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Large amount of security faults existing in software systems could be complex and hard to identify during the fault analysis. Therefore, it is not always possible to fully mitigate the internal or external security faults (vulnerabilities or threats) within the system. On the other hand, existence of faults in the system may eventually lead to a security failure. To avoid security failure of the target system it is required to make the system flexible and tolerant in the presence of security faults. This paper proposes a goal-based modeling approach to develop security requirements of Security-Critical Systems (SCSs) through explicitly factoring the faults into the requirement engineering process. Our approach establishes the Security Requirement Model (SRM) of the system based on its respective Security Fault Model (SFM). We incorporate fault tolerance into the SRM through considering the partial satisfaction of security goals. The proposed approach factors this partiality into the goals by using proper mitigation techniques during the refinement process. This approach eventually contributes to a fault tolerant model for security requirements of the target system.

In this paper, we propose the genetic design of fuzzy neural networks with multi-output based on interval type-2 fuzzy set (IT2FSFNNm) for pattern recognition. IT2FSFNNm is the networks of combination between the fuzzy neural networks (FNNs) and interval type-2 fuzzy set with uncertainty. The premise part of the networks is composed of the fuzzy partition of respective input spaces and the consequence part of the networks is represented by polynomial functions with interval set. We also consider real-coded genetic algorithms to estimate the optimal values of the parameters of IT2FSFNNm. The numerical experimentation is used for evaluating the proposed networks for pattern recognition.

Object oriented technology accommodates a set of relationships that affect the quality of coding and, therefore, output programs. In this paper, we present a hybrid technique that relies on the paradigm of dependency graph to detect the impact of change over time on object oriented relationships such as: inheritance, class size, coupling and cohesion. Our goal is to provide a systematic framework to measure how rate of change affects such features in a concurrent environment. The answer of such question takes place in code analysis and model checking of concurrent programs. The contribution of this research is to study the static and dynamic aspects that contribute in enhancing the quality of coded programs, which implies increasing maintainability. In order to satisfy our research goal, we performed experiments on a collection of concurrent Java programs, analyzed them, and then concluded the behavior of these programs over time. The results indicated that there is a significant positive correlation between some of these features and rate of change.

In this paper, concepts for high-efficiency induction motor with sharing core 37kw and below are established and a prototype is constructed. The need for developing high-efficiency induction motor die can be analyzed in terms of electromagnetic design and analyzing technique, mechanical design technology, material technology, and production technique. In order to produce prototypes, motor design, property analysis, and comparison of material cost and efficiency are needed. Methods for such include basic motor design, experiment design, equivalent circuit method, and finite element method. Among these, experiment design can be assessed according to optimal design, main factors, optimal regions, and others. For producing die for constructing prototype, inspection of die development process during die design, understanding of details stage by stage, and optimization of strip layout are essential. When constructing dies, improvement on causes of wear of punch, improvement of equipment and surrounding devices, adjustment of post structure for improvement of die durability, and shear load distribution analysis are necessary. Based on these factors, high-efficiency induction motor prototypes for 5.5kw, 7.5kw, 11kw, 4 polar, and 6 polar sharing cores are constructed.

This paper examines the application of a support vector regression (SVR) approach to identifying relationships between land use characteristics and damages caused by natural hazards. Our empirical results show the outperformance of a SVR model over a multiple ordinary least squares (OLS) regression model in terms of the predictive performance. Nonlinear relationships between land use characteristics and damages are revealed by a SVR model.

The task of requirement reprioritization is an important activity initiated due to the dynamism in the developing & the working environment of the software. Reprioritization is focused on the re-allotment of priorities to both implemented as well as non implemented ones. Focusing the implemented ones will ensure quality of regression testing and stressing on non implemented ones will ensure to consider the delayed as well as new or changed requirements. The reprioritization is complex decision making and effortful activity. Thus this task will require some heuristic parameters that could lead to the minimization of reprioritization efforts without any negative fabrication of quality of the current version. This paper proposed a multilayered dynamic approach for requirement reprioritization that is well suited for both agile and non agile development methodologies employing any prioritization method. The proposal is illustrated with the help of live case study of Library Management Software System developed in the Computer Programming laboratory of National Institute of Technology, Hamirpur.

Detecting data races in multi-threaded programs is a challenging problem in debugging, because the races could reveal nondeterministic program behavior in execution of such pro- grams. Nondeterministic runtime eects of a race are hard to identify and it is dicult to decide whether the reported data races can appear or not in the actual program executions. Previous techniques for detecting races cannot provide method to locate rst races in par- allel programs with random synchronization including lock/unlock mechnism. This paper presents an algorithm which extracts rst races by replaying the program and checking con- currency between sequenced traced data and candidate accesses, which are from a particular execution of parallel programs. We also present the correctness of our algorithm by showing that all the rst races are included in the traced accesses which are composed of key accesses.

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

In this paper, a new spot matching algorithm is proposed. The algorithm compares topological patterns on two central spots to be matched that are selected each from reference and target 2D-PAGE images. Similarity transform is applied to one of two patterns in order to consider and correct global and local distortion before comparison of topological patterns. Matching between neighbor spots from two patterns is performed and similarity is evaluated using the normalized Hausdorff distance(NHD). Finally, matching of spots is determined by the number of neighbor matched pairs, the number of outlier spots and the NHD in turn. The proposed algorithm shows good results, even better matching performance when the relative topology is well preserved and no outlier is included.

This paper proposes a new image segmentation algorithm that involves local area splitting and merging based on intensity change. Most image segmentation algorithms take advantage of features such as pixel intensity and edge to split or merge an image. Therefore, in addition to susceptibility to noise, the latter algorithms have a problem in that they achieve different results depending on the initially selected seed location. The proposed method adaptively changes pixel intensity during the process of region segmentation to the representative intensity of the adjacent sub-area of high homogeneity. Therefore, this method is not affected by the initial seed location, and it also eliminates pre-process, such as noise removal, because the pixel intensity is progressively stabilized to the average value of object. In addition, this method preserves the edges of segmented objects and reduces the phenomenon of excessive region merger by determining the direction of the next merger upon splitting a local area into small sub-areas. Our experimental results demonstrated that the proposed method accurately segments images higher credibility than the existing image segmentation algorithms.

Histogram equalization, which stretches the dynamic range of intensity, is the most common method for enhancing the contrast of image. Contrast Limited Adaptive Histogram Equalization (CLAHE), proposed by K. Zuierveld, has two key parameters: block size and clip limit. These parameters are mainly used to control image quality, but have been heuristically determined by users. In this paper, we propose a novel method of determining two parameters of the CLAHE using entropy of image. The key idea is based on the characteristics of entropy curves: clip limit vs entropy and block size vs entropy. Clip limit and block size are determined at the point with maximum curvature on entropy curve. Experimental results show that the proposed method improves images with very low contrast.

Over the past few years, the emergence of Web 2.0 technologies such as weblogs, wikis, and social networks has created new opportunies and challenges in the field of foreign language learning. The characteristics of Web 2.0, which facilitate interactive information sharing, user-centered designs, and online collaboration, are recognized to have great potential for more effective teaching and learning foreign languages. Consequently, a growing number of researchers and teachers have been exploring the ways to integrating various Web 2.0 technologies into foreign language classrooms. Weblogs have become one of the best-received Web 2.0 technologies in foreign language education. This study explores the significant issues of using Weblogs in foreign language learning, examining the theoretical justification of using Web 2.0 in foreign language learning, identifying possibilities and challenges of weblogs implemented in foreign language classrooms, and discussing advantages and disadvantages of using weblogs.

To encode the speech quality with reduce the redundancy within samples that resulted from domain processing method like PCM and LPC, Source coding or Waveform coding methods can be considered. However, it is well known that when conventional sampling methods are applied directly to speech signal, the required amount of data is comparable to or more than that of uniform sampling method. To overcome this problem, a new hybrid methods is proposed, in which hybrid domain coding is applied to two low-pass filters in lower bandwidth and the remain signals are compensated by the Gaussian white signal and harmonics, which is used to get high quality speech in higher bandwidth.

The relics and cultural assets are an important property which shows a spirit of the time and has historical, academic value. However, in protecting the relics and cultural assets, due to the lack of recognition and deficiency of expertise, a problem in preserving and managing the relics and cultural assets has appeared. This paper has studied digital restoration skills for original shape of cultural assets and would propose digital restoration skill for realizing virtual experience contents; including residential environment and natural environment of Hanyang in Chosun Dynasty, by means of digital restoration skills using virtual reality, such as interaction, navigation, file size and restoration of non-existing ruins.

Red tide is a temporary natural phenomenon involving harmful algal blooms (HABs) in company with a changing sea color from normal to red or reddish brown, and which has a bad influence on coast environments and sea ecosystems. Recent wide spread and persistent HABs give seriously impact public health and the economy of fisheries along coasts of many countries, since it mainly kills fish mainly and results in occasional shellfish poisoning. If we can predict the occurrence of red tide, we will be able to minimize the damage of HABs by a quick preparation of mitigation activity. To enhance the automatic forecast of red tide, this paper proposes a red tide prediction method that uses ensemble method. The proposed method can improves the precision of forecast results since the ensemble classifier is enhanced by optimal data of the proposed preprocessing.

The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy to use, and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes. In this research, we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction. Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.

A software development process describes a structured set of activities or tasks that must performed by software stakeholders during the different phases of software manufacturing. These activities can be divided also into smaller portions at time called increments that are produced in a progressive way. Thereby, every change occurring in one increment performs a new iteration and affects absolutely the other increments that depend on it. These changes may cause a waste in modeling time or re-coding programs and make iterations more complex. The aim of this work is to reduce this complexity and economize the modeling time by applying a model-driven approach to automate the generation of some important increments. Therefore, the proposal presents an approach to semi-automate the generation through a model transformation of one among of the most important increment of the design phase, the sequence diagram of system’s internal behavior using the sequence diagram of system’s external behavior. For this and to trace correctly the interactions in the SDSIB we propose a semantic based on the LARMAN operation contract that integrates new details to better describe system’s states and propose an improved and formal syntax to determine operations and their concerned source and responsible objects.

Software metrics proposed in various theories and literature have considerable variation among them. The sole purpose of all these theories is to improve the quality of software projects by producing and using quantitative measures. All these theories improve the quality of a software project and mend all the issues relating to software refactoring. As the software industry moves to a more mature state, the need for employing more effective tools, techniques and benchmarks for managing software projects has become significantly critical to minimize the negative risk factors as well as ensuring augmented adherence to quality assurance. In this study, we intend to explore how distinctive metrics with respect to different knowledge areas, especially in design engineering, could be useful to manage knowledgeably the software projects. The focus of this research study is to evaluate and highlight the importance of various performance metrics and measures to track the software project performance followed by some already proposed metrics for software design, development and management.

This paper presents an approach for extending the constraint model defined for conformity testing of a given method of class to its overriding method in subclass using inheritance principle. The main objective of the proposed work is to find the relationship between the test model of an overriding method and its overridden method using the constraint propagation. Our approach shows that the test cases developed for testing an original method can be used for testing its overriding method in a subclass and then the number of test cases can be reduced considerably. The implementation of this approach is based on a random generation of test data and analysis by formal proof.

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. In this paper, we study on the problem of clustering categorical data, where data objects are made up of non-numerical attributes. We propose MECC (Minimum Error Classification Clustering), an alternative technique for categorical data clustering using VPRS taking into account minimum error classification. The technique is implemented in MATLAB. Experimental results on two benchmark UCI datasets show that MECC technique is better than the baseline categorical data clustering techniques with respect to selecting the clustering attribute.

Random numbers are widely used in various applications. In the majority of cases, a pseudo-random number generator is used since true random number generators are slow and they are barely suitable for the hardware implementation. In this paper, we present new architecture of URNG (uniform random number generator) employing Leap-Ahead LFSR architecture for hardware implementation. In particular, the proposed URNG consists of two more segmented Leap-Ahead LFSRs to overcome the drawback of the conventional URNG employing Leap-Ahead architecture, that is, the sharp decrease of a maximum period of the generated random numbers. Thus, the proposed URNG with segmented LFSR architecture can generate multiple bits random number in a cycle without the frequent diminishing of maximum period of the generated random numbers. We prove the efficiency of the proposed segmented LFSR-architecture through the mathematical analysis. The simulation results show that the proposed URNG employing segmented Leap-Ahead LFSR architecture can be increased 2.5 times of the maximum period of generated random numbers compared to the URNG using the conventional Leap-Ahead architecture.

This study has conducted a user testing based on the student’s and lecturer’s experience using the Moodle application. The main focus is the Moodle application as an e-learning platform in a local college .A user testing method was used to identify problems and possible solutions that could best improve the current Moodle application. The data gathering technique employed was focus group interviews. The findings from the analysed results show that the usability issue encountered by both users that holds the highest average mean was the layout design. The results also prove that the current Moodle application needs to be amended for its interface. The recommendations for interface design were furthered discussed and described, together with the adaptive user interface techniques that can best implement the interface.

Proper resource allocation on research requires accurate forecasting for the future research activities. Forecasting task can be done using judgmental or numerical analysis. Bibliometric analysis is a quantitative method to determine the trend of research area by counting the frequency of certain keywords using journal publication or patents. This paper reports the implementation of our new forecast combination method which selects the best methods used by similar validation dataset on Indonesian journal database, namely the Garuda dataset, especially on the subject of Science and Technology. The experimental result indicates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, the emerging research topics for the next few years can be objectively identified.

Decision tree induction has gained its popularity as an effective automated method for data classification mainly because of its simple, easy-to-understand, and noise-tolerant characteristics. The induced tree reveals the most informative attributes that can best characterize training data and accurately predict classes of unseen data. Despite its predictive power, the tree structure can be overly expanded or deeply grown when the training data do not show explicit patterns. Such bushy and deep trees are difficult to comprehend and interpret by humans. We thus propose a logic-based method to query over a complicate tree structure to extract only parts of the tree model that are really relevant to users’ interest. The implementation using ECLiPSe constraint language to perform constrained search over a decision tree model is given in this paper. The illustrative examples on medical domains support our hypothesis regarding simplicity of constrained tree-based patterns.

A very important issue in many applied fields is to construct the fitting curve that approximates a given set of data points optimally in the sense of least-squares. This problem arises in a number of areas, such as computer-aided design & manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. This is also a hard problem, because it is highly nonlinear, over-determined and typically involves a large number of unknown variables. A critical step in this process is to obtain a suitable parameterization of the data points. In this context, this paper introduces a new method to obtain an optimal solution for the parameterization problem of the least-squares fitting B큖zier curve. Our method is based on a local search metaheuristic approach for optimization problems called tabu search. The method is applied to some simple yet illustrative examples for the cases of 2D and 3D curves. The proposed method is simple to understand, easy to implement and can be applied to any kind of smooth data points. Our experimental results show that the presented method performs very well, being able to fit the data points with a high degree of accuracy.

In this paper, a novel affine invariant descriptor for object matching is proposed. The advantage of Maximally Stable Extremal Regions (MSER) method is applied to get the most stable regions in the image. Inside each region, we pick the seeds as keypoints since MSER regions are invariant to affine transformation. Besides that, Voronoi diagram is employed to split the image into small Voronoi cells or local regions based on the key points picked in the previous step. Finally, local features inside each local region including color, texture and geometric properties are extracted to generate the descriptor. Our experiments demonstrate that the proposed affine invariant local descriptor based on Voronoi tessellation is more stable and robust to object matching than SIFT descriptor while using the same keypoints.

Knowledge engineering applied to the Component Based Software Engineering (CBSE) field is an important research area that focuses on identification, specification and validation of knowledge and experiences acquired during various phases of component based software development cycle. This work presents a systematic approach to knowledge identification in dependency analysis process in context of CBSE, which results a well defined approach for representing dependency analysis information in form of knowledge artifacts. The identified knowledge artifacts and knowledge relationships may help in design optimization, fault management, configuration management and testing processes.

The principle main goal of the image zooming technique is to fit a smaller (or larger) images to larger (or smaller) sized display device. In order to satisfy this operation, image zooming technique must be able to alter the image size to the other one. The proposed method consists of four steps. They are ‘obtain original image,’ ‘image downscaling by image zooming,’ ‘artifact removal process by circle shaped low pass filtering,’ and ‘detail preserving process by unsharp masking.’ The simulation results approve that the proposed method removed blocking artifact and remained image details in a prosperous manner.

This paper proposes a solution for storing and conjugating the traditional knowledge archives system which draws international attention. The world’s traditional knowledge is disappearing so fast that its documentation and archives should be considered urgently and significantly. We have created the traditional knowledge archives system based on the Web 2.0 platform to enhance the preservation of traditional knowledge. The major contributions of our system in the areas of both digital archives and traditional knowledge are twofold: an integrated archive structure using high technical mechanism and a multi-functional application for the public.

There are many applications using LBS such as navigations, location tracking, the whereabouts of a friend or employee. Most of these applications have the feature of active service mechanism. In this paper, we propose a system for sensing the stray of any objects within user-specified region as passive service mechanism. The proposed system has been initially authenticated the target objects and then matched between smartphone and a target object using LBS in smartphones. After a period of time, if the object has been break away from the specified boundary region, it is that the smartphone detects the straying status of the object. This system presents a virtual barrier such as a geofence. Therefore, the proposed mechanism will can be used of many applications such as anti-theft/anti-lost of any objects, preventing of stray children. Especially, it will be more opportunities combining with active service mechanism.