Search results for: Statistical Pattern Recognition.

Authors: Murat Koklu, Yavuz Unal

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Data mining can be called as a technique to extract information from data. It is the process of obtaining hidden information and then turning it into qualified knowledge by statistical and artificial intelligence technique. One of its application areas is medical area to form decision support systems for diagnosis just by inventing meaningful information from given medical data. In this study a decision support system for diagnosis of illness that make use of data mining and three different artificial intelligence classifier algorithms namely Multilayer Perceptron, Naive Bayes Classifier and J.48. Pima Indian dataset of UCI Machine Learning Repository was used. This dataset includes urinary and blood test results of 768 patients. These test results consist of 8 different feature vectors. Obtained classifying results were compared with the previous studies. The suggestions for future studies were presented.

Keywords: Artificial Intelligence, Classifiers, Data Mining, Diabetic Patients.

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Authors: M.H.Ghaffari

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The present study is concerned with effect of exciting boundary layer on increase in heat transfer from flat surfaces. As any increase in heat transfer between a fluid inside a face and another one outside of it can cause an increase in some equipment's efficiency, so at this present we have tried to increase the wall's heat transfer coefficient by exciting the fluid boundary layer. By a collision between flow and the placed block at the fluid way, the flow pattern and the boundary layer stability will change. The flow way inside the channel is simulated as a 2&3-dimensional channel by Gambit TM software. With studying the achieved results by this simulation for the flow way inside the channel with a block coordinating with Fluent TM software, it's determined that the figure and dimensions of the exciter are too important for exciting the boundary layer so that any increase in block dimensions in vertical side against the flow and any reduction in its dimensions at the flow side can increase the average heat transfer coefficient from flat surface and increase the flow pressure loss. Using 2&3-dimensional analysis on exciting the flow at the flow way inside a channel by cylindrical block at the same time with the external flow, we came to this conclusion that the heat flux transferred from the surface, is increased considerably in terms of the condition without excitation. Also, the k-e turbulence model is used.

Keywords: Cooling, Heat transfer, Turbulence, Excitingboundary layer.

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Authors: Saleem Z. Ramadan, Gürsel A. Süer

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In this paper a genetic algorithm (GA) with dual-fitness function is proposed and applied to solve the deterministic identical machine scheduling problem. The mating fitness function value was used to determine the mating for chromosomes, while the selection fitness function value was used to determine their survivals. The performance of this algorithm was tested on deterministic identical machine scheduling using simulated data. The results obtained from the proposed GA were compared with classical GA and integer programming (IP). Results showed that dual-fitness function GA outperformed the classical single-fitness function GA with statistical significance for large problems and was competitive to IP, particularly when large size problems were used.

Keywords: Machine scheduling, Genetic algorithms, Due dates, Number of tardy jobs, Number of early jobs, Integer programming, Dual Fitness functions.

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Authors: H. Nouri, I. E. Achouri, A. Grimes, H. Ait Said, M. Aissou, Y. Zebboudj

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This paper aims to analysis the behavior of DC corona discharge in wire-to-plate electrostatic precipitators (ESP). Currentvoltage curves are particularly analyzed. Experimental results show that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method of least squares. Least squares problems that of into two categories: linear or ordinary least squares and non-linear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. A closed-form solution (or closed form expression) is any formula that can be evaluated in a finite number of standard operations. The non-linear problem has no closed-form solution and is usually solved by iterative.

Keywords: Electrostatic precipitator, current-voltage characteristics, Least Squares method, electric field, magnetic field.

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Authors: Alexander Wachtel, Michael T. Franzen, Walter F. Tichy

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Programming requires years of training. With natural language and end user development methods, programming could become available to everyone. It enables end users to program their own devices and extend the functionality of the existing system without any knowledge of programming languages. In this paper, we describe an Interactive Spreadsheet Processing Module (ISPM), a natural language interface to spreadsheets that allows users to address ranges within the spreadsheet based on inferred table schema. Using the ISPM, end users are able to search for values in the schema of the table and to address the data in spreadsheets implicitly. Furthermore, it enables them to select and sort the spreadsheet data by using natural language. ISPM uses a machine learning technique to automatically infer areas within a spreadsheet, including different kinds of headers and data ranges. Since ranges can be identified from natural language queries, the end users can query the data using natural language. During the evaluation 12 undergraduate students were asked to perform operations (sum, sort, group and select) using the system and also Excel without ISPM interface, and the time taken for task completion was compared across the two systems. Only for the selection task did users take less time in Excel (since they directly selected the cells using the mouse) than in ISPM, by using natural language for end user software engineering, to overcome the present bottleneck of professional developers.

Keywords: Natural language processing, end user development; natural language interfaces, human computer interaction, data recognition, dialog systems, spreadsheet.

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Authors: H. Katkhuda, B. Hanayneh, N. Shatarat

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The main objective of this paper is to determine the isolated effect of silica fume on tensile, compressive and flexure strengths on high strength lightweight concrete. Many experiments were carried out by replacing cement with different percentages of silica fume at different constant water-binder ratio keeping other mix design variables constant. The silica fume was replaced by 0%, 5%, 10%, 15%, 20% and 25% for a water-binder ratios ranging from 0.26 to 0.42. For all mixes, split tensile, compressive and flexure strengths were determined at 28 days. The results showed that the tensile, compressive and flexure strengths increased with silica fume incorporation but the optimum replacement percentage is not constant because it depends on the water–cementitious material (w/cm) ratio of the mix. Based on the results, a relationship between split tensile, compressive and flexure strengths of silica fume concrete was developed using statistical methods.

Keywords: Silica fume, Lightweight, High strength concrete, and Strength.

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Authors: W. T. Hsu, H. L. Tsai

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The rapid aging of the population is a common trend in the world. However, the progress of modern medical technology has increased the average life expectancy. The global population structure has changed dramatically, and the elderly population has risen rapidly. In the face of rapid population growth, it must be noted issues of the aging population must face up to, which are the physiological, psychological, and social problems associated with aging. This study aims to investigate how insurance agents are actively dealing with an aging society, their own aging anxiety, and their exercise habits. Purposive sampling was the sampling method of this study, a total of 204 respondents were surveyed and 204 valid surveys were returned. The returned valid ratio was 100%. Statistical method included descriptive statistics, t-test, and one-way ANOVA. The results of the study found that the insurance agent’s age, seniority, exercise habits to aging anxiety are significantly different.

Keywords: Insurance agent, aging anxiety, exercise habits, elderly.

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Authors: Hazem M. El-Bakry

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Neural processors have shown good results for detecting a certain character in a given input matrix. In this paper, a new idead to speed up the operation of neural processors for character detection is presented. Such processors are designed based on cross correlation in the frequency domain between the input matrix and the weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the searching process. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately by using a single faster neural processor. Furthermore, faster character detection is obtained by using parallel processing techniques to test the resulting sub-images at the same time using the same number of faster neural networks. In contrast to using only faster neural processors, the speed up ratio is increased with the size of the input image when using faster neural processors and image decomposition. Moreover, the problem of local subimage normalization in the frequency domain is solved. The effect of image normalization on the speed up ratio of character detection is discussed. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.

Keywords: Fast Character Detection, Neural Processors, Cross Correlation, Image Normalization, Parallel Processing.

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Authors: Haining Wang, Hong Zhang

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Problems exist in the present construction industry in China. Conflicts hinder the development of the whole society, such as contradictions between resource reservation and a huge population, living space needs and low building production efficiency, as well as environment protection and high pollution production pattern. In order to solve the problems and find a solution, research is needed to explore a building system. By investigating the whole architectural process and contrasting analysis of light structures and heavy structures, the paper raised the concepts to cope with the existing challenges, such as design conception based on product and real construction processes, design methods focusing on components, and maximum utilization of the temporary building by optimizing the construction speed and building performance. The project was not only designed in virtual reality, but was also physically constructed in the real world. A series of aluminum light structure housing systems were dictated at last, with the characteristics of high performance, extremely rapid construction speed and also flexible function. It can be used in lots of aspects ranging from a single building in a remote area to a large residential community.

Keywords: Aluminum house, light structure, rapid assembly, repeat construction.

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Authors: A. Karimzad Ghavidel, M. Zadshakouyan

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Laser cutting is a very common production method for cutting 2D polymeric parts. Developing of polymer composites with nano-fibers makes important their other properties like laser workability. The aim of this research is investigation of the influence different laser cutting conditions on the dimensional accuracy of parts and holes from poly methyl methacrylate (PMMA)/carbon nanotubes (CNTs) material. Experiments were carried out by considering of CNTs (in four level 0,0.5, 1 and 1.5% wt.%), laser power (60, 80, and 100 watt) and cutting speed 20, 30, and 40 mm/s as input variable factors. The results reveal that CNTs adding improves the laser workability of PMMA and the increasing of power has a significant effect on the part and hole size. The findings also show cutting speed is effective parameter on the size accuracy. Eventually, the statistical analysis of results was done, and calculated mathematical equations by the regression are presented for determining relation between input and output factor.

Keywords: Dimensional accuracy-PMMA-CNTs-laser cutting.

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Authors: Kong Fah Tee, Lutfor Rahman Khan, Hongshuang Li

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An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.

Keywords: Underground pipelines, Probability of failure, Reliability and Subset Simulation.

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Authors: Yi-Zhong Weng, Chien-Kang Huang, Yu-Feng Huang, Chi-Yuan Yu, Darby Tien-Hao Chang

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Search for a tertiary substructure that geometrically matches the 3D pattern of the binding site of a well-studied protein provides a solution to predict protein functions. In our previous work, a web server has been built to predict protein-ligand binding sites based on automatically extracted templates. However, a drawback of such templates is that the web server was prone to resulting in many false positive matches. In this study, we present a sequence-order constraint to reduce the false positive matches of using automatically extracted templates to predict protein-ligand binding sites. The binding site predictor comprises i) an automatically constructed template library and ii) a local structure alignment algorithm for querying the library. The sequence-order constraint is employed to identify the inconsistency between the local regions of the query protein and the templates. Experimental results reveal that the sequence-order constraint can largely reduce the false positive matches and is effective for template-based binding site prediction.

Keywords: Protein structure, binding site, functional prediction

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Authors: Bremananth R., Veerabadran C. S., Andy W. H. Khong

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Automatic reading of handwritten cheque is a computationally complex process and it plays an important role in financial risk management. Machine vision and learning provide a viable solution to this problem. Research effort has mostly been focused on recognizing diverse pitches of cheques and demand drafts with an identical outline. However most of these methods employ templatematching to localize the pitches and such schemes could potentially fail when applied to different types of outline maintained by the bank. In this paper, the so-called outline problem is resolved by a cheque information tree (CIT), which generalizes the localizing method to extract active-region-of-entities. In addition, the weight based density plot (WBDP) is performed to isolate text entities and read complete pitches. Recognition is based on texture features using neural classifiers. Legal amount is subsequently recognized by both texture and perceptual features. A post-processing phase is invoked to detect the incorrect readings by Type-2 grammar using the Turing machine. The performance of the proposed system was evaluated using cheque and demand drafts of 22 different banks. The test data consists of a collection of 1540 leafs obtained from 10 different account holders from each bank. Results show that this approach can easily be deployed without significant design amendments.

Keywords: Cheque reading, Connectivity checking, Text localization, Texture analysis, Turing machine, Signature verification.

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Authors: Sunil Gupta, Harsh Kumar Verma, AL Sangal

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Wireless sensor networks can be used to measure and monitor many challenging problems and typically involve in monitoring, tracking and controlling areas such as battlefield monitoring, object tracking, habitat monitoring and home sentry systems. However, wireless sensor networks pose unique security challenges including forgery of sensor data, eavesdropping, denial of service attacks, and the physical compromise of sensor nodes. Node in a sensor networks may be vanished due to power exhaustion or malicious attacks. To expand the life span of the sensor network, a new node deployment is needed. In military scenarios, intruder may directly organize malicious nodes or manipulate existing nodes to set up malicious new nodes through many kinds of attacks. To avoid malicious nodes from joining the sensor network, a security is required in the design of sensor network protocols. In this paper, we proposed a security framework to provide a complete security solution against the known attacks in wireless sensor networks. Our framework accomplishes node authentication for new nodes with recognition of a malicious node. When deployed as a framework, a high degree of security is reachable compared with the conventional sensor network security solutions. A proposed framework can protect against most of the notorious attacks in sensor networks, and attain better computation and communication performance. This is different from conventional authentication methods based on the node identity. It includes identity of nodes and the node security time stamp into the authentication procedure. Hence security protocols not only see the identity of each node but also distinguish between new nodes and old nodes.

Keywords: Authentication, Key management, Wireless Sensornetwork, Elliptic curve cryptography (ECC).

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Authors: Z. D. Mansor, C. P. Mun, B. S. Nurul Farhana, Wan Aisyah Nasuha Wan Mohamed Tarmizi

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The aim of this research is to determine the influence of transformation leadership style on employee engagement among Generation Y. The growing of Generation Y employees in Malaysia has raised concerns about how to engage and motivate this cohort. Transformation Leadership style is one of the key factors to increase employee engagement levels in the organization. This study has proven to be important for the researchers and the organization to properly understand the concept of employee engagement, transformation leadership style and their relationship. The samples in this study included 221 respondents of Generation Y who are currently working in Selangor and Klang Valley area in Malaysia. The data were collected using questionnaires and analyzed by using Statistical Package for Social Science (SPSS). The results show that there is a significant relationship between the dimension of intellectual stimulation, inspiration motivation and individual consideration on employee engagement. In contrast, the results have revealed that there is no significant relationship between idealized influences of a leader on employee engagement among Generation Y.

Keywords: Employee engagement, gen Y, transformational leadership styles, survey.

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Authors: Sanga Monthatipkul, Pio Iovenitti, Igor Sbarski

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Bicycle usage for exercise, recreation, and commuting to work in Australia shows that pedal cycling is the fourth most popular activity with 10.6% increase in participants between 2001 and 2007. As with other means of transport, accident and injury becomes common although mandatory bicycle helmet wearing has been introduced. The research aims to develop a face surrogate made of sandwich of rigid foam and rubber sheets to represent human facial bone under blunt impact. The facial surrogate will serve as an important test device for further development of facial-impact protection for cyclist. A test procedure was developed to simulate the energy of impact and record data to evaluate the effect of impact on facial bones. Drop tests were performed to establish a suitable combination of materials. It was found that the sandwich structure of rigid extruded-polystyrene foam (density of 40 kg/m3 with a pattern of 6-mm-holes), Neoprene rubber sponge, and Abrasaflex rubber backing, had impact characteristics comparable to that of human facial bone. In particular, the foam thickness of 30 mm and 25 mm was found suitable to represent human zygoma (cheekbone) and maxilla (upper-jaw bone), respectively.

Keywords: Facial impact protection, face surrogate, cyclist, accident prevention

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Authors: Sa-aat Niwitpong

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Recent articles have addressed the problem to construct the confidence intervals for the mean of a normal distribution where the parameter space is restricted, see for example Wang [Confidence intervals for the mean of a normal distribution with restricted parameter space. Journal of Statistical Computation and Simulation, Vol. 78, No. 9, 2008, 829–841.], we derived, in this paper, analytic expressions of the coverage probability and the expected length of confidence interval for the normal mean when the whole parameter space is bounded. We also construct the confidence interval for the normal variance with restricted parameter for the first time and its coverage probability and expected length are also mathematically derived. As a result, one can use these criteria to assess the confidence interval for the normal mean and variance when the parameter space is restricted without the back up from simulation experiments.

Keywords: Confidence interval, coverage probability, expected length, restricted parameter space.

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Authors: Shatha A. Yousif, Qasim M. Zamil, Hasan Y. Al Muhi, Jamal A. Al Shammari

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Potato (Solanum tuberosum L.) is one of the major vegetable crops that are grown world –wide because of its economic importance. This experiment investigated the effect of local sands (River Base, Al-Ekader and Karbala) on number and total weight of minitubers. Statistical analysis revealed that there were no significant differences among sand cultures in number of stem/plant, chlorophyll index and tubers dry weight. River Base sand had the highest plant height (74.9 cm), leaf number/plant number (39.3), leaf area (84.4 dcm2⁄plant), dry weight/plant (26.31), tubers number/plant (8.5), tubers weight/plant (635.53 gm) and potato tuber yields/trove (28.60 kg), whereas the Karbala sand had lower performance. All the characters had positive and significant correlation with yields except the traits number of stem and tuber dry weight.

Keywords: Correlation, Potato, Sand Culture, Yield.

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Authors: R. J. Chang

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A cyclostationary Gaussian linearization method is formulated for investigating the time average response of nonlinear system under sinusoidal signal and white noise excitation. The quantitative measure of cyclostationary mean, variance, spectrum of mean amplitude, and mean power spectral density of noise are analyzed. The qualitative response behavior of stochastic jump and bifurcation are investigated. The validity of the present approach in predicting the quantitative and qualitative statistical responses is supported by utilizing Monte Carlo simulations. The present analysis without imposing restrictive analytical conditions can be directly derived by solving non-linear algebraic equations. The analytical solution gives reliable quantitative and qualitative prediction of mean and noise response for the Duffing system subjected to both sinusoidal signal and white noise excitation.

Keywords: Cyclostationary, Duffing system, Gaussian linearization, sinusoidal signal and white noise.

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Authors: Pradeep Singh, Sukhwinder Singh, Gurjinder Kaur

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In recent years, rapid advances in software and hardware in the field of information technology along with a digital imaging revolution in the medical domain facilitate the generation and storage of large collections of images by hospitals and clinics. To search these large image collections effectively and efficiently poses significant technical challenges, and it raises the necessity of constructing intelligent retrieval systems. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images[5]. Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes.

Keywords: Classification, clustering, content-based image retrieval (CBIR), relevance feedback (RF), statistical similarity matching, support vector machine (SVM).

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Authors: Ε. Giovanis

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In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

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Authors: Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin

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Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from Textile export, requires producing less defective textile for minimizing production cost and time. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying fabric defects requires more automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect Recognizer which uses computer vision methodology with the combination of multi-layer neural networks to identify four classifications of textile defects. The recognizer, suitable for LDC countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time. In order to generate input set for the neural network, primarily the recognizer captures digital fabric images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later, the output of the processed image, the area of the faulty portion, the number of objects of the image and the sharp factor of the image, are feed backed as an input layer to the neural network which uses back propagation algorithm to compute the weighted factors and generates the desired classifications of defects as an output.

Keywords: Computer vision, image acquisition device, machine vision, multi-layer neural networks.

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Authors: C. Kalamani, K. Paramasivam

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Test data compression is an efficient method for reducing the test application cost. The problem of reducing test data has been addressed by researchers in three different aspects: Test Data Compression, Built-in-Self-Test (BIST) and Test set compaction. The latter two methods are capable of enhancing fault coverage with cost of hardware overhead. The drawback of the conventional methods is that they are capable of reducing the test storage and test power but when test data have redundant length of runs, no additional compression method is followed. This paper presents a modified Run Length Coding (RLC) technique with Multilevel Selective Huffman Coding (MLSHC) technique to reduce test data volume, test pattern delivery time and power dissipation in scan test applications where redundant length of runs is encountered then the preceding run symbol is replaced with tiny codeword. Experimental results show that the presented method not only improves the test data compression but also reduces the overall test data volume compared to recent schemes. Experiments for the six largest ISCAS-98 benchmarks show that our method outperforms most known techniques.

Keywords: Modified run length coding, multilevel selective Huffman coding, built-in-self-test modified selective Huffman coding, automatic test equipment.

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Authors: N. Fujisaka

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This sociological case study analyzes a sequence of student maltreatment in an elementary school in Japan, based on narratives from former students. Among various forms of student maltreatment, emotional maltreatment has received less attention. One reason for this is that emotional maltreatment is often considered part of education and is difficult to capture in surveys. To discuss the challenge of recognizing emotional maltreatment, it is necessary to consider the social background in which student maltreatment occurs. Therefore, from the perspective of the sociology of education, this study aims to clarify the process through which emotional maltreatment was embraced by students within a Japanese classroom. The research employs retrospective narrative data collected through interviews and autoethnography. The research results imply a pattern of emotional maltreatment that is challenging to differentiate from education. The finding is analyzed and discussed in conjunction with the cycle of violence theory and the deschooling theory. The cycle of violence theory explains how violence in a specific relationship can be tolerated. The deschooling theory provides a sociological explanation for how emotional maltreatment can be overlooked in society. Analyzing the case in association with these two theories highlights the characteristics of teachers’ behaviors that rationalize maltreatment as education and hinder students from escaping emotional maltreatment. This study deepens our understanding of the causes of student maltreatment and provides a perspective for future qualitative and quantitative research.

Keywords: Emotional maltreatment, education, student maltreatment, Japan.

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Authors: Guojian Cheng, Tianshi Liu, Jiaxin Han, Zheng Wang

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The competitive learning is an adaptive process in which the neurons in a neural network gradually become sensitive to different input pattern clusters. The basic idea behind the Kohonen-s Self-Organizing Feature Maps (SOFM) is competitive learning. SOFM can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappings are topology preserving, feature mappings and probability distribution approximation of input patterns. To overcome some limitations of SOFM, e.g., a fixed number of neural units and a topology of fixed dimensionality, Growing Self-Organizing Neural Network (GSONN) can be used. GSONN can change its topological structure during learning. It grows by learning and shrinks by forgetting. To speed up the training and convergence, a new variant of GSONN, twin growing cell structures (TGCS) is presented here. This paper first gives an introduction to competitive learning, SOFM and its variants. Then, we discuss some GSONN with fixed dimensionality, which include growing cell structures, its variants and the author-s model: TGCS. It is ended with some testing results comparison and conclusions.

Keywords: Artificial neural networks, Competitive learning, Growing cell structures, Self-organizing feature maps.

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Authors: Timothy Yoonsuk Lee, Jinhwan Yu, Somi Nah

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Hollywood has produced various blockbusters on the subject of disasters. Entering the 2000s, disaster films began to be produced in the East Asian region as well, and as most of them were successful, disaster films have settled as a popular genre in the region. East Asian disaster films utilize a plot structure similar to Hollywood films but, at the same time, represent East Asian people-s unique value system. East Asian people-s social behavior pattern defined as collectivism is a characteristic that distinguishes this region from other cultural regions. In order to examine Confucian culture in disaster films on the premise of the difference, the author conducts this research as follows.This study first reviews the concepts disaster and disaster film, and understands the genre through analyzing the narrative structure and style. In addition, it discusses collectivism, a characteristic of the East Asian region distinguished from the West, and investigates Confucian culture in films and examines differences among Korean, Chinese and Japanese Confucianism. Films selected for this study are Tidal Wave (Korea, 2009), After Shock (China, 2006), and The Sinking of Japan (Japan, 2006). Using the characters in these films, we analyze how Confucian thought is described and reproduced.

Keywords: Confucianism, Disaster Film, East Asian Films, Three Basic Principles and Five Moral Disciplines in Human Relations

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Authors: Shen Weishou, Ji Di, Zhang Hui, Yan Shouguang, Li Haidong, Lin Naifeng

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Based on a long-term vegetation index dataset of NDVI and meteorological data from 68 meteorological stations in the Qinghai-Tibet plateau and their relations with major climate factors were analyzed. The results show the following: 1) The linear trends of temperature in the Qinghai-Tibet plateau indicate that the temperature in the plateau generally increased, but it rose faster in the last 20 years. 2) The most significant NDVI increase occurred in the eastern and southern plateau. However, the western and northern plateau demonstrate a decreasing trend. 3) There is a significant positive linear correlation between NDVI and temperature and a negative correlation between NDVI and mean wind speed. However, no significant statistical relationship was found between NDVI and relative humidity, precipitation or sunshine duration.4) The changes in NDVI for the plateau are driven by temperature-precipitation, but for the desert and forest areas, the relation changes to precipitation-temperature-wind velocity and wind velocity-temperature-precipitation.

Keywords: Qinghai-Tibet plateau, NDVI, climate warming.

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Authors: Hiba Basim Alwan, Ku Ruhana Ku-Mahamud

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A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACO_R-SVM, will tune SVM parameters, while the second IACO_MV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.

Keywords: Continuous ant colony optimization, incremental continuous ant colony, simultaneous optimization, support vector machine.

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Authors: Yehjune Heo

Abstract:

Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: Anti-spoofing, CNN, fingerprint recognition, GAN.

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Authors: M. Pervan, T. Pavic Kramaric

Abstract:

The aim of this paper is to investigate the influence of market share and diversification on the nonlife insurers- performance. The underlying relationships have been investigated in different industries and different disciplines (economics, management...), still, no consistency exists either in the magnitude or statistical significance of the relationship between market share (and diversification as well) on one side and companies- performance on the other side. Moreover, the direction of the relationship is also somewhat questionable. While some authors find this relationship to be positive, the others reveal its negative association. In order to test the influence of market share and diversification on companies- performance in Croatian nonlife insurance industry for the period from 1999 to 2009, we designed an empirical model in which we included the following independent variables: firms- profitability from previous years, market share, diversification and control variables (i.e. ownership, industrial concentration, GDP per capita, inflation). Using the two-step generalized method of moments (GMM) estimator we found evidence of a positive and statistically significant influence of both, market share and diversification, on insurers- profitability.

Keywords: Diversification, market share, nonlife insurance

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