Integrating multimodal message across heterogeneous networks
Bibliographic record
Abstract
We introduce a seamless massaging system for the management of personal messages. The aim of it is to intercept, filter, interpret, and deliver multi-modal messages (voice, fax, and/or e-mail messages). Messages are delivered to the recipient regardless of their target messaging device. Seamless messaging involves finding the person (if urgent) and delivering the information to them on their cellular phone, pager, laptop, nearest fax, telephone, or desktop computer. The system includes a set of personal agents that classify and act on incoming messages based on their content. The user specifies the classes and actions to the agent as a set of high-level rules. This allows the user to specify rules that are independent of the messaging system and target devices. A personal agent "Secretary" is responsible for mediating between the different messaging environments, the target devices, and other interacting applications (e.g., calendars, e-mail programs, etc.). The design of this seamless messaging system is based on the integration of three: technologies, ubiquitous computing, information filtering, and telematics. The system has been implemented on a Lotus Notes platform. What makes the system unique is its approach to treating a message in a universal manner, its ability to mediate between different messaging devices, and its ability to try to determine the availability of the user.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".