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 Showing 1201 - 872 of 872 Journals sorted alphabetically Software:Practice and Experience       (Followers: 12) Southern Communication Journal       (Followers: 3) Spatial Cognition & Computation       (Followers: 6) Spreadsheets in Education       (Followers: 1) Statistics, Optimization & Information Computing       (Followers: 3) Stochastic Analysis and Applications       (Followers: 3) Stochastic Processes and their Applications       (Followers: 6) Structural and Multidisciplinary Optimization       (Followers: 12) Studia Universitatis Babeș-Bolyai Informatica Studies in Digital Heritage       (Followers: 3) Supercomputing Frontiers and Innovations       (Followers: 1) Superhero Science and Technology       (Followers: 5) Sustainability Analytics and Modeling       (Followers: 5) Sustainable Computing : Informatics and Systems Sustainable Energy, Grids and Networks       (Followers: 4) Sustainable Operations and Computers       (Followers: 1) Swarm Intelligence       (Followers: 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Similar Journals
 Vietnam Journal of Computer ScienceNumber of Followers: 2     Open Access journal ISSN (Print) 2196-8888 - ISSN (Online) 2196-8896 Published by SpringerOpen  [229 journals]
• Modeling and objectification of blood vessel calcification with using of
multiregional segmentation

• Abstract: Abstract In a clinical practice of the angiography, the blood vessel analysis is substantially important mainly in a sense of an objectification and modeling of the pathological spots such as the blood vessel calcifications. An amount of the calcification is commonly just estimated by naked eyes; therefore, the automatic modeling may be beneficial in a context of an extraction of the blood vessel features well representing a level of the blood vessel deterioration. In this work, we have proposed a fully automatic software environment (BloodVessCalc) for processing the blood vessel images acquired by the CT (computer tomography). The main function of the SW is the multiregional image segmentation allowing for an extraction of the physiological blood vessel location from the calcification spots. This model offers the calcium score calculation in a form of amount of the calcification. In the last part of our analysis, the predictive intervals of the average value and median for calcium score are calculated.
PubDate: 2018-08-09

• Genetic algorithm as self-test path and circular self-test path design
method

• Abstract: Abstract The paper presents the use of Genetic Algorithm to search for non-linear Autonomous Test Structures (ATS) in Built-In Testing approach. Such structures can include essentially STP and CSTP and their modifications. Non-linear structures are more difficult to analyze than the widely used structures such as independent Test Pattern Generator and the Test Response Compactor realized by Linear Feedback Shift Registers. To reduce time-consuming test simulation of sequential circuit, it was used an approach based on the stochastic model of pseudo-random testing. The use of stochastic model significantly affects the time effectiveness of the search for evolutionary autonomous structures. In test simulation procedure, the block of sequential circuit memory is not disconnected. This approach does not require a special selection of memory registers such as BILBOs. A series of studies to test circuits set ISCAS’89 are made. The results of the study are very promising.
PubDate: 2018-07-27

• Mining and applications of repeating patterns

• Abstract: Abstract Mining the valuable knowledge from real data has been a hot topic for a long time. Repeating pattern is one of the important knowledge, occurring in many real applications such as musical data and medical data. In this paper, our purposes are to contribute an efficient mining algorithm for repeating patterns and to conduct a real application using the repeating patterns mined. In terms of mining the repeating patterns, although a number of past studies were made on this issue, the performance cannot still earn the users’ satisfactions especially for large data sets. For this issue, in this paper, we propose an efficient algorithm named Fast Mining of Repeating Patterns, which achieves high performance of discovering the repeating patterns by a novel index called Quick-Pattern Index. In terms of applications, a music recommender system named repeating-pattern-based music recommender system is proposed to deal with problems in music recommendation. Even facing a very sparse rating matrix, the recommendation can still be completed. The experimental results show that our proposed mining algorithm and recommender system outperform the previous works in terms of efficiency and effectiveness, respectively.
PubDate: 2018-06-14

• Short-term load forecasting in smart meters with sliding window-based
ARIMA algorithms

• Abstract: Abstract Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.
PubDate: 2018-06-06

• Aggregative context-aware fitness functions based on feature selection for
evolutionary learning of characteristic graph patterns

• Abstract: Abstract We propose aggregative context-aware fitness functions based on feature selection for evolutionary learning of characteristic graph patterns. The proposed fitness functions estimate the fitness of a set of correlated individuals rather than the sum of fitness of the individuals, and specify the fitness of an individual as its contribution degree in the context of the set. We apply the proposed fitness functions to our evolutionary learning, based on Genetic Programming, for obtaining characteristic block-preserving outerplanar graph patterns and characteristic TTSP graph patterns from positive and negative graph data. We report some experimental results on our evolutionary learning of characteristic graph patterns, using the context-aware fitness functions.
PubDate: 2018-06-02

• Recognizing the pattern of binary Hermitian matrices by quantum kNN and
SVM methods

• Abstract: Abstract The article contains a description of two quantum circuits for pattern recognition. The first approach is realized with use of k nearest neighbors algorithm and the second with support vector machine. The task is to distinguish between Hermitian and non-Hermitian matrices. The quantum circuits are constructed to accumulate elements of a learning set. After this process, circuits are able to produce a quantum state which contains the information if a tested element fits to the trained pattern. To improve the efficiency of presented solutions, the matrices were uniquely labeled with feature vectors. The role of the feature vectors is to highlight some features of the objects which are crucial in the process of classification. The circuits were implemented in Python programming language and some numeric experiments were conducted to examine the capacity of presented solutions in pattern recognition.
PubDate: 2018-05-30

• A new multilevel reversible bit-planes data hiding technique based on
histogram shifting of efficient compressed domain

• Abstract: Abstract In this paper, we proposed a new technique for reversible data hiding based on efficient compressed domain with multiple bit planes. We conducted a sequence of experiments to use block division scheme to appraise the result with different parameters and amended the probability of zero point in every block of histogram. This scheme attained more embedding capacity and high-quality of stego-image. Experimental consequences prove that the proposed method effectively achieved the objective of high embedding capacity and sustaining the quality of image.
PubDate: 2018-05-29

• Movie indexing and summarization using social network techniques

• Abstract: Abstract Movie summarization and indexing is the study which takes into account the understanding of the audiences. Besides, movie summarization focuses on reducing the length of a movie. Regarding this work, we propose a character network analysis to index and summarize the given movie. The method is based on the discovery and analysis of characters with respect to their appearance and the relationships among them in the movie. The strategy analysis is used to detect scenes, to segment the sub-plots of the story, and to extract character network and the main storyline of the movie. As a result, the social strength of each character in the social network is measured using measurement techniques that will then be used to mine the main plotline of the movie. In the final stage, the main storyline is used to provide a summarized version of the movie based on the social power of the characters. Experiments were carried out with 17 series of the Star Wars, the Lord of the Rings and the Harry Potter. The experimental evaluation results show that this study should index and extract summarization versions while keeping the understanding of the audiences.
PubDate: 2018-05-28

• Three local search-based methods for feature selection in credit scoring

• Abstract: Abstract Credit scoring is a crucial problem in both finance and banking. In this paper, we tackle credit scoring as a classification problem where three local search-based methods are studied for feature selection. The feature selection is an interesting technique that can be launched before the data classification task. It permits to keep only the relevant variables and eliminate the redundant ones which enhances the classification accuracy. We study the local search method (LS), the stochastic local search method (SLS) and the variable neighborhood search method (VNS) for feature selection. Then, we combine these methods with the support vector machine (SVM) classifier to find the best described model from a dataset with the correct class variable. The proposed methods (LS+SVM, SLS+SVM and VNS+SVM) are evaluated on both German and Australian credit datasets and compared with some well-known classifiers. The numerical results are promising and show a good performance in favor of our methods.
PubDate: 2018-05-28

• A hybrid mobile call fraud detection model using optimized fuzzy C-means
clustering and group method of data handling-based network

• Abstract: Abstract A novel two-stage fraud detection system in mobile telecom networks has been presented in this paper that identifies the malicious calls among the normal ones in two stages. Initially, a genetic algorithm-based optimized fuzzy c-means clustering is applied to the user’s historical call records for constructing the calling profile. Thereafter, the identification of the fraudulent calls occurs in two stages. In the first stage, each incoming call is passed to the clustering module that identifies the call as genuine, malicious or suspicious. This is done by comparing the distance value of the new calling instance from the profile cluster centers against two predefined threshold values. The calls detected as genuine or malicious are not further processed. However, the call records that are found to be suspicious are additionally scrutinized in the second stage by a previously trained group method of data handling model for final decision making. The legitimate and forged labeled call records generated out of the clustering module are utilized for training the supervised classifier. Experimentation is done on a real-world call dataset to exhibit the effectiveness of the proposed model. A comparative analysis of the current approach with one of our earlier propositions and another recent fraud detection system clearly illustrates the efficacy of the developed model.
PubDate: 2018-05-26

• Analyzing predictive performance of linear models on high-frequency
currency exchange rates

• Abstract: Abstract We generate a large number of predictive models by applying linear kernel SVR to historical currency rates’ bid data for three currency pairs obtained from high-frequency trading. The bid tick data are converted into equally spaced (1 min) data. Differences of price between the previous successive timeframes are used as features to predict the direction of movement of the price in the next timeframe. Different values for the number of training samples, number of features, and the length of the timeframes are used when learning the models. These models are used to conduct simulated currency trading in the year following the one in which the model was learned. Profits (sum of realized differences in best bid prices when order is executed), hit ratios, and number of trades executed using these models are recorded. The experiments indicate that while it is difficult to construct models using only historical data that consistently perform well, there are models that show good performance under certain pre-defined conditions, and that many of these models have an interesting property. Upon examining the parameters of these models, we discover that they have all negative coefficients and a negligibly small intercept, while having positive profits and good hit ratio. This suggests a simple yet effective trading strategy. Finally, we examine the historical data to find corroboration for the pattern suggested by the generated models and present the results.
PubDate: 2018-05-26

• Revisiting urban air quality forecasting: a regression approach

• Abstract: Abstract We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean $$\hbox {PM}_{10}$$ concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.
PubDate: 2018-05-24

• Evaluation of educational applications in terms of communication delay
between tablets with Bluetooth or Wi-Fi Direct

• Abstract: Abstract This study conducted a survey to implement educational applications that can share information even in environments where access points cannot be used. In particular, we investigated whether Bluetooth (widely used for many years) or Wi-Fi Direct (developed recently) is more suitable when creating educational applications using an ad hoc network. To survey the influence of hand movements on delay time while operating tablets, we created a paint application that shares a drawing screen across two tablets and conducted an experiment. In addition, to survey the influence of human presence on delay time, we conducted an experiment in which we changed the number of students seated between the two tablets in the classroom. From the results of these experiments, we conclude that Bluetooth is less influenced by hand movements and human presence than Wi-Fi Direct. We also verified the practicality of educational applications using Bluetooth communication. We developed an educational application, and students used the application in their actual class. A questionnaire to investigate whether they were conscious of communication delay was administered.
PubDate: 2018-05-23

• Estimating the similarity of social network users based on behaviors

• Abstract: Abstract Recently, with the express growth of social network, users have joined more and more of these networks and live their life virtually. Consequently, they create a huge data on these social networks: their profile, interest, and behavior such as post, comment, like, joining groups or communities, etc. This brings some new challenges to researchers: do users having the same profile/interest show the same behavior' And vice versa, do users having the same behavior have interest in the same things' One of the basic issues in these challenges is the problem of estimating the similarity among users on these social networks based on their profile, interest, and behavior. This paper presents a model for estimating the similarity between users based on their behavior on social networks. The considered behaviors are activities including posting entries, liking these entries, commenting and liking the comment in these entries. The model is then evaluated with a dataset-collected users from Twitter. The results show that the model estimates correctly the similarity among users in the majority of the cases.
PubDate: 2018-05-19

• Precomputing architecture for flexible and efficient big data analytics

• Abstract: Abstract The rising of big data brings revolutionary changes to many aspects of our lives. Huge volume of data, along with its complexity poses big challenges to data analytic applications. Techniques proposed in data warehousing and online analytical processing, such as precomputed multidimensional cubes, dramatically improve the response time of analytic queries based on relational databases. There are some recent works extending similar concepts into NoSQL such as constructing cubes from NoSQL stores and converting existing cubes into NoSQL stores. However, only limited attention in literature have been devoted to precomputing structure within the NoSQL databases. In this paper, we present an architecture for answering temporal analytic queries over big data by precomputing the results of granulated chunks of collections which are decomposed from the original large collection. In extensive experimental evaluations on drill-down and roll-up temporal queries over large amount of data we demonstrated the effectiveness and efficiency under different settings.
PubDate: 2018-05-19

• Failures in discrete-event systems and dealing with them by means of Petri
nets

• Abstract: Abstract An approach based on Petri nets pointing to the manner how to deal with failures in discrete-event systems is presented. It uses the reachability tree and/or reachability graph of the Petri net-based model of the real system as well as the synthesis of a supervisor to remove the possible deadlock(s). To illustrate the applicability of the approach to the detection and recovery of failures in DES modelled by Petri nets the case study on a railroad crossing is introduced.
PubDate: 2018-05-19

• Functional querying in graph databases

• Abstract: Abstract The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual and data level. The notions of graph conceptual schema and graph database schema are considered. The notion of a typed attribute is used as a basic structure both on the conceptual and database level. As a formal approach to declarative graph database querying a version of typed lambda calculus is used. This approach allows to use a logic necessary for querying, arithmetic as well as aggregation function. Another advantage is the ability to deal with relations and graphs in one integrated environment.
PubDate: 2018-05-01

• Control of autonomous robot behavior using data filtering through adaptive
resonance theory

• Abstract: Abstract The aim of the article is to use neural networks to control autonomous robot behavior. The type of the controlling neural network was chosen a backpropagation neural network with a sigmoidal transfer function. The focus in this article is put on the use adaptive resonance theory (ART1) for data filtering. ART1 is used for preprocessing of the training set. This allows finding typical patterns in the full training set and thus covering the whole space of solutions. The neural network adapted by a reduced training set has a greater ability of generalization. The work also discusses the influence of vigilance parameter settings for filtering the training set. The proposed approach to data filtering through ART1 is experimentally verified to control the behavior of an autonomous robot in an unknown environment with varying degrees of difficulty regarding the location of obstacles. All obtained results are evaluated in the conclusion.
PubDate: 2018-05-01

• From the Editor

• PubDate: 2018-02-01

• Concordance-based Kendall’s correlation for computationally-light vs.
computationally-heavy centrality metrics: lower bound for correlation

• Abstract: Abstract We identify three different levels of correlation (pairwise relative ordering, network-wide ranking, and linear regression) that could be assessed between a computationally-light centrality metric and a computationally-heavy centrality metric for real-world networks. The Kendall’s concordance-based correlation measure could be used to quantitatively assess how well we could consider the relative ordering of two vertices $$v_{i}$$ and $$v_{j}$$ with respect to a computationally-light centrality metric as the relative ordering of the same two vertices with respect to a computationally-heavy centrality metric. We hypothesize that the pairwise relative ordering (concordance)-based assessment of the correlation between centrality metrics is the most strictest of all the three levels of correlation and claim that the Kendall’s concordance-based correlation coefficient will be lower than the correlation coefficient observed with the more relaxed levels of correlation measures (linear regression-based Pearson’s product–moment correlation coefficient and the network-wide ranking-based Spearman’s correlation coefficient). We validate our hypothesis by evaluating the three correlation coefficients between two sets of centrality metrics: the computationally-light degree and local clustering coefficient complement-based degree centrality metrics and the computationally-heavy eigenvector centrality, betweenness centrality, and closeness centrality metrics for a diverse collection of 50 real-world networks.
PubDate: 2017-06-28

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