Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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A Novel Kernel for Text Classification Based on Semantic and Statistical Information
Get PDFIn text categorization, a document is usually represented by a vector space model which can accomplish the classification task, but the model cannot deal with Chinese synonyms and polysemy phenomenon. This paper presents a novel approach which takes into account both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting. According to our experimental results, the proposed approach could achieve state-of-the-art or competitive results as compared with traditional approaches such as the k-Nearest Neighbor (KNN), the Naive Bayes and deep learning models like convolutional networks
Ranking-Based Differential Evolution for Large-Scale Continuous Optimization
Get PDFLarge-scale continuous optimization has gained considerable attention in recent years. Differential evolution (DE) is a simple yet efficient global numerical optimization algorithm, which has been successfully used in diverse fields. Generally, the vectors in the DE mutation operators are chosen randomly from the population. In this paper, we employ the ranking-based mutation operators for the DE algorithm to improve DE's performance. In the ranking-based mutation operators, the vectors are selected according to their rankings in the current population. The ranking-based mutation operators are general, and they are integrated into the original DE algorithm, GODE, and GaDE to verify the enhanced performance. Experiments have been conducted on the large-scale continuous optimization problems. The results indicate that the ranking-based mutation operators are able to enhance the overall performance of DE, GODE, and GaDE in the large-scale continuous optimization problems
Htab2RDF: Mapping HTML Tables to RDF Triples
Get PDFThe Web has become a tremendously huge data source hidden under linked documents. A significant number of Web documents include HTML tables generated dynamically from relational databases. Often, there is no direct public access to the databases themselves. On the other hand, RDF (Resource Description Framework) gives an efficient mechanism to represent directly data on the Web based on a Web-scalable architecture for identification and interpretation of terms. This leads to the concept of Linked Data on the Web. To allow direct access to data on the Web as Linked Data, we propose in this paper an approach to transform HTML tables into RDF triples. It consists of three main phases: refining, pre-treatment and mapping. The whole process is assisted by a domain ontology and the WordNet lexical database. A tool called Htab2RDF has been implemented. Experiments have been carried out to evaluate and show efficiency of the proposed approach
Using a Full Spectral Raytracer for Calculating Light Microclimate in Functional-Structural Plant Modelling
Get PDFRaytracers that allow the spatially explicit calculation of the fate of light beams in a 3D scene allow the consideration of shading, reflected and transmitted light in functional-structural plant models (FSPM). However, the spectrum of visible light also has an effect on cellular and growth processes. This recently created the interest to extend this modelling paradigm allowing the representation of detailed spectra instead of monochromatic or white light and to extend existing FSPM platforms accordingly. In this study a raytracer is presented which supports the full spectrum of light and which can be used to compute spectra from arbitrary light sources and their transformation at the organ level by absorption, reflection and transmission in a virtual canopy. The raytracer was implemented as an extension of the FSPM platform GroIMP
SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction
Get PDFIn this paper, we present SEMAG - a novel semantic-agent learning recommendation mechanism which utilizes the advantages of instructional Semantic Web rules and multi-agent technology, in order to build a competitive and interactive learning environment. Specifically, the recommendation-making process is contingent upon chapter-quiz results, as usual; but it also checks the students' understanding at topic-levels, through personalized questions generated instantly and dynamically by a knowledge-based algorithm. The learning space is spread to the social network, with the aim of increasing the interaction between students and the intelligent tutoring system. A field experiment was conducted in which the results indicated that the experimental group gained significant achievements, and thus it supports the use of SEMAG
Load Balancing Scheduling Algorithm for Concurrent Workflow
Get PDFConcurrent workflow scheduling algorithm works in three phases, namely rank computation, tasks selection, and resource selection. In this paper, we introduce a new ranking algorithm that computes the rank of a task, based on its successor rank and its predecessors average communication time, instead of its successors rank. The advantage of this ranking algorithm is that two dependent tasks are assigned to the same machine and as a result the scheduled length is reduced. The task selection phase selects a ready task from each workflow and creates a task pool. The resource selection phase initially assigns tasks using min-min heuristic, after initial assignment, tasks are moved from the highly loaded machines to the lightly loaded machines. Our resource selection algorithm increases the load balance among the resources due to tasks assignment heuristic and reassignment of tasks from the highly loaded machines. The simulation results show that our proposed scheduling algorithm performs better over existing approaches in terms of load balance, makespan and turnaround time
Estimation of A Posteriori Computational Error by the Higher Order Multipoint Meshless FDM
Get PDFThe main objective of this paper is to present a possibility of high quality a posteriori error evaluation based on reference solutions obtained by means of the new multipoint meshless finite difference method. Due to its higher order approximation, the multipoint results may be used as improved reference solution instead of the true analytical one to estimate the errors. Several types of a posteriori error estimators which can be used to evaluate the calculation error are described here. The results of selected numerical benchmarks are considered
Background Subtraction Based on Perception-Contained Piecewise Memorizing Framework
Get PDFA key issue for full-time video surveillance is to search or establish a reference image of background which corresponds to current video frame. However, background that was ever in presence long time ago is enclosed or discarded due to background forgetting assumption. How to rapidly pick up or even rebuild long-term background needs to be discussed. This paper aims to present a framework for background maintenance in order to solve the problem. A piecewise memorizing framework is proposed for matching, updating and even rebuilding long-term background. Based on the metaphors of psychological selective attention theory, the framework is composed of a prior piecewise perception processor for intensity stationary test. Besides, a hierarchical memorizing mechanism constitutes the other part of the framework for overcoming the exponential forgetting of long period background appearances. Applied to Gaussian mixture model (GMM), this framework is capable of maintaining short-term background states, identifying long period background appearances, and rapidly adjusting to new background states according to different expressions derived from the prior perception of scene intensity changes. Its effectiveness can be demonstrated by experimental results for solving various typical problems
Ellipse Detection in Forensic Blood Stain Images Analysis
Get PDFThis paper presents an algorithm for ellipse detection on stains of blood, which is directly suited for the needs of forensic analysis. The algorithm is of the edge-analyzing type. It performs convexity detection and is able to split contours of overlapping ellipses by concave regions analysis. A filtering is applied to the fit ellipses to reduce the results only to those results necessary for blood drop trajectory tracing. Results for running time and fitting quality tests performed on real-life and artificial data are presented. The solution answers to both quality and running time expectations of the field of application
New Approach to Categorical Semantics for Procedural Languages
Get PDFThe semantics of programs written in some languages is concerned with the interpretation in various types of models. The purpose of structural operational semantics is to describe how a computation is performed. This method is one of the most popular semantic methods in the community of software engineers. It describes program behavior in the form of state changes caused by the execution of elementary steps. This feature predestinates the usage of the structural operational semantics for implementation of programming languages and also for verification purposes. Another semantic method, denotational semantics, defines changes of states by functions. In this paper a new approach to semantics is presented: behavior of programs, i.e., changes of states are modeled in the category of states. The morphisms category expresses elementary execution steps and the program execution is an oriented path in the category, i.e. composition of morphisms. Our categorical model is constructed for a simple procedural language that contains all basic van Dijkstra's constructs. We enriched our approach also with procedures forming a collection of categories interconnected by functors. This method enables the repeated call of procedures, nesting of procedure calls and recursive calls. Moreover, it allows to illustrate and accentuate dynamics of the program execution. The simplicity of this method does not exclude its mathematical exactness