Long-Term Impact of Welfare Reform: Biopsychosocial Barriers to Successful Transition Away from Welfare Reliance Among Rural Women in Louisiana

The discussion regarding government benefits and reliance on welfare benefits is one that takes place in arenas of policymaking and academia alike. These discussions often focus on poverty that exists in densely populated metropolitan areas, resulting in a scarcity of research regarding unique characteristics of rural poverty. Eighty-four rural Louisiana women participated in a longitudinal study of the impacts of welfare reform in their lives. Twenty years later, two (N = 2) rural Louisiana women, each former welfare recipients, participated in an in-depth qualitative case study examining their transition away from welfare programs. Data show that neither woman was able to function independently of welfare through employment following the welfare-to-work transition that took place as a consequence of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996. The integrated data from their four interviews each, including the retrospective interview they engaged in during summer 2019, revealed biological, psychological, and social factors that negatively impacted their transition away from public assistance. These findings suggest that policymakers should take into account the unique challenges inherent to rural communities during the development of welfare policy. The study also revealed a lack of evidence based practices during policy implementation, particularly an absence of working alliance between government agencies and participants, which proved disadvantageous to participants as they navigated the welfare reform transition

One-Class Classification for Intrusion Detection on Vehicular Networks

Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning methods to detect and report these attacks, but common methods are not robust towards unknown attacks. These methods usually rely on there being a sufficient representation of attack data, which may not be available due to there either not being enough data present to adequately represent its distribution or the distribution itself is too diverse in nature for there to be a sufficient representation of it. With the use of one-class classification methods, this issue can be mitigated as only normal data is required to train a model for the detection of anomalous instances. Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks. In this paper, we investigate the performance of various state-of-the-art one-class classification methods for detecting injection attacks on Controller Area Network bus traffic. We investigate the effectiveness of these techniques on attacks launched on Controller Area Network buses from two different vehicles during normal operation and while being attacked. We observe that the Subspace Support Vector Data Description method outperformed all other tested methods with a Gmean of about 85%.Comment: 7 pages, 2 figures, 4 tables. Accepted at IEEE Symposium Series on Computational Intelligence 202