Swarm-based Sequencing Recommendations in E-learning

To be presented at the International Workshop on Recommender Agents and Adaptive Web-based Systems (RAAWS 2005) held in conjunction with the Intelligent Systems Design and Applications 2005 Conference (ISDA 2005), Wroclaw, Poland, September 8-10, 2005. Proceedings 5th International Conference on Intelligent Systems Design and Applications, (Eds) Kwasnicka, H. & Paprzycki, M., IEEE Computer Society, 2005, pp.488-493Open and distance Learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organisation theory. It describes an architecture which supports the recording, processing and presentation of collective learner behaviour designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach

The role of artificial intelligence, knowledge and wisdom in automatic image understanding

In the paper, the roles of intelligence, knowledge, learning and wisdom are discussed in the context of image content understanding. The known model of automatic image understanding is extended by the role of learning. References to example implementations are also given

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view