Manual identification of visual bugs in video games is a resource-intensive and costly process, often demanding specialized domain knowledge. While supervised visual bug detection models offer a promising solution, their reliance on extensive labeled datasets presents a significant challenge due to the infrequent occurrence of such bugs. To overcome this limitation, we propose a hybrid Co-FineTuning (CFT) method that effectively integrates both labeled and unlabeled data. Our approach leverages labeled samples from the target game and diverse co-domain games, additionally incorporating unlabeled data to enhance feature representation learning. This strategy maximizes the utility of all available data, substantially reducing the dependency on labeled examples from the specific target game. The developed framework demonstrates enhanced scalability and adaptability, facilitating efficient visual bug detection across various game titles. Our experimental results show the robustness of the proposed method for game visual bug detection, exhibiting superior performance compared to conventional baselines across multiple gaming environments. Furthermore, CFT maintains competitive performance even when trained with only 50% of the labeled data from the target game.

Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3's daily ridership fluctuates owing to variables such as holidays, working days, and other unexpected issues. Commuters do not know how many other commuters are on their route on a given day, which may hinder their ability to plan an efficient itinerary. Currently, the DOTr depends on spreadsheets containing historical data, which might be challenging to examine. This study presents a time series prediction of daily traffic to anticipate future attendance at a particular station on specific days.

Establish a baseline performance for State-of-the-Art (SoTA) computer vision models with ease and use tuning to take them a step further. Today's world of Computer Vision offers different models for tasks like Image Classification, Object Detection, and Instance Segmentation. Each of these models has its strengths like some being faster, some being more accurate or some being good at specifics like identifying small objects, detecting anomalies, etc. The optimal model to go for depends on the user scenario at hand. With Azure AutoML for Images, users can explore and select from a variety of SoTA algorithms for a computer vision task and optionally tune the hyperparameters to optimize model performance with ease.

Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal ``human-in-the-loop'' involvement. We present \system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of different system components and benchmark our feature selection algorithm on real-world datasets.