The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compress...
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The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users has become a challenge. However, existing anime recommendati...
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ISBN:
(纸本)9789819755547;9789819755554
The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users has become a challenge. However, existing anime recommendation systems (RS) have focused on text features but still need to integrate image features. In addition, most multi-modal (MM) RS research is constrained by tightly coupled datasets, limiting its applicability to illustrations RS. We propose the User-aware Multimodal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine painting style with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of anime text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the MM fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight MM features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree MM crosses effectively. UMAIR-FPS surpasses the SOTA baselines on large real-world datasets.
This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analy...
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In the transition to the"digital" era, many large enterprises have adopted enterprise architecture methodologies for top-level design, business architecture as the core content of enterprise architecture, it...
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Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accurac...
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The study main purpose is to address the effectiveness of a computer-aided diagnosis (CADx) scheme developed to assist radiologists in evaluating nodules in digital mammography images. Unlike traditional CADe systems,...
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This paper presents the development of a dedicated ground station optimized for LoRa satellite communication. The station is equipped with crucial components, including an antenna, transceiver, modem, RF amplifier, tr...
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Enterprise systems are complex solutions to diverse needs of companies, but their extent does not typically span firm boundaries. Instead, there are different ways to handle inter-organizational IT requirements, inclu...
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Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to explainable classification systems. Classical association rule mining algori...
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ISBN:
(纸本)9783031777301;9783031777318
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there is still no efficient way to mine association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.
When users apply for an account in the "Tianqing" system, they can only fill in a maximum of 3 IP addresses bound to the account, so the software developed based on "Tianqing" can only theoreticall...
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