Enhancing the Performance of Eye and Head Mice: A Validated Assessment Method and an Investigation into the Performance of Eye and Head Based Assistive Technology Pointing Devices

This work poses the question "Could eye and head based assistive technology device interaction performance approach that of basic hand mouse interaction?" To this aim, the work constructs, validates, and applies a detailed and comprehensive pointing device assessment method suitable for assistive technology direct pointing devices, it then uses this method to add enhancement to these devices, finally it then demonstrates that such enhanced eye or head based pointing can approach that of basic hand mouse interaction and be a viable and usable interaction method for people with high-level motor disabilities. Eye and head based pointing devices, or eye and head mice, are often used by high-level motor disabled people to enable computer interaction in the place of a standard desktop hand mouse. The performance of these eye and head mice pointing devices when used for direct manipulation on a standard graphical user interface has generally been regarded as poor in comparison to that of a standard desktop hand mouse, thus putting users of head and eye mice at a disadvantage when interacting with computers. The performance of eye and head based pointing devices during direct manipulation on a standard graphical user interface has not previously been investigated in depth, and the reasons why these devices seem to demonstrate poor performance have not been determined in detail. Few proven methods have been demonstrated and investigated that enhance the performance of these devices based on their performance during direct manipulation. Importantly, and key to this work is that, no validated assessment method has been constructed to allow such an investigation. This work seeks to investigate the performance of eye and head based pointing devices during direct manipulation by constructing and verifying a test method suitable for the detailed performance assessment of eye and head based assistive technology pointing devices. It then uses this method to determine the factors influencing the performance of eye and head mice during direct manipulation. Finally, after identifying these factors, this work hypothesises, and then demonstrates that applying suitable methods for addressing these factors can result in enhanced performance for eye and head mice. It shows that the performance of these enhanced devices can approach the performance of standard desktop hand mice with the use of highly experienced users, together with the enhancement of a supporting modality for object manipulation, and a supporting interface enhancement for object size magnification; thus demonstrating that these devices can approach and equal the performance of basic hand mouse interaction

A ranking framework and evaluation for diversity-based retrieval

There has been growing momentum in building information retrieval (IR) systems that consider both relevance and diversity of retrieved information, which together improve the usefulness of search results as perceived by users. Some users may genuinely require a set of multiple results to satisfy their information need as there is no single result that completely fulfils the need. Others may be uncertain about their information need and they may submit ambiguous or broad (faceted) queries, either intentionally or unintentionally. A sensible approach to tackle these problems is to diversify search results to address all possible senses underlying those queries or all possible answers satisfying the information need. In this thesis, we explore three aspects of diversity-based document retrieval: 1) recommender systems, 2) retrieval algorithms, and 3) evaluation measures. This first goal of this thesis is to provide an understanding of the need for diversity in search results from the users’ perspective. We develop an interactive recommender system for the purpose of a user study. Designed to facilitate users engaged in exploratory search, the system is featured with content-based browsing, aspectual interfaces, and diverse recommendations. While the diverse recommendations allow users to discover more and different aspects of a search topic, the aspectual interfaces allow users to manage and structure their own search process and results regarding aspects found during browsing. The recommendation feature mines implicit relevance feedback information extracted from a user’s browsing trails and diversifies recommended results with respect to document contents. The result of our user-centred experiment shows that result diversity is needed in realistic retrieval scenarios. Next, we propose a new ranking framework for promoting diversity in a ranked list. We combine two distinct result diversification patterns; this leads to a general framework that enables the development of a variety of ranking algorithms for diversifying documents. To validate our proposal and to gain more insights into approaches for diversifying documents, we empirically compare our integration framework against a common ranking approach (i.e. the probability ranking principle) as well as several diversity-based ranking strategies. These include maximal marginal relevance, modern portfolio theory, and sub-topic-aware diversification based on sub-topic modelling techniques, e.g. clustering, latent Dirichlet allocation, and probabilistic latent semantic analysis. Our findings show that the two diversification patterns can be employed together to improve the effectiveness of ranking diversification. Furthermore, we find that the effectiveness of our framework mainly depends on the effectiveness of the underlying sub-topic modelling techniques. Finally, we examine evaluation measures for diversity retrieval. We analytically identify an issue affecting the de-facto standard measure, novelty-biased discounted cumulative gain (α-nDCG). This issue prevents the measure from behaving as desired, i.e. assessing the effectiveness of systems that provide complete coverage of sub-topics by avoiding excessive redundancy. We show that this issue is of importance as it highly affects the evaluation of retrieval systems, specifically by overrating top-ranked systems that repeatedly retrieve redundant information. To overcome this issue, we derive a theoretically sound solution by defining a safe threshold on a query-basis. We examine the impact of arbitrary settings of the α-nDCG parameter. We evaluate the intuitiveness and reliability of α-nDCG when using our proposed setting on both real and synthetic rankings. We demonstrate that the diversity of document rankings can be intuitively measured by employing the safe threshold. Moreover, our proposal does not harm, but instead increases the reliability of the measure in terms of discriminative power, stability, and sensitivity

Label Ranking with Probabilistic Models

Diese Arbeit konzentriert sich auf eine spezielle Prognoseform, das sogenannte Label Ranking. Auf den Punkt gebracht, kann Label Ranking als eine Erweiterung des herkömmlichen Klassifizierungproblems betrachtet werden. Bei einer Anfrage (z. B. durch einen Kunden) und einem vordefinierten Set von Kandidaten Labels (zB AUDI, BMW, VW), wird ein einzelnes Label (zB BMW) zur Vorhersage in der Klassifizierung benötigt, während ein komplettes Ranking aller Label (zB BMW> VW> Audi) für das Label Ranking erforderlich ist. Da Vorhersagen dieser Art, bei vielen Problemen der realen Welt nützlich sind, können Label Ranking-Methoden in mehreren Anwendungen, darunter Information Retrieval, Kundenwunsch Lernen und E-Commerce eingesetzt werden. Die vorliegende Arbeit stellt eine Auswahl an Methoden für Label-Ranking vor, die Maschinelles Lernen mit statistischen Bewertungsmodellen kombiniert. Wir konzentrieren wir uns auf zwei statistische Ranking-Modelle, das Mallows- und das Plackett-Luce-Modell und zwei Techniken des maschinellen Lernens, das Beispielbasierte Lernen und das Verallgemeinernde Lineare Modell

Use Case Oriented Medical Visual Information Retrieval & System Evaluation

Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation

Creativity and Information Systems: A Theoretical and Empirical Investigation of Creativity in IS

Be productive. Since the industrial revolution, managers have had an almost singular focus on equipping employees with productivity tools in productivity-supportive environments. Information technologies—systems designed to increase productivity—entered the marketplace in the 1980\u27s and were initially credited with the subsequent boom. Eventually, innovation was shown to be the primary spark, and the managerial focus shifted. Increasingly, the imperative is: be creative. This dissertation investigates how a technology environment designed to be fast and mechanistic influences the slow and organic act of creativity. Creativity—the production of novel and useful solutions—can be an elusive subject and has a varied history within Information Systems (IS) research so the first essay is devoted to conducting an historical analysis of creativity research across several domains and developing a holistic, technologically-aware framework for researching creativity in modern organizations. IS literature published in the Senior Scholar\u27s journals is then mapped to the proposed framework as a means of identifying unexplored regions of the creativity phenomenon. This essay concludes with a discussion of future directions for creativity research within IS. The second essay integrates task-technology fit and conservation of resources theory and employs an experimental design to explore the task of being creative with an IS. Borrowing from fine arts research, the concept of IS Mastery is introduced as a resource which, when deployed efficiently, acts to conserve resources and enhance performance on cognitively demanding creative tasks. The third essay investigates an expectedly strong but unexpectedly negative relationship between technology fit and creative performance. This finding launches an exploration into alternate study designs, theoretical models and performance measures as we search for the true nature of the relationship between creativity and technology fit. The essay concludes with an updated map of the technology-to-performance chain. These essays contribute to IS research by creating a technology-aware creativity framework for motivating and positioning future research, by showing that the IS is neither a neutral nor frictionless collaborator in creative tasks and by exposing the inhibiting effects of a well-fitting technology for creative performance

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Exploiting Social Semantics for Multilingual Information Retrieval

In this thesis we consider how user-generated content that is assembled by different popular Web portals can be exploited for Multilingual Information Retrieval. We define the knowledge that can be derived from such portals as Social Semantics. We present to approaches, Cross-lingual Explicit Semantic Analysis and Discriminative Retrieval Models, that are able to support multilingual retrieval models by integrating Social Semantics derived from Wikipedia and Yahoo! Answers

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute