Indexed Captioned Searchable Videos: A Learning Companion for STEM Coursework

Videos of classroom lectures have proven to be a popular and versatile learning resource. A key shortcoming of the lecture video format is accessing the content of interest hidden in a video. This work meets this challenge with an advanced video framework featuring topical indexing, search, and captioning (ICS videos). Standard optical character recognition (OCR) technology was enhanced with image transformations for extraction of text from video frames to support indexing and search. The images and text on video frames is analyzed to divide lecture videos into topical segments. The ICS video player integrates indexing, search, and captioning in video playback providing instant access to the content of interest. This video framework has been used by more than 70 courses in a variety of STEM disciplines and assessed by more than 4000 students. Results presented from the surveys demonstrate the value of the videos as a learning resource and the role played by videos in a students learning process. Survey results also establish the value of indexing and search features in a video platform for education. This paper reports on the development and evaluation of ICS videos framework and over 5 years of usage experience in several STEM courses.     

Chat mining: Predicting user and message attributes in computer-mediated communication

The focus of this paper is to investigate the possibility of predicting several user and message attributes in text-based, real-time, online messaging services. For this purpose, a large collection of chat messages is examined. The applicability of various supervised classification techniques for extracting information from the chat messages is evaluated. Two competing models are used for defining the chat mining problem. A term-based approach is used to investigate the user and message attributes in the context of vocabulary use while a style-based approach is used to examine the chat messages according to the variations in the authors’ writing styles. Among 100 authors, the identity of an author is correctly predicted with 99.7% accuracy. Moreover, the reverse problem is exploited, and the effect of author attributes on computer-mediated communications is discussed.     

Architecture of a grid-enabled Web search engine

Search Engine for South-East Europe (SE4SEE) is a socio-cultural search engine running on the grid infrastructure. It offers a personalized, on-demand, country-specific, category-based Web search facility. The main goal of SE4SEE is to attack the page freshness problem by performing the search on the original pages residing on the Web, rather than on the previously fetched copies as done in the traditional search engines. SE4SEE also aims to obtain high download rates in Web crawling by making use of the geographically distributed nature of the grid. In this work, we present the architectural design issues and implementation details of this search engine. We conduct various experiments to illustrate performance results obtained on a grid infrastructure and justify the use of the search strategy employed in SE4SEE.