Research on knowledge hiding has largely focused on its antecedents while overlooking its consequences. Drawing on moral cleansing theory, we adopt a “perpetrator-centric view” and posit that employees who engage in playing dumb and evasive hiding–two specific knowledge hiding behaviors that involve deception–will subsequently perform more organizational citizenship behavior directed toward individuals (OCB-I) because they perceive a loss of moral credits following their moral transgression. Further, we propose that the indirect effects are contingent on perpetrators’ moral identity internalization. We tested (...) our hypotheses using a time-lagged research design with a sample of 362 respondents from a large pharmaceutical group company. Consistent with our hypotheses, we found that employees who engaged in playing dumb and evasive hiding subsequently exhibited more OCB-I as they perceived a loss of moral credits, whereas employees who engaged in rationalized hiding did not. In addition, the positive relationships between playing dumb and evasive hiding with perceived loss of moral credits were stronger when perpetrators had high moral identity internalization, as were the indirect effects of playing dumb and evasive hiding on OCB-I via perceived loss of moral credits. Our research contributes to the understanding of when and how engaging in knowledge hiding affects perpetrators and their compensatory behaviors toward coworkers. (shrink)

Fourth-order autonomous nonlinear differential equations can exhibit chaotic properties. In this study, we propose a family of fourth-order chaotic systems with infinite equilibrium points whose equilibria form closed curves of different shapes. First, the phase diagrams and Lyapunov exponents of the system family are simulated. The results show that the system family has complex phase diagrams and dynamic behaviors. Simulation analysis of the Poincarè mapping and bifurcation diagrams shows that the system has chaotic characteristics. The circuit simulation model is constructed (...) and simulated in Multisim. The circuit simulation results coincide with the numerical simulation results, which verifies the circuit feasibility of the system. Then, based on Lyapunov stability theory and the adaptive control method, the synchronous control of the system with infinite equilibria is designed. Numerical simulation results verify that the system synchronization with the adaptive control method is well. Finally, the synchronous drive system is used for image encryption, the response system is used for decryption, and color image encryption is realized by combining deoxyribonucleic acid coding and operating rules. Therefore, this study not only enriched the research on infinite equilibria chaotic systems but also further expanded secure communication technology by combining chaotic synchronization control and DNA coding in image encryption. (shrink)

Identifying important entities in software systems has many implications for effective resource allocation. Complex network research opens new opportunities for identifying important entities from software networks. However, the existing methods only focus on identifying important classes. Little work has been done on the identification of important packages. Moreover, the metrics they used to quantify the class importance are only designed for unweighted software networks and cannot fit in with the weighted software networks. To overcome these limitations, in this article, we (...) introduce the weighted k-core decomposition method to identify the important packages. First, we use a weighted software network to describe packages and their internal dependencies. Second, we use Wk-core to partition a software network into a layered structure. Then, the packages that are denoted by the nodes within the main core are the identified important packages. To evaluate our method, we use a variant of the susceptible–infectious–recovered model to examine the spreading influence of the nodes in six real weighted software networks. The results show that our method can well identify influential nodes, better than other four methods. Furthermore, we demonstrate our method on two software networks and show that the important packages identified by our method are more meaningful from a software engineering perspective when compared with the other methods. (shrink)

The original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, (...) our approach uses a bipartite network to represent classes, features, and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach, and two community detection algorithms, namely a graph theoretic clustering algorithm and a fast algorithm for community detection. Our approach provides a new way to do refactoring from the perspective of software structure. (shrink)

For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic control, and many other fields, is of great importance. By taking the effect of the spreading rate on information entropy into account, we proposed an improved information entropy method. Compared to the benchmark methods in the six different empirical networks, the IIE method has been found with a better performance on Kendall’s Tau and imprecision function under (...) the Susceptible Infected Recovered model. Especially in the Facebook network, Kendall’s Tau can grow by 120% as compared with the original IE method. And, there is also an equally good performance in the comparative analysis of imprecise functions. The imprecise functions’ value of the IIE method is smaller than the benchmark methods in six networks. (shrink)