In closing, data crime can safely be said to be the wave of the future. Databases and not banks, will increasingly attract the attention of criminal cartels. Unlike bank robbers who, if apprehended and prosecuted, stand to face life imprisonment; data criminals rarely ever see the inside of a prison. Management, more than ever, needs to play a role in ensuring that data thefts do not pay.
In this paper, we analyzed how the CAFE standard has affected improvements in the fuel economy of vehicles, as examined in other preceding studies, but in addition, we also analyzed how these standards have affected the level of consumer interest in fuel economy. Our goal was to determine what effects the government intervention has had on consumers, and whether such intervention ought to be continued. The results showed that not only has the CAFE standard had a direct and significant impact on improving fuel economy and increasing the market share of fuel-efficient vehicles, it has also boosted the development of technologies for enhancing fuel economy and raised consumer interest in fuel economy, thus indirectly contributing to overcoming market failure. The significance of this study is that we used publically available observed data and analyzed the recent impact of the CAFE standard specifically with a focus on the behavior and strategies exhibited by consumers and automakers. Another significance of this study is that it extends our purview to examine the effects that the CAFE standard has had in other countries (Korea).
By using these points to initiate questions or consider key issues, accurate risk profiles and risk mitigation measures can be assessed and implemented. A formal business risk assessment will help determine priority issues and major concerns.
The current study provides an empirical testing of the victim-offender overlap in online platforms due to the scarcity of studies examining this overlapping victim-offending dynamic. Two types of cyber-interpersonal violence are examined: Cyber-harassment (including cyber-sexual harassment) and cyber-impersonation. Using Choi's (2008) integrated theory of Cyber-Routine Activities Theory, a sample of 272 college students at a Massachusetts university are examined. Three major findings are revealed: (1) Respondents who engage in risky online leisure activities are more likely to experience interpersonal violence in cyberspace, (2) poor online security management can contribute to the likelihood of being victimized by interpersonal violence on social networking sites (SNS), and (3) respondents who engage in risky social networking site activities are likely to commit cyber-interpersonal violence. For the two types of cyber-interpersonal violence examined in this study, it could also be predicted that females are more likely to have higher levels of victimization. Cybersecurity management and sex had no significant effects on cyber-interpersonal violence offending. The hope is that education on the potential hazards of the Internet and of cyber-interpersonal violence will induce more responsible online activity and engagement.
Cryptocurrency price forecasting plays an important role in financial markets. Traditional approaches face two challenges: (1) it is difficult to ascertain the influential factors related to price forecasting; and (2) due to the 24/7 trading policy, cryptocurrencies’ prices face very large fluctuations, thus weakening the forecasting power of traditional models. To address these issues, we focus on Bitcoin and identify the influential factors related to its price forecasting from the perspective of underlying blockchain transactions. We then propose a price forecasting model WT-CATCN, which leverages Wavelet Transform (WT) and Casual Multi-Head Attention (CA) Temporal Convolutional Network (TCN), to forecast cryptocurrency prices. Our model can capture important positions of input sequences and model the correlations among different data features. Using real-world Bitcoin trading data, we test and compare WT-CATCN with other state-of-the-art price forecasting models. The experiment results show that our model improves the price forecasting performance by 25%.
