A framework for investigating the interaction in information retrieval

To increase retrieval effectiveness, information retrieval systems must offer better supports to users in their information seeking activities. To achieve this, one major concern is to obtain a better understanding of the nature of the interaction between a user and an information retrieval system. For this, we need a means to analyse the interaction in information retrieval, so as to compare the interaction processes within and across information retrieval systems. We present a framework for investigating the interaction between users and information retrieval systems. The framework is based on channel theory, a theory of information and its flow, which provides an explicit ontology that can be used to represent any aspect of the interaction process. The developed framework allows for the investigation of the interaction in information retrieval at the desired level of abstraction. We use the framework to investigate the interaction in relevance feedback and standard web search

Remote sensing of cloud properties using ground-based measurements of zenith radiance

We have conducted the first extensive field test of two new methods to retrieve optical properties for overhead clouds that range from patchy to overcast. The methods use measurements of zenith radiance at 673 and 870 nm wavelengths and require the presence of green vegetation in the surrounding area. The test was conducted at the Atmospheric Radiation Measurement Program Oklahoma site during September–November 2004. These methods work because at 673 nm (red) and 870 nm (near infrared (NIR)), clouds have nearly identical optical properties, while vegetated surfaces reflect quite differently. The first method, dubbed REDvsNIR, retrieves not only cloud optical depth τ but also radiative cloud fraction. Because of the 1-s time resolution of our radiance measurements, we are able for the first time to capture changes in cloud optical properties at the natural timescale of cloud evolution. We compared values of τ retrieved by REDvsNIR to those retrieved from downward shortwave fluxes and from microwave brightness temperatures. The flux method generally underestimates τ relative to the REDvsNIR method. Even for overcast but inhomogeneous clouds, differences between REDvsNIR and the flux method can be as large as 50%. In addition, REDvsNIR agreed to better than 15% with the microwave method for both overcast and broken clouds. The second method, dubbed COUPLED, retrieves τ by combining zenith radiances with fluxes. While extra information from fluxes was expected to improve retrievals, this is not always the case. In general, however, the COUPLED and REDvsNIR methods retrieve τ to within 15% of each other

Mesoscopic effective material parameters for thin layers modeled as single and double grids of interacting loaded wires

As an example of thin composite layers we consider single and double grids of periodically arranged interacting wires loaded with a certain distributed reactive impedance. Currents induced to the wires by a normally incident plane wave are rigorously calculated and the corresponding dipole moment densities are determined. Using this data and the averaged fields we assign mesoscopic material parameters for the proposed grid structures. These parameters depend on the number of grids, and measure the averaged induced polarizations. It is demonstrated that properly loaded double grids possess polarization response that over some frequency range can be described by assigning negative values for the mesoscopic parameters. Discussion is conducted on the physical meaningfulness to assign such material parameters for thin composite slabs. The results predicted by the proposed method for the double-grid structures are compared with the results obtained using the commonly adopted S-parameter retrieval procedure.Comment: 36 pages, 23 figure

VMEXT: A Visualization Tool for Mathematical Expression Trees

Mathematical expressions can be represented as a tree consisting of terminal symbols, such as identifiers or numbers (leaf nodes), and functions or operators (non-leaf nodes). Expression trees are an important mechanism for storing and processing mathematical expressions as well as the most frequently used visualization of the structure of mathematical expressions. Typically, researchers and practitioners manually visualize expression trees using general-purpose tools. This approach is laborious, redundant, and error-prone. Manual visualizations represent a user's notion of what the markup of an expression should be, but not necessarily what the actual markup is. This paper presents VMEXT - a free and open source tool to directly visualize expression trees from parallel MathML. VMEXT simultaneously visualizes the presentation elements and the semantic structure of mathematical expressions to enable users to quickly spot deficiencies in the Content MathML markup that does not affect the presentation of the expression. Identifying such discrepancies previously required reading the verbose and complex MathML markup. VMEXT also allows one to visualize similar and identical elements of two expressions. Visualizing expression similarity can support support developers in designing retrieval approaches and enable improved interaction concepts for users of mathematical information retrieval systems. We demonstrate VMEXT's visualizations in two web-based applications. The first application presents the visualizations alone. The second application shows a possible integration of the visualizations in systems for mathematical knowledge management and mathematical information retrieval. The application converts LaTeX input to parallel MathML, computes basic similarity measures for mathematical expressions, and visualizes the results using VMEXT.Comment: 15 pages, 4 figures, Intelligent Computer Mathematics - 10th International Conference CICM 2017, Edinburgh, UK, July 17-21, 2017, Proceeding

A Nine Month Report on Progress Towards a Framework for Evaluating Advanced Search Interfaces considering Information Retrieval and Human Computer Interaction

This is a nine month progress report detailing my research into supporting users in their search for information, where the questions, results or even thei

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed