Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge. In this project, we extend the Popper ILP system to make use of multi-task learning. We implement the state-of-the-art approach and several new strategies to improve search performance. Furthermore, we introduce constraint preservation, a technique that improves overall performance for all approaches. Constraint preservation allows the system to transfer knowledge between updates on the background knowledge set. Consequently, we reduce the amount of repeated work performed by the system. Additionally, constraint preservation allows us to transition from the current state-of-the-art iterative deepening search approach to a more efficient breadth first search approach. Finally, we experiment with curriculum learning techniques and show their potential benefit to the field.

Object detection for street-level objects can be applied to various use cases, from car and traffic detection to the self-driving car system. Therefore, finding the best object detection algorithm is essential to apply it effectively. Many object detection algorithms have been released, and many have compared object detection algorithms, but few have compared the latest algorithms, such as YOLOv5, primarily which focus on street-level objects. This paper compares various one-stage detector algorithms; SSD MobileNetv2 FPN-lite 320x320, YOLOv3, YOLOv4, YOLOv5l, and YOLOv5s for street-level object detection within real-time images. The experiment utilizes a modified Udacity Self Driving Car Dataset with 3,169 images. Dataset is split into train, validation, and test; Then, it is preprocessed and augmented using rescaling, hue shifting, and noise. Each algorithm is then trained and evaluated. Based on the experiments, the algorithms have produced decent results according to the inference time and the values of their precision, recall, F1-Score, and Mean Average Precision (mAP). The results also shows that YOLOv5l outperforms the other algorithms in terms of accuracy with a mAP@.5 of 0.593, MobileNetv2 FPN-lite has the fastest inference time among the others with only 3.20ms inference time. It is also found that YOLOv5s is the most efficient, with it having a YOLOv5l accuracy and a speed almost as quick as the MobileNetv2 FPN-lite. This shows that various algorithm are suitable for street-level object detection and viable enough to be used in self-driving car.

In 1959, Arthur Samuel, a pioneer in the field of Artificial Intelligence defined the term Machine Learning [1] as the "field of study that gives computers the ability to learn without being explicitly programmed". In the field of Machine Learning, an important technique called Deep Learning allows "computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction" [2]. In recent years, many accurate decision support systems based on Deep Learning have been constructed as black boxes [3], that is as systems that hide their internal logic to the user. Thus, the purpose of an Explainable Artificial Intelligence [4-7] system is to make its behavior more intelligible to humans by providing explanations [8]. A popular approach to addressing the problem of opacity of black-box machine learning models is the use of post-hoc explainability methods: these methods approximate the logic of underlying machine learning models with the aim of explaining their internal workings, so that the user can understand them [9]. Unfortunately, these methods provide explanations that are not faithful to what the black-box model computes and can be misleading [10]. A recent and highly cited perspective [10] highlighted the need for white box models (i.e.

A precise forecast for droughts is of considerable value to scientific research, agriculture, and water resource management. With emerging developments of data-driven approaches for hydro-climate modeling, this paper investigates an AutoML-based framework to forecast droughts in the U.S. Compared with commonly-used temporal deep learning models, the AutoML model can achieve comparable performance with less training data and time. As deep learning models are becoming popular for Earth system modeling, this paper aims to bring more efforts to AutoML-based methods, and the use of them as benchmark baselines for more complex deep learning models.

This manuscript gives an overview of my research work carried out within the FEMTO-ST institute in Besan\c{c}on, more particularly in the Automatic and Micro-Mechatronic Systems (AS2M) department. It is above all the result of my (co)-supervision of interns, PhD students and postdocs. I would like to pay tribute to them, for their major contribution to scientific research, here and elsewhere.

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on edge devices to enable instant data processing and response. A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices. These characteristics make it difficult to build deep learning solutions that unleash the potential of edge devices while complying with their constraints. A promising approach to addressing this challenge is to automate the design of effective deep learning models that are lightweight, require only a little storage, and incur only low computational overheads. This survey offers comprehensive coverage of studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey then proceeds to cover three categories of the state-of-the-art of deep model design automation techniques: automated neural architecture search, automated model compression, and joint automated design and compression. Finally, the survey covers open issues and directions for future research.

Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text-to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope that this survey can serve as quick access to existing work and motivate future research.

Autonomous driving is the key technology of intelligent transportation system, and also a very promising engineering project that could fundamentally change the life of human society. Although there has been a lot of progress in both academia and industry in the past decades, autonomous driving is still an important research topic nowadays, especially inspired by the development of computer vision and deep learning recently. One of the crucial module of autonomous driving is perception, which targets to perceiving the surrounding environment and extracting information related to navigation, including object detection, tracking, semantic segmentation and so on. Perception used to be regarded as the technique bottleneck of autonomous driving. In the past few years, perception performance has been significantly improved with increasing large-scale training data and developments of deep learning algorithms. However, it is not enough to meet the demand of practical high level of autonomous capacity because high level or fully autonomous vehicles do not require human supervision and totally rely on the perception of vehicles.

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term ``deepfake''. Based on both the research literature and resources in English and in Chinese, this paper gives a comprehensive overview of deepfake, covering multiple important aspects of this emerging concept, including 1) different definitions, 2) commonly used performance metrics and standards, and 3) deepfake-related datasets, challenges, competitions and benchmarks. In addition, the paper also reports a meta-review of 12 selected deepfake-related survey papers published in 2020 and 2021, focusing not only on the mentioned aspects, but also on the analysis of key challenges and recommendations. We believe that this paper is the most comprehensive review of deepfake in terms of aspects covered, and the first one covering both the English and Chinese literature and sources.

Designing and implementing explainable systems is seen as the next step towards increasing user trust in, acceptance of and reliance on Artificial Intelligence (AI) systems. While explaining choices made by black-box algorithms such as machine learning and deep learning has occupied most of the limelight, systems that attempt to explain decisions (even simple ones) in the context of social choice are steadily catching up. In this paper, we provide a comprehensive survey of explainability in mechanism design, a domain characterized by economically motivated agents and often having no single choice that maximizes all individual utility functions. We discuss the main properties and goals of explainability in mechanism design, distinguishing them from those of Explainable AI in general. This discussion is followed by a thorough review of the challenges one may face when working on Explainable Mechanism Design and propose a few solution concepts to those.