Abstract: The amount of social events has increased significantly and location-based services have become an integral part of our life. This makes the recommendation of activity sequences an important emerging application. Recently, the notion of a distributed event (e.g., music festival or cruise) that gathers multiple competitive activities has appeared in the literature. An attendee of such events is overwhelmed with numerous possible activities and faces the problem of activity selection with the goal to maximise satisfaction of experience. This selection is subject to various uncertainties. In this paper, we formulate the problem of recommendation of activity sequences as a combination of personalised event recommendation and scheduling problem. We present a novel integrated framework to solve it and two computation strategies to analyse the categorical, temporal and textual users' interests. We mine the users' historical traces to extract their behavioural patterns and use them in the construction of the itinerary. The evaluation of our approach on a dataset built over a cruise program shows an average improvement of 10.4% over the state-of-the-art.

Abstract: Pricing is one of the most vital and highly demanded component in the mix of marketing along with the Product, Place and Promotion. An organization can adopt a number of pricing strategies, typically based on corporate objectives. This paper proposes a methodology to define an optimal pricing strategy for convenience stores based on dimension reduction methods and uncertainty of data. The solution approach involves a multiple linear regression as well as a linear programming optimization model using several variables to consider. A strategy to select a set of important variables among a large number of predictors using mix of PCA and best subset methods is presented. A linear optimization model then in solved using uncertainty data and diverse business rules. To show the value of the proposed methodology computation of optimal prices are compared with previous results obtained in a pilot performed for selected stores. This strategy provides an alternative solution that allows the decision maker include proper business rules of their particular environment in order to define a price strategy that meet the objective business goals.

Abstract: This paper presents an algorithm for designing a cryptographic system, in which the derivative disproportion functions (key functions) are used. This cryptographic system is used for an operative identification of a differential equation describing the movement of quasi-stationary objects. The symbols to be transmitted are encrypted by the sum of at least two of these functions combined with random coefficients. A new algorithm is proposed for decoding the received messages making use of important properties of the derivative disproportion functions. Numerical experiments are reported to demonstrate the algorithm’s reliability and robustness.

Abstract: The performance of coded modulation scheme based on the application of integer codes to TQAM constellation with $2^{2m}$ points is investigated. A method of calculating the exact value of SER in the case of TQAM over AWGN channel combined with encoding by integer codes is described. The results (SER and BER) of simulations in the case of coded 16, 64, and 256-TQAM simulations are given.

Abstract: Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire propagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) to predict the fire propagation. However, these parameters are usually difficult to measure or estimate precisely, and there is a high degree of uncertainty in many of them. This uncertainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.