Effective decision-making often relies on timely insights from complex visual data. While Information Visualization (InfoVis) dashboards can support this process, they rarely adapt to users’ cognitive state, and less so in real time. We present SYMBIOTIK, an intelligent, context-aware adaptive visualization system that leverages neurophysiological signals to estimate mental workload (MWL) and dynamically adapt visual dashboards using reinforcement learning (RL). Through a user study with 120 participants and three visualization types, we demonstrate that our approach improves task performance and engagement. SYMBIOTIK offers a scalable, real-time adaptation architecture, and a validated methodology for neuroadaptive user interfaces.

A method for identifying shape features of local nature on the shape's boundary, in a way that is facilitated by the presence of noise is presented. The boundary is seen as a real function. A study of a certain distance function reveals, almost counter-intuitively, that vertices can be defined and localized better in the presence of noise, thus the concept of noising, as opposed to smoothing, is conceived and presented. The method works on both smooth and noisy shapes, the presence of noise having an effect of improving on the results of the smoothed version. Experiments with noise and a comparison to state of the art validate the method.

We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.

A fully automated wrapper for information extraction from Web pages is presented. The motivation behind such systems lies in the emerging need for going beyond the concept of "human browsing". The World Wide Web is today the main "all kind of information" repository and has been so far very successful in disseminating information to humans. By automating the process of information retrieval, further utilization by targeted applications is enabled. The key idea in our novel system is to exploit the format of the Web pages to discover the underlying structure in order to finally infer and extract pieces of information from the Web page. Our system first identifies the section of the Web page that contains the information to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. STAVIES can operate without human intervention and does not require any training. The main innovation and contribution of the proposed system consists of introducing a signal-wise treatment of the tag structural hierarchy and using hierarchical clustering techniques to segment the Web pages. The importance of such a treatment is significant since it permits abstracting away from the raw tag-manipulating approach. Experimental results and comparisons with other state of the art systems are presented and discussed in the paper, indicating the high performance of the proposed algorithm.