Inference 1:
Input unit ideas:
Learning a workout often requires research for beginners.
Strength training and workouts with equipment especially require research.
An experienced instructor can guide trainees to perform such activities.
Advances in computer vision create opportunities for technology to teach workouts without human intervention.
In this study, Kinect V2 will be used to detect and track the subject's joints.
Kinect V2 will capture the start and end positions of the workout movements.
Using the RGB-D sensor, the model will track joint positions.
The model will assess if the posture is correct for five specific workouts.
The five workouts include: dumbbell lateral raise, dumbbell shoulder press, barbell front raise, dumbbell shrug, and barbell upright row.
Java and KinectPV2 library will collect data by recording joint coordinates for each subject.
Scikit-learn will be used to develop the model, training and testing datasets.
Extremely Randomized Trees (ERT) is an ensemble method for supervised classification and regression that introduces randomness.
ERT will be used for the proposed model and compared against K-Nearest Neighbors (KNN).

Reconstruction by No-Bias Baseline Averaged Standard American English Model:
Learning workouts, especially strength training and exercises with equipment, often requires research for beginners. An experienced instructor can guide trainees in performing these activities correctly. However, advances in computer vision technology now provide opportunities for workout instruction without human intervention. In this study, Kinect V2 will be used to detect and track joint movements to analyze workout posture accuracy. The Kinect V2 device, equipped with an RGB-D sensor, will capture the start and end positions of specific workout movements and track joint positions for five exercises: dumbbell lateral raise, dumbbell shoulder press, barbell front raise, dumbbell shrug, and barbell upright row. Java, along with the KinectPV2 library, will collect joint coordinate data for each subject, while Scikit-learn will be employed to develop the model for training and testing. The proposed model will utilize Extremely Randomized Trees (ERT), an ensemble method introducing randomness in supervised classification and regression, and its performance will be compared against the K-Nearest Neighbors (KNN) algorithm.

Reconstruction by an Average Filipino:
Learning how to perform a workout usually requires a little bit of research for people without experience, especially if it's in strength training or a workout that uses an equipment. Having an experienced instructor guide a trainee to perform such activity is enough, but with the advancement of computer vision, it opens up an opportunity to use technology to teach us without human intervention. In this study, Kinect V2 will be used to detect the joints of the subject and capture the starting and the end position of the workout. By tracking the position of the joints through the RGB-D sensor, the model will determine if the posture was performed correctly by the subject of these 5 (five) workouts: dumbbell lateral raise, dumbbell shoulder press, barbell front raise, dumbbell shrug, and barbell upright row. The data will be collected using the Java language and the KinectPV2 library to get the coordinates of the joints for each subject. Scikit-learn will be the chosen platform in the model creation for training and testing the datasets. Extremely Randomized Trees is a tree-based ensemble method for supervised classification and regression that will randomize. ERT will be used to the model proposed and compare it to the KNN.

Inference 2:
Input unit ideas:
The short film by @ig_life tited "Instagram life versus real life" explores the contrast between online and offline personas.
The film highlights the difference between a digital/ideal self and the real self.
It cautions viewers not to believe everything on social media, as perfect shots are often edited.
People often portray a perfect version of themselves on Instagram, which is different from their real lives.
In the film, a girl contrasts her real life with her Instagram life.
Her Instagram life shows a healthy and happy lifestyle, which contrasts with her reality.
The girl feels happy when she receives many likes on her posts.
She becomes depressed when she doesn't get any reactions to her posts.

Reconstruction by No-Bias Baseline Averaged Standard American English Model:
The short film "Instagram Life Versus Real Life" by @ig_life contrasts the curated online persona with the authentic offline self. It emphasizes the discrepancy between idealized digital images and lived reality, warning viewers against the deceptive nature of social media's carefully edited perfection. The film centers on a young woman whose meticulously crafted Instagram feed portrays a healthy and happy lifestyle, starkly contrasting with her actual struggles. Her emotional well-being is demonstrably tied to the validation of likes and engagement, highlighting the potential for social media to negatively impact self-esteem and mental health. The film serves as a cautionary tale about the pitfalls of seeking validation through online platforms and the importance of self-acceptance.

Reconstruction by an Average Filipino:
The short film titled "@ig_life/ Instagram life versus real life" is another story which gives us lesson about the difference of online and offline or how we distinguish the digital self/ideal self and real self. It also reminds us not to believe everything we see on social media because for every perfect angled are just edited glamorous shot. Many people portray on Instagram what they want to look in their life, they wanted to look perfect but it is far from reality. In the film it shows how girl contrast her real life and her Instagram life. In her Instagram post/ Instagram life, she lives a healthy lifestyle and happy but it does not show in reality, or it is contrast to her real life behind the camera. She looks like happy when she got lots of heart/ reaction in her Instagram post but when the time she doesn't get any reaction, it's depressing.

Formalize the English characteristics and traits of Average Filipino, for each characteristic/trait, give the pure raw unit idea in english, and the pure raw unit idea in filipino, and the average filipino reconstruction and the technical explanation and cultural context for each.