Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays
Abstract: Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.
Extracted statement — Algorithm: cnvPartition
Research field: Science
Title: Assessment of copy number variation using the Illumina Infinium 1M SNP-array: A comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study
Abstract: High‐throughput single nucleotide polymorphism (SNP)‐array technologies allow to investigate copy number variants (CNVs) in genome‐wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation‐dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KIw=98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19–0.28). In contrast, specificity was very high (0.97–0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome‐wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.Hum Mutat 32:1–10, 2011. © 2011 Wiley‐Liss, Inc.
Extracted statement — Algorithm: cnvPartition
Research field: Science
Title: On the value of user preferences in search-based software engineering: A case study in software product lines
Abstract: Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature maps) using various search-based software engineering methods. As we increase the number of optimization objectives, we find that methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0% violations of domain constraints. Our conclusion is that we need to change our methods for search-based software engineering, particularly when studying complex decision spaces.
Extracted statement — Algorithm(s): SPEA2
Research field: Science
Title: Generating Integration Test Orders for Aspect-Oriented Software with Multi-objective Algorithms
Abstract: The problem known as CAITO refers to the determination of an order to integrate and test classes and aspects that minimizes stubbing costs. Such problem is NP-hard and to solve it efficiently, search based algorithms have been used, mainly evolutionary ones. However, the problem is very complex since it involves different factors that may influence the stubbing process, such as complexity measures, contractual issues and so on. These factors are usually in conflict and different possible solutions for the problem exist. To deal properly with this problem, this work explores the use of multi-objective optimization algorithms. The paper presents results from the application of two evolutionary algorithms - NSGA-II and SPEA2 - to the CAITO problem in four real systems, implemented in AspectJ. Both multi-objective algorithms are evaluated and compared with the traditional Tarjan's algorithm and with a mono-objective genetic algorithm. Moreover, it is shown how the tester can use the found solutions, according to the test goals.
Extracted statement — Algorithm(s): SPEA2
Research field: Science
Title: Establishing Integration Test Orders of Classes with Several Coupling Measures
Abstract: During the inter-class test, a common problem, named Class Integration and Test Order (CITO) problem, involves the determination of a test class order that minimizes stub creation effort, and consequently test costs. The approach based on Multi-Objective Evolutionary Algorithms (MOEAs) has achieved promising results because it allows the use of different factors and measures that can affect the stubbing process. Many times these factors are in conflict and usually there is no a single solution for the problem. Existing works on MOEAs present some limitations. The approach was evaluated with only two coupling measures, based on the number of attributes and methods of the stubs to be created. Other MOEAs can be explored and also other coupling measures. Considering this fact, this paper investigates the performance of two evolutionary algorithms: NSGA-II and SPEA2, for the CITO problem with four coupling measures (objectives) related to: attributes, methods, number of distinct return types and distinct parameter types. An experimental study was performed with four real systems developed in Java. The obtained results point out that the MOEAs can be efficiently used to solve this problem with several objectives, achieving solutions with balanced compromise between the measures, and of minimal effort to test.
Extracted statement — Algorithm(s): SPEA2
Research field: Science
Title: The Multi- Objective Next Release Problem
Abstract: This paper is concerned with the Multi-Objective Next Release Problem (MONRP), a problem in search-based requirements engineering. Previous work has considered only single objective formulations. In the multi-objective formulation, there are at least two (possibly conflicting) objectives that the software engineer wishes to optimize. It is argued that the multi-objective formulation is more realistic, since requirements engineering is characterised by the presence of many complex and conflicting demands, for which the software engineer must find a suitable balance. The paper presents the results of an empirical study into the suitability of weighted and Pareto optimal genetic algorithms, together with the NSGA-II algorithm, presenting evidence to support the claim that NSGA-II is well suited to the MONRP. The paper also provides benchmark data to indicate the size above which the MONRP becomes non--trivial.
Extracted statement — Algorithm(s): NSGA-II
Research field: Science
Title: A Study of the Multi-Objective Next Release Problem
Abstract: One of the first issues which has to be taken into account by software companies is to determine what should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while this entails a minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total cost of including new features into a software package and maximizing the total satisfaction of customers are contradictory objectives, the problem has a multi-objective nature. In this work we study the NRP problem from the multi-objective point of view, paying attention to the quality of the obtained solutions, the number of solutions, the range of solutions covered by these fronts, and the number of optimal solutions obtained.Also, we evaluate the performance of two state-of-the-art multi-objective metaheuristics for solving NRP: NSGA-II and MOCell. The obtained results show that MOCell outperforms NSGA-II in terms of the range of solutions covered, while this latter is able of obtaining better solutions than MOCell in large instances. Furthermore, we have observed that the optimal solutions found are composed of a high percentage of low-cost requirements and, also, the requirements that produce most satisfaction on the customers.
Extracted statement — Algorithm(s): NSGA-II
Research field: Science
Title: Generating Integration Test Orders for Aspect-Oriented Software with Multi-objective Algorithms
Abstract: The problem known as CAITO refers to the determination of an order to integrate and test classes and aspects that minimizes stubbing costs. Such problem is NP-hard and to solve it efficiently, search based algorithms have been used, mainly evolutionary ones. However, the problem is very complex since it involves different factors that may influence the stubbing process, such as complexity measures, contractual issues and so on. These factors are usually in conflict and different possible solutions for the problem exist. To deal properly with this problem, this work explores the use of multi-objective optimization algorithms. The paper presents results from the application of two evolutionary algorithms - NSGA-II and SPEA2 - to the CAITO problem in four real systems, implemented in AspectJ. Both multi-objective algorithms are evaluated and compared with the traditional Tarjan's algorithm and with a mono-objective genetic algorithm. Moreover, it is shown how the tester can use the found solutions, according to the test goals.
Extracted statement — Algorithm(s): NSGA-II
Research field: Science
Title: Establishing Integration Test Orders of Classes with Several Coupling Measures
Abstract: During the inter-class test, a common problem, named Class Integration and Test Order (CITO) problem, involves the determination of a test class order that minimizes stub creation effort, and consequently test costs. The approach based on Multi-Objective Evolutionary Algorithms (MOEAs) has achieved promising results because it allows the use of different factors and measures that can affect the stubbing process. Many times these factors are in conflict and usually there is no a single solution for the problem. Existing works on MOEAs present some limitations. The approach was evaluated with only two coupling measures, based on the number of attributes and methods of the stubs to be created. Other MOEAs can be explored and also other coupling measures. Considering this fact, this paper investigates the performance of two evolutionary algorithms: NSGA-II and SPEA2, for the CITO problem with four coupling measures (objectives) related to: attributes, methods, number of distinct return types and distinct parameter types. An experimental study was performed with four real systems developed in Java. The obtained results point out that the MOEAs can be efficiently used to solve this problem with several objectives, achieving solutions with balanced compromise between the measures, and of minimal effort to test.
Extracted statement — Algorithm(s): NSGA-II
Research field: Science
Title: Multiobjective Simulation Optimisation in Software Project Management
Abstract: Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which considers a single objective (or multiple objectives combined into a single fitness function). This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimations is determined. The generated data is combined with a well-known multiobjective optimisation algorithm, NSGA-II, to find optimal solutions for the output variables. The NSGA-II algorithm was able to quickly converge to a set of optimal solutions composed of multiple and conflicting variables from a medium size software project simulation model. Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managers for decision making. Furthermore, visual representations of such solutions are intuitive and can help project managers in their decision making process.
Extracted statement — Algorithm(s): NSGA-II
Research field: Science
Title: A Hybrid Approach to Solve the Agile Team Allocation Problem
Abstract: The success of the team allocation in a agile software development project is essential. The agile team allocation is a NP-hard problem, since it comprises the allocation of self-organizing and cross-functional teams. Many researchers have driven efforts to apply Computational Intelligence techniques to solve this problem. This work presents a hybrid approach based on NSGA-II multi-objective metaheuristic and Mamdani Fuzzy Inference Systems to solve the agile team allocation problem, together with an initial evaluation of its use in a real environment.
Extracted statement — Algorithm(s): NSGA-II
Research field: Science
Title: The effect of socio-economic status and ethnicity on the comparative oral health of Asian and White Caucasian 12-year-old children
Abstract: OBJECTIVE To investigate the oral health of 12-year-old children of different deprivation but similar fluoridation status from South Asian and White Caucasian ethnic groups. DESIGN An epidemiological survey of 12-year-old children using BASCD criteria, with additional tooth erosion, ethnic classification and postcode data. CLINICAL SETTING Examinations were completed in schools in Leicestershire and Rutland, England, UK. Participants A random sample of 1,753 12-year-old children from all schools in the study area. MAIN OUTCOME MEASURES Caries experience was measured using the DMFT index diagnosed at the caries into dentine (D3) threshold, and tooth erosion using the index employed in the Children's Dental Health UK study reported in 1993. RESULTS The overall prevalence of caries was greater in White than Asian children, but varied at different levels of deprivation and amongst different Asian religious groups. There was a significant positive association between caries and deprivation for White children, but the reverse was true for non-Muslim Asians. White Low Deprivation children had significantly less tooth erosion, but erosion experience increased with decreasing deprivation in non-Muslim Asians. CONCLUSIONS Oral health is associated with ethnicity and linked to deprivation on an ethnic basis. The intra-Asian dental health disadvantage found in the primary dentition of Muslim children is perpetuated into the permanent dentition.
Extracted statement — n: 1,753
Research field: Science
Title: Overview of the Face Recognition Grand Challenge
Abstract: Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
Extracted statement — Amount of data: 50,000
Research field: Science
Title: Tracking the Middle-Income Trap: What is it, Who is in it, and Why?
Abstract: This paper provides a working definition of what the middle-income trap is. We start by defining four income groups of GDP per capita in 1990 PPP dollars: low-income below $2,000; lower-middle-income between $2,000 and $7,250; upper-middle-income between $7,250 and $11,750; and high-income above $11,750. We then classify 124 countries for which we have consistent data for 1950–2010. In 2010, there were 40 low-income countries in the world, 38 lower-middle-income, 14 upper-middle-income, and 32 high-income countries. Then we calculate the threshold number of years for a country to be in the middle-income trap: a country that becomes lower-middle-income (i.e., that reaches $2,000 per capita income) has to attain an average growth rate of per capita income of at least 4.7 percent per annum to avoid falling into the lower-middle-income trap (i.e., to reach $7,250, the upper-middle-income threshold); and a country that becomes upper-middle-income (i.e., that reaches $7,250 per capita income) has to attain an average growth rate of per capita income of at least 3.5 percent per annum to avoid falling into the upper-middle-income trap (i.e., to reach $11,750, the high-income level threshold). Avoiding the middle-income trap is, therefore, a question of how to grow fast enough so as to cross the lower-middle-income segment in at most 28 years, and the upper-middle-income segment in at most 14 years. Finally, the paper proposes and analyzes one possible reason why some countries get stuck in the middle-income trap: the role played by the changing structure of the economy (from low-productivity activities into high-productivity activities), the types of products exported (not all products have the same consequences for growth and development), and the diversification of the economy. We compare the exports of countries in the middle-income trap with those of countries that graduated from it, across eight dimensions that capture different aspects of a country’s capabilities to undergo structural transformation, and test whether they are different. Results indicate that, in general, they are different. We also compare Korea, Malaysia, and the Philippines according to the number of products that each exports with revealed comparative advantage. We find that while Korea was able to gain comparative advantage in a significant number of sophisticated products and was well connected, Malaysia and the Philippines were able to gain comparative advantage in electronics only.
Extracted statement — Sample Period: 1950–2010
Research field: Science
Title: Tracking the Middle-Income Trap: What is it, Who is in it, and Why?
Abstract: This paper provides a working definition of what the middle-income trap is. We start by defining four income groups of GDP per capita in 1990 PPP dollars: low-income below $2,000; lower-middle-income between $2,000 and $7,250; upper-middle-income between $7,250 and $11,750; and high-income above $11,750. We then classify 124 countries for which we have consistent data for 1950–2010. In 2010, there were 40 low-income countries in the world, 38 lower-middle-income, 14 upper-middle-income, and 32 high-income countries. Then we calculate the threshold number of years for a country to be in the middle-income trap: a country that becomes lower-middle-income (i.e., that reaches $2,000 per capita income) has to attain an average growth rate of per capita income of at least 4.7 percent per annum to avoid falling into the lower-middle-income trap (i.e., to reach $7,250, the upper-middle-income threshold); and a country that becomes upper-middle-income (i.e., that reaches $7,250 per capita income) has to attain an average growth rate of per capita income of at least 3.5 percent per annum to avoid falling into the upper-middle-income trap (i.e., to reach $11,750, the high-income level threshold). Avoiding the middle-income trap is, therefore, a question of how to grow fast enough so as to cross the lower-middle-income segment in at most 28 years, and the upper-middle-income segment in at most 14 years. Finally, the paper proposes and analyzes one possible reason why some countries get stuck in the middle-income trap: the role played by the changing structure of the economy (from low-productivity activities into high-productivity activities), the types of products exported (not all products have the same consequences for growth and development), and the diversification of the economy. We compare the exports of countries in the middle-income trap with those of countries that graduated from it, across eight dimensions that capture different aspects of a country’s capabilities to undergo structural transformation, and test whether they are different. Results indicate that, in general, they are different. We also compare Korea, Malaysia, and the Philippines according to the number of products that each exports with revealed comparative advantage. We find that while Korea was able to gain comparative advantage in a significant number of sophisticated products and was well connected, Malaysia and the Philippines were able to gain comparative advantage in electronics only.
Extracted statement — Time period: 1950–2010
Research field: Science
Title: Structural breaks, electricity consumption and economic growth: evidence from Turkey
Abstract: This paper investigates the short-run and long-run causality issues between electricity consumption and economic growth in Turkey by using the co-integration and vector error-correction models with structural breaks. It employs annual data covering the period 1968–2005. The study also explores the causal relationship between these variables in terms of the three error-correction based Granger causality models. The empirical results are as follows: i) Both variables are nonstationary in levels and stationary in the first differences with/without structural breaks, ii) there exists a longrun relationship between variables, iii) there is unidirectional causality running from the electricity consumption to economic growth. The overall results indicate that “growth hypothesis” for electricity consumption and growth nexus holds in Turkey. Thus, energy conservation policies, such as rationing electricity consumption, may harm economic growth in Turkey.
Extracted statement — Period: 1968–2005
Research field: Science
Title: An Updated ERP Systems Annotated Bibliography: 2001-2005
Abstract: This study provides an updated annotated bibliography of ERP publications published in the main IS conferences and journals during the period 2001-2005, categorizing them through an ERP lifecycle-based framework that is structured in phases. The first version of this bibliography was published in 2001 (Esteves and Pastor, 2001c). However, so far, we have extended the bibliography with a significant number of new publications in all the categories used in this paper. We also reviewed the categories and some incongruities were eliminated.
Extracted statement — Coverage: 2001-2005