Shafay Shamail

Determinants of Electronic Commerce in Pakistan: Preliminary Evidence from Small and Medium Enterprises

This study investigates the extent of e-commerce (EC) adoption along with the factors that predict EC adoption among 54 SMEs in Pakistan. The study found that 84% of the organizations have an Internet account; whereas 46% of the organizations have claimed average to above average adoption of EC (mean = 2.76). Several of the organizational factors such as organizational culture, management support and motivation to adopt EC along with technological and environmental factors were studied. Statistics further reveal that 59% of the organizations have either an in-house or vendor supported Web server and 67% of the surveyed organizations have a homepage. Factors such as perceived benefits, task variety, organizational culture and government support remain the significant predictors of EC adoption. All other factors including management support remained insignificant. Based upon findings, we made some recommendations to policy makers and relevant authorities for devising and implementation of a strategic plan t...

Enabling Self-Configuration in Autonomic Systems Using Case-Based Reasoning with Improved Efficiency

Autonomic computing is an emerging philosophy which promises to enable self-management capabilities in software systems. These self-management properties include self-configuration, self-healing, self-protection, self-optimization, self-awareness and self-governance. Enabling any of these properties in software systems is an open challenge. Exhibiting such self-management behavior is a continuous process in the software life cycle. Case-based reasoning is a problem solving methodology which exploits past experience. Past experience is maintained in the form of problem-solution pairs, also called cases. On the arrival of new problem, solution of past similar problems is used after appropriate adaptation. This problem solving technique can be used to achieve some of the properties of autonomic systems based on experience. To find this solution, entire experience space is searched which reduces efficiency. To overcome this efficiency problem, we restrict the fast growth of case repository, so that every time we have to search a very limited number of cases. We applied the proposed approach on a simulation of Autonomic Forest Fire application for self-configuration capability. Our results show that the proposed approach is quite promising in terms of accuracy as well as efficiency.

Survey of Frameworks, Architectures and Techniques in Autonomic Computing

A variety of frameworks, architectures and techniques have been proposed and used in the field of autonomic computing for self-management. There are also many applications and systems available that exhibit autonomic behavior. However all techniques and applications do not explicitly use autonomic or self-* terminologies to describe their autonomic characteristics. In this survey paper, a review of existing autonomic frameworks, architectures and self-management techniques is presented. It gives a panoramic picture of the researchfields of autonomic computing. Further analysis is done to categorize the surveyed frameworks, architectures, infrastructures and techniques.

On Vowels Segmentation and Identification Using Formant Transitions in Continuous Recitation of Quranic Arabic

This paper provides an analysis of cues to identify Arabic vowels. A new algorithm for vowel identification has been developed that uses formant frequencies. The algorithm extracts the formants of already segmented recitation audio files and recognizes the vowels on the basis of these extracted formants. The investigation has been done in context of recitation principles of Holy Quran which are commonly known as Tajweed rules. Primary objective of this work is to be able to identify zabar /a/, zair /e/ and pesh /u/ mistakes of the recitor during the recitation. Acoustic Analysis was performed on 150 samples of different recitors and a corpus comprising recitation of five experts was used to validate the results. The vowel identification system developed here has shown up to 90% average accuracy on continuous speech files comprising around 1000 vowels.

Software quality prediction techniques: A comparative analysis

There are many software quality prediction techniques available in literature to predict software quality. However, literature lacks a comprehensive study to evaluate and compare various prediction methodologies so that quality professionals may select an appropriate predictor. To find a technique which performs better in general is an undecidable problem because behavior of a predictor also depends on many other specific factors like problem domain, nature of dataset, uncertainty in the available data etc. We have conducted an empirical survey of various software quality prediction techniques and compared their performance in terms of various evaluation metrics. In this paper, we have presented comparison of 30 techniques on two standard datasets.

Achieving Self-configuration Capability in Autonomic Systems Using Case-Based Reasoning with a New Similarity Measure

A lot of activities inside human body are carried out intelligently without the explicit intervention of human itself, e.g. various actions of nervous systems, blood circulation system etc. Inspired from these natural systems, autonomic computing is an emerging concept which promises to enable such kind of self-management capabilities inside software systems. Case-based reasoning (CBR) is a methodology to solve current problems using the solutions of past problems of the similar nature. In this paper, we propose to use CBR to achieve self-configuration in autonomic systems. We introduce a new similarity measure to find nearest neighbors. We have also suggested the case preparation, case retrieval and case reuse and refinement methods to enable self-configuration in autonomic systems. To support our proposed methodology, we illustrate a case-study of Autonomic Forest Fire Application.

Eliminating process of normalization in relational database design

The relational database design approach requires the process of normalization in order to minimize data redundancy and update anomalies in the relational schema. Algorithms defined in normalization theory depend upon various dependencies namely functional, multivalued, join and inclusion dependencies that should be carefully defined for a database application. Identification of these dependencies and a minimal cover is a complex and time consuming task for almost all practical problems. This work discusses how the normalization process can be eliminated from the required steps of database design. It explores various constructs of entity relationship diagram (ERD) and their transformation to relational schema. This work elaborates how un-normalized relations are created during the entity relationship (ER) model to relational schema transformation. A set of rules is presented which if followed at the stage of conceptual modeling would always generate a relational schema that satisfies normal forms up to Boyce-Codd normal form (BCNF), thus eliminating the need for normalization. The motivation behind this paper is to save the database designers valuable time and effort otherwise required in defining dependencies, in finding a minimal cover and in normalizing a given relational schema.

Comparative Study of Various Artificial Intelligence Techniques to Predict Software Quality

Software quality prediction models are used to identify software modules that may cause potential quality problems. These models are based on various metrics available during the early stages of software development life cycle like product size, software complexity, coupling and cohesion. In this survey paper, we have compared and discussed some software quality prediction approaches based on Bayesian belief network, neural networks, fuzzy logic, support vector machine, expectation maximum likelihood algorithm and case-based reasoning. This study gives better comparative insight about these approaches, and helps to select an approach based on available resources and desired level of quality.

Continuous Arabic Speech Segmentation using FFT Spectrogram

This paper describes a phoneme segmentation algorithm that uses fast Fourier transform (FFT) spectrogram. The algorithm has been implemented and tested for utterances of continuous Arabic speech of 10 male speakers that contain almost 2346 phonemes in total. The recognition system determines the phoneme boundaries and identifies them as pauses, vowels and consonants. The system uses intensity and phoneme duration for separating pauses from consonants. Intensity in particular is used to detect two specific consonants (/r/, /hf) when they are not detected through the spectrographic information. Segmentation accuracy of 95.39% for the overall system has been achieved

Ineffectiveness of Use of Software Science Metrics as Predictors of Defects in Object Oriented Software

Software science metrics (SSM) have been widely used as predictors of software defects. The usage of SSM is an effect of correlation of size and complexity metrics with number of defects. The SSM have been proposed keeping in view the procedural paradigm and structural nature of the programs. There has been a shift in software development paradigm from procedural to object oriented (OO) and SSM have been used as defect predictors of OO software as well. However, the effectiveness of SSM in OO software needs to be established. This paper investigates the effectiveness of use of SSM for: a)classification of defect prone modules in OO software b) prediction of number of defects. Various binary and numeric classification models have been applied on dataset kc1 with class level data to study the role of SSM. The results show that the removal of SSM from the set of independent variables does not significantly affect the classification of modules as defect prone and the prediction of number of defects. In most of the cases the accuracy and mean absolute error has improved when SSM were removed from the set of independent variables. The results thus highlight the ineffectiveness of use of SSM in defect prediction in OO software.