Toward Pervasive Intelligence Systems: Looking Ahead in Cyber-physical World in Pervasive Computing
Bibliographic record
Abstract
The physical world is becoming filled with communication and computing units that communicate with one another and users; almost everything will be able to gather data and react to relevant stimuli. Through sensing, computing, and transmission components, real-world elements interact with cyberspace in this technologically advanced situation, leading to the so-called convergence of the Cyber-Physical World (CPW). However, the ubiquitous computing environment and IoT devices are susceptible to various threats due to their dynamic operation and the requirement to manage private and sensitive data. A high degree of safety guarantee is necessary for smart environments, such as trusted context producers and consumers, which should shield private data from exposure or surveillance. A unique, lightweight security architecture that authenticates and preserves the context of providers and receivers is proposed in the research, along with a discussion of the primary cyber threats in smart environments. Several recent research studies have proposed a definition of CPS that encompasses all the characteristics mentioned in the various domains. We contrast those several CPS plans, talk about some related concepts and technology, and wrap up by outlining the primary research issues in the field.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".