Summarizing source code through heterogeneous feature fusion and extraction

Code summarization, which seeks to automatically produce a succinct natural-language description to summarize the functionality of source code, plays an essential role in maintaining the software. Currently, plentiful approaches have been proposed to first encode the source code based on its Abstract Syntax Tree (AST), and then decode it into a textual summary. However, most existing works interpret the AST-based syntax structure as a homogeneous graph, without discriminating the different relations between graph nodes (e.g., the parent–child and sibling relations) in a heterogeneous way. To mitigate this issue, this paper proposes HETCOS to extract the syntactic and sequential features of source code by exploring its inherent heterogeneity for code summarization. Specifically, we first build a Heterogeneous Code Graph (HCG) that fuses the syntax structure and code sequence with eight types of edges/relations designed between graph nodes. Moreover, we present a heterogeneous graph neural network for capturing the diverse relations in HCG. The represented HCG is then fed into a Transformer decoder, followed by a multi-head attention-based copying mechanism to support high-quality summary generation. Extensive experiments on the major Java and Python datasets illustrate the superiority of our approach over sixteen state-of-the-art baselines. To promote reproducibility studies, we make the implementation of HETCOS publicly accessible at https://github.com/GJCEXP/HETCOS.</p

Social risk factors of transportation PPP projects in China: A sustainable development perspective

Public-private partnerships (PPPs) have become increasingly important in improving the sustainability of society in China, with transportation being the largest investment area. However, the Social Risk Factors (SRFs) of transportation PPPs in China, which serve as a useful tool for distinguishing strengths and weaknesses for effective social risk management (SRM), have not been clearly identified. A conceptual model including 3 risk dimensions and 15 SRFs was proposed to mitigate social risks and improve the social sustainability of transportation PPP projects. A questionnaire survey conducted to investigate stakeholders’ opinions on the proposed SRFs demonstrated that all the SRFs were important. The SRFs can be used to evaluate social risks from economic, environmental, and social dimensions. Confirmatory factor analysis (CFA) verified the classification of the SRFs and indicated that all the risk dimensions contributed to social risks. The social and environmental impacts on social sustainability may contribute more to the generation of social risks. Furthermore, the concept of people-first PPPs was proposed to reduce social risks from the perspective of different stakeholders, with the interactions among different stakeholders being prioritized. The identified SRFs and their relationships can improve our understanding of SRM in the delivery of social sustainability and improve social resilience

Providing prediction reliability through deep neural networks for recommender systems

Deep learning-based recommendation approaches have shown significant improvement in the accuracy of recommender systems (RSs). However, beyond accuracy, reliability measures are gaining attention to evaluate the validity of predictions and enhance user satisfaction. Such measures can ensure that the recommended items are high-scoring items with high reliability. To integrate the native concept of reliability into a deep learning model, this paper proposes a deep neural network-based recommendation framework with prediction reliability. This framework filters out unreliable prediction ratings according to a pre-defined reliability threshold, ensuring the credibility and reliability of top-N recommendation. The proposed framework relies solely on user ratings for reliability, making it highly generalizable and scalable. Additionally, we design a data pre-processing method to address the issue of uneven distribution of ratings before model training, which effectively improves the effectiveness and fairness. The experiments on four benchmark datasets demonstrate that the proposed scheme is superior to other comparison methods in evaluation metrics. Furthermore, our framework performs better on sparse datasets than on dense datasets, indicating its ability to make strong predictions even with insufficient information

Social risk factors of transportation PPP projects in China: A sustainable development perspective

Public-private partnerships (PPPs) have become increasingly important in improving the sustainability of society in China, with transportation being the largest investment area. However, the Social Risk Factors (SRFs) of transportation PPPs in China, which serve as a useful tool for distinguishing strengths and weaknesses for effective social risk management (SRM), have not been clearly identified. A conceptual model including 3 risk dimensions and 15 SRFs was proposed to mitigate social risks and improve the social sustainability of transportation PPP projects. A questionnaire survey conducted to investigate stakeholders’ opinions on the proposed SRFs demonstrated that all the SRFs were important. The SRFs can be used to evaluate social risks from economic, environmental, and social dimensions. Confirmatory factor analysis (CFA) verified the classification of the SRFs and indicated that all the risk dimensions contributed to social risks. The social and environmental impacts on social sustainability may contribute more to the generation of social risks. Furthermore, the concept of people-first PPPs was proposed to reduce social risks from the perspective of different stakeholders, with the interactions among different stakeholders being prioritized. The identified SRFs and their relationships can improve our understanding of SRM in the delivery of social sustainability and improve social resilience

School autonomy policies lead to increases in principal autonomy and job satisfaction

School autonomy policies lead to increases in principal autonomy and job satisfactio

An Empirical Study of Dynamic Triobjective Optimisation Problems

Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics. © 2018 IEEE.</p

Sustained high-altitude hypoxia increases cerebral oxygen metabolism (vol 114, pg 11, 2013)

Sustained high-altitude hypoxia increases cerebral oxygen metabolism (vol 114, pg 11, 2013

Mechanical behavior and deformation mechanisms of AZ31 Mg alloy at liquid nitrogen temperature

The mechanical behavior and deformation mechanisms of AZ31 magnesium alloy at liquid nitrogen temperature were investigated by compressing samples with different original textures to fracture at liquid nitrogen and ambient temperatures with different constant true strain rates. The results showed that the samples compressed at liquid nitrogen temperature exhibit higher strength especially when compression axis parallel to extrusion direction. Mechanical anisotropy is more remarkable during compressing at liquid nitrogen temperature. Twinning tends to be inhibited at liquid nitrogen temperature, regardless of original orientation and strain rate. The two main deformation mechanisms at liquid nitrogen temperature are twinning in several certain grains and grain rotation. © 2013 Elsevier B.V

Rail rolling contact fatigue formation and evolution with surface defects

Surface defects can induce serious rolling contact fatigue (RCF) damage at wheel/rail interfaces and even cause fracture failure of rail material. This study aims to explore the formation mechanism of surface defects on rails, and to trace the evolution process of RCF behavior of material around the surface defect. Experimental studies were conducted on a wheel/rail twin-disc machine considering two forms of defects: indentation defects caused by ballast impacts (IDBs) and indentation defects caused by cone penetration head impacts (IDCs). Results indicate that IDB can cause RCF cracks that propagate downward deep into the subsurface of rail due to the formation of a material hardening layer (MHL), causing severe damage. IDCs with different sizes and angles were grouped into an affected group and a non-affected group by considering a critical size dividing line and whether the MHLs existed on the defect surface or not. The evolution process of a crack in the affected group includes four main periods: fracture of the MHL, crack initiation, the rail steel matrix filling up the MHL gap and crack propagation downward. Further, the increase in both the angle and the depth of the IDC would lead to severe RCF damage