结合二次聚类编码的生成式可逆信息隐藏方法

为避免纹理生成式信息隐藏和马赛克拼图式信息隐藏存在的拼接痕迹和产生有意义图像,并提高抗攻击能力,提出一种结合二次聚类编码的生成式可逆信息隐藏方法。将不同灰度阶随机二值噪点小块作为候选样本小块,由二次聚类编码筛选出用于二值秘密信息表示的编码样本小块。然后结合随机二值参考图和嵌密位置来放置编码样本小块以生成含密掩体。在提取时,按二次聚类编码产生编码样本小块,结合参考图和嵌密位置提取秘密信息。所提方法不产生拼接痕迹,也不涉及任何修改式嵌入,可生成视觉质量良好的有意义图像并可无损地恢复为原掩体。所提方法具有较好的抗攻击能力,且嵌入和提取过程完全依赖于用户密钥,具有较高的安全性。

SDAEC算法在单细胞测序数据批次校正中的应用

目的 提出深度堆叠降噪自编码嵌套聚类(stacked denoising auto encoder embedded cluster, SDAEC)算法并用于单细胞mRNA测序(single cell mRNA sequence, scRNA-seq)数据的批次效应移除,对其移除批次效应性能进行评估。方法 基于单细胞数据具有高维度、高稀疏性及高度非线性误差特点,通过将单细胞Louvain聚类算法嵌入堆叠降噪自动编码器(stacked denoising auto encoder, SDAE)算法中,形成SDAEC算法,用于单细胞测序数据的批次效应移除。结合实际卵巢癌组织scRNA-seq数据,利用分布邻域嵌入(t-distributed stochastic neighbor embedding, tSNE)、 k最近邻批次效应检测(k-nearest-neighbor batch-effect test, kBET)、调整兰德系数(adjusted rand index, ARI)、标准化互信息(normalized mutual information, NMI)、平均轮廓宽度(average silhouette width, ASW)评价其移除批次效应性能。结果 利用SDAEC方法对scRNA-seq数据批次效应移除性能高于Combat、相互最近邻(mutual nearest neighbors, MNN)、分布匹配残差网络(maximum mean discrepancy distribution-matching residual networks, MMD-ResNet)和基于零膨胀负二项的方差提取法(zero-inflated negative binomial-based wanted variation extraction, ZINB-WaVE)。结论 SDAEC算法能够移除scRNA-seq数据的批次效应,提高scRNA-seq数据下游分析的有效性,具有实际应用价值。

大量目标识别的光学相关与神经网络融合方法

本文基于光学相关与神经网络学习算法和编码聚类原理的融合 ,提出一种大数量目标的高效率分类识别方法 ,并以 67个字符 ,其中包括2 6个英文字母、1 0个数字和 31个省和直辖市的简称汉字的分类识别作为实例 ,给出详细的原理说明和结果。

一种求解图着色的单亲遗传算法

给出了一种求解图着色问题的新算法,即单个个体的单亲遗传算法.算法采用顶点序号的聚类编码将个体的某个子串随机分配到其他子串中的变异方法.并对该算法的时间复杂度进行了分析比较,结果表明该算法具有较好的运行效率与收敛速度.

EFFECTIVE FEATURE ANALYSIS FOR COLOR IMAGE SEGMENTATION

An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.

Identifying Anomaly Aircraft Trajectories in Terminal Areas Based on Deep Autoencoder and Its Application in Trajectory Clustering

Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis.Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control(ATC)actions.However,the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging.To alleviate these two challenges,this paper proposes a novel framework which combines robust deep auto-encoder(RDAE)model and density peak(DP)clustering algorithm.Specifically,the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations.Then,the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows.An experiment on a terminal airspace at Guangzhou Baiyun Airport(ZGGG)with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement.The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.

Bag-of-visual-words model for artificial pornographic images recognition

It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.

中国政府与“保护的责任”辩论:基于安理会相关辩论发言的分析

近年来,"保护的责任"的理念迅速发展,对中国外交传统话语构成了挑战,已成为中国外交研究的热门议题。文章通过梳理中国常驻联合国代表团在安理会"武装冲突中保护平民"辩论中的表态,以及达尔富尔、利比亚、叙利亚等关键投票后的解释性发言,以编码聚类与词频统计的方式,归纳了中方话语调整的基本特征,凸显了其延续性与灵活性。文章认为,中方巳形成了对"保护的责任"有条件的支持、强调对其应用进行适当约束的基本立场。在相关辩论中,中方论述集中于如何"负责任"地实践"保护的责任"。伴随事态发展,话语焦点也从"有条件地适用"转为"反对滥用"。中国政府力图防止因"保护的责任"概念过快发展而对国际秩序与自身利益构成冲击,并努力推动"负责任保护"的实现。中方无意对抗或颠覆国际人道秩序,相反还是其重要的建设性力量。