By leveraging curvature information for improved performance, Newton’s method offers significant advantages over first-order methods for distributed learning problems. However, the practical applicability of Newton’...
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Graph-based clustering has been shown to be promising, partly due to the rich data relationship encoded in affinity graphs. However, the graph representation also means a large computation and storage load for large-s...
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This literature review examines the use of machine learning (ML) algorithms for landslide identification and provides an overview of recent studies in this field. The most used algorithms for landslide identification ...
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Achieving the complexity of graph algorithms in conventional languages with programs based on graph transformation rules is challenging because of the cost of graph matching. Previous work demonstrated that with so-ca...
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Drone Routing Problems (DRP) focus on finding optimal paths for autonomous drones in a graph-based environment, minimizing movement costs and avoiding collisions. DRP is modeled as a cooperative multi-agent problem, f...
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In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis. This paper presents a novel approach for bone pathology locali...
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With the continuous development of Internet of Things technologies, Wireless Body Area Networks (WBAN) have shown great application potentials in the healthcare industry. However, adversaries may masquerade as legitim...
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The classification of reviews or comments provided by the customers after shopping has a wide scope in terms of the categories it can be classified. Big companies like Walmart, Tesco and Amazon have customers from all...
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The importance of exploring the presenting location and type of tactile stimuli has grown along with various applications of haptic feedback. We measured the just noticeable differences (JNDs) for the vibrotactile and...
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Can negation be depicted? It has been claimed in various areas, including philosophy, cognitive science, and AI, that depicting negation through visual expressions such images and pictures is challenging. Recent empir...
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ISBN:
(纸本)9783031552441;9783031552458
Can negation be depicted? It has been claimed in various areas, including philosophy, cognitive science, and AI, that depicting negation through visual expressions such images and pictures is challenging. Recent empirical findings have shown that humans can indeed understand certain images as expressing negation, whereas this ability is not exhibited by machine learning models trained on image data. To elucidate the computational ability underlying the understanding of negation in images, this study first focuses on the image captioning task, specifically the performance of models pre-trained on large linguistic and image datasets for generating text from images. Our experiment demonstrates that a state-of-the-art model achieves some success in generating consistent captions from images, particularly in photographs rather than illustrations. However, when it comes to generating captions containing negation from images, the model is not as proficient as humans. To further investigate the performance of machine learning models in a more controlled setting, we conducted an additional analysis using a Visual Question Answering (VQA) task. This task enables us to specify where in the image the model should focus its attention when answering a question. As a result of this setting, the model's performance was improved. These results will shed light on the disparities in the attentional focus between humans and machine learning models.
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