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Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni and Italo Zoppis
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for r...
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Seokjoon Kwon, Jae-Hyeon Park, Hee-Deok Jang, Hyunwoo Nam and Dong Eui Chang
Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even ...
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Yusuf Brima, Ulf Krumnack, Simone Pika and Gunther Heidemann
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are ...
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Jie Ren, Changmiao Li, Yaohui An, Weichuan Zhang and Changming Sun
Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature representa...
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Chenhao Wu, Longgang Xiang, Libiao Chen, Qingcen Zhong and Xiongwei Wu
With the development of location-based services and data collection equipment, the volume of trajectory data has been growing at a phenomenal rate. Raw trajectory data come in the form of sequences of ?coordinate-time-attribute? triplets, which require c...
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Jun Yeong Kim, Chang Geun Song, Jung Lee, Jong-Hyun Kim, Jong Wan Lee and Sun-Jeong Kim
In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among the va...
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Jawaher Alghamdi, Yuqing Lin and Suhuai Luo
The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorporati...
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Sufyan Danish, Asfandyar Khan, L. Minh Dang, Mohammed Alonazi, Sultan Alanazi, Hyoung-Kyu Song and Hyeonjoon Moon
Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immersive ...
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Xin Tian and Yuan Meng
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the excha...
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Adriano Mancini, Francesco Solfanelli, Luca Coviello, Francesco Maria Martini, Serena Mandolesi and Raffaele Zanoli
Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting s...
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