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The revolutionary developments in the digital technologies have indicated a requirement for technology, that systematizes the huge images dataset for easy search and retrieval. This yields an essential demand for developing highly effective retrieval systems. Recently, wide-ranging research efforts have been made in the field of annotated images. However, image Annotation can tag by one term, which...
Recorded provenance facilitates reproducible science. Provenance metadata can help determine how data were possibly transformed, processed, and derived from original sources. While provenance is crucial for verification and validation, there remains the issue of the granularity — detail at which provenance data must be provided to a user, especially for conducting reproducible science. When data are...
Visualization is the process of representing data graphically and interacting with these representations in order to gain insight into the data and to assist human information processing by reducing demands on attention, working memory, and long-term memory. The graphical representation of data is also used in the Web as a mean which carries visual and easy to understand information. However, graphics...
Due to the explosive increase of online images, content-based image retrieval has gained a lot of attention. The success of deep learning techniques such as convolutional neural networks have motivated us to explore its applications in our context. The main contribution of our work is a novel end-to-end supervised learning framework that learns probability-based semantic-level similarity and feature-level...
We present a novel approach to semi-supervised learning for text classification based on the higher-order co-occurrence paths of words. We name the proposed method as Semi-Supervised Semantic Higher-Order Smoothing (S3HOS). The S3HOS is built on a tri-partite graph based data representation of labeled and unlabeled documents that allows semantics in higher-order co-occurrence paths between terms (words)...
This paper describes an early step in approaching implicit meaning computationally. It outlines various types of implicit meanings and then presents a method of finding the so called defaults - omissions that are universally reconstructable and, of course, interpretible without much additional reasoning. The defaults are analyzed on the example of the 1000 instances of TerminateLife events, and a...
Authorship attribution is a stylometric technique that associates text to authors based on the type of writing styles. Researchers have looked for ways to analyze the context of these texts, in some cases with limited results. Most of the approaches view information at the syntactic and physical levels and tend to ignore information from the semantic levels. In this paper, we present a technique that...
We introduce comparisons with respect to information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. As bisimilarity is a natural notion for characterizing indiscernibility in modal and description logics, it is useful for concept learning in description logics also when inconsistencies occur. We give preservation results and the Hennessy-Milner...
Response time plays an important factor in determining the Service Level Agreement (SLA). For the reason that actual measurement costs a large amount of resource, theoretical/numerical analysis based on Stochastic Process Algebra (SPA) is a good choice to obtain the response time of concurrent systems. Among all SPAs, Performance Evaluation Process Algebra (PEPA) is the most popular one due to its...
Recently, workflow fragments gains increasing momentum for reuse and re-purpose in Cyber-Physical Systems. To the end, this article proposes to detect and recommend workflow fragments gratifying e-Scientist requirement. Specifically, most common workflow fragments in the form of layer hierarchy are extracted from scientific workflows, which are collected in the myExperiment repository. Annotations...
Magnetic resonance imaging (MRI) can aid in assessing post-ablation scar formation. Automatic segmentation of left atrium (LA) offers great benefits for an accurate statistical assessment of LA region. However, how to robustly segment LA is still remaining as a challenging task for its high anatomical variability. In this paper, a robust segmentation method that exploits semantic information from...
This paper proposes a novel hierarchical behavior planner with a multi-layered confabulation based behavior selection structure for robots to perform tasks. The proposed planner integrates a STRIPS based behavior selection approach and cogent confabulation approach. The STRIPS based behavior selection approach is a goal tree search that induces goal-oriented sequences of behaviors, while the cogent...
This paper proposes a novel scheme of integrating episodic memory into semantic memory based task planner. Task planners have taken an important role in AI research along with semantic memory to better perform tasks for robots. Episodic memory memorizes and retrieves temporal sequence of situated behaviors by which temporal relationship between behaviors can be defined. None of any research, however,...
Image segmentation and image recognition are challenging processes, and the methods of merging those two processes like semantic segmentation have been studied. However, it is a lot of labor to construct the processes of segmentation and recognition manually, so automatic construction of those approaches using machine learning or evolutionary computation have been proposed. In this paper, we propose...
To solve symbolic regression problems, Genetic Programming (GP) is often used for evolving tree structural numerical expressions. Recently, new crossover operators based on semantics of tree structures have attracted many attentions. In the semantics-based crossover, offspring is created from its parental individuals so that the offspring can inherit the characteristics of the parents not structurally...
In order to achieve enhanced credibility of social networking services such as twitter, it is necessary to (1) identify the topic and to (2) check, if the majority of the tweets having the same topic show the same opinion. Therefore, it is indicated to improve the accuracy for analyzing the caller's emotional expression of the “emotional polarity classification”, which is used for opinion classification...
In recent automated production lines, industrial robots are required to recover from unexpected errors autonomously. In this paper, a hierarchical planning system that realizes autonomous error recovery of robots is proposed with some virtually demonstrated results. In order to realize a variety of flexible error recovery functions, a partial order planning scheme as well as a “deliberate” re-planning...
The purpose of this research is to provide a puzzle-based framework to study how global and local information is interacted on human's visual perception during the decision making process. Since the Deep Convolutional Neural Networks (DCNN) has shown the state of the art performance in image classification and object detection, DCNN can output scores to reflect the level of global information, which...
This dataset is used to detect undoing style in CSS code. In total, this dataset contains 41 subjects. Each subject has its own folder, which contains the captured states, a states.html file, is used to load all captured states in one document, and a folder called results, which contains the detected undoing styles, the refactored style sheets and the detected semantic changes. The file states.html...
This tool detects undoing style in CSS code and is able to apply refactoring opportunities to eliminate a subset of these instances of undoing style, while preserving the semantics of the web application.
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