Differences in treatment and intervention needs of girls arrested for status versus criminal offenses

The purpose of this research was to explore the differences in factors associated with girls' status and criminal arrests. This study used data from six juvenile justice programs in multiple states, which was derived from the Juvenile Assessment and Intervention System (JAIS). The sample of 908 adolescent girls (ages 13-19) was ethnically and racially diverse (41% African American, 32% white, 12% Hispanic, 11% Native American and 4% Other). A structural equation model (SEM) was analyzed which tested the potential effects of adolescent substance use, truancy, suicidal ideation/attempt, self-harm, peer legal trouble, parental criminal history and parental and non-parental abuse on type of offense (status and criminal) and whether any of these relationships varied as a function of race/ethnicity. ^ Complex relationships emerged regarding both status and more serious criminal arrests. One of the most important findings was that distinct and different patterns of factors were associated with status arrests compared to criminal arrests. For example, truancy and parental abuse were directly associated with status offenses, whereas parental criminal history was directly related to criminal arrests. However, both status and criminal arrests shared common associations, including substance use, which signifies that certain variables are influential regarding both non-criminal and more serious crimes. In addition, significant meditating influences were observed which help to explain some underlying mechanisms involved in girls' arrest patterns. Finally, race/ethnicity moderated a key relationship, which has serious implications for treatment. ^ In conclusion, the present study is an important contribution to research regarding girls' delinquency in that it overcomes limitations in the existing literature in four primary areas: (1) it utilizes a large, multi-state, ethnically and racially diverse sample of justice system-involved girls, (2) it examines numerous co-occurring factors influencing delinquency from multiple domains (family, school, peers, etc.) simultaneously, (3) it formally examines race/ethnicity as a moderator of these multivariate relationships, and (4) it looks at status and criminal arrests independently in order to highlight possible differences in the patterning of risk factors associated with each. These findings have important implications for prevention, treatment and interventions with girls involved in the juvenile justice system.^

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.