Master of Computer Science, Miami University, 2023, Computer Science and Software Engineering

Participatory modeling creates a shared and comprehensive representation of a complex societal problem. The necessity and benefits of participatory modeling are particularly apparent in interdisciplinary and multi-factorial problems such as obesity and suicide. However, comprehensive models built by many stakeholders can have too many factors to comprehend. Participatory modeling is thus only transparent when participants contribute their knowledge, as they cannot always benefit from the group-level knowledge contained in larger maps. Furthermore, the interdisciplinary nature of a problem and the increasingly common practice of global (and therefore remote) collaborations between participants result in a heightened need for emerging collaborative technologies as part of the modeling process. In this thesis, I designed, implemented, and evaluate new systems to solve the issues of transparency in causal maps and support remote collaborations. First, I created a series of systems to transform a network-based model (causal maps and fuzzy cognitive maps) into textual reports, using state-of-the-art large-scale pretrained large language models (LLMs) such as GPT 3 and GPT 3.5. Second, I created the first application leveraging mixed reality devices to accommodate collaborative modeling. The evaluations of my systems demonstrate their potential through metrics that include accuracy (for natural language generation) or time-to-completion (for usability).

Committee: Philippe Giabbanelli J. (Advisor); John Femiani C. (Committee Member); Xianglong Feng (Committee Member) Subjects: Computer Science

MARCH, University of Cincinnati, 2017, Design, Architecture, Art and Planning: Architecture

In an era where we are becoming ever more immersed in the digital world, personalization and the on-demand economy are coming to the forefront. The use of technology and its presence in our everyday lives is growing exponentially due to its increasing availability and rapid technological breakthroughs. Emerging technologies are opening endless possibilities to design fields. As designers of space, architects can use these platforms as a way to enhance a person's immediate surroundings, proposing that sensory kinematics respond to our personalized space within our living environments. Transitioning kinetic architectural elements from their typical use on exterior facades towards ambient, interior soft wall technologies, sensory kinematics can respond to our personalized spatial needs within our built environments. By analyzing the typical relationships between humans and space within a typical housing unit, this thesis shows how breaking from standard stationary walls will allow for individuals to curate space to their personalized moment and activity specific needs. Design professionals can begin to inform dynamic preferences and activities of human life in a similar manner to how design professionals have qualitatively and quantifiably understood building-oriented elements such as environmental or space-efficiency factors. This thesis proposes a coupling of analysis through emerging technologies and folding techniques, paired with soft studies such as avatar-based life scenarios as a way to rigorously study the effects of space on human elements of experience.

Committee: Christoph Klemmt A.A. Dipl. (Committee Chair); Terry Boling (Committee Member) Subjects: Architecture

Doctor of Philosophy, The Ohio State University, 2008, Electrical and Computer Engineering

This dissertation presents several methods to more efficiently use the computational resources availableon a Heterogeneous Chip Multiprocessor (H-CMP). Using task scheduling techniques, three challenges to the effective usage of H-CMPs are addressed: the emergence of reconfigurable hardware in general purpose computing, utilization of the network on a chip (NoC), and fault tolerance. To utilize reconfigurable hardware, we introduce the Mutually Exclusive Processor Groups reconfiguration model, and an accompanying task scheduler, the Heterogeneous Earliest Finish Time with Mutually Exclusive Processor Groups (HEFT-MEG) scheduling heuristic. HEFT-MEG schedules reconfigurations using a novel back-tracking algorithm to evaluate how different reconfiguration decisions affect previously scheduled tasks. In both simulation and real execution, HEFT-MEG successfully schedules reconfiguration allowing the architecture to adapt to changing application requirements. After an analysis of IBM's Cell Processor NoC and generation of a simple stochastic model, we propose a hybrid task scheduling system using a Compile- and Run-time Scheduler (CtS and RtS) that work in concert. The CtS, Contention Aware HEFT (CA-HEFT), updates task start and finish times when scheduling to account for network contention. The RtS, the Contention Aware Dynamic Scheduler (CADS), adjusts the schedule generated by CA-HEFT to account for variation in the communication pattern and actual task finish times, using a novel dynamic block algorithm. We find that using a CtS and RtS in concert improves the performance of several application types in real execution on the Cell processor. To enhance fault tolerance, we modify the previously proposed hybrid scheduling system to accommodate variability in the processor availability. The RtS is divided into two portions, the Fault Tolerant Re-Mapper (FTRM) and the Reconfiguration and Recovery Scheduler (RRS). FTRM examines the current processor availability and remap (open full item for complete abstract)

Committee: Fusun Ozguner (Advisor); Umit Catalyurek (Committee Member); Eylem Ekici (Committee Member) Subjects: Computer Science; Electrical Engineering