Exogenous Coordination for Building Fog-Based Cyber Physical Social Computing and Networking Systems
Bibliographic record
Abstract
With the proliferation of smart embedded devices, cyber physical social computing and networking systems (CPSCN) are emerging as a next generation of social networks. Unlike traditional social networks that run on cloud-based infrastructure, CPSCN systems usually depend on a large number of distributed, heterogeneous devices, such as mobile phones, smart vehicles, or network access points. These computing resources, which are often referred to as fog computing systems, provide a gateway to the physical world, and thus offer new possibilities for social applications. Unfortunately, building CPSCN systems that leverage fog computing infrastructure is not straightforward. Significant challenges arise from the large scale distribution of computing resources over a wide area, and the dynamic nature of multiple, possibly mobile, hosts. In this paper, we extend our previous work on a distributed dataflow programming model and propose an application platform for realizing CPSCN systems. A key aspect of our work is the development of an exogenous coordination model, which exhibits a separation of concern between computation and communication activities, and helps resolve some of the challenges brought about by the dynamic and large scale nature of CPSCN systems.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".