The mediation between participative leadership and employee exploratory innovation: Examining intermediate knowledge mechanisms

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.We examine mediation effects of coworker knowledge sharing and absorptive capacity on the participative leadership–employee exploratory innovation relationship in R&D units of Taiwanese technology firms. Deploying a time-lagged questionnaire method implemented over four business quarters, data is generated from 1600 paired samples (managers and employees) in R&D units of Taiwanese technology firms. The structural equation modeling results reveal that (1) participative leadership is positively related to employee exploratory innovation; (2) coworker knowledge and (3) absorptive capacity partially mediate the relationship between participative leadership and employee exploratory innovation independently; and, (4) coworker knowledge sharing in combination with absorptive capacity partially mediates this relationship. The results extend previous research on participative leadership and innovation by demonstrating that participative leadership is related to employee exploratory innovation (Lee and Meyer-Doyle, 2017; Mom et al., 2009).Results also confirm that participative leadership drives employee exploratory innovation through employee absorptive capacity. This reinforces the need highlighted by Lane et al. (2006) to investigate the role of absorptive capacity at the individual-level. Collectively, while participative leadership is important for employee exploratory innovation it is the knowledge mechanisms existing and interacting at the employee-level that are central to generating increased employee exploratory innovation from this leadership approach

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

Extraconnectivity of k-ary n-cube networks

AbstractGiven a graph G and a non-negative integer g, the g-extraconnectivity of G is the minimum cardinality of a set of vertices in G, if such a set exists, whose deletion disconnects G and leaves every remaining component with more than g vertices. This study shows that the 2-extraconnectivity of a k-ary n-cube Qnk for k≥4 and n≥5 is equal to 6n−5

"A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?"

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction

Task-oriented communication offers ample opportunities to alleviate the communication burden in next-generation wireless networks. Most existing work designed the physical-layer communication modules and learning-based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimizing conventional communication metrics, such as throughput maximization, delay minimization, or bit error rate minimization. The inconsistency between the design objectives may hinder the exploitation of the full benefits of task-oriented communications. In this paper, we consider a specific task-oriented communication system for multi-device edge inference over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i.e., feature encoding and classification) and communication (i.e., precoding) modules are designed with the same goal of inference accuracy maximization. Instead of end-to-end learning which involves both the task dataset and wireless channel during training, we advocate a separate design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR2) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem. We cast valuable insights into this formulation and develop a block coordinate descent (BCD) solution algorithm. Moreover, the MCR2 objective also serves the loss function of the feature encoding network, based on which we characterize the received features as a Gaussian mixture (GM) model, facilitating a maximum a posteriori (MAP) classifier to infer the result. Simulation results on both the synthetic and real-world datasets demonstrate the superior performance of the proposed method compared to various baselines.Comment: submitted to IEEE for possible publicatio

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors

Android is one of the most popular operating systems used in mobile devices. Its popularity also renders it a common target for attackers. We propose an efficient and accurate three-phase behavior-based approach for detecting and classifying malicious Android applications. In the proposed approach, the first two phases detect a malicious application and the final phase classifies the detected malware. The first phase quickly filters out benign applications based on requested permissions and the remaining samples are passed to the slower second phase, which detects malicious applications based on system call sequences. The final phase classifies malware into known or unknown types based on behavioral or permission similarities. Our contributions are three-fold: First, we propose a self-contained approach for Android malware identification and classification. Second, we show that permission requests from an Application are beneficial to benign application filtering. Third, we show that system call sequences generated from an application running inside a virtual machine can be used for malware detection. The experiment results indicate that the multi-phase approach is more accurate than the single-phase approach. The proposed approach registered true positive and false positive rates of 97% and 3%, respectively. In addition, more than 98% of the samples were correctly classified into known or unknown types of malware based on permission similarities.We believe that our findings shed some lights on future development of malware detection and classification