scholarly journals Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7401
Author(s):  
Abdulaziz Alhumam
Keyword(s):  
Critical Role ◽  
Fault Localization ◽  
Rank Aggregation ◽  
Fault Prediction ◽  
Rank Test ◽  
Features Selection ◽  
Dynamic Features ◽  
Complex Network Theory ◽  
Rank List ◽  
Proposed Model

The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.

scholarly journals Graph contextualized attention network for predicting synthetic lethality in human cancers

2021 ◽  
Author(s):  
Yahui Long ◽  
Min Wu ◽  
Yong Liu ◽  
Jie Zheng ◽  
Chee Keong Kwoh ◽  
...  
Keyword(s):  
Synthetic Lethality ◽  
Critical Role ◽  
Design Feature ◽  
Cost Effective ◽  
Attention Network ◽  
New Genes ◽  
Lab Experiments ◽  
Proposed Model ◽  
Multiple Feature ◽  
Wet Lab

Abstract Motivation Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners. Results In this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrated that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validated the effectiveness of our proposed model in identifying novel SL pairs. Availability Python codes and dataset are freely available on GitHub (https://github.com/longyahui/GCATSL) and Zenodo (https://zenodo.org/record/4522679) under the MIT license.

Investigation of the relationships among educational application (APP) quality, computer anxiety and student engagement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jung-Chieh Lee ◽  
Liang Nan Xiong
Keyword(s):  
Student Engagement ◽  
Computer Anxiety ◽  
Critical Role ◽  
System Quality ◽  
Is Success ◽  
Content Type ◽  
Mediating Role ◽  
Proposed Model ◽  
Traditional Classroom Teaching ◽  
Educational Applications

PurposeNumerous educational applications (APP) have been developed to assist traditional classroom teaching and student learning. APP quality plays a critical role in influencing students' learning behaviors. However, the role negative mindsets, especially computer anxiety, play in how APP quality affects student engagement remains unknown. To address the relationships among APP quality, computer anxiety and student engagement in an APP-based learning environment, we developed an extended information system (IS) success model that includes interface and instructor quality.Design/methodology/approachTo empirically test the proposed model, we conducted a survey with a sample of 225 university students and examined the hypotheses using the partial least squares (PLS) method.FindingsComputer anxiety was demonstrated to fully mediate the relationships between student engagement and interface quality and service quality and system quality. In addition, the instructor quality acts as a partial mediator of the relationship between computer anxiety and student engagement.Originality/valueThis study reveals the important mediating role of computer anxiety in APP-assisted learning and the special status of instructor quality and user experience in influencing student engagement. The findings of this study shed meaningful light on the practical implications for instructors and APP software developers.

The Finnish Climate Change Act: Architecture, Functions, and Challenges

Climate Law ◽  
2014 ◽  
Vol 4 (3-4) ◽  
pp. 301-326 ◽  
Author(s):  
Ismo Pölönen
Keyword(s):  
Climate Change ◽  
Climate Policy ◽  
Policy Making ◽  
Dynamic Features ◽  
Policy Choices ◽  
Legal Implications ◽  
Proposed Model ◽  
Term Energy ◽  
Public Scrutiny ◽  
The Cost

The article examines the key features and functions of the proposed Finnish Climate Change Act (fcca). It also analyses the legal implications of the Act and the qualities and factors which may limit its effectiveness. The paper argues that, despite its weak legal implications, the fcca would provide the regulatory preconditions for higher-quality climate policy-making in Finland, and it has the capacity to play an important role in national climate policy. The fcca would deliver regulatory foundations for systematic and integrated climate policy-making, also enabling wide public scrutiny. The proposed model leaves room for manifold climate-policy choices in varying societal and economical contexts. The cost of dynamic features is the relalow predictability in terms of sectorial paths on emission reductions. Another relevant challenge relates to the intended preparation of overlapping mid-term energy and climate plans with instruments of the fcca.

Strategic Human Resource Development Alignment

2016 ◽  
pp. 85-100
Author(s):  
Ann Herd ◽  
Meera Alagaraja
Keyword(s):  
Human Resource Development ◽  
Human Resource ◽  
Critical Role ◽  
Resource Development ◽  
Future Research ◽  
Global Marketplace ◽  
Strategic Objectives ◽  
Organizational Learning Culture ◽  
Proposed Model ◽  
And Performance

The critical role of human resource development (HRD) in helping organizations identify and meet their strategic objectives in today's competitive and ever-changing global marketplace is increasingly being recognized by both scholars and practitioners. While many HRD scholars have examined the importance of HRD alignment with the organization's strategic objectives, there exist few conceptualizations of this alignment from the employee's perspective. Drawing on strategic HRD and management “line of sight” literature, the purpose of this chapter is to explore the theoretical conceptualization and a proposed model of employee perceptions of the strategic alignment of HRD in their organizations. Strategic HRD alignment from the employee's perspective is explored, and future research directions are discussed, in relation to strategic HRD, organizational learning culture, perceived investment in employee development, and performance-related outcomes for which HRD scholar-practitioners strive in their quest to facilitate organizational strategic objectives.

A Framework for Homogeneous Cross-Project Defect Prediction

2021 ◽  
Vol 9 (1) ◽  
pp. 52-68
Author(s):  
Lipika Goel ◽  
Mayank Sharma ◽  
Sunil Kumar Khatri ◽  
D. Damodaran
Keyword(s):  
Class Imbalance ◽  
The Other ◽  
Training Data ◽  
Defect Prediction ◽  
Rank Test ◽  
Ensemble Model ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Proposed Model ◽  
Cross Project

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.

scholarly journals Structural and functional analyses of the N-terminal domain of the A subunit of aBacillus megateriumspore germinant receptor

2019 ◽  
Vol 116 (23) ◽  
pp. 11470-11479 ◽  
Author(s):  
Yunfeng Li ◽  
Kai Jin ◽  
Abigail Perez-Valdespino ◽  
Kyle Federkiewicz ◽  
Andrew Davis ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Abc Transporters ◽  
Spore Germination ◽  
Critical Role ◽  
Chemical Shift Perturbation ◽  
Terminal Domain ◽  
Proposed Model ◽  
Strong Homology ◽  
A Subunit ◽  
Functional Analyses

Germination ofBacillusspores is induced by the interaction of specific nutrient molecules with germinant receptors (GRs) localized in the spore’s inner membrane. GRs typically consist of three subunits referred to as A, B, and C, although functions of individual subunits are not known. Here we present the crystal structure of the N-terminal domain (NTD) of the A subunit of theBacillus megateriumGerK3GR, revealing two distinct globular subdomains bisected by a cleft, a fold with strong homology to substrate-binding proteins in bacterial ABC transporters. Molecular docking, chemical shift perturbation measurement, and mutagenesis coupled with spore germination analyses support a proposed model that the interface between the two subdomains in the NTD of GR A subunits serves as the germinant binding site and plays a critical role in spore germination. Our findings provide a conceptual framework for understanding the germinant recruitment mechanism by which GRs trigger spore germination.

scholarly journals A Computational Framework for Exploring the Socio-Cognitive Features of Teams and their Influence on Design Outcomes

2019 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Harshika Singh ◽  
Gaetano Cascini ◽  
Hernan Casakin ◽  
Vishal Singh
Keyword(s):  
Size Structure ◽  
Critical Role ◽  
Computational Framework ◽  
New Members ◽  
Learning Abilities ◽  
Team Members ◽  
Agent Based ◽  
Proposed Model ◽  
Dependent Parameter ◽  
The Impact

AbstractThe dynamics of design teams play a critical role in product development, mainly in the early phases of the process. This paper presents a conceptual framework of a computational model about how cognitive and social features of a design team affect the quality of the produced design outcomes. The framework is based on various cognitive and social theories grounded in literature. Agent-Based Modelling (ABM) is used as a tool to evaluate the impact of design process organization and team dynamics on the design outcome. The model describes key research parameters, including dependent, independent, and intermediates. The independent parameters include: duration of a session, number of times a session is repeated, design task and team characteristics such as size, structure, old and new members. Intermediates include: features of team members (experience, learning abilities, and importance in the team) and social influence. The dependent parameter is the task outcome, represented by creativity and accuracy. The paper aims at laying the computational foundations for validating the proposed model in the future.

scholarly journals Impact of Gene Polymorphisms in GAS5 on Urothelial Cell Carcinoma Development and Clinical Characteristics

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 260 ◽  
Author(s):  
Wei-Chun Weng ◽  
Chih-Jung Chen ◽  
Pei-Ni Chen ◽  
Shian-Shiang Wang ◽  
Ming-Ju Hsieh ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Tumor Size ◽  
Noncoding Rna ◽  
Cohort Analysis ◽  
Critical Role ◽  
Growth Arrest ◽  
Urothelial Cell ◽  
Rank Test ◽  
Urothelial Cell Carcinoma ◽  
Female Patients

Urothelial cell carcinoma (UCC) is the commonest malignant tumor of the urinary tract and the second most common kidney cancer malignancy. Growth arrest-specific 5 (GAS5), a long noncoding RNA, is encoded by the GAS5 gene and plays a critical role in cellular growth arrest and apoptosis. In the current study, two single nucleotide polymorphisms (SNPs) in the GAS5 gene, rs145204276 and rs55829688, were selected to investigate correlations between these single SNPs and susceptibility to UCC. A total of 430 UCC cases and 860 ethnically matched healthy controls were included. SNP rs145204276 and SNP rs55829688 were determined using a TaqMan genotyping assay. Logistic regression models demonstrated that female patients with UCC carrying the rs145204276 GAS5 Ins/Del or Del/Del genotype had a 3.037-fold higher risk of larger tumor status (95% confidence interval 1.259–7.324) than did rs145204276 wild type (Ins/Ins) carriers (p  =  0.011). The Cancer Genome Atlas validation cohort analysis demonstrated that the expression of GAS5 in female patients with bladder urothelial carcinoma (BLCA) with larger tumor size was much lower than that in patients with a smaller tumor size (p = 0.041). Kaplan-Meier curve analysis and the log–rank test revealed that female patients with BLCA and lower GAS5 expression had poorer overall survival than those with higher GAS5 expression. In conclusion, genetic variations in GAS5 rs145204276 may serve as a critical predictor of the clinical status of female patients with UCC.

Is the oxygen atom static or dynamic? The effect of generating animations on students' mental models of atomic structure

2016 ◽  
Vol 17 (4) ◽  
pp. 788-807 ◽  
Author(s):  
Sevil Akaygun
Keyword(s):  
High School Students ◽  
Oxygen Atom ◽  
Mental Models ◽  
Atomic Structure ◽  
Chemistry Education ◽  
Rank Test ◽  
Dynamic Features ◽  
School Students ◽  
Significant Difference ◽  
Software Programs

Visualizing the chemical structure and dynamics of particles has been challenging for many students; therefore, various visualizations and tools have been used in chemistry education. For science educators, it has been important to understand how students visualize and represent particular phenomena –i.e., their mental models– to design more effective learning environments. This study aimed to investigate and compare students'staticand dynamic representations of mental models for a fundamental concept of chemistry, atomic structure. Static representations of mental models were expressed as drawings and explanations given on paper, withdynamicones being generated by using animation-developing software. This mixed-method study was implemented in three parts. A total of 523 10th (N= 277) and 11th (246) grade high school students participated in a workshop where they first learned how to use one of three animation-developing software programs (K-Sketch, Chemsense or Pencil;N= 162, 204, 157, respectively), and then prepared an animation of an oxygen atom using that program. Before and after creating the animation, students were asked to draw the structure of the atom and to storyboard the oxygen atom for three seconds. After students generated their animations they were asked to explain their animations in 2–3 minute interviews (N= 324). The static and dynamic representations of mental models were compared statistically by the Wilcoxon Signed Rank Test within each group, and they were compared by the Kruskall Wallis Test between the groups. The results of the analysis showed that in all the groups, a significant difference (p= 0.000) between the initial and final static representations of mental models suggested that students modified their mental models towards a more refined and accurate representation of the atomic structure. Regardless of the software program used, students included significantly more dynamic features (p= 0.000) in their static representations of mental models after generating animations than they did initially. No significant difference (p> 0.05) between any of the features was conveyed in static representations of mental models of students who worked with different software programs. In addition, student-generated animations revealed some misconceptions, such as the movement of the parts of the atom or the atom itself besides electrons, which were not detected on paper.

scholarly journals Cine MR-Based Radiomics to Predict Myocardial Segments With Infarction

2021 ◽  
Author(s):  
Yongjia Peng ◽  
Yan Wang ◽  
Kongyang Wu ◽  
Yan Luo ◽  
Jing Liu ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Pearson Correlation ◽  
Statistical Significance ◽  
Univariate Analysis ◽  
Rank Test ◽  
Good Prediction ◽  
Features Selection ◽  
Cine Mr ◽  
Radiomics Signature ◽  
Cine Cmr

Abstract Background: Although myocardial infarction (MI) can be assessed quantitatively and qualitatively by using late gadolinium-enhanced (LGE) cardiovascular magnetic resonance (CMR) imaging, intravenous administration of gadolinium can expose patients to high risk of nephrogenic systemic fibrosis, especially in those with cardiovascular diseases. The purpose of this study is to harness cine CMR-based radiomics for predicting MI without introducing gadolinium.Methods: In this retrospective study, we included 48 patients with acute myocardial infarction (AMI) confirmed by later gadolinium enhancement (LGE) at CMR. CMR examinations were performed within 2 to 6 days after PCI. According to the LGE, each myocardial segment was dichotomized into with and without MI. Radiomic features of myocardial segments were extracted from cine CMR images and the myocardial segments were divided into training and validation sets randomly at a ratio of 0.7:0.3. Pearson correlation and Mann-Whitney U rank test were used to eliminate redundant and irrelevant features. A least absolute shrinkage and selection operator (LASSO) algorithm was used for features selection in the training set. Radiomic signatures were constructed in both the training and validation sets and its predictive performance was assessed using area under the cure of receiver operating characteristic (AUC-ROC).Results：Of 768 myocardial segments in the 48 patients, there were 291 (38%) segments with MI and 477 (62%) segments without MI. After univariate analysis, there were 22 RFs related to MI with statistical significance. LASSO regression selected 18 RFs for radiomics signature builting. AUC-ROC of radiomic signatures in prediction of segments with MI was 0.74(95% CI:0.69-0.78)and 0.68 (95%CI: 0.60-0.75) in the training and validation sets, respectively. The difference was not statistically significant (p=0.14).Conclusion: Cine MR-based radiomics signature can achieve a good prediction performance for MI, which showed the potential to be a promising imaging biomarker for MI without the administration of contrast agent.

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