-26页

作者： Guanghui Liang lin Lu Zhonggui chen chenglei Yang school of computer science and technology Shandong University Department of computer science Xiamen University

Poisson disk sampling is an important problem in computer graphics and has a wide variety of applications in imaging, geometry, rendering, etc. In this paper, we propose a novel Poisson disk sampling algorithm based on disk packing. The key idea uses the observation that a relatively dense disk packing layout naturally satisfies the Poisson disk distribution property that each point is no closer to the others than a specified minimum distance, i.e., the Poisson disk radius. We use this property to propose a relaxation algorithm that achieves a good balance between the random and uniform properties needed for Poisson disk distributions. Our algorithm is easily adapted to image stippling by extending identical disk packing to unequal disks. Experimental results demonstrate the efficacy of our approaches.

关键词： disk packing image stippling Poisson disk sampling power diagram

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