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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhou, Fengxi Chang, Baoming Wen, Yu Meng, Dan University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (纸本)9798350330717

The detection and analysis of Advanced Persistent Threats (APTs) are pivotal for contemporary network security. Provenance graphs, constructed from audit logs, offer a wealth of contextual information to identify and analyze threats and are popular in APT detection field. However, existing approaches frequently fall short in offering explanatory capabilities for their detection results, placing an additional burden on security analysts. Confronted with coarse-grained detection outcomes, analysts must delve into provenance graphs or audit logs to precisely pinpoint attack entities and events, which can significantly delay the response to threats. In this paper, we propose ProDE, a novel approach that enhances APT detection by providing interpretable results using an encoder-decoder architecture. ProDE initiates the detection process by comparing the encoded representations of the true graph and the predicted graph. Upon detecting abnormalities, the encoder-decoder model is able to decode the encodings into provenance graphs, thereby revealing inconsistencies between the decoded graph and real graph that serve as interpretable results. We evaluate ProDE on two widely used datasets, while taking into account the detection performance, the result shows ProDE can provided the more detailed detection results which provide the interpretation for analysts compared with existing approaches. © 2023 IEEE.

关键词： Advanced Persistent Threats (APTs) encoder-decoder interpretable

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Dynamic energy system modeling using hybrid physics-based and machinelearning encoder–decoder models

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Energy and AI 2022年第3期9卷 128-138页

作者： Derek Machalek Jake Tuttle Klas Andersson Kody M.Powell Department of Chemical Engineering University of UtahSalt Lake CityUTUnited States of America Taber International LLCUnited States of America Department of Space Earthand EnvironmentUniversity of ChalmersGothenburgSweden Department of Mechanical Engineering University of UtahSalt Lake CityUTUnited States of America

Three model configurations are presented for multi-step time series predictions of the heat absorbed by thewater and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into thefuture, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, anovel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. Thehybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, waterwall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared totarget values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. Thehybrid model with the incomplete dataset highlights the importance of the manipulated variables to the *** hybrid model, compared to the pure machine learning model, is over 10% more accurate on averageover 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a localsensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in theboiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

关键词： Hybrid model encoder-decoder Time series Automatic differentiation Thermal power plant

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Innovative Disease Management: An encoder-decoder Solution for Tomato Black Mold Detection 4

Innovative Disease Management: An Encoder-Decoder Solution f...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Vashisht, Divyam Kukreja, Vinay Sharma, Rishabh Jain, Ayushi Choudhury, Ankur Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350305258

The worldwide spread of tomato black mold disease is a major concern since it reduces crop output and quality. Effective disease control and environmentally responsible farming methods depend on rapid and precise disease identification. Using a dense dilated convolution methodology based on an encoder-decoder pair, the suggested presents a novel method for detecting the black mold illness that can occur in tomatoes in this study. The well-known VGG-16 model is used for feature extraction, and 25, 000 photos of tomatoes that the authors collected themselves are used to train and evaluate the model. Accurate segmentation and localization of infected areas are shown to have been achieved by the model, as evidenced by the high values of the Dice Similarity Coefficient (DSC) and the Intersection over Union (IoU). The model's success in approximating ground truth bounds is further supported by the small Hausdorff Distance. To better ensure the safety of our food supply, our studies aid in the development of precision agriculture by providing farmers with a reliable method of disease detection. Our suggested method offers a viable path for early and precise detection of tomato black mold disease by leveraging the power of modern computer vision techniques and deep learning (DL) models, which will help the agricultural community and promote sustainable crop management practices. © 2023 IEEE.

关键词： Deep Learning Dense Dilated Convolution encoder-decoder Segmentation Tomato black mold disease

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encoder-decoder Neural Network Architecture for solving Job Shop Scheduling Problems using Reinforcement Learning

Encoder-Decoder Neural Network Architecture for solving Job ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Magalhaes, Ricardo Martins, Miguel Vieira, Susana Santos, Filipe Sousa, Joao Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (纸本)9781728190488

This paper proposes an encoder-decoder neural network architecture with Attention Mechanism for solving the DRC-FJSSP using Deep Q-Learning. In the DRC-FJSSP the number of operations to schedule is problem dependent. Current state-of-the-art reinforcement learning methods arbitrarily simplify the input information to a fixed-size feature input vector. This way, they end up losing relevant problem information for a large enough number of operations. Furthermore, on the one hand, human schedulers tend to optimize production schedules by moving operations individually into more adequate positions in the schedule. On the other hand, the aforementioned state-of-the-art methods apply heuristics recurrently as their optimization procedure. These limitations come as the cost of the neural network architecture, which is limited to fixed-size inputs and outputs. The architecture proposed in this paper is a Recurrent Neural Network, which enables it to work with inputs and outputs of variable sizes. This decisive feature makes it possible for the agent to move a specific operation to a more adequate position in the schedule and receive explicit problem information, such as the processing times of all operations. In the end, this approach proved to be competitive with a state-of-the-art metaheuristic method, the KGFOA. This promising results come even with a limitation in the available computational resources, which only allowed the development of scarcely trained agent.

关键词： Job-Shop Scheduling Reinforcement Learning Deep Q-Learning encoder-decoder Attention Mechanism Pointer Networks

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A classification method based on encoder-decoder structure with paper content

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第9期34卷

作者： Yin, Yi Ouyang, Lin Wu, Zhixiang Yin, Shuifang Wuhan Univ Comp Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Comp Sci & Technol Sch Sci Wuhan Peoples R China

The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder-decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder-decoder structure is formed with labels and words. This improves the original paper classification method based on encoder-decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder-decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results.

关键词： CNN encoder-decoder hidden information word embedding

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Skip-attention encoder-decoder framework for human motion prediction

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MULTIMEDIA SYSTEMS 2022年第2期28卷 413-422页

作者： Zhang, Ruipeng Shu, Xiangbo Yan, Rui Zhang, Jiachao Song, Yan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Inst Technol Artificial Intelligence Inst Ind Technol Nanjing Peoples R China

Human motion prediction aims to automatically predict the future motion sequence based on an observed human motion sequence. In this paper, we propose a novel skip-attention encoder-decoder (SAED) framework to model human motion dependences in spatiotemporal space, by utilizing the encoder and decoder to encode the observed motions, and decode the predicted motions, respectively. Overall, this framework has two main insights. First, we design a new self-renewing ConvGRU as the unit of encoder and decoder to effectively capture temporal and spatial skeleton-motion dependencies. Second, we present a new skip-attention mechanism (SAM) to aggregate the motion information of all layers based on their importance. In experiments, quantitative and qualitative results on the Human3.6M and CMU motion capture datasets show the effectiveness of the proposed SAED compared with the related methods.

关键词： encoder-decoder Human motion prediction Skip attention

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IDM-Net: A Multi-Task Supported encoder-decoder Framework for Magnetic Field Inverse Design

IDM-Net: A Multi-Task Supported Encoder-Decoder Framework fo...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Wang, Jiaqi Zhang, Qiankun Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350301571

This study presents a novel end-to-end trainable network named IDM-Net (Inverse Design Network for Magnetic Fields) that facilitates multi-task supported inverse design of magnetic fields. Employing the encoder-decoder idea, IDM-Net accomplishes the inverse design of magnet parameters by leveraging magnetic field data for backpropagation. The encoder is harnessed to extract magnetic field features and classify magnet shapes, while the decoder predicts other properties, including magnet size and position. This innovative approach breaks the constraints of single magnet types in existing research, enabling the inverse design of properties for magnets with diverse shapes. Our experimental results demonstrate remarkable performance, with a 95.2% accuracy in magnet shape classification and a mere 0.28% error in magnet property prediction. By introducing the encoder-decoder idea in the field of inverse design for magnetic fields, we showcase significantly enhanced accuracy and pave the way for broader applications of this technology. © 2023 IEEE.

关键词： deep learning design optimization encoder-decoder inverse design magnetic field

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RUN-AND-BACK STITCH SEARCH: NOVEL BLOCK SYNCHRONOUS DECODING FOR STREAMING encoder-decoder ASR 47

RUN-AND-BACK STITCH SEARCH: NOVEL BLOCK SYNCHRONOUS DECODING...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Tsunoo, Emiru Narisetty, Chaitanya Hentschel, Michael Kashiwagi, Yosuke Watanabe, Shinji Sony Grp Corp Tokyo Japan Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665405409

A streaming style inference of encoder-decoder automatic speech recognition (ASR) systems is important for reducing latency, which is essential for interactive use cases. To this end, we propose a novel blockwise synchronous decoding algorithm with a hybrid approach that combines endpoint prediction and endpoint post-determination. In the endpoint prediction, we compute the expectation of the number of tokens that are yet to be emitted in the encoder features of the current blocks using the CTC posterior. Based on the expectation value, the decoder predicts the endpoint to realize continuous block synchronization, as a running stitch. Meanwhile, endpoint post-determination probabilistically detects backward jump of the source-target attention, which is caused by the misprediction of endpoints. Then it resumes decoding by discarding those hypotheses, as back stitch. We combine these methods into a hybrid approach, namely run-and-back stitch search, which reduces the computational cost and latency. Evaluations of various ASR tasks show the efficiency of our proposed decoding algorithm, which achieves a latency reduction, for instance in the Librispeech test set from 1487 ms to 821 ms at the 90th percentile, while maintaining a high recognition accuracy.

关键词： Streaming automatic speech recognition (ASR) encoder-decoder end-to-end Transformer CTC

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Deep Architecture Based Spalling Severity Detection System Using encoder-decoder Networks 17th

Deep Architecture Based Spalling Severity Detection System U...

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17th International Symposium on Visual Computing (ISVC)

作者： Yasmin, Tamanna Le, Chuong Hung Manh La Univ Nevada Reno NV 89557 USA

ISBN: (纸本)9783031207150;9783031207167

Proper maintenance of concrete structures is a significant issue to avoid any hazardous situation in civil infrastructure. Spalling is a significant surface concrete distress in bridges and buildings. Correctly detecting the severity level of spalling can make it happen to detect and maintain the harmful spalling promptly to avoid any accidents [10]. While previous works have been on surface defects, like cracks and spallings, few have addressed spalling severity detection. In this paper, we have proposed a deep learning-based approach to detect the exact location of spalling according to severity level by using pixel-by-pixel classification. Our network labels each pixel as no-spalling, small, or large spalling. To get the optimal proposed deep architecture, we tested several encoder-decoder networks to compare and analyze the performance of the detection processes.

关键词： Spalling detection Spalling severity Deep architecture encoder-decoder

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Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based encoder-decoder ASR 23

Internal Language Model Estimation Through Explicit Context ...

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Interspeech Conference

作者： Liu, Yufei Ma, Rao Xu, Haihua He, Yi Ma, Zejun Zhang, Weibin ByteDance AI LAB Beijing Peoples R China South China Univ Technol Guangzhou Peoples R China

An end-to-end (E2E) ASR model implicitly learns a prior Internal Language Model (ILM) from the training transcripts. To fuse an external LM using Bayes posterior theory, the log-likelihood produced by the ILM has to be accurately estimated and subtracted. In this paper we propose two novel approaches to estimate the ILM based on the Listen-Attend-Spell (LAS) framework. The first method is to replace the context vector of the LAS decoder at every time step with a vector that is learned with the training transcripts. The second approach is to use a lightweight feed-forward network to directly map query vectors to context vectors in a dynamic sense. Since the context vectors are learned by minimizing the perplexities on the training transcripts, and their estimation is independent of the encoder output, hence the ILMs are accurately learned for both methods. Experiments show that the ILMs achieve the lowest perplexity, indicating the efficacy of the proposed methods. In addition, they also significantly outperform the shallow fusion method, as well as two previously proposed ILM Estimation (ILME) approaches on several datasets.

关键词： speech recognition language model attention-based encoder-decoder internal language model estimation

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