Graded noise reduction
Introduction: Devices and applications demand our attention with notifications. Have you ever felt overwhelmed by the notifications from every application including games on your smartphone? Wouldn't it be nice if there was an automated way to turn off notifications depending on your interest level? Sure, we could turn on or off notifications from individual apps but how often do we toggle that. Besides how do we know which applications' notifications do we turn on or off at this time? Instead if there was a single slider that would do this for us the right way each time, wouldn't it be more convenient? This is the purpose of this writeup.
Description: Consider the number of on-off switches to be one each for every application. Since there can be many applications, this list may run into several pages. Some of these applications may be merely games which are notorious for their notifications. Outlook and Calendar, on the other hand, would only serve reminders if they are configured. All these applications are decided jointly. If we could string through the apps with a single switch, then we could turn off the notifications all at once. Since the applications are decided dynamically, it could choose to include the noisy ones depending on our tolerance levels. Therefore, we have a sliding scale of tolerance and as we move the slider over it, apps will start going silent starting from a few and ending with all. And the greatest benefit is the enhanced control for the region in between. Its takes away the effort involved in figuring out which applications to silence at any given time and learns this dynamically based on users' initial selections and the rate at which notifications are sent by the applications. This articulation of a single control gives us graded silence so we can adjust it instead of resorting to mute our phone all the time which still does not address the cluttered notifications on the screen and the requirement to browse them before dismissing one or all and that too repeatedly. Internally the sliding scale uses a simple weighted average of the number of notifications from the applications in a full day and the severity of the application. Reminders will be considered a high severity application while games and ads will be considered less severe. This easy setting of notifications will also reflect the toggled state before and after the switch is flipped. The applications are then ranked by priority and the threshold set by the user with the help of the slider is used to determine which applications are affected. These applications are then individually silenced. The algorithm to determine this can also use grouping and ranking techniques. Additionally, the means of notification may also be made consistent across all applications.
Testing: If there is a metric for the number and relevance of the notifications corresponding to the bar set by the slider such as the F1 score, then it is appropriate to grade the sliding scale and check if the notifications permitted match the setting for any and all assortment of applications.
Testing: If there is a metric for the number and relevance of the notifications corresponding to the bar set by the slider such as the F1 score, then it is appropriate to grade the sliding scale and check if the notifications permitted match the setting for any and all assortment of applications.
Conclusion: A single control across all applications that more useful than the mute button on the smartphone has some appeal to improve productivity for the owner.
#codingexercise
We were discussing finding the largest sum submatrix in a matrix of integers.
As we evaluated the growing range from column to column, we iterated inwards to outwards but the reverse is more efficient.
The advantage here is that the outer bounds of the entire matrix has the largest possible array to apply Kadane's algorithm which we discussed earlier as the one used for finding the max subarray.
#codingexercise
We were discussing finding the largest sum submatrix in a matrix of integers.
As we evaluated the growing range from column to column, we iterated inwards to outwards but the reverse is more efficient.
The advantage here is that the outer bounds of the entire matrix has the largest possible array to apply Kadane's algorithm which we discussed earlier as the one used for finding the max subarray.
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