1,249 research outputs found
Cooperative Cognitive Automobiles
Safety requirements are among the most ambitious challenges for autonomous guidance and control of automobiles. A human-like understanding of the surrounding traffic scene is a key element to fulfill these requirements, but is a still missing capability of today's intelligent vehicles. Few recent proposals for driver assistance systems approach this issue with methods from the AI research to allow for a reasonable situation evaluation and behavior generation. While the methods proposed in this contribution are lend from cognition in order to mimic human capabilities, we argue that in the long term automated cooperation among traffic participants bears the potential to improve traffic efficiency and safety beyond the level attainable by human drivers. Both issues are major objectives of the Transregional Collaborative Research Centre 28 'cognitive automobiles,' TCRC28 that is outlined in the paper. Within this project the partners focus on systematic and interdisciplinary research on machine cognition of mobile systems as the basis for a scientific theory of automated machine behavior
A Knowledge Graph for Industry 4.0
One of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality
ProofWatch: Watchlist Guidance for Large Theories in E
Watchlist (also hint list) is a mechanism that allows related proofs to guide
a proof search for a new conjecture. This mechanism has been used with the
Otter and Prover9 theorem provers, both for interactive formalizations and for
human-assisted proving of open conjectures in small theories. In this work we
explore the use of watchlists in large theories coming from first-order
translations of large ITP libraries, aiming at improving hammer-style
automation by smarter internal guidance of the ATP systems. In particular, we
(i) design watchlist-based clause evaluation heuristics inside the E ATP
system, and (ii) develop new proof guiding algorithms that load many previous
proofs inside the ATP and focus the proof search using a dynamically updated
notion of proof matching. The methods are evaluated on a large set of problems
coming from the Mizar library, showing significant improvement of E's standard
portfolio of strategies, and also of the previous best set of strategies
invented for Mizar by evolutionary methods.Comment: 19 pages, 10 tables, submitted to ITP 2018 at FLO
Facilitating the analysis of COVID-19 literature through a knowledge graph
At the end of 2019, Chinese authorities alerted the World Health Organization (WHO) of the outbreak of a new strain of the coronavirus, called SARS-CoV-2, which struck humanity by an unprecedented disaster a few months later. In response to this pandemic, a publicly available dataset was released on Kaggle which contained information of over 63,000 papers. In order to facilitate the analysis of this large mass of literature, we have created a knowledge graph based on this dataset. Within this knowledge graph, all information of the original dataset is linked together, which makes it easier to search for relevant information. The knowledge graph is also enriched with additional links to appropriate, already existing external resources. In this paper, we elaborate on the different steps performed to construct such a knowledge graph from structured documents. Moreover, we discuss, on a conceptual level, several possible applications and analyses that can be built on top of this knowledge graph. As such, we aim to provide a resource that allows people to more easily build applications that give more insights into the COVID-19 pandemic
The OpenCitations Data Model
A variety of schemas and ontologies are currently used for the
machine-readable description of bibliographic entities and citations. This
diversity, and the reuse of the same ontology terms with different nuances,
generates inconsistencies in data. Adoption of a single data model would
facilitate data integration tasks regardless of the data supplier or context
application. In this paper we present the OpenCitations Data Model (OCDM), a
generic data model for describing bibliographic entities and citations,
developed using Semantic Web technologies. We also evaluate the effective
reusability of OCDM according to ontology evaluation practices, mention
existing users of OCDM, and discuss the use and impact of OCDM in the wider
open science community.Comment: ISWC 2020 Conference proceeding
Making Neural Networks FAIR
Research on neural networks has gained significant momentum over the past few
years. Because training is a resource-intensive process and training data
cannot always be made available to everyone, there has been a trend to reuse
pre-trained neural networks. As such, neural networks themselves have become
research data. In this paper, we first present the neural network ontology
FAIRnets Ontology, an ontology to make existing neural network models findable,
accessible, interoperable, and reusable according to the FAIR principles. Our
ontology allows us to model neural networks on a meta-level in a structured
way, including the representation of all network layers and their
characteristics. Secondly, we have modeled over 18,400 neural networks from
GitHub based on this ontology, which we provide to the public as a knowledge
graph called FAIRnets, ready to be used for recommending suitable neural
networks to data scientists
Heat Treated NiP–SiC Composite Coatings: Elaboration and Tribocorrosion Behaviour in NaCl Solution
Tribocorrosion behaviour of heat-treated NiP and NiP–SiC composite coatings was investigated in a 0.6 M NaCl solution. The tribocorrosion tests were performed in a linear sliding tribometer with an electrochemical cell interface. It was analyzed the influence of SiC particles dispersion in the NiP matrix on current density developed, on coefficient of friction and on wear volume loss. The results showed that NiP–SiC composite coatings had a lower wear volume loss compared to NiP coatings. However, the incorporation of SiC particles into the metallic matrix affects the current density developed by the system during the tribocorrosion test. It was verified that not only the volume of co-deposited particles (SiC vol.%) but also the number of SiC particles per coating area unit (and consequently the SiC particles size) have made influence on the tribocorrosion behaviour of NiP–SiC composite coatings
Improved performance of the LHCb Outer Tracker in LHC Run 2
The LHCb Outer Tracker is a gaseous detector covering an area of with 12 double layers of straw tubes. The performance of the detector is
presented based on data of the LHC Run 2 running period from 2015 and 2016.
Occupancies and operational experience for data collected in , pPb and
PbPb collisions are described. An updated study of the ageing effects is
presented showing no signs of gain deterioration or other radiation damage
effects. In addition several improvements with respect to LHC Run 1 data taking
are introduced. A novel real-time calibration of the time-alignment of the
detector and the alignment of the single monolayers composing detector modules
are presented, improving the drift-time and position resolution of the detector
by 20\%. Finally, a potential use of the improved resolution for the timing of
charged tracks is described, showing the possibility to identify low-momentum
hadrons with their time-of-flight.Comment: 29 pages, 20 figures, minor changes to match the published versio
Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching
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