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.