Highly influential users (IUs) play a vital role in disseminating information on online social networks (OSNs). Recognizing IUs is crucial for brand awareness, strategic marketing and consumer engagement. Researchers ...
详细信息
Social media is a tool to share ideas, knowledge, and information with a wide group of people. Some people misuse the social networking site for criminal activities. The systematic literature review (SLR) focused on t...
详细信息
This research work presents a novel language intervention system for Tamil-speaking children with autism spectrum disorder (ASD). The system satisfies the considerable requirement for tools aimed at one more section o...
详细信息
The existing limitations in extractive text summarization encompass challenges related to preserving contextual features, limited feature extraction capabilities, and handling hierarchical and compositional aspects. T...
详细信息
Knowledge about fish species with continuous monitoring play a dominant role in determining short and long-term effects on ecosystems and generating ways to manage the problem through specific treatment. Categori...
详细信息
While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing su...
详细信息
While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing such feasibility assumptions, and in particular address the problem in the linear bandit setting, thus characterising the costs of feasibility testing for an unknown linear program using bandit feedback. Concretely, we test if ∃x : Ax ≥ 0 for an unknown A ∈ m×d, by playing a sequence of actions xt ∈ d, and observing Axt + noise in response. By identifying the hypothesis as determining the sign of the value of a minimax game, we construct a novel test based on low-regret algorithms and a nonasymptotic law of iterated logarithms. We prove that this test is reliable, and adapts to the 'signal level,' Γ, of any instance, with mean sample costs scaling as Õ(d2/Γ2). We complement this by a minimax lower bound of Ω(d/Γ2) for sample costs of reliable tests, dominating prior asymptotic lower bounds by capturing the dependence on d, and thus elucidating a basic insight missing in the extant literature on such problems. Copyright 2024 by the author(s)
Artificial intelligence (AI) technologies have a significant impact on developments in research and creative processes. With the quick development of computer vision, the extensive need for interaction with machines a...
详细信息
Inverse tone mapping technique is widely used to restore the lost textures from a single low dynamic range ***,many stack‐based deep inverse tone mapping networks have achieved impressive results by estimating a set ...
详细信息
Inverse tone mapping technique is widely used to restore the lost textures from a single low dynamic range ***,many stack‐based deep inverse tone mapping networks have achieved impressive results by estimating a set of multi‐exposure images from a single low dynamic range ***,there are still some *** the one hand,these methods usually set a fixed length for the estimated multi‐exposure stack,which may introduce computational redundancy or cause inaccurate *** the other hand,they neglect that the difficulties of estimating each exposure value are different and use the identical model to increase or decrease exposure *** solve these problems,the authors design an exposure decision network to adaptively determine the number of times the exposure of low dynamic range input should be increased or ***,the authors decouple the increasing/decreasing process into two sub‐modules,exposure adjustment and optional detail recovery,based on the characteristics of different variations of exposure *** these improvements,this method can fast and flexibly estimate the multi‐exposure stack from a single low dynamic range *** on several datasets demonstrate the advantages of the proposed method compared to state‐of‐the‐art inverse tone mapping methods.
Latest measurements correlated to the cloud computing technology, found to be very unreliable. For smooth conduction of cloud technology, the report is getting more than 100 values i.e., being added to the cost of the...
详细信息
Graphs find wide applications in numerous domains, ranging from simulating physical systems to learning molecular fingerprints, predicting protein interfaces, diagnosing diseases, etc. These applications encompass sim...
详细信息
Graphs find wide applications in numerous domains, ranging from simulating physical systems to learning molecular fingerprints, predicting protein interfaces, diagnosing diseases, etc. These applications encompass simulations in non-Euclidean space, in which a graph serves as an ideal representation, and are also an indispensable means of illustrating the connections and interdependencies among its various constituents. Graph neural networks (GNNs) are specific types of neural networks that are specifically built to handle data possessing a graph structure. They are highly effective at capturing intricate relationships among different entities. Nonetheless, their "black-box" characteristics pose difficulties in transparency, trust, and interpretability, especially in critical sectors like heath care, banking, and autonomous systems. Explainable artificial intelligence (XAI) has emerged to clarify these obscure decision-making processes, thus enhancing trust and accountability in AI systems. This survey paper delves into the intricate interplay between GNNs and XAI, including an exhaustive taxonomy of the various explainability methods designed for graph-structured data. It classifies the existing explainability methods into post hoc and self-interpretable models. The paper analyzes their practical applications in diversified fields, highlighting the significance of transparent GNNs in essential sectors such as fraud detection, drug development, and network security. The survey also delineates evaluation parameters for assessing explainability along with addressing persistent issues in scalability and fairness. The paper concludes by addressing prospective advancements in the subject, including the creation of innovative XAI methodologies tailored for GNN architectures, integration with federated learning, and utilization of these models in interdisciplinary fields. This study bridges the gap between GNNs and XAI, providing an essential resource for researchers and p
暂无评论