English language & third generation programming language pedagogical practice analysis

In an effort to provide better computer programming instruction to more students at an introductory level, pedagogical methods could be improved using a paradigm of instruction based on the same strategies used to teach students spoken languages. Although many different methodologies of instruction have been explored in the past, this document identifies relationships between spoken languages and computer languages that encourage the exploration of the best practices of teaching English Language Arts so that they could be applied to computer programming instruction. Those with backgrounds in mathematics and science initially completed programming tasks. Much literature about the problem solving aspects of programming is available; however, the researcher of this document found it difficult to obtain literature about the opportunities for growth provided by the humanities. This research is an attempt to foster the programming skills of students based on language skills. Given the similarities between spoken languages and object-oriented programming languages that have emerged, there is much encouragement that there may be possibilities for a new instructional paradigm. Following is an analysis of how computer languages are taught and how English is taught, as well as a description of fundamental learning theories. Example demonstrations in a high-level programming language and example problem sets that could be used in an introductory programming course are included that use the best practices employed in English classes, the best practices of computer programming instruction, and the generally accepted learning principles defined by educational theorists

Classification of dual language audio-visual content: Introduction to the VideoCLEF 2008 pilot benchmark evaluation task

VideoCLEF is a new track for the CLEF 2008 campaign. This track aims to develop and evaluate tasks in analyzing multilingual video content. A pilot of a Vid2RSS task involving assigning thematic class labels to video kicks off the VideoCLEF track in 2008. Task participants deliver classification results in the form of a series of feeds, one for each thematic class. The data for the task are dual language television documentaries. Dutch is the dominant language and English-language content (mostly interviews) is embedded. Participants are provided with speech recognition transcripts of the data in both Dutch and English, and also with metadata generated by archivists. In addition to the classification task, participants can choose to participate in a translation task (translating the feed into a language of their choice) and a keyframe selection task (choosing a semantically appropriate keyframe for depiction of the videos in the feed)

Hand in hand: automatic sign Language to English translation

In this paper, we describe the first data-driven automatic sign-language-to- speech translation system. While both sign language (SL) recognition and translation techniques exist, both use an intermediate notation system not directly intelligible for untrained users. We combine a SL recognizing framework with a state-of-the-art phrase-based machine translation (MT) system, using corpora of both American Sign Language and Irish Sign Language data. In a set of experiments we show the overall results and also illustrate the importance of including a vision-based knowledge source in the development of a complete SL translation system

Overview of VideoCLEF 2008: Automatic generation of topic-based feeds for dual language audio-visual content

The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of and access to multilingual multimedia content. In its first year, VideoCLEF piloted the Vid2RSS task, whose main subtask was the classification of dual language video (Dutchlanguage television content featuring English-speaking experts and studio guests). The task offered two additional discretionary subtasks: feed translation and automatic keyframe extraction. Task participants were supplied with Dutch archival metadata, Dutch speech transcripts, English speech transcripts and 10 thematic category labels, which they were required to assign to the test set videos. The videos were grouped by class label into topic-based RSS-feeds, displaying title, description and keyframe for each video. Five groups participated in the 2008 VideoCLEF track. Participants were required to collect their own training data; both Wikipedia and general web content were used. Groups deployed various classifiers (SVM, Naive Bayes and k-NN) or treated the problem as an information retrieval task. Both the Dutch speech transcripts and the archival metadata performed well as sources of indexing features, but no group succeeded in exploiting combinations of feature sources to significantly enhance performance. A small scale fluency/adequacy evaluation of the translation task output revealed the translation to be of sufficient quality to make it valuable to a non-Dutch speaking English speaker. For keyframe extraction, the strategy chosen was to select the keyframe from the shot with the most representative speech transcript content. The automatically selected shots were shown, with a small user study, to be competitive with manually selected shots. Future years of VideoCLEF will aim to expand the corpus and the class label list, as well as to extend the track to additional tasks