Importance Ranking Research Articles

Parallel dual-channel multi-label feature selection

In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use single-channel structure to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the concept of dual channel and design the algorithm model as two independent modules. The algorithm obtained different feature correlation sequences, thus avoided relevant feature loss. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective.

The predictors of death within 1 year in acute ischemic stroke patients based on machine learning.

To explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms. This study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator. Multivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year. The network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.

Effects of roughness on hydrodynamic characteristics of a submerged floating tunnel subject to steady currents

The effects of surface roughness as induced by marine fouling on the hydrodynamic forces on a submerged floating tunnel (SFT) are experimentally and numerically investigated in detail at Reynolds numbers Re = 8.125 × 103–5.25 × 104. A sensitivity analysis to different roughness parameters including roughness height, skewness, coverage ratio, and spatial arrangement is performed. In addition, an optimized parametric cross-section for an SFT is proposed, and the hydrodynamic performance of the parametric shape and circular SFT cross-section shape with roughness elements is compared. The pressure distribution along the SFT, flow separation and wake characteristics are analyzed to provide a systematic insight into the fundamental mechanism relating the roughness parameters and flow around an SFT. In order to better understand the nonlinear relationships among structural geometry, roughness parameters, flow states, and structural response, an artificial intelligence method using Random Forest (RF) for feature importance ranking is applied. The results show that with the parametric shape, the hydrodynamic forces on the fouled SFT can be effectively mitigated. The roughness height and coverage ratio affect the equivalent blockage and hence, change flow separation and recirculation length in the wake. Lower skewness of the roughness elements can increase the critical Re by changing the relative roughness parameter. Horizontal arrangement of the roughness elements on an SFT generally results in the largest hydrodynamic forces, compared to staggered and vertical distributions. Throughout the feature importance ranking, the flow regime is found to be the most important feature of the hydrodynamics of the SFT. In addition, the SFT cross-section shape and roughness coverage ratio play a dominant role.

Fields of interest and demands for a digital proving ground twin

The general understanding among scientists in the field of intelligent vehicles is that physical testing alone is not sufficient to achieve full driving automation in an economically acceptable period. Virtual testing methods are perceived as an effective option to decrease development times and costs for intelligent vehicles. We propose to utilize digital proving ground twins in order to verify the reliability of driving simulations by test replication under real environmental conditions. To investigate this proposal, we conducted a potential users survey to infer specifications for such a digital proving ground twin. The survey shows a consensus among engineers regarding the expected benefit of a digital proving ground twin. Next to the track geometries, environmental conditions, such as adverse weather, were stated as the most important aspect of a digital proving ground twin. Vehicle-in-the-loop, hardware-in-the-loop and software-in-the-loop test methods achieved the highest importance rank for applications of the twin. Engineers in the field of automated driving systems and engineers with a work focus on driving simulators expect the highest benefit of it. This article verifies the added value of test track digitalization and provides essential information on how and where to implement virtual simulation infrastructure for transportation system tests. The revealed knowledge can be used to accelerate the development of automated driving systems. Thus, it contributes to a faster reduction of road accidents and emissions. The results are highly relevant to strategists and managers that determine the development of automated driving systems by establishing policies or assigning investment budgets.

Comparison of local and global sensitivity analysis methods and application to thermal hydraulic phenomena

The best estimate plus uncertainty analysis have been widely applied for the safety assessment of nuclear systems, and sensitivity analysis is one of the essential steps to identify and rank the important phenomena and parameters. The conventionally used local sensitivity methods are based on linear hypothesis and have limitations in the application process of nonlinear systems, while the more complicated global sensitivity methods may have problem in time costs. To compare and propose the proper method for evaluation of thermal-hydraulic calculation in a reactor system, local methods such as Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient, global methods such as the variance-based method, moment-independent method, and elementary effects method are reviewed and applied for numerical examples and specific reactor thermal-hydraulic problem in this paper. Results show that the rank of parameter importance measured by various local analysis methods is the same, but away from the global methods. Different types of global sensitivity analysis methods have similar results for parameters with higher importance, and among these methods, the elementary effects method has the lowest computational cost with the same number of samples.

Development and Internal Validation of Risk Assessment Models for Chronic Obstructive Pulmonary Disease in Coal Workers.

Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.

Development of a Pancreatic Fistula Prediction Model After Pancreaticoduodenectomy Based on a Decision Tree and Random Forest Algorithm.

The incidence of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is high. We sought to develop a POPF prediction model based on a decision tree (DT) and random forest (RF) algorithm after PD and to explore its clinical value. The case data of 257 patients who underwent PD in a tertiary general hospital from 2013 to 2021 were retrospectively collected in China. The RF model was used to select features by ranking the importance of variables, and both algorithms were used to build the prediction model after automatic adjustment of parameters by setting the respective hyperparameter intervals and resampling as a 10-fold cross-validation method, etc. The prediction model's performance was assessed by the receiver operating characteristic curve (ROC) and the area under curve (AUC). Postoperative pancreatic fistula occurred in 56 cases (56/257, 21.8%). The DT model had an AUC of .743 and an accuracy of .840, while the RF model had an AUC of .977 and an accuracy of .883. The DT plot visualized the process of inferring the risk of pancreatic fistula from the DT model on independent individuals. The top 10 important variables were selected for ranking in the RF variable importance ranking. This study successfully developed a DT and RF algorithm for the POPF prediction model, which can be used as a reference for clinical health care professionals to optimize treatment strategies to reduce the incidence of POPF.

Dynamic Habitat Indices and Climatic Characteristics Explain Species Richness Patterns on the Mongolian Plateau

Global climate change affects biodiversity patterns, especially in arid and semi-arid regions such as the Mongolian plateau, one of the most ecologically fragile regions in the world. Three dynamic habitat indices (DHIs) were related to the productivity hypothesis and calculated based on FAPAR, including cumulative productivity (DHIcum indicates the availability of resources such as food supply and habitat in a year, representing available energy), minimum productivity (DHImin indicates the limitations of food and habitat resources in a year, representing environmental stress), and seasonal productivity (DHIsea denotes the change in productivity in a year, representing environmental stability). In this paper, we investigated the distribution pattern of species richness on the Mongolian Plateau based on the productivity hypothesis. We constructed models of the richness of three species (mammals, birds, and amphibians) using DHIs and climate variables to explain patterns of species richness on the Mongolian Plateau. The results revealed that, on the Mongolian plateau, there is a relatively high correlation between DHIs and species richness, especially with DHIcum (R = 0.59 for mammals, R = 0.73 for birds, and R = 0.58 for amphibians). There was a significant non-linear relationship between DHIs and species richness, as the model predictive power was significantly enhanced with GAM and RF. The inclusion of climate variables significantly improved the explanatory power of various models for the mammal, bird, and amphibian species richness on the Mongolian Plateau, with the best results for RF (0.89, 0.94, and 0.91, respectively). The influence of climate variables on species richness patterns in the importance ranking was higher than that of DHIs. Climate also has an influence on species richness. Vegetation productivity and climatic factors are good determinants of species richness on the Mongolian Plateau and should be carefully considered in future studies.

PKI: A bioinformatics method of quantifying the importance of nodes in gene regulatory network via a pseudo knockout index

BackgroundGene regulatory network (GRN) is a model that characterizes the complex relationships between genes and thereby provides an informatics environment to measure the importance of nodes. The evaluation of important nodes in a GRN can effectively refer to their functional implications severing as key players in particular biological processes, such as master regulator and driver gene. Currently, it is mainly based on network topological parameters and focuses only on evaluating a single node individually. However, genes and products play their functions by interacting with each other. It is worth noting that the effects of gene combinations in GRN are not simply additive. Key combinations discovery is of significance in revealing gene sets with important functions. Recently, with the development of single-cell RNA-sequencing (scRNA-seq) technology, we can quantify gene expression profiles of individual cells that provide the potential to identify crucial nodes in gene regulations regarding specific condition, e.g., stem cell differentiation. ResultsIn this paper, we propose a bioinformatics method, called Pseudo Knockout Importance (PKI), to quantify the importance of node and node sets in a specific GRN structure using time-course scRNA-seq data. First, we construct ordinary differential equations to approach the gene regulations during cell differentiation. Then we design gene pseudo knockout experiments and define PKI score evaluation criteria based on the coefficient of determination. The importance of nodes can be described as the influence on the ODE system of removing variables. For key gene combinations, PKI is derived as a combinatorial optimization problem of quantifying the in silico gene knockout effects. ConclusionsHere, we focus our analyses on the specific GRN of embryonic stem cells with time series gene expression profile. To verify the effectiveness and advantage of PKI method, we compare its node importance rankings with other twelve kinds of centrality-based methods, such as degree and Latora closeness. For key node combinations, we compare the results with the method based on minimum dominant set. Moreover, the famous combinations of transcription factors in induced pluripotent stem cell are also employed to verify the vital gene combinations identified by PKI. These results demonstrate the reliability and superiority of the proposed method.

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients' care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking.

A machine learning approach for predicting descending thoracic aortic diameter.

To establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients. A total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared. We identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications. The predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.

Synergism of Multi-Modal Data for Mapping Tree Species Distribution—A Case Study from a Mountainous Forest in Southwest China

Accurately mapping tree species is crucial for forest management and conservation. Most previous studies relied on features derived from optical imagery, and digital elevation data and the potential of synthetic aperture radar (SAR) imagery and other environmental factors have, generally, been underexplored. Therefore, the aim of this study is to evaluate the potential of fusing freely available multi-modal data for accurately mapping tree species. Sentinel-2, Sentinel-1, and various environmental datasets over a large mountainous forest in Southwest China were obtained and analyzed using Google Earth Engine (GEE). Seven data cases considering the individual or joint performance of different features, and four additional cases considering a novel clustering-based feature selection method, were analyzed. All 11 cases were assessed using three machine learning algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting tree (XGBoost). The best performance, with an overall accuracy of 77.98%, was attained from the case with all features and the random forest classifier. Sentinel-2 data alone exhibited similar performance as environmental data in terms of overall accuracy. Similar species, such as oak and birch, cannot be spectrally discriminated based on Sentinel-2-based features alone. The addition of SAR features improved discrimination, especially when distinguishing between some coniferous and deciduous species, but also decreased accuracy for oak. The analysis based on different data cases and feature importance rankings indicated that environmental features are important. The random forest outperformed other models, and a better prediction was achieved for planted tree species compared to that for the natural forest. These results suggest that accurately mapping tree species over large mountainous areas is feasible with freely accessible multi-modal data, especially when considering environmental factors.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase.

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

A Big Data-Driven Intelligent Knowledge Discovery Method for Epidemic Spreading Paths

The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data.

Development and validation of a machine learning-based predictive model for secondary post-tonsillectomy hemorrhage.

The main obstacle to a patient's recovery following a tonsillectomy is complications, and bleeding is the most frequent culprit. Predicting post-tonsillectomy hemorrhage (PTH) allows for accurate identification of high-risk populations and the implementation of protective measures. Our study aimed to investigate how well machine learning models predict the risk of PTH. Data were obtained from 520 patients who underwent a tonsillectomy at The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army. The age range of the patients was 2-57 years, and 364 (70%) were male. The prediction models were developed using five machine learning models: decision tree, support vector machine (SVM), extreme gradient boosting (XGBoost), random forest, and logistic regression. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best-performing model. The frequency of PTH was 11.54% among the 520 patients, with 10.71% in the training group and 13.46% in the validation set. Age, BMI, season, smoking, blood type, INR, combined secretory otitis media, combined adenoidectomy, surgical wound, and use of glucocorticoids were selected by mutual information (MI) method. The XGBoost model had best AUC (0.812) and Brier score (0.152). Decision curve analysis (DCA) showed that the model had a high clinical utility. The SHAP method revealed the top 10 variables of MI according to the importance ranking, and the average of the age was recognized as the most important predictor variable. This study built a PTH risk prediction model using machine learning. The XGBoost model is a tool with potential to facilitate population management strategies for PTH.

Abstract HUP4: Svin Mt2020 + Global Mechanical Thrombectomy Access Barrier Score

Introduction: Mechanical thrombectomy (MT) is a highly safe and effective standard of care for acute ischemic strokes with large vessel occlusion. However, timely access to MT is extremely limited on a global scale, with vast disparities in access between countries. MT2020+, a global non-profit initiative of SVIN, aimed to create a semi-quantitative global MT access barrier score (MTABS) to objectively measure the barriers impeding the implementation of treatment worldwide. Methods: Four independent investigators performed an in-depth systematic literature review using the peer review of electronic search strategies. Barriers to access were identified and categorized into 3 groups: information and diagnostic, physical, and financial. An international expert panel was created and scored each attribute using a modified Delphi process with the assistance of consultants from the University of Calgary W21C. A 1-9-point scale was used, with 1 being not at all important and 9 being extremely important. A meeting was held for the attributes that require deliberation. After an agreement, a list of attributes for access was elaborated. Next, a ranking of importance and individual weighting was done. We assigned a presence of or lack of an attribute a numerical value (1 for yes, 0 for no) and multiply by its weight to determine a final score. Results: After an initial screening of 2864 abstracts, 121 studies were included in the final systematic review. A total of 34 possible attributes that are barriers to access were identified. After the modified Delphi process, 26 individual attributes were selected. The MTABS was made with possible results from 0-62 points, with higher scores meaning higher barriers to access to MT. Conclusions: The global MTABS represents the first tool to quantify barriers to MT access worldwide. Its implementation will objectively measure the magnitude and identify key barriers to guide regional public health interventions to improve MT workflow and access.

Abstract TMP43: Svin Mt2020 + Global Mechanical Thrombectomy Access Barrier Score

Introduction: Mechanical thrombectomy (MT) is a highly safe and effective standard of care for acute ischemic strokes with large vessel occlusion. However, timely access to MT is extremely limited on a global scale, with vast disparities in access between countries. MT2020+, a global non-profit initiative of SVIN, aimed to create a semi-quantitative global MT access barrier score (MTABS) to objectively measure the barriers impeding the implementation of treatment worldwide. Methods: Four independent investigators performed an in-depth systematic literature review using the peer review of electronic search strategies. Barriers to access were identified and categorized into 3 groups: information and diagnostic, physical, and financial. An international expert panel was created and scored each attribute using a modified Delphi process with the assistance of consultants from the University of Calgary W21C. A 1-9-point scale was used, with 1 being not at all important and 9 being extremely important. A meeting was held for the attributes that require deliberation. After an agreement, a list of attributes for access was elaborated. Next, a ranking of importance and individual weighting was done. We assigned a presence of or lack of an attribute a numerical value (1 for yes, 0 for no) and multiply by its weight to determine a final score. Results: After an initial screening of 2864 abstracts, 121 studies were included in the final systematic review. A total of 34 possible attributes that are barriers to access were identified. After the modified Delphi process, 26 individual attributes were selected. The MTABS was made with possible results from 0-62 points, with higher scores meaning higher barriers to access to MT. Conclusions: The global MTABS represents the first tool to quantify barriers to MT access worldwide. Its implementation will objectively measure the magnitude and identify key barriers to guide regional public health interventions to improve MT workflow and access.

Prediction of Traffic Accident Severity Based on Random Forest

This paper used the data of automobile traffic accidents from 2018 to 2020 in the Chinese National Automobile Accident In-Depth Investigation System. The prediction features of traffic accident severity are innovated. Four accident features that did not participate in the importance ranking were added: accident location, accident form, road information, and collision speed. Eight accident features (engine capacity, hour of day, age of vehicle, month of year, day of week, age band of drivers, vehicle maneuver, and speed limit) have been used in previous studies. Random forest was used to rank the importance of 12 accident features, and 7 important accident features were finally adopted. By comparing the algorithms and optimizing the results, the prediction model of traffic accident degree with higher accuracy is finally obtained.

Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA

Infodemics are intertwined with the COVID-19 pandemic, affecting people's perception and social order. To curb the spread of COVID-19 related false rumors, fuzzy-set qualitative comparative analysis (fsQCA) is used to find configurational pathways to enhance rumor refutation effectiveness. In this paper, a total of 1,903 COVID-19 related false rumor refutation microblogs on Sina Weibo are collected by a web crawler from January 1, 2022 to April 20, 2022, and 10 main conditions affecting rumor refutation effectiveness index (REI) are identified based on “three rules of epidemics”. To reduce data redundancy, five ensemble machine learning models are established and tuned, among which Light Gradient Boosting Machine (LGBM) regression model has the best performance. Then five core conditions are extracted by feature importance ranking of LGBM. Based on fsQCA with the five core conditions, REI enhancement can be achieved through three different pathway elements configurations solutions: “Highly influential microblogger * high followers’ stickiness microblogger”, “high followers’ stickiness microblogger * highly active microblogger * concise information description” and “high followers’ stickiness microblogger * the sentiment tendency of the topic * concise information description”. Finally, decision-making suggestions for false rumor refutation platforms and new ideas for improving false rumor refutation effectiveness are proposed. The innovation of this paper reflects in exploring the REI enhancement strategy from the perspective of configuration for the first time.

An Empirical Study on Log Level Prediction for Multi-Component Systems

Logging statements are used to trace the execution of a software system. Practitioners leverage different logging information (e.g., the content of a log message) to decide for each logging statement an appropriate log level, which is leveraged to adjust the verbosity of logs so that only important log messages are traced. Deciding for the log level can be done differently from one to another component of a multi-component system, such as OpenStack and its 28 components. For example, a component might aim for increasing the verbosity of its log messages, while another component for the same multi-component system might aim at decreasing such a verbosity. Such different logging strategies can exist since each component can be developed and maintained by a different team. While a prior work leveraged an ordinal regression model to recommend the appropriate log level for a new logging statement, their evaluation did not consider the particularities that each component can have within a multi-component system. For instance, their model might not perform well at each component level of a multi-component system. The same model’s interpretability can mislead the developers of each component that has its unique logging strategy. In this paper, we quantify the impact of the particularities of each component of a multi-component system on the performance and interpretability of the log level prediction model of prior work. We observe that the performance of the log level prediction models that are trained at the whole project level (aka., global models) have lower performances (AUC) on 72% to 100% of the components of our five evaluated multi-component systems, compared to the same models when evaluated on the whole multi-component system. We observe that the models that are trained at the component level (aka., local models) statistically outperform the global model on 33% to 77% of the components of our evaluated multi-component systems. Furthermore, we observe that the rankings of the most important features that are obtained from the global models are statistically different from the feature importance rankings of 50% to 87% of the local models of our evaluated multi-component systems. Finally, we observe that 60% and 35% of the Spring and OpenStack components do not have enough data points to train their own local models (aka., data lacking components). Leveraging a peer-local model for such type of components is more promising than using the global model.