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In industrial power generation plants, subsystem monitoring and analytics play a vital role in quantifying the knowledge about different factors that impact their overall performance. Multi-dimensional performance metrics, e.g. thermal efficiency, in-service time, mean-time-to-failure etc., are calculated that may have different data constraints, modelling techniques, and execution frameworks. Automating...
Modelers face multiple challenges in their work. In this paper, we focus on two of them. First, multiple modeling methods and tools are currently available. Modelers are sometimes limited by their tools or paradigms. Second, when multiple models are proposed for the same case, a decision maker needs criteria to decide which model to choose for his/her objective.
Classifying educational resources such as videos and articles can be challenging in low-resource languages due to lack of appropriate tools and sufficient labeled data. To overcome this problem, a crosslingual classification method that utilizes resources created in one high-resource language, such as English, to perform classification in many low-resource languages, is proposed. Data scarcity issue...
Complex adaptive systems (CAS) exhibit properties beyond complex systems such as self-organization, adaptability and modularity. Designing models of CAS is typically a non-trivial task as many components are made up of sub-components and rely on a large number of complex interactions. Studying features of these models also requires specific work for each system. Moreover, running these models as simulations...
Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming...
As high performance computing (HPC) infrastructures continue to grow in capability and complexity, so do the applications that they serve. HPC and distributed-area computing (DAC) (e.g. grid and cloud) users are looking increasingly toward workflow solutions to orchestrate their complex application coupling, pre- and post-processing needs. To that end, the US Department of Energy Integrated end-to-end...
Knowledge management becomes increasingly more important for individuals since it would make the best use of knowledge through helping learners to better understand, administer and transfer the knowledge. Externalize what the learners already know is one of the most important aspects of knowledge management. Although mind tools, ontology and knowledge graph are the most popular methodologies to interpret...
This paper presents the first results of a functional prototype implementing a linguistic model focused on regulations in Spanish. Its global architecture, the reasoning model, a case-study and short statistics are provided for the prototype named PTAH. It mainly has a conversational robot linked to an Expert System by a module with many intelligent linguistic filters, implementing the reasoning model...
Deep Convolutional Neural Networks(DCNNs) have recently shown great performance in many high-level vision tasks, such as image classification, object detection and more recently outdoor semantic segmentation. However, the convolutional layer only process the local regions in the image, ignoring the global context information. To overcome this poor localization property of Convolutional Neural Networks(CNNs),...
Process models are becoming more and more widespread in contemporary organizations. For the purpose of reducing cost and improve model quality, the ability to rapidly tailor a reference process to satisfy the changing of business requirements is necessary for organizations. In this context, how to provide a suitable reference process model for a specific domain becomes a challenging question. This...
Topic modeling continues to grow as a popular technique for finding hidden patterns, as well as grouping collections of new types of text and non-text data. Recent years have witnessed a growing body of work in developing metrics and techniques for evaluating the quality of topic models and the topics they generate. This is particularly true for text data where significant attention has been given...
Porting applications from one cloud platform to another is difficult, making vendor lock-in a major impediment to cloud adoption. Model-driven engineering could be used to determine how applications might run on different platforms, if platform schemas could be matched. However, schema matching typically relies on linguistic and structural similarities, and cloud schema terms diverge so much that...
Modern software and hardware designs are mostly hierarchical. Moreover, while the design specification is defined up-down, the design implementation and verification are done down-up. In such a case, as a rule, coverage properties for simulation-based verification are defined inconsistently for different stages of the design flow. The fact leads to the well known explosion of bug rate, when we pass...
Semantic Web services (SWs) and P2P computing have emerged as new paradigms for solving complex problems by enabling large-scale aggregation and sharing of distributed computational resources. In this paper, we present a scalable approach based on epidemic discovery algorithm to discover new distributed and heterogeneous collaborative applications of large-scale distributed systems in a P2P network,...
Document clustering is to group documents according to a certain semantic features defined on the document set for measuring the similarities between two documents. The keyword models such as the TFIDF model of document have been widely used as features for document clustering. But it lacks of semantic structure, which limit its further usage in document analysis. Topic model has been developed to...
Getting good viewpoints has been considered important for promoting the efficiency when investigating a model. Many view selection methods therefore have been proposed. In particular, measuring semantic meaning of the model features through segmentation is regarded more effective to get optimal viewpoints. Unfortunately, the semantic meanings of the model are usually obtained through experiences,...
As model driven development has been promoted to the focus of engineers during the software development, engineers find themselves dealing with a large collection of models. Without managing these models efficiently, the wheel is reinvented over and over, resulting in more duplicated artifacts and an aggravated maintenance effort. Models' matching is a basic operation for different model management...
This paper mainly introduces a new method for image classification. The traditional Bag of Visual Words model (BoVW) is a promising image representation technique for image classification. But its limitation is that much valuable information is lost when building the codebook of BoVW simply by clustering visual features in the Euclidian space. In this paper, we take full advantage of image semantic...
In this paper, we present a model-based document information content extraction approach and perform in-depth evaluation based on clients' relevance. Real-world users i.e., clients first provide a set of key fields from the document image which they think are important. These are used to represent a graph where nodes (i.e., fields) are labelled with dynamic semantics including other features and edges...
The development cost of safety-critical embedded systems is dominated today by the cost of software including verification and validation. This cost is typically related to the complexity of the software functions implementing the desired system behavior in nominal and off-nominal conditions. A widely used measure of complexity is the cyclomatic number, which is computed on the implementation code...
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