Abstract: Dempster’s rule is traditionally interpreted as an operator for fusing belief functions. While there are different types of belief fusion, there has been considerable confusion regarding the exact type of operation that Dempster’s rule performs. Many alternative operators for belief fusion have been proposed, where some are based on the same fundamental principle as Dempster’s rule, and others have a totally different basis, such as the cumulative and averaging fusion operators. In this article, we analyze Dempster’s rule from a statistical and frequentist perspective and compare it with cumulative and averaging belief fusion. We prove, and illustrate by examples on colored balls, that Dempster’s rule in fact represents a method for serial combination of stochastic constraints. Consequently, Dempster’s rule is not a method for cumulative fusion of belief functions under the assumption that subjective beliefs are an extension of frequentist beliefs. Having identified the true nature of Dempster’s rule, appropriate applications of Dempster’s rule of combination are described such as the multiplication of orthogonal belief functions, and the combination of preferences dictated by different parties. © 2012 Wiley Periodicals, Inc.

Abstract: In this age of dramatic technology shift, one of the most significant development has been the emergence of digital video as an important aspect of daily life While the Internet has significantly changed the way in which we obtain the information, it is much more attractive because of the powerful medium of video In this paper we have described kernel based object tracking algorithm using mean shift method The goal of an object tracking algorithm is to generate the trajectory of an object over time by locating its position in every frame of the video There are various applications of object tracking in the field of computer vision A smart camera is a very important component for many applications such as, video surveillance, traffic monitoring system and for mobile robots

Abstract: The paper presents an efficient Content Based Image Retrieval (CBIR) system using color and texture. In proposed system, two different feature extraction techniques are employed. A universal content based image retrieval system uses color, texture and shape based feature extraction techniques for better matched images from the database. In proposed CBIR system, color and texture features are used. The texture features were extracted from the query image by applying block wise Discrete Cosine Transforms (DCT) on the entire image and from the retrieved images the color features were extracted by using moments of colors (Mean, Deviation and Skewness) theory. The proposed system has used Corel database of 1000 images. The feature vectors of the query image will then be compared with feature vectors of the database to obtain similar images. Individual and combined vectors using color and texture features were computed and the combined feature vector results were comparatively better. The proposed system provides an efficiency of 60%.

Abstract: treatment (CT) is the supplementary process to conventional heat treatment process in steels, by deep- freezing materials at cryogenic temperatures to enhance the mechanical and physical properties of materials being treated. Cryogenic treatment (CT) of materials has shown significant improvement in their properties .Various advantages like increase in hardness, increase in wear resistance, reduced residual stresses, fatigue Resistance, increased dimensional stability, increased thermal conductivity, toughness, by transformation of retained austenite to martensite, the metallurgical aspects of eta-carbide formation, precipitation of ultra fine carbides, and homogeneous crystal structure. Different approaches have been applied for CT to study the effect on different types of steels and other materials. This paper aims at the comprehensive analysis of strategies followed in CTs and their effects on properties of different types of steels by application of appropriate types of CTs from cryogenic conditioning of the process. The conclusion of the paper discusses the development and outlines the trends for the research in this field.

Abstract: clinicians and researchers alike buried in a sea of EEG paper records. The advent of computers and the technologies associated with them has made it possible to effectively apply a host of methods to quantify EEG changes [4]. The EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands: delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Since the EEG signals are non-stationary, the parametric methods are not suitable for frequency decomposition of these signals. A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications. Since the WT is appropriate for analysis of non-stationary signals and this represents a major advantage over spectral analysis, it is well suited to locating transient events, which may occur during epileptic seizures. Wavelet’s feature extraction and representation properties can be used to analyze various transient events in biological signals. Adeli et al. [2] gave an overview of the discrete wavelet transform (DWT) developed for recognizing and quantifying spikes, sharp waves and spike-waves. They used wavelet transform to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. The techniques have been used to address this problem such as the analysis of EEG signals for epileptic seizure detection using the autocorrelation function; frequency domain features, time–frequency analysis, and wavelet transform (WT). The results of the studies in the literature have demonstrated that the WT is the most promising method to extract features from the EEG signals. In this respect, in the present study for epileptic seizure detection in patients with absence seizures (petit mal), the WT was used for feature extraction from the EEG signals belonging to the normal and the patient with absence seizure [11].

Abstract: Determining the beginning and the termination of speech in the presence of background noise is a complicated problem. This paper is concerned with labeling sections of speech samples based on whether they are silence, voiced or unvoiced speech. The labeling is done using calculations over the speech samples; zero crossing and short-term energy functions. The short-term energy and zero crossing rate of speech have been extensively used to detect the endpoints of an utterance. General Terms Speech Recognition, Voice, Unvoice.

Abstract: In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e-marketplaces that is attractive to users, through its promotion of honesty. © 2012 Wiley Periodicals, Inc.

Abstract: Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approaches have been developed for skin detection. Skin detectors typically transform a given pixel into an appropriate color space and then use a skin classifier to label the pixel whether it is a skin or a non-skin pixel. In this paper, an efficient method for skin color segmentation on color photos is implemented. This

Abstract: Face detection is an essential application of visual object detection and it is one of the main components of face analysis and understanding with face localization and face recognition It becomes a more and more complex domain used in a large number of applications, among which we find security, new communication interfaces, biometrics and many others The goal of face detection is to detect human faces in still images or videos, in different situations We will focus on a detector which processes images very quickly while achieving high detection

Abstract: Current research on qualitative spatial representation and reasoning mainly focuses on one single aspect of space. In real-world applications, however, multiple spatial aspects are often involved simultaneously. This paper investigates problems arising in reasoning with combined topological and directional information. We use the RCC8 algebra and the rectangle algebra (RA) for expressing topological and directional information, respectively. We give examples to show that the bipath-consistency algorithm Bipath-Consistency is incomplete for solving even basic RCC8 and RA constraints. If topological constraints are taken from some maximal tractable subclasses of RCC8, and directional constraints are taken from a subalgebra, termed DIR49, of RA, then we show that Bipath-Consistency is able to separate topological constraints from directional ones. This means, given a set of hybrid topological and directional constraints from the above subclasses of RCC8 and RA, we can transfer the joint satisfaction problem in polynomial time to two independent satisfaction problems in RCC8 and RA. For general RA constraints, we give a method to compute solutions that satisfy all topological constraints and approximately satisfy each RA constraint to any prescribed precision. © 2012 Wiley Periodicals, Inc.

Abstract: A focused crawler is an efficient tool used to traverse the Web to gather documents on a specific topic It can be used to build domain-specific Web search portals and online personalized search tools Focused crawlers can only use information obtained from previously crawled pages to estimate the relevance of a newly seen URL Therefore, good performance depends on powerful modeling of context as well as the quality of the current observations To address this challenge, we propose capturing sequential patterns along paths leading to targets based on probabilistic models We model the process of crawling by a walk along an underlying chain of hidden states, defined by hop distance from target pages, from which the actual topics of the documents are observed When a new document is seen, prediction amounts to estimating the distance of this document from a target Within this framework, we propose two probabilistic models for focused crawling, Maximum Entropy Markov Model (MEMM) and Linear-chain Conditional Random Field (CRF) With MEMM, we exploit multiple overlapping features, such as anchor text, to represent useful context and form a chain of local classifier models With CRF, a form of undirected graphical models, we focus on obtaining global optimal solutions along the sequences by taking advantage not only of text content, but also of linkage relations We conclude with an experimental validation and comparison with focused crawling based on Best-First Search (BFS), Hidden Markov Model (HMM), and Context-graph Search (CGS) © 2012 Wiley Periodicals, Inc

Abstract: Nuclei segmentation of the epithelial cells of a Pap smear image is an important step in order to have correct morphometric measures. This task is non trivial due to the complexities of the Pap smear images. Our paper presents a novel method on nuclei segmentation using morphological operation and watershed transformation. The proposed segmentation method was evaluated with respect to its nuclei area and its shape-similarity in comparison to the pathologist truth. It showed that the segmentation results are promising and it can be used for further analysis such as cell quantification or abnormality cell detection.

Abstract: Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e-commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation-resistant, the researchers require a flexible, easy-to-use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game-theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well-known trust models against both individual and group attacks. © 2012 Wiley Periodicals, Inc.

Abstract: Auctions have been used to deal with resource allocation in multiagent environments, especially in service-oriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources. © 2012 Wiley Periodicals, Inc.

Abstract: Electrocardiograms (ECGs) are signals that originate from the action of the human heart. The ECG is the key biosignal for aiding the clinical staff in disease diagnosis. The recognition and analysis of the ECG signals is a very important task. This could be difficult, because the size and form of these signals may change eventually and can be noised. ECG noise removal is complicated due to the time varying nature of ECG signals. The traditional approach to remove high frequency noise from ECG signal is to employ a low-pass filter [1]. However, the cut-off frequency is difficult to determine and it may introduce some additional artifacts to the signal, especially on the QRS wave. Other filtering techniques that have been proposed are reviewed here. The next step is extracting feature from the signal. One cardiac cycle in an ECG signal

Abstract: People buy billions of dollars worth of goods and services every year many which harm the environment in how they are harvested, made, or used. Environmentalists support green marketing to encourage people to use environmentally preferable alternatives, and to offer incentives to manufacturers that develop more environmentally beneficial products. World-wide evidence indicates people are concerned about the environment and are changing their behavior accordingly. As a result there is a growing market for sustainable and socially

Abstract: Scheduling is a capital problem when using distributed heterogeneous computing (HC) and grid environments to solve complex problems. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made to develop efficient methods for solving the problem. However, few works have faced realistic grid-sized problem instances. This work presents a parallel CHC (pCHC) evolutionary algorithm codified over MALLBA, a general-purpose library for combinatorial optimization, for solving the scheduling problem in HC and grid environments. Efficient numerical results are reported in the experimental analysis performed on both a standard benchmark and a set of large-sized problem instances specially designed in this work. The comparative study shows that pCHC is able to achieve high problem solving efficacy, significantly improving over traditional deterministic scheduling methods, while also showing a good scalability behavior when solving large problem instances. © 2012 Wiley Periodicals, Inc.

Abstract: Neurocomputers supporting very-large-scale artificial neural networks are in demand. In this paper, a synchronous digital neurocomputing architecture called Neuron Machine is proposed. In this architecture, memories are arranged such that data for a large number of neural connections can be stored and accessed simultaneously. This memory structure enables both parallel computation of multiple connections and pipelining of a series of computation stages, thereby exploiting a large amount of parallelism. In addition, there are no fundamental limitations on the network size and topology of the artificial neural networks that it can compute. The proposed architecture was implemented on a field-programmable gate array (FPGA), and it was demonstrated that a learning speed in excess of 70 giga connection updates per second (GCUPS) can be achieved using a single chip. This architecture can be used to implement large-scale general-purpose neuro-computers or neurochips in real-time applications.

Abstract: Dependency parsing has attracted considerable interest from researchers and developers in natural language processing. However, to obtain a high-accuracy dependency parser, supervised techniques require a large volume of hand-annotated data, which are extremely expensive. This paper presents a simple and effective approach for improving dependency parsing with subtrees derived from unannotated data, which are easy to obtain. First, we use a baseline parser to parse large-scale unannotated data. Then, we extract subtrees from dependency parse trees in the auto-parsed data. Next, the extracted subtrees are classified into several sets according to their frequency. Finally, we design new features based on the subtree sets for parsing algorithms. To demonstrate the effectiveness of our proposed approach, we conduct experiments on the English Penn Treebank and Chinese Penn Treebank. The results show that our approach significantly outperforms baseline systems. It also achieves the best accuracy for the Chinese data and an accuracy competitive with the best known systems for the English data. © 2012 Wiley Periodicals, Inc.

Abstract: This paper proposes a new method for translating software requirements specified using natural language to formal specification (in this context is executable and translatable Unified Modeling Language class diagram). Requirements specification written in a scenario-like format will be transformed into class diagram's components.

Abstract: “Oxford College,” well-known as “the earliest edification institution in northern Taiwan,” was planned by Rev. George Leslie Mackay. It is a typical Chinese and Western style architecture with rich historical and cultural content, now is a Class 2 national monument. This paper took “Oxford College” as an example to develop a 3D augmented reality mobile navigation system that supports the function of indoor positioning. This system collected the historical data to develop the 3D models according to the ratio of actual objectives, and constructed the 3D external and internal structures of Oxford College of the past and present. Moreover, this system combined RFID positioning function with the technology of markerless augmented reality to actively detect the location of visitors and to further instantaneously present 3D and multimedia navigation information on mobile devices.

Abstract: Ensemble learning is inspired by the human group decision making process, and it has been found beneficial in various application domains. Decision tree and artificial neural network are two popular types of classification algorithms often used to construct classic ensembles. Recently, researchers proposed to use the mixture of both types to construct hybrid ensembles. However, researchers use decision trees and artificial neural networks together in an ensemble without further discussion. The focus of this paper is on the hybrid ensemble constructed by using decision trees and artificial neural networks simultaneously. The goal of this paper is not only to show that the hybrid ensemble can achieve comparable or even better classification performance, but also to provide an explanation of why it works.

Abstract: In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model. © 2012 Wiley Periodicals, Inc.

Abstract: The Concept of classification and learning will suit well to medical applications, especially those that need complex diagnostic measurements. Therefore classification technique can be used for cancer disease prediction. This approach is very much interesting as it is part of a growing demand towards predictive diagnosis. From the available studies it is evident that classification and learning methods can be used effectively to improve the accuracy of predicting a disease and its recurrence. In the present work classification techniques namely Support Vector Machine [SVM] and Random Forest [RF] are used to learn, classify and compare cancer disease data with varying kernels and kernel parameters. Results with Support Vector Machines and Random Forest are compared for different data sets. The results with different kernels are tuned with proper parameters selection. Results are analyzed with confusion matrix.

Abstract: In this paper, a technique for the recognition of unconstrained Arabic printed text is proposed. Features that measure the image characteristics at local scales are applied. A line image is divided into a set of one-pixel width windows which is sliding a cross that text line. Run length encoding is used to extract features from each window. A unique method is chosen to select best number of transitions for each window. The proposed recognition system is trained and tested on the APTI (Arabic Printed Text Image) database. In order to select the optimal parameters for feature extraction and for the HMM classifier, the APTI training dataset is further divided into a smaller training subset and a verification set. The estimated parameters are, then, used in the testing phase. The presented technique provides state-of-the-art recognition results on the APTI database using HMMs. The achieved average recognition rates is 96.65% on the letter level using the HMM classifier.

Abstract: Green supply chain management is defined as "the process of using environmentally friendly inputs and transforming these inputs into outputs that can be reclaimed and re-used at the end of their life cycle thus, creating a sustainable supply chain. GSCM is one of the recent innovations for the enhancement of capabilities of Supply Chain Management. The purpose of this paper is to briefly review the literature of the green supply chain management (GSCM) over the last thirty years. The major activities that came out of the literature are: green operations, green design, green manufacturing, reverse logistics and waste management .This paper also discusses the key drivers for green initiatives include government compliance, improved customer and public relations.

Abstract: The purpose of a Machine Translation (MT) system is to decode one language into another. Every language has its own different lexical and syntactic structure. A hybrid language does not have its own structure; it is an amalgamation of two or more languages in a sentence. To understand the structure and to decode a hybrid language into a formal language, hybrid parsing techniques are required. Hindi and English have Subject Object Verb (SOV) and Subject Verb Object (SVO) word orders, respectively. The basic requirement of parsers is to transform a SOV word order to a SVO word order and vice versa and Part of Speech (POS) tagging is essential for word grouping. The purpose of this paper is to bring out the concepts of parsers and POS tagging techniques to which hybrid translation can takes place to a formal language.

Abstract: In this paper, Bayesian-Networks (BN) and Ant Colony Optimization (ACO) techniques are combined to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work is that the amount of pheromones released is variable. This amount is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required. By using ACO and BN, a fitness function, responsible of automatically selecting the next course in the learning graph, is defined. This is done by generating a path that maximizes the probability of each user's success in the course. Therefore, the path can change to improve learners’ average performance, taking into account the pedagogical weight of each learning unit and the social behavior of the system. Furthermore, a discrete dynamical system is obtained and its stability is studied. How to wrap an existing Learning Management System is also described in this work. Finally, an experiment compares this approach with the old on-line learning system being used previously. (These initial values were agreed with the Pedagogical Team. In addition, all the edges of the learning graph were initialized with zero pheromones. © 2012 Wiley Periodicals, Inc.)

Abstract: This research is purposed to increase computer function into a time driven to support real time system so that the processor can work according to determined time variable and can work optimally in a certained deadline. The first approachment of this research is a processor which has a priority arbiter/border of a task. The second approachment, is a numerator processor with variable precision (VP) computing. The third approachment is by functioning statistic control of the emergence task that will be observing with the help of coprocessor which is placed in the front section of bitspace architecture of the second approachment above. The last aprroachment is by adding certainty precision in form of arithmetic interval that is able to cut the data/task. The data/task-cut is in the form of upper and lower border from the bounds. These four approachments can be structured orthoganally or stand alone into a processor/several processors. Initial hyphothesis of these four approachments can produce a processor that can work optimally in time variable and in a fast, accurate, reliable and robust daedline in supporting real time data/task finishing.