Bibliographic record
Abstract
The benefit of using WiFi for Internet connection is obvious: cost-effective and powerful. WiFi gives us the flexibility and convenience of not being tied to a fixed location. Nowadays, more and more electronic devices and gadgets, such as mobile phones, cameras, gaming devices, TV and entertainment equipment, are WiFi enabled. WiFi also enables your devices to share files instantly. WiFi broadcasting devices, such as Chromecast, give you extra convenience by allowing you to stream video and audio contents from your mobile phone to your TV using WiFi connection. However, this kind of flexibility and convenience comes with a cost. Sharing files, streaming contents or even accessing the Internet via WiFi means signals are being transmitted and they can be captured by anyone with a computer or mobile phone installed with appropriate software. Therefore, it is important to let WiFi users know their security risks and how to minimize them. Educating WiFi users to reduce the WiFi security risk is one of our on-going missions. Basing on empirically collected data, this paper is report of a comprehensive study on the use of WiFi and WiFi networking and the knowledge of WiFi users of the risks and security issues involved in using WiFi in Hong Kong. Findings of the study highlight the WiFi security knowledge gaps of the users in Hong Kong so that stakeholders can take action to improve Internet security by eliminating the security gaps identified.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".