740 research outputs found
Indexing the Event Calculus with Kd-trees to Monitor Diabetes
Personal Health Systems (PHS) are mobile solutions tailored to monitoring
patients affected by chronic non communicable diseases. A patient affected by a
chronic disease can generate large amounts of events. Type 1 Diabetic patients
generate several glucose events per day, ranging from at least 6 events per day
(under normal monitoring) to 288 per day when wearing a continuous glucose
monitor (CGM) that samples the blood every 5 minutes for several days. This is
a large number of events to monitor for medical doctors, in particular when
considering that they may have to take decisions concerning adjusting the
treatment, which may impact the life of the patients for a long time. Given the
need to analyse such a large stream of data, doctors need a simple approach
towards physiological time series that allows them to promptly transfer their
knowledge into queries to identify interesting patterns in the data. Achieving
this with current technology is not an easy task, as on one hand it cannot be
expected that medical doctors have the technical knowledge to query databases
and on the other hand these time series include thousands of events, which
requires to re-think the way data is indexed. In order to tackle the knowledge
representation and efficiency problem, this contribution presents the kd-tree
cached event calculus (\ceckd) an event calculus extension for knowledge
engineering of temporal rules capable to handle many thousands events produced
by a diabetic patient. \ceckd\ is built as a support to a graphical interface
to represent monitoring rules for diabetes type 1. In addition, the paper
evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show
how indexing events using kd-trees improves scalability with respect to the
current state of the art.Comment: 24 pages, preliminary results calculated on an implementation of
CECKD, precursor to Journal paper being submitted in 2017, with further
indexing and results possibilities, put here for reference and chronological
purposes to remember how the idea evolve
A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)
This dissertation addresses a problem found in supervised machine learning (ML) classification, that the target variable, i.e., the variable a classifier predicts, has to be identified before training begins and cannot change during training and testing. This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories: Stanovich\u27s tripartite framework, which proposes learning results from interactions between conscious and unconscious processes; and, the Integrated Information Theory (IIT) of Consciousness, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows for retaining and reasoning-over data sets in a non-ontological, non-hierarchical qualia space (QS). This novel computational approach supports concept drift, by allowing the target variable to change ad infinitum without re-training while achieving classification accuracy comparable to or greater than benchmark classifiers. Additionally, the research produced a functioning model of Stanovich\u27s framework, and a computationally tractable working solution for a representation of qualia, which when exposed to new examples, is able to match the causal structure and generate new inferences
Topological Properties in Ontology-based Applications
Proceedings of: 11th International Conference on Intelligent Systems Design and Applications, CĂłrdoba, Spain, 22 â 24 November, 2011.Representation and reasoning with spatial properties is essential in several application domains where ontologies
are being successfully applied; e.g., Information Fusion systems. This requires a full characterization of the semantics of relations such as adjacent, included, overlapping, etc. Nevertheless, ontologies
are not expressive enough to directly support widely-use spatial or topological theories, such as the Region Connection Calculus (RCC). In addition, these properties must be properly instantiated in the ontology, which may require expensive calculations. This paper presents a practical approach to represent
and reason with topological properties in ontology-based systems, as well as some optimization techniques that have been applied in a video-based Information Fusion application.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC,CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-02.Publicad
Description Logic for Scene Understanding at the Example of Urban Road Intersections
Understanding a natural scene on the basis of external sensors is a task yet to be solved by computer algorithms. The present thesis investigates the suitability of a particular family of explicit, formal representation and reasoning formalisms for this task, which are subsumed under the term Description Logic
Primacy of Quantum Logic in the Natural World
This paper presents evidence from the fields of cognitive science and quantum information theory suggesting quantum theory to be the dominant fundamental logic in the natural world, in direct challenge to the long-held assumption that quantum logic only need be considered âin the quantum realm.' A summary of the evolution of quantum logic and quantum theory is presented, along with an overview for the necessity of incomplete quantum knowledge, and some representative aspects of quantum logic. A case can be made that classical logic and theory is a subset of quantum logic and theory, given that elements of quantum physics exist that can never admit classical understanding, including: Bell's theorem, Hardy's theorem, and the Pusey-Barrett-Rudolph theorem. Support can be found for the primacy of quantum logic in the natural world in the cognitive sciences, where recent research studies recognize quantum logic in studies of: the subconscious, decisions involving unknown interconnected variables, memory, and question sequencing
A Practical Approach to the Development of Ontology-Based Information Fusion Systems
Proceedings of: NATO Advanced Study Institute (ASI) on Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems, Salamanca, 19-30 September, 2011Ontology-based representations are gaining momentum among other alternatives to implement the knowledge model of high-level fusion applications. In this paper, we provide an introduction to the theoretical foundations of ontology-based knowledge representation and reasoning, with a particular focus on the issues that appear in maritime security âwhere heterogeneous regulations, information sources, users, and systems
are involved. We also present some current approaches and existing technologies for high-level fusion based on ontological representations. Unfortunately, current tools for the practical implementation of ontology-based systems are not fully standardized, or even prepared to work together in medium-scale systems. Accordingly, we discuss different alternatives to face problems such as spatial and temporal knowledge representation
or uncertainty management. To illustrate the conclusions drawn from this research, an ontology-based semantic tracking system is briefly presented. Results and latent capabilities of this framework are shown at the end of the paper, where we also envision future opportunities for this kind of applications.This research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS 2008-07029-C02-02.Publicad
High-Level Information Fusion in Visual Sensor Networks
Information fusion techniques combine data from multiple sensors, along with additional information and knowledge, to obtain better estimates of the observed scenario than could be achieved by the use of single sensors or information sources alone. According to the JDL fusion process model, high-level information fusion is concerned with the computation of a scene representation in terms of abstract entities such as activities and threats, as well as estimating the relationships among these entities. Recent experiences confirm that context knowledge plays a key role in the new-generation high-level fusion systems, especially in those involving complex scenarios that cause the failure of classical statistical techniques âas it happens in visual sensor networks. In this chapter, we study the architectural and functional issues of applying context information to improve high-level fusion procedures, with a particular focus on visual data applications. The use of formal knowledge representations (e.g. ontologies) is a promising advance in this direction, but there are still some unresolved questions that must be more extensively researched.The UC3M Team gratefully acknowledges that this research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02
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