The rapid development of remote sensing technologies and the availability of many satellite and aerial sensors have boosted the collection of large volumes of high-resolution images, promoting progress in a wide range of applications. As a consequence, Object detection (OD) in aerial images has gained much interest in the last few years. However, the development of object detectors requires a massive amount of carefully labeled data. Since annotating datasets is very time-consuming and may require expert knowledge, a consistent number of weakly supervised object localization (WSOL) and detection (WSOD) methods have been developed. These approaches exploit only coarse-grained metadata, typically whole image labels, to train object detectors. However, many challenges remain open due to the missing location information in the training process of WSOD approaches and to the complexity of remote sensing images. Furthermore, methods studied for natural images may not be directly applicable to remote sensing images (RSI) and may require carefully designed adaptations. This work provides a comprehensive survey of the recent achievements of remote sensing weakly supervised object detection (RSWSOD). An analysis of the challenges related to RSWSOD is presented, the advanced techniques developed to improve WSOD are summarized, the available benchmarking datasets are described and a discussion of future directions of RSWSOD research is provided.

Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors , that implement security requirements. A key challenge is ensuring the LLMs have enough knowledge to address specific security requirements, such as information about existing attacks. For this, we propose an approach integrating Retrieval Augmented Generation (RAG) and Self-Ranking into the LLM pipeline. RAG enhances the robustness of the output by incorporating external knowledge sources, while the Self-Ranking technique, inspired by the concept of Self-Consistency, generates multiple reasoning paths and creates ranks to select the most robust detector. Our extensive empirical study targets code generated by LLMs to detect two prevalent injection attacks in web security: Cross-Site Scripting (XSS) and SQL injection (SQLi). Results show a significant improvement in detection performance while employing RAG and Self-Ranking, with an increase of up to 71%pt (on average 37%pt) and up to 43%pt (on average 6%pt) in the F2-Score for XSS and SQLi detection, respectively.

Retrieval-Augmented Generation (RAG)-based systems are increasingly deployed in high-stakes settings where correct behaviour depends not only on the language model but also on the retrieval component that selects external documents at inference time. While existing RAG evaluation metrics assess retrieval and generation quality on a per-query basis, typically relying on query-level test oracles such as reference answers or relevance annotations, they provide limited insight into whether a test suite adequately exercises the retrieval behaviour of the system as a whole. In this paper, we introduce Chunk Coverage (CC), an oracle-independent test adequacy criterion for testing the retrieval component of RAG systems. CC measures the fraction of corpus chunks that are retrieved at least once across a test suite, providing a structural view of which parts of the retrieval space have been exercised. We further show how CC can be used to guide test selection and generation by prioritising queries that expand coverage of previously unexercised retrieval regions. We evaluate CC on clinical and financial RAG system scenarios. CC-guided testing reaches 50% of attainable coverage 1.7x faster than random selection and 4.2x faster than redundancy-biased strategies. Moreover, CC improves fault detection effectiveness (APFD) by 10% to 25% over random, indicating earlier discovery of distinct retrieval faults. These results show that CC captures retrieval diversity relevant to effective testing without requiring test oracles.

Reinforcement learning (RL) algorithms, due to their reliance on external systems to learn from, require digital environments (e.g., simulators) with very simple interfaces, which in turn constrain significantly the implementation of such environments. In particular, these environments are implemented either as separate processes or as state machines, leading to synchronization and communication overheads in the first case, and to unstructured programming in the second. We propose a new domain-specific, co-routine-based, compiled language, called Rulebook, designed to automatically generate the state machine required to interact with machine learning (ML) algorithms and similar applications, with no performance overhead. Rulebook allows users to express programs without needing to be aware of the specific interface required by the ML components. By decoupling the execution model of the program from the syntactical encoding of the program, and thus without the need for manual state management, Rulebook allows to create larger and more sophisticated environments at a lower development cost.