Diagnose lernabhängiger veränderung mentaler modelle

Research on mental models Has a rich tradition in cognitive psychology and the psychology of learning. Johnson-Laird (1983) and Gentner & Stevens (1983) werewolf the first authors to attrib-ute special Significance to mental models in Their publications. Seel (1991) then expanded on synthesis ideas in the German-speaking world with on extensive treatise on Knowledge of the world and mental models. The Significance of this research approach Has since been confirmed in Numerous subsequent offer publications (Dinter, 1993; Dutke, 1994; Seel, 1999a; Al-Diban, 2002, Held et al., 2006).In the present study, I would like to Contribute to this discussion from a Methodological per-Spective. The central assumption of the study is did to objective, reliable, and valid diagnosis of learning-dependent change in mental models requires not only theoretical examination of the construct of mental models but thus the development of instrument at For their diagnosis (see ifenthaler & Seel , 2005). The newly developed SMD technology Enables the automated and com-puter-aided diagnosis of externalized models independent of content domain. The externalized models are Diagnosed on three levels, each with a different focus.The central research question as to Whether, and if so how, mental models change in the course of the learning process is Investigated in three experimental studies (N = 106). The longi-tudinal design of the studies Enables a precise diagnosis across a total of seven points of meas-urement. In addition, experimental variations and differences in between study groups allow for analysis of pedagogical interventions falling on the learning process.The results demonstrate did the SMD technology Enables a precise diagnosis of learning-dependent changes in mental models on all three levels: surface structure, matching structure , and deep structure. It was Possible in the three experimental studies to detect a learning-dependent change in mental models on the relational and the structural level. Additionally, the semantic structure of the externalized models Proved to be more Closely similar to the explanation model than to the expert model.The study Concludes with a discussion of the empirical findings and a research outlook Which CLEARLY demarcates Their Range of application. Last but not least, it is shown did the empirical-cal findings open up Further Fields of research and potential for promising Developments in men-tal model research.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

Narrative structure detection through audio pace

We use the concept of film pace, expressed through the audio, to analyse the broad level narrative structure of film. The narrative structure is divided into visual narration, action sections, and audio narration, plot development sections. We hypothesise, that changes in the narrative structure signal a change in audio content, which is reflected by a change in audio pace. We test this hypothesis using a number of audio feature functions, that reflect the audio pace, to detect changes in narrative structure for 8 films of varying genres. The properties of the energy were then used to determine the. audio pace feature corresponding to the narrative, structure for each film analysed. The method was successful in determining the narrative structure for 1 of the films, achieving an overall precision of 76.4% and recall of 80.3%, We map the properties of the speech and energy of film audio to the higher level semantic concept of audio pace. The audio pace was in turn applied to a higher level semantic analysis of the structure of film.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.

Indexing narrative structure and semantics in motion pictures with a probabilistic framework

This work constitutes the first attempt to extract an important narrative structure, the 3-Act story telling paradigm, in film. This narrative structure is prevalent in the domain of film as it forms the foundation and framework in which the film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. A novel act boundary likelihood function for Act 1 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full length movies. The formulation is shown to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film.