Abdulhussain E. Mahdi

Some new horizons in magnetic sensing: high-Tc SQUIDs, GMR and GMI materials

Driven by rapid progress in microelectronics and thin film technologies, magnetic sensors development continues to expand. This paper presents issues related to the principles, categorisation and applications of magnetic sensors. Special attention is paid to two types of sensors and their present and future impact: superconducting quantum interference devices (SQUID) magnetometers with their unsurpassed sensitivity, and giant magnetoresistance (GMR)/giant magnetoimpedance (GMI) based sensors as the most promising technology. A review of recent developments in SQUID technology and a discussion of limitations and aspects that could contribute to the wider acceptance of this technology, are presented. The giant magnetoresistance and giant magnetoimpedance effects have already found applications in magnetic sensing and have promise in other applications. Their unique characteristics and miniaturisation potential have contributed to the rapid acceptance of these technologies. The article describes the principles of the GMR and GMI effects along with recent developments in these technologies particularly in manufacturing techniques and materials.

New output-based perceptual measure for predicting subjective quality of speech

The paper proposes a new output-based system for prediction of subjective speech quality, and evaluates its performance. The system is based on computing objective distance measures, such as the median minimum distance, between perceptually-based parameter vectors representing the voiced parts of the speech signal and appropriately matched reference vectors extracted from a pre-formulated codebook. The distance measures are then mapped into equivalent mean opinion scores (MOS) using regression. The codebook of the system is formed by optimally clustering the large number of speech parameter vectors extracted from an undistorted source speech database. The required clustering and matching processes are achieved by using an efficient data mining technique known as the self-organising map. The perceptually-based speech parameters are derived using perceptual linear prediction (PLP) and bark spectrum analyses. Reported evaluation results show that the proposed system is robust against speaker, utterance and distortion variations.

Output-based objective speech quality measure using self-organizing map

This paper proposes a new output-based method for assessing speech quality and evaluates its performance. The measure is based on comparing the output speech to an artificial reference signal representing the closest match from an appropriately formulated codebook. The codebook holds a number of optimally clustered speech parameter vectors, extracted from an undistorted clean speech database, and provides a reference for computing objective auditory distance measures for distorted speech. The median minimum distance is used as a measure of the objective auditory distance. The required clustering and matching processes are achieved by using an efficient data mining technique known as the self-organising map. Speech parameters derived from perceptual linear prediction (PLP) and bark spectrum analysis are used to provide speaker independent information as required by an output-based objective approach for speech quality measure.

A citation-based approach to automatic topical indexing of scientific literature

Topical indexing of documents with keyphrases is a common method used for revealing the subject of scientific and research documents to both human readers and information retrieval tools, such as search engines. However, scientific documents that are manually indexed with keyphrases are still in the minority. This article describes a new unsupervised method for automatic keyphrase extraction from scientific documents which yields a performance on a par with human indexers. The method is based on identifying references cited in the document to be indexed and, using the keyphrases assigned to those references, for generating a set of high-likelihood keyphrases for the document. We have evaluated the performance of the proposed method by using it to automatically index a third-party testset of research documents. Reported experimental results show that the performance of our method, measured in terms of consistency with human indexers, is competitive with that achieved by state-of-the-art supervised methods.

Automatic keyphrase annotation of scientific documents using Wikipedia and genetic algorithms

Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents to both human readers and information retrieval systems. This article describes a machine learning-based keyphrase annotation method for scientific documents that utilizes Wikipedia as a thesaurus for candidate selection from documents’ content. We have devised a set of 20 statistical, positional and semantical features for candidate phrases to capture and reflect various properties of those candidates that have the highest keyphraseness probability. We first introduce a simple unsupervised method for ranking and filtering the most probable keyphrases, and then evolve it into a novel supervised method using genetic algorithms. We have evaluated the performance of both methods on a third-party dataset of research papers. Reported experimental results show that the performance of our proposed methods, measured in terms of consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised and unsupervised methods.

An unsupervised approach to automatic classification of scientific literature utilizing bibliographic metadata

This article describes an unsupervised approach for automatic classification of scientific literature archived in digital libraries and repositories according to a standard library classification scheme. The method is based on identifying all the references cited in the document to be classified and, using the subject classification metadata of extracted references as catalogued in existing conventional libraries, inferring the most probable class for the document itself with the help of a weighting mechanism. We have demonstrated the application of the proposed method and assessed its performance by developing a prototype software system for automatic classification of scientific documents according to the Dewey Decimal Classification scheme. A dataset of 1000 research articles, papers, and reports from a well-known scientific digital library, CiteSeer, were used to evaluate the classification performance of the system. Detailed results of this experiment are presented and discussed.

Perceptual non‐intrusive speech quality assessment using a self‐organizing map

Purpose – This paper seeks to propose a new non‐intrusive method for the assessment of speech quality of voice communication systems and evaluate its performance.Design/methodology/approach – The method is based on measuring perception‐based objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre‐formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into equivalent subjective mean opinion scores (MOSs). The required clustering and matching processes are achieved by an efficient data‐mining tool known as the self‐organizing map (SOM). The proposed method was examined using a wide range of distortion including speech compression, wireless channel impairments, VoIP channel impairments, and modifications to the signal from features such as AGC.Findings – The experimental results reported ...

Automatic mapping of user tags to Wikipedia concepts

The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding Wikipedia articles, as well-formed concepts, offers multifaceted benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags and improvement of the interchangeability of existing subject metadata. In this article, we propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. We have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website, StackOverflow.com, as our test platform. Currently, around 20 million posts in StackOverflow are annotated with about 37,000 unique user tags, from which we have chosen a subset of 1256 tags to evaluate the accuracy performance of our proposed mapping method. We have evaluated the performance of our method using the standard information retrieval measures of precision, recall and F1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F1 scores as high as 99.6% were achieved.

A new text representation scheme combining Bag-of-Words and Bag-of-Concepts approaches for automatic text classification

This paper introduces a new approach to creating text representations and apply it to a standard text classification collections. The approach is based on supplementing the well-known Bag-of-Words (BOW) representational scheme with a concept-based representation that utilises Wikipedia as a knowledge base. The proposed representations are used to generate a Vector Space Model, which in turn is fed into a Support Vector Machine classifier to categorise a collection of textual documents from two publically available datasets. Experimental results for evaluating the performance of our model in comparison to using a standard BOW scheme and a concept-based scheme, as well as recently reported similar text representations that are based on augmenting the standard BOW approach with concept-based representations.

New single-ended objective measure for non-intrusive speech quality evaluation

This article proposes a new output-based method for non-intrusive assessment of speech quality of voice communication systems and evaluates its performance. The method requires access to the processed (degraded) speech only, and is based on measuring perception-motivated objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre-formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into objective Mean Opinion listening quality scores. An efficient data-mining tool known as the self-organizing map (SOM) achieves the required clustering and mapping/reference matching processes. In order to obtain a perception-based, speaker-independent parametric representation of the speech, three domain transformation techniques have been investigated. The first technique is based on a perceptual linear prediction (PLP) model, the second utilises a bark spectrum (BS) analysis and the third utilises mel-frequency cepstrum coefficients (MFCC). Reported evaluation results show that the proposed method provides high correlation with subjective listening quality scores, yielding accuracy similar to that of the ITU-T P.563 while maintaining a relatively low computational complexity. Results also demonstrate that the method outperforms the PESQ in a number of distortion conditions, such as those of speech degraded by channel impairments.