Identification of Most Frequently Occurring Lexis in Body-enhancement Medicinal Unsolicited Bulk e-mails

e-mail has become an important means of electronic communication but the viability of its usage is marred by Unsolicited Bulk e-mail (UBE) messages. UBE consists of many types like pornographic, virus infected and 'cry-for-help' messages as well as fake and fraudulent offers for jobs, winnings and medicines. UBE poses technical and socio-economic challenges to usage of e-mails. To meet this challenge and combat this menace, we need to understand UBE. Towards this end, the current paper presents a content-based textual analysis of more than 2700 body enhancement medicinal UBE. Technically, this is an application of Text Parsing and Tokenization for an un-structured textual document and we approach it using Bag Of Words (BOW) and Vector Space Document Model techniques. We have attempted to identify the most frequently occurring lexis in the UBE documents that advertise various products for body enhancement. The analysis of such top 100 lexis is also presented. We exhibit the relationship between occurrence of a word from the identified lexis-set in the given UBE and the probability that the given UBE will be the one advertising for fake medicinal product. To the best of our knowledge and survey of related literature, this is the first formal attempt for identification of most frequently occurring lexis in such UBE by its textual analysis. Finally, this is a sincere attempt to bring about alertness against and mitigate the threat of such luring but fake UBE.

Bond Graph and Bayesian Networks for Reliable Diagnosis

Bond Graph as a unified multidisciplinary tool is widely used not only for dynamic modelling but also for Fault Detection and Isolation because of its structural and causal proprieties. A binary Fault Signature Matrix is systematically generated but to make the final binary decision is not always feasible because of the problems revealed by such method. The purpose of this paper is introducing a methodology for the improvement of the classical binary method of decision-making, so that the unknown and identical failure signatures can be treated to improve the robustness. This approach consists of associating the evaluated residuals and the components reliability data to build a Hybrid Bayesian Network. This network is used in two distinct inference procedures: one for the continuous part and the other for the discrete part. The continuous nodes of the network are the prior probabilities of the components failures, which are used by the inference procedure on the discrete part to compute the posterior probabilities of the failures. The developed methodology is applied to a real steam generator pilot process.

Estimation of Skew Angle in Binary Document Images Using Hough Transform

This paper includes two novel techniques for skew estimation of binary document images. These algorithms are based on connected component analysis and Hough transform. Both these methods focus on reducing the amount of input data provided to Hough transform. In the first method, referred as word centroid approach, the centroids of selected words are used for skew detection. In the second method, referred as dilate & thin approach, the selected characters are blocked and dilated to get word blocks and later thinning is applied. The final image fed to Hough transform has the thinned coordinates of word blocks in the image. The methods have been successful in reducing the computational complexity of Hough transform based skew estimation algorithms. Promising experimental results are also provided to prove the effectiveness of the proposed methods.

A Dual Method for Solving General Convex Quadratic Programs

In this paper, we present a new method for solving quadratic programming problems, not strictly convex. Constraints of the problem are linear equalities and inequalities, with bounded variables. The suggested method combines the active-set strategies and support methods. The algorithm of the method and numerical experiments are presented, while comparing our approach with the active set method on randomly generated problems.

Using Perspective Schemata to Model the ETL Process

Data Warehouses (DWs) are repositories which contain the unified history of an enterprise for decision support. The data must be Extracted from information sources, Transformed and integrated to be Loaded (ETL) into the DW, using ETL tools. These tools focus on data movement, where the models are only used as a means to this aim. Under a conceptual viewpoint, the authors want to innovate the ETL process in two ways: 1) to make clear compatibility between models in a declarative fashion, using correspondence assertions and 2) to identify the instances of different sources that represent the same entity in the real-world. This paper presents the overview of the proposed framework to model the ETL process, which is based on the use of a reference model and perspective schemata. This approach provides the designer with a better understanding of the semantic associated with the ETL process.

Geometric Data Structures and Their Selected Applications

Finding the shortest path between two positions is a fundamental problem in transportation, routing, and communications applications. In robot motion planning, the robot should pass around the obstacles touching none of them, i.e. the goal is to find a collision-free path from a starting to a target position. This task has many specific formulations depending on the shape of obstacles, allowable directions of movements, knowledge of the scene, etc. Research of path planning has yielded many fundamentally different approaches to its solution, mainly based on various decomposition and roadmap methods. In this paper, we show a possible use of visibility graphs in point-to-point motion planning in the Euclidean plane and an alternative approach using Voronoi diagrams that decreases the probability of collisions with obstacles. The second application area, investigated here, is focused on problems of finding minimal networks connecting a set of given points in the plane using either only straight connections between pairs of points (minimum spanning tree) or allowing the addition of auxiliary points to the set to obtain shorter spanning networks (minimum Steiner tree).

The Development of the Multi-Agent Classification System (MACS) in Compliance with FIPA Specifications

The paper investigates the feasibility of constructing a software multi-agent based monitoring and classification system and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. The agents function autonomously to provide continuous and periodic monitoring of excels spreadsheet workbooks. Resulting in, the development of the MultiAgent classification System (MACS) that is in compliance with the specifications of the Foundation for Intelligent Physical Agents (FIPA). However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies that are Windows Communication Foundation (WCF) services, Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). The Microsoft's .NET widows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW that is in order to satisfy the monitoring and classification of the multiple developer aspect. ODM was used to automate the classification phase of MACS.

Non-equilibrium Statistical Mechanics of a Driven Lattice Gas Model: Probability Function, FDT-violation, and Monte Carlo Simulations

The study of non-equilibrium systems has attracted increasing interest in recent years, mainly due to the lack of theoretical frameworks, unlike their equilibrium counterparts. Studying the steady state and/or simple systems is thus one of the main interests. Hence in this work we have focused our attention on the driven lattice gas model (DLG model) consisting of interacting particles subject to an external field E. The dynamics of the system are given by hopping of particles to nearby empty sites with rates biased for jumps in the direction of E. Having used small two dimensional systems of DLG model, the stochastic properties at nonequilibrium steady state were analytically studied. To understand the non-equilibrium phenomena, we have applied the analytic approach via master equation to calculate probability function and analyze violation of detailed balance in term of the fluctuation-dissipation theorem. Monte Carlo simulations have been performed to validate the analytic results.

Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features

This paper presents an effective traffic lights detection method at the night-time. First, candidate blobs of traffic lights are extracted from RGB color image. Input image is represented on the dominant color domain by using color transform proposed by Ruta, then red and green color dominant regions are selected as candidates. After candidate blob selection, we carry out shape filter for noise reduction using information of blobs such as length, area, area of boundary box, etc. A multi-class classifier based on SVM (Support Vector Machine) applies into the candidates. Three kinds of features are used. We use basic features such as blob width, height, center coordinate, area, area of blob. Bright based stochastic features are also used. In particular, geometric based moment-s values between candidate region and adjacent region are proposed and used to improve the detection performance. The proposed system is implemented on Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the urban and rural road videos. Through the test, we show that the proposed method using PF, BMF, and GMF reaches up to 93 % of detection rate with computation time of in average 15 ms/frame.

Hybrid Coding for Animated Polygonal Meshes

A new hybrid coding method for compressing animated polygonal meshes is presented. This paper assumes the simplistic representation of the geometric data: a temporal sequence of polygonal meshes for each discrete frame of the animated sequence. The method utilizes a delta coding and an octree-based method. In this hybrid method, both the octree approach and the delta coding approach are applied to each single frame in the animation sequence in parallel. The approach that generates the smaller encoded file size is chosen to encode the current frame. Given the same quality requirement, the hybrid coding method can achieve much higher compression ratio than the octree-only method or the delta-only method. The hybrid approach can represent 3D animated sequences with higher compression factors while maintaining reasonable quality. It is easy to implement and have a low cost encoding process and a fast decoding process, which make it a better choice for real time application.

Robust Power System Stabilizer Design Using Particle Swarm Optimization Technique

Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, particle swarm optimization (PSO) technique is applied to design a robust power system stabilizer (PSS). The design problem of the proposed controller is formulated as an optimization problem and PSO is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions; disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. Further, all the simulations results are compared with a conventionally designed power system stabilizer to show the superiority of the proposed design approach.

A New Concept for Deriving the Expected Value of Fuzzy Random Variables

Fuzzy random variables have been introduced as an imprecise concept of numeric values for characterizing the imprecise knowledge. The descriptive parameters can be used to describe the primary features of a set of fuzzy random observations. In fuzzy environments, the expected values are usually represented as fuzzy-valued, interval-valued or numeric-valued descriptive parameters using various metrics. Instead of the concept of area metric that is usually adopted in the relevant studies, the numeric expected value is proposed by the concept of distance metric in this study based on two characters (fuzziness and randomness) of FRVs. Comparing with the existing measures, although the results show that the proposed numeric expected value is same with those using the different metric, if only triangular membership functions are used. However, the proposed approach has the advantages of intuitiveness and computational efficiency, when the membership functions are not triangular types. An example with three datasets is provided for verifying the proposed approach.

Damping Mechanism in Welded Structures

Response surface methodology with Box–Benhken (BB) design of experiment approach has been utilized to study the mechanism of interface slip damping in layered and jointed tack welded beams with varying surface roughness. The design utilizes the initial amplitude of excitation, tack length and surface roughness at the interfaces to develop the model for the logarithmic damping decrement of the layered and jointed welded structures. Statistically designed experiments have been performed to estimate the coefficients in the mathematical model, predict the response, and check the adequacy of the model. Comparison of predicted and experimental response values outside the design conditions have shown good correspondence, implying that empirical model derived from response surface approach can be effectively used to describe the mechanism of interface slip damping in layered and jointed tack welded structures.

An Approach for Integration of Industrial Robot with Vision System and Simulation Software

Utilization of various sensors has made it possible to extend capabilities of industrial robots. Among these are vision sensors that are used for providing visual information to assist robot controllers. This paper presents a method of integrating a vision system and a simulation program with an industrial robot. The vision system is employed to detect a target object and compute its location in the robot environment. Then, the target object-s information is sent to the robot controller via parallel communication port. The robot controller uses the extracted object information and the simulation program to control the robot arm for approaching, grasping and relocating the object. This paper presents technical details of system components and describes the methodology used for this integration. It also provides a case study to prove the validity of the methodology developed.

Interactive Concept-based Search using MOEA:The Hierarchical Preferences Case

An IEC technique is described for a multi-objective search of conceptual solutions. The survivability of solutions is influenced by both model-based fitness and subjective human preferences. The concepts- preferences are articulated via a hierarchy of sub-concepts. The suggested method produces an objectivesubjective front. Academic example is employed to demonstrate the proposed approach.

An Intelligent Cascaded Fuzzy Logic Based Controller for Controlling the Room Temperature in Hydronic Heating System

Heating systems are a necessity for regions which brace extreme cold weather throughout the year. To maintain a comfortable temperature inside a given place, heating systems making use of- Hydronic boilers- are used. The principle of a single pipe system serves as a base for their working. It is mandatory for these heating systems to control the room temperature, thus maintaining a warm environment. In this paper, the concept of regulation of the room temperature over a wide range is established by using an Adaptive Fuzzy Controller (AFC). This fuzzy controller automatically detects the changes in the outside temperatures and correspondingly maintains the inside temperature to a palatial value. Two separate AFC's are put to use to carry out this function: one to determine the quantity of heat needed to reach the prospective temperature required and to set the desired temperature; the other to control the position of the valve, which is directly proportional to the error between the present room temperature and the user desired temperature. The fuzzy logic controls the position of the valve as per the requirement of the heat. The amount by which the valve opens or closes is controlled by 5 knob positions, which vary from minimum to maximum, thereby regulating the amount of heat flowing through the valve. For the given test system data, different de-fuzzifier methods have been implemented and the results are compared. In order to validate the effectiveness of the proposed approach, a fuzzy controller has been designed by obtaining a test data from a real time system. The simulations are performed in MATLAB and are verified with standard system data. The proposed approach can be implemented for real time applications.

Protein Residue Contact Prediction using Support Vector Machine

Protein residue contact map is a compact representation of secondary structure of protein. Due to the information hold in the contact map, attentions from researchers in related field were drawn and plenty of works have been done throughout the past decade. Artificial intelligence approaches have been widely adapted in related works such as neural networks, genetic programming, and Hidden Markov model as well as support vector machine. However, the performance of the prediction was not generalized which probably depends on the data used to train and generate the prediction model. This situation shown the importance of the features or information used in affecting the prediction performance. In this research, support vector machine was used to predict protein residue contact map on different combination of features in order to show and analyze the effectiveness of the features.

Augmentation Opportunity of Transmission Control Protocol Performance in Wireless Networks and Cellular Systems

The advancement in wireless technology with the wide use of mobile devices have drawn the attention of the research and technological communities towards wireless environments, such as Wireless Local Area Networks (WLANs), Wireless Wide Area Networks (WWANs), and mobile systems and ad-hoc networks. Unfortunately, wired and wireless networks are expressively different in terms of link reliability, bandwidth, and time of propagation delay and by adapting new solutions for these enhanced telecommunications, superior quality, efficiency, and opportunities will be provided where wireless communications were otherwise unfeasible. Some researchers define 4G as a significant improvement of 3G, where current cellular network’s issues will be solved and data transfer will play a more significant role. For others, 4G unifies cellular and wireless local area networks, and introduces new routing techniques, efficient solutions for sharing dedicated frequency bands, and an increased mobility and bandwidth capacity. This paper discusses the possible solutions and enhancements probabilities that proposed to improve the performance of Transmission Control Protocol (TCP) over different wireless networks and also the paper investigated each approach in term of advantages and disadvantages.

Real-time ROI Acquisition for Unsupervised and Touch-less Palmprint

In this paper we proposed a novel method to acquire the ROI (Region of interest) of unsupervised and touch-less palmprint captured from a web camera in real-time. We use Viola-Jones approach and skin model to get the target area in real time. Then an innovative course-to-fine approach to detect the key points on the hand is described. A new algorithm is used to find the candidate key points coarsely and quickly. In finely stage, we verify the hand key points with the shape context descriptor. To make the user much comfortable, it can process the hand image with different poses, even the hand is closed. Experiments show promising result by using the proposed method in various conditions.

Elimination Noise by Adaptive Wavelet Threshold

Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.