For the past decade, mobile health applications are seeing greater acceptance due to their potential to remotely monitor and increase patient engagement, particularly for chronic disease. Sickle Cell Disease is an inherited chronic disorder of red blood cells requiring careful pain management. A significant number of mHealth applications have been developed in the market to help clinicians collect and monitor information of SCD patients. Surveys are the most common way to self-report patient conditions. These are non-engaging and suffer from poor compliance. The quality of data gathered from survey instruments while using technology can be questioned as patients may be motivated to complete a task but not motivated to do it well. A compromise in quality and quantity of the collected patient data hinders the clinicians' effort to be able to monitor patient's health on a regular basis and derive effective treatment measures. This research study has two goals. The first is to monitor user compliance and data quality in mHealth apps with long and repetitive surveys delivered. The second is to identify possible motivational interventions to help improve compliance and data quality. As a form of intervention, will introduce intrinsic and extrinsic motivational factors within the application and test it on a small target population. I will validate the impact of these motivational factors by performing a comparative analysis on the test results to determine improvements in user performance. This study is relevant, as it will help analyze user behavior in long and repetitive self-reporting tasks and derive measures to improve user performance. The results will assist software engineers working with doctors in designing and developing improved self-reporting mHealth applications for collecting better quality data and enhance user compliance.

When software design teams attempt to collaborate on different design docu-

ments they suffer from a serious collaboration problem. Designers collaborate either in person or remotely. In person collaboration is expensive but effective. Remote collaboration is inexpensive but inefficient. In, order to gain the most benefit from collaboration there needs to be remote collaboration that is not only cheap but also as efficient as physical collaboration.

Remotely collaborating on software design relies on general tools such as Word, and Excel. These tools are then shared in an inefficient manner by using either email, cloud based file locking tools, or something like google docs. Because these tools either increase the number of design building blocks, or limit the number

of available times in which one can work on a specific document, they drastically decrease productivity.

This thesis outlines a new methodology to increase design productivity, accom- plished by providing design specific collaboration. Using version control systems, this methodology allows for effective project collaboration between remotely lo- cated design teams. The methodology of this paper encompasses role management, policy management, and design artifact management, including nonfunctional re- quirements. Version control can be used for different design products, improving communication and productivity amongst design teams. This thesis outlines this methodology and then outlines a proof of concept tool that embodies the core of these principles.

Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of defining, documenting and maintaining a software system's requirements. When researchers decided to blend these two streams in, there was research on automating the process of classification of software requirements statements into categories easily comprehensible for developers for faster development and delivery, which till now was mostly done manually by software engineers - indeed a tedious job. However, most of the research was focused on classification of Non-functional requirements pertaining to intangible features such as security, reliability, quality and so on. It is indeed a challenging task to automatically classify functional requirements, those pertaining to how the system will function, especially those belonging to different and large enterprise systems. This requires exploitation of text mining capabilities. This thesis aims to investigate results of text classification applied on functional software requirements by creating a framework in R and making use of algorithms and techniques like k-nearest neighbors, support vector machine, and many others like boosting, bagging, maximum entropy, neural networks and random forests in an ensemble approach. The study was conducted by collecting and visualizing relevant enterprise data manually classified previously and subsequently used for training the model. Key components for training included frequency of terms in the documents and the level of cleanliness of data. The model was applied on test data and validated for analysis, by studying and comparing parameters like precision, recall and accuracy.

Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption.

To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.

This reports investigates the general day to day problems faced by small businesses, particularly small vendors, in areas of marketing and general management. Due to lack of man power, internet availability and properly documented data, small business cannot optimize their business. The aim of the research is to address and find a solution to these problems faced, in the form of a tool which utilizes data science. The tool will have features which will aid the vendor to mine their data which they record themselves and find useful information which will benefit their businesses. Since there is lack of properly documented data, One Class Classification using Support Vector Machine (SVM) is used to build a classifying model that can return positive values for audience that is likely to respond to a marketing strategy. Market basket analysis is used to choose products from the inventory in a way that patterns are found amongst them and therefore there is a higher chance of a marketing strategy to attract audience. Also, higher selling products can be used to the vendors' advantage and lesser selling products can be paired with them to have an overall profit to the business. The tool, as envisioned, meets all the requirements that it was set out to have and can be used as a stand alone application to bring the power of data mining into the hands of a small vendor.

A well-defined Software Complexity Theory which captures the Cognitive means of algorithmic information comprehension is needed in the domain of cognitive informatics & computing. The existing complexity heuristics are vague and empirical. Industrial software is a combination of algorithms implemented. However, it would be wrong to conclude that algorithmic space and time complexity is software complexity. An algorithm with multiple lines of pseudocode might sometimes be simpler to understand that the one with fewer lines. So, it is crucial to determine the Algorithmic Understandability for an algorithm, in order to better understand Software Complexity. This work deals with understanding Software Complexity from a cognitive angle. Also, it is vital to compute the effect of reducing cognitive complexity. The work aims to prove three important statements. The first being, that, while algorithmic complexity is a part of software complexity, software complexity does not solely and entirely mean algorithmic Complexity. Second, the work intends to bring to light the importance of cognitive understandability of algorithms. Third, is about the impact, reducing Cognitive Complexity, would have on Software Design and Development.

Despite the various driver assistance systems and electronics, the threat to life of driver, passengers and other people on the road still persists. With the growth in technology, the use of in-vehicle devices with a plethora of buttons and features is increasing resulting in increased distraction. Recently, speech recognition has emerged as an alternative to distraction and has the potential to be beneficial. However, considering the fact that automotive environment is dynamic and noisy in nature, distraction may not arise from the manual interaction, but due to the cognitive load. Hence, speech recognition certainly cannot be a reliable mode of communication.

The thesis is focused on proposing a simultaneous multimodal approach for designing interface between driver and vehicle with a goal to enable the driver to be more attentive to the driving tasks and spend less time fiddling with distractive tasks. By analyzing the human-human multimodal interaction techniques, new modes have been identified and experimented, especially suitable for the automotive context. The identified modes are touch, speech, graphics, voice-tip and text-tip. The multiple modes are intended to work collectively to make the interaction more intuitive and natural. In order to obtain a minimalist user-centered design for the center stack, various design principles such as 80/20 rule, contour bias, affordance, flexibility-usability trade-off etc. have been implemented on the prototypes. The prototype was developed using the Dragon software development kit on android platform for speech recognition.

In the present study, the driver behavior was investigated in an experiment conducted on the DriveSafety driving simulator DS-600s. Twelve volunteers drove the simulator under two conditions: (1) accessing the center stack applications using touch only and (2) accessing the applications using speech with offered text-tip. The duration for which user looked away from the road (eyes-off-road) was measured manually for each scenario. Comparison of results proved that eyes-off-road time is less for the second scenario. The minimalist design with 8-10 icons per screen proved to be effective as all the readings were within the driver distraction recommendations (eyes-off-road time < 2sec per screen) defined by NHTSA.

Driving is already a complex task that demands a varying level of cognitive and physical load. With the advancement in technology, the car has become a place for media consumption, a communications center and an interconnected workplace. The number of features in a car has also increased. As a result, the user interaction inside the car has become overcrowded and more complex. This has increased the amount of distraction while driving and has also increased the number of accidents due to distracted driving. This thesis focuses on the critical analysis of today’s in-car environment covering two main aspects, Multi Modal Interaction (MMI), and Advanced Driver Assistance Systems (ADAS), to minimize the distraction. It also provides deep market research on future trends in the smart car technology. After careful analysis, it was observed that an infotainment screen cluttered with lots of small icons, a center stack with a plethora of small buttons and a poor Voice Recognition (VR) results in high cognitive load, and these are the reasons for the increased driver distraction. Though the VR has become a standard technology, the current state of technology is focused on features oriented design and a sales driven approach. Most of the automotive manufacturers are focusing on making the VR better but attaining perfection in VR is not the answer as there are inherent challenges and limitations in respect to the in-car environment and cognitive load. Accordingly, the research proposed a novel in-car interaction design solution: Multi-Modal Interaction (MMI). The MMI is a new term when used in the context of vehicles, but it is widely used in human-human interaction. The approach offers a non-intrusive alternative to the driver to interact with the features in the car. With the focus on user-centered design, the MMI and ADAS can potentially help to reduce the distraction. To support the discussion, an experiment was conducted to benchmark a minimalist UI design. An engineering based method was used to test and measure distraction of four different UIs with varying numbers of icons and screen sizes. Lastly, in order to compete with the market, the basic features that are provided by all the other competitors cannot be eliminated, but the hard work can be done to improve the HCaI and to make driving safer.

Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities become more common and complicated, especially with regard to In-Car Interactive System. This work's main focus is on driver distraction caused by the in-car interactive System. There have been many User Interaction Designs (Buttons, Speech, Visual) for Human-Car communication, in the past and currently present. And, all related studies suggest that driver distraction level is still high and there is a need for a better design. Multimodal Interaction is a design approach, which relies on using multiple modes for humans to interact with the car & hence reducing driver distraction by allowing the driver to choose the most suitable mode with minimum distraction. Additionally, combining multiple modes simultaneously provides more natural interaction, which could lead to less distraction. The main goal of MMI is to enable the driver to be more attentive to driving tasks and spend less time fiddling with distractive tasks. Engineering based method is used to measure driver distraction. This method uses metrics like Reaction time, Acceleration, Lane Departure obtained from test cases.

Internet browsers are today capable of warning internet users of a potential phishing attack. Browsers identify these websites by referring to blacklists of reported phishing websites maintained by trusted organizations like Google, Phishtank etc. On identifying a Unified Resource Locator (URL) requested by a user as a reported phishing URL, browsers like Mozilla Firefox and Google Chrome display an 'active' warning message in an attempt to stop the user from making a potentially dangerous decision of visiting the website and sharing confidential information like username-password, credit card information, social security number etc.

However, these warnings are not always successful at safeguarding the user from a phishing attack. On several occasions, users ignore these warnings and 'click through' them, eventually landing at the potentially dangerous website and giving away confidential information. Failure to understand the warning, failure to differentiate different types of browser warnings, diminishing trust on browser warnings due to repeated encounter are some of the reasons that make users ignore these warnings. It is important to address these factors in order to eventually improve a user’s reaction to these warnings.

In this thesis, I propose a novel design to improve the effectiveness and reliability of phishing warning messages. This design utilizes the name of the target website that a fake website is mimicking, to display a simple, easy to understand and interactive warning message with the primary objective of keeping the user away from a potentially spoof website.