科学实验具备的几个特点

本文对科学实验的特点进行总结和归纳，对以后的实验研究工作者及学习者都有一定的指导意义。     

试论彝族文化学的学科特征

彝族文化学是一门构建自己理论知识体系的学科,是研究彝族及其文化的人文社会科学,具有科学性、民族性、复合性等学科特征。     

Automatically extracting sheet-metal features from solid model

With the development of modern industry,sheet-metal parts in mass production have been widely applied in mechanical,communication,electronics,and light industries in recent decades; but the advances in sheet-metal part design and manufacturing remain too slow compared with the increasing importance of sheet-metal parts in modern industry. This paper proposes a method for automatically extracting features from an arbitrary solid model of sheet-metal parts; whose characteristics are used for classification and graph-based representation of the sheet-metal features to extract the features embodied in a sheet-metal part. The extracting feature process can be divided for valid checking of the model geometry,feature matching,and feature relationship. Since the extracted features include abundant geometry and engineering information,they will be effective for downstream application such as feature rebuilding and stamping process planning.     

钱锺书论李贺诗旨赘证

李贺《高轩过》有“笔补造化天无功”一语,钱锺书认为其语“可以作为他的自我评赞”。章府为,“笔补造化”的动机是李贺人生难堪的遭际。“笔补”是李贺对自身生生活缺陷的补偿。     

初中语文教学中的美育浅议

在语文教学中，学生通过学习，除掌握必要的语文知识外，还要培养感受美、鉴赏美、创造美的能力，培养和提高学生对现实世界以及文艺作品的美的鉴别、欣赏和创造能力，以陶冶情操，提高生活情趣，使他们在思想感情等方面得以健康发展。     

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.     

Where I’m from? Third Culture Kids about their cultural identity shifts and belonging

This study explored the affect of Third Culture Kids (TCKs) towards their home and host culture(s) and how this affect may indicate possible cultural identity shifts as distinguished in Sussman’s (2000) cultural identity shift model. To this end, the method of poetic inquiry was used. The poems were concerned with TCKs’ affective experiences (Prendergast, 2009). We also investigated whether TCKs described their belonging in terms of personal relationships rather than in terms of geographical locations.Twenty TCKs, ranging in age from 26 to 70 years and from five ‘home cultures’, expressed their early cross-cultural experiences through the free verse poem of “Where I’m from”. A mixed method approach of qualitative and quantitative research was applied, by combining poetic inquiry using a free verse poem format and clustering these data by means of coding in Atlas.ti. TCKs’ poems were analyzed using belonging, affect, and practices-food-nature-events as key codes.Findings revealed that TCKs expressed stronger positive affect towards their host cultures than towards their ‘home’ cultures, indicating a subtractive cultural identity shift. We also found that TCKs defined their belonging more in terms of personal relationships than in terms of geographical locations. This study shows that TCKs’ sense of belonging seems more related to the question who than where I am from.