Development of self-archiving tools to support archiving, analysis and re-use of qualitative data

The potential to share and re-use qualitative archived data has garnered much interest in recent years. This increased attention can be attributed mainly to advances in both data documentation standards and digital archiving technologies, which provide users with the ability to archive, share and disseminate qualitative research materials. However, there remain theoretical and epistemological barriers to and implications for the sharing and re-use of qualitative study data. One way to address these issues is by studying research practices (with practitioners’ active involvement), in combination with developing software tools that support digital archiving of qualitative studies. Semantic technologies, combined with metadata standards and documentation schemas have the potential to enhance qualitative data documentation, archiving and analysis. In fact, it has been established that data documentation is one of the key elements that enables data archiving. The use of appropriate standard documentation frameworks is crucial to data archives’ exposure and has a direct impact on the discoverability, search and retrieval of archived data. The technological aspect of this study has been the development of a self-archiving toolkit that makes use of such technologies. The purpose of this work was to allow users, with varying levels of research experience (e.g. from undergraduate student researchers up to more experienced senior researchers) to avail of the benefits offered by qualitative digital archiving. To complement the technological developments undertaken, the present study also explored the practices of different researchers: undergraduate student researchers, researchers involved in teaching research-oriented modules, as well as senior researchers. This exploration focused on the collection, organisation, analysis and presentation of qualitative data and how these relate to and can be supported by digital archiving to enable researchers to organise, disseminate, and visualise research collections

Context-Aware Service Creation On The Semantic Web

With the increase of the computational power of mobile devices, their new capabilities and the addition of new context sensors, it is possible to obtain more information from mobile users and to offer new ways and tools to facilitate the content creation process. All this information can be exploited by the service creators to provide mobile services with higher degree of personalization that translate into better experiences. Currently on the web, many data sources containing UGC provide access to them through classical web mechanisms (built on a small set of standards), that is, custom web APIs that promote the fragmentation of the Web. To address this issue, Tim Berners-Lee proposed the Linked Data principles to provide guidelines for the use of standard web technologies, thus allowing the publication of structured on the Web that can be accessed using standard database mechanisms. The increase of Linked Data published on the web, increases opportunities for mobile services take advantage of it as a huge source of data, information and knowledge, either user-generated or not. This dissertation proposes a framework for creating mobile services that exploit the context information, generated content of its users and the data, information and knowledge present on the Web of Data. In addition we present, the cases of different mobile services created to take advantage of these elements and in which the proposed framework have been implemented (at least partially). Each of these services belong to different domains and each of them highlight the advantages provided to their end user

Evaluating performance for procurement: A structured method for assessing the usability of future speech interfaces

Procurement is a process by which organizations acquire equipment to enhance the effectiveness of their operations. Equipment will only enhance effectiveness if it is usable for its purpose in the work environment, i.e. if it enables tasks to be performed to the desired quality with acceptable costs to those who operate it. Procurement presents a requirement, then, for evaluations of the performance of human-machine work systems. This thesis is concerned with the provision of information to support procurers in performing such evaluations. The Ministry of Defence (an equipment procurer) has presented a particular requirement for a means of assessing the usability of speech interfaces in the establishment of the feasibility of computerized battlefield work systems. A structured method was developed to meet this requirement, the scope, notation and process of which sought to be explicit and proceduralized. The scope was specified in terms of a conceptualization of human-computer interaction: the method supported the development of representations of the task, device and user, which could be implemented as simulations and used in empirical evaluations of system performance. Notations for representations were proposed, and procedures enabling the use of the notations. The specification and implementation of the four sub-methods is described, and subsequent enhancement in the context of evaluations of speech interfaces for battlefield observation tasks. The complete method is presented. An evaluation of the method was finally performed with respect to the quality of the assessment output and costs to the assessor. The results suggested that the method facilitated systematic assessment, although some inadequacies were identified in the expression of diagnostic information which was recruited by the procedures, and in some of the procedures themselves. The research offers support for the use of structured human factors evaluation methods in procurement. Qualifications relate to the appropriate expression of knowledge of device-user interaction, and to the conflict between requirements for flexibility and low-level proceduralization

Design of hardware architectures for HMM–based signal processing systems with applications to advanced human-machine interfaces

In questa tesi viene proposto un nuovo approccio per lo sviluppo di interfacce uomo–macchina. In particolare si tratta il caso di sistemi di pattern recognition che fanno uso di Hidden Markov Models per la classificazione. Il progetto di ricerca è partito dall’ideazione di nuove tecniche per la realizzazione di sistemi di riconoscimento vocale per parlato spontaneo. Gli HMM sono stati scelti come lo strumento algoritmico di base per la realizzazione del sistema. Dopo una fase di studio preliminare gli obiettivi sono stati estesi alla realizzazione di una architettura hardware in grado di fornire uno strumento riconfigurabile che possa essere utilizzato non solo per il riconoscimento vocale, ma in qualsiasi tipo di classificatore basato su HMM. Il lavoro si concentra quindi sullo sviluppo di architetture hardware dedicate, ma nuovi risultati sono stati ottenuti anche a livello di applicazione per quanto riguarda la classificazione di segnali elettroencefalografici attraverso gli HMM. Innanzitutto state sviluppata una architettura a livello di sistema applicabile a qualsiasi sistema di pattern recognition che faccia usi di HMM. L’architettura stata concepita in modo tale da essere utilizzabile come un sistema stand–alone. Definita l’architettura, un processore hardware per HMM, completamente riconfigurabile, stato decritto in linguaggio VHDL e simulato con successo. Un array parallelo di questi processori costituisce di fatto il nucleo di processamento dell’architettura sviluppata. Sulla base del progetto in VHDL, due piattaforme di prototipaggio rapido basate su FPGA sono state selezionate per dei test di implementazione. Diverse configurazioni costituite da array paralleli di processori HMM sono state implementate su FPGA. Le soluzioni che offrivano un miglior compromesso tra prestazioni e quantità di risorse hardware utilizzate sono state selezionate per ulteriori analisi. Un sistema software per il pattern recognition basato su HMM stato scelto come sistema di riferimento per verificare la corretta funzionalità delle architetture implementate. Diversi test sono stati progettati per validare che il funzionamento del sistema corrispondesse alle specifiche iniziali. Le versioni implementate del sistema sono state confrontate con il software di riferimento sulla base dei risultati forniti dai test. Dal confronto è stato possibile appurare che le architetture sviluppate hanno un comportamento corrispondente a quello richiesto. Infine le implementazioni dell’array parallelo di processori HMM `e sono state applicate a due applicazioni reali: un riconoscitore vocale, ed un classificatore per interfacce basate su segnali elettroencefalografici. In entrambi i casi l’architettura si è dimostrata in grado di gestire l’applicazione senza alcun problema. L’uso del processamento hardware per il riconoscimento vocale apre di fatto la strada a nuovi sviluppi nel campo grazie al notevole incremento di prestazioni ottenibili in termini di tempo di esecuzione. L’applicazione al processamento dell’EEG, invece, introduce di fatto un approccio completamente nuovo alla classificazione di questo tipo di segnali, e mostra come in futuro potrebbe essere possibile lo sviluppo di interfacce basate sulla classificazione dei segnali generati dal pensiero spontaneo. I possibili sviluppi del lavoro iniziato con questa tesi sono molteplici. Una direzione possibile è quella dell’implementazione completa dell’architettura proposta come un sistema stand–alone riconfigurabile per l’accelerazione di sistemi per pattern recognition di qualsiasi natura purchè basati su HMM. Le potenzialità di tale sistema renderebbero possibile la realizzazione di classificatiori in tempo reale con un alto grado di complessità, e quindi allo sviluppo di interfacce realmente multimodali, con una vasta gamma di applicazioni, dai sistemi di per lo spazio a quelli di supporto per persone disabili.In this thesis a new approach is described for the development of human–computer interfaces. In particular the case of pattern recognition systems based on Hidden Markov Models have been taken into account. The research started from he development of techniques for the realization of natural language speech recognition systems. The Hidden Markov Model (HMM) was chosen as the main algorithmic tool to be used to build the system. After the early work the goal was extended to the development of an hardware architecture that provided a reconfigurable tool to be used in any pattern recognition task, and not only in speech recognition. The whole work is thus focused on the development of dedicated hardware architectures, but also some new results have been obtained on the classification of electroencephalographic signals through the use of HMMs. Firstly a system–level architecture has been developed to be used in HMM based pattern recognition systems. The architecture has been conceived in order to be able to work as a stand–alone system. Then a VHDL description has been made of a flexible and completely reconfigurable hardware HMM processor and the design was successfully simulated. A parallel array of these processors is actually the core processing block of the developed architecture. Then two suitable FPGA based, fast prototyping platforms have been identified to be the targets for the implementation tests. Different configurations of parallel HMM processor arrays have been set up and mapped on the target FPGAs. Some solutions have been selected to be the best in terms of balance between performance and resources utilization. Furthermore a software HMM based pattern recognition system has been chosen to be the reference system for the functionality of the implemented subsystems. A set of tests have been developed with the aim to test the correct functionality of the hardware. The implemented system was compared to the reference system on the basis of the tests’ results, and it was found that the behavior was the one expected and the required functionality was correctly achieved. Finally the implementation of the parallel HMM array was tested through its application to two real–world applications: a speech recognition task and a brain–computer interface task. In both cases the architecture showed to be functionally suitable and powerful enough to handle the task without problems. The application of the hardware processing to speech recognition opens new perspectives in the design of this kind of systems because of the dramatic increment in performance. The application to brain–computer interface is really interesting because of a new approach in the classification of EEG that shows how could be possible a future development of interfaces based on the classification of spontaneous thought. The possible evolution directions of the work started with this thesis are many. Effort could be spent of the implementation of the developed architecture as a stand–alone reconfigurable system suitable for any kind of HMM–based pattern recognition task. The potential performance of such a system could open the way to extremely complex real–time pattern recognition systems, and thus to the realization of truly multimodal interfaces, with a variety of applications, from space to aid systems for the impaired

HealthCyberMap: Mapping the Health Cyberspace Using Hypermedia GIS and Clinical Codes

HealthCyberMap () is a Semantic Web service for healthcare professionals and librarians, patients and the public m general that aims at mappmg parts of medical/ health information resources in cyberspace in novel ways to improve their retrieval and navigation. The Semantic Web ( and ) aims to be the next-generation World Wide Web by giving machine-readable semantics and context to the currently presentation-based Web pages. HealthCyberMap features an unconventional use of GIS (Geographic Information Systems) to map conceptual spaces occupied by collections of medical/ health information resources. Besides mapping the semantic and non-geographical aspects of these resources using suitable spatial metaphors, HealthCyberMap also collects and maps the geographical provenance of these resources. Some of HealthCyberMap Web interfaces are visual (maps for browsing resources by clinical/ health topic, by provenance and by type), while others are textual (multilingual interfaces for browsing resources by language, and a directory of topical resource categories, besides HealthCyberMap Semantic Subject Search Engine that goes beyond conventional free-text and keyword-based search engines, and supports synonyms, disease variants, subtypes, as well as some semantic relationships between terms). HealthCyberMap adopts a clinical metadata framework built upon a clinical coding scheme (vocabulary or ontology—ICD-9-CM* clinical classification in the current pilot service). Clinical coding schemes serve as a reliable common backbone for topical resource indexing, automated topical classification, topical visualisation and navigation of coded resource pools (using suitable metaphors), and enhanced information retrieval and linking. A resource metadata base based on Dublin Core metadata set with HealthCyberMap’s own extensions holds information about selected high-quality resources. HealthCyberMap then uses GIS spatialisation methods to generate interactive navigational cybermaps from the metadata base. These visual cybermaps are based on familiar metaphors for image-word association to give users a broad overview and understanding of what is available in this complex conceptual space of medical/ health Internet resources and help them navigate it more efficiently and effectively. HealthCyberMap cybermaps can be considered as semantically-spatialised, ontology-based browsing views of the underlying resource metadata base. Using a clinical coding scheme as a metric for spatialisation (“semantic distance”) is unique to HealthCyberMap and is very much suited for the semantic categorisation and navigation of medical/ health Internet information resources. HealthCyberMap also introduces a useful form of cyberspatial analysis for the detection of topical coverage gaps in its resource pool using choropleth (shaded) maps of human body systems. The project features a cost-effective method for serving Web hypermaps with dynamic metadata base drill-down functionality. It also demonstrates the feasibility of Electronic Patient Record to Online Information Services (like HealthCyberMap) Problem to Knowledge Linking using clinical codes as crisp problem-knowledge linkers or knowledge hooks. The Semantic Subject Search Engine queries the same HealthCyberMap resource metadata base. Explicit concepts in resource metadata map onto a brokering domain ontology (ICD-9-CM) allowing the search engine to infer implicit meanings (synonyms and semantic relationships) not directly mentioned in either the resource or its metadata. Similarly, user queries would map to the same ontology allowing the search engine to infer the implicit semantics of user queries and use them to optimise retrieval. A formative evaluation study of HealthCyberMap pilot service using an online user evaluation questionnaire, in addition to analysis of HealthCyberMap server transaction log, has been conducted during the period from 18 April 2002 to 1 June 2002 with very encouraging results. This two-method evaluation approach was guided by methodologies described in NIH Web Site Evaluation and Performance Measures Toolkit among other resources. Many exciting future possibilities have been also investigated by the author, including the further development of HealthCyberMap as a customisable, location-based medical/ health information service

"Shouldn't I use a polarquestion?" Proper Question Forms Disentangling Inconsistencies in Dialogue Systems

This work reports on the description of a specific class of clarification requests, adopted for the negotiation of pieces of information part of the common ground for argumentation strategies in human-machine interaction. Two studies are carried out to prove the adequateness of a specific form of polar question in a specific pragmatic situation, where a presupposition is contradicted by a new evidence. Whereas the first one proves the appropriateness of the negative form, the second one also demonstrate how the use of such a form, in the aforementioned pragmatic situation, can affect the principle of robustness, in terms of observability and recoverability, important in human–machine interaction applications. Given the results obtained in the two studies, dialogue systems with such capabilities are, therefore, a desirable goal, as they are expected to lead to improved usability and naturalness in conversation. For this reason, I present here a system capable of detecting conflicts and of using argumentation strategies to signal them consistently with previous observations