Showing papers by "Aly A. Fahmy published in 2015"

A Discrete Bat Algorithm for the Community Detection Problem

Abstract: Community detection in networks has raised an important research topic in recent years. The problem of detecting communities can be modeled as an optimization problem where a quality objective function that captures the intuition of a community as a set of nodes with better internal connectivity than external connectivity is selected to be optimized. In this work the Bat algorithmwas used as an optimization algorithm to solve the community detection problem. Bat algorithm is a new Nature-inspired metaheuristic algorithm that proved its good performance in a variety of applications. However, the algorithm performance is influenced directly by the quality function used in the optimization process. Experiments on real life networks show the ability of the Bat algorithm to successfully discover an optimized community structure based on the quality function used and also demonstrate the limitations of the BA when applied to the community detection problem.

Community Detection Algorithm Based on Artificial Fish Swarm Optimization

Abstract: Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this paper Artificial Fish Swarm optimization (AFSO) has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures and other well-known methods. Experiments on real life networks show the capability of the AFSO to successfully find an optimized community structure based on the quality function used.

Analysis and feedback of erroneous Arabic verbs

Abstract: Arabic language is strongly structured and considered as one of the most highly inflected and derivational languages. Learning Arabic morphology is a basic step for language learners to develop language skills such as listening, speaking, reading, and writing. Arabic morphology is non-concatenative and provides the ability to attach a large number of affixes to each root or stem that makes combinatorial increment of possible inflected words. As such, Arabic lexical (morphological and phonological) rules may be confusing for second language learners. Our study indicates that research and development endeavors on spelling, and checking of grammatical errors does not provide adequate interpretations to second language learners’ errors. In this paper we address issues related to error diagnosis and feedback for second language learners of Arabic verbs and how they impact the development of a web-based intelligent language tutoring system. The major aim is to develop an Arabic intelligent language tutoring system that solves these issues and helps second language learners to improve their linguistic knowledge. Learners are encouraged to produce input freely in various situations and contexts, and are guided to recognize by themselves the erroneous functions of their misused expressions. Moreover, we proposed a framework that allows for the individualization of the learning process and provides the intelligent feedback that conforms to the learner's expertise for each class of error. Error diagnosis is not possible with current Arabic morphological analyzers. So constraint relaxation and edit distance techniques are successfully employed to provide error-specific diagnosis and adaptive feedback to learners. We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic weak verbs formed using complex morphological rules. As a proof of concept, we have implemented the components that diagnose learner's errors and generate feedback which have been effectively evaluated against test data acquired from real teaching environment. The experimental results were satisfactory, and the performance achieved was 74.34 percent in terms of recall rate.

Arabic Anaphora Resolution: Corpus of the Holy Qur'an Annotated with Anaphoric Information

Abstract: This paper reports on compiling a large Arabic corpus of the Holy Qur'an script, annotated with anaphoric relation and other anaphoric information, providing multi-dimensional feature vector rich with most of basic anaphoric information needed in statistical anaphora resolution systems. About 24,653 personal pronouns are tagged with their antecedents and other anaphoric information like distance between the anaphor and its antecedent in terms of verses, words, and segments, gender, number, person, and other information which can be used to implement the feature vector of a statistical anaphora resolution system. In addition, it describes the compilation of a bank of sentence patterns consisting of 481 antecedent patterns; each pattern represents particular part-of-speech tag corresponding to its antecedent phrase. The aim is to provide a valuable resource that enables future research in Arabic anaphora resolution, and help in future work in analyzing Quran script. Also, it will be a valuable resource that can be used for training and testing anaphora resolution systems, and evaluating. General Terms Natural language processing, Computational linguistics, Anaphora resolution, Corpus development.

Community detection in social networks using logic-based probabilistic programming

Abstract: Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure-function relationship; therefore, detecting communities can be a way to identify substructures that could correspond to important functions. Social networks can be formalised by a generative process in which interactions between actors are generated based on some assumptions, i.e., a model with some parameters. Based on that idea, a probabilistic inference technique can be used to infer the community structure of the network. We propose a generative model to describe how network interactions are generated and show the use of a logic-based probabilistic modelling technique such as PRISM, to solve the community detection problem. The proposed model works well with directed and undirected networks, and with weighted and un-weighted networks. We use the deterministic annealing expectation maximisation algorithm in the learning process, which prove to yield a very promising result when is applied to the community detection problem.

Stakeholders of Egypt Public Universities and Research Institutions: An Information Technology Governance Perspective

Abstract: Egypt public higher education and institutions (HEIs) have recognized the need to reassess their functions of teaching, research, and community services. Successful organizations are these providing value for their stakeholders. HEIs are indifference and their management need to identify their stakeholders’ needs and to reposition their institutions towards the fulfillment of these needs. On their quest to enhance their competencies, Information Technology (IT) plays an important role of these institutions. Consequently, governance of It (or ITG) becomes a necessity. From the view point of, this paper aims to identify Egypt public HEIs stakeholders and their needs as the first and necessary step towards the successful implementation of ITG in Egypt public HEIs.