Bibliographic record
Abstract
Firewalls filter information as it flows through a network. This filter can be implemented in hardware or software and can be used to protect computers from unwanted access. While software firewalls are considered easier to set up and use, hardware firewalls are often considered faster and more secure. Absent from the marketplace is an embedded hardware solution applicable to desktop systems. Traditional software firewalls use the CPU of the computer to filter packets; this is disadvantageous because the computer can become unusable during a network attack when the CPU is swamped by the firewall process. Traditional hardware firewalls are usually implemented in a single location, between a private network and the Internet. Depending on the size of the private network, a hardware firewall may be responsible for filtering the network traffic of hundreds of clients. This not only makes the required hardware firewall quite expensive, but dedicates those financial resources to a single point that may fail. The dynamic silicon firewall project implements a hardware firewall using a soft-core processor with a custom peripheral designed using a hardware description language. Embedding this hardware firewall on each network interface card in a network would offer many benefits. It would avoid the aforementioned denial of service problem that software firewalls are susceptible to since the custom peripheral handles the filtering of packets. It could also reduce the complexity required to secure a large private network, and eliminate the problem of a single point of failure. Also, the dynamic silicon firewall requires little to no administration since the filtering rules change with the user's network activity. The design of the dynamic silicon firewall incorporates the best features from traditional hardware and software firewalls, while minimizing or avoiding the negative aspects of both.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".