Search results for "Information Science"
showing 10 items of 3627 documents
Knowledge acquisition through introspection in Human-Robot Cooperation
2018
Abstract When cooperating with a team including humans, robots have to understand and update semantic information concerning the state of the environment. The run-time evaluation and acquisition of new concepts fall in the critical mass learning. It is a cognitive skill that enables the robot to show environmental awareness to complete its tasks successfully. A kind of self-consciousness emerges: the robot activates the introspective mental processes inferring if it owns a domain concept or not, and correctly blends the conceptual meaning of new entities. Many works attempt to simulate human brain functions leading to neural network implementation of consciousness; regrettably, some of thes…
Concentrated Solar Power: Ontologies for Solar Radiation Modeling and Forecasting
2014
This chapter considers the possibility of formally representing implicit and explicit knowledge of solar radiation modeling and forecasting by means of ontologies, with particular reference to the implications of concentrated solar power. The various applications discussed in the literature include various methods, such as spectral, parametric and empirical models, artificial neural networks and fuzzy logic approaches, as well as satellite and ground based imaging techniques. We want to use the principles of semantic technologies and formal ontologies, to represent knowledge in solar radiation models. The purpose is to capture the semantics of information and realize a system for sharing an…
A Pragmatic Characterization of Concept Algebra
2017
Taking into account the framework of denotational mathematics as seen by Yingxu Wang, in this paper the author wishes to implement a possible further pragmatic (context-depend) dimension into the algebraic structure of concept algebra. One of the main problems of software science is that regarding context-depend question of a programming language. Indeed, attention has been paid above all to syntactic and semantic dimensions of a programming language, neglecting the pragmatic one concerning context. The author has tried to face this question providing a first denotational mathematics structure taking into account a possible pragmatic dimension.
Concurrent Computing with Shared Replicated Memory
2019
Any concurrent system can be captured by a concurrent Abstract State Machine (cASM). This remains valid, if different agents can only interact via messages. It even permits a strict separation between memory managing agents and other agents that can only access the shared memory by sending query and update requests. This paper is dedicated to an investigation of replicated data that is maintained by a memory management subsystem, where the replication neither appears in the requests nor in the corresponding answers. We specify the behaviour of a concurrent system with such memory management using concurrent communicating ASMs (ccASMs), provide several refinements addressing different replic…
A Restricted-Weakly Connected Dominating Set for Role Assignment in a Multichannel MAC for Wireless Mesh Network
2009
International audience; We propose an efficient way of constructing the wireless mesh structure associated with Molecular MAC, a multichannel access method designed for efficient packet forwarding. We base our role assignment on a restricted Weakly Connected Dominating Set structure. After presenting a formal definition of the role assignment problem, we prove its NP-completeness. Then, we propose a centralized 2-approximation algorithm that maximizes the sum of radio link capacities in the molecular structure. Finally, we extend this protocol so that it can operate in a distributed way still providing the same guarantee. This distributed protocol is self-stabilizing thus robust to topology…
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Least-squares community extraction in feature-rich networks using similarity data
2021
We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may …
MetNet: A two-level approach to reconstructing and comparing metabolic networks
2021
Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways a…
ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability.
2020
In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. Th…
Improving a traffic operator interface using an ontology and intelligent rules
2007
The Transportation and traffic domain is cross-disciplinary; Different areas must be covered to solve complex problems. It also involves the use of different systems for monitoring, visualization, information distribution, control, communication, etc. All of these systems are implemented using different levels of technology. In most cases the control of these systems is handled by [graphic] user interfaces. In the past years the number of systems has grown substantially and because of their "monolytic" design, they have different and in most cases not-compatible user-interfaces. A generic interface would be useful to improve not only the different processes but also the efficiency of the op…