Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Bibliographic record
Abstract
It is our great pleasure to welcome you to the Ninth Annual ACM/IEEE International Conference on Human-Robot Interaction. HRI 2014 is a highly selective conference that aims to showcase the very best interdisciplinary and multidisciplinary research in human-robot interaction with roots in robotics, social psychology, cognitive science, HCI, human factors, artificial intelligence, design, engineering, and many more. We invite broad participation and encourage discussion and sharing of ideas across a diverse audience. Robotics is growing increasingly multidisciplinary as it moves towards realizing capable and collaborative robots that are studied in both laboratory and real world settings. Concurrent development of technical, social, and designed aspects of systems, with a concern for how they will improve the world, is needed. This year's theme "(E)Merging Perspectives" seeks to combine both user and system perspectives to advance new and possibly unorthodox methodologies. To extend the current singular approaches, our program demonstrates the usage of novel empirical methods, the integration of empirical findings into complex robot systems, and holistic approaches in system evaluation. The call for papers attracted 132 submissions from Asia, Canada, Europe, Africa, and the United States. Full Papers submitted to the conference were thoroughly reviewed and discussed. The process utilized a rebuttal process and a worldwide team of dedicated, interdisciplinary reviewers. This year's conference continues the tradition of selectivity, the program committee selected 32 of the submissions (24%). Due to the joint sponsorship of ACM and IEEE, papers are archived in both the ACM Digital Library and IEEE Xplore. For the second year, the conference also features papers from a journal special issue. Six papers were accepted for the Journal of Human-Robot Interaction's special issue on Design, and will be presented throughout the conference program. Accompanying the full papers are the Late Breaking Reports, Videos, and Demos. For the LBR, 109 of 127 two-page papers were accepted and will be presented at the conference poster session. 14 short videos were accepted and will be presented during the video session, and we have 7 demos of robot systems for all participants to be able to interact with during the conference. Rounding out the program are three keynote speakers who will discuss topics relevant to HRI: Dr. Maja Mataric, Dr. Maja Pantic, and Dr. Helge Ritter.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.135 | 0.058 |
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".