Bibliographic record
Abstract
Originated from distributed artificial intelligence, agents represent an exciting and promising approach to building a wide range of distributed software applications.The Internet of Things (IoT) refers to uniquely identifiable objects as well as their virtual representations in an Internet-like structure.It is related to a number of disciplines and technologies that enable the Internet to reach out into the real world of physical objects and their environments.It has been hailed as the most potentially disruptive technological revolution of our lifetime after the Web and mobile accessibility.It becomes even more promising with smart applications like smart cities, smart Grid, smart factories, smart buildings, smart homes, and smart cars.On the other hand, Big Data is a broad term for data sets so large or complex that traditional data processing technologies are inadequate.It has been considered as a technology and become a very active research area primarily involving topics related to machine learning, database, and distributed computing.Recent developments and fast advancements of Cloud/Fog/Edge Computing, Internet of Things, Cyber-Physical Systems, and Big Data provide new opportunities for applications of intelligent software agents, but also bring a lot of new research challenges.Based on 29 years of first-hand research experience on agents, Internet of Things (IoT), Big Data, and their industrial applications, this talk will provide an overview of agents, IoT and Big Data, including stat-of-the-art and future trends, with a focus on how agents, IoT and Big Data are linked with and applied in various industrial domains and societies.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.763 | 0.697 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".