Special issue on Computational Science and Its Applications
Bibliographic record
Abstract
This issue features a special issue on ‘Computational Science and Its Applications’. Computational Science is the main pillar of most of the present research, industrial and commercial activities and plays a unique role in exploiting ICT innovative technologies. Owing to the latest development and the availability of high-performance computing, including parallel computing, grid computing, and cloud computing, there is a critical need to employ efficient and effective computational methods and algorithms in various applications, including computational biology, computational geometry, computational physics, computation chemistry, computational finance, graphics and visualization, scientific data management, data mining, etc. This issue features selected papers from the International Conference on Computational Science and Its Applications (ICCSA2009) held in 29 June–1 July, 2009, Kyung Hee University, Suwon, South Korea. In addition to extended papers from ICCSA2009, a special issue CFP has been distributed to a wider community through various mailing lists. Finally, we selected five papers to be included in this issue. The first paper discusses data and knowledge grids. It basically combines grid computing and real-time service management and execution paradigms, which makes use of the service-oriented architecture and paradigm suited for the grid platform. The second focuses on grid and P2P systems, especially in the context of sharing paradigm. It discusses middleware and libraries for grid and P2P systems. The third paper also focuses on P2P, whereby it describes scalable group communication protocols. The fourth paper focuses on road network query processing, which can be adopted in a mobile environment. It particularly concentrates on range search in a continuous mobile dynamic. Finally, the fifth paper introduces context-aware semantic network similarity model, using an ontological approach. As general co-chairs and program co-chairs of ICCSA2009, as well as the guest editors of the special issue on Computational Science in the Concurrency and Computation journal, we would like to congratulate the authors whose papers appeared in this special issue. We would also like to thank the PC members of ICCSA2009 who conducted the initial reviewing process for the conference and external reviewers who conducted further reviews of extended papers submitted to this special issue.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".