Bibliographic record
Abstract
This thesis studies next-generation web user interaction definition languages, as well as browser software architectures. The motivation comes from new end-user requirements for web applications: demand for higher interaction, adaptation for mobile and multimodal usage, and rich multimedia content. At the same time, there is a requirement for non-programmers to be able to author, customize, and maintain web user interfaces.\n\nCurrent user interface tools do not support well these new kinds of requirements. Thus, the main research problem of this Thesis is the definition of a device and modality independent model for high-interaction web user interfaces.\n\nThis Thesis proposes a set of criteria for user interface tools, and evaluates current tools against the criteria. It proposes a taxonomy of tools based on authoring style, consisting of procedural, declarative and hybrid tools. Based on an analysis, declarative languages are chosen, the main advantage being higher semantic level, which enables ease-of-authoring and adaptation based on context of use, current device, and user's preferences.\n\nA layered model, consisting of mostly declarative languages, is proposed. It is composed of well-defined, and proven XML languages, and is divided into layers. The abstract UI layer contains, among others, interaction, document structure, and security, and is modality independent. The modality-dependent layer allows more detailed control over the renderings for each modality, such as visual and aural. It is shown that it is possible to automatically produce multimodal user interfaces from a single declarative user interface definition using the proposed model. This thesis focuses specifically on user interaction, where the use of XForms language is proposed. The author has co-specified the XForms language in the World Wide Web Consortium. In the proposed model, procedural scripting is only used to provide specialized modality-specific widgets in a reusable manner.\n\nFinally, as a proof-of-concept, this thesis describes the author's implementation of the proposed model. The implementation is part of the open-source X-Smiles user agent and includes full implementations of most of the proposed languages and techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".