The Evolution of Information Networks around Data-shifting Paradigms
Bibliographic record
Abstract
Our perception of the Internet is shifting by two main factors, an explosive growth of proactive mobile devices, and an overabundance of data that is growing beyond tractable operation. Thus, the increasing volume of data is in fact becoming less accessible in terms of coherence and synergy, contrary to what search engines would want us to believe! There was a time when IP address space was the major hindrance. Now, the bottleneck has shifted from connecting new devices to handling their data demands (both generated and requested) and cascading replications over the network. In this talk we overview the growing momentum for Information Centric Networking (ICN); a paradigm that envisions networks built around data, rather than the latter being a mere constituent of stale architectures. We highlight two major factors that drive ICN, namely, globalizing the utility of resources that serve the network architecture and cost-effective data harvesting and delivery. We first elaborate on our research in establishing networks on the fly. As networks grow, and the demand for data readiness becomes more stringent, the adoption of application-specific networks presents a major hindrance. At any given location, we need to find the resources that could collect data, process it, and delivers it to designated backhauls at the least cost. To the first end, we overview our work in real-time data collection over opportunistic and participatory sensing networks, overarching a major realization in VANets. We then present our work in optimizing data delivery in dense and infrastructure-less realizations of transient networks. We consider factors of placement, localization, time delivery constraints and cost of delivery over multi-proprietary networks.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".