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24th International Conference on Control, Automation and Systems

作者： Tahira, Nusrat J. Suresh, Patel D. Park, Jang S. Kyungsung Univ Dept Elect Engn Busan 48434 South Korea

ISBN: (纸本)9798331517939;9788993215380

This paper introduces two methods for estimating crowd characteristics - a convLSTM approach for estimating crowd density, and an encoder-decoder network for estimating crowd flow. By analyzing sequential density maps, the approaches capture the dynamic nature of crowds and learn patterns to enable accurate prediction. Evaluation on real-world data advances crowd management techniques. Understanding crowd dynamics is crucial for effective crowd control and public safety. The flow estimation method enables precise crowd counts and facilitates proactive decision-making for crowd control and emergency response. This empowers authorities to take preventive measures in dense areas, contributing to safer urban environments that prioritize public well-being. Accurately estimating crowd density and flow can optimize resource allocation, enhance crowd movement, and prevent overcrowding to maintain public safety. This research significantly advances the understanding of crowd dynamics and informs strategies to promote individual welfare and security in urban settings.

关键词： Crowd Dynamics Density Prediction convLSTM encoder-decoder Deep Learning

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Coarse-to-Fine Response Generation for Document Grounded Conversations 20th

Coarse-to-Fine Response Generation for Document Grounded Con...

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20th Pacific Rim International Conference on Artificial Intelligence

作者： Zhao, Huan Li, Shan Li, Bo Zha, Xupeng Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (纸本)9789819970186;9789819970193

Generating fluent and informative responses is crucial for dialogue systems. Most existing studies introduce documents as additional document to improve dialogue generation performance. However, understanding complex and multi-topic documents as well as extracting useful information related to the dialogue context from them is challenging. To solve this problem, we propose a Coarse-to-Fine Response Generation (CFRG) model with an encoder-decoder for document-grounded conversations,where the encoder is used to aware the themes of the dialogue context, and the decoder parse the semantics of replies. Specifically, the CFRG extracts coarse-grained features guided by the response from the context and documents and then uses them as input of decoder. It further interacts with the current sequence in the cross-attention layer to generate fine-grained features, on which a non-linear transformation function is trained to map to a word space. The experimental results on datasets CMU DoG and Wizard of WikiPedia show that our model demonstrates superior performance compared to the majority of other models and achieves the best or comparable results.

关键词： Coarse-to-fine features Document-driven dialogue encoder-decoder Knowledge integration

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Modelling monthly rainfall of India through transformer-based deep learning architecture

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2024年第3期10卷 3119-3136页

作者： Nayak, G. H. Harish Alam, Wasi Singh, K. N. Avinash, G. Ray, Mrinmoy Kumar, Rajeev Ranjan ICAR Indian Agr Res Inst Grad Sch New Delhi 110012 India ICAR Indian Agr Stat Res Inst New Delhi 110012 India

In the realm of Earth systems modelling, the forecasting of rainfall holds crucial significance. The accurate prediction of monthly rainfall in India is paramount due to its pivotal role in determining the country's agricultural productivity. Due to this phenomenon's highly nonlinear dynamic nature, linear models are deemed inadequate. Parametric non-linear models also face limitations due to stringent assumptions. Consequently, there has been a notable surge in the adoption of machine learning approaches in recent times, owing to their data-driven nature. However, it is acknowledged that machine learning algorithms lack automatic feature extraction capabilities. This limitation has propelled the popularity of deep learning models, particularly in the domain of rainfall forecasting. Nevertheless, conventional deep learning architectures typically engage in the sequential processing of input data, a task that can prove challenging and time-consuming, especially when dealing with lengthy sequences. To address this concern, the present article proposes a rainfall modelling algorithm founded on a transformer-based deep learning architecture. The primary distinguishing feature of this approach lies in its capacity to parallelize sequential input data through an attention mechanism. This attribute facilitates expedited processing and training of larger datasets. The predictive performance of the transformer-based architecture was assessed using monthly rainfall data spanning 41 years, from 1980 to 2021, in India. Comparative evaluations were conducted with conventional recurrent neural networks, long short-term memory, and gated recurrent unit architectures. Experimental findings reveal that the transformer architecture outperforms other conventional deep learning architectures based on root mean square error and mean absolute percentage error. Furthermore, the accuracy of each architecture's predictions underwent testing using the Diebold-Mariano test. The conclusive fin

关键词： Attention mechanism encoder-decoder Gated recurrent units (GRUs) Long short-term memory (LSTM) Recurrent neural network (RNN) Transformer-encoder

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CENET: CONTENT-AWARE ENHANCED NETWORK FOR PRACTICAL SCENE PARSING 49

CENET: CONTENT-AWARE ENHANCED NETWORK FOR PRACTICAL SCENE PA...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Song, Kai Wang, Zhengtan Dai, Huhe Zheng, Yuan Inner Mongolia Univ Hohhot Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Attention mechanisms are widely adopted in existing scene parsing methods due to their excellent performance, especially spatial self-attention. However, spatial self-attention suffers from high computational complexity, which limits the practical applications of the scene parsing methods on mobile devices with limited resources. In view of this, we propose a simple yet effective spatial attention module, namely Content-Aware Attention Module (CA2 M). CA2M is a lightweight spatial attention module that consists of several convolution and pooling operations, compared to various spatial self-attention modules. Moreover, it is able to adaptively select spatial pixel information which is helpful for scene parsing task. With CA2M, we present a Content-aware Enhanced Network for scene parsing (CENet), where CA2M is introduced into the lateral connections at four different scales, resulting in a semantic alignment at adjacent scales and an effective semantic propagation. To validate the performance of the proposed CA2 M and CENet, we conduct extensive experiments and achieve consistently improved performances on three popular benchmarks. Furthermore, we verify their generalization ability when using different baseline models and backbone networks. Code is available at https://***/ZY-IMU-CV/CENET_SK_2023.

关键词： Spatial attention image semantic segmentation encoder-decoder

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A FEDERATED GRAPH TO EMBEDDING APPROACH FOR KNOWLEDGE GRAPH COMPLETION 49

A FEDERATED GRAPH TO EMBEDDING APPROACH FOR KNOWLEDGE GRAPH ...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sun, Hongliang Bi, Xiaofeng Sui, Dianbo Tu, Zhiying Harbin Inst Technol Weihai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Knowledge graph completion (KGC) tasks have been developed to address the inherent incompleteness of KGs. Recently, knowledge graph embedding (KGE) methods have gained popularity for embedding entities and relations, proving effective in KGC. However, privacy concerns make it challenging to collect privacy KG data from different institutions in the actual application. Federated learning has emerged as a solution for training models with decentralized data, eliminating the need for collecting private data. However, existing federated KGE methods overlook the implicit graph structural information of entities and relations, resulting in fragmented and incomplete representations within federated clients. Moreover, these methods often struggle with capturing multiple relational representations. To address these challenges, we propose a Federated Graph to Embedding (FedGE) approach based on encoder-decoder to capture interactions among entities and relations. Extensive experiments on two common KG datasets demonstrate the superiority of our method. The code is available at https://***/s460305450/***.

关键词： knowledge graph completion federated learning privacy protection encoder-decoder graph convolutional network

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Efficient Lung Segmentation for Tumour Detection 19th

Efficient Lung Segmentation for Tumour Detection

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19th International Symposium on Visual Computing

作者： Hiraman, Anura Viriri, Serestina Gwetu, Mandlenkosi Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa

ISBN: (纸本)9783031773914;9783031773921

Over the past decade, deep learning has significantly impacted medical imaging, particularly in segmentation tasks. encoder-decoder methods have advanced medical image segmentation by restoring feature map resolution and minimizing information loss during decoding. This paper introduces three key contributions: a method for region of interest (RoI) detection that isolates the lung region by classifying CT scan slices using two convolutional neural networks-one for slices above the lungs;an architecture inspired by U-Net, which employs a convolutional encoder-decoder-encoder-decoder pattern to enhance learning by feeding output feature maps from the first decoder into the second encoder;and concatenation layers between the encoder-decoder networks that reintroduce important features lost in the initial structure, improving the learning of complex features. Evaluation results demonstrate the effectiveness of these methods for lung segmentation, achieving an average DSC of 92.68%.

关键词： CNN encoder-decoder Lung segmentation Region of interest

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A stroke of genius: Predicting the next move in badminton

A stroke of genius: Predicting the next move in badminton

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ibh, Magnus Grasshof, Stella Hansen, Dan Witzner IT Univ Copenhagen Machine Learning Grp Copenhagen Denmark

ISBN: (纸本)9798350365474

This paper presents, RallyTemPose, a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position, skeleton poses, and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks, which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion, the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons. The code can be found at: This https url.

关键词： Action Forecasting Computer Vision encoder-decoder Skeleton-data Sports Application

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Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第4期11卷 846-850页

作者： Xie, Wenwu Xiao, Jian Zhu, Peng Yu, Chao Yang, Liang Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Adv Cryptog & Syst Secur Key Lab Sichuan Prov Chengdu 610103 Peoples R China

To reduce the pilot overhead of cascaded channel estimation for RIS-aided Massive MIMO communication system, we proposed a deep compressed sensing-based channel estimation scheme, where U-shaped network (U-Net), an encoder-decoder with skip connection, is used to recover the high-dimensional cascaded channel matrix from limited pilot overhead. The skip connections between encoder and decoder can fuse features of different scales and semantic by concatenating the feature map, which enhance the reconstruction performance of cascaded channel. To further improve the feature extraction ability of U-Net, we design a ResU-Net architecture with stacked residual units to increase the depth of network. Simulation results show the channel estimation of ResU-Net is more accurate than conventional algorithm and other network model. Meanwhile, ResU-Net has good generalization and robustness for different pilot lengths and phase quantization errors.

关键词： Channel estimation Azimuth Quantization (signal) Estimation Sensors Compressed sensing Chaos Reconfigurable intelligent surface channel estimation deep compressed sensing encoder-decoder

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Improving offline handwritten Chinese text recognition with glyph-semanteme fusion embedding

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第2期13卷 485-496页

作者： Zhan, Hongjian Lyu, Shujing Lu, Yue Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai 200062 Peoples R China

In this paper, we propose the Glyph-Semanteme fusion Embedding (GSE) for Chinese character and apply it to Offline Handwritten Chinese Text Recognition (offline-HCTR). It is well known that the number of Chinese characters is very large and the glyphs of these characters are complex, but few researchers realize that the underlying reason for this phenomenon is that Chinese is a form of ideogram, which indicates that there are correlations between the glyph and semanteme of a character. In order to utilize this feature and create better representations for Chinese characters, firstly, we extract the glyph embedding and semanteme embedding for each Chinese character;then we propose a parameterized gated fusion strategy to automatically calculate the Glyph-Semanteme fusion Embedding for each character by fusing its glyph embedding and semanteme embedding. We apply the proposed GSE to an attention-based encoder-decoder network for the offline-HCTR task. Furthermore, two kinds of GSE, Character-level GSE (CGSE) and Text-level GSE (TGSE), are applied to the decoder phase to yield the predictions. On the standard benchmark ICDAR-2013 HCTR competition dataset, the proposed method achieves 96.65% character-level recognition accuracy, which demonstrates the effectiveness of the proposed glyph-semanteme fusion embedding.

关键词： Offline Handwritten Chinese text recognition Glyph embedding Semanteme embedding Embedding fusion encoder-decoder

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Intelligent machinery health prognostics under variable operation conditions with limited and variable-length data

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ADVANCED ENGINEERING INFORMATICS 2022年第0期53卷

作者： Ding, Peng Jia, Minping Ding, Yifei Cao, Yudong Zhao, Xiaoli Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

High accuracy health prognostics are significant to machinery intelligent operation and maintenance. Current data-driven prognostics achieve great success that benefits from amply learning samples. In fact, data scarcity challenge widely exists in machinery prognostics and health management, especially for high-end equipment. This study aims to solve this dilemma and proposes a novel meta learning algorithm reconstructed by classic variable-length prediction mode and attention mechanism, namely meta attention recurrent neural network (MARNN). Specifically, we first develop the encoder-decoder with attention mechanism (EDA) cell to perform episodic learning for the subtask-level upgrade. Then multiple subtasks with EDA as prediction models are aggregated to accomplish meta-level upgrade, thus mining the general degradation knowledge from historical datasets. Finally, cross-domain prognostics tasks can be easily realized through fine-tuning tricks, and three rotating machinery run-to-failed experiments are conducted to prove the generalizations of MARNN, which can obtain desired results even when the on-site adaptation data is reduced to one-twentieth.

关键词： Limiteddatadrivenhealthprognostics encoder-decoder Attentionmechanism Metalearning Rotatingmachinery

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