A framework for transforming, analyzing, and realizing software designs in unified modeling language

Unified Modeling Language (UML) is the most comprehensive and widely accepted object-oriented modeling language due to its multi-paradigm modeling capabilities and easy to use graphical notations, with strong international organizational support and industrial production quality tool support. However, there is a lack of precise definition of the semantics of individual UML notations as well as the relationships among multiple UML models, which often introduces incomplete and inconsistent problems for software designs in UML, especially for complex systems. Furthermore, there is a lack of methodologies to ensure a correct implementation from a given UML design. The purpose of this investigation is to verify and validate software designs in UML, and to provide dependability assurance for the realization of a UML design.^ In my research, an approach is proposed to transform UML diagrams into a semantic domain, which is a formal component-based framework. The framework I proposed consists of components and interactions through message passing, which are modeled by two-layer algebraic high-level nets and transformation rules respectively. In the transformation approach, class diagrams, state machine diagrams and activity diagrams are transformed into component models, and transformation rules are extracted from interaction diagrams. By applying transformation rules to component models, a (sub)system model of one or more scenarios can be constructed. Various techniques such as model checking, Petri net analysis techniques can be adopted to check if UML designs are complete or consistent. A new component called property parser was developed and merged into the tool SAM Parser, which realize (sub)system models automatically. The property parser generates and weaves runtime monitoring code into system implementations automatically for dependability assurance. The framework in the investigation is creative and flexible since it not only can be explored to verify and validate UML designs, but also provides an approach to build models for various scenarios. As a result of my research, several kinds of previous ignored behavioral inconsistencies can be detected.^

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Dynamics of ecosystem metabolism and flocculent detritus transport in estuarine Taylor River

Estuaries and estuarine wetlands are ecologically and societally important systems, exhibiting high rates of primary production that fuel offshore secondary production. Hydrological processes play a central role in shaping estuarine ecosystem structure and function by controlling nutrient loading and the relative contributions of marine and terrestrial influences on the estuary. The Comprehensive Everglades Restoration Plan includes plans to restore freshwater delivery to Taylor Slough, a shallow drainage basin in the southern Everglades, ultimately resulting in increased freshwater flow to the downstream Taylor River estuary. The existing seasonal and inter-annual variability of water flow and source in Taylor River affords the opportunity to investigate relationships between ecosystem function and hydrologic forcing. Estimates of aquatic ecosystem metabolism, derived from free-water, diel changes in dissolved oxygen, were combined with assessments of wetland flocculent detritus quality and transport within the context of seasonal changes in Everglades hydrology. Variation in ecosystem gross primary production and respiration were linked to seasonal changes in estuarine water quality using multiple autoregression models. Furthermore, Taylor River was observed to be net heterotrophic, indicating that an allochthonous source of carbon maintained ecosystem respiration in excess of autochthonous primary production. Wetland-derived detritus appears to be an important vector of energy and nutrients across the Everglades landscape; and in Taylor River, is seasonally flushed into ponded segments of the river where it is then respired. Lastly, seasonal water delivery appears to govern feedbacks regulating water column phosphorus availability in the Taylor River estuary.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

The eye of the beholder: The impact of a district implementation plan of state mandates as perceived by the implementers

The purpose of this inquiry was to investigate the impact of a large, urban school district's experience in implementing a mandated school improvement plan and to examine how that plan was perceived, interpreted, and executed by those charged with the task. The research addressed the following questions: First, by whom was the district implementation plan designed, and what factors were considered in its construction? Second, what impact did the district implementation plan have on those charged with its implementation? Third, what impact did the district plan have on the teaching and learning practices of a particular school? Fourth, what aspects of the implemention plan were perceived as most and least helpful by school personnel in achieving stated goals? Last, what were the intended and unintended consequences of an externally mandated and directed plan for improving student achievement? The implementation process was measured against Fullan's model as expounded upon in The Meaning of Educational Change (1982) and The New Meaning of Educational Change (1990). The Banya implementation model (1993), because it added a dimension not adequately addressed by Fullan, was also considered.^ A case study was used as the methodological framework of this qualitative study. Sources of data used in this inquiry included document analysis, participant observations in situ, follow-up interviews, the "long" interview, and triangulation. The study was conducted over a twelve-month period. Findings were obtained from the content analysis of interview transcripts of multiple participants. Results were described and interpreted using the Fullan and Banya models as the descriptive framework. A cross-case comparison of the multiple perspectives of the same phenomena by various participants was constructed.^ The study concluded that the school district's implementation plan to improve student achievement was closely aligned to Fullan's model, although not intentionally. The research also showed that where there was common understanding at all levels of the organization as to the expectations for teachers, level of support to be provided, and availability of resources, successful implementation occured. The areas where successful implementation did not occur were those where the complexity of the changes were underestimated and processes for dealing with unintended consequences were not considered or adequately addressed. The unique perspectives of the various participants, from the superintendent to the classroom teacher, are described. Finally, recommendations for enhancement of implementation are offered and possible topics for further research studies are postulated. ^