Search results for: Equality of P and NP Complexity Classes.

Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

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This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: Designing, education management, information systems, matrix technology, process affinity.

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Authors: D. R. Jain

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The aim of this paper is to use matrix representation of Fuzzy soft sets for proving some equalities connected with Fuzzy soft sets based on set-operations.

Keywords: Equality, Fuzzy soft matrix, Fuzzy soft sets, operations.

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Authors: Laura Çami (Vorpsi), Xhon Skënderi

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In today's rapidly evolving technological landscape, the traditional principles of contract law are facing significant challenges. The emergence of new technologies, such as electronic signatures, smart contracts, and online dispute resolution mechanisms, is transforming the way contracts are formed, interpreted, and enforced. This paper examines the implications of these technological advancements on the constitutional principles of contract law. One of the fundamental principles of contract law is freedom of contract, which ensures that parties have the autonomy to negotiate and enter into contracts as they see fit. However, the use of technology in the contracting process has the potential to disrupt this principle. For example, online platforms and marketplaces often offer standard-form contracts, which may not reflect the specific needs or interests of individual parties. This raises questions about the equality of bargaining power between parties and the extent to which parties are truly free to negotiate the terms of their contracts. Another important principle of contract law is the requirement of consideration, which requires that each party receives something of value in exchange for their promise. The use of digital assets, such as cryptocurrencies, has created new challenges in determining what constitutes valuable consideration in a contract. Due to the ambiguity in this area, disagreements about the legality and enforceability of such contracts may arise. Furthermore, the use of technology in dispute resolution mechanisms, such as online arbitration and mediation, may raise concerns about due process and access to justice. The use of algorithms and artificial intelligence to determine the outcome of disputes may also raise questions about the impartiality and fairness of the process. Finally, it should be noted that there are many different and complex effects of technical improvements on the fundamental constitutional foundations of contract law. As technology continues to evolve, it will be important for policymakers and legal practitioners to consider the potential impacts on contract law and to ensure that the principles of fairness, equality, and access to justice are preserved in the contracting process.

Keywords: Technological advancements, constitutional principles, contract law, smart contracts, online dispute resolution, freedom of contract.

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Authors: Andrea Paltrinieri, Alberto Dreassi, Stefano Miani, Alex Sclip

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The financial crises caused a collapse in prices of most asset classes, raising the attention on alternative investments such as sukuk, a smaller, fast growing but often misunderstood market. We study diversification benefits of sukuk, their correlation with other asset classes and the effects of their inclusion in investment portfolios of institutional and retail investors, through a comprehensive comparison of their risk/return profiles during and after the financial crisis. We find a beneficial performance adjusted for the specific volatility together with a lower correlation especially during the financial crisis. The distribution of sukuk returns is positively skewed and leptokurtic, with a risk/return profile similarly to high yield bonds. Overall, our results suggest that sukuk present diversification opportunities, a significant volatility-adjusted performance and lower correlations especially during the financial crisis. Our findings are relevant for a number of institutional investors. Long term investors, such as life insurers would benefit from sukuk’s protective features during financial crisis yet keeping return and growth opportunities, whereas banks would gain due to their role of placers, advisors, market makers or underwriters.

Keywords: Asset allocation, asset performance, sukuk, zero beta asset.

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Authors: Nitai Biswas, Prasenjit Kapas, Arumay Jana, Asish Paul

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Introduction: Motivation is basic drive for all kinds of action. It has direct influence on academic achievement and sports performance that builds urge to incentive values of success. In other words, it can be defined as the need for success to attain excellence. Anxiety in pre competition especially in sports formulates positive inward settings in mind to overcome the challenge. There is a tendency to perceive competitive situations as some threatening issues and to respond them with feelings of apprehension and tension. Aim: Aim of the study was to compare the achievement motivation and competition anxiety among three different classes of students. Methods and Materials: To conduct the study the researcher has taken 131 male subjects from three different classes as Extra Department, Bachelor of Physical Education-I and Master of Physical EducationII, aged 19-28 years. Achievement motivation and sports competition anxiety were measured by the questionnaire. To analyze the data mean, standard deviation for each parameter as descriptive statistics and one way analysis of variance as inferential statistics were employed. Results: From the result of the study in achievement motivation (p ≥ 0.05) and competition anxiety (p ≥ 0.05) no significant differences were found among the said three groups. Conclusion: The study concluded that all three groups had almost the same state of achievement motivation and sports competition anxiety.

Keywords: Anxiety, sports psychology, sports competition anxiety, achievement motivation, academic hierarchy.

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, capsule network, capacity optimization, character recognition, data augmentation; semantic segmentation.

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Authors: Saeed Mian Qaisar, Laurent Fesquet, Marc Renaudin

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Mostly the systems are dealing with time varying signals. The Power efficiency can be achieved by adapting the system activity according to the input signal variations. In this context an adaptive rate filtering technique, based on the level crossing sampling is devised. It adapts the sampling frequency and the filter order by following the input signal local variations. Thus, it correlates the processing activity with the signal variations. Interpolation is required in the proposed technique. A drastic reduction in the interpolation error is achieved by employing the symmetry during the interpolation process. Processing error of the proposed technique is calculated. The computational complexity of the proposed filtering technique is deduced and compared to the classical one. Results promise a significant gain of the computational efficiency and hence of the power consumption.

Keywords: Level Crossing Sampling, Activity Selection, Rate Filtering, Computational Complexity, Interpolation Error.

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Authors: Ibrahim S AlNomay, Alaa Jaber, Ghada AlNasser

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A learning management system (commonly abbreviated as LMS) is a software application for the administration, documentation, tracking, and reporting of training programs, classroom and online events, e-learning programs, and training content (Ellis 2009). (Hall 2003) defines an LMS as \"software that automates the administration of training events. All Learning Management Systems manage the log-in of registered users, manage course catalogs, record data from learners, and provide reports to management\". Evidence of the worldwide spread of e-learning in recent years is easy to obtain. In April 2003, no fewer than 66,000 fully online courses and 1,200 complete online programs were listed on the TeleCampus portal from TeleEducation (Paulsen 2003). In the report \" The US market in the Self-paced eLearning Products and Services:2010-2015 Forecast and Analysis\" The number of student taken classes exclusively online will be nearly equal (1% less) to the number taken classes exclusively in physical campuses. Number of student taken online course will increase from 1.37 million in 2010 to 3.86 million in 2015 in USA. In another report by The Sloan Consortium three-quarters of institutions report that the economic downturn has increased demand for online courses and programs.

Keywords: LMS, Interactive Materials, Exam Centers, Learning Outcomes

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Authors: Predrag B. Petrović

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The paper deals with the estimation of amplitude and phase of an analogue multi-harmonic band-limited signal from irregularly spaced sampling values. To this end, assuming the signal fundamental frequency is known in advance (i.e., estimated at an independent stage), a complexity-reduced algorithm for signal reconstruction in time domain is proposed. The reduction in complexity is achieved owing to completely new analytical and summarized expressions that enable a quick estimation at a low numerical error. The proposed algorithm for the calculation of the unknown parameters requires O((2M+1)2) flops, while the straightforward solution of the obtained equations takes O((2M+1)3) flops (M is the number of the harmonic components). It is applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise RMS measurements (for power and energy) of a periodic signal based on the presented signal reconstruction. The paper investigates the errors related to the signal parameter estimation, and there is a computer simulation that demonstrates the accuracy of these algorithms.

Keywords: Band-limited signals, Fourier coefficient estimation, analytical solutions, signal reconstruction, time.

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Authors: Elena Ezhova, Vadim Mottl, Olga Krasotkina

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The problem of estimating time-varying regression is inevitably concerned with the necessity to choose the appropriate level of model volatility - ranging from the full stationarity of instant regression models to their absolute independence of each other. In the stationary case the number of regression coefficients to be estimated equals that of regressors, whereas the absence of any smoothness assumptions augments the dimension of the unknown vector by the factor of the time-series length. The Akaike Information Criterion is a commonly adopted means of adjusting a model to the given data set within a succession of nested parametric model classes, but its crucial restriction is that the classes are rigidly defined by the growing integer-valued dimension of the unknown vector. To make the Kullback information maximization principle underlying the classical AIC applicable to the problem of time-varying regression estimation, we extend it onto a wider class of data models in which the dimension of the parameter is fixed, but the freedom of its values is softly constrained by a family of continuously nested a priori probability distributions.

Keywords: Time varying regression, time-volatility of regression coefficients, Akaike Information Criterion (AIC), Kullback information maximization principle.

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Authors: Mimi N. A. Mohamed

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The complexity of teaching English in higher institutions by non-native speakers within a second/foreign language setting has created continuous discussions and research about teaching approaches and teaching practises, professional identities and challenges. In addition, there is a growing awareness that teaching English within discipline-specific contexts adds up to the existing complexity. This awareness leads to reassessments, discussions and suggestions on course design and content and teaching approaches and techniques. In meeting expectations teaching at a university specified in a particular discipline such as engineering, English language educators are not only required to teach students to be able to communicate in English effectively but also to teach soft skills such as problem solving skills. This paper is part of a research conducted to investigate how English language educators negotiate with the complexities of teaching problem solving skills through English language teaching at a technical university. This paper reports the way an English language educator identified himself and the way he approached his teaching in this institutional context.

Keywords: English Language Teaching, Teacher Agency, Problem Solving Skills, Professional Identities.

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Authors: Chien-Jen Liu, Shu Ching Yang

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Using the technology acceptance model (TAM), this study examined the external variables of technological complexity (TC) to acquire a better understanding of the factors that influence the acceptance of computer application courses by learners at Active Aging Universities. After the learners in this study had completed a 27-hour Facebook course, 44 learners responded to a modified TAM survey. Data were collected to examine the path relationships among the variables that influence the acceptance of Facebook-mediated community learning. The partial least squares (PLS) method was used to test the measurement and the structural model. The study results demonstrated that attitudes toward Facebook use directly influence behavioral intentions (BI) with respect to Facebook use, evincing a high prediction rate of 58.3%. In addition to the perceived usefulness (PU) and perceived ease of use (PEOU) measures that are proposed in the TAM, other external variables, such as TC, also indirectly influence BI. These four variables can explain 88% of the variance in BI and demonstrate a high level of predictive ability. Finally, limitations of this investigation and implications for further research are discussed.

Keywords: Technology acceptance model (TAM), technological complexity, partial least squares (PLS), perceived usefulness.

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Authors: Aimilia P. Doukeli, Athanasios S. Lioumpas, George K. Karagiannidis, Panayiotis V. Frangos, P. Takis Mathiopoulos

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In diversity rich environments, such as in Ultra- Wideband (UWB) applications, the a priori determination of the number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized by a variety of power delay profiles (PDPs). Several Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this aim, we introduce two adaptive Rake receivers, which combine a subset of the resolvable paths considering simultaneously the quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path. These schemes achieve better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The proposed receivers compromise between the power consumption, complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.

Keywords: Adaptive Rake receivers, diversity techniques, fading channels, UWB channel.

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.

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Authors: G. Rajesh, B. Vinayaga Sundaram, C. Aarthi

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In wireless sensor network, sensor node transmits the sensed data to the sink node in multi-hop communication periodically. This high traffic induces congestion at the node which is present one-hop distance to the sink node. The packet transmission and reception rate of these nodes should be very high, when compared to other sensor nodes in the network. Therefore, the energy consumption of that node is very high and this effect is known as the “funneling effect”. The tree based-data aggregation technique (TBDA) is used to reduce the energy consumption of the node. The throughput of the overall performance shows a considerable decrease in the number of packet transmissions to the sink node. The proposed scheme, TBDA, avoids the funneling effect and extends the lifetime of the wireless sensor network. The average case time complexity for inserting the node in the tree is O(n log n) and for the worst case time complexity is O(n2).

Keywords: Data Aggregation, Funneling Effect, Traffic Congestion, Wireless Sensor Network.

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Authors: D. A. K. S. Gunaratna, N. D. Kodikara, H. L. Premaratne

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Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec" comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes- characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions.

Keywords: Artificial intelligence, linear transformation and pattern recognition.

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Authors: A. Elyousfi, A. Tamtaoui, E. Bouyakhf

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The H.264/AVC standard uses an intra prediction, 9 directional modes for 4x4 luma blocks and 8x8 luma blocks, 4 directional modes for 16x16 macroblock and 8x8 chroma blocks, respectively. It means that, for a macroblock, it has to perform 736 different RDO calculation before a best RDO modes is determined. With this Multiple intra-mode prediction, intra coding of H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression standards, but computational complexity is increased significantly. This paper presents a fast intra prediction algorithm for H.264/AVC intra prediction based a characteristic of homogeneity information. In this study, the gradient prediction method used to predict the homogeneous area and the quadratic prediction function used to predict the nonhomogeneous area. Based on the correlation between the homogeneity and block size, the smaller block is predicted by gradient prediction and quadratic prediction, so the bigger block is predicted by gradient prediction. Experimental results are presented to show that the proposed method reduce the complexity by up to 76.07% maintaining the similar PSNR quality with about 1.94%bit rate increase in average.

Keywords: Intra prediction, H.264/AVC, video coding, encodercomplexity.

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Authors: Adrien Derock, Pascal Veron

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Taking into account the link between the efficiency of a detector and the complexity of a stealth mechanism, we propose in this paper a new formalism for stealth using graph theory.

Keywords: Detection, eradication, graph, rootkit, stealth.

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Authors: A. Preetha Priyadharshini, S. B. M. Priya

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In this paper, we consider the MU-MISO downlink scenario, under imperfect channel state information (CSI). The main issue in imperfect CSI is to keep the probability of each user achievable outage rate below the given threshold level. Such a rate outage constraints present significant and analytical challenges. There are many probabilistic methods are used to minimize the transmit optimization problem under imperfect CSI. Here, decomposition based large deviation inequality and Bernstein type inequality convex restriction methods are used to perform the optimization problem under imperfect CSI. These methods are used for achieving improved output quality and lower complexity. They provide a safe tractable approximation of the original rate outage constraints. Based on these method implementations, performance has been evaluated in the terms of feasible rate and average transmission power. The simulation results are shown that all the two methods offer significantly improved outage quality and lower computational complexity.

Keywords: Imperfect channel state information, outage probability, multiuser- multi input single output.

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Authors: M. Govindarajan, R. M.Chandrasekaran

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Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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Authors: Parviz Fattahi

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Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, it quit difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. In this paper, a Pareto approach to solve the multi objective flexible job shop scheduling problems is proposed. The objectives considered are to minimize the overall completion time (makespan) and total weighted tardiness (TWT). An effective simulated annealing algorithm based on the proposed approach is presented to solve multi objective flexible job shop scheduling problem. An external memory of non-dominated solutions is considered to save and update the non-dominated solutions during the solution process. Numerical examples are used to evaluate and study the performance of the proposed algorithm. The proposed algorithm can be applied easily in real factory conditions and for large size problems. It should thus be useful to both practitioners and researchers.

Keywords: Flexible job shop, Scheduling, Hierarchical approach, simulated annealing, tabu search, multi objective.

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Authors: Ja'far Alqatawna, Jawed Siddiqi, Babak Akhgar, Mohammad Hjouj Btoush

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A great deal of research works in the field information systems security has been based on a positivist paradigm. Applying the reductionism concept of the positivist paradigm for information security means missing the bigger picture and thus, the lack of holism which could be one of the reasons why security is still overlooked, comes as an afterthought or perceived from a purely technical dimension. We need to reshape our thinking and attitudes towards security especially in a complex and dynamic environment such as e- Business to develop a holistic understanding of e-Business security in relation to its context as well as considering all the stakeholders in the problem area. In this paper we argue the suitability and need for more inductive interpretive approach and qualitative research method to investigate e-Business security. Our discussion is based on a holistic framework of enquiry, nature of the research problem, the underling theoretical lens and the complexity of e-Business environment. At the end we present a research strategy for developing a holistic framework for understanding of e-Business security problems in the context of developing countries based on an interdisciplinary inquiry which considers their needs and requirements.

Keywords: e-Business Security, Complexity, Methodological considerations, interpretive qualitative research and Case study method.

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Authors: Mrs.K.Kavitha, S.Arivazhagan

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A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.

Keywords: Multi-class, Run Length features, PCA, ICA, classification and Support Vector Machines.

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Authors: Sameru Sharma, Sanjay Sharma, Derick Engles

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Multiple-input multiple-output (MIMO) systems are widely in use to improve quality, reliability of wireless transmission and increase the spectral efficiency. However in MIMO systems, multiple copies of data are received after experiencing various channel effects. The limitations on account of complexity due to number of antennas in case of conventional decoding techniques have been looked into. Accordingly we propose a modified sphere decoder (MSD-1) algorithm with lower complexity and give rise to system with high spectral efficiency. With the aim to increase signal diversity we apply rotated quadrature amplitude modulation (QAM) constellation in multi dimensional space. Finally, we propose a new architecture involving space time trellis code (STTC) concatenated with space time block code (STBC) using MSD-1 at the receiver for improving system performance. The system gains have been verified with channel state information (CSI) errors.

Keywords: Channel State Information , Diversity, Multi-Antenna, Rotated Constellation, Space Time Codes.

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Authors: Mohammad Seifi, Azura Bt. Che Soh, Noor Izzrib. Abd. Wahab, Mohd Khair B. Hassan

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Solar energy has a major role in renewable energy resources. Solar Cell as a basement of solar system has attracted lots of research. To conduct a study about solar energy system, an authenticated model is required. Diode base PV models are widely used by researchers. These models are classified based on the number of diodes used in them. Single and two-diode models are well studied. Single-diode models may have two, three or four elements. In this study, these solar cell models are examined and the simulation results are compared to each other. All PV models are re-designed in the Matlab/Simulink software and they examined by certain test conditions and parameters. This paper provides comparative studies of these models and it tries to compare the simulation results with manufacturer-s data sheet to investigate model validity and accuracy. The results show a four- element single-diode model is accurate and has moderate complexity in contrast to the two-diode model with higher complexity and accuracy

Keywords: Fill Factor (FF), Matlab/Simulink, Maximum PowerPoint (MPP), Maximum Power Point Tracker (MPPT), Photo Voltaic(PV), Solar cell, Standard Test Condition (STC).

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Authors: Yoshiaki Shikata, Nobutane Hanayama

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We present a prioritized, limited multi-server processor sharing (PS) system where each server has various capacities, and N (≥2) priority classes are allowed in each PS server. In each prioritized, limited server, different service ratio is assigned to each class request, and the number of requests to be processed is limited to less than a certain number. Routing strategies of such prioritized, limited multi-server PS systems that take into account the capacity of each server are also presented, and a performance evaluation procedure for these strategies is discussed. Practical performance measures of these strategies, such as loss probability, mean waiting time, and mean sojourn time, are evaluated via simulation. In the PS server, at the arrival (or departure) of a request, the extension (shortening) of the remaining sojourn time of each request receiving service can be calculated by using the number of requests of each class and the priority ratio. Utilising a simulation program which executes these events and calculations, the performance of the proposed prioritized, limited multi-server PS rule can be analyzed. From the evaluation results, most suitable routing strategy for the loss or waiting system is clarified.

Keywords: Processor sharing, multi-server, various capacity, N priority classes, routing strategy, loss probability, mean sojourn time, mean waiting time, simulation.

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Authors: Yoshiaki Shikata, Wataru Katagiri, Yoshitaka Takahashi

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We propose a novel prioritized limited processor-sharing (PS) rule and a simulation algorithm for the performance evaluation of this rule. The performance measures of practical interest are evaluated using this algorithm. Suppose that there are two classes and that an arriving (class-1 or class-2) request encounters n1 class-1 and n2 class-2 requests (including the arriving one) in a single-server system. According to the proposed rule, class-1 requests individually and simultaneously receive m / (m * n1+ n2) of the service-facility capacity, whereas class-2 requests receive 1 / (m *n1 + n2) of it, if m * n1 + n2 ≤ C. Otherwise (m * n1 + n2 > C), the arriving request will be queued in the corresponding class waiting room or rejected. Here, m (
1) denotes the priority ratio, and C ( ∞), the service-facility capacity. In this rule, when a request arrives at [or departs from] the system, the extension [shortening] of the remaining sojourn time of each request receiving service can be calculated using the number of requests of each class and the priority ratio. Employing a simulation program to execute these events and calculations enables us to analyze the performance of the proposed prioritized limited PS rule, which is realistic in a time-sharing system (TSS) with a sufficiently small time slot. Moreover, this simulation algorithm is expanded for the evaluation of the prioritized limited PS system with N
3 priority classes.

Keywords: PS rule, priority ratio, service-facility capacity, simulation algorithm, sojourn time, performance measures

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Authors: Parvinder Singh Sandhu, Dalwinder Singh Salaria, Hardeep Singh

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Software Reusability is primary attribute of software quality. There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. In this paper, we have devised the framework of metrics that uses McCabe-s Cyclometric Complexity Measure for Complexity measurement, Regularity Metric, Halstead Software Science Indicator for Volume indication, Reuse Frequency metric and Coupling Metric values of the software component as input attributes and calculated reusability of the software component. Here, comparative analysis of the fuzzy, Neuro-fuzzy and Fuzzy-GA approaches is performed to evaluate the reusability of software components and Fuzzy-GA results outperform the other used approaches. The developed reusability model has produced high precision results as expected by the human experts.

Keywords: Software Reusability, Software Metrics, Neural Networks, Genetic Algorithm, Fuzzy Logic.

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Authors: Nahid Ardalani, Ahmadreza Khoogar, H. Roohi

Abstract:

In this paper we compare the response of linear and nonlinear neural network-based prediction schemes in prediction of received Signal-to-Interference Power Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The nonlinear predictor is Multilayer Perceptron MLP and the linear predictor is an Adaptive Linear (Adaline) predictor. We solve the problem of complexity by using the Minimum Mean Squared Error (MMSE) principle to select the optimal predictors. The optimized Adaline predictor is compared to optimized MLP by employing noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an urban environment. The results show that the Adaline predictor can estimates SIR with the same error as MLP when the user has the velocity of 5 km/h and 60 km/h but by increasing the velocity up-to 120 km/h the mean squared error of MLP is two times more than Adaline predictor. This makes the Adaline predictor (with lower complexity) more suitable than MLP for closed-loop power control where efficient and accurate identification of the time-varying inverse dynamics of the multi path fading channel is required.

Keywords: Power control, neural networks, DS/CDMA mobilecommunication systems.

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Authors: Prasansha Kumari

Abstract:

This paper intends to identify and analyze customer adoption and attitudes towards mobile banking facilities. The study uses six perceived characteristics of innovation that can be used to form a favorable or unfavorable attitude toward an innovation, namely: Relative advantage, compatibility, complexity, trailability, risk, and observability. Collected data were analyzed using Pearson Chi-Square test. The results showed that mobile bank users were predominantly males. There is a growing trend among young, educated customers towards converting to mobile banking in Sri Lanka. The research outcomes suggested that all the six factors are statistically highly significant in influencing mobile banking adoption and attitude formation towards mobile banking in Sri Lanka. The major reasons for adopting mobile banking services are the accessibility and availability of services regardless of time and place. Over the 75 percent of the respondents mentioned that savings in time and effort and low financial costs of conducting mobile banking were advantageous. Issue of security was found to be the most important factor that motivated consumer adoption and attitude formation towards mobile banking. Main barriers to mobile banking were the lack of technological skills, the traditional cash‐carry banking culture, and the lack of awareness and insufficient guidance to using mobile banking.

Keywords: Compatibility, complexity, mobile banking, risk.

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