Abstract The Marching Cubes algorithm is based on the estimate of the isovalue in order to determine all the pixels that are belonging to the volume to be reconstructed. This estimation is done by the user in an interactive and way without any orientation; this is no longer an intuitive process. Solutions are proposed to detect more than one isovalue and have... The Marching Cubes algorithm is based on the estimate of the isovalue in order to determine all the pixels that are belonging to the volume to be reconstructed. This estimation is done by the user in an interactive and way without any orientation; this is no longer an intuitive process. Solutions are proposed to detect more than one isovalue and have been used to display multiple isosurfaces and not to adjust or automatically determine isovalue. Other solutions propose to explore histogram to estimate isovalue automatically; those remain insufficient because they still require user intervention. The proposed method acts directly in the isovalue estimation phase. In fact, we propose to make this estimate by using automatic methods of sampling, which have shown their performance in estimating a threshold in several works. This paper proposes to make the use of existing automated isovalue selection methods that take into account histogram and the dynamics of the image. The automatic Marching Cubes algorithm reduces user interaction and makes the selection process more intuitive. The obtained results will show that this adaptation minimizes the computing time and that the obtained volumes are of better quality than those obtained by the classical Marching Cubes algorithm. Read More Read Less

Abstract The number of citations has been used for measuring the significance of a paper. Moreover, we have the following question: which paper is the most important if there are some papers with the same number of citations? Some measures have been introduced to answer this question: one of them is PageRank. We use the Science Citation Index Expanded from 1981 to 2015... The number of citations has been used for measuring the significance of a paper. Moreover, we have the following question: which paper is the most important if there are some papers with the same number of citations? Some measures have been introduced to answer this question: one of them is PageRank. We use the Science Citation Index Expanded from 1981 to 2015 to calculate the number of citations and the Google number in the citation network consisting of 34,666,719 papers and 591,321,826 citations. We clarify the positive linear relationship between the number of citations and the Google number, as well as extract some outliers from this positive linear relationship. These outliers are considered to be extremely prestige papers. Furthermore, we calculate the mean values of the number of citations and the Google number for all journals, construct a new measure of journal influence, and extract extremely prestige journals. This new measure has a positive and medium correlation with the impact factor, Eigenfactor score, and SCImago Journal Rank. Read More Read Less

Abstract This paper seeks to depict online rumor retransmission using three main constructs, namely, message characteristics, user characteristics and retransmission outcomes. In particular, it teases retransmission outcomes into volume, immediacy and lifespan of rumor tweets. The dataset was drawn from 322 original Twitter messages which generated some 5,700 retweets about the rumored death of Lee Kuan Yew (LKY). Content analysis and statistical tests... This paper seeks to depict online rumor retransmission using three main constructs, namely, message characteristics, user characteristics and retransmission outcomes. In particular, it teases retransmission outcomes into volume, immediacy and lifespan of rumor tweets. The dataset was drawn from 322 original Twitter messages which generated some 5,700 retweets about the rumored death of Lee Kuan Yew (LKY). Content analysis and statistical tests were conducted. The results confirm the robustness of the research model. Specifically, rumor message characteristics affect retransmission in terms of volume, immediacy and lifespan. Additionally, user characteristics are positively related to volume and immediacy. Finally, user characteristics have a moderating effect on the relationships between rumor message characteristics and retransmission outcomes. This paper illustrates the applicability of the information diffusion model on online rumor retransmission which hitherto has yet to be attempted. Moreover, it granularizes the concept of online rumor retransmission into three important outcomes, namely, volume, immediacy and lifespan. On the practical front, this paper has implications for organizations seeking to combat rumors. Read More Read Less

Abstract Computing efficiency is a key in biometric identification systems for automatic facial expression recognition. It was integrated within advanced pattern recognition as an excellent paradigm while users shifted towards underlying patterns. Most existing face recognition models suffer from a low recognition rate and significant execution time. To overcome these drawbacks, we propose a new Local Gradient Neighborhood (LGN) descriptor for... Computing efficiency is a key in biometric identification systems for automatic facial expression recognition. It was integrated within advanced pattern recognition as an excellent paradigm while users shifted towards underlying patterns. Most existing face recognition models suffer from a low recognition rate and significant execution time. To overcome these drawbacks, we propose a new Local Gradient Neighborhood (LGN) descriptor for effective face and facial expression recognition. Firstly, the LGN components obtained by applying LGN for each block of the face image which is represented by 9-size vector. Secondly, the system concatenates features vectors of different blocks to obtain the final feature vector for the face image. Finally, it applies SVM and KNN techniques to classify the input images. Unlike other similar works, the new proposed descriptor is evaluated on two benchmarks, for face recognition and facial expression recognition respectively. The experimental results show an excellent recognition rate and fast execution time. The recognition rate for the ORL face database is 98.50% and the recognition rate for the JAFEE database is 84.28%. Read More Read Less