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In this paper, a new pseudo-random number generator (PRNG) based on improved onedimensional discrete-space chaotic map is proposed. Like the original, the improved map relies on bijective mapping of permutations and natural numbers. Instead of using standard Lehmer code, we use a mapping computable in linear time, which significantly speeds up the PRNG. Results of NIST 800-22 test suite and TestU01 test suite confirm that the proposed approach can be used for generation of pseudo-random numbers. Due to discrete nature of used chaotic map, the proposed PRNG is not influenced by dynamical degradation and has virtually unlimited key space. Proposed approach has much better ratio between required memory and security level than previous secure one-dimensional discrete-space chaotic PRNGs. Also, proposed PRNG is much faster than other secure PRNGs of the same type. Satisfactory speed and small memory requirements indicate that proposed PRNG has properties desirable for use in devices with limited memory space, such as wireless sensor networks.

An approach for using lasso (Least Absolute Shrinkage and Selection Operator) regression in creating sparse 3D models of soil properties for spatial prediction at multiple depths is presented. Modeling soil properties in 3D benefits from interactions of spatial predictors with soil depth and its polynomial expansion, which yields a large number of model variables (and corresponding model parameters). Lasso is able to perform variable selection, hence reducing the number of model parameters and making the model more easily interpretable. This also prevents overfitting, which makes the model more accurate. The presented approach was tested using four variable selection approaches – none, stepwise, lasso and hierarchical lasso, on four kinds of models – standard linear model, linear model with polynomial expansion of depth, linear model with interactions of covariates with depth and linear model with interactions of covariates with depth and its polynomial expansion. This framework was used to predict Soil Organic Carbon (SOC) in three contrasting study areas: Bor (Serbia), Edgeroi (Australia) and the Netherlands. Results show that lasso yields substantial improvements in accuracy over standard and stepwise regression — up to 50 % of total variance. It yields models which contain up to five times less nonzero parameters than the full models and that are usually more sparse than models obtained by stepwise regression, up to three times. Extension of the standard linear model by including interactions typically improves the accuracy of models produced by lasso, but is detrimental to standard and stepwise regression. Regarding computation time, it was demonstrated that lasso is several orders of magnitude more efficient than stepwise regression for models with tens or hundreds of variables (including interactions). Proper model evaluation is emphasized. Considering the fact that lasso requires meta-parameter tuning, standard cross-validation does not suffice for adequate model evaluation, hence a nested cross-validation was employed. The presented approach is implemented as publicly available sparsereg3D R package.

A new method for obtaining pseudo-random numbers, based on discrete-space chaotic map, is presented. The proposed method uses a discrete chaotic map, based on the composition of permutations. The randomness of pseudo-random sequences generated by proposed method is verified using NIST 800-22 test suite and TestU01. Proposed method is not affected by dynamical degradation, so the process of generation of pseudo-random numbers is not influenced by approximations of any kind. The advantage of the proposed method is that it has virtually unlimited key space and the ability to generate same number of different pseudo-random sequences as other secure discrete-space chaotic methods, but with significantly lower memory space requirements. Also, higher level of security and great cycle lengths can be achieved. Small memory requirements could make proposed PRNG applicable in devices with limited memory space.

For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.

This article deals with teaching students how to review research articles of other authors and how to respond to reviewer comments related to their own articles. By teaching these skills, one educates students how to do something which is undoubtedly much more important for their professional development: how to properly do their own research and how to properly write their own research articles. The guidelines given are primarily intended for students of computer science. Nevertheless, the notions presented here are applicable to areas other than computing. Since there are so many branches within computing, each one characterized by its own particular features, this article also includes a section on variations of presented methodology, which are of interest for each particular branch of computer science covered by the authors of this article. In general, the review process consists of two main parts: formal review and quality review. Led by the lack of results in the available literature, an analysis based on readability index evaluates average article acceptance rates, review times, and delay period for publishing accepted articles depending on the following: article types, willingness of reviewers to do reviews, journal impact factor, and the scientific field. Quality review must be taken into account for all types of articles. Appendix A describes specific experiences from various scientific fields. Appendix B gives specific guidelines for a number of fields covered by the authors of this article.

Evaluating improvements to modern SAT solvers and comparison of two arbitrary solvers is a challenging and important task. Relative performance of two solvers is usually assessed by running them on a set of SAT instances and comparing the number of solved instances and their running time in a straightforward manner. In this paper we point to shortcomings of this approach and advocate more reliable, statistically founded methodologies that could discriminate better between good and bad ideas. We present one such methodology and illustrate its application.

Demographic aspects of prehistoric populations have an important role in current archaeological theory and empirical research. In this study, we develop a method to estimate population dynamics and population size and apply it to data on house remains at one of key European Mesolithic-Neolithic transitional sites - Lepenski Vir (Serbia). Lepenski Vir is a site located in the Danube Gorges, well-known for its trapezoidal house floors and stone sculpture. It was most intensively occupied between similar to 6200 and similar to 6000 cal BC, the so called Transitional phase, which corresponds to the beginning of the Neolithic in Central Balkans. We combine archaeological evidence and ethnographic information with mathematical models of population dynamics and house accumulation within a Bayesian framework (Approximate Bayesian Computation) to derive posterior distributions of growth rate and population size estimates for the Lepenski Vir population in this period.

The solution of a class of third order ordinary differential equations possessing two parameter Lie symmetry group is obtained by group theoretic means. It is shown that reduction to quadratures is possible according to two scenarios: 1) if upon first reduction of order the obtained second order ordinary differential equation besides the inherited point symmetry acquires at least one more new point symmetry (possibly a hidden symmetry of Type II). 2) First, reduction paths of the fourth order differential equations with four parameter symmetry group leading to the first order equation possessing one known (inherited) symmetry are constructed. Then, reduction paths along which a third order equation possessing two-parameter symmetry group appears are singled out and followed until a first order equation possessing one known (inherited) symmetry are obtained. The method uses conditions for preservation, disappearance and reappearance of point symmetries., Comment: Part 2 of the paper consists of tables

We present a new, CDCL-based approach for automated theorem proving in coherent logic -- an expressive semi-decidable fragment of first-order logic that provides potential for obtaining human readable and machine verifiable proofs. The approach is described by means of an abstract state transition system, inspired by existing transition systems for SAT and represents its faithful lifting to coherent logic. The presented transition system includes techniques from which CDCL SAT solvers benefited the most (backjumping and lemma learning), but also allows generation of human readable proofs. In contrast to other approaches to theorem proving in coherent logic, reasoning involved need not to be ground. We prove key properties of the system, primarily that the system yields a semidecision procedure for coherent logic. As a consequence, the semidecidability of another fragment of first order logic which is a proper superset of coherent logic is also proven.

Execution of most of the modern DPLL-based SAT solvers is guided by a number of heuristics. Decisions made during the search process are usually driven by some fixed heuristic policies. Despite the outstanding progress in SAT solving in recent years, there is still an appealing lack of techniques for selecting policies appropriate for solving specific input formulae. In this paper we present a methodology for instance-based selection of solver's policies that uses a data-mining classification technique. The methodology also relies on analysis of relationships between formulae, their families, and their suitable solving strategies. The evaluation results are very good, demonstrate practical usability of the methodology, and encourage further efforts in this direction.