Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining

In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built

Matters of Biocybersecurity with Consideration to Propaganda Outlets and Biological Agents

The modern era holds vast modalities in human data utilization. Within Biocybersecurity (BCS), categories of biological information, especially medical information transmitted online, can be viewed as pathways to destabilize organizations. Therefore, analysis of how the public, along with medical providers, process such data, and the methods by which false information, particularly propaganda, can be used to upset the flow of verified information to populations of medical professionals, is important for maintenance of public health. Herein, we discuss some interplay of BCS within the scope of propaganda and considerations for navigating the field

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve \u3e90% accuracy on the detection of seagrass scars

The dual standard model and the 750 GeV events at the LHC

The aim of this short paper is to discuss the recently observed excess at 750 GeV by both CMS and ATLAS in the light of the dual standard model. Within this framework it is natural to introduce neutral spin 0 and/or spin 2 SU(2) glue mesons which could easily account for this observation if it is confirmed. The model predicts that these glue mesons would be part of SU(2) triplets and that there must thus be charged counterparts of these glue mesons carrying a QED charge of _1 with a spin 0 and/or 2 as well