Many areas of Bangkok and its environs are currently blanketed with fine dust with dangerous levels of PM2.5. High levels of PM2.5 have a negative impact on human health. In this study, support vector regression, begg...
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In this paper we propose a new fitness function for Evolutionary Computation purposes, based on a weighted by neighborhood average distance between two sequences of points within any metric space. We will apply this f...
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ISBN:
(纸本)9783031070150;9783031070143
In this paper we propose a new fitness function for Evolutionary Computation purposes, based on a weighted by neighborhood average distance between two sequences of points within any metric space. We will apply this fitness function to the field of computer-Assisted Composition focusing on the problem of thematic bridging, consisting in the evolutionary creation of a soft set of transitions between two given different melodies, the initial and the final one. Several self-adaptive strategies will be used to perform the search. A symbolic melody will be geno-typically mapped into a sequence of genes, each of then containing the information of duration, frequency and time distance to following note. We will test the implementation of the fitness function by means of two experiments, showing some of the intermediate melodies generated in a successful run, and benchmarking every experiment with performance indicators for any of the three distinct evolutionary strategies implemented. The results prove this novel fitness function to be a quick and suitable way for individual evaluation in genetic algorithms.
In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and securely i...
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Convolutional neural networks are used to classify dermoscopic skin lesion images. The high accuracy of deep learning models is well documented;however, those models do not perform very well on testing (unseen data) s...
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Pre-trained language models have demonstrated state-of-the-art performance in various downstream tasks such as summarization, sentiment classification, and question answering. Leveraging vast amounts of textual data d...
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ISBN:
(纸本)9783031778490
Pre-trained language models have demonstrated state-of-the-art performance in various downstream tasks such as summarization, sentiment classification, and question answering. Leveraging vast amounts of textual data during training, these models inherently hold a certain amount of factual knowledge, which is particularly beneficial for knowledge-driven tasks such as question answering. However, the knowledge implicitly contained within the language models is not complete. Consequently, many studies incorporate additional knowledge from Semantic Web resources such as knowledge graphs, which provide an explicit representation of knowledge in the form of triples. Seamless integration of this knowledge into language models remains an active research area. Direct pre-training of language models on knowledge graphs followed by fine-tuning on downstream tasks has proven ineffective, primarily due to the catastrophic forgetting effect. Many approaches suggest fusing language models with graph embedding models to enrich language models with information from knowledge graphs, showing improvement over solutions that lack knowledge graph integration in downstream tasks. However, these methods often require additional computational overhead, for instance, by training graph embedding models. In our work, we propose a novel adapter-based method for integrating knowledge graphs into language models through pre-training. This approach effectively mitigates catastrophic forgetting that can otherwise affect both the original language modeling capabilities and the access to pre-trained knowledge. Through this scheme, our approach ensures access to both the original capabilities of the language model and the integrated Semantic Web knowledge during fine-tuning on downstream tasks. Experimental results on multiple choice question answering tasks demonstrate performance improvements compared to baseline models without knowledge graph integration and other pre-training-based knowledge inte
We consider a stochastic gradient descent (SGD) algorithm for solving linear inverse problems (e.g., CT image reconstruction) in the Banach space framework of variable exponent Lebesgue spaces (pn)(R). Such non-standa...
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The introduction of the World Wide Web and the fast assumption of online social media and the entertainment stages (like Facebook, Twitter, LinkedIn, etc.) prepared for data dissemination that has never been seen in t...
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The landscape of malicious emails and cyber social engineering attacks in general are constantly evolving. In order to design effective defenses against these attacks, we must deeply understand the Psychological Tacti...
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