A remotely controlled Bluetooth enabled environment
Bibliographic record
Abstract
Ad-hoc networks using Bluetooth enabled devices have piqued the interest of the networking community. Network developers are looking into different means of remotely controlling the devices comprising such networks, so as to have the flexibility to change various device parameters without actually being present near them. We present a method of remotely controlling devices in a Bluetooth enabled environment in the home or office from any part of the world via the Internet. A Web page applet programmed in Java can be accessed from any Java-enabled browser connected to the Internet and is used to control parameters of remote Bluetooth devices. It is also used to display the current state of the Bluetooth devices. A novel application of using Bluetooth devices for remote control is that a passive electronic device can be given processing power simply by connecting a Bluetooth chip to it. Any passive device in the Bluetooth enabled scatternet can have processed data send to it by the program resident in the Web server. The paper addresses issues related to internetworking such networks to form scatternets and presents a scatternet formation algorithm to solve the problem of connecting all the Bluetooth enabled devices in any home or office environment.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".