In this thesis we develop models for sentence compression. This text rewriting task has recently attracted a lot of attention due to its relevance for applications (e.g., summarisation) and simple formulation by means of word deletion. Previous models for sentence compression have been inherently local and thus fail to capture the long range dependencies and complex interactions involved in text rewriting. We present a solution by framing the task as an optimisation problem with local and global constraints and recast existing compression models into this framework. Using the constraints we instil syntactic, semantic and discourse knowledge the models otherwise fail to capture. We show that the addition of constraints allow relatively simple local models to reach state-of-the-art performance for sentence compression. The thesis provides a detailed study of sentence compression and its models. The differences between automatic and manually created compression corpora are assessed along with how compression varies across written and spoken text. We also discuss various techniques for automatically and manually evaluating compression output against a gold standard. Models are reviewed based on their assumptions, training requirements, and scalability. We introduce a general method for extending previous approaches to allow for more global models. This is achieved through the optimisation framework of Integer Linear Programming (ILP). We reformulate three compression models: an unsupervised model, a semi-supervised model and a fully supervised model as ILP problems and augment them with constraints. These constraints are intuitive for the compression task and are both syntactically and semantically motivated. We demonstrate how they improve compression quality and reduce the requirements on training material. Finally, we delve into document compression where the task is to compress every sentence of a document and use the resulting summary as a replacement for the original document. For document-based compression we investigate discourse information and its application to the compression task. Two discourse theories, Centering and lexical chains, are used to automatically annotate documents. These annotations are then used in our compression framework to impose additional constraints on the resulting document. The goal is to preserve the discourse structure of the original document and most of its content. We show how a discourse informed compression model can outperform a discourse agnostic state-of-the-art model using a question answering evaluation paradigm.

Even though Broca's area has been associated with speech and language processing since the 19th century, the exact role that it plays is still a matter of debate. Recent models on the neuroanatomical substrates of language have assigned Broca's area to different processes: syllabification (Indefrey and Levelt 2004), articulatory code storage (Hickok and Poeppel 2004) and verbal working memory (Chein and Fiez 2001; Chein et al. 2002). The subject of this doctoral dissertation, is to examine language production and disambiguate the role of Broca's area. This issue was addressed in a series of functional magnetic resonance imaging studies (fMRI) involving speech production, where the phonological properties of pseudowords were manipulated in a way that differentiated between syllabification and articulatory code generation. The load on verbal working memory was also changed. The behaviour of Broca's area was then examined in response to these manipulations to determine the dependence of the observed results on the different levels of processing and verbal working memory. The results from the present studies suggest that the dorsal premotor cortex has a consistent role in articulatory code generation irrespective of verbal working memory demands. In contrast, Broca's area, specifically Brodmann area 44, showed a main effect of phonetic encoding only during delayed response tasks. Interestingly, area BA44 was also found to be functionally segregated between the dorsal and ventral part. The dorsal part was sensitive to articulatory and phonological load, such as stimulus length. The ventral part on the other hand was sensitive to sub-lexical stimulus properties, but only during delayed response trials. These findings suggest that BA44 is not a homogeneous region, but it is divided into a dorsal premotor and a ventral prefrontal part. These results add another dimension of complexity to the study of Broca's area, its functional segregation and its role in language production.

This is a submission to the 3rd Annual Social Informatics SIG Research Symposium: The Social Web, Social Computing and the Social Analysis of Computing. To date the no empirical research has been done to visualize the discipline of social informatics. This work presents the early stages of a domain analysis of social informatics in terms of its authors. The names of those most frequently cocited with Rob Kling from 1974 to 2007 were retrieved from Social Scisearch via Dialog. The top 48 authors were submitted to author cocitation analysis.

This is a submission to the "Interrogating the social realities of information and communications systems pre-conference workshop, ASIST AM 2006."

This is a submission to the "Interrogating the Social Realities of Information Systems" Preconference Symposium at ASIST 2006. This abstract describes an investigation of the changing access and use rights of licensed scholarly digital resources, particularly the rights associated with digital works protected by technological protection measures (TPM â also known as digital rights management systems or DRM)

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Why can some people study, read books, and work while listening to music or with noise in the background while other people simply cannot? This was the question that prompted this research study. The aim of this project was to assess the impact of binaural beats on participants during the performance of a task. The participants were exposed to different binaural beats that changed the dominant brainwaves while they were engaging in the task. A braincomputer interface was used to monitor the performance of the task in which a Lego Mindstorm robot was controlled as it moved through a course. To accomplish the aim of the project, the effects of binaural tones on participantsâ task performance were investigated in relation to participantsâ levels of frustration, excitement, engagement, meditation and performance. Participants were monitored by means of using an Emotiv EPOC neuroheadset. Although previous studies on binaural beats have been done, most of these studies were done on Attention deficit-hyperactivity disorder (ADHD) children, with users performing everyday tasks. In these studies, time was the only metric used. The researcher collected data by means of questionnaires that were completed by the participants to obtain personal information and measure the user experience. The aspects of frustration, excitement, engagement, meditation and performance were determined using the Emotiv headset in combination with the Emotiv software development kit, Microsoft Robotics Studio and software created by the researcher. After intensive statistical analysis, the researcher found that different sound frequencies did indeed affect user performance. Sessions where no sound frequency was applied were associated with more errors and longer time durations compared with all other frequencies. It can be concluded that invoking a participantâs dominant brainwave by means of binaural tones can change his/her state of mind. This in turn can affect the long-term excitement, short-term excitement, engagement, meditation, frustration or performance of a participant while performing a task. Much remains to be learned, in particular regarding the combination of brain-computer interfaces and human-computer interaction. The possibility of new cutting-edge technologies that could provide a platform for further in-depth research is an exciting prospect

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In recent years computer applications have been deployed to manage spatial data with Geographic Information Systems (GIS) to store and analyze data related to domains such as transportation and tourism. Recent developments have shown that there is an urgent need to develop systmes for mobile devices and particularly for Location Based Services (LBS) such as proximity analysis that helps in finding the nearest neighbors, for example. restaurant, and the facilities that are located within a circle area around the user's location, known as a buffer area, for example, all restaurants within 100 meters. The mobile market potential is across geographical and cultural boundaries. Hence the visualization of queries becomes important especially that the existing visual query languages have a number of limitations. They are not tailored for mobile GIS and they do not support dynamic complex queries (DCQ) and visual query formation. Thus, the first aim of this research is to develop a new visual query language (IVQL) for mobile GIS that handles static and DCQ for proximity analysis. IVQL is designed and implemented using smiley icons that visualize operators, values, and objects. The evaluation results reveal that it has an expressive power, easy-to-use user interface, easy query building, and a high user satisfaction. There is also a need that new optimization strategies consider the scale of mobile user queries. Existing query optimization strategies are based on the sharing and push-down paradigms and they do not cover multiple-DCQ (MDCQ) for proximity analysis. This leads to the second aim of this thesis which is to develop the query melting processor (QMP) that is responsible for processing MDCQs. QMP is based on the new Query Melting paradigm which consists of the sharing paradigm, query optimization, and is implemented by a new strategy "Melting Ruler". Moreover, with the increase in volume of cost sensitive mobile users, the need emerges to develop a time cost optimizer for processing MDCQs. Thus, the thirs aim of the thesis is to develop a new Decision Making Mechanism for time cost optimization (TCOP) and prove its cost effectiveness. TCOP is based on the new paradigm "Sharing global execution plans by MDCQs with similar scenarios". The experimental evaluation results, using a case study based on the map of Paris, proved that significant saving in time can be achieved by employing the newly developed strategies.

Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies for enhanced genetic data analysis. Microarray is a well known technology for the evaluation of thousands of gene expression profiles. By considering these profiles as digital signals, the power of DSP methods can be applied to produce robust and unsupervised clustering of microarray samples. This can be achieved by transferring expression profiles into spectral components which are interpreted as a measure of profile similarity. This thesis introduces enhanced signal processing algorithms for robust clustering of micro array gene expression samples. The main aim of the research is to design and validate novel genomic signal processing methodologies for micro array data analysis based on different DSP methods. More specifically, clustering algorithms based on Linear prediction coding, Wavelet decomposition and Fractal dimension methods combined with Vector quantisation algorithm are applied and compared on a set of test microarray datasets. These techniques take as an input microarray gene expression samples and produce predictive coefficients arrays associated to the microarray data that are quantised in discrete levels, and consequently used for sample clustering. A variety of standard micro array datasets are used in this work to validate the robustness of these methods compared to conventional methods. Two well known validation approaches, i.e. Silhouette and Davies Bouldin index methods, are applied to evaluate internally and externally the genomic signal processing clustering results. In conclusion, the results demonstrate that genomic signal processing based methods outperform traditional methods by providing more clustering accuracy. Moreover, the study shows that the local features of the gene expression signals are better clustered using wavelets compared to the other DSP methods.