Search results

This paper aims to propose a set of metamodels applicable to the architecture modeling of air traffic management systems|air traffic management system (ATMS) under the UAF methodology. The designing of metamodels also needs to meet modeling requirements for the introduction of new supersonic airliners into the ATMS.

In order to complete the designing of metamodels and the architecture modeling work in the case study, the GOPPRR method and the M0–M3 modeling framework are used in this paper. The design and modeling work carried out in this paper was done in the multi-architecture modeling tool Airdraw.

In this paper, the set of metamodels applicable to the architecture modeling of ATMS under the UAF methodology was proposed, which has a quantity of 102 object metamodels, eight point metamodels, 98 property metamodels, 41 relationship metamodels, 36 role metamodels and 65 graph metamodels.

The metamodel design proposed in this thesis allows for architectural modeling of the ATMS. Comparing with the traditional method of system engineering, which uses files to define, the model-based ATMS architecture can be updated for different ATMS and different aircraft types by modifying the parameters of the corresponding views or adding relevant supplementary model views in the architecture model library, which greatly improves the compatibility and modifiability of the system definition.

The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

In this chapter, we estimate the Expected Shortfall (ES) in conditional autoregressive expectile models by using a nonparametric multiple expectile regression via gradient tree boosting. This approach has the advantages generated by the flexibility of not having to rely on data assumptions and avoids the drawbacks and fragilities of a restrictive estimator such as Historical Simulation. We consider distinct specifications for the information sets that produce the ES estimates. The results obtained with simulated and real market data indicate that the proposed approach has good performance, with some distinctions between the specifications.

We study the potential effects of pension underfunding on corporate investment, financial constraints and improved employee bonding using 10 Pacific-Basin countries (including the United States, Australia, and eight Asian countries) at heterogeneous economic development stages and different regulatory environments. We document that corporate pensions are significantly underfunded in most countries of our sample in the period of 2001–2017, when interest rates were ultralow in most countries. In addition, firms from countries with stronger employee protection and more generous retirement benefits tend to show higher levels of underfunding in their defined benefit (DB) pension plans. To the extent of pension underfunding imposing constraints on corporate investment, we find that firms in these countries can face more constraints on investment when their pension is underfunded.

In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs.

This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions.

There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions.

The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.

Paying to view others' answers is a new mode for question and answer (Q&A) platforms. The purpose is to build a model to explore the determinants of the number of listeners and further explore certain meaningful characteristics of the model in the context of different types of questions and answerers.

The authors develop an empirical model and use real panel data to test the hypothesis. Specifically, cues from the answerer and from the question elicit the listener's trust in the answerer (including direct and indirect trust) and perceived value in the question (including intrinsic and extrinsic attributes), respectively.

The authors find that cues from answerers (experience for paid Q&As and popularity for free Q&As) and questions (length, sentence structure, value and number of likes) all have positive effects on the number of listeners. The impact of answerer authentication is more significant than the popularity of free Q&As. Moreover, the length of the question matters only for subjective questions, while sentence structure matters only for objective questions. In addition, the answerer's own attributes and the behavior and feedback of others have greater impacts when the answerer is below average in popularity.

The authors summarize the unique features of the mode of paying to view others' answers in contrast with the traditional mode of paid Q&As. In addition, the authors focus on the characteristics of the question (including the subjectivity and the sentence structure of the question), a topic which has not been studied previously. Our research provides a reference for exploring user behavior patterns. The practical implications for knowledge platforms are also concretely described.

The purpose of this paper is to investigate the influence of trust and perceived risk on investment willingness considering the bidirectional relationship between trust and perceived risk in peer-to-peer (P2P) lending.

Data were collected from a leading Chinese P2P platform, PPDAI.com. In total, 328 valid responses were received and analyzed using structural equation modeling (SEM).

The results show that the influence of trust on investment willingness is significant, whereas that of perceived risk is insignificant. The results also indicate that platform reputation has a positive effect on trust, and the quality of alternatives is positively associated with perceived risk. In addition, the bidirectional perspective should be preferred to cope with the bidirectional relationship between trust and perceived risk in P2P lending.

This study extends existing research on the influence of trust and perceived risk on investment willingness from a bidirectional perspective, which has not been addressed in the P2P lending context. In addition, this research enriches the current literature about trust and perceived risk by providing more evidence that the relationship between trust and perceived risk is bidirectional and thus the bidirectional model should be preferred. For practice, the study suggests that managers can earn trust and reduce the perceived risk of lenders by continuously providing high-quality products, services and enhancing platform reputation, ultimately improving their investment willingness.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

Building on the person–supervisor fit theory, this paper examines how and when leader–follower moqi congruence positively impacts task performance.

With data collected from 174 leader–follower dyads in 41 project teams in Shanghai, China, the authors use polynomial regression and response surface plots to test the hypotheses on the effects of leader–follower moqi congruence.

Leader–follower moqi congruence positively affects followers' task performance, mediated by coordination. Task coordination was of higher quality when the congruence is achieved at a high level of moqi than at a low level. The effect of leader–follower moqi congruence on task performance (mediated by coordination) was weaker when leader-member exchange was low than when it was high.

This study identifies why leader–follower moqi can improve coordination and task performance. It extends person–supervisor fit theory and is an enhancement for moqi research and practice.

Flexible automation robotic systems and off‐line programming methods have recently received much attention. Studies the problem of robot auto‐marking and auto‐cutting of shipbuilding panels, using an integrated computer aided design/manufacturing (CAD/CAM) system based on computer technology and off‐line programming of the robot. The following three points are focused on in this paper: marking and cutting information of the panel’s CAD model; measurement of the panel’s deformation and its compensation algorithm; robot auto‐making and auto‐cutting of the panel using the CAM system. Robot auto‐marking and auto‐cutting of shipbuilding panels solves the difficulty associated with panel marking and cutting by hand. Furthermore this system possesses high processing precision and automatically compensates for the deformation of the panel. Our experiments prove the feasibility and efficiency of this system at the end of this paper.