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Network Traffic Prediction with Attention-based Spatial-Temporal Graph Network 24

Network Traffic Prediction with Attention-based Spatial-Temp...

引用

IEEE 24th International Conference on High Performance Switching and Routing, (HPSR)

作者： Peng, Yufei Guo, Yingya Hao, Run Lin, Junda Fuzhou Univ Dept Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou Peoples R China

ISBN: (纸本)9781665476409

Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention-based Spatial-Temporal Graph Network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.

关键词： Network Traffic Prediction Attention Mechanism Graph Neural Network Temporal and Spatial encoder-decoder

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Multi-Service Demand Forecasting using Graph Neural Networks 17

Multi-Service Demand Forecasting using Graph Neural Networks

引用

17th IEEE International Congress on Intelligent and Service-Oriented Systems Engineering (CISOSE)

作者： Theodoropoulos, Theodoros Makris, Antonios Kontopoulos, Ioannis Maroudis, Angelos-Christos Tserpes, Konstantinos Harokopio Univ Athens Dept Informat & Telemat Kallithea Greece

ISBN: (纸本)9798350322392

Accurate service demand forecasting is crucial towards achieving effective resource allocation and service orchestration. However, existing solutions require separate prediction models for each service, which consumes significant computation resources. In this paper, we propose a novel approach that introduces a global prediction model capable of generating accurate multi-step predictions involving multiple services and that can potentially leveraged for facilitating proactive strategies for orchestrating multiple services. The proposed model is based on an encoder-decoder architecture that utilizes Graph Neural Networks (GNNs) to capture interdependencies among input variables and their trends. By iteratively updating graph node representations, our model effectively incorporates historical trends and potential dependencies. Furthermore, the incorporation of GNNs into the encoder-decoder architecture enables the proposed approach to leverage the correlations among input variables, thus making it suitable for multivariate forecasting. Experimental results demonstrate the superiority of the proposed approach in terms of accurately conducting multi-step multiservice demand predictions, when compared against numerous contemporary deep learning models.

关键词： graph neural networks service demand forecasting edge deep learning encoder-decoder

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Sequence Prediction under Missing Data: An RNN Approach without Imputation 22

Sequence Prediction under Missing Data: An RNN Approach with...

引用

31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Pachal, Soumen Achar, Avinash IIT Madras Res Pk Consultancy Serv Res Chennai Tamil Nadu India

ISBN: (纸本)9781450392365

Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data directly without trying to impute data either before or during model building. Our encoding is lossless and achieves compression. It can be employed for both sequence classification and forecasting. We focus on forecasting here in a general context of multi-step prediction in presence of possible exogenous inputs. In particular, we propose novel variants of encoder-decoder (Seq2Seq) RNNs for this. The encoder here adopts the above mentioned pattern encoding, while at the decoder which has a different structure, multiple variants are feasible. We demonstrate the utility of our proposed architecture via multiple experiments on both single and multiple sequence (real) data-sets. We consider both scenarios where (i)data is naturally missing and (ii)data is synthetically masked.

关键词： Recurrent Neural Networks encoder-decoder Seq2Seq time-series Missing Data

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Explainability for Medical Image Captioning 11

Explainability for Medical Image Captioning

引用

11th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Beddiar, Djamila Oussalah, Mourad Tapio, Seppanen Univ Oulu CMVS Oulu Finland MIPT Fac Med Oulu Finland

ISBN: (纸本)9781665469647

Medical image captioning is the process of generating clinically significant descriptions to medical images, which has many applications among which medical report generation is the most frequent one. In general, automatic captioning of medical images is of great interest for medical experts since it offers assistance in diagnosis, disease treatment and automating the workflow of the health practitioners. Recently, many efforts have been put forward to obtain accurate descriptions but medical image captioning still provides weak and incorrect descriptions. To alleviate this issue, it is important to explain why the model produced a particular caption based on some specific features. This is performed through Artificial Intelligence Explainability (XAI), which aims to unfold the 'black-box' feature of deeplearning based models. We present in this paper an explainable module for medical image captioning that provides a sound interpretation of our attention-based encoder-decoder model by explaining the correspondence between visual features and semantic features. We exploit for that, self-attention to compute word importance of semantic features and visual attention to compute relevant regions of the image that correspond to each generated word of the caption in addition to visualization of visual features extracted at each layer of the Convolutional Neural Network (CNN) encoder. We finally evaluate our model using the ImageCLEF medical captioning dataset.

关键词： Image Captioning Medical images encoder-decoder Attention-maps Artificial Intelligence Explainability

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Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network 22nd

Biomarker Localization by Combining CNN Classifier and Gener...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhang, Rong Tan, Shuhan Wang, Ruixuan Manivannan, Siyamalan Chen, Jingjing Lin, Haotian Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China MOE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China Univ Jaffna Dept Comp Sci Jaffna Sri Lanka Sun Yat Sen Univ Zhongshan Ophthalm Ctr Guangzhou Peoples R China

ISBN: (纸本)9783030322397;9783030322380

This paper proposes a novel deep neural network architecture to effectively localize potential biomarkers in medical images, when only the image-level labels are available during model training. The proposed architecture combines a CNN classifier and a generative adversarial network (GAN) in a novel way, such that the CNN classifier and the discriminator in the GAN can effectively help the encoder-decoder in the GAN to remove biomarkers. Biomarkers in abnormal images can then be easily localized and segmented by subtracting the output of the encoder-decoder from its original input. The proposed approach was evaluated on diabetic retinopathy images with real biomarkers and on skin images with simulated biomarkers, showing state-of-the-art performance in localizing biomarkers even if biomarkers are irregularly scattered and are of various sizes in images.

关键词： Biomarker localization encoder-decoder Generative adversarial networks

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SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE

SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE

引用

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

作者： Bonada, Jordi Blaauw, Merlijn Univ Pompeu Fabra Mus Technol Grp Barcelona Spain

ISBN: (纸本)9781728176055

We propose a semi-supervised singing synthesizer, which is able to learn new voices from audio data only, without any annotations such as phonetic segmentation. Our system is an encoder-decoder model with two encoders, linguistic and acoustic, and one (acoustic) decoder. In a first step, the system is trained in a supervised manner, using a labeled multi-singer dataset. Here, we ensure that the embeddings produced by both encoders are similar, so that we can later use the model with either acoustic or linguistic input features. To learn a new voice in an unsupervised manner, the pretrained acoustic encoder is used to train a decoder for the target singer. Finally, at inference, the pretrained linguistic encoder is used together with the decoder of the new voice, to produce acoustic features from linguistic input. We evaluate our system with a listening test and show that the results are comparable to those obtained with an equivalent supervised approach.

关键词： Singing synthesis semi-supervised encoder-decoder autoregressive convolutional

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RBBA: ResNet - BERT - Bahdanau Attention for Image Caption Generator 14

RBBA: ResNet - BERT - Bahdanau Attention for Image Caption G...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Hoang, Duc-Hieu Tran, Anh-Khoa Minh Dang, Duc Ngoc Tran, Phuong-Nam Dang-Ngoc, Hanh Nguyen, Cuong Tuan Ton Duc Thang University Faculty of Electrical & Electronics Eng. Ho Chi Minh City Viet Nam Ton Duc Thang University Faculty of Electrical and Electronics Ho Chi Minh City Viet Nam Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam University of Technology Sydney School of Electrical and Data Engineering SydneyNSW Australia Fpt University Dept. of Information Technology Specialization Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350313277

In recent years, the topic of image caption generators has gained significant attention. Several successful projects have emerged in this field, showcasing notable advancements. Image caption generators automatically generate descriptive captions for images through the encoder and decoder mechanisms. The encoder leverages computer vision models, while the decoder utilizes natural language processing models. In this study, we aim to assess a comprehensive set of seven distinct methodologies, including six existing methods from prior research and one newly proposed. These methods are trained and evaluated with bilingual evaluation (BLEU) on the Flickr8K dataset. In our experiments, the proposed ResNet50 - BERT - Bahdanau Attention model outperforms the other models in terms of the BLEU-1 score of 0.532143 and BLEU-4 score of 0.126316. © 2023 IEEE.

关键词： BLEU Deep learning encoder-decoder Flickr8K Image Caption Natural language processing

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Improving Long-Term Traffic Prediction with Online Search Log Data 47

Improving Long-Term Traffic Prediction with Online Search Lo...

引用

47th IEEE-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Matsunaga, Itsuki Kosugi, Yuto Ge Hangli Michikata, Takashi Koshizuka, Noboru Univ Tokyo Grad Sch Interdisciplinary Informat Studies Tokyo Japan Univ Tokyo Interfac Initiat Informat Studies Tokyo Japan

ISBN: (纸本)9798350326970

Long-term traffic prediction is essential for both road managers and users to prepare for future congestion. However, most existing studies have only focused on short-term prediction. Moreover, few studies have effectively incorporated external data into long-term traffic prediction, even though traffic conditions are complexly influenced by various spatiotemporal factors. In this paper, we propose a novel method that utilizes online search log data for long-term traffic prediction on expressways. Online search logs reflect drivers' travel intentions and external factors, such as weather conditions and events, which cannot be represented by historical traffic data. Based on a new analysis of the correlation between online search log data and real-world traffic, we use online search log data as potential future traffic volume in an LSTM-based encoder-decoder model. Experiments using a real-world dataset on an expressway known for frequent congestion show that the use of online search log data improves the metrics of MAE, RMSE, and MAPE in next-day traffic volume prediction by 8.1%, 12.5%, and 7.2% on average, respectively. Similarly, in speed prediction, the MAE, RMSE, and MAPE are reduced by 3.7%, 2.1%, and 11.8%, respectively. It is also shown that online search log data is particularly effective in predicting irregular congestion caused by sudden increases in traffic demand.

关键词： Traffic prediction online search log intelligent transportation system encoder-decoder spatiotemporal data

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Abstractive Meeting Summarization based on an Attentional Neural Model 13

Abstractive Meeting Summarization based on an Attentional Ne...

引用

13th International Conference on Machine Vision

作者： Dammak, Nouha BenAyed, Yassine Univ Sousse ISITCom Sousse 4011 Tunisia Univ Sfax MIRACL Lab Sfax 3021 Tunisia

ISBN: (纸本)9781510640412

Through the ages, in all nations, at all times, people spend a lot of their time on discussing new and important issues either on meetings or in conferences. With the evolution and the abundance of Automatic Speech Recognition (ASR) frameworks, automatic transcripts and even automatic meeting summarization are getting more and more interesting. Recently, automatic summarization faces deeper progresses on speech summarization. Neural models had been introduced to tackle with many difficulties of abstractive summarization. Our contribution in this paper focuses on these weaknesses of neural abstractive meeting summarization and suggests an encoder-decoder model based on an attentional algorithm on the decoding sequence. We proposed a deep encoder-decoder model based on attention mechanism (DEDA) for ASR transcripts. Experiments on the AMI Dataset demonstrates that our proposed method ensured competitive results with the state of the art even on extractive or abstractive models. The experimental analyses also put the stress on the performance of the summarized utterances as well as the reduction of the occurrence repetition in summaries.

关键词： Summarization meetings deep learning encoder-decoder LSTM RNN attention model

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SMALL BOWEL MUCOSA SEGMENTATION FOR FRAME CHARACTERIZATION IN VIDEOS OF ENDOSCOPIC CAPSULES 16

SMALL BOWEL MUCOSA SEGMENTATION FOR FRAME CHARACTERIZATION I...

引用

16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Pinheiro, Gil Coelho, Paulo Mourao, Mariana Salgado, Maria Oliveira, Helder P. Cunha, Antonio Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Inst Politecn Leiria Leiria Portugal Univ Tras Os Montes & Alto Douro Vila Real Portugal Ctr Hosp Porto Porto Portugal Univ Porto Fac Ciencias Porto Portugal

ISBN: (纸本)9781538636411

Endoscopic capsules are vitamin-sized devices that leverage from a small wireless camera to create 8 to 10 hour videos of the patients' entire digestive tract, still being the leading tool to diagnose small bowel diseases. The revision of the produced videos is a very time-consuming task, currently conducted manually and frame-by-frame by an expert. Since endoscopic videos usually contain a considerable amount of frames where the mucosa is not clearly visible, the segmentation of the informative regions is a vital component to reduce the necessary time to review each exam. In this work, a CNN encoder-decoder architecture is applied to segment informative regions in small bowel frames of videos of endoscopic capsules. The network was trained and tested with a dataset of 2,929 manually annotated images, achieving a 91.2% Dice coefficient and 83.9% IoU. Furthermore, a video-wise analysis based on the amount of informative pixels in each frame is done.

关键词： Endoscopic capsule mucosa segmentation informative frames encoder-decoder deep learning

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