Bibliographic record
Abstract
The 2nd International Conference on Blockchain and Applications 2020 (BLOCKCHAIN’20), held in the Heritage city of L’Aquila (Italy), has been a meeting point for both experienced and young researchers investigating in the areas of blockchain and artificial intelligence (AI). The conference has acted as a forum at which the attendees listened to lectures, and shared and discussed ideas, projects and advances associated with these technologies and their application domains. Within the scientific community, blockchain and AI are viewed as a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and AI should be used together. Combining blockchain mechanisms and artificial intelligence is still a particularly challenging task, and the BLOCKCHAIN’20 conference has been a milestone towards its achievement. The BLOCKCHAIN’20 conference has been devoted to promoting the investigation of cutting-edge blockchain technology, exploring the latest blockchain- and AI-related ideas, innovations, guidelines, theories, models, technologies, applications and tools for the industry. Critical issues and challenges have been identified so that researchers and practitioners may address them in future research. The technical programme has been carefully designed to offer a fresh and balanced selection of advances and results in blockchain and AI, encouraging focus on novel and interdisciplinary topics. The technical programme has been diverse and of high quality, and it focused on contributions to both well-established and evolving areas of research. More than 40 papers have been submitted to 20 from over 20 different countries (Canada, France, Germany, India, Ireland, Italy, Jordan, Luxembourg, Malaysia, Malta, Morocco, Netherlands, Oman, Portugal, Slovenia, Spain, Sweden, UAE and USA). We would like to thank all the contributing authors, the members of the Programme Committee, the sponsors (IBM, Indra, EurAI, AEPIA, AFIA, APPIA and AIR Institute) and the Organizing Committee for their hard and highly valuable work. We thank the funding supporting with the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing” (Id. RTI2018-095390-B-C32); their work contributed to the success of the BLOCKCHAIN’20 event, and finally, we thank the Local Organization members and the Programme Committee members for their hard work, which was essential for the success of BLOCKCHAIN’20.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.574 | 0.422 |
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".