Combination of connectors with loosely coupled architecture based on aspect-oriented computing
Bibliographic record
Abstract
Software architecture has a vital role in achieving quality goals for large scale software systems which is made up of components and connectors. For reducing the complexity of software, components and connectors are applied to understanding, designing, and implementing software, especially connectors residing in distributed systems. To satisfy requirements of interaction between various components, it is time and cost consuming process to create a connector. In particular, it is often difficult to select only one type of connector to develop connectors in distributed systems. To address the difficulties, our research focuses on the issue: how do traditional types of connectors, in combination with new technologies in distributed systems, provide systems architectures with loosely coupled structures. In this paper, we propose an approach to the combination of connectors in order to provide distributed systems with loosely coupled interaction. Our approach involves AOP technology and design pattern, as well as messaging systems. In the end, we present an example of our approach in which we show a connector designed by combining the AspectJ, shared memory, UDP Socket and publish-subscribe design pattern with the aim of designing a loosely coupled system architecture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".