Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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1506 research outputs found
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CL-Vis: Visualization Platform for Understanding and Checking the OpenCL Programs
Get PDFDue to GPU's improved hardware performance, many researchers have tried to utilize the GPU for computer vision, image processing, cryptography, and artificial intelligence. As results, the GPU could successfully speed up algorithms from tens to hundreds of times in many cases. However, GPU programming is still known to be difficult because of its different characteristics from the traditional CPU programming. Also, it is hard to find the root causes of software failures because the failures are irreproducible in many cases. Our goal is to simplify the process of verifying intended actions when debugging GPGPU programs. To achieve this goal, we use the visualization method of executed codes because it can increase the human's understanding through seeing and analyzing the real actions by each thread. We developed a platform that can visualize the running OpenCL codes and algorithms that can identify data race, barrier divergence, and infinite loop in the GPU. To the best of our knowledge, this is the first study on the visualizations of OpenCL operations and detection of infinite loops in the programs. We also suggest an algorithm for detecting data race with GPU-specific lock-step execution and barrier function
Web Person Name Disambiguation Using Social Links and Enriched Profile Information
Get PDFIn this article, we investigate the problem of cross-document person name disambiguation, which aimed at resolving ambiguities between person names and clustering web documents according to their association to different persons sharing the same name. The majority of previous work often formulated cross-document name disambiguation as a clustering problem. These methods employed various syntactic and semantic features either from the local corpus or distant knowledge bases to compute similarities between entities and group similar entities. However, these approaches show limitations regarding robustness and performance. We propose an unsupervised, graph-based name disambiguation approach to improve the performance and robustness of the state-of-the-art. Our approach exploits both local information extracted from the given corpus, and global information obtained from distant knowledge bases. We show the effectiveness of our approach by testing it on standard WePS datasets. The experimental results are encouraging and show that our proposed method outperforms several baseline methods and also its counterparts. The experiments show that our approach not only improves the performances, but also increases the robustness of name disambiguation
Networked Ontologies with Contextual Alignments
Get PDFThe problem of knowledge heterogeneity in the Semantic Web or in the context of information systems remains a major challenge for the scientific community, in particular when several ontologies developed independently and separately have to be exploited to exchange their knowledge. Several works have addressed the semantic heterogeneity issue in ontologies and proposed to align them with additional knowledge. Recently a formalism taking into account the challenge of applied techniques to represent and reason on aligned ontologies was proposed by the authors. The authors proposed a contribution that can be seen as an extension of existing work on the heterogeneous ontologies integration. This formalism allows dealing with contextual representation and reasoning where ontologies and alignments by pairs of ontologies are developed in different and incompatible context. In this paper, some aspects of multi-level networked knowledge are recalled, detailing its semantics and discussing the comparison of the two semantics, DL-approach and DDL-approach, according to certain criteria, in order to measure their relevance and to give to readers a way to choose one semantics rather than another according to the context or the intended application
Variable Neighborhood Search Approach for Solving Roman and Weak Roman Domination Problems on Graphs
Get PDFIn this paper Roman and weak Roman domination problems on graphs are considered. Given that both problems are NP hard, a new heuristic approach, based on a Variable Neighborhood Search (VNS), is presented. The presented algorithm is tested on instances known from the literature, with up to 600 vertices. The VNS approach is justified since it was able to achieve an optimal solution value on the majority of instances where the optimal solution value is known. Also, for the majority of instances where optimization solvers found a solution value but were unable to prove it to be optimal, the VNS algorithm achieves an even better solution value
Model Variations and Automated Refinement of Domain-Specific Modeling Languages for Robot-Motion Control
Get PDFThis paper presents an approach to handling frequent variations of modeling languages and models. The approach is based on Domain-Specific Modeling and linking of modeling tools with adaptive Run-Time Systems. The applicability of our solution is illustrated on an example of domain-specific languages for robot control. Special attention was given to the following problems: 1) model-level debugging; 2) performing fast transformation of models to native code for various hardware platforms and operating systems; and 3) specification of views and view-based generation of applications for validation of meta-models, models, and generated code. The feedback for automated refinement of models and meta-models is provided by a custom adaptive Run-Time System. For the purpose of synchronizing models, meta-models, and the target Run-Time System, we introduce action reports, which allow model-level debugging. In order to simplify handling of frequent model variations, we have introduced the linguistic concept of a modifier
A New Open Information Extraction System Using Sentence Difficulty Estimation
Get PDFThe World Wide Web has a considerable amount of information expressed using natural language. While unstructured text is often difficult for machines to understand, Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract assertions directly from massive and heterogeneous corpora. Allocation of low-cost computational resources is a main demand for Open Relation Extraction (ORE) systems. A large number of ORE methods have been proposed recently, covering a wide range of NLP tools, from ``shallow'' (e.g., part-of-speech tagging) to ``deep'' (e.g., semantic role labeling). There is a trade-off between NLP tools depth versus efficiency (computational cost) of ORE systems. This paper describes a novel approach called Sentence Difficulty Estimator for Open Information Extraction (SDE-OIE) for automatic estimation of relation extraction difficulty by developing some difficulty classifiers. These classifiers dedicate the input sentence to an appropriate OIE extractor in order to decrease the overall computational cost. Our evaluations show that an intelligent selection of a proper depth of ORE systems has a significant improvement on the effectiveness and scalability of SDE-OIE. It avoids wasting resources and achieves almost the same performance as its constituent deep extractor in a more reasonable time
Agent-Based System for Mobile Service Adaptation Using Online Machine Learning and Mobile Cloud Computing Paradigm
Get PDFAn important aspect of modern computer systems is their ability to adapt. This is particularly important in the context of the use of mobile devices, which have limited resources and are able to work longer and more efficiently through adaptation. One possibility for the adaptation of mobile service execution is the use of the Mobile Cloud Computing (MCC) paradigm, which allows such services to run in computational clouds and only return the result to the mobile device. At the same time, the importance of machine learning used to optimize various computer systems is increasing. The novel concept proposed by the authors extends the MCC paradigm to add the ability to run services on a PC (e.g. at home). The solution proposed utilizes agent-based concepts in order to create a system that operates in a heterogeneous environment. Machine learning algorithms are used to optimize the performance of mobile services online on mobile devices. This guarantees scalability and privacy. As a result, the solution makes it possible to reduce service execution time and power consumption by mobile devices. In order to evaluate the proposed concept, an agent-based system for mobile service adaptation was implemented and experiments were performed. The solution developed demonstrates that extending the MCC paradigm with the simultaneous use of machine learning and agent-based concepts allows for the effective adaptation and optimization of mobile services
New Methods of Uncheatable Grid Computing
Get PDFGrid computing is the collection of computer resources from multiple locations to reach a common goal. According to the task publisher's computing power, we will classify the deceptive detection schemes into two categories, and then analyze the security of deceptive detection schemes based on the characteristics of computational task function. On the basis of double check, we proposed an improved scheme at the cost of time sacrifice called the secondary allocation scheme of double check. In our scheme, the security of double check has been greatly strengthened. Finally, we analyzed the common problem of High-Value Rare Events, improved the deceptive detection scheme due to [1], and then put forward a new deceptive detection scheme with better security and efficiency. This paper is revised and expanded version of a paper entitled "Deceptive Detection and Security Reinforcement in Grid Computing" [2] presented at 2013 5th International Conference on Intelligent Networking and Collaborative Systems, Xi'an city, Shanxi province, China, September 9-11, 2013
Discovering Foreign Keys on Web Tables with the Crowd
Get PDFForeign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to minimize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost
A Cooperative Local Search Method for Solving the Traveling Tournament Problem
Get PDFConstrained optimization is the process of optimizing a certain objective function subject to a set of constraints. The goal is not necessarily to find the global optimum. We try to explore the search space more efficiently in order to find a good approximate solution. The obtained solution should verify the hard constraints that are required to be satisfied. In this paper, we propose a cooperative search method that handles optimality and feasibility separately. We take the traveling tournament problem (TTP) as a case study to show the applicability of the proposed idea. TTP is the problem of scheduling a double round-robin tournament that satisfies a set of related constraints and minimizes the total distance traveled by the teams. The proposed method for TTP consists of two main steps. In the first step, we ignore the optimization criterion. We reduce the search only to feasible solutions satisfying the problem's constraints. For this purpose, we use constraints programming model to ensure the feasibility of solutions. In the second step, we propose a stochastic local search method to handle the optimization criterion and find a good approximate solution that verifies the hard constraints. The overall method is evaluated on benchmarks and compared with other well-known techniques for TTP. The computational results are promising and show the effectiveness of the proposed idea for TTP