图像重建迭代算法的研究

对于图像重建中的代数迭代算法而言，松弛因子的选择会影响图像重建的质量。经典算法一般采用常数形式的松弛因子进行重建，给出一种动态选择松弛因子的方法。实验结果表明，使用该方法进行图像重建时，有较好的边界效果。     

论科技评价在科学制度化建设中的地位与作用及对我国的启示

对科技评价在科学制度化建设中的必要性以及科技评价的实质等方面进行了论述,阐明了科技评价在科学制度化建设中的重要地位与作用,并根据我国目前科技评价的现状,从科技评价的角度提出有利于我国今后科技制度建设的几点建议。     

Graph neural topic model with commonsense knowledge

Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents’ topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods.     

用正则化方法求一类Abel型积分方程数值解

本文利用吉洪诺夫正则化方法 ,线性插值给出了Abel型积分方程反问题的稳定化数值方法     

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Automatic text summarization attempts to provide an effective solution to today’s unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL is used to achieve sentence semantic parsing whose word tokens are represented as a vector of weighted Wikipedia concepts using ESA method. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC 2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. On the other hand, the testing also demonstrates the scalability of the system, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectorial semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.     

AHAB: Aligning heterogeneous knowledge bases via iterative blocking

With the development of information extraction, there have been an increasing number of large-scale knowledge bases available in different domains. In recent years, a great deal of approaches have been proposed for large-scale knowledge base alignment. Most of them are based on iterative matching. If a pair of entities has been aligned, their compatible neighbors are selected as candidate entity pairs. The limitation of these methods is that they discover candidate entity pairs depending on aligned relations, which cannot be used for aligning heterogeneous knowledge bases. Only few existing methods focus on aligning heterogeneous knowledge bases, which discover candidate entity pairs just for once by traditional blocking methods. However, the performance of these methods depends on blocking keys heavily, which are hard to select. In this paper, we present an approach for aligning heterogeneous knowledge bases via iterative blocking (AHAB) to improve the discovery and refinement of candidate entity pairs. AHAB iteratively utilizes different relations for blocking, and then matches block pairs based on matched entity pairs. The Cartesian product of unmatched entities in matched block pairs forms candidate entity pairs. By filtering out dissimilar candidate entity pairs, matched entity pairs will be found. The number of matched entity pairs proliferates with iterations, which in turn helps match block pairs in each iteration. Experiments on real-world heterogeneous knowledge bases demonstrate that AHAB is able to yield a competitive performance.     

基于子空间投影技术的椭圆拟合算法

通过两次子空间投影,把拟合误差矢量正交分解为3个分量,并使它们的范数同时最小化,导致一个有约束总体最小化过程,该过程在同时约束其中的2个正交分量(即误差均值和误差矢量在中心化一阶子空间的投影分量)为0的条件下使另1个分量(即误差矢量在中心化一阶子空间的正交投影分量)的范数最小化.通过结合子空间投影与奇异值分解,采用渐进计算技术来完成椭圆参数矢量的3个分量的最优估计.理论分析和实验表明,该算法具有快速、精确、抗噪能力强、拟合成功率高等优点.     

信息化时代高校学生档案规范化管理研究

高等学校学生档案是国家人事档案的重要组成部分,刚刚修订通过的《中华人民共和国档案法》顺应时代要求,增设了“档案信息化建设”等内容.通过对湖北省部分高校的学生档案管理进行调查得知,高校学生档案要与时俱进,加速电子化建设进程、实现信息化管理、维护学生利益、推动学校稳定健康发展,就应探索标准化规范化管理方法,制定符合实际的实...     

网络课程开发的工程化方法研究

为了降低课程开发风险．提高课程质量,解决网络课程开发过程中缺乏系统化方法、进度不可控的问题．北京邮电大学网络课程“高级程序语言（JAVA）”的开发过程引入工程化的开发方法,按项目管理的思想组建开发团队,以专业特长划分课程开发小组,将开发任务拆分成不同阶段,明确各小组在不同开发阶段的职责和任务,并运用迭代式方法,将课程开发看作是一个动态的过程,持续改进课程,提高开发效率和质量。     

运动车辆车牌图像的超分辨率重建

为了改善实际交通环境中运动车辆车牌图像的质量,提出一种新的超分辨率重建方法,即通过融合低分辨率图像间的互补信息得到一幅高分辨率车牌图像.首先,在超分辨率重建正则化框架下引入梯度残差项作为一个梯度强制项来改善重建图像的质量.其次,为了提高重建算法的鲁棒性,用L1范数度量数据残差项和梯度残差项.最后,用最速下降法求解相应的最小能量泛函.模拟和实际视频图像序列的实验结果验证了所提方法的有效性和实用性,所提方法在重建图像的信噪比指标和视觉效果方面均优于双三次插值和DAMRF法.