Trustworthiness of a website relies foremost on a good first impression which includes the visitor’s perception of the user interface. The focus of this research is to investigate the effects of website design elements on user perception of trustworthiness of a site and provide a set of guidelines for website designers. The research design is based on Yosef Jabardeen’s (2009) “conceptual framework analysis”. In this research paper, a holistic model is developed to depict the relationships among website design elements and trustworthiness. The model was tested, validated and updated using the results of the repertory grid technique, a process that elicits perceptions about a topic from an individual. For this research, the topic was website trust, the objects were the website design elements, and the constructs were elicited perceptions regarding those website design elements. The repertory grid technique was applied in two stages to a set of participants made up of website users and website designers. Analysis yielded useful information regarding website design associations and correlations of perceptions. The research findings confirmed original suggestions regarding associations and produced an updated, validated model of website design elements. The research indicated that while all design elements had their importance regarding trust, those elements that provided for the function and security of the website rated the highest in importance and expectation. The validated model will aid website designers in understanding what elements are appealing to the visual senses and conjure credibility and trust. Most importantly, this new understanding may help designers to create websites that attract and retain new users and establishing a successful presence on the Internet.

Fog Computing is an emerging computing paradigm that shifts certain processing closer to the Edge of a network, generally within one network hop, where latency is minimized, and results can be obtained the quickest. However, not a lot of research has been done on the effectiveness of Fog in real-world applications. The aim of this research is to show the effectiveness of the Fog Computing paradigm as the middle layer in a 3-tier architecture between the Internet of Things (IoT) and the Cloud. Two applications were developed: one utilizing Fog in a 3-tier architecture and another application using IoT and Cloud with no Fog. A quantitative and qualitative analysis followed the application development, with studies focused on application response time and walkthroughs for AWS Greengrass and Amazon Machine Learning. Furthermore, the application itself demonstrates an architecture which is of both business and research value, providing a real-life coffee shop use-case and utilizing a newly available Fog offering from Amazon known as Greengrass. At the Cloud level, the newly available Amazon Machine Learning API was used to perform predictive analytics on the data provided by the IoT devices. Results suggest that Fog-enabled applications have a much lower range of response times as well as lower response times overall. These results suggest Fog-enabled solutions are suitable for applications which require network stability and reliably lower latency.

Energy consumption by various modern symmetric key encryption protocols (DES, 3-DES, AES and, Blowfish) is studied from an algorithmic perspective. The work is directed towards redesigning or modifying the underlying algorithms for these protocols to make them consume less energy than they currently do. This research takes the approach of reducing energy consumption by parallelizing the consecutive memory accesses of symmetric key encryption algorithms. To achieve parallelization, an existing energy complexity model is applied to symmetric key encryption algorithms. Inspired by the popular DDR3 architecture, the model assumes that main memory is divided into multiple banks, each of which can store multiple blocks. Each block in a bank can only be accessed from a cache of its own, that can hold exactly one block. However all the caches from different banks can be accessed simultaneously. In this research, experiments are conducted to measure the difference in energy consumption by varying the level of parallelization, i.e. variations of, number of banks that can be accessed in parallel. The experimental results show that the higher the level of parallelism, smaller is the energy consumption.

Richard Mason proposed a social framework for addressing the major ethical issues of the information age in his pivotal 1986 article “Four Ethical Issues of the Information Age.” In 2006, Alan Peslak validated the framework by measuring the current attitudes of students, IT professionals, and university faculty and staff toward the four key issues proposed by Mason: privacy, accuracy, property, and accessibility (referred to as PAPA). This study continues this inquiry into the seven-year period after Peslak’s research. Previously collected data was analyzed for 312 university computing majors taking a senior-level ethics course where Mason was taught and discussed. Demographic influences as well as differences over the period were considered. A single exam question administered consistently over the period was the focus. Results indicate, with Mason’s framework as a foundation, computing students can identify all of Mason’s ethical issues, selecting privacy as the most relevant issue of concern in their current environment. Age, gender, and computing work experience resulted in no differences in selection of relevant PAPA factors. All genders, all age groups, and all levels of computing work experience select privacy as the most relevant factor for society today. Privacy increased in importance over the seven-year period as the primary ethical issue for computing students. The ever-changing technology environment and new threats to society posed by these changes is discussed, including social networks, data breaches, consumer privacy, internet neutrality, and emerging technologies.

Front-end developers are tasked with keeping websites up-to-date while optimizing user experiences and interactions. Tools and systems have been developed to give these individuals granular analytic insight into who, with what, and how users are interacting with their sites. These systems maintain a historical record of user interactions that can be leveraged for design decisions. Developing a framework to aggregate those historical usage records and using it to anticipate user interactions on a webpage could automate the task of optimizing web pages. In this research a system called Active Analytics was created that takes Google Analytics historical usage data and provides a dynamic front-end system for automatically updating web page navigational elements. The previous year’s data is extracted from Google Analytics and transformed into a summarization of top navigation steps. Once stored, a responsive front-end system selects from this data a timespan of three weeks from the previous year: current, previous and next. The most frequently reached pages, or their parent pages, will have their navigational UI elements highlighted on a top-level or landing page to attempt to reduce the effort to reach those pages. The Active Analytics framework was evaluated by eliciting volunteers by randomly assigning two versions of a site, one with the framework, one without. It was found that users of the framework-enabled site were able to navigate a site more easily than the original.

As increasing functionality in mobile devices leads to rapid battery drain, energy management has gained increasing importance. However, differences in user’s usage contexts and patterns can be leveraged for saving energy. On the other hand, the increasing sensitivity of users’ data, coupled with the need to ensure security in an energy-aware manner, demands careful analyses of trade-offs between energy and security. The research described in this thesis addresses this challenge by 1)modeling the problem of context-adaptive energy-aware security as a combinatorial optimization problem (Context-Sec); 2) proving that the decision version of this problem is NP-Complete, via a reduction from a variant of the well-known Knapsack problem; 3) developing three different algorithms to solve a related offline version of Context-Sec; and 4) implementing tests and compares the performance of the above three algorithms with data-sets derived from real-world smart-phones on wireless networks. The first algorithm presented is a pseudo-polynomial dynamic programming (DP)algorithm that computes an allocation with optimal user benefit using recurrence of the relations; the second algorithm is a greedy heuristic for allocation of security levels based on user benefit per unit of power consumption for each level; and the third algorithm is a Fully Polynomial Time Approximation Scheme (FPTAS) which has a polynomial time execution complexity as opposed to the pseudo-polynomialDP based approach. To the best of the researcher’s knowledge, this is the first work focused on modeling, design, implementation and experimental performance.

In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, a limited number of studies have examined the impact of collaborative information search processes on novices’ query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users’ patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices. This thesis investigates the influence of collaboration between experts and novices as well as the use of a collaborative web search tool on novices’ query behavior. We used SearchTeam as our collaborative search tool. This empirical study involves four collaborative team conditions: SearchTeam and expert-novice team, SearchTeam and novice-novice team, traditional and expert-novice team, and traditional and novice-novice team. We analyzed participants’ query behavior in two dimensions: quantitatively (e.g. the query success rate), and qualitatively (e.g. the query reformulation patterns). The findings of this study reveal that the successful query rate is higher in expert-novice collaborative teams, who used the collaborative search tools. Participants in expert-novice collaborative teams who used the collaborative search tools, required less time to finalize all tasks compared to expert-novice collaborative teams, who used the traditional search tools. Self-issued queries and chat logs were major sources of terms that novice participants in expert-novice collaborative teams who used the collaborative search tools used. Novices as part of expert-novice pairs who used the collaborative search tools, employed New and Specialization more often as query reformulation patterns. The results of this study contribute to the literature by providing detailed investigation regarding the influence of utilizing collaborative search tool (SearchTeam) in the context of software troubleshooting and development. This study highlights the possible collaborative information seeking (CIS) activities that may occur among software developers’ interns and their mentors. Furthermore, our study reveals that there are specific features, such as awareness and built-in instant messaging (IM), offered by SearchTeam that can promote the CIS activities among participants and help increase novices’ query success rates. Finally, we believe the use of CIS tools, designed to support collaborative search actions in big software development companies, has the potential to improve the overall novices’ query behavior and search strategies.

The objective of this research is to determine the effects over time of a dynamic system that adapts itself to a user's current state of expertise, in terms of the application domain, by constantly monitoring the user throughout use of the system, placing them in appropriate user models when this expertise has changed. A dynamic system, named ER-by-Design version 2.0, is presented, consisting of an inference component, a help system, a help/assistance screen, and user models. The user models are responsible for adapting the system interface to the level of expertise of the user. The system monitors and analyzes a user's interactions in order to evaluate user expertise, placing the user in the most appropriate model based on this evaluation. Through analysis of data collected from participants' sessions with both versions of the system, it is shown that over time, through the use of ER-by-Design version 2.0, users accessed help less often and perceived the system as more beneficial when compared to a system with a static, generic interface. In addition, users who had the least experience with ER modeling concepts created more correct diagrams with ER-by-Design version 2.0 than with a static version of the system.

Knowledge-based expert systems are used to enhance and automate manual processes through the use of a knowledge base and modern computing power. The traditional methodology for creating knowledge-based expert systems has many commonly encountered issues that can prevent successful implementations. Complications during the knowledge acquisition phase can prevent a knowledge-based expert system from functioning properly. Furthermore, the time and resources required to maintain a knowledge-based expert system once implemented can become problematic. There are several concepts that can be integrated into a proposed methodology to improve the knowledge-based expert system lifecycle to create a more efficient process. These methods are commonly used in other disciplines but have not traditionally been incorporated into the knowledge-based expert system lifecycle. A container-loading knowledge-based expert system was created to test the concepts in the proposed methodology. The results from the container-loading knowledge-based expert system test were compared against the historical records of thirteen container ships loaded between 2008 and 2011.