Mobile games success and failure:mining the hidden factors

Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released eachday. However, a few of them succeed while the majority fail. Toward the goal of investigating the potential correlationbetween the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousand gameswere considered for that reason. We show that IAPs (In-App Purchases), genre, number of supported languages, developerprofile, and release month have a clear effect on the success of a mobile game. We also develop a novel success scorereflecting multiple objectives. Furthermore, we show that game icons with certain visual characteristics tend to be asso-ciated with more rating counts. We employ different machine learning models to predict a novel success score metric of amobile game given its attributes. The trained models were able to predict this score, as well as the expected rating averageand rating count for a mobile game with 70% accuracy

Recognition of Traffic Signs with Artificial Neural Networks:A Novel Dataset and Algorithm

Traffic sign classification is a prime issue for autonomous platform industries such as autonomous cars. Towards the goal of recognition, most recent classification methods deploy Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs). In this work, we provide a novel dataset and a hybrid ANN that achieves accurate results that are very close to the state-of-the-art ones. When training and testing on German Traffic Sign Recognition Benchmarks (GTSRB) a top-5 classification accuracy of 80% was achieved for 43 classes. On the other hand, a top-2 classification accuracy of 95% was reached on our novel dataset for 10 classes. This accomplishment can be linked to the fact that the proposed hybrid ANN combines 9 different models trained on color intensity, HOG (Histograms of Oriented Gradients) and LBP (Local Binary Pattern) features

Silver: Novel Rendering Engine for Data Hungry Computer Vision Models

Large-scale synthetic data is needed to support the deep learning big-bang that started in the recent decade and influenced almost all scientific fields. Most of the synthetic data generation solutions are task-specific or unscalable while the others are expensive, based on commercial games, or unreliable. In this work, a new rendering engine called Silver is presented in detail. Photo-realism, diversity, scalability, and full 3D virtual world generation at run-time are the key aspects of this work. The photo-realism was approached by utilizing the state-of-the-art High Definition Render Pipeline (HDRP) of the Unity game engine. In parallel, the Procedural Content Generation (PCG) concept was employed to create a full 3D virtual world at run-time, while the scalability of the system was attained by taking advantage of the modular approach followed as we built the system from scratch. Silver can be used to provide clean, unbiased, and large-scale training and testing data for various computer vision tasks

Using synthetic data for person tracking under adverse weather conditions

Robust visual tracking plays a vital role in many areas such as autonomous cars, surveillance and robotics. Recent trackers were shown to achieve adequate results under normal tracking scenarios with clear weather conditions, standard camera setups and lighting conditions. Yet, the performance of these trackers, whether they are correlation filter-based or learning-based, degrade under adverse weather conditions. The lack of videos with such weather conditions, in the available visual object tracking datasets, is the prime issue behind the low performance of the learning-based tracking algorithms. In this work, we provide a new person-tracking dataset of real-world sequences (PTAW172Real) captured under foggy, rainy and snowy weather conditions to assess the performance of the current trackers. We also introduce a novel person-tracking dataset of synthetic sequences(PTAW217Synth) procedurally generated by our NOVA framework spanning the same weather conditions in varying severity to mitigate the problem of data scarcity. Our experimental results demonstrate that the performances of the state-of-the-art deep trackers under adverse weather conditions can be boosted when the available real training sequences are complemented with our synthetically generated dataset during training

Leveraging synthetic data to learn video stabilization under adverse conditions

Stabilization plays a central role in improving the quality of videos. However, current methods perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather video stabilization algorithm that dispenses real data for training, relying solely on synthetic data. Our approach leverages specially generated synthetic data to avoid the feature extraction issues faced by current methods. To achieve this, we present a novel data generator to produce the required training data with an automatic ground-truth extraction procedure. We also propose a new dataset, VSAC105Real, and compare our method to five recent video stabilization algorithms using two benchmarks. Our method generalizes well on real-world videos across all weather conditions and does not require large-scale synthetic training data. Implementations for our proposed video stabilization algorithm, generator, and datasets are available at https://github.com/A-Kerim/SyntheticData4VideoStabilization_WACV_2024

Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions

Stabilization plays a central role in improving the quality of videos. However, current methods perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather video stabilization algorithm that dispenses real data for training, relying solely on synthetic data. Our approach leverages specially generated synthetic data to avoid the feature extraction issues faced by current methods. To achieve this, we present a novel data generator to produce the required training data with an automatic ground-truth extraction procedure. We also propose a new dataset, VSAC105Real, and compare our method to five recent video stabilization algorithms using two benchmarks. Our method generalizes well on real-world videos across all weather conditions and does not require large-scale synthetic training data. Implementations for our proposed video stabilization algorithm, generator, and datasets are available at https://github.com/A-Kerim/SyntheticData4VideoStabilization_WACV_2024

Semantic Segmentation under Adverse Conditions:A Weather and Nighttime-aware Synthetic Data-based Approach

Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, timeconsuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model’s performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a novel architecture specifically designed for using synthetic training data for domain adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using weather and time-of-the-day supervisors trained with multi-task learning, making it both weather and nighttime aware, which improves its mIoU accuracy by 14 percentage points on the ACDC dataset while maintaining a score of 75% mIoU on the Cityscapes dataset. Our code is available at https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions

Semantic segmentation under adverse conditions: a weather and nighttime-aware synthetic data-based approach

Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model’s performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a novel architecture specifically designed for using synthetic training data for domain adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using weather and time-of-the-day supervisors trained with multi-task learning, making it both weather and nighttime aware, which improves its mIoU accuracy by 14 percentage points on the ACDC dataset while maintaining a score of 75% mIoU on the Cityscapes dataset. Our code is available at https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Synthetic Data for Machine Learning

Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studies Key Features - Avoid common data issues by identifying and solving them using synthetic data-based solutions - Master synthetic data generation approaches to prepare for the future of machine learning - Enhance performance, reduce budget, and stand out from competitors using synthetic data - Purchase of the print or Kindle book includes a free PDF eBook Book Description The machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges. This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data. By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML. What you will learn - Understand real data problems, limitations, drawbacks, and pitfalls - Harness the potential of synthetic data for data-hungry ML models - Discover state-of-the-art synthetic data generation approaches and solutions - Uncover synthetic data potential by working on diverse case studies - Understand synthetic data challenges and emerging research topics - Apply synthetic data to your ML projects successfully Who this book is for If you are a machine learning (ML) practitioner or researcher who wants to overcome data problems, this book is for you. Basic knowledge of ML and Python programming is required. The book is one of the pioneer works on the subject, providing leading-edge support for ML engineers, researchers, companies, and decision makers. Table of Contents 1. Machine Learning and the Need for Data 2. Annotating Real Data 3. Privacy Issues in Real Data 4. An Introduction to Synthetic Data 5. Synthetic Data as a Solution 6. Leveraging Simulators and Rendering Engines to Generate Synthetic Data 7. Exploring Generative Adversarial Networks 8. Video Games as a Source of Synthetic Data 9. Exploring Diffusion Models for Synthetic Data 10. Case Study 1 – Computer Vision 11. Case Study 2 – Natural Language Processing 12. Case Study 3 – Predictive Analytics 13. Best Practices for Applying Synthetic Data 14. Synthetic-to-Real Domain Adaptation 15. Diversity Issues in Synthetic Data 16. Photorealism in Computer Vision 17. Conclusio