The NP-Complete Face of Information-Theoretic Security

The problem of perfectly secure communication has enjoyed considerable theoretical treatment over the last decades. Results in this area include the identification of multipath transmission as a necessary ingredient, as well as quantum key distribution （QKD）, which can perfectly protect direct lines, Combining the advantages of the quantum and multipath transmission paradigm, as well as rigorously analyzing the security of such combined techniques, is possible by virtue of game-theory. Based on a game-theoretic measure of channel vulnerability, the authors prove the problem of setting up infrastructures for QKD-based multipath transmission to be NP-complete. The authors consider the problem in two flavors, both being computationally hard. Remarkably, the authors＇ results indicate that the P-vs-NP-question is only of minor effect for confidentiality, because either nowadays public-key cryptosystems remain secure （in case that P, NP） or infrastructures facilitating perfectly confidential communication can be constructed efficiently （in case that P = NP）.

Information Interaction Is the Fifth Type of Fundamental Interactions

Information is an integral part of the Universe. By its physical essence information is heterogeneity of matter and energy. Therefore information is inseparably connected with matter and energy. The universal measure of information in physical heterogeneity is the Shannon information entropy. An information approach along with a physical one allows to obtain new, sometimes more general data in relation to data obtained on the ground of physical rules only. The results presented in this paper show the effectiveness of informational approach for studying the interactions in the Universe. The paper shows that, along with the physical interactions the gravitational, electromagnetic, strong, weak interactions exists fifth type of fundamental interactions--information interaction, whose magnitude is not dependent on distance. The existence of information interaction is determined by the entanglement of quantum states, of quantum subsystems. The magnitude of information interaction is measured in bits.

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample information from the AOI （area of interest） for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR （building coverage ratio） and FAR （floor area ratio） are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM （normalized digital surface model） comprised of upsampled elevation data with considerable improvement regarding region filling and ＂straightness＂ of elevation discontinuities. Following this step, a MFNN （multilayer feedforward neural network） is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.