<title>Novel architecture for using HDTV as the home terminal of a remote computer server</title>
Bibliographic record
Abstract
Recently established high definition television (HDTV) standard is expected to replace the conventional analog television standards such as NTSC, PAL and SECAM in the next few years. However the high cost of HDTV is proving to be a major factor impeding its popularity. Integrating HDTV with other home appliances is likely to increase its usefulness and popularity. In this paper, we propose an efficient scheme to use HDTV as a computer monitor in addition to its entertainment role. In the proposed scheme, we assume that the main computer is situated in a remote location. The computer raster in the remote server is compressed using an MPEG-2 encoder and transmitted to the home. The built in MPEG-2 decoder in HDTV decompresses the bit stream, and displays the raster. The HDTV will be fitted with a mouse and keyboard, through which the interaction with the remote computer server can be performed. The HDTV can thus be used as a high-resolution computer terminal. The experimental results show that the performance of HDTV as a remote terminal is very good, with marginal degradation in text quality due to compression noise.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".