The multi-granularity decision rule is better than single granularity decision rule, two-way decision rule, and frequent decision rule. ... Leite et al. (2020) described an online incremental learning method for the optimal design of granular rule-based models from data streams. Feng et al. (2010) ...
Abstract. Graph contrastive learning is an unsupervised learning method for graph data. It aims to learn useful representations by maximizing the similarity between similar instances and minimizing the similarity between dissimilar instances. Despite the success of the existing GCL methods, they generally overlook the hierarchical structures …
We also design a gate mechanism based on market-aware technical indicators to fuse the multi-granularity features at each time step adaptively. Extensive experiments on three real-world datasets show significant improvements of our approach over the state-of-the-art baselines on stock trend prediction and profitability in real investing scenarios.
In this paper, we go beyond the typical lattice structure and propose a novel Multi-Granularity Contrastive Learning framework (MCL), that aims to optimize the inter-granularity distribution distance and emphasize the critical matched words in the lexicon. By carefully combining cross-granularity contrastive learning and bi-granularity ...
Module partitioning is beneficial for engineers to gain a better understanding of the structure and function of complex mechanical products (CMPs). It plays an important role in the entire life cycle of the CMPs. However, owing to the large number of mechanical parts and the complex relationships, existing module partition approaches cannot obtain …
Download : Download high-res image (341KB) Download : Download full-size image Fig. 2. General procedure of the proposed MGCEA with three core components, including multi-interval sampling (dividing decision variables into noncritical ones and critical ones), multi-granularity clustering (dividing decision variables into multiple …
We design a sequential fusion framework that recombines template and search images based on tracking results from chronological frames, fusing updated tracking states in training. ... (STCFormer), which uses a sequential fusion framework with multi-granularity consistency constraints to learn spatiotemporal context information. We …
Figure 1 demonstrates an overview of our method to assess BPC between process models and regulatory documents at design-time, which consists of three parts. (1) Process Model Disassembly This step involves extracting semantic components and control flow details from the labels associated with structured process models. (2) Regulatory …
To overcome these challenges, we propose a multi-granularity-aware network (MGA-Net). Specifically, we design a multi-granularity hybrid feature fusion module (MGHF2M) to extract more representative local detail and global semantic information, enhancing the model's capability to represent ship features to adapt to …
A method to model user preferences and detect preference drift, based on a hierarchical topic tree and tilted time window, and design a hierarchical classification tree, named HAT-tree, to maintain the history of the user's preferences at multi-topic and multi-time granularity. Since the advent of Web 2.0 culture, there as been an explosion of …
To this end, we further design a novel multi-granularity regularization term that enables the model to consider the correlations of classes in addition to re-balancing the data. A class hierarchy is first constructed by ontology or grouping semantically or visually similar classes. The multi-granularity regularization then transforms the one ...
In this paper, we focus on the problem of multi-granularity and multi-level feature expression of text semantics in question and answer understanding, and design a question-answering understanding method for multi-granularity hierarchical features. First, we extract features from two aspects, the traditional language model and the deep …
a Multi-granularity Intent Heterogeneous Session Graph (MIHSG). In this heterogeneous graph [39], nodes (CIUs) with different num-bers of items are categorized into different groups and the tran-sition edges among the same type of nodes capture the spatial continuity of the user-item interactions in the corresponding in-tent granularity.
A large-scale classification task can be divided into a group of small sub-classification tasks using the semantic hierarchy [46], [37], [14].The hierarchical feature selection method selects a feature subset for each sub-class to eliminate feature redundancy in layers of each granularity [36].The hierarchical feature selection method …
for super-resolution [11], [12], we design a Transformer-based RSISR method termed Multi-granularity Backprojection Transformer (MBT). Specifically, we employ the backpro-jection learning strategy to learn low-resolution feature rep-resentations at different granularities. Firstly, we design the
A multi-granularity feasibility evaluation model of the PDD was constructed based on the complex product's hierarchical structure, which not only described the evaluation indices from the product level to the component level but also presented methods and rules to quantify them. ... Aguiar J, Oliverira SJ (2017) A design tool to …
Software systems are a typical kind of man-made complex systems. Understanding their evolutions can lead to better software engineering practices. In this paper, the authors use complex network theory as a tool to analyze the evolution of object-oriented (OO) software from a multigranularity perspective. First, a multi-granularity …
This paper introduces a Multi-Granularity based Domain Design Space Exploration tool (MG-DmDSE) to improve both average application throughput as well as platform generality. The key contributions of MG-DmDSE are: (1) Applying a multi-granular decomposition of coarse grain application functions into more granular compute kernels. ...
Conclusion. Multiple granularity locking is a locking mechanism used in DBMS. It provides different levels of locks for different database objects. It increases concurrency, reduces locking overhead, and improves performance by reducing conflicts and deadlocks. It allows transactions to lock different levels of granularity.
The multi-granularity relationship, as an important auxiliary relationship, forms a multi-granularity knowledge graph (MGKG). Fig. 2 shows a visualization of an MGKG constructed from the semantic dependencies between classes in WordNet [14]. ... First, we design a channel and spatial multi-scale feature fusion network, which fuses …
We design a transformer encoder-based model which abstracts sentence-level semantics from word-level semantics by removing the multi-granularity semantics representations. That is to say the inner-chunk self-attention layer and the inter-chunk self-attention layer are replaced by the inter-word self-attention layer.
Multi-granularity structural information is shown to be effective for document-level biomedical relation extraction by some analytical experiments. ... these methods require a significant level of manual design. In recent years, deep neural networks have been widely used for sentence-level RE without feature engineering. Convolutional …
Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis, ignoring disease-level semantic correspondences. In this paper, we present a novel Multi …
With the help of the preceding multi-granularity DR modeling and the two-stage access strategy, DR granularity is enlarged and initiative design capability is …
Mathematical modeling and optimization of the design parameters of the working chamber and the executive body (roll) of a single-roll gyratory shaft crusher, …
Genetic algorithm is used to optimize the crusher crank-rocker mechanism for multi-object design and an optimum result is obtained. According to the …
In this paper, we design a Matrix Capsule-based multi-granularity framework (MaCa) for this task. Specifically, we first introduce a word-level encoder to obtain the token-aware representations. Then, two sentence-level extractors are used to generate emotion prediction and cause prediction.
Such crushers are promising for use in mines to crush rocks laid in the developed space. Mathematical modeling and optimization of the design parameters of …
The multi-objective optimization of the crusher performances is performed. ... multi-objective optimization is carried out through Design-Expert software, and the …
An optimization method of product green design scheme based on…. Expand. 1. 1 Excerpt. Semantic Scholar extracted view of "Supply chain low-carbon design of multi-granularity complex product" by Yang Chuan-ming et al.
We design a Transformer-based network named Multi-granularity Aggregation Transformer (MAT) to dynamically learn the complementary information between sub-aperture images in this paper. MAT is mainly implemented with the proposed multi-granularity aggregation blocks, which process sub-aperture images with three different …
The intelligent optimization algorithm has the advantage of giving feasible solutions in polynomial time when solving complex problems in reality. Its performance depends on its strategy design and parameter configuration. The key to achieving computational intelligence and self-organization is parameter configuration. However, …
In Section 3, the processes used to design multi-dimensional information granules, using the principle of justifiable granularity, are explained in detail, and the correlation index is introduced. In Section 4, the method for processing inhibitory data in multi-dimensional space is presented, and the effects of confidence levels on …
In this paper, we design an Integrating Multi-granularity Sessions and Multi-type Interests (IMSMI) model, which can construct user-course interaction sequences as …
In the real-world 24/7 surveillance systems, the images collected during the day and night are visible light images and infrared images, respectively. Infrared images lack color and texture information. In this case, it is more practical to use cross-modality person re-identification (re-ID) to process visible-infrared images. In fact, the cross …