Modeling robot co-representation: state-of-the-art, open issues, and predictive learning as a possible framework

SummaryObjective: Semantic interoperability of the Electronic Health Record (EHR) requires a rigorous and precise modelling of clinical information. Our objective is to facilitate the representation of clinical facts based on formal principles.Methods: We here explore the potential of ontology content patterns, which are grounded on a formal and semantically rich ontology model and can be specialised and composed.Results: We describe and apply two content patterns for the representation of data on tobacco use, rendered according to two heterogeneous models, represented in openEHR and in HL7 CDA. Finally, we provide some query exemplars that demonstrate a data interoperability use case.Conclusion: The use of ontology content patterns facilitate the semantic representation of clinical information and therefore improve their semantic interoperability. There are open issues such as the scalability and performance of the approach if a logic-based language is used. Implementation decisions might determine the final degree of semantic interoperability, influenced by the state of the art of the semantic technologies.Citation: Martínez-Costa C, Schulz S. Ontology content patterns as bridge for the semantic rRepresentation of clinical information Appl Clin Inf 2014; 5: 660–669http://dx.doi.org/10.4338/ACI-2014-04-RA-0031

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CostNet: A Concise Overpass Spatiotemporal Network for Predictive Learning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040209 ◽

2020 ◽

Vol 9 (4) ◽

pp. 209

Author(s):

Fengzhen Sun ◽

Shaojie Li ◽

Shaohua Wang ◽

Qingjun Liu ◽

Lixin Zhou

Keyword(s):

State Of The Art ◽

Vertical Direction ◽

Bottom Layer ◽

Spatiotemporal Data ◽

Short Term ◽

The Core ◽

Predictive Learning ◽

Fast Gradient ◽

Unit Output ◽

Cross Connection

Predicting the futures from previous spatiotemporal data remains a challenging topic. There have been many previous works on predictive learning. However, mainstream models suffer from huge memory usage or the gradient vanishing problem. Enlightened by the idea from the resnet, we propose CostNet, a novel recursive neural network (RNN)-based network, which has a horizontal and vertical cross-connection. The core of this network is a concise unit, named Horizon LSTM with a fast gradient transmission channel, which can extract spatial and temporal representations effectively to alleviate the gradient propagation difficulty. In the vertical direction outside of the unit, we add overpass connections from unit output to the bottom layer, which can capture the short-term dynamics to generate precise predictions. Our model achieves better prediction results on moving-mnist and radar datasets than the state-of-the-art models.

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Special Issue on Intelligent Systems in Sensor Networks and Internet of Things

Sensors ◽

10.3390/s20113182 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3182

Author(s):

Chang Choi ◽

Gianni D’Angelo ◽

Francesco Palmieri

Keyword(s):

Sensor Networks ◽

Internet Of Things ◽

Intelligent Systems ◽

State Of The Art ◽

Special Issue ◽

Research Experiences ◽

Original State ◽

Art Research ◽

Open Issues

This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges.

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When the answer comes into question in question-answering: survey and open issues

Natural Language Engineering ◽

10.1017/s1351324911000350 ◽

2012 ◽

Vol 19 (1) ◽

pp. 1-32 ◽

Cited By ~ 7

Author(s):

ANA CRISTINA MENDES ◽

LUÍSA COHEUR

Keyword(s):

Question Answering ◽

Information Sources ◽

State Of The Art ◽

Research Community ◽

The State ◽

Present Survey ◽

Cooperative Answering ◽

Open Issues

AbstractThe answer determines the success of a Question-Answering (QA) system. In redundancy-based QA systems, a common approach is to extract the candidate answers from the information sources and select the most frequent answers as the final answers. However, this strategy has some pitfalls. For instance, if a system is not able to detect equivalences between the candidate answers, their frequencies might be erroneously calculated. Moreover, the user who posed the question should also be taken into account when answering: different persons require different (correct) answers. This can involve the use of suitable vocabulary and/or information details. In these situations, the generation of a response can be a more suitable strategy, instead of the extraction and direct retrieval of the answer from the information sources. The present survey targets the state of the art in the answering task in QA under three different lines of research. First, we present several works that focus on relating candidate answers. Then, we recover the concept of cooperative answer – a correct, useful, and non-misleading answer – and we bring up attempts to address cooperative answering. Finally, we investigate the research community endeavors on response generation. We will also present our perspective on each of these three topics throughout this paper.

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Internet of Things: State-of-the-art, Challenges, Applications, and Open Issues

International Journal of Intelligent Computing Research ◽

10.20533/ijicr.2042.4655.2018.0112 ◽

2018 ◽

Vol 9 (3) ◽

pp. 928-938 ◽

Cited By ~ 11

Author(s):

Hany F. Atlam ◽

Robert J. Walters ◽

Gary B. Wills

Keyword(s):

Internet Of Things ◽

State Of The Art ◽

Open Issues

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Current Approaches, Challenges, and Perspectives on Spatial OLAP for Agri-Environmental Analysis

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2016100103 ◽

2016 ◽

Vol 7 (4) ◽

pp. 32-49 ◽

Cited By ~ 2

Author(s):

Sandro Bimonte

Keyword(s):

Urban Development ◽

Environmental Analysis ◽

Scientific Community ◽

State Of The Art ◽

Environmental Context ◽

Research Community ◽

The State ◽

Environmental Data ◽

Multidimensional Model ◽

Open Issues

Spatial OLAP (SOLAP) systems are powerful GeoBusiness Intelligence tools for analysing massive volumes of geo-referenced datasets. Therefore, these technologies are receiving considerable attention in the research community and in the database industry as well. Applications of these technologies are current in several domains such as ad marketing, healthcare, and urban development, to name a few. Contrary to other application domains, in the context of agri-environmental data and analysis, SOLAP systems have been underexploited. Therefore, in this paper, the author makes an exhaustive survey of most of the published studies in the domain of the SOLAP analysis of agri-environmental data with an emphasis on the reasons why only few recent works investigate the use of SOLAP systems in the agri-environmental context. In particular, the author focuses on the complexity of the spatio-multidimensional model and its implementation. Moreover, based on surveying the state of the art in this domain, this paper identifies some general guidelines that must be considered by the scientific community to design and implement efficient SOLAP approaches to the analysis of geo-referenced agri-environmental datasets. Finally, open issues about warehousing and OLAPing agri-environmental data are also shown in the paper.

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