datavisualization is an important part of dataanalysis and is used in all fields, antenna engineering being one of them. Antenna engineers mostly use closed-source tools to run simulations for electromagnetic applic...
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Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e...
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ISBN:
(纸本)9783031438486;9783031438493
Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search in digital libraries and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and datasets are publicly available (https://***/TIBHannover/PatentImageClassification).
Commodities play a pivotal role in the global economy, and their dramatic price fluctuations can affect national security and global economic stability. This paper focus on the field of commodities, utilizing the Web ...
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The study aims to highlight the bibliometric analysis of published literature on mobile healthcare in the past one decade. The data consist of literature indexed in Scopus database from 2011 to 2020. The keywords used...
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In this brief paper, the textile materials, clothing, and shoes trade network along one belt one road countries was constructed and analyzed (mainly based on STIC 83 and STIC 84 hybrid trade data in 2004-2019). Based ...
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ISBN:
(纸本)9781665478960
In this brief paper, the textile materials, clothing, and shoes trade network along one belt one road countries was constructed and analyzed (mainly based on STIC 83 and STIC 84 hybrid trade data in 2004-2019). Based on basic characters calculation, trade member community detective, membership importance ranking by page rank value, bridging centrality calculation, and network visualizationanalysis, the topological structure and evolution trend of the One Belt One Road (OBOR) trade network was analyzed on details. The network diameter and average length of the path between countries is relatively small with the development of world globalization process. The analysis of node PR value's distribution analysis showed that, Czechia, India, Singapore, United Arab Emirates, Viet Nam, Myanmar, Pakistan, Poland, Sri Lanka, Turkey, Ukraine, China, Indonesia, Malaysia, Romania, Russian Federation, and Thailand behaved important function for trade communications. The analysis of node betweenness centrality value's distribution analysis showed that, Czechia, India, Singapore, United Arab Emirates, Viet Nam, Myanmar, Pakistan, Poland, Sri Lanka, Turkey, Ukraine, China, Indonesia, Malaysia, Romania, Russian Federation, Thailand behaved most important media Junction. We also found that, the 65 One Belt One Road countries in the trade network behaves like a three layers small-world network, and it can be divided into 3 main community according to geography address on the earth along the OBOR line according to community analysis, in which China occupies a very important position in the whole trade network. We also tried to check whether the maximal weighted spanning tree will occupy most of such a trade network or not. The analysis of maximal weighted spanning tree of the trade network further revealed that, the betweenness centrality and bridging centrality distributions of constructed MST is different from each other. The betweenness centrality distribution of weighted MST is some
The paper presents results of three-dimensional numerical simulations of an annular rotating detonation engine using the author’s software package. The program was based on a mathematical model of multicomponent gas ...
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Grounded theory can help scholars solve complex management problems and build a link between practice and theory and has received much attention in recent years. This paper takes CNKI as primary data source, construct...
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Digital Technologies are significant in improving transparency and reducing wastage in organizational settings. This study examines the digital technologies and innovations involved in the food supply chain. The liter...
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ISBN:
(数字)9798331530983
ISBN:
(纸本)9798331530990
Digital Technologies are significant in improving transparency and reducing wastage in organizational settings. This study examines the digital technologies and innovations involved in the food supply chain. The literature outlines that these technologies can significantly reduce waste and enhance transparency throughout the supply chain. Therefore, this study aims to identify sustainable technologies within the food supply chain that contribute to reducing food waste at sourcing, production, and distribution. This paper’s methodology involves conducting a bibliometric analysis. The analysis uses data from Scopus database articles published between 2004 and 2024 (October). The analysis evaluates the trends in journals, authorship, and keywords to analyze research findings. The Visual representations of the above analysis are generated using VOSviewer. This study outlines that literature is discussed more on blockchain and IoT as key technologies. Blockchain improves transparency and traceability. IoT monitors conditions like temperature to prevent spoilage. AI and predictive analytics optimize operations and reduce waste. Mobile apps enhance collaboration and logistics. These technologies offer significant benefits. However, adoption requires customized strategies due to cost and complexity
Extracting singing melody from polyphonic music is an important topic in the field of music information retrieval. In this paper, we propose a singing melody extraction network consisting of five stacked multi-scale f...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Extracting singing melody from polyphonic music is an important topic in the field of music information retrieval. In this paper, we propose a singing melody extraction network consisting of five stacked multi-scale feature time-frequency aggregation (MF-TFA) modules. In the same network, deeper layers generally contain more contextual information than shallower layers. To help the shallower layers enhance the ability of task-relevant feature extraction, we propose a self-distillation and multi-level supervision (SD-MS) method, which leverages the feature distillation from the deepest layer to the shallower one and multi-level supervision to guide network training. visualizationanalysis shows that by introducing SD-MS, the same-level layer in the network can obtain a clearer representation of fundamental frequency components, while the shallower layers can even learn more task-relevant semantic information. Ablation study results indicate that SD-MS applies to existing melody extraction models and can consistently improve performance. Experimental results show that our proposed method, MF-TFA with SD-MS, outperforms six compared state-of-the-art methods, achieving overall accuracy (OA) scores of 87.1%, 89.9%, and 76.6% on the ADC 2004, MIREX 05, and MEDLEY DB datasets, respectively. The main code will be available at https://***/SmoothJing/MF-TFA_SD-MS.
Singing melody extraction from polyphonic music is a complex but important task in music information retrieval. Harmonic relationships have been shown to be crucial in this task, but most existing models based on Conv...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Singing melody extraction from polyphonic music is a complex but important task in music information retrieval. Harmonic relationships have been shown to be crucial in this task, but most existing models based on Convolutional Neural Networks (CNNs) struggle to capture long-range harmonic dependencies. To address this, we propose a Harmonic Attention-based Network (HANet) for singing melody extraction from polyphonic music, which includes multiple sampling layers. Specifically, each sampling layer uses three parallel Harmonic Attention Modules (HAMs) with CNNs of different kernel sizes to capture harmonic relationships across 1 to 6 octaves along the frequency axis. Additionally, the Channel Attention Module (CAM) is used to adaptively model long-range harmonic features across various octave ranges. Experimental results show that HANet achieves state-of-the-art performance on multiple public music melody datasets. The overall accuracy on the ADC2004dataset reached a peak of 95.2%, with the lowest voicing false alarm result of 2.1%. visualization results directly demonstrate that harmonic information effectively reduces octave errors. Our code is available online: https://***/wwsjj/HANet
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