Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks, unexplainable discrimination against disadvantaged groups, or excessive resource consumption in edge computing environments. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness. To this end, we propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.

Cities worldwide are rapidly adopting smart technologies, transforming urban life. Despite this trend, a universally accepted definition of 'smart city' remains elusive. Past efforts to define it have not yielded a consensus, as evidenced by the numerous definitions in use. In this paper, we endeavored to create a new 'compromise' definition that should resonate with most experts previously involved in defining this concept and aimed to validate one of the existing definitions. We reviewed 60 definitions of smart cities from industry, academia, and various relevant organizations, employing transformer architecture-based generative AI and semantic text analysis to reach this compromise. We proposed a semantic similarity measure as an evaluation technique, which could generally be used to compare different smart city definitions, assessing their uniqueness or resemblance. Our methodology employed generative AI to analyze various existing definitions of smart cities, generating a list of potential new composite definitions. Each of these new definitions was then tested against the pre-existing individual definitions we have gathered, using cosine similarity as our metric. This process identified smart city definitions with the highest average cosine similarity, semantically positioning them as the closest on average to all the 60 individual definitions selected.

A transformative approach to mental health therapy lies at the crossroads of cultural heritage and advanced technology. This paper introduces an innovative method that fuses machine learning techniques with traditional Emirati motifs, focusing on the United Arab Emirates (UAE). We utilize the Stable Diffusion XL (SDXL) model, enhanced with Low-Rank Adaptation (LoRA), to create culturally significant coloring templates featuring Al-Sadu weaving patterns. This novel approach leverages coloring therapy for its recognized stress-relieving benefits and embeds deep cultural resonance, making it a potent tool for therapeutic intervention and cultural preservation. Specifically targeting Generalized Anxiety Disorder (GAD), our method demonstrates significant potential in reducing associated symptoms. Additionally, the paper delves into the broader implications of color and music therapy, emphasizing the importance of culturally tailored content. The technical aspects of the SDXL model and its LoRA fine-tuning showcase its capability to generate high-quality, culturally specific images. This research stands at the forefront of integrating mental wellness practices with cultural heritage, providing a groundbreaking perspective on the synergy between technology, culture, and healthcare. In future work, we aim to employ biosignals to assess the level of engagement and effectiveness of color therapy. A key focus will be to examine the impact of the Emirati heritage Al Sadu art on Emirati individuals and compare their responses with those of other nationalities. This will provide deeper insights into the cultural specificity of therapeutic interventions and further the understanding of the unique interplay between cultural identity and mental health therapy.

In recent years, formal methods have been extensively used in the design of autonomous systems. By employing mathematically rigorous techniques, formal methods can provide fully automated reasoning processes with provable safety guarantees for complex dynamic systems with intricate interactions between continuous dynamics and discrete logics. This paper provides a comprehensive review of formal controller synthesis techniques for safety-critical autonomous systems. Specifically, we categorize the formal control synthesis problem based on diverse system models, encompassing deterministic, non-deterministic, and stochastic, and various formal safety-critical specifications involving logic, real-time, and real-valued domains. The review covers fundamental formal control synthesis techniques, including abstraction-based approaches and abstraction-free methods. We explore the integration of data-driven synthesis approaches in formal control synthesis. Furthermore, we review formal techniques tailored for multi-agent systems (MAS), with a specific focus on various approaches to address the scalability challenges in large-scale systems. Finally, we discuss some recent trends and highlight research challenges in this area.

Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: github.com/ayanban011/GraphKD

With the exponential growth of the life science literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. Identifying named entities (e.g., diseases, drugs, or genes) in texts and their linkage to reference knowledge bases are crucial steps in BTM pipelines to enable information aggregation from different documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied in the wild, i.e., on application-dependent text collections different from those used for the tools' training, varying, e.g., in focus, genre, style, and text type. This raises the question of whether the reported performance of BTM tools can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for named entity extraction, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five for an in-depth analysis on three publicly available corpora encompassing four different entity types. Comparison between tools results in a mixed picture and shows that, in a cross-corpus setting, the performance is significantly lower than the one reported in an in-corpus setting. HunFlair2 showed the best performance on average, being closely followed by PubTator. Our results indicate that users of BTM tools should expect diminishing performances when applying them in the wild compared to original publications and show that further research is necessary to make BTM tools more robust.

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.

Achieving a universally high accuracy in object detection is quite challenging, and the mainstream focus in the industry currently lies on detecting specific classes of objects. However, deploying one or multiple object detection networks requires a certain amount of GPU memory for training and storage capacity for inference. This presents challenges in terms of how to effectively coordinate multiple object detection tasks under resource-constrained conditions. This paper introduces a lightweight fine-tuning strategy called Calibration side tuning, which integrates aspects of adapter tuning and side tuning to adapt the successful techniques employed in transformers for use with ResNet. The Calibration side tuning architecture that incorporates maximal transition calibration, utilizing a small number of additional parameters to enhance network performance while maintaining a smooth training process. Furthermore, this paper has conducted an analysis on multiple fine-tuning strategies and have implemented their application within ResNet, thereby expanding the research on fine-tuning strategies for object detection networks. Besides, this paper carried out extensive experiments using five benchmark datasets. The experimental results demonstrated that this method outperforms other compared state-of-the-art techniques, and a better balance between the complexity and performance of the finetune schemes is achieved.

Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases. Introduced by Lasry and Lions, and Huang, Caines and Malham\'e, Mean Field Games (MFGs) rely on a mean-field approximation to allow the number of players to grow to infinity. Traditional methods for solving these games generally rely on solving partial or stochastic differential equations with a full knowledge of the model. Recently, Reinforcement Learning (RL) has appeared promising to solve complex problems at scale. The combination of RL and MFGs is promising to solve games at a very large scale both in terms of population size and environment complexity. In this survey, we review the quickly growing recent literature on RL methods to learn equilibria and social optima in MFGs. We first identify the most common settings (static, stationary, and evolutive) of MFGs. We then present a general framework for classical iterative methods (based on best-response computation or policy evaluation) to solve MFGs in an exact way. Building on these algorithms and the connection with Markov Decision Processes, we explain how RL can be used to learn MFG solutions in a model-free way. Last, we present numerical illustrations on a benchmark problem, and conclude with some perspectives.

Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use of such generative AI tools, in this work we investigate how these tools can be deployed in a non-safety critical, strategic traffic flow management setting. Specifically, we train an LLM, CHATATC, based on a large historical data set of Ground Delay Program (GDP) issuances, spanning 2000-2023 and consisting of over 80,000 GDP implementations, revisions, and cancellations. We test the query and response capabilities of CHATATC, documenting successes (e.g., providing correct GDP rates, durations, and reason) and shortcomings (e.g,. superlative questions). We also detail the design of a graphical user interface for future users to interact and collaborate with the CHATATC conversational agent.