abstract: Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only. In this thesis, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by making the mapping between the language constructs and the system primitives customizable. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation purposes and machine learning models for language processing tasks. It has been evaluated within a simulation environment consisting of objects and a robot. This environment has been deployed as a web application, providing anonymous user testing for evaluation, and generating training data for machine learning components. Performance evaluation has been done on metrics such as time taken for a task or the number of instructions given by the user to the robot to accomplish a task. Additionally, the framework has been used to create a natural language interface for a database system to demonstrate its domain independence. / Dissertation/Thesis / Masters Thesis Software Engineering 2020

Newer technologies are being developed all the time in order to help us in our daily tasks. Smart Assistant (SA) is one such broad category of devices which, when instructed through speech or command, perform certain tasks. Most SAs have two primary tasks: identify the user, and perform the taskcommunicated by the user. The task of identification can be achieved in severalways and one such method involves face recognition interface integrated within SA. Further interaction with the user is achieved via either a touch screen or a speech-based interface integrated within SA. Since the primary objective of a SA involves interaction with an individual user, it is necessary to identify users' concerns related to the usability of SA.In this thesis, the goal is to perform a usability study of Smart Assistant(SA) in the context of providing assistance in scheduling meetings at the workplace. Adopting a qualitative approach and we performed the usability study by designing a questionnaire to document public perception of new technologies like a smart assistant. We performed an online survey and collected responses from a section of the population. We also performed a similar survey with employees at Cybercom who have experience in interacting with the SAbeing developed in their company, and which is meant for providing assistance in scheduling meetings.In the process, we also provide relevant background literatureon human-computer interaction to contextualize the results of our survey. Themain outcome of this work is a list of guidelines to be followed for developing asocially interactive speech-based SA.The results of our survey indicate that there are several privacy and security related concerns that users have when using a face recognition interface and speech-based interaction. Although some of the concerns require technological advancements, a few of the concerns can be addressed by adopting certain suggested strategies.

Cloud-based assistants like the Google Home and the Amazon Alexa have become ubiquitous in our homes. Users can simply communicate with the devices using a smartphone application. There are privacy concerns associated with cloud-based assistants. For example, users do not know what type of information is being sent to the device manufacturer, if the device is stealthily listening to conversations, data retention, or who else has access to the data. Privacy is about perception. The goal of this study is to determine user privacy concerns regarding cloud-based assistants by adopting a quantitative research method. The study used a privacy decision framework that lists three core components, which are technology controls, understanding user privacy preference, and government regulations. The research used Dervin's sensemaking model to describe users' privacy perception using the privacy decision framework and improved on a privacy perception survey instrument from previous dissertations. An understanding of user privacy concerns with cloud-based assistants is required to provide a comprehensive privacy guidance to stakeholders. The significance of this study is in the identification of the privacy perception of users of cloud-based assistants and the extent to which the components of the theoretical framework can impact user privacy perception. The results of this study serve as a guide for device manufacturers and other stakeholders in prioritizing privacy design decisions.