The skewness of computer science

Computer science is a relatively young discipline combining science, engineering, and mathematics. The main flavors of computer science research involve the theoretical development of conceptual models for the different aspects of computing and the more applicative building of software artifacts and assessment of their properties. In the computer science publication culture, conferences are an important vehicle to quickly move ideas, and journals often publish deeper versions of papers already presented at conferences. These peculiarities of the discipline make computer science an original research field within the sciences, and, therefore, the assessment of classical bibliometric laws is particularly important for this field. In this paper, we study the skewness of the distribution of citations to papers published in computer science publication venues (journals and conferences). We find that the skewness in the distribution of mean citedness of different venues combines with the asymmetry in citedness of articles in each venue, resulting in a highly asymmetric citation distribution with a power law tail. Furthermore, the skewness of conference publications is more pronounced than the asymmetry of journal papers. Finally, the impact of journal papers, as measured with bibliometric indicators, largely dominates that of proceeding papers.Comment: I applied the goodness-of-fit methodology proposed in: A. Clauset, C. R. Shalizi, M. E. J. Newman. Power-law distributions in empirical data. SIAM Review 51, 661-703 (2009

KNN FOR CLASSIFICATION OF FRUIT TYPES BASED ON FRUIT FEATURES

Research related to the recognition of fruit types has been done previously. Research related to the recognition of many types of fruit applies computer vision and artificial intelligence. The purpose of this research is to apply artificial intelligence science with the KNN method to identify the type of fruit. The KNN method has a good performance in previous studies. We tried to use KNN by determining the most optimal K value. There are five types of fruit images used in this study, namely Apples, Grapes, Oranges, Mangoes, and Strawberries. The fruit image is extracted with colour, texture, and shape features with a total of 15 features, namely the average value of R, the average value of G, the average value of B, the value of skewness R, the value of skewness G, the value of skewness B, the value of grayscale entropy. , grayscale contrast value, grayscale energy value, grayscale correlation value, grayscale homogeneity value, binary area value, binary circumference value, binary major axis value, and binary minor axis value. The dataset used in this study was taken from Kaggle, with a dataset of 2750 images, each type of fruit contained 550 images, 2500 training images were used and 250 images were used for testing. The experimental results show that the KNN method with K=1 has the highest accuracy, which is 99.6%. The KNN method can be used optimally in classifying fruit types based on colour, texture, and shape features

An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level

The paper provides an empirical examination of how research productivity distributions differ across scientific fields and disciplines. Productivity is measured using the FSS indicator, which embeds both quantity and impact of output. The population studied consists of over 31,000 scientists in 180 fields (10 aggregate disciplines) of a national research system. The Characteristic Scores and Scale technique is used to investigate the distribution patterns for the different fields and disciplines. Research productivity distributions are found to be asymmetrical at the field level, although the degree of skewness varies substantially among the fields within the aggregate disciplines. We also examine whether the field productivity distributions show a fractal nature, which reveals an exception more than a rule. Differently, for the disciplines, the partitions of the distributions show skewed patterns that are highly similar

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201