Computer Science & Information Technology
Bibliographic record
Abstract
Conference on Advanced Natural Language Processing (AdNLP 2020) was collocated with 9 th International Conference on Advanced Information Technologies and Applications (ICAITA 2020).The conferences attracted many local and international delegates, presenting a balanced mixture of intellect from the East and from the West.The goal of this conference series is to bring together researchers and practitioners from academia and industry to focus on understanding computer science and information technology and to establish new collaborations in these areas.Authors are invited to contribute to the conference by submitting articles that illustrate research results, projects, survey work and industrial experiences describing significant advances in all areas of computer science and information technology.The ICAITA 2020, CDKP 2020, SAI 2020, NCO 2020, CMC 2020, SOFT 2020, MLT 2020 and AdNLP 2020 Committees rigorously invited submissions for many months from researchers, scientists, engineers, students and practitioners related to the relevant themes and tracks of the workshop.This effort guaranteed submissions from an unparalleled number of internationally recognized top-level researchers.All the submissions underwent a strenuous peer review process which comprised expert reviewers.These reviewers were selected from a talented pool of Technical Committee members and external reviewers on the basis of their expertise.The papers were then reviewed based on their contributions, technical content, originality and clarity.The entire process, which includes the submission, review and acceptance processes, was done electronically.In closing, ICAITA 2020, CDKP 2020, SAI 2020, NCO 2020, CMC 2020, SOFT 2020, MLT 2020 and AdNLP 2020 brought together researchers, scientists, engineers, students and practitioners to exchange and share their experiences, new ideas and research results in all aspects of the main workshop themes and tracks, and to discuss the practical challenges encountered and the solutions adopted.The book is organized as a collection of papers from the ICAITA 2020, CDKP 2020, SAI 2020, NCO 2020, CMC 2020, SOFT 2020, MLT 2020 and AdNLP 2020.We would like to thank the General and Program Chairs, organization staff, the members of the Technical Program Committees and external reviewers for their excellent and tireless work.We sincerely wish that all attendees benefited scientifically from the conference and wish them every success in their research.It is the humble wish of the conference organizers that the professional dialogue among the researchers, scientists, engineers, students and educators continues beyond the event and that the friendships and collaborations forged will linger and prosper for many years to come.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.098 |
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".