Abstract: Evidential database has showed its potential application in many fields of study. This specific database framework allows frequent patterns and associative classification rules to be extracted in a more efficient way from uncertain and imprecise data. The definition of support and confidence measures plays an important role in the extraction process of meaningful patterns and rules. In this present work, we proposed a new definition of support and confidence measures based on interval representation. Moreover, a new algorithm, named EBS-Apriori, based on these bounded measures and several pruning strategies was developed. Experiments were conducted using several database benchmarks. Performance analysis showed a better prediction outcome for our proposed approach in comparison with several literature-based methods.

Abstract: This paper proposes a concise overview of Probabilistic Semantics according to a technology-oriented perspective. Indeed, while the progressive consolidation of Semantic Technology in a wide context and on a large scale is going to be a fact, the non-deterministic character of many problems and environments suggests the rise of additional researches around semantics to integrate the mainstream. Probabilistic extensions and their implications to the current semantic ecosystems are discussed in this paper with an implicit focus on the Web and its evolution. The critical literature review undertaken shows valuable theoretical works, effective applications, evidences of an increasing research interest as the response to real problems, as well as largely unexplored research areas.

Abstract: Data assimilation in weather forecasting is a well-known technique used to obtain an improved estimation of the current atmosphere state or data analysis. Data assimilation methods such as LAPS (Local Analysis and Prediction System) and STMAS (Space-Tim Multiscale Analysis System) provide reasonable results when dealing with a low resolution model, but they have restrictions when high real time resolution analysis is required for surface parameters. In particular, the Meteorological Service of Catalunya (SMC) is seeking for a real time high resolution analysis of surface parameters over Catalonia (north-east of Spain), in order to know the current weather conditions at any point of that region. For this purpose, a comparative study among several data assimilation methods, including the Altava's method designed in this weather forecast center, has been performed to determine which one delivers better results. The classical data assimilation techniques combine observational data with numerical weather prediction models to estimate the current state of the atmosphere and the multi-regression technique proposed by the SMC are included in this comparison analysis. The comparison has been done using as true state independent observational data the one provided by the Spanish Meteorological State Agency (Agencia Estatal de METeorologia, AEMET). The results show that the multi-regression technique provides more accurate analyses of temperature and relative humidity than the data assimilation methods because the multi-regression methodology only uses observations and consequently the model biases are avoided.

Abstract: In this paper, we analyze the concept of psychological warfare in Internet as the continuous opinion opposition forwarded into influencing at public opinion in some area and showed via manifold mass media publications. We formulated the basic research questions concerning psychological war and suggested simple steps to provide initial analysis. In our research, we decided to take Twitter as a typical representative of the social networking phenomenon as it is one of most subscribed social networks. To determine points of view related to chosen theme the algorithm of keyword clusterization is provided. We proposed a network composing method and performed network properties analysis, which we could interpret in the context of psychological war analysis.

Abstract: Our work addresses the influence of the growing adoption of information and communication technologies (ICT) for campaigning purposes on the evolving dynamics of information flows from the eminently social beings that candidates are. Our approach combines an analysis of contents to technological and methodological concerns. The breadth of these objectives as well as the amount of data to be considered pointed to a need for collaboration between several researchers for sharing out tasks and bringing together expertise from various disciplines. This paper presents results concerning three of the data collections life cycle phases: collection, cleaning, and storage. The result is a data collection ready to be analysed for different purposes. In particular, in our experimental validation it has been used for comparing political campaigns behaviour in France and the United Kingdom during the European elections in 2015.

Abstract: We study a special bilevel programming problem that arises in transactions between a Natural Gas Shipping Company and a Pipeline Operator. Because of the business relationships between these two actors, the timing and objectives of their decision-making process are different. In order to model that, bilevel programming was traditionally used in previous works. The problem theoretically studied to facilitate its solution; this included linear reformulation, heuristic approaches, and branch-and-bound techniques. We present a linear programming reformulation of the latest version of the model, which is easier and faster to solve numerically. This reformulation makes it easier to theoretically analyze the problem, allowing us to draw some conclusions about the nature of the solution. Since elements of uncertainty are definitely present in the bilevel natural gas cash-out problem, its stochastic formulation is developed in the form of a bilevel multi-stage stochastic programming model with recourse. After reducing the original formulation to a bilevel linear problem, a stochastic scenario tree is defined by its node events, and time series forecasting is used to produce stochastic values for data of natural gas price and demand. Numerical experiments were run to compare the stochastic solution with the perfect information solution and the expected value solutions.

Abstract: This paper considers a real life case study that determines the minimum number of sellers required to attend a set of customers located in a certain region taking into account the weekly schedule plan of the visits, as well as the optimal route. The problem is formulated as a combination of assignment, scheduling and routing problems. In the new formulation, case studies of small size subset of customers of the above type can be solved optimally. However, this subset of customers is not representative within the business plan of the company. To overcome this limitation, the problem is divided into three phases. A greedy algorithm is used in Phase I in order to identify a set of cost-effective feasible clusters of customers assigned to a seller. Phase II and III are then used to solve the problem of a weekly program for visiting the customers as well as to determine the route plan using MILP formulation. Several real life instances of different sizes have been solved demonstrating the efficiency of the proposed approach.

Abstract: In the last years development of exascale computing technology lead to need of obtaining evaluation for the energy consumption when large-scale problems are solved with different high-performance computing (HPC) systems. In this paper we study the energy efficiency of a class of Monte Carlo (MC) and Quasi-Monte Carlo(QMC) algorithms for given integral equation using hybrid HPC systems. The algorithms are applied to solve quantum kinetic integral equations describing ultra-fast transport in quantum wire. We compare the energy performance for both algorithms using a GPU-based computer platform and CPU-based computer platform with and without hyper threading (HT) technology. We use SPRNG library and CURAND generator to produce parallel pseudo-random (PPR) sequences in case of MC algorithms over CPU-based and GPU -based platforms, respectively. In the case of QMC algorithms Sobol and Halton sequences are used to produce parallel quasi-random (PQR) sequences. We compare the obtained results by the tested algorithms with respect to given energy metrics. The results of our study demonstrate the importance to take into account not only scalability of the HPC intensive algorithms but also their energy efficiency. They also show the need of further optimisation of the QMC algorithms when a GPU-based computing platform are used.