Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
Not a member yet
1506 research outputs found
Sort by
Social Data Visualization System for Understanding Diffusion Patterns on Twitter: A Case Study on Korean Enterprises
Get PDFOnline social media have been playing an important role of creating and diffusing information to many users. It means the users can get cognitive influence to the other users. Thus, it is important to understand how the information can be diffused by interactions among users through online social media. In this paper, we design a social media monitoring system (called "TweetPulse'') which can analyze and show meaningful diffusion patterns (DP) among the users. Particularly, TweetPulse focuses on visualizing information diffusion in Twitter, given a certain time duration. Also, this work has investigated the relationships 1) between DP and event detecting, 2) between DP and emotional words, and 3) between DP and the number of followers of the users. Thereby, to understand the continuous patterns of the information diffusion, we propose two different types of analytic methods, which are 1) macroscopic approach and 2) microscopic approach. For evaluating the proposed method, we have collected and preprocessed the dataset during about 4 months (14 March 2012 to 12 July 2012). As a conclusion, TweetPulse has helped users to easily understand DP from a large scale dataset streaming through Twitter
Extending the Communication Capabilities of Agents
Get PDFAgent technology is in principle well suited for realizing various kinds of distributed systems, but in practice agents are seldomly chosen for realizing real-world applications. One reason hindering agents being used in practice is their cumbersome communication mechanism focused on speech act based message exchange which makes them hard for practitioners used to work in an object oriented way. To broaden the application spectrum of agent technology in practice and make them more accessible for object-oriented developers, this paper presents additional communication means for agents. First, it will be shown how agents can interact using strongly typed service interfaces resorting to asynchronous future based methods. These allow keeping agents autonomous and further support several recurrent interaction patterns within one method call, i.e. without having to use complex message protocols. Second, an extension for binary data streaming via virtual connections will be presented. Its usage resembles established input and output streaming APIs and lets developers transfer data between agents in the same simple way as e.g. a file is written to hard disk. Furthermore, virtual connections allow failure tolerant transmission by multiplexing data across different physical connections. Usefulness of the extensions will be further explained with a real-word example application from the area of business intelligence workflows
Real-Time Recognition and Tracking of Moving Objects
Get PDFA multi-camera monitoring system for online recognition of moving objects is considered. The system consists of several autonomous vision subsystems. Each of them is able to monitor an area of interest with the aim to reveal and recognize characteristic patterns and to track the motion of the selected configuration. Each subsystem recognizes the existence of the predefined objects in order to report expected motion while automatically tracking the selected object. Simultaneous tracking by two or more cameras is used to measure the instant distance of the tracked object. A modular conception enables simple extension by several static and mobile cameras mechanically oriented in space by the pan and tilt heads. The open architecture of the system allows additional subsystems integration and the day and night image processing algorithms extension
Knowledge Granulation, Rough Entropy and Uncertainty Measure in Incomplete Fuzzy Information System
Get PDFMany real world problems deal with ordering of objects instead of classifying objects, although most of research in data analysis has been focused on the latter. One of the extensions of classical rough sets to take into account the ordering properties is dominance-based rough sets approach which is mainly based on substitution of the indiscernibility relation by a dominance relation. In this paper, we address knowledge measures and reduction in incomplete fuzzy information system using the approach. Firstly, new definitions of knowledge granulation and rough entropy are given, and some important properties of them are investigated. Then, dominance matrix about the measures knowledge granulation and rough entropy is obtained, which could be used to eliminate the redundant attributes in incomplete fuzzy information system. Lastly, a matrix algorithm for knowledge reduction is proposed. An example illustrates the validity of this method and shows the method is applicable to complex fuzzy system. Experiments are also made to show the performance of the newly proposed algorithm
A Technique to Stock Market Prediction Using Fuzzy Clustering and Artificial Neural Networks
Get PDFStock market prediction is essential and of great interest because successful prediction of stock prices may promise smart benefits. These tasks are highly complicated and very difficult. Many researchers have made valiant attempts in data mining to devise an efficient system for stock market movement analysis. In this paper, we have developed an efficient approach to stock market prediction by employing fuzzy C-means clustering and artificial neural network. This research has been encouraged by the need of predicting the stock market to facilitate the investors about buy and hold strategy and to make profit. Firstly, the original stock market data are converted into interpreted historical (financial) data i.e. via technical indicators. Based on these technical indicators, datasets that are required for analysis are created. Subsequently, fuzzy-clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy system module, is employed to predict the stock price. The results for the stock market prediction are validated through evaluation metrics, namely mean absolute deviation, mean square error, root mean square error, mean absolute percentage error used to estimate the forecasting accuracy in the stock market. Comparative analysis is carried out for single Neural Network (NN) and existing technique neural. The obtained results show that the proposed approach produces better results than the other techniques in terms of accuracy
Passive Fault-Tolerance Management in Component-Based Embedded Systems
Get PDFIt is imperative to accept that failures can and will occur even in meticulously designed distributed systems and to design proper measures to counter those failures. Passive replication minimizes resource consumption by only activating redundant replicas in case of failures, as typically, providing and applying state updates is less resource demanding than requesting execution. However, most existing solutions for passive fault tolerance are usually designed and configured at design time, explicitly and statically identifying the most critical components and their number of replicas, lacking the needed flexibility to handle the runtime dynamics of distributed component-based embedded systems. This paper proposes a cost-effective adaptive fault tolerance solution with a significant lower overhead compared to a strict active redundancy-based approach, achieving a high error coverage with a minimum amount of redundancy. The activation of passive replicas is coordinated through a feedback-based coordination model that reduces the complexity of the needed interactions among components until a new collective global service solution is determined, hence improving the overall maintainability and robustness of the system
GPU-Accelerated and CPU SIMD Optimized Monte Carlo Simulation of φ4 Model
Get PDFIn this contribution we describe an efficient GPU implementation of the Monte-Carlo simulation of the Ginzburg-Landau model. We achieve the performance close to 50 % of the peak performance of the used GPU. We compare this performance with a parallelized and vectorized CPU code and discuss the observed differences
Generation of Neuronal Trees by a New Three Letters Encoding
Get PDFA neuronal tree is a rooted tree with n leaves whose each internal node has at least two children; this class not only is defined based on the structure of dendrites in neurons, but also refers to phylogenetic trees or evolutionary trees. More precisely, neuronal trees are rooted-multistate phylogenetic trees whose size is defined as the number of leaves. In this paper, a new encoding over an alphabet of size 3 (minimal cardinality) is introduced for representing the neuronal trees with a given number of leaves. This encoding is used for generating neuronal trees with n leaves in A-order with constant average time and O(n) time complexity in the worst case. Also, new ranking and unranking algorithms are presented in time complexity of O(n) and O(n log n), respectively
Evolving the System's Core: A Case Study on the Identification and Characterization of Key Developers in Apache Ant
Get PDFSoftware systems usually include a limited number of important classes that a large number of other classes depend upon. These files comprise the technical core of a software system. Evolving this core is naturally difficult and requires caution, since inappropriate changes may induce side and ripple effects. Indeed, despite the rhetoric about openness in free/libre open source software, modifications to the technical core are usually accomplished by a small set of contributors known as key developers. Automatically identifying key developers is relevant for a number of reasons, such as supporting the recruitment of specialists, assigning tutors to newcomers, and estimating the longevity likelihood of projects. Moreover, little is known about how key developers evolve and about the social skills that support them in their technical tasks. In this paper, we describe a case study involving the Apache Ant project. Our goal was to identify key developers and characterize them in terms of their social activity and contributions. We conceived, implemented, and applied a method to identify key developers. Such method identified four individuals, who were the main target of our investigation. We built communication networks from mailing list data and a coordination requirements network from their development traces. The analysis of these two networks indicated that three key developers socialized more than the others. They acted as bridges connecting other developers and communicated with almost everyone they were supposed to. The other key developer showed a very distinct behavior, as he participated very rarely in the mailing list. We also analyzed key developers' contributions and found some patterns. Core commits and non-core commits were often interleaved and key developers also contributed to peripheral portions of the system. Finally, we observed that the set of key developers was indistinguishable from the set of top committers. We expect this characterization to foster the definition of key developers' profiles that take into account their social activities and contribution characteristics
PatOMat - Versatile Framework for Pattern-Based Ontology Transformation
Get PDFThe purpose of the PatOMat transformation framework is to bridge between different modeling styles of web ontologies. We provide a formal model of pattern-based ontology transformation, explain its implementation in PatOMat, and manifest the flexibility of the framework on diverse use cases