Requirements ranking based on crowd-sourcing high-end product USs

Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.

Ranking academic institutions based on the productivity,impact,and quality of institutional scholars

Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and quality.Design/methodology/approach:The institutional ranking process developed here considers all institutions in all countries and regions,thereby including those that are established,as well as those that are emerging in scholarly prowess.Rankings of individual scholars worldwide are first generated using the recently introduced,fully indexed ScholarGPS database.The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor,in 14 individual Fields,in 177 Disciplines,and in approximately 350,000 unique Specialties.Rankings associated with five specific Fields(Medicine,Engineering&Computer Science,Life Sciences,Physical Sciences&Mathematics,and Social Sciences),and in two Disciplines(Chemistry,and Electrical&Computer Engineering)are presented as examples,and changes in the rankings over time are discussed.Findings:For the Fields considered here,the Top 20 institutional rankings in Medicine have undergone the least change(lifetime versus last five years),while the rankings in Engineering&Computer Science have exhibited significant change.The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions,although this emergence is shown to be highly Field-and Discipline-dependent.Practical implementations:Existing rankings of academic institutions have:(i)often been restricted to pre-selected institutions,clouding the potential discovery of scholarly activity in emerging institutions and countries;(ii)considered only broad areas of research,limiting the ability of university leadership to act on the assessments in a concrete manner,or in contrast;(iii)have considered only a narrow area of research for comparison,diminishing the broader applicability and impact of the assessment.In general,existing institutional rankings depend on which institutions are included in the ranking process,which areas of research are considered,the breadth(or granularity)of the research areas of interest,and the methodologies used to define and quantify research performance.In contrast,the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall(all Fields)level,to the level of the Specialty.The methods may also assist identification of the root causes of shifts in institution rankings,and how these shifts vary across hundreds of thousands of Fields,Disciplines,and Specialties of scholarly endeavor.Originality/value:This study provides the first ranking of all academic institutions worldwide over Fields,Disciplines,and Specialties based on a unique methodology that quantifies the productivity,impact,and quality of individual scholars.

一种基于潜变量的Ranking模型构造算法

现有的Ranking算法获得的模型全部来自训练数据,因为很多模型的有用信息并不能完全从训练数据中得到,因此这样得到的模型不够精确,对此,提出一种基于潜变量的Ranking算法。该算法以结构化SVM为学习工具,将除训练数据外的其他有用信息以潜变量形式引入算法的框架中,并在此基础上定义了面向NDCG的目标函数。针对该目标函数非凸非平滑,首先使用"凹-凸过程"进行逼近,然后用"近似Bundle法"展开优化计算。基准数据集上的实验结果表明:相比完全依靠训练数据的Ranking算法,本文算法获得的模型更为精确。

线性程序的Ranking函数自动合成

针对判定一个程序终止性的经典方法Ranking函数法,运用半代数系统的概念,把程序终止性问题转换为求半代数系统的Ranking函数。然后运用符号计算工具DISCOV-ERER和Farkas引理,求出函数参数存在的充分必要条件,并根据符号计算理论的方法自动合成Ranking函数。通过计算代数理论的证明和试验的验证,并与其他方法做了比较,这种方法是高效合理的。

A Review on Ranking of Z-numbers

There are numerous studies about Z-numbers since its inception in 2011.Because Z-number concept reflects human ability to make rational decisions,Z-number based multi-criteria decision making problems are one of these studies.When the problem is translated from linguistic information into Z-number domain,the important question occurs that which Z-number should be selected.To answer this question,several ranking methods have been proposed.To compare the performances of these methods,benchmark set of fuzzy Z-numbers has been created in time.There are relatively new methods that their performances are not examined yet on this benchmark problem.In this paper,we worked on these studies which are relative entropy based Z-number ranking method and a method for ranking discrete Z-numbers.The authors tried to examine their performances on the benchmark problem and compared the results with the other ranking algorithms.The results are consistent with the literature,mostly.The advantages and the drawbacks of the methods are presented which can be useful for the researchers who are interested in this area.

基于pairwise的改进ranking算法

传统基于pairwise的ranking算法,学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好,对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据,但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难以直接优化的特点,提出使用割平面算法进行学习,不仅解决了上述问题,而且使算法迭代的次数不再依赖于训练样本对数。最后基于基准数据集的实验证明了算法的有效性。

Personalized web pages ranking algorithm based on user preferences

In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.

基于非凸上界的ranking模型构造算法

现有的ranking算法均通过最小化原目标函数的凸上界构造ranking模型,得到的模型不够精确.为此,文中提出一种基于非凸上界的ranking算法.该算法首先给出一个基于多类支持向量机(SVM)的框架,然后定义面向NDCG的目标函数,在此基础上设计一个比现有的凸上界更为紧凑的非凸上界逼近原目标函数;针对上界函数的非凸非光滑,提出使用凹-凸过程进行凸逼近,并采用割平面算法进行求解;最后,通过在基准数据集上的实验对该算法进行验证,并与现有算法进行对比.结果表明,相比现有的基于凸上界的ranking算法,文中算法得到的模型不但更为精确,而且更加稳定.

Analyzing the activities of visitors of the Leiden Ranking website

Purpose： To get a better understanding of the way in which university rankings are used.Design/methodology/approach： Detailed analysis of the activities of visitors of the website of the CWTS Leiden Ranking.Findings： Visitors of the Leiden Ranking website originate disproportionally from specific countries. They are more interested in impact indicators than in collaboration indicators, while they are about equally interested in size-dependent indicators and size-independent indicators. Many visitors do not seem to realize that they should decide themselves which criterion they consider most appropriate for ranking universities.Research limitations： The analysis is restricted to the website of a single university ranking. Moreover, the analysis does not provide any detailed insights into the motivations of visitors of university ranking websites.Practical implications： The Leiden Ranking website may need to be improved in order to make more clear to visitors that they should decide themselves which criterion they want to use for ranking universities.Originality/value： This is the first analysis of the activities of visitors of a university ranking website.

基于多源知识和Ranking SVM的中文微博命名实体链接

命名实体是文本中承载信息的重要单元,正确分析存在歧义的命名实体对文本的理解起着关键性作用。该文提出基于多源知识和Ranking SVM的中文微博命名实体链接,结合同义词词典、百科资源等知识产生初始候选实体集合,同时从文本中抽取多种组合特征,利用Ranking SVM对候选实体集合进行排序,从而得到目标实体。在NLP&CC20141中文微博实体链接评测数据集上进行了实验,获得了89.40%的平均准确率,与NLP&CC2014中文微博实体链接评测取得最好成绩的系统相比,本文的系统具有一定的优势。

单纯形算法在统计机器翻译Re-ranking中的应用

近年来,discriminative re-ranking技术已经被应用到很多自然语言处理相关的分支中,像句法分析,词性标注,机器翻译等,并都取得了比较好的效果,在各自相应的评估标准下都有所提高。本文将以统计机器翻译为例,详细地讲解利用单纯形算法(Simplex Algorithm)对翻译结果进行re-rank的原理和过程,算法的实现和使用方法,以及re-rank实验中特征选择的方法,并给出该算法在NIST-2002(开发集)和NIST-2005(测试集)中英文机器翻译测试集合上的实验结果,在开发集和测试集上,BLEU分值分别获得了1.26%和1.16%的提高。

Risk assessment and ranking of pesticide residues in Chinese pears

The presence of pesticide residues in pears is a serious health concern. This study presents the results from a 2-year investigation （2013-2014） that used gas chromatography, GS/MS and UPLC/MS-MS to measure the levels of 104 pesti- cides in 310 pear samples. In 93.2% of the samples, 43 pesticides were detected, of which the maximum residue levels （MRLs） were exceeded in 2.6% of the samples. Multiple residues （two to eight compounds） were present in 69.7% of the samples; one sample contained nine pesticides and one sample contained 10. Only 6.8% of the samples did not contain residues. To assess the health risks, the pesticide residue data have been combined with daily pear consumption data for children and adult populations. A deterministic model was used to assess the chronic and acute exposures based on the Joint Meeting on Pesticide Residues （JMPR） method. A potential acute risk was demonstrated for children in the case of bifenthrin, which was found to be present at 105.36% of the acute reference dose （ARfD） value. The long- term exposure of the Chinese consumer to pesticide residues through the consumption of raw pears was far below the acceptable daily intake （ADI） criterion. Additionally, the matrix ranking scheme was used to classify risk subgroups of pesticides and pear samples. In general, 95.5% of samples were deemed to be safe and nine pesticides were classified as being of a relatively high risk. The findings indicated that the occurrence of pesticide residues in pears should not be considered a serious public health problem. Nevertheless, a more detailed study is required for vulnerable consumer groups, especially children. Continuous monitoring of pesticides in pears and tighter regulation of pesticide residue standards are recommended.

Assessing field vulnerability to phosphorus loss in Beijing agricultural area using Revised Field Phosphorus Ranking Scheme

Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve the P management strategies in Guishui River watershed are important for the safety of drinking water in this region. In this study, a Revised Field P Ranking Scheme （PRS） was developed to reflect the field vulnerability of P loss at the field scale based on the Field PRS. In this new scheme, six factors are included, and each one was assigned a relative weight and a determination method. The affecting factors were classified into transport factors and source factors, and, the standards of environmental quality on surface water and soil erosion classification and degradation of the China were used in this scheme. By the new scheme, thirty-four fields in the Guishui River were categorized as ＂low＂, ＂medium＂ or ＂high＂ potential for P loss into the runoff. The results showed that the P loss risks of orchard and vegetable fields were higher than that of corn and soybean fields. The source factors were the main factors to affect P loss from the study area. In the study area, controlling P input and improving P usage efficiency are critical to decrease P loss. Based on the results, it was suggested that more attention should be paid on the fields of vegetable and orchard since they have extremely high usage rate of P and high soil test of P. Compared with P surplus by field measurements, the Revised Field PRS was more suitable for reflecting the characteristics of fields, and had higher potential capacity to identify critical source areas of P loss than PRS.

Influential Node Ranking and Invulnerability of Air Traffic Cyber Physical System

To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.

Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis

With the fast growth of Chinese economic, more and more capital will be invested in environmental projects. How to select the environmental investment projects （alternatives） for obtaining the best environmental quality and economic benefits is an important problem for the decision makers. The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria. A model for ranking the projects of municipal sewage treatment plants is proposed by using exports＇ information and the data of the real projects. And, the ranking result is given based on the PROMETHEE method. Furthermore, by means of the concept of the weight stability intervals （WSI）, the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed. The result shows that some criteria, such as “proportion of benefit to project cost”, will influence the ranking result of alternatives very strong while others not. The influence are not only from the value of criterion but also from the changing the weight of criterion. So, some criteria such as “proportion of benefit to project cost” are key critera for ranking the projects. Decision makers must be cautious to them.

Full ranking procedure based on best and worst frontiers

In the traditional data envelopment analysis （DEA） structure, the efficiency score for one decision making unit （DMU） is calculated by measuring the distance of the evaluated DMU to the best practice frontier. Recent researches have provided the reasonability of considering the worst practice frontier as a supple- ment to the traditional DEA techniques. The existing researches take only one type of frontier into account, and they cannot com- pare the evaluated DMU with both the best and the worst perform- ing DMUs. A DEA-based procedure is developed to consider the best and the worst frontiers in the same scenario where the ratio of two distances （RDS） measure is proposed. The principal appli- cation of this approach is for ranking, and, as a complement tool, for performance evaluation. The proposed approach can be used in a wide range of applications such as the performance evaluation of employees and others. Finally, a bookstore data set is used to illustrate the proposed approach.

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

Node Ranking Strategy in Virtual Network Embedding: An Overview

Network virtualization(NV)is a highprofile way to solve the ossification problem of the nowadays Internet,and be able to support the diversified network naturally.In NV,Virtual Network Embedding(VNE)problem has been widely considered as a crucial issue,which is aimed to embed Virtual Networks(VNs)onto the shared substrate networks(SNs)efficiently.Recently,some VNE approaches have developed Node Ranking strategies to drive and enhance the embedding efficiency.Node Ranking Strategy rank/sort the nodes according to the attributes of the node,including both residual local attributes(CPU,Bandwidth,storage,Etc.)and the global topology attributes(Number of neighborhood Nodes,Delay to other nodes,Etc.).This paper presents an overview of Node Ranking Strategies in Virtual Network Embedding,and possible directions of VNE Node Ranking Strategy.

Eigenvector Method for Ranking Alternatives with Vague Value Measurements

An eigenvector method for ranking alternatives whose measurements are given as vague values is provided. Firstly, a positive matrix is constructed which is defined as evaluation information matrix （EIM）. Based on four assumptions for evaluating alternatives, a ranking eigenvector is defined. And then it is proved, based on positive matrix theory, that the EIM＇s eigenvector corresponding to the maximal eigenvalue is the ranking vector. For alternatives whose characteristics are presented by vague sets, the proposed techniques can evaluate the degree of suitability to which an alternative satisfies the decision-maker＇ s requirement efficiently.

Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.