Quantitative analysis of Spanish university scientific output in the area of Legal and Forensic Medicine: International exposure

We set out to analyse and quantify the papers published (for an international readership) by Spanish universities in the field of Legal and Forensic Medicine. For this, we used the MEDLINE data base, to analyse research articles in which a Spanish university teacher (whose sole employment was with a university, as registered by the Ministry of Education in July 2005, (n = 67), appeared as author or co-author in this field. The years covered are 1952 (First year that a Spanish author appears for an article on Legal and Forensic Medicine in this service) to July 2005. A total of 770 articles were counted; the productivity in this area was increasing substantially from the 1980’s onwards. English language scientific journals were the preferred channel of communication. Slightly more than 85% of the works can be classified into four themes, of which Genetics is the most prolific. The number of papers published in English journals represented 84% of the total and only 13% was published in Spanish journals. There was a close relationship between growth in the authority index and inter-institutional co-operation, which boosted the production of articles. When at least one of the authorship of a published paper was a Spanish university teacher, the research was led by a university in 62.4% of cases, and of this 85.6% were Spanish universities.     

Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm

In recent days, the gigantic generation of medical data from smart healthcare applications requires the development of big data classification methodologies. Medical data classification can be utilized for visualizing the hidden patterns and finding the presence of disease from the medical data. In this article, we present an efficient multi-kernel support vector machine (MKSVM) and fruit fly optimization algorithm (FFOA) for disease classification. Initially, FFOA is employed to choose the finest features from the available set of features. The selected features from the medical dataset are processed and provided to the MKSVM for medical data classification purposes. The proposed chronic kidney disease (CKD) classification method has been simulated in MATLAB. Next, testing of the dataset takes place using the own benchmark CKD dataset from UCI machine learning repositories such as Kidney chronic, Cleveland, Hungarian, and Switzerland. The performance of the proposed CKD classification method is elected by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate. The investigational outcome specifies that the proposed CKD classification method achieves maximum classification precision value of 98.5% for chronic kidney dataset, 90.42904% for Cleveland, 89.11565% for Hungarian, and 86.17886% for Switzerland dataset than existing hybrid kernel SVM, fuzzy min-max GSO neural network, and SVM methods.     

复杂背景下多目标提取的高灵敏度方法

对电视经纬仪拍摄到的飞行小目标图像进行了归一化高斯模板相关。传统的模板相关方法不能将真假目标集合充分分离,导致真目标的漏检或引入虚假目标。基于模板相关的高斯函数拟合方法(Gaussian Function Fitting Method, GFFM),对模板相关所得到的目标集(含有真目标和假目标)中的每一个元素进行高斯函数拟合,并引入了一个更为灵敏的检验量—高斯函数拟合误差,可以将真假目标集合明显区分开,减小阈值确定的难度。实验表明:当相关系数阈值rth=0.8时,传统模板相关方法漏检率20%,虚警率40%;而GFFM方法则检出了所有真目标,且无虚假目标。     

Fairly sharing the credit of multi-authored papers and its application in the modification of <Emphasis Type="Italic">h</Emphasis>-index and <Emphasis Type="Italic">g</Emphasis>-index

Except the alphabetic ordering authorship papers, the citations of multi-authored papers are allocated to the authors based on their contributions to the paper. For papers without clarification of contribution proportion, a function of author number and rank is presented to rightly determine the credit allocated proportion and allocated citations of each author. Our citation allocation scheme is between the equally fractional counting and the one using the inverse of author rank. It has a parameter to adjust the credit distribution among the different authors. The allocated citations can either be used alone to indicate one’s performance in a paper, or can be applied in the modification of h-index and g-index to represent the achievement of a scientist on the whole. The modified h-index and g-index of an author makes use of more papers in which he or she played important roles. Our method is suitable for the papers with wide range of author numbers.     

一适用于超多类手写汉字识别的新改型Adaboost算法

提出一种适用于超多类手写汉字识别的新改型Adaboost算法,采用基于描述性模型的多类分类器（modified quadratic discriminant function,MQDF）作为Adaboost基元分类器,可直接进行多类分类,无需将多类问题转化为多个两类问题处理,其训练复杂度大大低于已有的多类Adaboost算法。算法提出根据广义置信度更新样本权重,实验证明这种算法适用于大规模多类分类问题。为了降低算法的识别复杂度,提出从所有训练后得到的Adaboost基元分类器组中选择一个最优的基元分类器作为最终分类器的方法进行删减。在HCL2000及THOCR-HCD数据集上进行实验证明,所提改型Adaboost算法提高了识别率的有效性,该算法的相对错误率比现有最优算法分别下降了14.3 %,8.1 %和19.5 %。     

Quantity versus impact of software engineering papers: a quantitative study

According to the data from the Scopus publication database, as analyzed in several recent studies, more than 70,000 papers have been published in the area of Software Engineering (SE) since late 1960’s. According to our recent work, 43% of those papers have received no citations at all. Since citations are the most commonly used metric for measuring research (academic) impact, these figures raise questions (doubts) about the (non-existing) impact of such a large set of papers. It is a reality that typical academic reward systems encourage researchers to publish more papers and do not place a major emphasis on research impact. To shed light on the issue of volume (quantity) versus citation-based impact of SE research papers, we conduct and report in this paper a quantitative bibliometrics assessment in four aspects: (1) quantity versus impact of different paper types (e.g., conference versus journal papers), (2) ratios of uncited (non-impactful) papers, (3) quantity versus impact of papers originating from different countries, and (4) quantity versus impact of papers by each of the top-10 authors (in terms of number of papers). To achieve the above objective, we conducted a quantitative exploratory bibliometrics assessment, comprised of four research questions, to assess quantity versus impact of SE papers with respect to the aspects discussed above. We extracted the data through a systematic, automated and repeatable process from the Scopus paper database, which we also used in two previous papers. Our results show that the distribution of SE publications has a major inequality in terms of impact overall, and also when categorized in terms of the above four aspects. The situation in the SE literature is similar to the other areas of science as studied by previous bibliometrics studies. Also, among our results is the fact that journal articles and conference papers have been cited 12.6 and 3.6 times on average, confirming the expectation that journal articles have more impact, in general, than conference papers. Also, papers originated from English-speaking countries have in general more visibility and impact (and consequently citations) when compared to papers originated from non-English-speaking countries. Our results have implications for improvement of academic reward systems, which nowadays mainly encourage researchers to publish more papers and usually neglect research impact. Also, our results can help researchers in non-English-speaking countries to consider improvements to increase their research impact of their upcoming papers.     

The relationship between the author byline and contribution lists: a comparison of three general medical journals

The author byline is an indispensable component of a scientific paper. Some journals have added contribution lists for each paper to provide detailed information of each author’s role. Many papers have explored, respectively, the byline and contribution lists. However, the relationship between the two remains unclear. We select three prominent general medical journals: Journal of the American Medical Association (JAMA), Annals of Internal Medicine (Annals), and PLOS Medicine (PLOS). We analyze the relationship between the author byline and contribution lists using four indexes. Four main findings emerged. First, the number, forms, and names of contribution lists significantly differed among the three journals, although they adopted the criteria of the International Committee of Medical Journal Editors. Second, a U-shaped relationship exists between the extent of contribution and author order: the participation levels in contribution lists were highest for first authors, followed by last and second authors, and then middle authors with the lowest levels. Third, regarding the consistency between author order in the contribution list and byline, every contribution category has a high consistency in JAMA and Annals, while PLOS shows a low consistency, in general. Fourth, the three journals have a similar distribution for the first authors in the contribution category; the first author in the byline contributes the highest proportion, followed by the middle and second authors, and then the last author with the lowest proportion. We also develop recommendations to modify academic and writing practice: implement structured cross-contribution lists, unify formats and standards of contribution lists, draft the author contribution criteria in the social sciences and humanities, and consider author contribution lists in scientific evaluation.     

工程图扫描图像中基于连通群的分类与识别算法

本文提出基于连通群的图像分割和分类的基础理论,给出了连通群的分类依据,并将其用于工程图纸的识别,对宇串的方向定位算法作了描述。文中关于连通群的理论分析为解决工程图中字符、数字和图形的正确分割、分类及排除噪声的干扰提供了依据,连通群的迭代算法可准确定位字符串的方向。本研究针对建筑结构图的识别作了试验性研究,矢量化与识别精度较高。     

Relation of seminal nanotechnology document production to total nanotechnology document production — South Korea

This study evaluates trends in quality of nanotechnology and nanoscience papers produced by South Korean authors. The metric used to gauge quality is ratio of highly cited nanotechnology papers to total nanotechnology papers produced in sequential time frames. In the first part of this paper, citations (and publications) for nanotechnology documents published by major producing nations and major producing global institutions in four uneven time frames are examined. All nanotechnology documents in the Science Citation Index [SCI, 2006] for 1998, 1999–2000, 2001–2002, 2003 were retrieved and analyzed in March 2007. In the second part of this paper, all the nanotechnology documents produced by South Korean institutions were retrieved and examined. All nanotechnology documents produced in South Korea (each document had at least one author with a South Korea address) in each of the above time frames were retrieved and analyzed. The South Korean institutions were extracted, and their fraction of total highly cited documents was compared to their fraction of total published documents. Non-Korean institutions that co-authored papers were included as well, to offer some perspective on the value of collaboration.     

Experimental research of unsupervised Cameron/maximum-likelihood classification method for fully polarimetric synthetic aperture radar data

In this study, experimental research on classification is applied to fully polarimetric data in X-band from China. Considering the amplitude and phase error between H and V channels in the system, the authors firstly correct the error in original data. The authors also deduce the formula of Cameron?s classification method for the real data in our study. Then Cameron?s method is used to initially classify the site image. Finally, the initial classification map defines training sets for the maximum-likelihood (ML) classifier. The advantages of this method are the automated classification and interpretation of each class based on the scattering mechanism. The experiment demonstrates the feasibility of the proposed approach, which dramatically improves the X-band data classification result compared with the Cameron method and H/α/ML method.     

A novel algorithm for shape parameter selection in radial basis functions collocation method

Many Radial Basis Functions (RBFs) contain a free shape parameter that plays an important role for the application of Meshless method to the analysis of multilayered composite and sandwich plates. In most papers the authors end up choosing this shape parameter by trial and error or some other ad hoc means. In this paper a novel algorithm for shape parameter selection, based on a convergence analysis, is presented. The effectiveness of this algorithm is assessed by static analyses of laminated composite and sandwich plates.     

An approach to verifying and debugging simulation models governed by ordinary differential equations: Part 2. Residuals analysis and a case study

In Part 1 of this paper a methodology for back‐to‐back testing of simulation software was described. Residuals with error‐dependent geometric properties were generated. A set of potential coding errors was enumerated, along with a corresponding set of feature matrices, which describe the geometric properties imposed on the residuals by each of the errors. In this part of the paper, an algorithm is developed to isolate the coding errors present by analysing the residuals. A set of errors is isolated when the subspace spanned by their combined feature matrices corresponds to that of the residuals. Individual feature matrices are compared to the residuals and classified as ‘definite’, ‘possible’ or ‘impossible’. The status of ‘possible’ errors is resolved using a dynamic subset testing algorithm. To demonstrate and validate the testing methodology presented in Part 1 and the isolation algorithm presented in Part 2, a case study is presented using a model for biological wastewater treatment. Both single and simultaneous errors that are deliberately introduced into the simulation code are correctly detected and isolated. Copyright © 2003 John Wiley & Sons, Ltd.     

Discovery of factors influencing citation impact based on a soft fuzzy rough set model

In this paper, the machine learning tools were used to identify key features influencing citation impact. Both the papers?? external and quality information were considered in constructing papers?? feature space. Based on the feature space, the soft fuzzy rough set was used to generate a series of associated feature subsets. Then, the KNN classifier was used to find the feature subset with the best classification performance. The results show that citation impact could be predicted by objectively assessed factors. Both the papers?? quality and external features, mainly represented as the reputation of the first author, are contributed to future citation impact.     

Automatic validation of phosphopeptide identifications from tandem mass spectra

We developed and compared two approaches for automated validation of phosphopeptide tandem mass spectra identified using database searching algorithms. Phosphopeptide identifications were obtained through SEQUEST searches of a protein database appended with its decoy (reversed sequences). Statistical evaluation and iterative searches were employed to create a high-quality data set of phosphopeptides. Automation of postsearch validation was approached by two different strategies. By using statistical multiple testing, we calculate a p value for each tentative peptide phosphorylation. In a second method, we use a support vector machine (SVM; a machine learning algorithm) binary classifier to predict whether a tentative peptide phosphorylation is true. We show good agreement (85%) between postsearch validation of phosphopeptide/spectrum matches by multiple testing and that from support vector machines. Automatic methods conform very well with manual expert validation in a blinded test. Additionally, the algorithms were tested on the identification of synthetic phosphopeptides. We show that phosphate neutral losses in tandem mass spectra can be used to assess the correctness of phosphopeptide/spectrum matches. An SVM classifier with a radial basis function provided classification accuracy from 95.7% to 96.8% of the positive data set, depending on search algorithm used. Establishing the efficacy of an identification is a necessary step for further postsearch interrogation of the spectra for complete localization of phosphorylation sites. Our current implementation performs validation of phosphoserine/phosphothreonine-containing peptides having one or two phosphorylation sites from data gathered on an ion trap mass spectrometer. The SVM-based algorithm has been implemented in the software package DeBunker. We illustrate the application of the SVM-based software DeBunker on a large phosphorylation data set.     

Detection of brain tumour from MRI image using modified region growing and neural network

Abstract

Region growing is an important application of image segmentation in medical research for detection of tumour. In this paper, we propose an effective modified region growing technique for detection of brain tumour. It consists of four steps which includes: (i) pre-processing; (2) modified region growing by the inclusion of an additional orientation constraint in addition to the normal intensity constrain; (3) feature extraction of the region; and (4) final classification using the neural network. The performance of the proposed technique is systematically evaluated using the magnetic resonance imaging (MRI) brain images received from the public sources. For validating the effectiveness of the modified region growing, we have considered the quantity rate parameter. For the evaluation of the proposed technique of tumour detection, we make use of sensitivity, specificity and accuracy values which we compute from finding out false positive, false negative, true positive and true negative. Comparative analyses were made of the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. From the results obtained, we could see that the proposed technique achieved the accuracy of 80% for the testing dataset, which clearly demonstrated the effectiveness of the modified region growing when compared to the normal technique.     

基于声信号和一维卷积神经网络的电机故障诊断研究

针对电机故障诊断问题,设计一种新型的一维卷积神经网络结构(1D-CNN),提出一种基于声信号和1D-CNN的电机故障诊断方法.为了验证1D-CNN算法在电机故障识别领域的有效性,以一组空调故障电机作为实验对象,搭建电机故障诊断平台,对4种状态的空调电机进行声信号采集实验,制作电机故障声信号数据集,并运用1D-CNN算法...     

Tracking the follow-up of work in progress papers

Academic conferences offer numerous submission tracks to support the inclusion of a variety of researchers and topics. Work in progress papers are one such submission type where authors present preliminary results in a poster session. They have recently gained popularity in the area of Human Computer Interaction (HCI) as a relatively easier pathway to attending the conference due to their higher acceptance rate as compared to the main tracks. However, it is not clear if these work in progress papers are further extended or transitioned into more complete and thorough full papers or are simply one-off pieces of research. In order to answer this we explore self-citation patterns of four work in progress editions in two popular HCI conferences (CHI2010, CHI2011, HRI2010 and HRI2011). Our results show that almost 50% of the work in progress papers do not have any self-citations and approximately only half of the self-citations can be considered as true extensions of the original work in progress paper. Specific conferences dominate as the preferred venue where extensions of these work in progress papers are published. Furthermore, the rate of self-citations peaks in the immediate year after publication and gradually tails off. By tracing author publication records, we also delve into possible reasons of work in progress papers not being cited in follow up publications. In conclusion, we speculate on the main trends observed and what they may mean looking ahead for the work in progress track of premier HCI conferences.