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.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

Cultural influence and learning to teach: Beginning teachers' perceptions of teacher preparation programs and school support systems

Beginning teachers in the field of English Language Arts and Reading are responsible for providing literacy instruction to students. Teachers need a broad background in teaching reading, writing, listening, speaking, and viewing, as well as critical thinking. In secondary schools in particular, beginning English Language Arts and Reading teachers are also faced with the challenge of preparing students to be proficient enough readers and writers to meet required State standards. Beginning teachers must navigate compelling challenges that exist during the first years of teaching. The school support systems available to new teachers are an integral part of their educational development. ^ This qualitative study was conceptualized as an in-depth examination of the experiences and perceptions of eight beginning teachers. They represented different racial/ethnic groups, attended different teacher preparation programs, and taught in different school cultures. The data were collected through formal and informal interviews and classroom observations. A qualitative system of data analysis was used to examine the patterns relating to the interrelationship between teacher preparation programs and school support systems. ^ The experiences of the beginning teachers in this study indicated that teacher education programs should provide preservice teachers with a critical knowledge base for teaching literature, language, and composition. A liberal arts background in English, followed by an extensive program focusing on pedagogy, seems to provide a thorough level of curriculum and instructional practices needed for teaching in 21st century classrooms. The data further suggested that a school support system should pair beginning teachers with mentor teachers and provide a caring, professional environment that seeks to nurture the teacher as she/he develops during the first years of teaching. ^

Perceptions of underprepared community college students regarding their educational achievement

The purpose of this study was to understand the perceptions of underprepared college students who had participated in learning communities and who persisted to complete developmental classes and earned at least 30 college-level credit hours to graduate and the perceptions of their peers who had dropped out of college. The theories posed by Tinto, Astin, and Freire formed the framework for this case study. The 22 participants were graduates or transfer students now attending a public university, currently-enrolled sophomores, and students no longer enrolled at the time of the study. Semi-structured individual interviews and a group interview provided narrative data which were transcribed, coded, and analyzed to gain insights into the experiences and perspectives of the participants. The group interview provided a form of member checking to increase accuracy in interpreting themes. A peer reviewer provided feedback on the researcher’s data analysis procedures. The analysis yielded four themes and 14 sub-themes which captured the essence of the participants’ experiences. The pre-college characteristics/traits theme described the students’ internal values and attributes acquired prior to college. The external college support/community influence theme described the encouragement to attend college the students received from family, friends, and high school teachers. The social involvement theme described the students’ participation in campus activities and their interactions with other members of the campus. The academic integration theme described students’ use of campus resources and their contacts with the faculty. The persisters reported strong family and peer support, a sense of responsibility, appreciation for dedicated and caring faculty, and a belief that an education can be a liberatory means to achieve their goals. The non-persisters did not report having the same sense of purpose, goal orientation, determination, obligation to meet family expectations, peer support, campus involvement, positive faculty experiences, and time management skills. The researcher offers an emerging model for understanding factors associated with persistence and three recommendations for enhancing the academic experience of underprepared college students: (a) include a critical pedagogy perspective in coursework where possible, (b) integrate co-curricular activities with the academic disciplines, and (c) increase student-faculty interaction.

Early career special education teachers' perceptions of school site induction support

Research highlights teacher attrition as one of the biggest challenges facing public schools and their attempts to provide a quality teacher for every student (Ingersoll & Smith, 2003). The teacher shortage is particularly daunting in special education where teachers are over twice as likely to leave the field. The first few years of teaching are the most critical in determining whether or not a beginning teacher will stay in the teaching profession (Whitaker, 2000). ^ A mixed-methods sequential explanatory design was utilized to examine research questions focused on the components of induction support that early career teachers received at their school site, including what they considered most valuable to their long-term retention in the classroom and their development as a quality teacher. Eighty seven early career special education teachers were surveyed during the first phase of the study and six participants were interviewed during the second phase. ^ Data analysis of the Likert-scale survey used in the study revealed that the majority of the respondents received at least 21 of the 25 listed induction components. Moreover, early career special education teachers indicated that they valued all 25 induction components. In addition, findings revealed that over two thirds of the respondents indicated a desire to remain a special education teacher. Overall, early career special education teachers felt confident in their abilities to teach students with disabilities; however, nearly half of the respondents did not feel satisfied with the induction they received. Independent t-tests showed a statistically significant difference between teachers who indicated a desire to remain in special education and those that did not on the level of satisfaction with their induction experience. ^ The six interviews provided elaboration and clarification of the survey responses. The participants expressed their passion for the art of teaching, their dedication to students with disabilities, and their frustration with being a beginning teacher. Furthermore, it was reported that the overall school culture was not very supportive. Participants offered relevant ideas for additional or alternate induction components that would be more effective.^

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Santeria as an Informal Psychosocial Support Among Latinas Living with Cancer

Among Latinos, Santería functions as both a religion and a health care system in occurrences of health versus illness within various Latino sub-groups in the U.S. This exploratory study offers a comprehensive analysis of the function of the folk healing tradition Santería as a culturally congruent informal mental health support that assists with coping with the psychosocial sequelae of living with cancer among Latinas in Miami-Dade County, FL. It (a) determined the attitudes of Latinas living with cancer towards Santería as an informal mental health support and (b) explored how Santería offers Latinas effective mental health support that assists in coping with the psychosocial sequelae of living with cancer. The mechanisms and characteristics underlying the motivations of Latinas living with cancer to seek and integrate this informal modality for their cancer care were identified. A purposive sample of 15 Latinas ages 18 and older in Miami-Dade County who had received a diagnosis of cancer were recruited from sites in Miami-Dade offering formal mental health support services and botánicas. Data collection incorporated in-depth interviews and a validation focus group. In an effort to generate theory through a modified Grounded Theory approach, data analysis was accomplished by means of multiple coding passes and the constant comparison method which resulted in higher levels codes that were grouped into three major themes: 1) Participants’ Experience with Folk Healers, 2) Influence of Santería on the Cancer Experience, and 3) Participants’ Experience with Conventional Healthcare and Mental Healthcare. Results illustrate how, among Latinas, the folk healing tradition of Santería co-occurs with professional medical and mental health treatment in what Arthur Kleinman defines as the popular sector, which identifies and sets the parameters for culturally acceptable forms of healthcare and mental health treatment options.

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

Perceptions of Underprepared Community College Students regarding their Educational Achievement

The purpose of this study was to understand the perceptions of underprepared college students who had participated in learning communities and who persisted to complete developmental classes and earned at least 30 college-level credit hours to graduate and the perceptions of their peers who had dropped out of college. The theories posed by Tinto, Astin, and Freire formed the framework for this case study. The 22 participants were graduates or transfer students now attending a public university, currently-enrolled sophomores, and students no longer enrolled at the time of the study. Semi-structured individual interviews and a group interview provided narrative data which were transcribed, coded, and analyzed to gain insights into the experiences and perspectives of the participants. The group interview provided a form of member checking to increase accuracy in interpreting themes. A peer reviewer provided feedback on the researcher’s data analysis procedures. The analysis yielded four themes and 14 sub-themes which captured the essence of the participants’ experiences. The pre-college characteristics/traits theme described the students’ internal values and attributes acquired prior to college. The external college support/community influence theme described the encouragement to attend college the students received from family, friends, and high school teachers. The social involvement theme described the students’ participation in campus activities and their interactions with other members of the campus. The academic integration theme described students’ use of campus resources and their contacts with the faculty. The persisters reported strong family and peer support, a sense of responsibility, appreciation for dedicated and caring faculty, and a belief that an education can be a liberatory means to achieve their goals. The non-persisters did not report having the same sense of purpose, goal orientation, determination, obligation to meet family expectations, peer support, campus involvement, positive faculty experiences, and time management skills. The researcher offers an emerging model for understanding factors associated with persistence and three recommendations for enhancing the academic experience of underprepared college students: (a) include a critical pedagogy perspective in coursework where possible, (b) integrate co-curricular activities with the academic disciplines, and (c) increase student-faculty interaction.