Khaled Nagi

Mobile agents for locating documents in ad hoc networks

The wide availability of mobile devices equipped with wireless communication capabilities results in highly dynamic communities of mobile users. An interesting application in such an environment is decentralized peer-to-peer file sharing. Locating files in a highly dynamic network while minimizing the consumption of scarce resources is challenging. Since the availability of files changes significantly over time, an asynchronous approach to searching is promising. In this paper, we show why existing file sharing systems cannot be used here and introduce our approach based on mobile agents.

Designing multi-agent unit tests using systematic test design patterns-(extended version)

Software agents are the basic building blocks in many software systems especially those based on artificial intelligence methods, e.g., reinforcement learning based multi-agent systems (MASs). However, testing software agents is considered a challenging problem. This is due to the special characteristics of agents which include its autonomy, distributed nature, intelligence, and heterogeneous communication protocols. Following the test-driven development (TDD) paradigm, we present a framework that allows MAS developers to write test scenarios that test each agent individually. The framework relies on the concepts of building mock agents and testing common agent interaction design patterns. We analyze the most common agent interaction patterns including pair and mediation patterns in order to provide stereotype implementation for their corresponding test cases. These implementations serve as test building blocks and are provided as a set of ready-for-reuse components in our repository. This way, the developer can concentrate on testing the business logic itself and spare him/her the burden of implementing tests for the underlying agent interaction patterns. Our framework is based on standard components such as the JADE agent platform, the JUnit framework, and the eclipse plug-in architecture. In this paper, we present in details the design and function of the framework. We demonstrate how we can use the proposed framework to define more stereotypes in the code repository and provide a detailed analysis of the code coverage for our designed stereotype test code implementations.

HadoopMutator: A Cloud-Based Mutation Testing Framework

Mutation testing is a software engineering methodology where code mutation is used to assess the quality of a testing technique. Mutation testing is carried out by injecting errors in the code and measuring the ability of a testing tool to detect these errors. However, it is a time-consuming process, as tests need to be run on many variants of the code, called mutants. Each mutant represents a version of the code under test, with an injected error. In this paper, we propose HadoopMutator; a cloud-based mutation testing framework that reuses the MapReduce programming model in order to speed up the generation and testing of mutants. We show, through experimentation, that we can significantly enhance the performance of automated mutation testing and provide a scalable solution that is applicable for large-scale software projects. Based on two use cases, we show that the performance can be enhanced 10 folds, on average, using our proposed framework. By treating source code as data, our work paves the way for new reuse opportunities of the novel data-centric frameworks.

Bringing search engines to the cloud using open source components

The usage of search engines is nowadays extended to do intelligent analytics of petabytes of data. With Lucene being at the heart of the vast majority of information retrieval systems, several attempts are made to bring it to the cloud in order to scale to big data. Efforts include implementing scalable distribution of the search indices over the file system, storing them in NoSQL databases, and porting them to inherently distributed ecosystems, such as Hadoop. We evaluate the existing efforts in terms of distribution, high availability, fault tolerance, manageability, and high performance. We believe that the key to supporting search indexing capabilities for big data can only be achieved through the use of common open-source technology to be deployed on standard cloud platforms such as Amazon EC2, Microsoft Azure, etc. For each approach, we build a benchmarking system by indexing the whole Wikipedia content and submitting hundreds of simultaneous search requests. We measure the performance of both indexing and searching operations. We stimulate node failures and monitor the recoverability of the system. We show that a system built on top of Solr and Hadoop has the best stability and manageability; while systems based on NoSQL databases present an attractive alternative in terms of performance.

HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase.

Question Answering over knowledge-based data is one of the most important Natural Language Processing tasks. Despite numerous efforts that have been made in this field, it is not yet in the mainstream. Question Answering can be formulated as a Relation Extraction task between the question focus entity and the expected answer. Therefore, it requires high accuracy to solve a dual problem where the relation and answer are unknown. In this work, we propose a HybQA, a Hybrid Relation Extraction system to provide high accuracy for the Relation Extraction and the Question Answering tasks over Freebase. We propose a hybrid model that combines different types of state-of-the-art deep networks that capture the relation type between the question and the expected answer from different perspectives and combine their outputs to provide accurate relations. We then use a joint model to infer the possible relation and answer pairs simultaneously. However, since Relation Extraction might still be prone to errors due to the large size of the knowledge-base corpus (Freebase), we finally use evidence from Wikipedia as an unstructured knowledge base to select the best relation-answer pair. We evaluate the system on WebQuestions data and show that the system achieves a statistical significant improvement over the existing state-of-the-art models and provides the best accuracy which is 57%.

AlQuAnS - An Arabic Language Question Answering System.

Building Arabic Question Answering systems is a challenging problem compared to their English counterparts due to several limitations inherent in the Arabic language and the scarceness of available Arabic training datasets. In our proposed Arabic Question Answering system, we combine several previously successful algorithms and add a novel approach to the answer extraction process that has not been used by any Arabic Question Answering system before. We use the state-of-the-art MADAMIRA Arabic morphological analyser for preprocessing questions and retrieved passages. We also enhance and extend the question classification and use the Explicit Semantic Approach (ESA) in the passage retrieval process to rank passages that most probably contain the correct answer. We also introduce a new answer extraction pattern, which matches the patterns formed according to the question type with the sentences in the retrieved passages in order to provide the correct answer. A performance evaluation study shows that our system gives promising results compared to other existing Arabic Question Answering systems, especially with the newly introduced answer extraction

Open-Source Search Engines in the Cloud

The key to the success of the analysis of petabytes of textual data available at our fingertips is to do it in the cloud. Today, several extensions exist that bring Lucene, the open-source de facto standard of textual search engine libraries, to the cloud. These extensions come in three main directions: implementing scalable distribution of the indices over the file system, storing them in NoSQL databases, and porting them to inherently distributed ecosystems. In this work, we evaluate the existing efforts in terms of distribution, high availability, fault tolerance, manageability, and high performance. We are committed to using common open-source technology only. So, we restrict our evaluation to publicly available open-source libraries and eventually fix their bugs. For each system under investigation, we build a benchmarking system by indexing the whole Wikipedia content and submitting hundreds of simultaneous search requests. By measuring the performance of both indexing and searching operations, we report of the most favorable constellation of open-source libraries that can be installed in the cloud.

ArabRelat: Arabic Relation Extraction using Distant Supervision

Relation Extraction is an important preprocessing task for a number of text mining applications, including: Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a novel Arabic relation extraction method that leverages linguistic features of the Arabic language in Web data to infer relations between entities. Due to the lack of labeled Arabic corpora, we adopt the idea of distant supervision, where DBpedia, a large database of semantic relations extracted from Wikipedia, is used along with a large unlabeled text corpus to build the training data. We extract the sentences from the unlabeled text corpus, and tag them using the corresponding DBpedia relations. Finally, we build a relation classifier using this data which predicts the relation type of new instances. Our experimental results show that the system reaches 70% for the F-measure in detecting relations.

Applying Simple Ontology Relations for Receiving Better Recommendations

Huge number of documents must be shared in virtual organizations; which render artifact recommender systems indispensable. Recommender systems use several information retrieval techniques to enhance the quality of their results. However, the problem arises when a user tries to search for some information in his/her peers’ exposed data due to the difference in classification systems in use. The seeker categories must be matched with the responders categories. In this work, we purpose a way to enhance the recommendation process based on using simple implicit ontology relations. This helps in recognizing better matched categories in the exposed data. We show that this approach improves the quality of the results using two different real-life datasets.

Performance analysis of locating files asynchronously in ad-hoc networks

Summary form only given. Peer-to-peer (P2P) file sharing systems as well as mobile ad hoc networks (MANETs) possess the same decentralized nature. However, P2P and MANETs operate on different network layers, a combination of both appears to be promising. In this paper, we study the use of mobile agents for locating documents asynchronously in this environment, and compare it to its counterpart of locating document synchronously. We are interested in locating more documents even if this takes a longer time. Meanwhile, we do not want to flood the network with requests, or misuse the resources at its nodes. The performance of the system is analyzed by simulating an ad-hoc network, studying the characteristics of this network and comparing it using both synchronous and asynchronous approaches in locating documents. The asynchronous approach shows more success in locating far-from-reach documents, while maintaining a very reasonable level of network traffic and resource consumption.