Using Online Games To Teach Personal Finance Concepts

This case study explores the use of online games to teach personal finance concepts at the college level. A number of free online games targeting such topics as budgeting and saving, risk and return, consumer credit, financial services, and investments were introduced to the experimental group as homework assignments. Statistical results indicate that integrating online games into coursework significantly enhanced student learning outcomes. We suggest extending our successful experience to groups of people who need financial knowledge the most

Hedonic Values And Utilitarian Values As Predicators Of Social Media Participation

This research proposes a model to investigate the behavior of posting articles and the continued use of social media via Babin’s value perspective. The antecedents of values are web quality and users’ emotions. The model was tested with PLS-Graph software based on its structural equation modeling approach. Data was gained from 310 users. The results revealed that antecedents have a strong impact on user values, which in turn influences users’ intention to post articles and continue to use social media. Several implications for research and practice have been derived from these findings

Sensor Selection and Integration to Improve Video Segmentation in Complex Environments

Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region, or frame level and designating any elements that deviate from the background model as foreground. However, most existing approaches are capable of segmenting a number of distinct components but unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest from different sensing modalities into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes. Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on two complex data sets demonstrate that our proposed technique significantly improves the accuracy and utility of state-of-the-art video segmentation technique. © 2014 Adam R. Reckley et al

An Evaluation Model for Foreign Direct Investment Performance of Free Trade Port Zones

With the tendency of internationalisation and globalisation, signing regional economic agreements among multiple countries has become a trend. Under such an integration environment, some free economic zones with port transportation functions have become crucial for FDI (foreign direct investment) investors in selecting investment locations. The free trade port zone (FTPZ) is argued to be one of the most well-known. This paper aims to assess the FDI performance of FTPZs. On the basis of the FTPZ\u27s features and relevant literature, assessment criteria (ACs) are initially identified. An evaluation model based on the fuzzy AHP (Analytic Hierarchy Process) approach is then introduced to evaluate the FTPZs\u27 FDI performance from foreign investors\u27 viewpoints. Finally, the FTPZ of the Kaohsiung port in Taiwan was empirically investigated to verify the assessment model. Results point out that for the FTPZ of Kaohsiung port, ACs with higher priorities needing improvement are raw material acquired, local government efficiency, and political stability and social security. Theoretical and practical recommendations for the FTPZ managers are discussed based on the results

Onsite Early Prediction of PGA Using CNN With Multi-Scale and Multi-Domain P-Waves as Input

Although convolutional neural networks (CNN) have been applied successfully to many fields, the onsite earthquake early warning by CNN remains unexplored. This study aims to predict the peak ground acceleration (PGA) of the incoming seismic waves using CNN, which is achieved by analyzing the first 3 s of P-wave data collected from a single site. Because the amplitude of P-wave data of large and small earthquakes can differ, the multi-scale input of P-wave data is proposed in this study in order to let the CNN observe the input data in different scales. Both the time and frequency domains of the P-wave data are combined into multi-domain input, and therefore the CNN can observe the data from different aspects. Since only the maximum absolute acceleration value of the time history of seismic waves is the target output of the CNN, the absolute value of the P-wave time history data is used instead of the raw value. The proposed arrangement of the input data shows its superiority to the one directly inputting the raw P-wave data into the CNN. Moreover, the predicted PGA accuracy using the proposed CNN approach is higher than the one using the support vector regression approach that employed the extracted P-wave features as its input. The proposed CNN approach also shows that the accuracy of the predicted PGA and the alert performances are acceptable based on data from two independent and damaging earthquakes