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Data mapping among different data standards in health institutes is often a necessity when data exchanges occur among different institutes. However, no matter rule-based approaches or traditional machine learning methods, none of these methods have achieved satisfactory results yet. In this work, we propose a deep learning method, mixture feature embedding convolutional neural network (MfeCNN), to...
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical non-parametric...
This paper aims to support the creation of high performance ‘Plug-and-Produce’ systems by proposing a new semantic model that targets the use of AutomationML (AML). In this direction, the focus is narrowed to the self-description of equipment modules that highlights the use of ‘Skill’ concept. An insight description on how the concept of ‘Skill Recipe’ can be used to execute the equipment ‘Skills’...
The article describes the representation of a mining model in a hierarchy of lists that can be processed concurrently. This representation is based at PMML standard that defines the mining model's elements and their relationships. We unify them and introduce special functions which allow us to split lists before parallel processing and merge them after this. As an example of the mining models are...
As the amount of data grows very fast inside and outside of an enterprise, it is getting important to analyze both of them for getting total business intelligence. While online analytical processing (OLAP) techniques have been proven very useful for analyzing structured data, they face challenges in handling unstructured data. To this end, new multidimensional models have been proposed for OLAP purposes...
In the past few years, we have witnessed a rapid development of online social media, from which we can access various short texts. Understanding the topic patterns of these short text is significant. Traditional topic models, like LDA, are not suitable when applied to short text topic analysis due to data sparsity. A lot of efforts have been made to solve this problem. However, there is still significant...
This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in...
Understanding or acquiring a user's information needs from their local information repository (e.g. a set of example-documents that are relevant to user information needs) is important in many applications. However, acquiring the user's information needs from the local information repository is very challenging. Personalised ontology is emerging as a powerful tool to acquire the information needs...
This paper is part of a doctoral thesis that aims to propose an evaluation model, for later application, using Educational Data Mining techniques to analyze the responses of students obtained during an Institutional Teaching Evaluation. Therefore, the authors propose an Institutional Teaching Evaluation model that applies, among others, the Sentiment Analysis to identify which teaching practices are...
This paper describes an innovative approach and infrastructure for a seamless data communication between CAE and automation systems' engineering tools. In this approach syntax, semantics and graphical representations of various CAE tools can be captured, analyzed, visualized and mapped to an intermediate syntax and semantic in a data hub. Different captures from different data sources can be merged...
Resource Space Model (RSM) is a semantic data model for specifying, storing, managing, and locating versatile resources based on multi-dimensional categorization. However, the structure of the model needs to be extended for managing and searching contents. This paper proposes an extensive model of RSM to solve the problem by building a new layer of RSM to store, manage and search the meaning units...
Data exchange is a critical issue within the multi-disciplinary engineering process of cyber physical production systems (CPPS). AutomationML (AML) is an emerging standard in the this field to represent and exchange artifacts between heterogeneous engineering tools used in mechanical, electrical, and software engineering domains. However, the interoperability of different exchange standards may be...
Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information...
In the upcoming age of semantic web there is a large number of relational databases being widely used. When time comes for a legacy relational database to migrate to semantic web or to be integrated with it, an important issue of determining similarity (compatibility) between two data models expressed in different ways arises. The goal of this paper is to describe the methodology for similarity assessment...
The growing amount of information produced by automatic systems is often associated with the paradigm of Big Data. Next to this approach it remains the interest in integrating information of different origin within a shared framework, conceptually able to be formalized by an ontology. The IEC Common Information Model (CIM) is a natural candidate to take on the role of reference ontology in the electrical...
The oneM2M standard is a global initiative led jointly by major standards organizations around the world in order to develop a unique architecture for M2M communications. Prior standards, and also oneM2M, while focusing on achieving interoperability at the communication level, do not achieve interoperability at the semantic level. An expressive ontology for IoT called IoT-O is proposed, making best...
To build a simple picture of M2M communication under service-oriented M2M architecture, a key innovation underlines the enablement of M2M device abstraction and semantics support. However, state of the art structures of M2M devices/software lack common M2M Service Layer for interoperability. Devices/software rely heavily on specific API implementation-oriented data services and the data definition...
Land Administration Spatial Data Supply Chains (SDSC) for state and territory jurisdictions in Australia require extensive investigation to address several contemporary issues and challenges that are hampering innovation and the use of spatial information across the land administration sector. The management of cadastral data involves multiple value and supply chains. Each has heterogeneous geo-processes,...
In this study, we address two major problems of spontaneous reporting systems for adverse drug events (ADEs): underreporting and low report content quality. In the scope of WEB-RADR project, we make use of relevant patient information available in electronic health record (EHR) systems to facilitate ADE reporting process and promote spontaneous reporting on mobile devices. By semi-automatically extracting...
In this paper, we propose a multi-scale model of energy demand that is consistent with observations at a macro scale, in our use-case standard load profiles for (residential) electric loads. We employ the model to study incentives to assume the risk of volatile market prices for intelligent energy cooperatives at different aggregation scales of energy consumption. Next to scale, we investigate the...
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