6,539 research outputs found
MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism
combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN has
sufficient expressive power for general-purpose knowledge representation and
reasoning. Developing a MEBN model to support a given application is a
challenge, requiring definition of entities, relationships, random variables,
conditional dependence relationships, and probability distributions. When
available, data can be invaluable both to improve performance and to streamline
development. By far the most common format for available data is the relational
database (RDB). Relational databases describe and organize data according to
the Relational Model (RM). Developing a MEBN model from data stored in an RDB
therefore requires mapping between the two formalisms. This paper presents
MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We
identify links between the two languages (RM and MEBN) and define four levels
of mapping from elements of RM to elements of MEBN. These definitions are
implemented in the MEBN-RM algorithm, which converts a relational schema in RM
to a partial MEBN model. Through this research, the software has been released
as a MEBN-RM open-source software tool. The method is illustrated through two
example use cases using MEBN-RM to develop MEBN models: a Critical
Infrastructure Defense System and a Smart Manufacturing System
Context-based Information Fusion: A survey and discussion
This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of \u201ccontext\u201d. It shows how its fortune in the distributed computing world eventually permeated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploitation dynamics and architectural aspects peculiar to the fusion domain are presented and discussed
Context classification for service robots
This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs.
As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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