Bibliographic record
Abstract
<p>WiFi is the fastest and most cost-effective way of wireless Internet connectivity. Nowadays, almost all of the mobile phones and an increasing number of home entertainment systems are WiFi-enabled. Being the key enabler of the “Internet of Everything”, WiFi brings including people, processes, data and devices, together and turns data into valuable information that makes life better and business thrive. With all mobile devices, wearable gadgets, home entertainment systems and home automation systems connected together and linked to the Internet, devices can now interact with one another and data be shared among the devices. However, transmitting information across the WiFi network means leaving your computer or devices vulnerable to attack, giving unscrupulous people the opportunity to intercept traffic, selectively eavesdrop on critical communications or even the administrative access and thus the ability to harvest all the information they want. All these threats highlight the growing importance of keeping your WiFi secure from unauthorized access and malicious attacks.</p><p>Basing on empirically collected quantitative data, this paper presents a comprehensive study on Hong Kong people’s knowledge about WiFi security and their use of WiFi in connecting the Internet. Findings of the study shed light on the knowledge gaps of Hong Kong WiFi users in using and setting up WiFi connections so that service providers, policy makers and stakeholders can devise appropriate security measures to improve the security of WiFi connection. The study also canvasses and analyses the views of the users on the connectivity and quality of free and commercial WiFi service in Hong Kong. The findings can help government and private WiFi operators to further improve the service provided. </p>
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) | 0.003 | 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".