Facilitating file retrieval on resource limited devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.Education Commission of Pakistan and the University of Engineering & Technology, Peshawa

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

Towards the Optimal Use of Machine Learning Algorithms in Text Mining: A Quick Review

This paper aims to provide a quick review to jump-start the research in the field of text mining where Machine Learning (ML) algorithms have been used and several accomplishments have been reported by the research community. There are different categories of text mining, and the implementation of ML algorithms and techniques have been supported in the literature to give promising results. However, in this area of study, most of the research activities in terms of time and efforts are consumed during the initial stages where implementations and experiments are carried out to evaluate various combinations. The accomplishments in this field can be further advanced by presenting early investigations concisely and analytically. Thus, the benefits of this paper are threefold: first, it will provide a platform for the new researchers to start quickly with a shorter literature review and knowing more precisely about the combinations of text mining and ML; secondly, clear analysis has been presented about the text mining categories where the performance of ML algorithms have been reported successful; and lastly, the problems have been identified for which the algorithms were used in various studies. This will enable the new researchers to directly target the problem instead of implementing the existing techniques. With the help of well-structured questions, the results are more analytical and present multidimensional views to this research issue. Main findings include that ML has been widely used in document classification and Support Vector Machine (SVM) is the most successful algorithm reported

Growth Parameters for Films of Hydrothermally Synthesized One-Dimensional Nanocrystals of Zinc Oxide

Zinc oxide has been the focus of material research due to its potential applications in a variety of novel fields. The material exhibits anisotropic growth in the form of single crystal rods/wires of length in microns and thickness in several tens of nanometers through a facile and low temperature hydrothermal route wherein size, morphology, orientation, and growth rate are strongly dependent on a number of synthesis parameters. In this review article we intend to present/discuss the effects of important growth parameters of zinc oxide that have been reported in the literature. These parameters include concentration of the precursor solution, growth time, role of hexamine, synthesis temperature, pH of the precursor, and seeding layer deposited on a substrate

A Taxonomy of Text Mining

With a rapid increase in the volume of textual data on the Internet, extracting useful information through innovative text mining techniques has become crucial. In this context, terminology jargon in the literature related to text-mining creates ambiguity and has made it very difficult for researchers to focus in a specific direction and bring innovation. For example, review mining and opinion mining may have different applications, however, from a technical perspective, they are very similar. In this paper, we propose a classification of the text mining terminologies from the perspectives of technical and text-mining processes. The classification is based on a comprehensive literature survey and analysis. This research study presents a clear classification of text mining terminologies based on technical and text mining processes to resolve the issue of terminology jargon. By utilizing the proposed classification, researchers will be able to easily choose a specific direction instead of diverging amongst similar research problems, thereby, driving innovation. Further, the proposed classification will help advance and improve the overall research progress in all text-mining related fields

A Review And Comparison Of The Traditional Collaborative And Online Collaborative Techniques For Requirement Elicitation

Requirement elicitation is one of the major phases of the software development life cycle. As per authors knowledge, among many reviews, there is no review available on a comparison between Online Collaborative Requirement Engineering (OCRE) and Traditional Collaborative Requirement Engineering (TCRE) techniques. In this review paper, OCRE and TCRE techniques are reviewed in terms of research methods employed in the related research. In addition, the techniques are compared in terms of software tools used in elicitation of the requirements and the types of software developed by using these techniques. The advantages and disadvantages mentioned in the literature are also highlighted in this research. The relevant papers were selected in a systematic way and data is extracted into the Excel files for analysis. The results revealed some interesting findings like the most important techniques in both OCRE and TCRE are literature review followed by experimentatio

Semantic file annotation and retrieval on mobile devices

Abstract. The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices such as mobile phones and PDAs. However, it has become a challenging issue for users to efficiently and effectively search for files of interest in a mobile environment involving a large number of mobile nodes. This paper presents SemFARM framework which facilitates users to publish, annotate and retrieve files which are geographically distributed in a mobile network enabled by Bluetooth. The SemFARM framework is built on semantic web technologies in support of file retrieval on low-end mobile devices. A generic ontology is developed which defines a number of keywords, their possible domains and properties. Based on semantic reasoning, similarity degrees are computed to match user queries with published file descriptions. The SemFARM prototype is implemented using the Java mobile platform (J2ME). The performance of SemFARM is evaluated from a number of aspects in comparison with traditional mobile file systems and enhanced alternatives. Experimental results are encouraging showing the effectiveness of SemFARM in file retrieval. We can conclude that the use of semantic web technologies have facilitated file retrieval in mobile computing environments maximizing user satisfaction in searching for files of interest

An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively

A novel image encryption scheme based on Arnold cat map, Newton-Leipnik system and Logistic Gaussian map

In the existing literature, numerous chaos-based multimedia encryption schemes have been presented. Benefiting from inherent properties such as ergodicity and key-sensitivity; this can potentially improve non-linearity in encrypted data. This paper introduces a hybrid scheme that encrypt color images using multiple chaotic maps such as the two-dimensional Arnold cat map (ACM), Newton-Leipnik dynamic system (NLDS), and a modified version of the Logistic Gaussian chaotic system (LOGAS). To make the scheme plaintext dependent, the input parameters (keys) of the chaotic maps are determined by passing the plaintext color image layers through SHA-512. Initially, the color image layers are shuffled by applying two-dimensional Arnold map scrambling. The permuted pixels were encrypted through a pseudo-random number sequences (PRNS) generated from the NLDS. To strengthen the suggested approach’s security level, the modified LOGAS were utilized to generate dynamic substitution boxes (S-boxes) to substitute the encrypted pixels. Moreover, the proposed approach is tested through various statistical tests. The adjacent pixel correlation and entropy value are close to 0 and 8, respectively. The NPCR and UACI tests values are 99% and 33%, respectively. The encrypted images histograms are flat which is close to optimal value, and the computational time is less than 2.0 sec for the proposed scheme. Thus, these findings indicate the effectiveness and better reliability of the proposed system