A distributed problem‐solving framework for probabilistic software effort estimation

Distributed problem‐solving (DPS) systems use a framework of human organizational notions and principles of intelligent systems to solve complex problems. Human organizational notions are used to decompose a complex problem into sub‐problems that can be solved using intelligent systems. The solutions of these sub‐problems are combined to solve the original complex problem. In this paper, we propose a DPS system for probabilistic estimation of software development effort. Using a real‐world software engineering dataset, we compare the performance of the DPS system with a neural network (NN) and show that the performance of the DPS system is equal to or better than that of the NN with the additional benefits of modularity, probabilistic estimates, greater interpretability, flexibility and capability to handle incomplete input data.     

Preliminary cost estimates for thin-film transistor liquid–crystal display inspection and repair equipment: A hybrid hierarchical approach

The thin-film transistor liquid–crystal display (TFT-LCD) industry has developed rapidly in recent years. Because TFT-LCD manufacturing is highly complex and requires different tools for different products, accurately estimating the cost of manufacturing TFT-LCD equipment is essential. Conventional cost estimation models include linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent evidence that a hierarchical structure outperforms a flat structure, this study proposes a hierarchical classification and regression (HCR) approach for improving the accuracy of cost predictions for TFT-LCD inspection and repair equipment. Specifically, first-level analyses by HCR classify new unknown cases into specific classes. The cases are then inputted into the corresponding prediction models for the final output. In this study, experimental results based on a real world dataset containing data for TFT-LCD equipment development projects performed by a leading Taiwan provider show that three prediction models based on HCR approach are generally comparable or better than three conventional flat models (LR, ANN, and SVR) in terms of prediction accuracy. In particular, the 4-class and 5-class support vector machines in the first-level HCR combined with individual SVR obtain the lowest root mean square error (RMSE) and mean average percentage error (MAPE) rates, respectively.     

Recovery of online service: Perceived justice and transaction frequency

Recovery of online service is an issue in need of study. The current study empirically examines (1) the relationships among perceived justice, satisfaction with recovery of online service and repurchase intention of online service/failure encounters; and (2) the moderating effects of transaction frequency on these relationships. The current study collects 187 self-administered questionnaires to gather customers’ perceptions of actual online service/failure encounters. Research findings demonstrate that distributive justice, interactional justice and procedural justice can positively lead to satisfaction with recovery of online service and repurchase intention toward online service. These results further show that customers with low transaction frequency tend to focus more on interactional justice to establish their satisfaction with recovery of online service. On the other hand, customers with high transaction frequency focus more on procedural justice.     

A non-monotone inexact regularized smoothing Newton method for solving nonlinear complementarity problems

In the paper [S.P. Rui and C.X. Xu, A smoothing inexact Newton method for nonlinear complementarity problems, J. Comput. Appl. Math. 233 (2010), pp. 2332–2338], the authors proposed an inexact smoothing Newton method for nonlinear complementarity problems (NCP) with the assumption that F is a uniform P function. In this paper, we present a non-monotone inexact regularized smoothing Newton method for solving the NCP which is based on Fischer–Burmeister smoothing function. We show that the proposed algorithm is globally convergent and has a locally superlinear convergence rate under the weaker condition that F is a P ₀ function and the solution of NCP is non-empty and bounded. Numerical results are also reported for the test problems, which show the effectiveness of the proposed algorithm.     

Stability analysis in a second-order differential equation with delays

A second-order differential equation with finite discrete delays is considered. Local stability of the zero equilibrium is investigated, and we obtain some sufficient conditions for the zero equilibrium is stable or unstable. Moreover, it is found that there exist the local Hopf bifurcations of the system when the delay varies.     

Image reconstruction from an efficient number of moment-based projections

This paper presents a new technique for computed tomography that is based on moment reconstruction. The proposed technique employs the Fourier and Haar coefficients for spectral and spatial moment-based image analyses, respectively. It provides a new approach to the problem of tomographic image reconstruction, where an X-ray image is obtained from a set of line projections. The experimental evaluation includes reconstructions of standard tomographic images in the presence of blur, caused by uniform linear motions. The results lead to the conclusion that the proposed method is more selective, efficient and robust. Another issue considered is the noise associated with normal transmission.     

The new neighbourhood in the internet era: network communities serving local communities

The past two decades have witnessed many attempts to transform online communities in new neighbourhoods of the Internet era. In particular, one of the most interesting applications of Internet Technologies in this field have been ‘network communities’, that differ from online communities because they refer to a specific territory and, for this reason, serve as a social catalyst for the corresponding territorial community. Network communities, as virtual neighbourhoods, have the purpose of allowing a better understanding of physical ones, contributing to the creation and the proliferation of services most suited to the needs of residents. For this reason, municipalities and local governments should consider the opportunity to exploit network communities as useful tools for local community management. Following this lead, this article analyses a real case study and highlights the existence of a positive correlation between a constructive utilisation of a network community by its members, their sense of community and the degree of their involvement in local problems.     

Developing an appropriate design of blended learning with web-enabled self-regulated learning to enhance students' learning and thoughts regarding online learning

The vocational schools in Taiwan regard professional certifications as a badge of skills achievement. The teaching in this context usually focuses on how to help students enhance their professional skills and pass the certificate examinations, particularly for computing courses. However, due to national education policy, pure online courses are not permitted here and in some other nations. In order to provide an appropriate design and arrangement of blended learning (BL) courses, the authors redesigned a course, integrating web-enabled self-regulated learning (SRL) with variations in online class frequency, and explored their effects on enhancing students' skills of deploying database management system (DBMS) and their thoughts regarding blended course and interventions concerning SRL. Three class sections with a total of 112 students were taken as three distinct groups. The results indicated that students in the group of SRL and BL with five online classes had the highest grades for using DBMS among the three groups, and had very positive thoughts regarding the interventions concerning BL and SRL. The authors also provide suggestions and implications for teachers and schools to adopt innovative teaching methods and technologies, and redesign their courses to help students learn.     

A multi-start iterated local search with tabu list and path relinking for the two-echelon location-routing problem

The two-echelon location-routing problem (LRP-2E) is raised by the design of transportation networks with two types of trips: first-level trips serving from one main depot a set of satellite depots, to be located, and second-level trips supplying customers from these satellites. In the proposed multi-start iterated local search (MS-ILS), three greedy randomized heuristics are used cyclically to get initial solutions. Each ILS run alternates between two search spaces: LRP-2E solutions, and travelling salesman (TSP) tours covering the main depot and the customers. The number of iterations allotted to a run is reduced whenever a known solution (stored in a tabu list) is revisited. MS-ILS can be reinforced by a path-relinking procedure (PR), used internally for intensification, as post-optimization, or both. On two sets with 24 and 30 LRP-2E instances, MS-ILS outperforms on average two GRASP algorithms and adding PR brings a further improvement. Our metaheuristic also surpasses a tabu search on 30 instances for a more general problem with several main depots. It is still effective on a particular case, the capacitated location-routing problem (CLRP): In a comparison with four published metaheuristics, only one (LRGTS, Prins et al., 2007) does better.     

基于字节频度的异常入侵检测影响因素研究

目前数据包负载异常检测缺乏针对性的测试数据。为此,构建一个模拟网络数据集,对基于字节频度分布的异常检测模型进行测试分析。实验结果表明,该数据集对模型测试具有可行性;数据包大小的分布特性对检测准确度有较大影响,必须根据特定网络服务数据包尺寸的密集分布区确定检测阈值,并尽量向小尺寸方向校准;数据包之间的频度差异对分组求频度平均值的模型有很大影响,组内数据包之间过大的频度差异将导致包模型失效,连接模型性能降低较大,改进的包模型则不受影响。