异质信息网络高阶层次化嵌入学习与推荐预测

异质信息网络是一种异质数据表示形式,如何融合异质数据复杂语义信息,是推荐系统面临的挑战之一.利用弱关系具有的丰富语义和信息传递能力,构建一种面向推荐系统的异质信息网络高阶嵌入学习框架,主要包括:初始化信息嵌入、高阶信息嵌入聚合与推荐预测3个模块.初始化信息嵌入模块首先采用基于弱关系的异质信息网络最佳信任路径筛选算法,有效地避免在全关系异质信息网络中,采样固定数量邻居造成的信息损失,其次利用新定义的基于多头图注意力的多任务共享特征重要性度量因子,筛选出节点的语义信息,并结合交互结构,有效地表征网络节点;高阶信息嵌入聚合模块通过融入弱关系及网络嵌入对知识良好的表征能力,实现高阶信息表达,并利用异质信息网络的层级传播机制,将被采样节点的特征聚合到待预测节点;推荐预测模块利用高阶信息的影响力推荐方法,实现了推荐任务.该框架具有嵌入节点类型丰富、融合共享属性和隐式交互信息等特点.最后,实验验证UI-HEHo学习框架可有效地改善评级预测的准确性,以及推荐生成的针对性、新颖性和多样性,尤其是在数据稀疏的应用场景中,具有良好的推荐效果.     

On the multi-level embedding of crypto-image reversible data hiding

Crypto-space reversible image steganography has attracted increasing attention, given its ability to embed authentication information without revealing the image content. This paper presents an efficient reversible data hiding scheme for crypto-images: a block predictor is applied to compute prediction errors, then an adaptive block mapping algorithm is utilized to compress them whose amplitudes are within a small threshold, finally, this strategy can be applied in a multi-level manner to achieve a higher embedding capacity. Due to the correlations among adjacent pixels in the block, images can be sufficiently compressed to reserve abundant space for additional data embedding. Different from the prior arts, the compression code of the image is fully encrypted. Experimental results verify that the embedded data and original image can be perfectly recovered, the security is higher compared with the state-of-the-arts, and a significant improvement in the average embedding rate is achieved on two large-scale image datasets.     

一种基于降维密度聚类的船舶异常轨迹识别方法

目的 有效分析和探索海洋船舶时空轨迹行为模式,提高船舶轨迹聚类的效率与质量,更好地检测真实船舶的异常行为。方法 针对当前船舶轨迹数据研究中存在的对多维特征信息利用不足、检测效率不高、检测精度较差等问题,提出一种精确度高、能自主识别分析多维特征的船舶异常轨迹识别方法。首先利用随机森林分类器评估多维特征重要性,构建轨迹特征的最优组合;然后提出一种降维密度聚类方法,将T–分布随机邻域嵌入(T–SNE)和自适应密度聚类(DBSCAN)模型结合,通过构建特征选择层和无监督聚类层实现对数据元素非线性关系的高效提取以及对聚类参数的智能选择;最后根据聚类结果构建类簇特征向量,计算距离阈值判别轨迹相似度,实现轨迹异常检测模型的构建。结果 以UCI数据集为例,降维密度聚类方法对4、13、30、64维特征数据集的F1分数能达到0.9 048、0.9 534、0.8 218、0.6 627,多个聚类指标均优于DBSCAN、K–Means等常见聚类算法的。结论 研究结果表明,降维密度聚类方法能有效提取数据多维特征结构,实现聚类参数自适应,弥补密度聚类中参数难以确定的问题,有效实现对多种类型船舶轨迹异常的识别。     

Cycles passing through a prescribed path in a hypercube with faulty edges

Let Qn denote an n-dimensional hypercube with n?2, P be a path of length h in Qn and F⊂E(Qn)\E(P). Recently, Tsai proved that if 1?h?n−1 and |F|?n−1−h, then in the graph Qn−F the path P lies on a cycle of every even length from 2h+2 to n², and P also lies on a cycle of length 2h if |F|?h−2. In this paper, we show that if 1?h?2n−3 and |F|?n−2−⌊h/2⌋, then in Qn−F the path P lies on a cycle of every even length from 2h+2 to n², and P also lies on a cycle of length 2h if P contains two edges of the same dimension or P is a shortest path and |F∩E(Qh)|?h−2, where Qh is the h-dimensional subcube containing the path P. Moreover, the upper bound 2n−3 of h is sharp and the upper bound n−2−⌊h/2⌋ of |F| is sharp for any given h with 1?h?2n−3.     

A reversible data hiding scheme using complementary embedding strategy

Obtaining good visual quality and high hiding capacity with reversible data hiding systems is a technically challenging problem. In this paper, we propose a simple reversible data hiding scheme that uses a complementary hiding strategy. The proposed method embeds one secret bit horizontally and vertically into one cover pixel of a grayscale cover image by decreasing odd-valued pixels and increasing even-valued pixels by one. Experimental results show that the hiding capacity measured by bit per pixel (bpp) of the proposed scheme is at least 1.21 bpp with a PSNR (peak signal-to-noise ratio) value greater than 52 dB for all standard test images. Especially in the case of four-layer embedding, the PSNR value of the proposed method is still greater than 51 dB at a hiding capacity of about 5 bpp for all standard test images. In addition, the proposed method is quite simple because it primarily uses additions and subtractions. These results indicate that the proposed scheme is superior to many existing reversible data hiding schemes introduced in the literature.     

Embedding multi-dimensional meshes into twisted cubes

The twisted cube is an important variant of the most popular hypercube network for parallel processing. In this paper, we consider the problem of embedding multi-dimensional meshes into twisted cubes in a systematic way. We present a recursive method for embedding a family of disjoint multi-dimensional meshes into a twisted cube with dilation 1 and expansion 1. We also prove that a single multi-dimensional mesh can be embedded into a twisted cube with dilation 2 and expansion 1. Our work extends some previously known results.     

一种基于边界约束的流形展开方法

在流形学习的谱方法中, 流形展开被表述为优化问题. 这些优化问题的解是退化的, 即所有的样本将被嵌入到同一个点. 为了避免退化解, 谱方法对嵌入坐标人为地强加了一个单位协方差矩阵约束. 然而, 该约束往往导致流形展开的失真非常明显. 本文提出一种新的流形学习方法, 彻底抛弃了人为的单位协方差矩阵约束. 主要思路是先对流形边界进行嵌入, 然后再求流形内部的嵌入; 流形边界的嵌入位置被确定后, 流形内部样本的嵌入位置将被边界拉开, 使得它们不会都收缩到一个点上, 从而避免了退化解的出现. 将流形边界的嵌入位置作为边界条件, 求解一个线性方程组来得到内部样本的嵌入; 该线性方程组反映了尽量保持邻近样本间距离不变的要求. 流形边界的嵌入由简化流形的嵌入求出; 为此, 本文还设计了一种流形边界检测算法以及一种流形简化算法. 与目前代表性的几种流形学习方法进行了比较实验, 结果表明了本文方法的有效性, 其展开失真比谱方法明显要小.     

An exact algorithm for minimum distortion embedding

Let G be an unweighted connected graph on n vertices. We show that an embedding of the shortest path metric of G into the line with minimum distortion can be found in time 5^n+o(n). This is the first algorithm breaking the trivial n!-barrier.     

Constructing the nearly shortest path in crossed cubes

A graph G is panconnected if each pair of distinct vertices u,v∈V(G) are joined by a path of length l for all d_G(u,v)?l?|V(G)|-1, where d_G(u,v) is the length of a shortest path joining u and v in G. Recently, Fan et. al. [J. Fan, X. Lin, X. Jia, Optimal path embedding in crossed cubes, IEEE Trans. Parall. Distrib. Syst. 16 (2) (2005) 1190-1200, J. Fan, X. Jia, X. Lin, Complete path embeddings in crossed cubes, Inform. Sci. 176 (22) (2006) 3332-3346] and Xu et. al. [J.M. Xu, M.J. Ma, M. Lu, Paths in Möbius cubes and crossed cubes, Inform. Proc. Lett. 97 (3) (2006) 94-97] both proved that n-dimensional crossed cube, CQ_n, is almost panconnected except the path of length d_CQn(u,v)+1 for any two distinct vertices u,v∈V(CQ_n). In this paper, we give a necessary and sufficient condition to check for the existence of paths of length d_CQn(u,v)+1, called the nearly shortest paths, for any two distinct vertices u,v in CQ_n. Moreover, we observe that only some pair of vertices have no nearly shortest path and we give a construction scheme for the nearly shortest path if it exists.