Huang, Shihong

A self-adaptive software is developed to predict the stock market. It’s Stock
Prediction Engine functions autonomously when its skill-set suffices to achieve its goal,
and it includes human-in-the-loop when it recognizes conditions benefiting from more
complex, expert human intervention. Key to the system is a module that decides of
human participation. It works by monitoring three mental states unobtrusively and in real
time with Electroencephalography (EEG). The mental states are drawn from the
Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the
three mental states are predictive of whether the Human Computer Interaction System
functions better autonomously (human with low scores on opportunity and/or
willingness, capability) or with the human-in-the-loop, with willingness carrying the
largest predictive power. This transdisciplinary software engineering research
exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs
allow for unobtrusive pre-interactions.

Retaining business value in a legacy commercial enterprise resource planning system today often entails more than just maintaining the software to preserve existing functionality. This type of system tends to represent a significant capital investment that may not be easily scrapped, replaced, or re-engineered without considerable expense. A legacy system may need to be frequently extended to impart new behavior as stakeholder business goals and technical requirements evolve. Legacy ERP systems are growing in prevalence and are both expensive to maintain and risky to evolve. Humans are the driving factor behind the expense, from the engineering costs associated with evolving these types of systems to the labor costs required to operate the result. Autonomic computing is one approach that addresses these challenges by imparting self-adaptive behavior into the evolved system. The contribution of this dissertation aims to add to the body of knowledge in software engineering some insight and best practices for development approaches that are normally hidden from academia by the competitive nature of the retail industry. We present a formal architectural pattern that describes an asynchronous, low-complexity, and autonomic approach. We validate the pattern with two real-world commercial case studies and a reengineering simulation to demonstrate that the pattern is repeatable and agnostic with respect to the operating system, programming language, and communication protocols.

Affecting one in every 88 children, Autism Spectrum Disorder ASD is one of the fastest growing developmental disabilities. Scientific research has proven that early behavioral intervention can improve learning, communication, and social skills. Similarly, studies have shown that the usage of off-the-shelf technology to this end boosts motivation in children under the spectrum while increasing their attention span and ability to interact socially. Embracing perspectives from different fields can lead to the development of an effective tool that can complement the treatment of those with ASD. This thesis documents the re-engineering and extension of Ying, an existing mobile software application designed to aid in the learning of autistic children. Ying’s original methodology combined expertise from different fields including developmental psychology, semantic learning, and computer science. In this work, Ying is modified to incorporate additional aspects of traditional treatment, like applied behavior analysis and verbal behavior therapy, while enhancing the user experience by improving the audio and visual capabilities of the application. Using cutting-edge software technology in areas like voice recognition and mobile device applications, this project aspires to enhance social behavior and reinforce verbal communication skills in children with ASD, while detecting and storing learning patterns for later study.

Affecting one in every 68 children, Autism Spectrum Disorder (ASD) is one
of the fastest growing developmental disabilities. Scientific research has proven that
early behavioral intervention can improve learning, communication, and social skills.
Similarly, studies have shown that the usage of of-the-shelf technology boosts
motivation in children diagnosed with ASD while increasing their attention span and
ability to interact socially. Embracing perspectives from different fields of study can
lead to the development of an effective tool to complement traditional treatment
of those with ASD. This thesis documents the re-engineering, extension, and evolu-
tion of Ying, an existing web application designed to aid in the learning of autistic
children. The original methodology of Ying combines expertise from other research
areas including developmental psychology, semantic learning, and computer science.
In this work, Ying is modifed to incorporate aspects of traditional treatment, such
as Applied Behavior Analysis. Using cutting-edge software technology in areas like
voice recognition and mobile device applications, this project aspires to use software
engineering approaches and audio-visual interaction with the learner to enhance social behavior and reinforce verbal communication skills in children with ASD, while
detecting and storing learning patterns for later study.