Contents of Volume 27 (2017)

1/2017 2/2017


  • [1] Kuklová J., Přibyl O. (CZ)
    Changeover from decision tree approach to fuzzy logic approach within highway management , 181-196

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    Abstract: {abstract} This paper deals with the changeover from the decision tree (bivalent logic) approach to the fuzzy logic approach to highway traffic control, particularly to variable speed limit displays. The usage of existing knowledge from decision tree control is one of the most suitable methods for identification of the new fuzzy model. However, such method introduces several difficulties. These difficulties are described and possible measures are proposed. Several fuzzy logic algorithms were developed and tested by a~microsimulation model. The results are presented and the finest algorithm is recommended for testing on the Prague City Ring Road in real conditions. This paper provides a~guidance for researchers and practitioners dealing with similar problem formulation.

  • [2] Popa M.C., Rothkrantz L.J.M., Wiggers P., Shan C. (NL)
    Assessment of Facial Expressions in Product Appreciation, 197-214

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    Abstract: In the marketing area, new trends are emerging, as customers are not only interested in the quality of the products or delivered services, but also in a stimulating shopping experience. Creating and influencing customers' experiences has become a valuable differentiation strategy for retailers. Therefore, understanding and assessing the customers' emotional response in relation to products/services represents an important asset. The purpose of this paper consists of investigating whether the customer's facial expressions shown during product appreciation are positive or negative and also which types of emotions are related to product appreciation. We collected a database of emotional facial expressions, by presenting a set of forty product related pictures to a number of test subjects. Next, we analysed the obtained facial expressions, by extracting both geometric and appearance features. Furthermore, we modeled them both in an unsupervised and supervised manner. Clustering techniques proved to be efficient at differentiating between positive and negative facial expressions in 78\% of the cases. Next, we performed more refined analysis of the different types of emotions, by employing different classification methods and we achieved 84\% accuracy for seven emotional classes and 95\% for the positive vs. negative.

  • [3] Seidlová R., Pozivil J., Seidl J., Malecl L. (CZ)
    Synthetic data generator for testing of classification rule algorithms, 215-229

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    Abstract: We developed a data generating system that is able to create systematically testing datasets that accomplish user’s requirements such as number of rows, number and type of attributes, number of missing values, class noise and imbalance ratio. These datasets can be used for testing of the algorithms designed for solving classification rule problem. We used them for optimizing of the parameters of the classification algorithm based on the behavior of ant colonies. But they can be advantageously used for other applications too. Program generates output files in ARFF format. Two standards and one user-define probability distributions are used in data generation: uniform distribution, normal distribution and irregular distribution for nominal attributes. To our knowledge, our system is probably the first synthetic data generation system that systematically generates datasets for examination and judgment of the classification rule algorithms.

  • [4] Gadri S., Moussaoui A. (Algeria)
    Application of a New Set of Pseudo-Distances in Documents Categorization, 231-245

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    Abstract: Automatic text classification is a very important task that consists in assigning labels (categories, groups, classes) to a given text based on a set of previously labeled texts called training set. The work presented in this paper treats the problem of automatic topical text categorization. It is a supervised classification because it works on a predefined set of classes and topical because it uses topics or subjects of texts as classes. In this context, we used a new approach based on $k$-NN algorithm, as well as a new set of pseudo-distances (distance metrics) known in the field of language identification. We also proposed a simple and effective method to improve the quality of performed categorization.

  • [5] Ye W., Liu S., Liu X. (China)
    Transition modes between spiking and bursting in a pacemaker neuron, 247-258

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    Abstract: Central pattern generators (CPGs) play an important role in controlling rhythmic movements in vivo. Increased insight into mechanisms of CPGs can be obtained by perturbing neuron activities so as to study a range of behaviors. By applying this method, a series of simulations were performed to research different transition modes between firing patterns in a pacemaker neuron model of stomatogastric ganglion (STG). Firstly, with the perturbation of parameters in model, such as external stimulus, parameters in compartments and connection between compartments, different firing patterns and bifurcation of inter-spike intervals (ISIs) were obtained to exhibit the impact of single parameter on the transions between spiking and bursting. Moreover, perturbing two parameters gCa, Iext simultaneously induced the continuous variation of the bifurcation mode, which implied the crucial role of calcium channel in regulating the rhythm generation. Finally, a two-dimensional parameter space (gCa, Iext) was constructed by spike-counting method to capture the distribution of the firing patterns and different transition mode between them in a comprehensive aspect. In this parameter space, three basic transition modes were concluded: bifurcation ring, period-doubling mode and period-adding mode.


  • Editorial Board of Neural Network World (CZ)
    Petr Hájek passed away, 1-2,       
    Full text

  • [1] Chaudhary P., Gupta P.P. (India)
    A Novel Framework to Alleviate Dissemination of XSS Worms in Online Social Network (OSN) using View Segregation , 5-26

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    Abstract: In this paper, we propose a client-server based framework that alleviates the dissemination of XSS worms from the OSN. The framework initially creates the views corresponding to retrieved request on the server-side. Such views indicate that which part of the generated web page on the server can be accessed by user depending on the generated Access Control List (ACL). Secondly, JavaScript attack vectors are retrieved from the HTTP response by referring the blacklist repository of attack vectors. Finally, injection of sanitization primitives will be done on the client-side in place of extracted JavaScript attack vectors. The framework will perform the sanitization on such attack vectors strictly in a context-aware manner. The experimental testing of our framework has performed on the two platforms of open source OSN-based web applications. The observed detection rate of JavaScript attack vectors was effective and acceptable as compared to other existing XSS defensive methodologies. The proposed framework has optimized the method of auto-context-aware sanitization in contrast to other existing approaches and hence incurs a low and acceptable performance overhead.

  • [2] S. Kamal, N. Dey, A. S. Ashour, S. Ripon,V. E. Balas, M. S. Kaysar (Bangladesh)
    FbMapping: An Automated System for Monitoring Facebook Data , 27-58

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    Abstract: In recent modernized era, the number of the Facebook users is increasing dramatically. Moreover, the daily life information on social networking sites is changing energetically over web. Teenagers and university students are the major users for the different social networks all over the world. In order to maintain rapid user satisfactions, information flow and clustering are essential. However, these tasks are very challenging due to the excessive datasets. In this context, cleaning the original data is significant. Thus, in the current work the Fishers Discrimination Criterion (FDC) is applied to clean the raw datasets. The FDC separates the datasets for superior fit under least square sense. It arranges datasets by combining linearly with greater ratios of between -- groups and within the groups. In the proposed approach, the separated data are handled by the Bigtable mapping that is constructed with Map specification, tabular representation and aggregation. The first phase organizes the cleaned datasets in row, column and timestamps. In the tabular representation, Sorted String Table (SSTable) ensures the exact mapping. Aggregation phase is employed to find out the similarity among the extracted datasets. Mapping, preprocessing and aggregation help to monitor information flow and communication over Facebook. For smooth and continuous monitoring, the Dynamic Source Monitoring (DSM) scheme is applied. Adequate experimental comparisons and synthesis are performed with mapping the Facebook datasets. The results prove the efficiency of the proposed machine learning approaches for the Facebook datasets monitoring.

  • [3] Yang Z., Li Z., Fan K., Huang J. (China)
    Exploiting Multi-Sources Query Expansion in Microblogging Filtering , 59-76

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    Abstract: Microblogging filtering is intended to filter out irrelevant content, and select useful, new, and timely content from microblogs. However, microblogging filtering suffers from the problem of insufficient samples which renders the probabilistic models unreliable. To mitigate this problem, a novel method is proposed in this study. It is believed that an explicit brief query is only an abstract of the user's information needs, and it’s difficult to infer users' actual searching intents and interests. Based on this belief, a filtering model is built where the multi-sources query expansion in microblogging filtering is exploited and expanded query is submitted as user’s interest. To manage the external expansion risk, a user filter graph inference method is proposed, which is characterized by combination of external multi-sources information, and a risk minimization filtering model is introduced to achieve the best reasoning through the multi-sources expansion. A series of experiments are conducted to evaluate the effectiveness of proposed framework on an annotated tweets corpus. The results of these experiments show that our method is effective in tweets retrieval as compared with the baseline standards.

  • [4] Yuan W., Guan D. (China)
    Optimized Trust-Aware Recommender System using Genetic Algorithm, 77-94

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    Abstract: Trust-aware recommender system (TARS) recommends ratings based on user trust. It greatly improves the conventional collaborative filtering by providing reliable recommendations when dealing with the data sparseness problem. One basic research issue of TARS is to improve the recommending efficiency, in which the key point is to find sufficient number of recommenders efficiently for active users. Existing works searched recommenders via a skeleton, which consists of a number of hub nodes. The hub nodes are those who have superior degrees based on the scale-freeness of the trust network. However, existing works did not consider the skeleton maintenance cost and the coverage overlap between nodes of the skeleton. They also failed to suggest the proper size of the skeleton. This paper proposes an optimized TARS model to solve the problems of existing works. By using the genetic algorithm, our model chooses the most suitable nodes for the skeleton of recommender searching. It can achieve the maximum prediction coverage with the minimum skeleton maintenance cost. Simulation results show that compared with existing works, our model can reduce more than 90{\%} of the skeleton maintenance cost with reasonable prediction coverage.

  • [5] H. Li, T. Zhao, N. Li, Q. Cai, J. Du (China)
    Feature Matching of Multi-view 3D Models Based on Hash Binary Encoding, 95-106

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    Abstract: Image data and 3D model data have emerged as resourceful foundation for information with proliferation of image capturing devices and social media. In this paper, a feature matching method based on hash binary encoding for multi-view 3D models in social media is proposed. SIFT algorithm is first used to extract features of the depth image, and then RANSAC is utilized as a filter. Finally, a cascade hash binary encoding algorithm is adapted to match the feature of multi-view models. Experimental results on SHREC2014 dataset have shown the effectiveness of the proposed method.

  • [6] A.A. Frolov, D. Húsek, E.V. Biryukova, P.D. Bobrov, O.A. Mokienkoy, A.V. Alexandrov (Russia,CZ)
    Principles of motor recovery in post-stroke Patients Using Hand Exoskeleton Controlled by the Brain-Computer Interface Based on Motor Imagery , 107-138

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    Abstract: Motor recovery in post-stroke and post-traumatic patients using exoskeleton controlled by the brain-computer interface (BCI) is a new and promising rehabilitation procedure. Its development is a multidisciplinary research which requires, the teamwork of experts in neurology, neurophysiology, physics, mathematics, biomechanics and robotics. Some aspects of all these fields of study concerning the development of this rehabilitation procedure are described in the paper. The description includes the principles and physiological prerequisites of BCI based on motor imagery, biologically adequate principles of exoskeleton design and control and the results of clinical application.

  • [7] T.M. Usha, S. Appavu alias Balamurugan (India)
    Computational Modeling of Electricity Consumption Using Econometric Variables Based on Neural Network Training Algorithms, 139-178

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    Abstract: Recently, there has been a significant emphasis in the forecasting of the electricity demand due to the increase in the power consumption. Energy demand forecasting is a very important task in the electric power distribution system to enable appropriate planning for future power generation. Quantitative and qualitative methods have been utilizedpreviously for the electricity demand forecasting. Due to the limitations inthe availability of data, these methods fail to provide effective results. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. This paper presents the computational modeling of electricity consumption based on the Neural Network (NN) training algorithms. The main aim of the work is to determine the optimal training algorithm for electricity demand forecasting. From the experimental analysis, it is concluded that the Bayesian regularization training algorithm exhibits low relative error and high correlation coefficient than other training algorithms. Thus, the Bayesian Regularization training algorithm is selected as the optimal training algorithm for the effective prediction of the electricity demand. Finally, the economic input attributes are forecasted for next 15 years using time series forecasting. Using this forecasted economic attributes and with the optimal Bayesian Regularization training algorithm, the electricity demand for the next 15 years ispredicted. The comparative analysis of the NN training algorithms for the proposed dataset and larger datasets obtained from the UCI repository and American Statistical Association shows that the Bayesian Regularization training algorithm yields higher correlation value and lower relative error than other training algorithms.