Re-C共渗渗层组织研究

研究了稀土元素庄渗碳过程中对渗层组织的影响。结果表明：稀土能使渗碳过程加快、渗层加厚、时间缩短;稀土对碳钢的渗层组织无明显的细化;稀土能使合金钢表层碳化物颗粒细化,均匀弥散分布,碳化物层加厚。     

Capturing the form of feature interactions in black-box models

Detecting feature interactions is an important post-hoc method to explain black-box models. The literature on feature interactions mainly focus on detecting their existence and calculating their strength. Little attention has been given to the form how the features interact. In this paper, we propose a novel method to capture the form of feature interactions. First, the feature interaction sets in black-box models are detected by the high dimensional model representation-based method. Second, the pairwise separability of the detected feature interactions is determined by a novel model which is verified theoretically. Third, the set separability of the feature interactions is inferred based on pairwise separability. Fourth, the interaction form of each feature in product separable sets is explored. The proposed method not only provides detailed information about the internal structure of black-box models but also improves the performance of linear models by incorporating the appropriate feature interactions. The experimental results show that the accuracy of recognizing product separability in synthetic models is 100%. Experiments on three regression and three classification tasks demonstrate that the proposed method can capture the product separable form of feature interactions effectively and improve the prediction accuracy greatly.     

基于模拟退火算法的Tile集生成优化

纹理合成技术是计算机图形学、计算机视觉和图像处理领域的研究热点之一,其中因Wang-Tile纹理合成方法可以实时地生成任意大小的纹理而受到人们的关注。Tile集的生成是Wang-Tile纹理合成方法的一个重要方面,但由于其中样本纹理选择的随意性使得合成纹理出现明显的菱形接缝。采用模拟退火算法来实现样本纹理选择过程的全局优化,提高了Tile集的质量,得到了理想的合成结果,对此进行了论述。     

一种新的基于SVM相关反馈的图像检索算法

提出了一种结合颜色和形状特征的图像检索方法,针对传统基于内容图像检索不能很好满足用户需求的问题,提出了一种基于支持向量机（SVM）的相关反馈算法来捕捉用户的检索意图。实验结果证明,算法能发挥用户在检索过程中的作用,具有较好检索性能。     

基于内容结构的网络Flash动画检索方法

Flash动画作为一种喜闻乐见、深受青少年和儿童喜爱的多媒体形式在互联网中广泛传播,成为不可或缺的网络多媒体信息资源。基于内容结构特征检索网络Flash动画的方法,既可以利用Flash动画的名称、内嵌文本的     

Adaptive multi-feature fusion via cross-entropy normalization for effective image retrieval

Multi-feature fusion has achieved gratifying performance in image retrieval. However, some existing fusion mechanisms would unfortunately make the result worse than expected due to the domain and visual diversity of images. As a result, a burning problem for applying feature fusion mechanism is how to figure out and improve the complementarity of multi-level heterogeneous features. To this end, this paper proposes an adaptive multi-feature fusion method via cross-entropy normalization for effective image retrieval. First, various low-level features (e.g., SIFT) and high-level semantic features based on deep learning are extracted. Under each level of feature representation, the initial similarity scores of the query image w.r.t. the target dataset are calculated. Second, we use an independent reference dataset to approximate the tail of the attained initial similarity score ranking curve by cross-entropy normalization. Then the area under the ranking curve is calculated as the indicator of the merit of corresponding feature (i.e., a smaller area indicates a more suitable feature.). Finally, fusion weights of each feature are assigned adaptively by the statistically elaborated areas. Extensive experiments on three public benchmark datasets have demonstrated that the proposed method can achieve superior performance compared with the existing methods, improving the metrics mAP by relatively 1.04% (for Holidays), 1.22% (for Oxf5k) and the N-S by relatively 0.04 (for UKbench), respectively.     

A novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer for feature selection

Existing personality detection methods based on user-generated text have two major limitations. First, they rely too much on pre-trained language models to ignore the sentiment information in psycholinguistic features. Secondly, they have no consensus on the psycholinguistic feature selection, resulting in the insufficient analysis of sentiment information. To tackle these issues, we propose a novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer (GWO) for feature selection (IDGWOFS). Specifically, we introduced the Gaussian Chaos Map-based initialization and neighbor search strategy into the original GWO to improve the performance of feature selection. To eliminate the bias generated when using mutual information to select features, we adopt symmetric uncertainty (SU) instead of mutual information as the evaluation for correlation and redundancy to construct the fitness function, which can balance the correlation between features–labels and the redundancy between features–features. Finally, we improve the common Spark-based parallelization design of GWO by parallelizing only the fitness computation steps to improve the efficiency of IDGWOFS. The experiments indicate that our proposed method obtains average accuracy improvements of 3.81% and 2.19%, and average F1 improvements of 5.17% and 5.8% on Essays and Kaggle MBTI dataset, respectively. Furthermore, IDGWOFS has good convergence and scalability.     

韩国农业农村立法若干问题研究

韩国十分重视农业农村立法。韩国的农业农村立法有6大特征：一是法律法规体系比较健全：二是配套法规建设自成体系;三是适时修改法律法规及规则;四是重视农村建设立法;五是重视农民教育立法;六是重视农业组织立法。经过30多年的发展,韩国形成了比较完善的农业农村法律体系。     

中国大学生心目中的“小人”人格结构初探

“小人”人格具有丰富的文化内涵,是中国人判定一个人人格高下的重要标尺。以原型说和特征表说的研究方法探究出中国大学生心目中的“小人”人格依次为卖国求荣、不仁不义、祸国殃民、阴险狡诈、言行可憎、不讲信义六大结构。“小人”人格结构符合实际,富有时代特色,能够帮助大学生认识、摒弃“小人”言行,从而有效地培育大学生理想的“君子”人格。     

中小学特色学校发展的思考与实践

特色学校是指具有自身独特色彩和风格的学校,它立于“特”,也优于“特”。特色学校的发展有着较为悠久的历史。新世纪以来,特色学校特有的功能性和教育价值使其再度受到人们的关注,成为教育研究新的生长点。发展特色学校要从目标优化、内容优化、系统优化和方法优化的模式出发,立足学校实际、准确定位特色、依托校本教研,紧紧围绕“特色”进行教育改革和学校建构。同时,还要充分发挥教师积极性,增强主人翁意识,摆脱以往学校管理中统得过死,整齐划一的管理模式,促进特色学校的健康发展。