Insta(nt) Pet Therapy: GAN-generated Images for Therapeutic Social Media Content

The positive therapeutic effect of viewing pet images online has been well-studied. However, it is difficult to obtain large-scale production of such content since it relies on pet owners to capture photographs and upload them. I use a Generative Adversarial Network-based framework for the creation of fake pet images at scale. These images are uploaded on an Instagram account where they drive user engagement at levels comparable to those seen with images from accounts with traditional pet photographs, underlining the applicability of the framework to be used for pet-therapy social media content.Comment: 7 pages, 7 figure

Creating a Competitive Environment for Defense Aerospace in a Protectionist Multipolar World: A Study of India and Israel

The paper studies protectionism in defense aerospace in a multipolar world and analyzes the strategies of two emerging powers: India and Israel. The emergence of protectionism in a multipolar world has left a visible and influential impact in the globally integrated defense industry. As the world has become increasingly multipolar, new military powers have emerged around the world. India and Israel are disparate in terms of their size, wealth, and international relations. There are interesting similarities between them when it comes to their defense strategies. As a result, they also present compelling case studies for understanding protectionism in a multipolar world, specifically in the defense aerospace sector. This paper studies the current strategies adopted by the two nations in their defense aerospace manufacturing sectors. The paper evaluates differences and similarities between the two nations in terms of the issues faced by the defense aerospace sectors of the two nations and the potential that lies ahead for them. In the recommendations made, it was discussed how Israel needs new defense partners to reduce its over dependence on the United States, while India needs to boost manufacturing in its defense aerospace industry through specific tax reforms and bureaucratic reforms. While India and Israel need to regulate the defense aerospace industry to some extent for national security reasons, they should open their industries to other countries and find favorable partners to do so

Machine Learning-based Live Predictive Warnings for Unstabilized Approaches in Aircraft

Unstabilized approaches are a major hazard for general aviation aircraft. An unstabilized approach can lead to runway excursions, structural damage on touchdown, or even Controlled Flight into Terrain (CFIT). The Aircraft Owners and Pilots Association reported that 3,257 general aviation accidents from 2009-2019 occurred during the landing phase of a flight. The advancement of machine learning technology offers the opportunity to develop low-cost and easily adaptable technology. This research is aimed at developing machine learning-based predictive warnings for pilots to abort an unstabilized approach and execute a go-around maneuver. As the first step, we collected feature-rich flight data which could be useful for making predictions of unstabilized approaches. The data utilized for the model preparation was derived from the Flight Data Monitoring (FDM) program of a Part-141 Flight Training Organization. As a first step of preprocessing, we decided to extract only the variables that would be determining factors when predicting approach stability, based on the developed criteria. Additionally, we structured the data by separating it into matrices corresponding to exactly one flight - defined as the period from one take-off through the subsequent landing - determined by the change of altitude, airspeed, and engine power variables. We will use deep neural networks to train our machine learning model to predict unstabilized approaches. Since the data is structured with data points corresponding to every second of the flight, i.e., it is time-series, we will use a Recurrent Neural Network which is specifically adept at modeling time-series data. To develop our model, we will use an 85% training set, a 5% development set, and a 10% testing set split for our complete dataset comprising approximately 42,000 flights. The deep neural network architecture will be designed using the Tensorflow 2 framework. The model developed in this project will be a low-cost, objective decision-making aid for pilots that will improve general aviation safety. The model will be integrable into avionics systems that are used by general aviation pilots, such as the Garmin G1000®, for their aircraft

Utilizing Deep Learning to Predict Unstabilized Approaches for General Aviation Aircraft

Unstabilized approaches pose a major hazard for general aviation aircraft. In the period from 2009 to 2019, 3,257 general aviation accidents occurred during the landing phase of flight in which loss of control was analyzed to be the leading cause of accidents [1]. Previous studies have explored the use of machine learning to develop low-cost and easily adaptable predictive tools as possible mitigation tools for unstabilized approaches. This study was aimed at developing a machine learning-based predictive warning system for pilots to abort an unstabilized approach and execute a go-around maneuver. Deep neural networks were trained predict unstabilized approaches for a light multi-engine general aviation aircraft. Since the data was structured with data points corresponding to every second of the flight and exhibited qualities of a time-series dataset, a Recurrent Neural Network architecture was used to model the timeseries relationships. To develop and validate the model, a dataset comprising of approximately 42,000 landings was used. The model developed in this study was able to predict an unstabilized approach with an accuracy of 84%, and the vertical speed of an aircraft was determined to be the most significant predictor of an unstabilized approach