基于数据分布的聚类联邦学习

联邦学习(federated learning)可以解决分布式机器学习中基于隐私保护的数据碎片化和数据隔离问题。在联邦学习系统中,各参与者节点合作训练模型,利用本地数据训练局部模型,并将训练好的局部模型上传到服务器节点进行聚合。在真实的应用环境中,各节点之间的数据分布往往具有很大差异,导致联邦学习模型精确度较低。为了解决非独立同分布数据对模型精确度的影响,利用不同节点之间数据分布的相似性,提出了一个聚类联邦学习框架。在Synthetic、CIFAR-10和FEMNIST标准数据集上进行了广泛实验。与其他联邦学习方法相比,基于数据分布的聚类联邦学习对模型的准确率有较大提升,且所需的计算量也更少。     

基于加权的无人机集群组网分簇算法

在无人机集群组网中,节点的高速移动会造成网络拓扑结构更新频繁,使网络管理变得更加复杂。分簇能够增大网络容量,实现空间资源的复用,是优化网络管理的有效手段之一。针对大规模、高速移动的环境进行了研究,提出了一种多参数加权分簇算法。该算法将最大速度相似度分簇算法中的分簇指标引入到加权分簇算法中,并且对链路保持率、节点度差、节点剩余能量进行改进,综合考虑这四种参数,通过加权组合的方式选举具有最大权重的网络节点作为簇头。仿真结果表明,该分簇算法不仅能够减少簇的数量和簇间切换率,提高分簇的稳定性,而且能够延长最小节点生存时间,改善网络的整体续航能力。     

面向聚类的平面反射数据扰动方法

面向聚类的数据隐藏通常使用数据扰动技术防止敏感信息泄露。针对现有的面向聚类的数据扰动方法隐私保护度低的问题,提出一种基于平面反射的数据扰动方法,将发布对象的全部属性两两配对构成平面上的点,再随机选择一条直线,作每对属性关于直线的对称点,转换后的数据即为发布的数据。实验结果表明,这种方法具有较好的隐私保护度和聚类可用性,且对高维数据有良好的适应性。     

基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法

针对企业电力负荷随机性强、稳定性低、预测精度不理想等问题,提出了一种基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法。首先对最大偏差相似性准则算法进行修改,并提出使用预测日的负荷特征向量与最大偏差相似性准则算法聚类之后的类中心负荷特征的距离来确定预测日的相似日类别;然后将聚类后的相似日类别负荷数据作为BP网络的训练数据,输出预测日起始的连续三天96整点负荷值。实验表明,该方法提出的短期电力负荷预测方法在精度和网络训练时间上都有较大的提升,具有较高的有效性和实用性。     

浙江省水利管理与服务能力现代化发展水平评价

遵循独立、科学、系统、层次和可操作性的原则,在充分分析国内外大量相关指标的基础上,结合浙江省实际情况,提出了包括防汛防台抗旱管理、水利工程管理、水行政管理、水利工程完好率、人才保障能力、资金保障能力等6项准则层的浙江省水利管理与服务能力现代化发展水平评估指标体系,在此基础上利用模糊聚类循环迭代法科学确定指标权重,并构建了发展水平综合评估模型。对浙江省2015年的水利管理与服务能力现代化水平进行评价,将结果与2020年的预期值进行对比,查找问题和薄弱环节并提出对策建议。     

Discovering social structures of local influence by using tweetStimuli

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communities.     

Image clustering based on sparse patch alignment framework

Image clustering methods are efficient tools for applications such as content-based image retrieval and image annotation. Recently, graph based manifold learning methods have shown promising performance in extracting features for image clustering. Typical manifold learning methods adopt appropriate neighborhood size to construct the neighborhood graph, which captures local geometry of data distribution. Because the density of data points’ distribution may be different in different regions of the manifold, a fixed neighborhood size may be inappropriate in building the manifold. In this paper, we propose a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds. Specifically, we assume that for each data point there exists a small neighborhood in which only the points that come from the same manifold lie approximately in a low-dimensional affine subspace. Based on the patch alignment framework, we propose an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point. After that, the whole alignment strategy is utilized to build the manifold. Experiments are conducted on four real-world datasets, and the results demonstrate the effectiveness of the proposed method.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

Continuous Levels‐of‐Detail and Visual Abstraction for Seamless Molecular Visualization

Molecular visualization is often challenged with rendering of large molecular structures in real time. We introduce a novel approach that enables us to show even large protein complexes. Our method is based on the level‐of‐detail concept, where we exploit three different abstractions combined in one visualization. Firstly, molecular surface abstraction exploits three different surfaces, solvent‐excluded surface (SES), Gaussian kernels and van der Waals spheres, combined as one surface by linear interpolation. Secondly, we introduce three shading abstraction levels and a method for creating seamless transitions between these representations. The SES representation with full shading and added contours stands in focus while on the other side a sphere representation of a cluster of atoms with constant shading and without contours provide the context. Thirdly, we propose a hierarchical abstraction based on a set of clusters formed on molecular atoms. All three abstraction models are driven by one importance function classifying the scene into the near‐, mid‐ and far‐field. Moreover, we introduce a methodology to render the entire molecule directly using the A‐buffer technique, which further improves the performance. The rendering performance is evaluated on series of molecules of varying atom counts.