UHYPERLINK: AN ORGANIZED METHOD TO COLLECT, MANAGE AND STORE OBJECT HYPERLINKS USING RFID
Bibliographic record
Abstract
Advancements in ubiquitous computing are allowing users to search and add information to the web for surrounding objects from any location at any time. With more and more information being added to the web, it is becoming hard for users to find the information about an object that surrounds the user at a given context. Current web based search engines are putting local organizations and local objects at a disadvantage in many cases. In recent years, a new era of object hyperlink has evolved which connects physical objects to web based content via graphical machine readable tags or automatic identification technology such as Radio Frequency Identification (RFID). Users can view the obtained information on the mobile device. However, users today may choose to process the obtained information on more than one computing device based on the activity or task that they are performing. The learning curve for transferring the obtained information to different devices is an addition to the information overload problem.\nIn this thesis, a user centered Radio Frequency Identification (RFID) based object hyperlink solution is proposed. First goal of this thesis is to provide users with the ability to easily collect information from any given object hyperlink location. UHyperlink is designed to provide users an ability to store object hyperlinks from different organizations to a central repository where users can analyze and recapitulate the collected information. UHyperlink is also designed to provide users with more than one object hyperlink where relevant links are presented to the user based on context and user request, reducing the information overload problem. From the experimental setup and evaluation of this thesis, it can be said that it is a novel and interactive approach to object hyperlink which provides users with different results based on user requirement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".