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Journal articles on the topic 'Embedding layer'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Susanty, Meredita, and Sahrul Sukardi. "Perbandingan Pre-trained Word Embedding dan Embedding Layer untuk Named-Entity Recognition Bahasa Indonesia." Petir 14, no. 2 (2021): 247–57. http://dx.doi.org/10.33322/petir.v14i2.1164.

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Named-Entity Recognition (NER) is used to extract information from text by identifying entities such as the name of the person, organization, location, time, and other entities. Recently, machine learning approaches, particularly deep-learning, are widely used to recognize patterns of entities in sentences. Embedding, a process to convert text data into a number or vector of numbers, translates high dimensional vectors into relatively low-dimensional space. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. The embedding process can be perf

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Lu, Ruili, Pengfei Jiao, Yinghui Wang, Huaming Wu, and Xue Chen. "Layer Information Similarity Concerned Network Embedding." Complexity 2021 (August 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/2260488.

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Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while igno

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Li, Saihan, and Bing Gong. "Word embedding and text classification based on deep learning methods." MATEC Web of Conferences 336 (2021): 06022. http://dx.doi.org/10.1051/matecconf/202133606022.

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Traditional manual text classification method has been unable to cope with the current huge amount of data volume. The improvement of deep learning technology also accelerates the technology of text classification. Based on this background, we presented different word embedding methods such as word2vec, doc2vec, tfidf and embedding layer. After word embedding, we demonstrated 8 deep learning models to classify the news text automatically and compare the accuracy of all the models, the model ‘2 layer GRU model with pretrained word2vec embeddings’ model got the highest accuracy. Automatic text c

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Liu, Haiyang, Yuki Endo, Jinho Lee, and Shunsuke Kamijo. "PREmbed: Balancing Conditional Generative Models with Embedding Pretraining and Regularization." Electronics 14, no. 2 (2025): 280. https://doi.org/10.3390/electronics14020280.

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Recent advancements in conditional generative models, e.g., Conditional Variational AutoEncoder (CVAE) and Conditional Denoising Diffusion Probabilistic Model (CDDPM), which utilize class-specific embeddings for generating images in specific classes, have demonstrated exceptional capabilities in producing high-quality images on balanced datasets. However, their performance decreases with imbalanced real-world data, affecting the fidelity and diversity of the generated images. This paper discusses the reasons and solutions for the imbalance issue in CVAE and CDDPM. By selectively reweighting th

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Sabbeh, Sahar F., and Heba A. Fasihuddin. "A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification." Electronics 12, no. 6 (2023): 1425. http://dx.doi.org/10.3390/electronics12061425.

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Sentiment analysis on social media platforms (i.e., Twitter or Facebook) has become an important tool to learn about users’ opinions and preferences. However, the accuracy of sentiment analysis is disrupted by the challenges of natural language processing (NLP). Recently, deep learning models have proved superior performance over statistical- and lexical-based approaches in NLP-related tasks. Word embedding is an important layer of deep learning models to generate input features. Many word embedding models have been presented for text representation of both classic and context-based word embed

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He, Tao, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, and Yuanfang Li. "SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4091–98. http://dx.doi.org/10.1609/aaai.v34i04.5832.

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Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is

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Jadon, Anil Kumar, and Suresh Kumar. "Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1933. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1933-1941.

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Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN wit

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Anil, Kumar Jadon Suresh Kumar. "Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1933–41. https://doi.org/10.11591/ijeecs.v35.i3.pp1933-1941.

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Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN wit

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Chowdhury, Shihabur Rahman, Sara Ayoubi, Reaz Ahmed, et al. "Multi-Layer Virtual Network Embedding." IEEE Transactions on Network and Service Management 15, no. 3 (2018): 1132–45. http://dx.doi.org/10.1109/tnsm.2018.2834315.

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Bao, Shudi, Tiantian Wang, Liliang Zhou, Guilan Dai, Geng Sun, and Jun Shen. "Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation." Applied Sciences 12, no. 15 (2022): 7369. http://dx.doi.org/10.3390/app12157369.

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Service recommendation is key to improving users’ online experience. The development of the Internet has accelerated the creation of many services, and whether users can obtain good experiences among the massive number of services mainly depends on the quality of service recommendation. It is commonly believed that deep learning has excellent nonlinear fitting ability in capturing the complex interactions between users and items. The advantage in learning intricacy relationships enables deep learning to become an important technology for present service recommendation. Recently, it is noticed

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Wang, Bin, Yu Chen, Jinfang Sheng, and Zhengkun He. "Attributed Graph Embedding Based on Attention with Cluster." Mathematics 10, no. 23 (2022): 4563. http://dx.doi.org/10.3390/math10234563.

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Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes. In recent years, the appearance of graph neural networks has significantly improved the accuracy of graph embedding. However, the influence of clusters was not considered in existing graph neural network (GNN)-based methods, so this paper proposes a new method to incorporate the influence of clusters into the generation of graph embedding. We use the attention mechanism to pass

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Noha, Ali, H. AbuEl-Atta Ahmed, and H. Zayed Hala. "Enhancing the performance of cancer text classification model based on cancer hallmarks." International Journal of Artificial Intelligence (IJ-AI) 20, no. 2 (2021): 316–23. https://doi.org/10.11591/ijai.v10.i2.pp316-323.

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Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed mod

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Li, Qizhi, Xianyong Li, Yajun Du, Yongquan Fan, and Xiaoliang Chen. "A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis." Applied Sciences 12, no. 20 (2022): 10236. http://dx.doi.org/10.3390/app122010236.

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Since some sentiment words have similar syntactic and semantic features in the corpus, existing pre-trained word embeddings always perform poorly in sentiment analysis tasks. This paper proposes a new sentiment-enhanced word embedding (S-EWE) method to improve the effectiveness of sentence-level sentiment classification. This sentiment enhancement method takes full advantage of the mapping relationship between word embeddings and their corresponding sentiment orientations. This method first converts words to word embeddings and assigns sentiment mapping vectors to all word embeddings. Then, wo

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Lee, Wonjun, Bumsub Ham, and Suhyun Kim. "Maximizing the Position Embedding for Vision Transformers with Global Average Pooling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18154–62. https://doi.org/10.1609/aaai.v39i17.33997.

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In vision transformers, position embedding (PE) plays a crucial role in capturing the order of tokens. However, in vision transformer structures, there is a limitation in the expressiveness of PE due to the structure where position embedding is simply added to the token embedding. A layer-wise method that delivers PE to each layer and applies independent LNs for token embedding and PE has been adopted to overcome this limitation. In this paper, we identify the conflicting result that occurs in a layer-wise structure when using the global average pooling (GAP) method instead of the class token.

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Li, Yu, Yuan Tian, Jiawei Zhang, and Yi Chang. "Learning Signed Network Embedding via Graph Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4772–79. http://dx.doi.org/10.1609/aaai.v34i04.5911.

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Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e

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Li, Meng, and MeiLian Lu. "A Virtual Network Embedding Algorithm Based On Double-Layer Reinforcement Learning." Computer Journal 64, no. 6 (2021): 973–89. http://dx.doi.org/10.1093/comjnl/bxab040.

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Abstract Virtual network embedding (VNE) algorithms dominate the effectiveness of resource sharing in network virtualization. Heuristic embedding algorithms generally make embedding decisions by artificially specified strategies, in which the node importance is measured by simply summing or multiplying several node attributes. However, the contributions of different attributes may be combined through complex functional relationships. The reinforcement learning-based VNE algorithms can optimize node embedding. However, the existing algorithms only consider the local node attributes, and only si

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Selvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. 12 (2022): 1312–29. http://dx.doi.org/10.3897/jucs.84130.

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidi

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Selvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. (12) (2022): 1312–29. https://doi.org/10.3897/jucs.84130.

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidi

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Liu, Shouyue, Chunying Zhang, Liya Wang, Pengchao Yang, Shaona Hua, and Tong Zhang. "Image Steganalysis of Low Embedding Rate Based on the Attention Mechanism and Transfer Learning." Electronics 12, no. 4 (2023): 969. http://dx.doi.org/10.3390/electronics12040969.

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In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We int

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Wang, Zhiqiang, Xiaorui Ren, Shuhao Li, Bingyan Wang, Jianyi Zhang, and Tao Yang. "A Malicious URL Detection Model Based on Convolutional Neural Network." Security and Communication Networks 2021 (May 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/5518528.

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With the development of Internet technology, network security is under diverse threats. In particular, attackers can spread malicious uniform resource locators (URL) to carry out attacks such as phishing and spam. The research on malicious URL detection is significant for defending against these attacks. However, there are still some problems in the current research. For instance, malicious features cannot be extracted efficiently. Some existing detection methods are easy to evade by attackers. We design a malicious URL detection model based on a dynamic convolutional neural network (DCNN) to

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Kumar, Rajeev, Neeraj Kumar, and Ki-Hyun Jung. "Reversible Data Hiding Using an Improved Pixel Value Ordering and Complementary Strategy." Symmetry 14, no. 12 (2022): 2477. http://dx.doi.org/10.3390/sym14122477.

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Reversible data hiding (RDH) schemes based on pixel value ordering have gained significant popularity due to their unique capability of providing high-quality marked images with a decent embedding capacity, while also enabling secret information extraction and the lossless recovery of the original images at the receiving side. However, the marked image quality may be distorted severely when the pixel value ordering (PVO) method is employed in a layer-wise manner to increase the embedding capacity. In this paper, a new high-capacity RDH scheme using a complementary strategy is introduced to ove

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Liang, Hsin Ying, Chia Hsin Cheng, Cheng Ying Yang, and Kun Fu Zhang. "A Modified Least Significant Bit Embedding with Error Correction." Applied Mechanics and Materials 284-287 (January 2013): 3256–59. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3256.

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This paper proposes a modified Least significant bit (LSB) embedding capable of both a high embedding payload and error correction. The method proposed in this paper combines the techniques of both LSB embedding and multilevel coding to produce stego images with error correction capability and high embedding payloads. The proposed method divides cover work into multiple blocks, and each LSB for all the pixels in each block is considered a layer. Reed-Muller codes are used to encode cipher and embed data into every layer. LSB embedding has no inherent capability to correct errors in cipher extr

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Nguyen, Van Quan, Tien Nguyen Anh, and Hyung-Jeong Yang. "Real-time event detection using recurrent neural network in social sensors." International Journal of Distributed Sensor Networks 15, no. 6 (2019): 155014771985649. http://dx.doi.org/10.1177/1550147719856492.

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We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on

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Dong, Siyu, Cheng Zhang, Yuxiang Zhou, et al. "High-Stability Hybrid Organic-Inorganic Perovskite (CH3NH3PbBr3) in SiO2 Mesopores: Nonlinear Optics and Applications for Q-Switching Laser Operation." Nanomaterials 11, no. 7 (2021): 1648. http://dx.doi.org/10.3390/nano11071648.

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Hybrid organic-inorganic perovskite shows a great potential in the field of photoelectrics. Embedding methyl ammonium lead bromide (MAPbBr3) in a mesoporous silica (mSiO2) layer is an effective method for maintaining optical performance of MAPbBr3 at room temperature. In this work, we synthesized MAPbBr3 quantum dots, embedding them in the mSiO2 layer. The nonlinear optical responses of this composite thin film have been investigated by using the Z-scan technique at a wavelength of 800 nm. The results show plural nonlinear responses in different intensities, corresponding to one- and two-photo

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Ali, Noha, Ahmed H. AbuEl-Atta, and Hala H. Zayed. "Enhancing the performance of cancer text classification model based on cancer hallmarks." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 316. http://dx.doi.org/10.11591/ijai.v10.i2.pp316-323.

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<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the e

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Zhang, Dehai, Haoxing Wang, Xiaobo Yang, Yu Ma, Jiashu Liang, and Anquan Ren. "Deep Interest Network Based on Knowledge Graph Embedding." Applied Sciences 13, no. 1 (2022): 357. http://dx.doi.org/10.3390/app13010357.

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Recommendation systems based on knowledge graphs often obtain user preferences through the user’s click matrix. However, the click matrix represents static data and cannot represent the dynamic preferences of users over time. Therefore, we propose DINK, a knowledge graph-based deep interest exploration network, to extract users’ dynamic interests. DINK can be divided into a knowledge graph embedding layer, an interest exploration layer, and a recommendation layer. The embedding layer expands the receptive field of the user’s click sequence through the knowledge graph, the interest exploration

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Meng, Di, and Gianluca Pollastri. "PUNCH2: Explore the strategy for intrinsically disordered protein predictor." PLOS ONE 20, no. 3 (2025): e0319208. https://doi.org/10.1371/journal.pone.0319208.

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Intrinsically disordered proteins (IDPs) and their intrinsically disordered regions (IDRs) lack stable three-dimensional structures, posing significant challenges for computational prediction. This study introduces PUNCH2 and PUNCH2-light, advanced predictors designed to address these challenges through curated datasets, innovative feature extraction, and optimized neural architectures. By integrating experimental datasets from PDB (PDB_missing) and fully disordered sequences from DisProt (DisProt_FD), we enhanced model performance and robustness. Three embedding strategies—One-Hot, MSA-based,

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Sinha, Swapnil, and Nicholas Alexander Meisel. "Influence of process interruption on mechanical properties of material extrusion parts." Rapid Prototyping Journal 24, no. 5 (2018): 821–27. http://dx.doi.org/10.1108/rpj-05-2017-0091.

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Purpose This paper aims to identify and quantify the effects of additive manufacturing (AM) process interruption on the tensile strength of material extrusion parts, and to find solutions to mitigate it. Design/methodology/approach Statistical analysis was performed to compare the tensile strength of specimens prepared with different process interruption time durations and different embedding methods. Subsequently, specimens were reheated at the paused layer before resuming, and tensile strengths were analyzed to observe any improvements. Findings Process interruption significantly reduced the

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Zhang, Jiarong, Jinsha Yuan, Jing Zhang, Zhihong Luo, and Aitong Li. "Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition." Applied Sciences 13, no. 20 (2023): 11325. http://dx.doi.org/10.3390/app132011325.

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The automatic extraction of key entities in mechanics problems is an important means to automatically solve mechanics problems. Nevertheless, for standard Chinese, compared with the open domain, mechanics problems have a large number of specialized terms and composite entities, which leads to a low recognition capability. Although recent research demonstrates that external information and pre-trained language models can improve the performance of Chinese Named Entity Recognition (CNER), few efforts have been made to combine the two to explore high-performance algorithms for extracting mechanic

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Yu, Paul L., Gunjan Verma, and Brian M. Sadler. "Wireless physical layer authentication via fingerprint embedding." IEEE Communications Magazine 53, no. 6 (2015): 48–53. http://dx.doi.org/10.1109/mcom.2015.7120016.

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BEIN, WOLFGANG W., LAWRENCE L. LARMORE, CHARLES O. SHIELDS, and I. HAL SUDBOROUGH. "EMBEDDING A COMPLETE BINARY TREE INTO A THREE-DIMENSIONAL GRID." Journal of Interconnection Networks 05, no. 02 (2004): 111–30. http://dx.doi.org/10.1142/s0219265904001052.

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We describe total congestion 1 embeddings of complete binary trees into three dimensional grids with low expansion ratio r. That is, we give a one-to-one embedding of any complete binary tree into a hexahedron shaped grid such that (a) the number of nodes in the grid is at most r times the number of nodes in the tree, and (b) no tree nodes or edges occupy the same grid positions. The first strategy embeds trees into cube shaped 3D grids. That is, 3D grids in which all dimensions are roughly equal in size, and which thus have no limit in the number of layers. The technique uses a recursive sche

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Meng, Qi, Xixiang Zhang, Yun Dong, Yan Chen, and Dezhao Lin. "A Combined Semantic Dependency and Lexical Embedding RoBERTa Model for Grid Field Relational Extraction." Applied Sciences 13, no. 19 (2023): 11074. http://dx.doi.org/10.3390/app131911074.

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Relationship extraction is a crucial step in the construction of a knowledge graph. In this research, the grid field entity relationship extraction was performed via a labeling approach that used span representation. The subject entity and object entity were used as training instances to bolster the linkage between them. The embedding layer of the RoBERTa pre-training model included word embedding, position embedding, and paragraph embedding information. In addition, semantic dependency was introduced to establish an effective linkage between different entities. To facilitate the effective lin

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Khosa, Saima, Arif Mehmood, and Muhammad Rizwan. "Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction." Computers 12, no. 7 (2023): 137. http://dx.doi.org/10.3390/computers12070137.

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The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. The data are stratified into train and test sets to ensure equal representation of each category. Word embeddings are generated using transformer models, with the last hidden layer selected as the embedding. Mean pooling calculates a single vector representation called sentence embedding, capturing the overal

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Zhang, Fan, Mei Tu, and Jinyao Yan. "Accelerating Neural Machine Translation with Partial Word Embedding Compression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14356–64. http://dx.doi.org/10.1609/aaai.v35i16.17688.

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Large model size and high computational complexity prevent the neural machine translation (NMT) models from being deployed to low resource devices (e.g. mobile phones). Due to the large vocabulary, a large storage memory is required for the word embedding matrix in NMT models, in the meantime, high latency is introduced when constructing the word probability distribution. Based on reusing the word embedding matrix in the softmax layer, it is possible to handle the two problems brought by large vocabulary at the same time. In this paper, we propose Partial Vector Quantization (P-VQ) for NMT mod

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Arham, Aulia, and Novia Lestari. "Arnold’s cat map secure multiple-layer reversible watermarking." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1536. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1536-1545.

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Reversible watermarking is a novel approach to digital copyright protection that allows the embedding of watermarks into digital data using multiple layers while retaining the ability to recover the original content without data loss. This method provides a unique solution for securing digital data while maintaining the integrity and quality of the content. Nonetheless, new challenges have emerged with the increase in attacks on this method, as reversible watermarking methods lack security keys, making it easy to extract and modify hidden data. In this paper, we present a method for multiple-l

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Arham, Aulia, and Novia Lestari. "Arnold's cat map secure multiple-layer reversible watermarking." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1536–45. https://doi.org/10.11591/ijeecs.v33.i3.pp1536-1545.

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Reversible watermarking is a novel approach to digital copyright protection that allows the embedding of watermarks into digital data using multiple layers while retaining the ability to recover the original content without data loss. This method provides a unique solution for securing digital data while maintaining the integrity and quality of the content. Nonetheless, new challenges have emerged with the increase in attacks on this method, as reversible watermarking methods lack security keys, making it easy to extract and modify hidden data. In this paper, we present a method for multiple-l

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Wu, Yanan, Shouliang Qi, Yu Sun, Shuyue Xia, Yudong Yao, and Wei Qian. "A vision transformer for emphysema classification using CT images." Physics in Medicine & Biology 66, no. 24 (2021): 245016. http://dx.doi.org/10.1088/1361-6560/ac3dc8.

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Abstract Objective. Emphysema is characterized by the destruction and permanent enlargement of the alveoli in the lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE). Automating emphysema classification can help precisely determine the patterns of lung destruction and provide a quantitative evaluation. Approach. We propose a vision transformer (ViT) model to classify the emphysema subtypes via CT images. First, large patches (61 × 61) are cropped from CT images which con

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Hu, Hefei, Sirui Zhang, and Yanan Wang. "An Interlayer Link Prediction Method Based on Edge-Weighted Embedding." Complexity 2023 (December 8, 2023): 1–13. http://dx.doi.org/10.1155/2023/3541437.

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Presently, users usually register accounts on online social networks (OSNs). Identifying the same user in different networks is also known as interlayer link prediction. Most existing interlayer link prediction studies use embedding methods, which represent nodes in a common representation space by learning mapping functions. However, these studies often directly model links within the pre-embedding layer as equal weights, fail to effectively distinguish the strength of edge relationships, and do not fully utilize network topology information. In this paper, we propose an interlayer link predi

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Ning, Nianwen, Qiuyue Li, Kai Zhao, and Bin Wu. "Multiplex Network Embedding Model with High-Order Node Dependence." Complexity 2021 (March 6, 2021): 1–18. http://dx.doi.org/10.1155/2021/6644111.

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Multiplex networks have been widely used in information diffusion, social networks, transport, and biology multiomics. They contain multiple types of relations between nodes, in which each type of the relation is intuitively modeled as one layer. In the real world, the formation of a type of relations may only depend on some attribute elements of nodes. Most existing multiplex network embedding methods only focus on intralayer and interlayer structural information while neglecting this dependence between node attributes and the topology of each layer. Attributes that are irrelevant to the netw

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陳鵬, 陳鵬, Jiancheng Zhao Peng Chen, and Xiaosheng Yu Jiancheng Zhao. "LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks." 網際網路技術學刊 23, no. 3 (2022): 621–29. http://dx.doi.org/10.53106/160792642022052303020.

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<p>Recommender systems have been extensively utilized to meet users&rsquo; personalized needs. Collaborative filtering is one of the most classic algorithms in the recommendation field. However, it has problems such as cold start and data sparsity. In that case, knowledge graphs and graph convolutional networks have been introduced by scholars into recommender systems to solve the above problems. However, the current graph convolutional networks fail to give full play to the advantages of graph convolution since they are employed either in the embedding representations of users and c

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Huang, Jiani, He Yan, Qixiu Chen, and Yingan Liu. "Multi-Granularity Temporal Embedding Transformer Network for Traffic Flow Forecasting." Sensors 24, no. 24 (2024): 8106. https://doi.org/10.3390/s24248106.

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Traffic flow forecasting is integral to transportation to avoid traffic accidents and congestion. Due to the heterogeneous and nonlinear nature of the data, traffic flow prediction is facing challenges. Existing models only utilize plain historical data for prediction. Inadequate use of temporal information has become a key problem in current forecasting. To address the problem, we must effectively analyze the influence of time periods while integrating the distinct characteristics of traffic flow across various time granularities. This paper proposed a multi-granularity temporal embedding Tra

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Cauwe, Maarten, Bjorn Vandecasteele, Johan De Baets, Jeroen van den Brand, Roel Kusters, and Ashok Sridhar. "Active and passive component embedding into low-cost plastic substrates aimed at smart system applications." International Symposium on Microelectronics 2013, no. 1 (2013): 000730–34. http://dx.doi.org/10.4071/isom-2013-wp51.

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The technology development for a low-cost, roll-to-roll compatible chip embedding process is described in this paper. Target applications are intelligent labels and disposable sensor patches. Two generations of the technology are depicted. In the first version of the embedding technology, the chips are embedded in an adhesive layer between a copper foil and a PET film. While this results in a very thin (&lt; 200 μm) and flexible system, the single-layer routing and the incompatibility with passive components restricts the application of this first generation. The double-sided circuitry emb

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Obidallah, Waeal J., Bijan Raahemi, and Waleed Rashideh. "Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques." Data 7, no. 5 (2022): 57. http://dx.doi.org/10.3390/data7050057.

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We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services

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Liu, Ying, Zengyu Wei, Long Chen, Cai Xu, and Ziyu Guan. "Multi-Modal Temporal Dynamic Graph Construction for Stock Rank Prediction." Mathematics 13, no. 5 (2025): 845. https://doi.org/10.3390/math13050845.

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Stock rank prediction is an important and challenging task. Recently, graph-based prediction methods have emerged as a valuable approach for capturing the complex relationships between stocks. Existing works mainly construct static undirected relational graphs, leading to two main drawbacks: (1) overlooking the bidirectional asymmetric effects of stock data, i.e., financial messages affect each other differently when they occur at different nodes of the graph; and (2) failing to capture the dynamic relationships of stocks over time. In this paper, we propose a Multi-modal Temporal Dynamic Grap

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Xiao, Yu, Hui Wu, Yisheng Chen, Chongcheng Chen, Ruihai Dong, and Ding Lin. "Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation." Remote Sensing 17, no. 2 (2025): 256. https://doi.org/10.3390/rs17020256.

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In recent years, large-scale point cloud semantic segmentation has been widely applied in various fields, such as remote sensing and autonomous driving. Most existing point cloud networks use local aggregation to abstract unordered point clouds layer by layer. Among these, position embedding serves as a crucial step. However, current methods of position embedding have limitations in modeling spatial relationships, especially in deeper encoders where richer spatial positional relationships are needed. To address these issues, this paper summarizes the advantages and disadvantages of mainstream

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Weng, Shaowei, Jeng-shyang Pan, and Leida Li. "Reversible data hiding based on an adaptive pixel-embedding strategy and two-layer embedding." Information Sciences 369 (November 2016): 144–59. http://dx.doi.org/10.1016/j.ins.2016.05.030.

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Dornaika, F. "Multi-layer linear embedding with feature subset selection." Knowledge and Information Systems 63, no. 4 (2021): 1029–43. http://dx.doi.org/10.1007/s10115-020-01535-3.

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Jiang, Junjun, Ruimin Hu, Zhongyuan Wang, Zhen Han, and Jiayi Ma. "Facial Image Hallucination Through Coupled-Layer Neighbor Embedding." IEEE Transactions on Circuits and Systems for Video Technology 26, no. 9 (2016): 1674–84. http://dx.doi.org/10.1109/tcsvt.2015.2433538.

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Xiushan Nie, Ju Liu, Jiande Sun, and Wei Liu. "Robust Video Hashing Based on Double-Layer Embedding." IEEE Signal Processing Letters 18, no. 5 (2011): 307–10. http://dx.doi.org/10.1109/lsp.2011.2126020.

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Yu, Seungmin, Hayun Lee, and Dongkun Shin. "Optimizing Computation of Tensor-Train Decomposed Embedding Layer." Journal of KIISE 50, no. 9 (2023): 729–36. http://dx.doi.org/10.5626/jok.2023.50.9.729.

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