Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules

Higher Variations of the Monty Hall Problem (3.0 and 4.0) and Empirical Definition of the Phenomenon of Mathematics, in Boole's Footsteps, as Something the Brain Does

Generalizations of the Monty Hall problem are studied according to George Boole's (1853) "An Investigation of the Laws of Thought, on Which Are Founded the Mathematical Theories of Logic and Probabilities

A New Trust Framework for E-Government in Cloud of Things

The concept of Cloud of Things becomes important for each e-government, facilitating its way of work, increasing its productivity and all that leading to cost savings. It will likely have a significant impact on the e-governments in the future. E-government diversity goals face many challenges. Trust is a major challenge when deploying Cloud of Things in e-government. In this paper, a new trust framework is proposed that supports trust between Internet of Things devices interconnected to the Cloud in order to support e-government services to be delivered in trusted manner. The proposed framework has been applied to a use case study to ensure the trustworthiness of the proposed framework in a real mission. The results show that the proposed trust framework is useful to ensuring a trust environment for Cloud of Things in order to continue providing and gathering data needed to provide services to users through the E-government services

An exact algorithm for first order probabilistic logic

Human beings often have to reason and make decisions based on uncertain knowledge of real world problems. Therefore, many artificial intelligence (AI) applications, such as expert systems, must have the ability to understand the way human beings reason from uncertain data or knowledge in order to reach a conclusion. Several approaches have been proposed in this respect to deal with various kinds of uncertainty in AI. Among these approaches, probabilistic theory is used in many research areas such as knowledge-based systems, data mining, etc. Nilsson revisited in 1986 the early work of Boole (1854) and of Hailperin (1976) on logic and probability. He proposed a generalization of logic in which the truth values of sentences are probability values. The main problem addressed by Nilsson is the probabilistic satisfiability ( PSAT) for both propositional and first-order logic: determine, given a set of sentences (i.e., clauses) and probabilities that these sentences are true, whether these probabilities are consistent. Since first-order logic is used in many AI applications due to its expressiveness to represent knowledge over propositional logic, our thesis proposes an extension of the mathematical modeling of PSAT to first-order logic, FOPSAT for short. We next propose an exact algorithm based on delayed column generation technique, to check consistency and, if consistency holds, to entail new probability values for an additional logical sentence to be true and such that the augmented set of sentences remains consistent. We illustrate the proposed algorithm on an example, and discuss its potential to solve medium size FOPSAT instances

An anytime deduction heuristic for first order probabilistic logic

This thesis describes an anytime deduction heuristic to address the decision and optimization form of the First Order Probabilistic Logic problem which was revived by Nilsson in 1986. Reasoning under uncertainty is always an important issue for AI applications, e.g., expert systems, automated theorem-provers, etc. Among the proposed models and methods for dealing with uncertainty, some as, e.g., Nilsson's ones, are based on logic and probability. Nilsson revisited the early works of Boole (1854) and Hailperin (1976) and reformulated them in an AI framework. The decision form of the probabilistic logic problem, also known as PSAT, consists of finding, given a set of logical sentences together with their probability value to be true, whether the set of sentences and their probability value is consistent. In the optimization form, assuming that a system of probabilistic formulas is already consistent, the problem is: Given an additional sentence, find the tightest possible probability bounds such that the overall system remains consistent with that additional sentence. Solution schemes, both heuristic and exact, have been proposed within the propositional framework. Even though first order logic is more expressive than the propositional one, more works have been published in the propositional framework. The main objective of this thesis is to propose a solution scheme based on a heuristic approach, i.e., an anytime deduction technique, for the decision and optimization form of first order probabilistic logic problem. Jaumard et al. [33] proposed an anytime deduction algorithm for the propositional probabilistic logic which we extended to the first order context

The Evidential Value of National Regulatory Infringement Decisions for the Purposes of Private Damages Actions: Trying to Establish what Really Does "Follow-on"

This article considers issues around the evidential value of final infringement decisions of national regulators and review courts for the purposes of follow-on damages actions, particularly in the context of Article 9(1) of the EU’s Antitrust Damages Directive, which purports to address this issue. Some of the key questions that potential follow-on claimants are faced with are considered and it is suggested that Article 9(1) does little to address these issues. A brief analysis of the transposition of Article 9(1) into national law is then considered. It is concluded that in the absence of the EU courts providing further elaboration on Article 9(1), it will largely be left to national courts to decide how final infringement decisions should be treated in practice in follow-on actions brought before them. This may well result in a wide variety of approaches being adopted. In this context, some significant recent decisions in follow-on cases in both Italy and the UK are considered. There is also some brief discussion of Article 9(2) of the Directive