2019 4th International Conference on Intelligent Computing and Signal Processing (ICSP 2019)
Bibliographic record
Abstract
Preface This issue of Proceedings gathers the papers presented at 2019 4th International Conference on Intelligent Computing and Signal Processing (ICSP 2019) held in Xi’an, China during March 29-31, 2019. ICSP 2019 is an international conference covering research and development in the field of intelligent computing and signal processing and participation from all over the world. More than 400 papers were finally accepted after a double blinded peer review process by international reviewers and academic committee members. Divided into 4 chapters, the papers provide a wide spectrum of researches on wide range of intelligent computing and signal processing. The chapters are devoted to Algorithm and Data Mining, Signal and Image Processing, Automation Engineering and Intelligent Application, Computer Modeling and Performance Structure. Specific research results by conference participants were presented and examined in the light of the frameworks outlined above, which is of interest to academics, researchers and professionals in this field. Two keynote speeches were presented from Prof. Weihua Zhuang, University of Waterloo, Canada, whose topic was about Service Provisioning in 5G Communication Networks; Prof. Nagula Sangary, University of Waterloo & Prudential Technology Ltd., whose topic was about Trends and Challenges in Terrestrial Satellite Wireless Communication Systems in mm-Wave range. All the talks were very impressive for the high level of professionalism, and in many cases original ideas and activities have been accomplished or proposed. List of Committees are availble in this PDF.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.219 | 0.178 |
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".