State-of-the-art watermarking and fingerprinting techniques for Large Language Models (LLMs) are explored, with our analysis spanning a wide array of methodologies designed to protect the intellectual property of LLMs. The review of watermarking techniques is based on embedding watermarks during the training, logits generation, and token sampling phases. Meanwhile, we investigate the application of watermarking technology in multimodal LLMs and potential attacks on watermarks. Moreover, our examination of fingerprinting techniques revealed the ingenuity behind methods used to identify LLMs. We discussed the development of fingerprints based on model behavior and using deep learning models to learn thresholds for fingerprint comparison. Our survey has underscored the importance of advancing security measures for LLMs, especially in light of the increasing sophistication of adversarial attacks. As LLMs continue to play a pivotal role in advancing AI technologies, developing and refining security measures that safeguard their intellectual property and ensure their ethical deployment is imperative.

Mathematical reasoning has long represented one of the most fundamental and challenging frontiers in artificial intelligence research. In recent years, large language models (LLMs) have achieved significant advances in this area. This survey examines the development of mathematical reasoning abilities in LLMs through two high-level cognitive phases: comprehension, where models gain mathematical understanding via diverse pretraining strategies, and answer generation, which has progressed from direct prediction to step-by-step Chain-of-Thought (CoT) reasoning. We review methods for enhancing mathematical reasoning, ranging from training-free prompting to fine-tuning approaches such as supervised fine-tuning and reinforcement learning, and discuss recent work on extended CoT and “test-time scaling”. Despite notable progress, fundamental challenges remain in terms of capacity, efficiency, and generalization. To address these issues, we highlight promising research directions, including advanced pretraining and knowledge augmentation techniques, formal reasoning frameworks, and meta-generalization through principled learning paradigms. This survey tries to provide some insights for researchers interested in enhancing reasoning capabilities of LLMs and for those seeking to apply these techniques to other domains.

Causal inference plays an important role in explanatory analysis and decision-making across a wide range of fields, including statistics, marketing, healthcare, and education. Its core objective is to estimate treatment effects and inform intervention policies. Most existing work focuses on the binary treatment setting, where each unit is assigned to either treatment or control. In practice, however, treatments are often more complex, encompassing multi-valued, continuous, or bundle interventions. We refer to such settings as complex treatments. In this paper, we provide a systematic and comprehensive survey of causal inference methods for complex treatments. We first revisit the problem formulation, core assumptions, and their possible variations under different settings. We sequentially review the representative methods for multi-valued, continuous, and bundle treatments. Within each setting, we organize the methods into two broad categories: those that rely on the unconfoundedness assumption and those that address violations of this assumption. We further discuss the intrinsic relationships among these methods and the assumption verification. Finally, we summarize available benchmark datasets and open-source codes, and outline several directions for future research.

The swift evolution of Large Language Models (LLMs) like the GPT family, LLaMA, ChatGLM, and Qwen represents significant progress in artificial intelligence research. Despite their remarkable capabilities in generating content, these models encounter substantial challenges when producing structured outputs and engaging in dynamic interactions, particularly when they need to retrieve external information in real time. To address these limitations, researchers have developed the ”Function Calling” paradigm. This approach enables language models to analyze user inquiries and engage with defined functions, thereby facilitating precise responses through connections to external sources, including databases, programming interfaces, and live data streams. This functionality has been successfully implemented across numerous sectors such as finance analytics, healthcare systems, and service operations. The implementation of function calling comprises three essential phases: preparation, execution, and processing. The preparation phase encompasses query analysis and function identification. During execution, the system evaluates whether a function is necessary, extracts relevant parameters, and oversees the operation. The processing phase concentrates on analyzing outcomes and crafting appropriate responses. Each phase presents unique difficulties, ranging from accurately selecting functions to managing complex parameter extraction and ensuring reliable execution. Researchers have established various evaluation frameworks and metrics to assess function calling performance, including success rates, computational efficiency, parameter extraction accuracy, and response quality indicators such as ROUGE-L evaluation scores. This survey systematically reviews the current landscape of function calling in LLMs, analyzing technical challenges, examining existing solutions, and discussing evaluation methodologies. We particularly focus on practical implementations and industrial applications, providing insights into both current achievements and future directions in this rapidly evolving field. For a comprehensive collection of related research papers and the Appendix file, please refer to our repository at GitHub.

The use of Unmanned Aerial Vehicles (UAVs) in various domains is continuously increasing. The benefits offered by UAVs enable the facilitation of functions or execution of tasks, such as search and rescue, which would otherwise be challenging or financially restrictive. Furthermore, UAVs are important for the military domain being considered as efficient weapons. Therefore, many countries and retail companies are investing in the massive production of UAVs for market as well as government purposes. Nevertheless, most UAVs in all domains, lack even basic cybersecurity mechanisms, thus, posing cybersecurity threats that significantly affect cybersecurity aspects or result in physical damage and even loss of human life. Many cybersecurity solutions have been proposed in the literature, encompassing both reactive and proactive measures, with the latter being preferable. Added to this, threat modeling emerges as an effective proactive measure with minimal cost and complexity. The current work conducts extensive and comprehensive research concerning the domain of UAVs. Initially, the paper outlines the historical development of UAVs and the different taxonomies in which they are classified according to different characteristics (e.g., number of rotors). The next sections detail a comprehensive description of the Internet of Drones (IoD) environment and the various relevant cybersecurity issues. Subsequently, the paper examines a wide range of diverse threat modeling approaches found in the literature, while it categorises relevant papers based on whether they describe solutions that integrate threat modeling or propose novel approaches for threat modeling.

Graphical User Interface (GUI) has become one of the most significant parts of mobile applications (apps). It is a direct bridge between mobile apps and end users, which directly affects the end user’s experience. Neglecting GUI quality can undermine the value and effectiveness of the entire mobile app solution. Significant research efforts have been devoted to GUI testing, one effective method to ensure mobile app quality. By conducting rigorous GUI testing, developers can ensure that the visual and interactive elements of the mobile apps not only meet functional requirements but also provide a seamless and user-friendly experience. However, traditional solutions, relying on the source code or layout files, have met challenges in both effectiveness and efficiency due to the gap between what is obtained and what app GUI actually presents. Vision-based mobile app GUI testing approaches emerged with the development of computer vision technologies and have achieved promising progress. In this survey article, we provide a comprehensive investigation of the state-of-the-art techniques on 271 articles, among which 92 are vision-based studies. This survey covers different topics of GUI testing, like GUI test generation, GUI test record & replay, GUI testing framework, and so on. In particular, we highlight the emerging role of vision-based techniques and analyze how they reshape traditional approaches to mobile app GUI testing. Based on the investigation of existing studies, we outline the challenges and opportunities of (vision-based) mobile app GUI testing and propose promising research directions with the combination of emerging techniques.

Videos and images undergo various distortions and artefacts during acquisition, processing, compression, transmission, and storage. These distortions and artefacts may impact the performance of high-level Computer Vision (CV) tasks. Therefore, identifying distortions/artefacts is crucial for developing and improving CV technologies. This article provides an overview of the distortions and artefacts that affect the quality of image and video signals. It also offers a framework highlighting the role of databases, especially in this era where data represents the fuel of current technologies. Various methods related to CV and image processing, focusing on distortion detection and classification in images/videos, are discussed. A wide range of applications and techniques developed in computer vision and associated databases are also analysed. Our study finds that while Machine Learning (ML) based approaches provide promising results for single distortion, multiple distortion classification remains challenging. Moreover, some distortions are still missing in the existing datasets, indicating room for constructing more databases with various distortions necessary to improve the ML-based models used in various CV-based applications. This article benefits researchers working in CV applications where visual data quality plays an important role. It provides insights for designing efficient ML-based solutions for several real-world CV applications.

Natural Language Processing (NLP) is revolutionising the way both professionals and laypersons operate in the legal field. The considerable potential for NLP in the legal sector, especially in developing computational assistance tools for various legal processes, has captured the interest of researchers for years. This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 154 studies, with a final selection of 131 after manual filtering. It explores foundational concepts related to NLP in the legal domain, illustrating the unique aspects and challenges of processing legal texts, such as extensive document lengths, complex language, and limited open legal datasets. We provide an overview of NLP tasks specific to legal text, such as Document Summarisation, Named Entity Recognition, Question Answering, Argument Mining, Text Classification, and Judgment Prediction. Furthermore, we analyse both developed legal-oriented language models, and approaches for adapting general-purpose language models to the legal domain. Additionally, we identify sixteen open research challenges, including the detection and mitigation of bias in artificial intelligence applications, the need for more robust and interpretable models, and improving explainability to handle the complexities of legal language and reasoning.

The rapid proliferation of generative AI has led to led to unprecedented capabilities in synthesizing realistic deepfakes (DFs) across multiple modalities. This raises significant concerns regarding privacy, security, and copyright protection. Unlike passive detection approaches that operate after DFs have been created and distributed, proactive defense mechanisms aim at preventing the generation of malicious synthetic content at its source. This article provides a comprehensive survey of current proactive DF defense strategies, including Disruption and Watermarking. Disruption approaches protect individuals’ data by introducing imperceptible perturbations that prevent unauthorized exploitation by generative models, while watermarking approaches embed verifiable messages into data or models to enable content authentication and attribution. We also analyze proactive approaches across various evaluation metrics (imperceptibility, protectability/detectability, transferability, traceability, and robustness), and examine their effectiveness in real-world settings. Furthermore, we review the evolution of DF generation techniques, highlighting their rapid developments. Finally, we identify key challenges and promising future research directions to enhance proactive defense mechanisms.

Next-generation transportation and industrial systems are characterized by a continuously increasing number of integrated software functionalities and computational performance requirements. Nevertheless, there is also a need to reduce the cost, number, size, power consumption, volume and weight of integrated dependable embedded systems. In this context, virtualization technology enables the reconciliation of these challenges supporting the integration of multiple software functionalities of mixed-criticality (real-time and non-real-time, safety and non-safety-related) on a single computing device. This survey provides an overview of virtualization solutions (hypervisors, microkernels, and separation kernels) and categorizes research contributions addressing virtualization, real-time, dependability, and domain-specific technological challenges.