We study the Combinatorial Group Testing (CGT) problem in a novel gametheoretic framework, with a solution concept of Adversarial Equilibrium (AE). In this new framework, we have n selfish autonomous agents, corresponding to the elements of the universe [n] = {0, 1,..., n 1}, and a hidden set K [n] of active agents of size |K| = k n. In each round of the game, each active agent decides if it is present in a query Q [n], and all agents receive some limited feedback on Q K. The goal of each active agent is to ensure that its id could be revealed from the feedback as early as possible. We present a comprehensive set of results for this new game, where we design and analyze adaptive algorithmic strategies of agents which are AE's. In particular, if k is known to the agents, then we show adaptive AE strategies with provably near-optimal maximum revealing time of O(k log(n/k)).

IC3K 2024 (16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management) received 175 paper submissions from 47 countries. To evaluate each submission, a double‐blind paper review was performed by the Program Committee. After a stringent selection process, 37 papers were published and presented as full papers, i.e. completed work (12 The organizing committee included the IC3K Conference Chair: Jorge Bernardino, Polytechnic University of Coimbra, Portugal and the IC3K 2024 Program Chairs: David Aveiro, University of Madeira, NOVA- LINCS and ARDITI, Portugal, Antonella Poggi, Università di Roma "La Sapienza", Italy, Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico (University of Lisbon), Portugal, Le Gruenwald, University of Oklahoma, School of Computer Science, United States, Elio Masciari, University of Napoli Federico II, Italy and Frans Coenen, University of Liverpool, United Kingdom. At the closing session, the conference acknowledged a few papers that were considered excellent in their class, presenting a "Best Paper Award", "Best Student Paper Award" and "Best Poster Award" for each of the co-located conferences. A short list of presented papers will be selected so that revised and extended versions of these papers will be published by Springer in a CCIS Series Book.

Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, including text, images, audio, and video. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning. Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview. Large-scale multimodal datasets are essential because they allow for thorough testing and training of these models. With an emphasis on their contributions to the discipline, the study examines a variety of datasets, including those for training, domain-specific tasks, and real-world applications. It also emphasizes how crucial benchmark datasets are for assessing models' performance in a range of scenarios, scalability, and applicability. Since multimodal learning is always changing, overcoming these obstacles will help AI research and applications reach new heights.

Reliability analysis is a sub-field of uncertainty quantification that assesses the probability of a system performing as intended under various uncertainties. Traditionally, this analysis relies on deterministic models, where experiments are repeatable, i.e., they produce consistent outputs for a given set of inputs. However, real-world systems often exhibit stochastic behavior, leading to non-repeatable outcomes. These so-called stochastic simulators produce different outputs each time the model is run, even with fixed inputs. This paper formally introduces reliability analysis for stochastic models and addresses it by using suitable surrogate models to lower its typically high computational cost. Specifically, we focus on the recently introduced generalized lambda models and stochastic polynomial chaos expansions. These emulators are designed to learn the inherent randomness of the simulator's response and enable efficient uncertainty quantification at a much lower cost than traditional Monte Carlo simulation. We validate our methodology through three case studies. First, using an analytical function with a closed-form solution, we demonstrate that the emulators converge to the correct solution. Second, we present results obtained from the surrogates using a toy example of a simply supported beam. Finally, we apply the emulators to perform reliability analysis on a realistic wind turbine case study, where only a dataset of simulation results is available.

Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety. It is worth noting that current multi-sensor fusion and multi-task learning models consume substantial power and heavily rely on high-power GPUs for inference. This contributes to increased carbon emissions, a concern that runs counter to the prevailing emphasis on environmental preservation and the pursuit of sustainable, low-carbon urban environments. In light of these concerns, this paper concentrates on low-power, lightweight, multi-task panoptic perception through the fusion of visual and 4D radar data, which is seen as a promising low-cost perception method. We propose a framework named Achelous++ that facilitates the development and comprehensive evaluation of multi-task water-surface panoptic perception models. Achelous++ can simultaneously execute five perception tasks with high speed and low power consumption, including object detection, object semantic segmentation, drivable-area segmentation, waterline segmentation, and radar point cloud semantic segmentation. Furthermore, to meet the demand for developers to customize models for real-time inference on low-performance devices, a novel multi-modal pruning strategy known as Heterogeneous-Aware SynFlow (HA-SynFlow) is proposed. Besides, Achelous++ also supports random pruning at initialization with different layer-wise sparsity, such as Uniform and Erdos-Renyi-Kernel (ERK). Overall, our Achelous++ framework achieves state-of-the-art performance on the WaterScenes benchmark, excelling in both accuracy and power efficiency compared to other single-task and multi-task models. We release and maintain the code at https://github.com/GuanRunwei/Achelous.

While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic linking patterns, wherein adjacent nodes may share dissimilar attributes and distinct labels. Therefore, GNNs smoothing node proximity holistically may aggregate both task-relevant and irrelevant (even harmful) information, limiting their ability to generalize to heterophilic graphs and potentially causing non-robustness. In this work, we propose a novel edge splitting GNN (ES-GNN) framework to adaptively distinguish between graph edges either relevant or irrelevant to learning tasks. This essentially transfers the original graph into two subgraphs with the same node set but exclusive edge sets dynamically. Given that, information propagation separately on these subgraphs and edge splitting are alternatively conducted, thus disentangling the task-relevant and irrelevant features. Theoretically, we show that our ES-GNN can be regarded as a solution to a disentangled graph denoising problem, which further illustrates our motivations and interprets the improved generalization beyond homophily. Extensive experiments over 11 benchmark and 1 synthetic datasets demonstrate that ES-GNN not only outperforms the state-of-the-arts, but also can be more robust to adversarial graphs and alleviate the over-smoothing problem.

Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To achieve accurate and robust perception capabilities, autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system. Among these fused sensors, radars and cameras enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. This review aims to provide a comprehensive guideline for radar-camera fusion, particularly concentrating on perception tasks related to object detection and semantic segmentation.Based on the principles of the radar and camera sensors, we delve into the data processing process and representations, followed by an in-depth analysis and summary of radar-camera fusion datasets. In the review of methodologies in radar-camera fusion, we address interrogative questions, including "why to fuse", "what to fuse", "where to fuse", "when to fuse", and "how to fuse", subsequently discussing various challenges and potential research directions within this domain. To ease the retrieval and comparison of datasets and fusion methods, we also provide an interactive website: https://radar-camera-fusion.github.io.

Panoptic perception is essential to unmanned surface vehicles (USVs) for autonomous navigation. The current panoptic perception scheme is mainly based on vision only, that is, object detection and semantic segmentation are performed simultaneously based on camera sensors. Nevertheless, the fusion of camera and radar sensors is regarded as a promising method which could substitute pure vision methods, but almost all works focus on object detection only. Therefore, how to maximize and subtly fuse the features of vision and radar to improve both detection and segmentation is a challenge. In this paper, we focus on riverway panoptic perception based on USVs, which is a considerably unexplored field compared with road panoptic perception. We propose Efficient-VRNet, a model based on Contextual Clustering (CoC) and the asymmetric fusion of vision and 4D mmWave radar, which treats both vision and radar modalities fairly. Efficient-VRNet can simultaneously perform detection and segmentation of riverway objects and drivable area segmentation. Furthermore, we adopt an uncertainty-based panoptic perception training strategy to train Efficient-VRNet. In the experiments, our Efficient-VRNet achieves better performances on our collected dataset than other uni-modal models, especially in adverse weather and environment with poor lighting conditions. Our code and models are available at \url{https://github.com/GuanRunwei/Efficient-VRNet}.

Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success. However, in many cases, the complexity of the learning objectives goes beyond the capabilities of the Markovian assumption, necessitating a more sophisticated reward mechanism. Reward machines and omega-regular languages are two formalisms used to express non-Markovian rewards for quantitative and qualitative objectives, respectively. This paper introduces omega-regular reward machines, which integrate reward machines with omega-regular languages to enable an expressive and effective reward mechanism for RL. We present a model-free RL algorithm to compute epsilon-optimal strategies against omega-egular reward machines and evaluate the effectiveness of the proposed algorithm through experiments.