Bibliographic record
Abstract
There are many websites specially designed for mobile phones. Some hackers write automated programs to abuse these website services and waste the website resources. Therefore, it is necessary to distinguish between human users and computer programs. Methods known for achieving this are known as CAPTCHA (Completely Automated Public Turing test to tell Computers and Human Apart). CAPTCHA methods are mainly based on the weaknesses of OCR (Optical Character Recognition) systems and ask the user to type a word. So using them is difficult in tools such as PDAs (Personal Digital Assistant) or mobile phones that lack a complete keyboard. In this paper, a new CAPTCHA system is proposed for touch-screen devices such as PDAs and mobile phones. In this system, a word is drawn in a random place on the screen and a number of arcs are drawn on the screen. Then the user is asked to highlight the word by the stylus. Due to the limitations of the PDA and mobile phone, OCR programs on these devices cannot recognize the shown word, while a human user can easily highlight the word. The proposed method is implemented by the JavaME (Java Platform Micro Edition) language and tested on a Sony Ericsson P990i mobile phone.
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
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".