Optimization of fuzzy rule sets using a bacterial evolutionary algorithm

In this paper we present a novel approach where we rst create a large set of (possibly) redundant rules using inductive rule learning and where we use a bacterial evolutionary algorithm to identify the best subset of rules in a subsequent step. This enables us to nd an optimal rule set with respect to a freely de nable global goal function, which gives us the possibility to integrate interpretability related quality criteria explicitly in the goal function and to consider the interplay of the overlapping fuzzy rulesPeer Reviewe

Connotation Frames: A Data-Driven Investigation

Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how people use language and give rise to the connotative meanings. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.Comment: 11 pages, published in Proceedings of ACL 201

Toward Sensor-Based Context Aware Systems

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information

Conceptual Spaces in Object-Oriented Framework

The aim of this paper is to show that the middle level of mental representations in a conceptual spaces framework is consistent with the OOP paradigm. We argue that conceptual spaces framework together with vague prototype theory of categorization appears to be the most suitable solution for modeling the cognitive apparatus of humans, and that the OOP paradigm can be easily and intuitively reconciled with this framework. First, we show that the prototypebased OOP approach is consistent with Gärdenfors’ model in terms of structural coherence. Second, we argue that the product of cloning process in a prototype-based model is in line with the structure of categories in Gärdenfors’ proposal. Finally, in order to make the fuzzy object-oriented model consistent with conceptual space, we demonstrate how to define membership function in a more cognitive manner, i.e. in terms of similarity to prototype

Supporting Complex Business Decisions with a Fuzzy Mobile Assistant

Researchers have recognized the importance of semantic expressiveness in understanding and solving complex problems and have identified a need to incorporate reasoning about uncertainty into decision tools that assist business managers. This study extends current approaches and develops new tools to allow artificial mobile assistants to manage rule-driven consultations by capturing and recommending problem solutions through natural language interfaces. A prototype assistant is described to support fuzzy knowledge representations and fuzzy rule-based consultations. The prototype’s application is implemented in a Windows 8 mobile device and applied in a case study of business outsourcing decisions

Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming

A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Automatic Dynamic Web Service Composition: A Survey and Problem Formalization

The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs. Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically. The objective of this article is to overview the issues of automatic dynamic Web service composition. We discuss the issues related to the semantics of services, which is important for automatic Web service composition. We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their relation to the semantics of services. We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas