Languages across the world have words, phrases, and behaviors—the taboos—that are avoided in public communication considering them as obscene or disturbing to the social, religious, and ethical values of society. However, people deliberately use these linguistic taboos and other language constructs to make hurtful, derogatory, and obscene comments. It is nearly impossible to construct a universal set of offensive or taboo terms because offensiveness is determined entirely by different factors such as socio-physical setting, speaker-listener relationship, and word choices. In this article, we present a detailed corpus-based study of offensive language in Nepali. We identify and describe more than 18 different categories of linguistic offenses including politics, religion, race, and sex. We discuss 12 common euphemisms, such as synonym, metaphor, and circumlocution. In addition, we introduce a manually constructed dataset of more than 1,000 offensive and taboo terms popular among contemporary speakers. We describe the first experiments that provide baseline results in detecting offensive language in Nepali. This in-depth study of offensive language and resource will provide a foundation for several downstream tasks, such as offensive language detection and language learning.

We present in this paper an innovative solution to the challenge of building effective educational technologies that offer tailored instruction to each individual learner. The proposed solution in the form of a conversational intelligent tutoring system, called DeepTutor, has been developed as a web application that is accessible 24/7 through a browser from any device connected to the Internet. The success of several large scale experiments with high-school students using DeepTutor is a solid proof that conversational intelligent tutoring at scale over the web is possible.

We present an overview of the design of a conversational intelligent tutoring system, called DeepTutor, based on the framework of Learning Progressions. Learning Progressions capture students' successful paths towards mastery. The assumption of the proposed tutor is that by guiding instruction based on Learning Progressions, the system will be more effective (and efficient for that matter).

Today, many common tasks (e.g. booking flights, ordering food) can be done by filling out web forms. Automatic processing of Web forms to support interactive speech input is useful for numerous reasons, including ease of use for mobile device users and accessibility for people with visual or print disabilities. In this paper, we propose an automated method to process web forms and convert them into dialog flows for spoken interaction. First we identify relevant information for each form element (including element type, label, values and help messages) and key relationships between form elements (including ordering and dependencies). We then generate two types of dialog flow for each Web form. Experimental results show that the method generates efficient and informative dialog flows for web tasks, a key step for building virtual assistants. An Android application has been realized as a use case of the generated dialog flows.

Peer-to-Peer (P2P) technology has been shown to be effective for content delivery on the Internet. Its applications such as video streaming on Mobile Ad hoc NETwork (MANET) has been explored while the issue of live video streaming on MANET is still a real challenge due to frequent changes in network topology, and the sensitiveness of radio links. To combat these challenges, we propose a novel Cross-Layer And P2P based Solution (CLAPS) that distributes a live video stream using an overlay constructed by the Multicast Overlay Spanning Tree (MOST) protocol. In this solution we have adopted the Multiple Description Coding (MDC) to create multiple video descriptions for a given video stream. CLAPS then distributes pieces to the closest peer which on its turn shares the pieces among the interested peers using the MOST protocol. We compared the performances of the CLAPS with that of the original MOST and we showed that in most of the cases CLAPS substantially increases the continuity index of the video stream and significantly, CLAPS performs better with high mobility and with more than one video stream.