Earticle

The real challenge in pattern recognition tasks and machine learning processes is to train a discriminator using labeled data and use it to distinguish between future data points as accurate as possible. However, most of the problems in the real world have numerous data. Therefore assigning labels to every data points in these problems are a cumbersome or even impossible matter. Semi-supervised learning is one approach to overcome these types of problems. It uses only a small set of labeled with the company of huge remain and unlabeled data to train the discriminator. In semi-supervised learning, it is very essential that which data is labeled and depend on position of data it effectiveness changes. In this paper, we proposed an evolutionary approach called Artificial Immune System (AIS) to determine which data is better to be labeled to get the high quality data. The experimental results represent the effectiveness of this algorithm in finding these data points.

In this paper, the Bayesian estimation of the parameters of mixture of two components of Gumbel type II distribution has been considered. A heterogeneous population has been modeled by means of two components mixture of the Gumbel type II distribution under type I censored data. The Bayes estimators of the said parameters have been derived under the assumption of informative priors using different loss functions. A censored mixture data is simulated by probabilistic mixing for the computational purpose. The comparisons among the estimators have been made in terms of corresponding risks.

Image compression is playing a key role in the development of various multimedia computer services and telecommunication applications. As image needs a huge amount of data to store it, there is pressing need to limit image data volume for transport along communication links. An ideal image compression system must yield good quality compressed images with good compression ratio, while maintaining minimal time cost. The goal of image compression techniques is to remove redundancy present in data in a way that enables image compression technique. There are numerous lossy and lossless image compression techniques. For the still digital image or video, a lossy compression is preferred. Wavelet-based image compression provides substantial improvements in picture quality at higher compression ratios. Contrary to traditional techniques for image compression, neural networks can also be used for data or image compression. In this paper both of these methods for compression of images to obtain better quality.

In this paper we are interested in the early development phases of a distributed software system, especially in the case of obtaining a system design from its global requirement specification. This is an important step in the development of complex systems and their automated derivation. The Model-Driven Engineering (MDE) approach allows the designers to model their systems at different abstraction levels. Such approach provides automatic model transformations to incrementally refine abstract models into concrete ones. This paper presents a MDE approach to derive the behavior of a given system component from its global requirements. The requirements are specified using UML activity diagram extended with collaborations. The derived behavior of the components is in the form of distributed UML state machines. The suggested approach is based on the definition of an appropriate source meta-model (requirements meta-model) and the definition of a target design meta-model. A set of model transformation rules has been designed to govern the derivation process. A real application of telediagnosis in neuroscience has been developed using this approach.

In unstructured P2P systems, peers organize themselves into a random overlay. A challenging problem in these systems is to efficiently locate appropriate peers to answer a specific query. This paper proposes a semantic method in , which a query can be routed for appropriate peers instead of broadcasting or using random selection. This semantic is generally built from the contents of the peers, but can also bring in the implicit behavior of the users. The main objective of our method is to achieve better results in non-supervised tasks through the incorporation of usage data obtained from past search queries. This type of method allows us to discover the motivations of users when visiting a certain documents and peers. The terms used in past queries can provide a better choice of features queries. Hence, for each peer, our method learns from past queries to represent correlation between sent queries terms and related peers. We implemented the proposed method, and compared its routing effectiveness in terms of both recall and messages traffic with a broadcasting scheme (without learning). Experimental results show that our method is efficient and performs better than other non-semantic query routing methods with respect to accuracy. In addition, our approach improves the recall rate nearly 90% while reducing message traffic dramatically compared with Gnutella protocol.

The quality of Natural Gas Piping Systems (NGPS) must be ensured against any manufacturing defects. For this purpose, we develop a special testing machine (STM) constructed at the lab to test (NGPS). The proposed (STM)[1] function is based on testing the weak points at the pipe connections e.g. pipe bends, and intermediate connections. For more than 1500 pieces of (NGPS), crack propagation simultaneously followed up and monitored on the output screen at the critical positions of the pipelines connections. The control system utilizes the LabVIEW™ tools for various signals acquisition and monitoring also for designing the control system strategy.

Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popularity. However, this algorithm is not applicable in the environment with large amount of data points or when the data point arrive in a sequential manner. In this paper, we aim to propose an incremental version of this algorithm called Incremental Constrained Discriminant Component Analysis (ICDCA) to reduce the computational cost of this algorithm in large datasets. The ICDCA updates the within class scatter matrix and between class scatter matrix instead of calculating it from scratch. This change significantly reduces the computational cost of feature extraction process while keep the accuracy of such features as close as possible to offline version of this algorithm. In the end the effectiveness of ICDCA is compared to other recently proposed incremental LDA. To ensure the reliability of these experiments, they are repeated with several UC I data set. In these comparisons, advantage of ICDCA in the accuracy and speed is demonstrated.

This paper reviews the trends and identifies the determinants of income inequality in Korea from 1980 to 2012. Both Kuznets’ hypothesis of inverted U-shaped relationship between income inequality and economic growth and Barro’s hypothesis of U-shaped relationship are not empirically supported. Macroeconomic index such as the government spending as a share of GNI was found to be statistically insignificant in affecting income inequality. The statistically significant negative estimate of the investment share in GDP shows that an increase in investment would decrease the income inequality. The statistically significant positive estimate of the variable for the share of the elderly in working population confirms the argument that the rise of aging population, which has been accelerating in Korea during the last two decades, is one of the important factors in widening the gap of income inequality.

The smart grid has received high interest for improving efficiency and reliability of power system. It adopts Information and System Technology for this purpose and several technologies are considered as system architecture of the smart grid. Consequently security issues of system should be considered for the smart grid. In this paper system architecture for the smart grid is presented. Requirements of security and solutions for cyber attack in the smart grid also discussed.

The advancement in last decade in electronics & communication, computer science and information technology domain has resulted in the new computing and communication era, known as Wireless Sensor Networks. The routing protocols differ on the basis of application and network architecture. With awareness is a compulsory design criterion, many new protocols have been specifically designed for routing, power management and data dissemination. Efficient routing in a sensor network requires that routing protocol must minimize network energy dissipation and maximize network lifetime. This paper analyses the lifetime of different routing protocols for wireless sensor networks.

E-Learning has attained significance in todays’ cyber world due to the obvious advantages of anywhere anytime learning, to reach the unreachable and so on. It is a well known fact that explosion of E-learning technologies were due to the wide spread adoption of certain technical enablers. However no specific standards were followed for the development of existing systems making them more customized. In this paper, we discuss the important technical enablers that are considered vital in the successful implementation of e-learning. These are specific to the regional requirements of Middle-east and have been sought after due discussion and opting qualitative research techniques.

Web service composition is a mature and effective way to realize the rapidly changing requirements of business in service-oriented solutions. Testing the compositions of web services is complex, due to their distributed nature and asynchronous behaviour. Control flow testing focuses on the transfer of control, while data flow testing focuses on the definitions of data and their subsequent use. The execution of a service is driven by the received and manipulated data; hence validation of data flow is very critical in composition. Colored Petri Nets (CPNs) is one model that combines data and control flow with behavioural aspects of a given business requirement. In this paper, a test framework based on a data-driven CPN model for functional testing of web service composition is proposed. The test framework also includes test design coverage measurement using three newly formulated metrics: Decision Coverage, Input Output Coverage and Implementation Coverage. The framework was applied to airline reservation system and the generated test sequences were evaluated against the coverage criteria.