User's Segmentation on Continued Knowledge Management System Use in the Public Sector

Knowledge management systems (KMS) can help an organization support knowledge management activities and thereby increase organizational performance. This study extends the expectation-confirmation model for predicting mandatory continued KMS use in the public sector. The models are assessed using data from a sample of 627 employees of the Kaohsiung City government in Taiwan and analyzed using the finite mixture partial least squares (FIMIX-PLS) method. The results of this study indicate that (1) data heterogeneity (i.e., educational level) segments two specific groups that show different perceptions toward continued KMS use; (2) the results of aggregate-based data analysis are different from the results of group-specific data analysis; (3) compatibility, relative to confirmation, has larger impact on perceived usefulness regardless of groups; (4) the effect of user satisfaction on continued usage behavior is significant different between the two groups; (5) cognition-driven continued use and emotion-driven continued use are identified in the two groups

Critical Condition Detection Using Lion Hunting Optimizer and SVM Classifier in a Healthcare WBAN

A timely critical condition detection and early notification are two essential requirements in a healthcare wireless body area network for the correct treatment of patients. However, most of the systems have limited capabilities and so could not detect the exact condition in a precise time interval. In addition to these it needs a reduction in the false alert rate, as issuing alerts for the deviation in each incoming packet increases the false alert rate and these false alerts consume more network resources. In order to fulfill the above-mentioned requirements, a dynamic alert system has been designed in this regard to make it more efficient, also, a new kind of hybridization approach is being introduced to it with the additive support of a nature-inspired optimization strategy named Lion Hunting and a machine-learning technique called support vector machine. The simulation is done using a network simulator NS-2.35, and the proposed alerting system outperforms others

The Internet of Things: Challenges and Considerations for Cybercrime Investigations and Digital Forensics

The Internet of Things (IoT) represents the seamless merging of the real and digital world, with new devices created that store and pass around data. Processing large quantities of IoT data will proportionately increase workloads of data centres, leaving providers with new security, capacity, and analytics challenges. Handling this data conveniently is a critical challenge, as the overall application performance is highly dependent on the properties of the data management service. This article explores the challenges posed by cybercrime investigations and digital forensics concerning the shifting landscape of crime – the IoT and the evident investigative complexity – moving to the Internet of Anything (IoA)/Internet of Everything (IoE) era. IoT forensics requires a multi-faceted approach where evidence may be collected from a variety of sources such as sensor devices, communication devices, fridges, cars and drones, to smart swarms and intelligent buildings

Threaded Discussion: The Role It Plays in E-Learning

This article presents the results of two studies that focus on the role that threaded discussion plays in student learning. Over a period of three and one-half years, researchers conducted a series of surveys of graduate and undergraduate students at a private, nonprofit university in Southwestern Pennsylvania to determine how students viewed the value of threaded discussions in enhancing their ability to learn course material. Students were asked which types of threaded discussions they preferred; whether they found the threaded discussion to be a better tool for learning than a written assignment; and, which learning environment they felt was more conducive to learning, classroom or online. Results from the combined studies revealed some statistically significant differences based on enrollment status and gender. Upon comparing study results, researchers found statistically significant differences with regard to a preference for classroom versus online instruction and the usefulness of threaded discussions to learning

Top Management's Role in Promoting Decision Support Systems Efficiency: An Exploratory Study in Government Sector in Saudi Arabia

Despite overwhelmingly positive reviews for decision support systems, the IS literature has produced inconsistent results regarding the role of top management and the effectiveness of these systems. IS researchers are concerned with there being a widening gap between research and practice, leading to the current study, focusing on the relevance of these two constituencies. This study employs the Delphi methodology in relation to Saudi Arabia to investigate the reality of the decision support systems in governmental organizations and the diverse issues related to making effective use of them by increasing the role of top management. The findings revealed that there is an absence of a role for IT in the decision-making process, and that there is a lack of robust data warehouse systems capable of supporting organizations' top management with high-quality information. The study revealed various required reforms of various governmental and institutional arrangements and obligational aspects of the efficiency of decision support systems

Concept Drift Detection in Data Stream Clustering and its Application on Weather Data

This article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study

Stochastic Modelling of Weather-Related Transmission Line Outages

The physical environment around transmission lines plays a major role in the resulting reliability of the power network. The inclusion of weather in the failure and repair process will lead to realistic modelling of the power network. This article suggests a modelling methodology to take into account weather-related failures. Besides a maintenance management strategy using dynamic programming, it is suggested to minimizing the cost of maintenance while accounting for weather-related failures. The data obtained from 220kV Transmission lines from Goa, India, is used to stochastically model the phenomenon. A three-state weather model is suggested, and accordingly the failure and repair phenomenon are segregated and stochastically modelled. Time-varying expressions for computing the availability in each weather condition is computed. This model can be used by the power utilities to realistically model weather-related failures

Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy

“Shylock's Return”: Translational Transactions in The Merchant of Venice on the Hebrew Stage

This article addresses monetary, cultural, political and religious transactions, exchanges, conversions and translations between Jews and non-Jews in the play, “The Merchant of Venice,” in relation with Hebrew performances of the play and their social and political contexts. The article examines Leopold Jessner's production from 1936, Tyrone Guthrie production from 1959, Yossi Izae'li's production from 1972, and Hanan Snir's production from 1995 (both in Israel and in Germany). The discussion will address various facets of the complicated intercultural relations that the Merchant of Venice has come to symbolize to Hebrew speaking audiences

Trust, Perceived Benefit, and Purchase Intention in C2C E-Commerce: An Empirical Examination in China

It is a class research question about how trust and perceived benefit affect consumers' purchase intentions. This research examines the relationship in a very different context: consumer-to-consumer (C2C) e-commerce in China. Specifically, this research empirically assesses the differences in effect size due to the change of context. First, a theoretical model linking trust, perceived benefit, and their antecedents to purchase intention is developed upon the literature. Then the model is evaluated using empirical data collected at Taobao, the largest C2C e-commerce website in China. Partial least squares based structural equation modeling (PLS-SEM) results strongly support the model and research hypotheses. A developing country context can indeed affect the strength of effect. These results contribute to the literature in that they provide new insights toward a more in-depth theoretical understanding. Meanwhile, they can also provide useful guidance for managers