Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

A Privacy-Preserving Attribute-Based Reputation System in Online Social Networks

Online social networks (OSNs) have revolutionarily changed the way people connect with each other. One of the main factors that help achieve this success is reputation systems that enable OSN users to ...     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。     

带权决策表的属性约简

属性约简是粗糙集理论的重要应用。考虑将决策表中的每行都作为一条决策规则时，若把表中出现相同决策规则的次数作为权，可得到带权决策表。提出了关于带权决策表的正域约简相应的辨识矩阵并给出了证明，从而得到了约简算法。相比于决策表中的正域约简时发现，通过将决策表转化为带权决策表后，再利用算法1进行约简时，其在一定程度上优于前者。提出了近似分类精度约简相应的辨识矩阵并给出了证明。对于2个算法，在选取的UCI数据集上进行了实验验证。通过实验进一步说明了所提出算法的可行性和有效性。     

基于边缘和局部熵融合的视觉安全性评估方法

针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性，引入图像的边缘特征，通过共有边缘来衡量加密图像与原始图像的边缘相似度，消除了块效应。由于局部熵对加密等级低的图像不敏感，边缘相似度对加密等级高的图像不敏感，将两个评估方法进行自适应融合，提出[SLEES]（Local Entropy and Edge Similarity，[SLEES]）指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试，实验结果表明，[SLEES]指标相比传统评估指标有更好的鲁棒性，评估范围更广。     

密文策略属性加密中的撤销控制方案

在云环境下使用数据共享功能时，由于云环境的复杂性，需要对数据进行安全保护和访问控制，这就要求使用加密机制。基于密文策略属性的加密（CP-ABE）是当前广泛使用的加密机制，它可以根据用户的属性来设置访问权限，任何具有合格访问权限的用户都可以访问数据。然而云是一个动态环境，有时可能只允许具有访问权限用户中的一部分用户访问数据，这就需要用户权限的撤销机制。然而，在CP-ABE中，访问权撤销或用户撤销是一个冗长且代价高昂的事件。所提出方案根据对CP-ABE流程的改进，在原密文中嵌入了可灵活控制的用户个人秘密，使得用户权限撤销时既不要求使用新访问策略的用户撤销数据，也不要求对数据进行重新加密，大幅减少撤销时的计算成本。与知名CP-ABE撤销方案对比，所提出方案的计算成本更低且具有良好的安全性。     

东营凹陷深部储层流度属性提取及应用

东营凹陷深部储层埋深大、储层薄,为干燥环境下沉积的红层,采用常规储层描述与预测方法难以取得好的效果。在流度属性表征方法计算公式详细推导和流度属性本质含义解剖的基础上,根据研究区岩石物理参数和流体置换的特征,建立了含油层和干层的理论模型,发现将储层主频作为优势频率提取的流度属性,具有很好的有利储层识别度,含油砂体的流度属性能量团比干层及其它地层的能量要强。对东营凹陷深部实际三维工区数据进行目标处理,过井剖面的有利储层得到很好展示;沿层流度属性切片,其储层厚度、储层流体类型及其分布范围与井点图吻合度很高,进一步验证了方法的有效性。研究成果拓展了流度属性的应用范围(以往仅局限于低频),也为深部储层预测开辟了一条新的途径,具有重要的理论意义和较好的推广价值。     

German consumer evaluations of milk in blind and nonblind tests

Greater differentiation is being made between types of milk according to product and process characteristics. This study investigated consumer preferences for 4 different types of milk in packaging-blind and nonblind experiments. The taste of long-life milk, conventional fresh whole milk, pasture-based milk, and organic milk was assessed by 138 randomly selected respondents. The average taste of packaging-blind versus nonblind milk types was statistically significantly different for each of the 4 milk types. Consumers appeared to be influenced by the milk's packaging and related credence-good attributes. It is noteworthy that the taste of organic milk during the blind tasting was given a lower rating, and the taste of long-life milk or UHT milk a higher rating, than when respondents were aware of the respective milk types. The study estimated multilevel mixed-effects logistic regression models with the response variable buying decision. Of all the factors studied in the blind and nonblind tasting of almost all milk types, buying decisions were influenced most by taste, overall assessment and income.     

Neural Emotion Detection via Personal Attributes

There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.