Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

基于双重注意力机制和GRU网络的短期负荷预测模型

电力负荷预测对电力系统的部署、规划和运行影响重大,但目前各输入特征对电网负荷情况影响的程度不稳定,且递归神经网络捕获负荷数据的长期记忆能力差,导致预测精度下降。提出一种基于双重注意力机制和GRU网络的预测新模型,利用特征注意力机制自主分析历史信息与输入特征间的关联关系,提取重要特征,并通过时序注意力机制自主选取GRU网络中关键时间点的历史信息,提升较长时间段预测效果的稳定性。在3个公开数据集上的实验结果表明,该模型在预测精度指标上表现良好,对比SVR、KPCA-ELM、DBN、GRU、Attention-GRU、CNN-LSTM、Attention-CNN-GRU模型预测精度分别提高了2.47、1.14、1.93、1.37、1.04、0.74、0.41个百分点。     

基于四元数胶囊网络的知识图谱补全模型

知识图谱采用RDF三元组的形式描述现实世界中的关系和头、尾实体,即（头实体,关系,尾实体）或（主语,谓语,宾语）。为补全知识图谱中缺失的事实三元组,将四元数融入胶囊神经网络模型预测缺失的知识,并构建一种新的知识图谱补全模型。采用超复数嵌入取代传统的实值嵌入来编码三元组结构信息,以尽可能全面捕获三元组全局特性,将实体、关系的四元数嵌入作为胶囊网络的输入,四元数结合优化的胶囊网络模型可以有效补全知识图谱中丢失的三元组,提高预测精度。链接预测实验结果表明,与CapsE模型相比,在数据集WN18RR中,该知识图谱补全模型的Hit@10与正确实体的倒数平均排名分别提高3.2个百分点和5.5%,在数据集FB15K-237中,Hit@10与正确实体的倒数平均排名分别提高2.5个百分点和4.4%,能够有效预测知识图谱中缺失的事实三元组。     

结合栈式监督AE与可变加权ELM的回归预测模型

在现代工业生产过程中,许多关键变量与产品质量或生产效率密切相关,关键变量的实时监测是实现利润最大化及节能降耗的有效途径。针对回归预测任务中目标特征提取不全面、预测精度较低等问题,提出一种基于栈式监督自编码器与可变加权极限学习机的回归预测模型。通过堆叠多层自编码器并在每层自编码器中添加回归网络,同时以有监督方式对栈式自编码器（SAE）进行逐层预训练,得到与输出变量相关的特征表示。利用反向传播算法对网络参数进行微调,优化自编码器模型参数。在分析提取特征与输出变量的相关性基础上,对极限学习机（ELM）的输入权值和偏置进行加权得到预测结果。实验结果表明,与基于ELM和SAE-ELM的回归预测模型相比,该模型在多晶硅铸锭的G6产品数据集上的均方根误差降低0.056 7和0.011 2、决定系数提高0.489 3和0.290 3,具有更高的回归预测准确性及更强的鲁棒性与泛化性能。     

基于网络资源流量的链路预测方法

在复杂网络中,现有基于结构相似性的链路预测方法较少考虑全局和局部拓扑信息之间平衡性、准确度和复杂度之间平衡性以及网络资源动态流动的问题。将网络资源流量作为相似性判断依据,提出一种准局部链路预测方法。根据网络中节点重要性的不同来为它们分配对应的资源,以保证资源分配的合理性。针对网络资源提出一种动态流动机制,将节点对双向流动的资源之和作为相似程度的量化指标。引入节点对之间中间路径节点的概念,分析中间路径节点在资源流动过程中的稀释作用。在此基础上,计算初始资源量和稀释作用量从而得到网络资源流量方法的性能评估指标值。在Jazz、NS等11个真实世界的网络中进行实验,对比该方法与CN、Salton等常见基准方法在准确度和鲁棒性方面的性能表现,结果表明,所提方法能够充分利用准局部信息,既能考虑资源流动性又能解决平衡性问题,可有效提高链路预测性能。     

E级高性能计算机的维护故障诊断系统研究

E级计算机系统规模巨大,使得故障异常总量随之增多,导致诊断发现的难度增加,因此,迫切需要一套更加准确高效的实时维护故障诊断系统,对硬件系统进行全面的异常及故障信息实时检测、故障诊断及故障预测。传统故障诊断系统在面对数万节点规模的诊断时存在执行效率低、异常检测误报率高的问题,异常检测及故障诊断的覆盖率不足。对异常及故障检测、故障诊断与故障预测相关技术进行研究,分析技术原理及适用性,并结合E级高性能计算机实际工程需求,设计一套满足数E级高性能计算机需求的维护故障诊断系统。基于维护系统的结构组成设计可扩展的边缘诊断架构,将高性能计算机系统知识、专家知识与数理统计、机器学习相融合给出故障检测、诊断及预测算法,并针对专用场景建立预测模型。实验结果表明,该系统具有较好的可扩展性,能在10 s内完成对十万个节点规模系统的故障诊断,与传统故障诊断系统相比,异常检测某特定指标误报率从3.3%降低到几乎为0,硬件故障检测覆盖率从90.2%提升至96%以上,硬件故障诊断覆盖率从71%提升至约94%,能较准确地预测多个重要应用场景下的故障。     

基于灰色预测与滑模变结构的ABS仿真分析

为研究汽车制动防抱死系统(antilock brake system,ABS)的超前性、实时性与鲁棒性,结合灰色预测和滑模变结构控制方法,提出基于灰色预测GM(1,1)模型与滑模变结构控制的ABS综合控制算法,建立仿真模型进行数据的对比分析.结果表明:相对于单滑模或bang-bang控制,灰色预测与滑模变结构综合控制方法使汽车ABS超前运行,缩短了制动时间与制动距离;有助于缓解滑模变结构方法自身带来的抖动缺陷.因此,灰色预测与滑模变结构综合控制方法有助于提升ABS的响应速度、实时鲁棒性与人体舒适性.     

机械结构相似度评价及可靠性预计研究

在用传统的相似产品法进行可靠性预计时,所选取的相似产品与评价对象之间差异较大,会造成预计结果的偏差较大。为解决这一问题,提出了一种基于"功能—运动—动作"结构化分解的可靠性预计新方法。通过明确产品的各大运动功能建立功能层,分析完成各功能的机械部件运动建立运动层,确定实现各机械部件运动的元动作运动单元建立元动作层,建立了可靠性预计的"功能—运动—元动作运动单元"层次模型。引入区间层次分析法,确定了"功能—运动—元动作运动单元"层次模型中各子代运动单元对父代运动单元的可靠性影响权重,并构建了由子代运动单元预计父代运动单元可靠性的数学模型。确定影响评价机械结构相似度的因素,构建了区间数综合评判云模型并选取与元动作运动单元相似的参考结构,以区间数综合评价云模型得出的相似度值修正参考结构的可靠性数据来预计元动作运动单元的可靠性。将元动作运动单元的可靠性预计值代入数学模型,层层预计父代运动单元的可靠性,从而得出整个产品的可靠性水平。最后以数控转台为例进行了分析,得出了可靠性预计值,并将该方法与传统方法进行了比较。结果表明,新的可靠性预计方法具有较好的可行性和较高的准确性。     

基于CNKI源的人因可靠性文献计量学分析

为了客观反映与评价国内人因可靠性研究现状与发展趋势,采用文献计量学分析方法,并以CNKI数据库为数据源,对2006-2016国内人因可靠性领域文献进行分析与评价。采用年份、学科、载文期刊、研究机构、基金资助、文献传播与扩散、关键词等进行分析评述。结果表明:近十年国内该领域文献总体数量呈增加趋势;该学科发展呈现出多学科相互交叉的特点;根据布拉德福文献离散定律,确定期刊分布的核心区域;利用Citespace软件对关键词进行分析,得出人因可靠性分析方法是当前的研究热点。