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9th International Conference on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Du, Shengdong Li, Tianrui Horng, Shi-Jinn Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781538694039

Time series forecasting has been regarded as a key research problem in various fields. such as financial forecasting, traffic flow forecasting, medical monitoring, intrusion detection, anomaly detection, and air quality forecasting etc. In this paper, we propose a sequence-to-sequence deep learning framework for multivariate time series forecasting, which addresses the dynamic, spatial-temporal and nonlinear characteristics of multivariate time series data by LSTM based encoder-decoder architecture. Through the air quality multivariate time series forecasting experiments, we show that the proposed model has better forecasting performance than classic shallow learning and baseline deep learning models. And the predicted PM2.5 value can be well matched with the ground truth value under single timestep and multi-timestep forward forecasting conditions. The experiment results show that our model is capable of dealing with multivariate time series forecasting with satisfied accuracy.

关键词： Time series forecasting LSTM encoder-decoder PM2.5 Sequence-to-sequence deep learning

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Plant Leaf Disease Detection and Classification Using Segmentation encoder Techniques

Open Agriculture Journal

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Open Agriculture Journal 2024年 18卷

作者： Trivedi, Payal Narayan, Yogendra Ravi, Vinayakumar Kumar, Prashant Kaur, Prabhjot Tabianan, Kayalvily Singh, Prabhishek Diwakar, Manoj Department of Biotechnology Chandigarh University Punjab Mohali India School of Technology Woxsen University Telangana Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun India Graphic Era Hill University Uttarakhand Dehradun India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Faculty of Information Technology Inti International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia

Aims: Agriculture is one of the fundamental elements of human civilization. Crops and plant leaves are susceptible to many illnesses when grown for agricultural purposes. There may be less possibility of further harm to the plants if the illnesses are identified and classified accurately and early on. Background: Plant leaf diseases are typically predicted and classified by farmers tediously and inaccurately. Manual identification of diseases may take more time and may not accurately detect the disease. There could be a major drop in production if crop plants are destroyed due to slow detection and classification of plant illnesses. Radiologists used to segment leaf lesions manually, which takes a lot of time and work. Objective: It is established that deep learning models are superior to human specialists in the diagnosis of lesions on plant leaves. Here, the “Deep Convolutional Neural Network (DCNN)” based encoder-decoder architecture is suggested for the semantic segmentation of leaf lesions. Methods: A proposed semantic segmentation model is based on the Dense-Net encoder. The LinkNet-34 segmentation model performance is compared with two other models, SegNet and PSPNet. Additionally, the two encoders, ResNeXt and InceptionV3, have been compared to the performance of DenseNet-121, the encoder used in the LinkNet-34 model. After that, two different optimizers, such as Adam and Adamax, are used to optimize the proposed model. Results: The DenseNet-121 encoder utilizing Adam optimizer has been outperformed by the LinkNet-34 model, with a dice coefficient of 95% and a Jaccard Index of 93.2% with a validation accuracy of 97.57%. Conclusion: The detection and classification of leaf disease with deep learning models gives better results in comparison with other models. © 2024 The Author(s).

关键词： Classification Convolutional neural network (CNN) Detection encoder-decoder Plant leaf disease Semantic segmentation

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MANet: A Modal Attention Network for Describing Videos 17

MANet: A Modal Attention Network for Describing Videos

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25th ACM International Conference on Multimedia (MM)

作者： Phan, Sang Miyao, Yusuke Satoh, Shin'ichi Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781450349062

Exploiting multimodal features has become a standard approach towards many video applications, including the video captioning task. One problem with the existing work is that it models the relevance of each type of features evenly, which neutralizes the impact of each individual modality to the word to be generated. In this paper, we propose a novel Modal Attention Network (MANet) to account for this issue. Our MANet extends the standard encoder-decoder network by adapting the attention mechanism to video modalities. As a result, MANet emphasizes the impact of each modality with respect to the word to be generated. Experimental results show that our MANet effectively utilizes multimodal features to generate better video descriptions. Especially, our MANet system was ranked among the top three systems at the 2nd Video to Language Challenge in both automatic metrics and human evaluations.

关键词： video captioning attention mechanism multimodal encoder-decoder

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Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser

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FRONTIERS IN PHYSICS 2022年 10卷

作者： Zhu, J. Lockmann, N. M. Czwalinna, M. K. Schlarb, H. Deutsch Elektronen Synchrotron DESY Hamburg Germany

Longitudinal properties of electron bunches are critical for the performance of a wide range of scientific facilities. In a free-electron laser, for example, the existing diagnostics only provide very limited longitudinal information of the electron bunch during online tuning and optimization. We leverage the power of artificial intelligence to build a neural network model using experimental data, in order to bring the destructive longitudinal phase space (LPS) diagnostics online virtually and improve the existing current profile online diagnostics which uses a coherent transition radiation (CTR) spectrometer. The model can also serve as a digital twin of the real machine on which algorithms can be tested efficiently and effectively. We demonstrate at the FLASH facility that the encoder-decoder model with more than one decoder can make highly accurate predictions of megapixel LPS images and coherent transition radiation spectra concurrently for electron bunches in a bunch train with broad ranges of LPS shapes and peak currents, which are obtained by scanning all the major control knobs for LPS manipulation. Furthermore, we propose a way to significantly improve the CTR spectrometer online measurement by combining the predicted and measured spectra. Our work showcases how to combine virtual and real diagnostics in order to provide heterogeneous and reliable mixed diagnostics for scientific facilities.

关键词： free-electron laser (FEL) longitudinal phase space machine learning encoder-decoder mixed diagnostics

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4G-VOS: Video Object Segmentation using guided context embedding

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KNOWLEDGE-BASED SYSTEMS 2021年 231卷

作者： Fiaz, Mustansar Zaheer, Muhammad Zaigham Mahmood, Arif Lee, Seung-Ik Jung, Soon Ki Mohamed Bin Zayed Univ Artificial Intelligence Comp Vis Dept Abu Dhabi U Arab Emirates Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Univ Sci & Technol Daejeon South Korea Elect & Telecommunicat Res Inst Daejeon South Korea Informat Technol Univ Dept Comp Sci Lahore Pakistan

Video Object Segmentation (VOS) is a fundamental task required in many high-level real-world computer vision applications. VOS becomes challenging due to the presence of background distractors as well as to object appearance variations. Many existing VOS approaches use online model updates to capture the appearance variations which incurs high computational cost. Template matching and propagation-based VOS methods, although cost-effective, suffer from performance degradation under challenging scenarios such as occlusion and background clutter. In order to tackle these challenges, we propose a network architecture dubbed 4G-VOS to encode video context for improved VOS performance to tackle these challenges. To preserve long term semantic information, we propose a guided transfer embedding module. We employ a global instance matching module to generate similarity maps from the initial image and the mask. Besides, we use a generative directional appearance module to estimate and dynamically update the foreground/background class probabilities in a spherical embedding space. Moreover, during feature refinement, existing approaches may lose contextual information. Therefore, we propose a guided pooled decoder to exploit the global and local contextual information during feature refinement. The proposed framework is an end-to-end learning architecture that is trained in an offline fashion. Evaluations over three VOS benchmark datasets including DAVIS2016, DAVIS2017, and YouTube-VOS have demonstrated outstanding performance of the proposed algorithm compared to 40 existing state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Video Object Segmentation Feature transfer and matching Spherical embedding Feature refinement Channel convolutional neural networks encoder-decoder

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Robust multi-scale time series prediction for building carbon emissions with explainable deep learning

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ENERGY AND BUILDINGS 2024年 312卷

作者： Chen, Chao Guo, Jing Zhang, Limao Wu, Xiaoying Yang, Zheng Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Glodon Corp Ltd Beijing 100000 Peoples R China Cent South Univ Sch Resources & Safety Engn Changsha 410006 Hunan Peoples R China

Accurate prediction dramatically enhances the effectiveness of carbon emission control, making it a valuable tool for achieving decarbonization goals. In this work, we propose a robust explainable encoder-decoder deep learning method for predicting multi-scale short-term carbon emissions and feature importance analysis to predict buildings ' emission patterns accurately. In the encoder, the initial inputs are transformed into a sequence to capture the time series and spatial information, where the model predicts the building emissions with an attention mechanism in the decoder. The hybrid model is trained and validated with a multi-scale dataset collected from a university. Results reveal that (1) After considering the buildings ' interactions, the R 2 values regarding individual building emissions predictions are 0.943, 0.938, 0.941, and 0.943, respectively, climbing to 0.952, 0.943, 0.943, and 0.950. (2) Benefiting from the proposed model ' s structure, the constructed model can accurately predict the community ' s emissions with the R 2 equal to 0.952 and keep excellent robustness after adding the white Gaussian noise data with all R 2 are above 0.919. (3) The historical hourly emissions, outdoor humidity and time of day are considered the most essential factors responsible for the building emissions. The novelty of this work lies in the multi-scale building emissions prediction that is conducted by the developed hybrid model simultaneously considering the impact of environmental factors and building interactions, allowing us to expand the model ' s application scenarios and provide valuable references for decarbonization decision-making.

关键词： encoder-decoder Time series prediction Building carbon emissions Explainable deep learning Decarbonization

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Revolutionizing sentiment classification: A deep learning approach using self-attention based encoding-decoding transformers with feature fusion

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Tejashwini, S. G. Aradhana, D. Ballari Inst technol & Management Dept Comp Sci & Engn Bellary Karnataka India

In recent times, the growth of human computer interaction and Artificial Intelligence attains more interest especially in facial expression detection. Though the field has attained tremendous progress, still many issues are pertained as the facial expressions are complex. Therefore the present study aims to increase the efficiency in classifying the emotions of humans from the images of FER-2013 dataset and strives to attain optimal accuracy. Most of the studies have averted multi-modal parameters like a video for emotion detection. Moreover, existing studies lacked a detection rate due to ineffective feature extraction that negatively impacted the classification rate. For avoiding such pitfalls, this study proposes suitable data mining-based algorithms with the main intention to attain high accuracy. The present study proposes Deep Location Attention Forest method for emotion detection with higher accuracy. To accomplish this, DLFAT (Deep Location Feed Attention Transformers) is used for feature extraction from various sub-space representation at different position of the image. CK-PCA (Canonical Kernel-Principal Component Analysis) is performed feature fusion to improve the performance of the classifier. MRF (Modified Random Forest) is proposed to perform classification and predicts the emotions based on the facial expressions. The main intention of RF relies on learning all the data subset weights that alleviate loss amongst the predicted value for accomplishing better input classification to the related label. The proposed system is validated by the performance metrics in which the outcome is found to be 0.96197 as accuracy, 0.96 as F1-Score, 0.97 as recall and 0.96 as precision and the outcomes reveal the efficacy of the system in emotion detection.

关键词： Emotion detection Data mining Canonical Kernel-Principal Component Analysis encoder-decoder Modified Random Forest

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"More Than Deep Learning": post-processing for API sequence recommendation

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EMPIRICAL SOFTWARE ENGINEERING 2022年第1期27卷 15-15页

作者： Chen, Chi Peng, Xin Chen, Bihuan Sun, Jun Xing, Zhenchang Wang, Xin Zhao, Wenyun Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Data Sci Shanghai Peoples R China Singapore Management Univ Singapore Singapore Australian Natl Univ Canberra ACT Australia

In the daily development process, developers often need assistance in finding a sequence of APIs to accomplish their development tasks. Existing deep learning models, which have recently been developed for recommending one single API, can be adapted by using encoder-decoder models together with beam search to generate API sequence recommendations. However, the generated API sequence recommendations heavily rely on the probabilities of API suggestions at each decoding step, which do not take into account other domain-specific factors (e.g., whether an API suggestion satisfies the program syntax and how diverse the API sequence recommendations are). Moreover, it is difficult for developers to find similar API sequence recommendations, distinguish different API sequence recommendations, and make a selection when the API sequence recommendations are ordered by probabilities. Thus, what we need is more than deep learning. In this paper, we propose an approach, named Cook, to combine deep learning models with post-processing strategies for API sequence recommendation. Specifically, we enhance beam search with code-specific heuristics to improve the quality of API sequence recommendations. We develop a clustering algorithm to cluster API sequence recommendations so as to make it easier for developers to find similar API sequence recommendations and distinguish different API sequence recommendations. We also propose a method to generate a summary for each cluster to help developers understand the API sequence recommendations. Our evaluation results have shown that (1) three deep learning models with our heuristic-enhanced beam search achieved better performance than with the original beam search in terms of CIDEr-1, CIDEr-5 and CIDEr-10 scores, with an average improvement of 1.8, 2.3 and 2.3, respectively;and (2) our clustering algorithm achieved high performance on six metrics and outperformed two variant clustering algorithms. Moreover, our user study with 24 participants

关键词： API Recommendation Deep learning encoder-decoder Post-processing

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A Transformer-Based Bridge Structural Response Prediction Framework

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SENSORS 2022年第8期22卷 3100页

作者： Li, Ziqi Li, Dongsheng Sun, Tianshu Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China

Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The proposed method can be applied in damage diagnosis and disaster warning of bridges.

关键词： bridge structural response prediction transformer deep learning structural health monitoring encoder-decoder

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Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation

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SENSORS 2021年第23期21卷 7844-7844页

作者： Li, Dongqian Fan, Cien Zou, Lian Zuo, Qi Jiang, Hao Liu, Yifeng Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Natl Engn Lab Risk Percept & Prevent NEL RPP Beijing 100041 Peoples R China

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder-decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.

关键词： semantic segmentation encoder-decoder feature balanced fusion Cityscapes

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