Universal Automated Blinds; The Effects of Light Devices on Human Behavior
Bibliographic record
Abstract
With the introduction of artificial light and smart technology the transformation of light has brought to attention the analysis of the possible effects that it can have on humans. Specifically human behavior, with new smart light devices emerging the effects that light could have on humans is not necessarily general knowledge. Too much light it is possibly get skin cancer. Too little light and it is possible to develop health problems from the lack of Vitamin D. These are the two extremes and do not discuss the positive effects of light nor the inverse effects that have appeared with the invention of artificial light. Artificial light has eclipsed the natural darkness of the night which will be later discussed to have severe impacts on society. The case study by the Department of Psychiatry at the McGill University located in Canada will discuss the possible effects that light therapy has on human behavior through quantitative and qualitative data. The STS and technical papers are slightly related, they both relate to light devices and the introduction of smart tech to artificial lighting. The technical portion consist of an automated blinds system that is designed and constructed by myself and my capstone team. The objective of the automated blinds is to provide an easier way for homeowners to adjust their blinds and have set states depending on the time of the day or the amount of ambient light that is coming through the window. Users may also have to ability to enable a privacy setting which will close the blinds if there is movement detected outside. A societal concern may arise from this as these blinds may allow, people passing by, the ability to scope the user’s home. Since this privacy state could be enable this risk is mitigated, but not completely gone as some issues may still arise. Another societal concern is the possible effects that the increased amount of light may have on the users. Further analysis will be done to express the possible concerns and the decision engineers have to make when designing these devices. Additionally, data is sent via Wi-Fi pertaining to any information about the blinds position or set states which is then is parsed and stored in a cloud database for future use. This raises ethical concerns on the design of these blinds and its transfer of data. The engineer tasked with this project, my team and I, are primarily responsible for the possible invasion of privacy. There are possibilities to collect data from users which could provide sensitive information (inferring when the users are home). Privacy laws indicate the correct steps for designers/engineers to take in these cases, where the information is not publicly listed nor does it have information relating to the users such as their names or address. The engineers have a duty to ensure the privacy of its users and to keep their health and safety in mind when designing new devices.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".