Information Systems (IS) Audit Services & Design of Mechanism for the Future E-Commerce
Bibliographic record
Abstract
Talking of security and its potential breaches has become a piece of common talk in all the deliberations related to the e-commerce activities all over the globe. The security of e-commerce is a serious concern of all the major players in the digital business arena who rely heavily on distributed processing in their routine daily operational chores. The security breaches and the related frauds have cost billions of dollars to the businesses and the industries as a whole. Consumers are also not far behind in the sufferings through frauds in the digital market place. It is possibly due to the fact that business models of transaction processing that are viable in the conventional commerce might be wrong ab initio in the context of digital business. And, therefore, we feel that there is a growing need for robust tools and equally rigorous auditable methodologies in the design and verification of the correctness of the digital processing systems that operate over the internet. This paper focuses on the economic reasoning of business process design. I have decided to go for development of a secure online auction protocol as an attempt at applying the design of mechanism reasoning framework in the direction of information systems audit and control of ecommerce.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".