Search results for: cloud computing privacy.

Authors: Mandla T. Nene, Edgar.Jembere, Matthew O. Adigun, Themba Shezi, Siyabonga S. Cebekhulu

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The drastic increase in the usage of SMS technology has led service providers to seek for a solution that enable users of mobile devices to access services through SMSs. This has resulted in the proposal of solutions towards SMS-based service invocation in service oriented environments. However, the dynamic nature of service-oriented environments coupled with sudden load peaks generated by service request, poses performance challenges to infrastructures for supporting SMS-based service invocation. To address this problem we adopt load balancing techniques. A load balancing model with adaptive load balancing and load monitoring mechanisms as its key constructs is proposed. The load balancing model then led to realization of Least Loaded Load Balancing Framework (LLLBF). Evaluation of LLLBF benchmarked with round robin (RR) scheme on the queuing approach showed LLLBF outperformed RR in terms of response time and throughput. However, LLLBF achieved better result in the cost of high processing power.

Keywords: SMS (Short Message Service), LLLBF (Least Loaded Load Balancing Framework), Service Oriented Computing (SOC).

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Authors: Hsin-Yun Lee

Abstract:

In this study, the pedestrian simulation VISWALK integration and application platform ant algorithms written program made to construct a renovation engineering schedule planning mode. The use of simulation analysis platform construction site when the user running the simulation, after calculating the user walks in the case of construction delays, the ant algorithm to find out the minimum delay time schedule plan, and add volume and unit area deactivated loss of business computing, and finally to the owners and users of two different positions cut considerations pick out the best schedule planning. To assess and validate its effectiveness, this study constructed the model imported floor of a shopping mall floor renovation engineering cases. Verify that the case can be found from the mode of the proposed project schedule planning program can effectively reduce the delay time and the user's walking mall loss of business, the impact of the operation on the renovation engineering facilities in the building to a minimum.

Keywords: Pedestrian, renovation, schedule, simulation.

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Authors: Mohamed Adel Serhani, Abdelghani Benharref, Rachida Dssouli, Rabeb Mizouni

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Nowadays, people are going more and more mobile, both in terms of devices and associated applications. Moreover, services that these devices are offering are getting wider and much more complex. Even though actual handheld devices have considerable computing power, their contexts of utilization are different. These contexts are affected by the availability of connection, high latency of wireless networks, battery life, size of the screen, on-screen or hard keyboard, etc. Consequently, development of mobile applications and their associated mobile Web services, if any, should follow a concise methodology so they will provide a high Quality of Service. The aim of this paper is to highlight and discuss main issues to consider when developing mobile applications and mobile Web services and then propose a framework that leads developers through different steps and modules toward development of efficient and secure mobile applications. First, different challenges in developing such applications are elicited and deeply discussed. Second, a development framework is presented with different modules addressing each of these challenges. Third, the paper presents an example of a mobile application, Eivom Cinema Guide, which benefits from following our development framework.

Keywords: Mobile applications, development of mobile applications, efficient mobile application, secure mobile application.

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Authors: Azali Saudi, Jumat Sulaiman

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Harmonic functions are solutions to Laplace’s equation that are known to have an advantage as a global approach in providing the potential values for autonomous vehicle navigation. However, the computation for obtaining harmonic functions is often too slow particularly when it involves very large environment. This paper presents a two-stage iterative method namely Modified Arithmetic Mean (MAM) method for solving 2D Laplace’s equation. Once the harmonic functions are obtained, the standard Gradient Descent Search (GDS) is performed for path finding of an autonomous vehicle from arbitrary initial position to the specified goal position. Details of the MAM method are discussed. Several simulations of vehicle navigation with path planning in a static known indoor environment were conducted to verify the efficiency of the MAM method. The generated paths obtained from the simulations are presented. The performance of the MAM method in computing harmonic functions in 2D environment to solve path planning problem for an autonomous vehicle navigation is also provided.

Keywords: Modified Arithmetic Mean method, Harmonic functions, Laplace’s equation, path planning.

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: Long-short-term memory, re-identification, security critical application, spatial-temporal awareness.

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Authors: Imen Mejri, Ahmed Mahmoudi, Mohamed A. Abbassi, Ahmed Omri

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Thermal radiation plays a very important role in the heat transfer combination through the various components of the SOFC fuel cell operating at high temperatures. Lattice Boltzmann method is used for treating conduction-radiation heat transfer in the electrolyte. The thermal radiation heat transfer is coupled to the overall energy conservation equations through the divergence of the local radiative flux. The equation of energy in one dimension is numerically resolved by using the Lattice Boltzmann method. A computing program (FORTRAN) is developed locally for this purpose in order to obtain fields of temperature in every element of the cell. The parameters investigated are: functioning temperature, cell voltages and electrolyte thickness. The results show that the radiation effect increases with increasing the electrolyte thickness, also increases with increasing the functioning temperature and decreases with the increase of the voltage of the cell.

Keywords: SOFC, lattice Boltzmann method, conduction, radiation, planar medium.

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: Surendran M, Palani G. S, Nagesh R. Iyer

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Multi-site damage (MSD) has been a challenge to aircraft, civil and power plant structures. In real life components are subjected to cracking at many vulnerable locations such as the bolt holes. However, we do not consider for the presence of multiple cracks. Unlike components with a single crack, these components are difficult to predict. When two cracks approach one another, their stress fields influence each other and produce enhancing or shielding effect depending on the position of the cracks. In the present study, numerical studies on fracture analysis have been conducted by using the developed code based on the modified virtual crack closure integral (MVCCI) technique and finite element analysis (FEA) software ABAQUS for computing SIF of plates with multiple cracks. Various parametric studies have been carried out and the results have been compared with literature where ever available and also with the solution, obtained by using ABAQUS. By conducting extensive numerical studies expressions for SIF have been obtained for collinear cracks and non-aligned cracks.

Keywords: Crack interaction, Fracture mechanics, Multiple site damage, stress intensity factor, collinear cracks, non-aligned cracks.

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Authors: Behnam Mehrkian, Arash Bahar, Ali Chaibakhsh

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Magneto-rheological (MR) fluid damper is a semiactive control device that has recently received more attention by the vibration control community. But inherent hysteretic and highly nonlinear dynamics of MR fluid damper is one of the challenging aspects to employ its unique characteristics. The combination of artificial neural network (ANN) and fuzzy logic system (FLS) have been used to imitate more precisely the behavior of this device. However, the derivative-based nature of adaptive networks causes some deficiencies. Therefore, in this paper, a novel approach that employ genetic algorithm, as a free-derivative algorithm, to enhance the capability of fuzzy systems, is proposed. The proposed method used to model MR damper. The results will be compared with adaptive neuro-fuzzy inference system (ANFIS) model, which is one of the well-known approaches in soft computing framework, and two best parametric models of MR damper. Data are generated based on benchmark program by applying a number of famous earthquake records.

Keywords: Benchmark program, earthquake record filtering, fuzzy logic, genetic algorithm, MR damper.

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Authors: R. Tur, A. Yardımcı

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Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.

Keywords: Fuzzy logic, decision support systems, construction industry.

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Authors: Maria Christofi, George Plastiras, Rafaella Elia, Vaggelis Tsiourtis, Theocharis Theocharides, Miltiadis Katsaros

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The rapidly expanding urban areas of the world constitute a challenge of how we need to make the transition to "the next urbanization", which will be defined by new analytical tools and new sources of data. This paper is about the production of a spatial application, the ‘FUMapp’, where space and its initiative will be available literally, in meters, but also abstractly, at a sensed level. While existing spatial applications typically focus on illustrations of the urban infrastructure, the suggested application goes beyond the existing: It investigates how our environment's perception adapts to the alterations of the built environment through a dataset construction of biophysical measurements (eye-tracking, heart beating), and physical metrics (spatial characteristics, size of stimuli, rhythm of mobility). It explores the intersections between architecture, cognition, and computing where future design can be improved and identifies the flexibility and livability of the ‘available space’ of specific examined urban paths.

Keywords: Biophysical data, flexibility of urban, livability, next urbanization, spatial application.

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Authors: Musa'ed N. Almarshad, Saleh A. Alshebeili, Mourad Barkat

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A challenging problem in radar signal processing is to achieve reliable target detection in the presence of interferences. In this paper, we propose a novel algorithm for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithm, termed the forward automatic censored cell averaging detector (F-ACCAD), consists of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background level and set the adaptive thresholds accordingly. The F-ACCAD algorithm does not require any prior information about the clutter parameters nor does it require the number of interfering targets. The effectiveness of the F-ACCAD algorithm is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments.

Keywords: CFAR, Log-normal clutter, Censoring, Probabilityof detection, Probability of false alarm, Probability of falsecensoring.

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Authors: P. Shunmugapriya, S. Kanmani, P. Jude Fredieric, U. Vignesh, J. Reman Justin, K. Vivek

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Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem and it is quite difficult to find an optimal solution consisting of a set of routes for vehicles whose total cost is minimum. Evolutionary and swarm intelligent (SI) algorithms play a vital role in solving optimization problems. While the SI algorithms perform search, the diversity between the solutions they exploit is very important. This is because of the need to avoid early convergence and to get an appropriate balance between the exploration and exploitation. Therefore, it is important to check how far the solutions are diverse. In this paper, we measure the similarity between solutions, which ABC exploits while optimizing VRP. The similar solutions found are discarded at the end of the iteration and only unique solutions are passed on to the next iteration. The bees of discarded solutions become scouts and they start searching for new solutions. This process is continued and results show that the solution is optimized at lesser number of iterations but with the overhead of computing similarity in all the iterations. The problem instance from Solomon benchmarked dataset has been used for evaluating the presented methodology.

Keywords: ABC algorithm, vehicle routing problem, optimization, Jaccard’s similarity measure.

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Authors: Zhoucheng Bao, Haiyan Zhu, Tingting Pang, Zuling Wang

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This paper presents a multi population Differential Evolution (DE) with adaptive mutation and local search for global optimization, named AMMADE in order to better coordinate the cooperation between the populations and the rational use of resources. In AMMADE, the population is divided based on the Euclidean distance sorting method at each generation to appropriately coordinate the cooperation between subpopulations and the usage of resources, such that the best-performed subpopulation will get more computing resources in the next generation. Further, an adaptive local search strategy is employed on the best-performed subpopulation to achieve a balanced search. The proposed algorithm has been tested by solving optimization problems taken from CEC2014 benchmark problems. Experimental results show that our algorithm can achieve a competitive or better result than related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Keywords: Differential evolution, multi-mutation strategies, memetic algorithm, adaptive local search.

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Authors: Mamoun F. Al-Mistarihi

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We have previously introduced an ultrasonic imaging approach that combines harmonic-sensitive pulse sequences with a post-beamforming quadratic kernel derived from a second-order Volterra filter (SOVF). This approach is designed to produce images with high sensitivity to nonlinear oscillations from microbubble ultrasound contrast agents (UCA) while maintaining high levels of noise rejection. In this paper, a two-step algorithm for computing the coefficients of the quadratic kernel leading to reduction of tissue component introduced by motion, maximizing the noise rejection and increases the specificity while optimizing the sensitivity to the UCA is presented. In the first step, quadratic kernels from individual singular modes of the PI data matrix are compared in terms of their ability of maximize the contrast to tissue ratio (CTR). In the second step, quadratic kernels resulting in the highest CTR values are convolved. The imaging results indicate that a signal processing approach to this clinical challenge is feasible.

Keywords: Volterra Filter, Pulse Inversion, Ultrasonic Imaging, Contrast Agent.

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Authors: Hassan Zohoor, Zaeem M. Moosavi

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The intermittent nature of solar energy and the energy requirements of buildings necessitate the storage of thermal energy. In this paper a hybrid system of storing solar energy has been analyzed. Adding a LHS medium to a commercial solar water heater, the required energy for heating a small room was obtained in addition to preparing hot water. In other words, the suggested hybrid storage system consists of two tanks: a water tank as a SHS medium; and a paraffin tank as a LHS medium. A computing program was used to find the optimized time schedule of charging the storage tanks during each day, according to the solar radiation conditions. The results show that the use of such system can improve the capability of energy gathering comparing to the individual water storage tank during the cold months of the year. Of course, because of the solar radiation angles and shorten daylight in December & January, the performance will be the same as the simple solar water heaters (in the northern hemisphere). But the extra energy stored in November, February, March & April, can be useful for heating a small room for 3 hours during the cold days.

Keywords: Hybrid, Optimization, Solar thermal energy, Storage.

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Authors: Amer A. Boushaala

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The heuristic decision rules used for project scheduling will vary depending upon the project-s size, complexity, duration, personnel, and owner requirements. The concept of project complexity has received little detailed attention. The need to differentiate between easy and hard problem instances and the interest in isolating the fundamental factors that determine the computing effort required by these procedures inspired a number of researchers to develop various complexity measures. In this study, the most common measures of project complexity are presented. A new measure of project complexity is developed. The main privilege of the proposed measure is that, it considers size, shape and logic characteristics, time characteristics, resource demands and availability characteristics as well as number of critical activities and critical paths. The degree of sensitivity of the proposed measure for complexity of project networks has been tested and evaluated against the other measures of complexity of the considered fifty project networks under consideration in the current study. The developed measure showed more sensitivity to the changes in the network data and gives accurate quantified results when comparing the complexities of networks.

Keywords: Activity networks, Complexity index, Networkcomplexity measure, Network topology, Project Network.

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Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

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Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

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Authors: Mohamed K. Watfa

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Like any sentient organism, a smart environment relies first and foremost on sensory data captured from the real world. The sensory data come from sensor nodes of different modalities deployed on different locations forming a Wireless Sensor Network (WSN). Embedding smart sensors in humans has been a research challenge due to the limitations imposed by these sensors from computational capabilities to limited power. In this paper, we first propose a practical WSN application that will enable blind people to see what their neighboring partners can see. The challenge is that the actual mapping between the input images to brain pattern is too complex and not well understood. We also study the connectivity problem in 3D/2D wireless sensor networks and propose distributed efficient algorithms to accomplish the required connectivity of the system. We provide a new connectivity algorithm CDCA to connect disconnected parts of a network using cooperative diversity. Through simulations, we analyze the connectivity gains and energy savings provided by this novel form of cooperative diversity in WSNs.

Keywords: Wireless Sensor Networks, Pervasive Computing, Eye Vision Application, 3D Connectivity, Clusters, Energy Efficient, Cooperative diversity.

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Authors: O. P. Rishi, Rekha Govil, Madhavi Sinha

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Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student-s learning behavior and presents to the student the learning material (content, questions-answers, assignments) accordingly. In today-s distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents. The paper describes the approach, the architecture, and the agent characteristics for such system. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students- learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and student model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system.

Keywords: CBR, ITS, student modeling, distributed system, intelligent agent.

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Authors: H. K. Sachidananda, K. Raghunandana, B. Shivamurthy

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The estimation of gear tooth stiffness is important for finding the load distribution between the gear teeth when two consecutive sets of teeth are in contact. Based on dynamic model a C-program has been developed to compute mesh stiffness. By using this program position dependent mesh stiffness of spur gear tooth for various profile shifts have been computed for a fixed center distance and altering tooth-sum gearing (100 by ± 4%). It is found that the C-program using dynamic model is one of the rapid soft computing technique which helps in design of gears. The mesh tooth stiffness along the path of contact is studied for both 20° and 25° pressure angle gears at various profile shifts. Better tooth stiffness is noticed in case of negative alteration tooth-sum gears compared to standard and positive alteration tooth-sum gears. Also, in case of negative alteration tooth-sum gearing better mesh stiffness is noticed in 20° pressure angle when compared to 25°.

Keywords: Altered tooth-sum gearing, bending fatigue, mesh stiffness, spur gear.

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Authors: Daniel Stamate

Abstract:

One of the most used assumptions in logic programming and deductive databases is the so-called Closed World Assumption (CWA), according to which the atoms that cannot be inferred from the programs are considered to be false (i.e. a pessimistic assumption). One of the most successful semantics of conventional logic programs based on the CWA is the well-founded semantics. However, the CWA is not applicable in all circumstances when information is handled. That is, the well-founded semantics, if conventionally defined, would behave inadequately in different cases. The solution we adopt in this paper is to extend the well-founded semantics in order for it to be based also on other assumptions. The basis of (default) negative information in the well-founded semantics is given by the so-called unfounded sets. We extend this concept by considering optimistic, pessimistic, skeptical and paraconsistent assumptions, used to complete missing information from a program. Our semantics, called extended well-founded semantics, expresses also imperfect information considered to be missing/incomplete, uncertain and/or inconsistent, by using bilattices as multivalued logics. We provide a method of computing the extended well-founded semantics and show that Kripke-Kleene semantics is captured by considering a skeptical assumption. We show also that the complexity of the computation of our semantics is polynomial time.

Keywords: Logic programs, imperfect information, multivalued logics, bilattices, assumptions.

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Authors: Liping Zhou, Liang Bao, Yiqin Lin, Yimin Wei, Qinghua Wu

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This article is devoted to the numerical solution of large-scale quadratic eigenvalue problems. Such problems arise in a wide variety of applications, such as the dynamic analysis of structural mechanical systems, acoustic systems, fluid mechanics, and signal processing. We first introduce a generalized second-order Krylov subspace based on a pair of square matrices and two initial vectors and present a generalized second-order Arnoldi process for constructing an orthonormal basis of the generalized second-order Krylov subspace. Then, by using the projection technique and the refined projection technique, we propose a restarted generalized second-order Arnoldi method and a restarted refined generalized second-order Arnoldi method for computing some eigenpairs of largescale quadratic eigenvalue problems. Some theoretical results are also presented. Some numerical examples are presented to illustrate the effectiveness of the proposed methods.

Keywords: Quadratic eigenvalue problem, Generalized secondorder Krylov subspace, Generalized second-order Arnoldi process, Projection technique, Refined technique, Restarting.

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: Time-series, features engineering methods for forecasting, energy demand forecasting, Azure machine learning.

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Authors: F.Alilat, S.Loumi, H.Merrad, B.Sansal

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In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.

Keywords: Neural Networks, fuzzy ART, fuzzy ARTMAP, Remote sensing, multispectral Classification.

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Authors: Rozo D. Juan Felipe, Ramírez L. Leonardo Juan, Puerta A. Gabriel Alberto

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The telemedicine services require correct computing resource management to guarantee productivity and efficiency for medical and non-medical staff. The aim of this study was to examine web management strategies to ensure the availability of resources and services in telemedicine so as to provide medical information management with an accessible strategy. In addition, to evaluate the quality-of-service parameters, the followings were measured: delays, throughput, jitter, latency, available bandwidth, percent of access and denial of services based of web management performance map with profiles permissions and database management. Through 24 different test scenarios, the results show 100% in availability of medical information, in relation to access of medical staff to web services, and quality of service (QoS) of 99% because of network delay and performance of computer network. The findings of this study suggest that the proposed strategy of web management is an ideal solution to guarantee the availability, reliability, and accessibility of medical information. Finally, this strategy offers seven user profile used at telemedicine center of Bogota-Colombia keeping QoS parameters suitable to telemedicine services.

Keywords: Availability, medical information, QoS, strategy, telemedicine.

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Authors: Pyae Pyae Hun

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Secure electronic payment system is presented in this paper. This electronic payment system is to be secure for clients such as customers and shop owners. The security architecture of the system is designed by RC5 encryption / decryption algorithm. This eliminates the fraud that occurs today with stolen credit card numbers. The symmetric key cryptosystem RC5 can protect conventional transaction data such as account numbers, amount and other information. This process can be done electronically using RC5 encryption / decryption program written by Microsoft Visual Basic 6.0. There is no danger of any data sent within the system being intercepted, and replaced. The alternative is to use the existing network, and to encrypt all data transmissions. The system with encryption is acceptably secure, but that the level of encryption has to be stepped up, as computing power increases. Results In order to be secure the system the communication between modules is encrypted using symmetric key cryptosystem RC5. The system will use simple user name, password, user ID, user type and cipher authentication mechanism for identification, when the user first enters the system. It is the most common method of authentication in most computer system.

Keywords: A 128-bit block cipher, Microsoft visual basic 6.0, RC5 encryption /decryption algorithm and TCP/IP protocol.

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Authors: Amr Almaddah, Sadi Vural, Yasushi Mae, Kenichi Ohara, Tatsuo Arai

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In this paper, we propose a robust face relighting technique by using spherical space properties. The proposed method is done for reducing the illumination effects on face recognition. Given a single 2D face image, we relight the face object by extracting the nine spherical harmonic bases and the face spherical illumination coefficients. First, an internal training illumination database is generated by computing face albedo and face normal from 2D images under different lighting conditions. Based on the generated database, we analyze the target face pixels and compare them with the training bootstrap by using pre-generated tiles. In this work, practical real time processing speed and small image size were considered when designing the framework. In contrast to other works, our technique requires no 3D face models for the training process and takes a single 2D image as an input. Experimental results on publicly available databases show that the proposed technique works well under severe lighting conditions with significant improvements on the face recognition rates.

Keywords: Face synthesis and recognition, Face illumination recovery, 2D spherical spaces, Vision for graphics.

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machine Training, Multi-ParameterKernels, Shared Memory Parallel Computing, Large Data

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

Abstract:

The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: Ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph.

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