IEEE Access Special Section Editorial: Cloud-Fog-Edge Computing in Cyber–Physical–Social Systems (CPSS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cyber–Physical–Social Systems (CPSS) integrate the cyber, physical, and social spaces together. One of the ultimate goals of cyber–physical–social systems is to make our lives more convenient and intelligent by providing prospective and personalized services for users. To achieve this goal, a wide range of data in CPSS are employed as the starting point for research, since the data contain the users’ historical behavior trajectory and the users’ demand preference. Generated and collected from social and physical spaces and integrated into the cyberspace, CPSS data are complex and heterogeneous, recording all aspects of users’ lives in the forms of image, audio, video, and text. Generally, the collected or generated data in CPSS satisfy the 4Vs (volume, variety, velocity, and veracity) of big data. Thus, knowing how to deal with CPSS big data efficiently is the key to providing services for users. From another perspective, CPSS big data are specified as the global historical data and the local real-time data. Cloud computing, as a powerful paradigm for implementing the data-intensive applications, has an irreplaceable role in processing global historical data. On the other hand, with the increasing computing capacity and communication capabilities of mobile terminal devices, fog-edge computing, as an important and effective supplement of cloud computing, has been widely used to process local real-time data. Therefore, the question of how to systematically and efficiently process CPSS big data (including both the global historical data and the local real-time data) in CPSS has become the key for providing services. The goal of this Special Section is therefore to provide insights and views into the area of Cloud-Fog-Edge Computing in CPSS, as well as to provide directions for research 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it