Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2 – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are a relatively small number of training cases available (). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered

Bone Mineral Density Reference Standards for Chinese Children Aged 3-18: Cross-Sectional Results of the 2013-2015 China Child and Adolescent Cardiovascular Health (CCACH) Study

Objectives: No nationwide paediatric reference standards for bone mineral density (BMD) are available in China. We aimed to provide sex-specific BMD reference values for Chinese children and adolescents (3-18 years). Methods: Data (10 818 participants aged 3-18 years) were obtained from cross-sectional surveys of the China Child and Adolescent Cardiovascular Health in 2015, which included four municipality cities and three provinces. BMD was measured using Hologic Discovery Dual Energy X-ray Absorptiometry (DXA) scanner. The DXA measures were modelled against age, with height as an independent variable. The LMS statistical method using a curve fitting procedure was used to construct reference smooth cross-sectional centile curves for dependent versus independent variables. Results: Children residing in Northeast China had the highest total body less head (TBLH) BMD while children residing in Shandong Province had the lowest values. Among children, TBLH BMD was higher for boys as compared with girls; but, it increased with age and height in both sexes. Furthermore, TBLH BMD was higher among US children as compared with Chinese children. There was a large difference in BMD for height among children from these two countries. US children had a much higher BMD at each percentile (P) than Chinese children; the largest observed difference was at P50 and P3 and the smallest difference was at P97. Conclusions: This is the first study to present a sex-specific reference dataset for Chinese children aged 3-18 years. The data can help clinicians improve interpretation, assessment and monitoring of densitometry results

Efimov States From Triple α Resonances

The Efimov trimers in excited 12C nuclei, which no observation exists yet, are discussed by means of analyzing the experimental data of 70(64)Zn(64Ni) +70(64)Zn(64Ni )reactions at beam energy of E/A=35 MeV/nucleon. In heavy ion collisions, the αs interact with each other and can form complex systems such as 8Be and 12C. For the 3α systems, multi resonance processes give rise to excited levels of 12C. The interaction between any two of the 3α particles provides events with one, two or three 8Be. Their interfering levels are clearly seen in the minimum relative energy distributions. Events of three couple αrelative energies consistent with the ground state of 8Be are observed with the decreasing of the instrumental error at the reconstructed 7.458 MeV excitation energy of 12C, which was suggested as the (Thomas) Efimov state

Intracytoplasmic sperm injection (ICSI) versus conventional in vitro fertilisation (IVF) in couples with non-severe male infertility (NSMI-ICSI) : protocol for a multicentre randomised controlled trial

Funding This study was supported by National Key Research and Development Program of China (2016YFC1000201; 2018YFC1002104) and the National Science Foundation of China (81730038). The study funders had no rule in the study design, implementation, analysis, manuscript, preparation or decision to submit this article for publication.Peer reviewedPublisher PD

A Small World Routing Model for Structured P2P Systems

Peer-to-Peer (P2P) technologies are considered as one of the fundamental technologies for the next generation Internet. P2P systems are classified into unstructured P2P systems or structured P2P systems depending on their topological structure of network. Most structured P2P systems are based on Chord protocol that is designed by using Distributed Hash Tables (DHTs). However, some issues have not still been addressed in structured P2P systems based on DHTs, which include high lookup failure rate, low effective proximity routing, and poor proximity neighbor selection. This paper proposes a Small World Routing Model (SWRM) based on Chord for solving the above issues. The idea of the SWRM comes from the small world characteristic in networks. The SWRM classifies nodes on a network into two classes, which include common nodes and super nodes. The classification principle depends on the status of links with other nodes. Every node in the SWRM can be congregated into different clusters depending on the congregating coefficients and the average shortest hop distance between two chosen nodes. Finally, this paper presents the experimental results of comparing the performance between SWRM and Chord. The experimental results show that it takes fewer lookup hops for the SWRM to find object nodes than Chord, and it takes fewer average lookup successful hops for the SWRM to find object nodes than Chord. Meanwhile, the SWRM has better proximity neighbor selection and is more consistent with the SWC than Chord. --ISISE conference held: Shanghai, China, 20-22 December, 2008

Design of a Wireless Sensor Module for Monitoring Conductor Galloping of Transmission Lines

Conductor galloping may cause flashovers and even tower collapses. The available conductor galloping monitoring methods often employ acceleration sensors to measure the conductor translations without considering the conductor twist. In this paper, a new sensor for monitoring conductor galloping of transmission lines based on an inertial measurement unit and wireless communication is proposed. An inertial measurement unit is used for collecting the accelerations and angular rates of a conductor, which are further transformed into the corresponding geographic coordinate frame using a quaternion transformation to reconstruct the galloping of the conductor. Both the hardware design and the software design are described in details. The corresponding test platforms are established, and the experiments show the feasibility and accuracy of the proposed monitoring sensor. The field operation of the proposed sensor in a conductor spanning 734 m also shows its effectiveness

A test paper generation algorithm based on diseased enhanced genetic algorithm

With the continuous progress of society, tests, and exams appear more and more frequently in people's lives. Faced with the ever-increasing demand for test papers, efficient test paper generation algorithms have become more important. In this paper, we improved and proposed a Diseased Enhanced Genetic Algorithm (DEGA) based on the Genetic Algorithm (GA), and applied it to the test paper generation algorithm. I the crossover operator, the crossover probability that will change in different situations of the population is adopted. According to the characteristics of the test paper generation algorithm, we use the method based on the hamming distance to calculate the distance between individuals in the population. Aiming at the shortcoming that the mutation operator is too random, we designed and used a disease operator that includes three modules: natural disease, infection, and mutation. It effectively guarantees the distance between individuals in the population and improves the shortcoming that GA is easy to fall into a locally optimal solution. Finally, using the College English Test Band 4 (CET-4) questions from 2014 to 2021 as the data set, comparative experiments were carried out on the test paper generation algorithm based on Random Sampling Algorithm (RSA), GA, Enhanced Genetic Algorithm (EGA) and DEGA. The results show that when using the test paper generation algorithm based on DEGA, the generation of test papers is faster, the number of iterations is less, and the algorithm results are significantly better than other algorithms

RST-based Discourse Coherence Quality Analysis Model for Students’ English Essays

Against the problems which can’t be solved by the word-level based local coherence analysis model, we propose a new discourse coherence quality analysis model (abbreviated RST-DCQA) by analyzing the full hierarchical discourse structure of English essays. Under the framework of rhetorical structure theory (RST), firstly, we design an RST-style discourse relations parser to capture the deep hierarchical discourse structure of essays; secondly, we transform the discourse relation information into a discourse relation matrix; finally, we design an algorithm to analyze the discourse coherence quality of student’s English essays. The experimental results show that the average error of our model’s score and teacher’s score is only 2.63, and the Pearson correlation coefficient is 0.71. Compared with the other models, our RST-DCQA model has a higher accuracy and better practicality in the field of students’ essays assessment

Off-topic English Essay Detection Model Based on Hybrid Semantic Space for Automated English Essay Scoring System

Aiming at the problem that the lack of accurate and efficient off-topic detection model for current Automated English Scoring System in China, an unsupervised off-topic essay detection model based on hybrid semantic space was proposed. Firstly, the essay and its essay prompt are respectively represented as noun phrases by using a neural-network dependency parser. Secondly, we introduce a method to construct a hybrid semantic space. Thirdly, we propose a method to represent the noun phrases of the essay and its prompt as vectors in hybrid semantic space and calculate the similarity between the essay and its prompt by using the noun phrase vectors of them. Finally, we propose a sort method to set the off-topic threshold so that the off-topic essays can be identified efficiently. The experimental results on four datasets totaling 5000 essays show that, compared to the previous off-topic essay detection models, the proposed model can detect off-topic essays with higher accuracy, and the accuracy rate over all essay data sets reaches 89.8%

RST-based Discourse Coherence Quality Analysis Model for Students’ English Essays

Against the problems which can’t be solved by the word-level based local coherence analysis model, we propose a new discourse coherence quality analysis model (abbreviated RST-DCQA) by analyzing the full hierarchical discourse structure of English essays. Under the framework of rhetorical structure theory (RST), firstly, we design an RST-style discourse relations parser to capture the deep hierarchical discourse structure of essays; secondly, we transform the discourse relation information into a discourse relation matrix; finally, we design an algorithm to analyze the discourse coherence quality of student’s English essays. The experimental results show that the average error of our model’s score and teacher’s score is only 2.63, and the Pearson correlation coefficient is 0.71. Compared with the other models, our RST-DCQA model has a higher accuracy and better practicality in the field of students’ essays assessment