The 14th International Conference on Modeling Decisions for Artificial Intelligence Kitakyushu, Japan October 18  20, 2017 http://www.mdai.cat/mdai2017 
Submission deadline:
USB DEADLINE: June 22nd, 2017 USB Submission open 
Abstract: Quite often decision makers have only partial information about the underlying uncertainty. This might happen, for instance, when information about the subject matter is obtained from different surveys/resources. We model such an information as a nonadditive probability. Consider a decision maker who has to choose between two portfolios, or between two groups of engineers, based on incomplete information about the uncertainty of the market, or about the productivity of the groups.How would the decision maker evaluate the expected return from each portfolio or expected productivity from each group? We present different schemes of aggregation with respect to nonadditive probabilities. These schemes might serve as decision tools in many fields, such as financial markets, production and more.
Abstract: In this study we present a first step toward nonlinear statistics by applying Choquet calculus to probability theory. Throughout the study we take a constructive approach. For nonlinear statistics, we consider a distorted probability space on the nonnegative real line. A distorted probability measure (Edwards 1953) is derived from a conventional probability measure by the monotone transformation with a generator (usually called a distortion function), where we deal with two classes of parametric generators. First we explore some properties of Choquet integrals of continuous functions with respect to distorted probabilities. Then we calculate basic statistics such as the distorted mean and variance of a random variable for uniform, exponential and Gamma distributions.
In general we can consider a fuzzy measure space（Sugeno 1974）for nonlinear statistics.
Abstract: Recently, sparsity has been playing a central role in signal processing, machine learning, and data science. Here we consider a problem of reconstructing (or learning) a signal (or a function) from observed data, which may be undersampled and disturbed by noise. To address this problem, a method called sparse modeling, also known as compressed sensing, has become a hot topic. In this talk, I will give a brief introduction to sparse modeling for signal estimation, and its applications to control. In particular, I will give an introduction to "maximum handsoff control," which has the minimum support length among all feasible solutions for saving energy and reducing CO2 emissions in control systems.
Abstract: Social networking service and sensing device have become more and more popular in recent years and data flow never stop to increase. Examples are genome sequences of same species, version controlled documents, and source codes in repositories. Since such a data is usually highlycompressible, adopting data compression techniques is a suitable way to process it. In addition, in order to catch up the speed of data grow, there is a strong demand for stream data compression, that is, fully online and really scalable compression. In this talk, I would like to focus on lossless data compression and introduce several stateoftheart technologies for stream data compression including their applications.
Abstract: This talk presents an overview of current needs on data provenance in relationship with data privacy. It discusses stateoftheart results in the area. In particular, the paper discusses properties and representation of data provenance, secure data provenance, and data provenance systems. Then, the talk will highlight the difficulties that we need to face in data provenance in relation to data privacy, and includes some lines of research that require further work.
University of Skövde
