This paper describes relevant classification methods applied to the cultural heritage context. In particular, a categorisation of the classification methods is provided according to tangible and intangible cultural heritage, where movable and immovable objects can be in the focus. A short description of each method is reported for each cultural heritage category in terms of feature representation, classification approach and obtained results. The proposed survey can be useful in the research community of pattern recognition and visual computing for exploring the current literature about the topic. It will hopefully provide new insights for the advancement of knowledge discovery in cultural heritage.

Scarlet an Artificial Teaching Assistant is a personal digital assistant that has been developed with main aim to assist students in their learning process by ensuring fast and efficiently search of documents and learning materials. Scarlet is able to give an adequate response to a specific question based on knowledge gathered by an unique algorithm which enables her to recognize context during file and web page content search. After finding the most appropriate answer Scarlet seeks for student feedback in order to improve future search. The metric proposed is based on the power law which occurs in natural language, that is the Zipfian distribution[1]. It is designed to work for any spoken language although it might work on some better than other depending on the nature of the language, the structure, grammar and semantics. The method uses this metric to derive context from data and then queries the data source looking for the best match. The whole implementation is rounded off by a learning module which gives the system a learning curve based on users (students) scoring how relevant the output is among other parameters.

Scarlet an Artificial Teaching Assistant is a personal digital assistant that has been developed with main aim to assist students in their learning process by ensuring fast and efficiently search of documents and learning materials. Scarlet is able to give an adequate response to a specific question based on knowledge gathered by an unique algorithm which enables her to recognize context during file and web page content search. After finding the most appropriate answer Scarlet seeks for student feedback in order to improve future search. The metric proposed is based on the power law which occurs in natural language, that is the Zipfian distribution[1]. It is designed to work for any spoken language although it might work on some better than other depending on the nature of the language, the structure, grammar and semantics. The method uses this metric to derive context from data and then queries the data source looking for the best match. The whole implementation is rounded off by a learning module which gives the system a learning curve based on users (students) scoring how relevant the output is among other parameters. All the main algorithms and newly proposed metrics like the “contextual similarity” are presented in the same paper.

The quality of higher education is of particular importance for the development and progress of modern society. Modern higher education institutions aim to improve their services, and establish a system of continuous quality assurance. Within the framework of the European Standards and Guidelines for Quality Assurance, Standard 1.6 requires the implementation of information systems for efficient management of study programs and other activities. During the phase of problem analysis and objectives, in regard to the application of information systems at the University "Dzemal Bijedic", the existence of heterogeneous internal and external data sources was established. In modern management, data is considered a key resource necessary for the survival and development of the institution. Accordingly, the research focus is on the development of models of business intelligence systems that will be based on existing data sources. This system would primarily be used to support internal quality assurance at the University, as well as management support for timely and optimal decision making process. This paper presents the tools and technology of business intelligence, and through practical example demonstrates the possibilities of the system.

Adaptive e learning systems are expensive and their development is consuming both in the sense of time and human resources. Therefore, it is not realistic to expect that such systems can be developed in BiH environment in the very near future. On the other hand, even in BiH institutions involved in e learning, there exist elements of adaptive systems in the form of e content and independent applications that support e learning, such as forums, testing modules, e learning content management modules, and applications for testing the learning styles. In this paper, we present a model that uses the existing solutions combined with one additional module which is collecting data from the individual applications in order to use them for achieving certain level of adaptivity. The suggested solution is possible to implement in the BiH environment is as step in a fully adaptive system development.