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.

A qualitative case study of teacher perceptions and concerns during the implementation of literacy folders in a select elementary school in Broward County, Florida

Classroom teachers are often required to implement new procedures or practices in response to local or federal education mandates. Attempts to implement innovations, often do not take into account the personal side of change; the perceptions, concerns and needs of those required to implement the innovation. One innovation that was required by the School Board of Broward County, Florida for all elementary classroom teachers was the implementation of Literacy Folders. ^ This study attempted to address the personal side of change by identifying teacher concerns during the implementation of Literacy Folders in a select elementary school in Broward County Florida. The Concerns Based Adoption model (CBAM) for change was used as the conceptual framework for this qualitative case study. ^ Sources of data for this study included participant interviews, observations and analysis of documents. Informal conversations with the participants and unscheduled classroom visits were also sources of data. Seven classroom teachers were interviewed using a predesigned interview guide developed based on the CBAM of change, specifically the Stages of Concern Dimension. Participant responses were coded into two categories, (a) recollections of past perceptions, and (b) present perceptions regarding the innovation. ^ Data analysis resulted in the emergence of one major theme and two subordinate themes. The themes were related to time and purpose of the innovation. The researcher also discovered that the participants exhibited responses typically representative of the CBAM for individuals who are in the process of adjusting to a new innovation. ^ Recommendations based on participant concerns are made for improving the implementation of the innovation. Recommendations for alternatives to the innovation and suggestions regarding areas for further research in the field of educational change are also made. ^

Belowground decomposition of mangrove roots in Florida coastal everglades

Mangrove root decomposition rates were measured by distributing mesh bags containing fine root material across six sites with different soil fertility and hydroperiod to compare ambient differences to substrate quality. Roots from a site with lower soil phosphorus concentration were used as a reference and compared to ambient roots at five other sites with increased phosphorus concentration. Four mesh bags of each root type (ambient versus reference), separated into four 10-cm replicate intervals, were buried up to 42 cm depth at each site and incubated for 250 d (initiation in May 2004). Mass loss of ambient mangrove roots was significant at all study sites and ranged from 17% to 54%; there was no significant difference with depth at any one site. Reference decomposition constants (−k) ranged from 0.0012 to 0.0018 d−1 among Taylor Slough sites compared to 0.0023–0.0028 d−1 among Shark River sites, indicating slower decomposition rates associated with lower soil phosphorous and longer flood duration. Reference roots had similar decomposition rates as ambient roots in four of the six sites, and there were no significant correlations between indices of root substrate quality and decomposition rates. Among these distinct landscape gradients of south Florida mangroves, soil environmental conditions have a greater effect on belowground root decomposition than root substrate quality.