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MPOCSR: optical chemical structure recognition based on multi-path Vision Transformer

COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 7553-7563页

作者： Lin, Fan Li, Jianhua East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

Optical chemical structure recognition (OCSR) is a fundamental and crucial task in the field of chemistry, which aims at transforming intricate chemical structure images into machine-readable formats. Current deep learning-based OCSR methods typically use image feature extractors to extract visual features and employ encoder-decoder architectures for chemical structure recognition. However, the performance of these methods is limited by their image feature extractors and the class imbalance of elements in chemical structure representation. This paper proposes MPOCSR (multi-path optical chemical structure recognition), which introduces the multi-path Vision Transformer (MPViT) and the class-balanced (CB) loss function to address these two challenges. MPOCSR uses MPViT as an image feature extractor, combining the advantages of convolutional neural networks and Vision Transformers. This strategy enables the provision of richer visual information for subsequent decoding processes. Furthermore, MPOCSR incorporates CB loss function to rebalance the loss weights among different categories. For training and validation of our method, we constructed a dataset that includes both Markush and non-Markush structures. Experimental results show that MPOCSR achieves an accuracy of 90.95% on the test set, surpassing other existing methods.

关键词： Optical chemical structure recognition Deep learning Multi-path Vision Transformer encoder-decoder architecture

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Research on prediction of nanocrystalline alloy hysteresis properties based on long short-term memory network

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Li, Hailin Zhang, Bo Shen, Yongpeng Zhang, Lei Liu, Kun Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450000 Peoples R China XJ Elect Co Ltd Xuchang 461000 Peoples R China

In order to predict the hysteresis characteristics of nanocrystalline alloy materials at different frequencies, a data-driven hysteresis prediction model based on the encoder-decoder architecture, which combines long short-term memory network and feedforward neural network, is proposed in this paper. The data-driven based magnetic hysteresis prediction model can take advantage of the powerful nonlinear learning ability of artificial neural network to train and learn its magnetic hysteresis characteristics of nanocrystalline alloy materials at different frequencies. Firstly, based on the encoder-decoder architecture, a hysteresis prediction model is constructed by combining long short-term memory network and feedforward neural network. Subsequently, in order to obtain the training set and validation set used for the data-driven based hysteresis prediction model, the Jiles-Atherton (J-A) hysteresis model is identified based on the B-H measurement data of a small number of nanocrystalline alloy materials at different frequencies for expediency since it is quite cumbersome and time-consuming to get these B-H data by measurement. Finally, the validity and accuracy of the data-driven based hysteresis prediction model are proved by the validation set. The maximum error is about 10.29%. The results show that the hysteresis model of neural network is able to predict hysteresis characteristics with considering the effect of frequency, which provides a new way for the simulation of hysteresis characteristics.

关键词： Magnetic hysteresis characteristic Data-driven Long short-term memory network encoder-decoder architecture

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GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第9期20卷 15809-15829页

作者： Zhuang, Jujuan Feng, Kexin Teng, Xinyang Jia, Cangzhi Dalian Maritime Univ Sch Sci Dalian 116026 Liaoning Peoples R China

Transcription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the contextual features of DNA sequences. In this paper, we develop an integrated spatiotemporal context-aware neural network framework, named GNet, for predicting TF-DNA binding signal at single nucleotide resolution by achieving three tasks: single nucleotide resolution signal prediction, identification of binding regions at the sequence level, and TF-DNA binding motif prediction. GNet extracts implicit spatial contextual information with a gated highway neural mechanism, which captures large context multi-level patterns using linear shortcut connections, and the idea of it permeates the encoder and decoder parts of GNet. The improved dual external attention mechanism, which learns implicit relationships both within and among samples, and improves the performance of the model. Experimental results on 53 human TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets shows that GNet outperforms the state-of-the-art methods in the three tasks, and the results of cross-species studies on 15 human and 18 mouse TF datasets of the corresponding TF families indicate that GNet also shows the best performance in cross-species prediction over the competitive methods.

关键词： transcription factor binding site gated highway neural network encoder-decoder architecture external attention mechanism

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Video description: A comprehensive survey of deep learning approaches

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第11期56卷 13293-13372页

作者： Rafiq, Ghazala Rafiq, Muhammad Choi, Gyu Sang Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Keimyung Univ Dept Game & Mobile Engn 1095 Dalgubeol Daero Daegu 42601 South Korea

Video description refers to understanding visual content and transforming that acquired understanding into automatic textual narration. It bridges the key AI fields of computer vision and natural language processing in conjunction with real-time and practical applications. Deep learning-based approaches employed for video description have demonstrated enhanced results compared to conventional approaches. The current literature lacks a thorough interpretation of the recently developed and employed sequence to sequence techniques for video description. This paper fills that gap by focusing mainly on deep learning-enabled approaches to automatic caption generation. Sequence to sequence models follow an encoder-decoder architecture employing a specific composition of CNN, RNN, or the variants LSTM or GRU as an encoder and decoder block. This standard-architecture can be fused with an attention mechanism to focus on a specific distinctiveness, achieving high quality results. Reinforcement learning employed within the encoder-decoder structure can progressively deliver state-of-the-art captions by following exploration and exploitation strategies. The transformer mechanism is a modern and efficient transductive architecture for robust output. Free from recurrence, and solely based on self-attention, it allows parallelization along with training on a massive amount of data. It can fully utilize the available GPUs for most NLP tasks. Recently, with the emergence of several versions of transformers, long term dependency handling is not an issue anymore for researchers engaged in video processing for summarization and description, or for autonomous-vehicle, surveillance, and instructional purposes. They can get auspicious directions from this research.

关键词： Deep learning encoder-decoder architecture Text description Video captioning techniques Video description approaches Video captioning Vision to text

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Transfer learning approach based on satellite image time series for the crop classification problem

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JOURNAL OF BIG DATA 2023年第1期10卷 54页

作者： Antonijevic, Ognjen Jelic, Slobodan Bajat, Branislav Kilibarda, Milan Univ Belgrade Fac Civil Engn Dept Geodesy & Geoinformat Belgrade Serbia

This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose Transformerencoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.

关键词： Transfer learning Remote sensing encoder-decoder architecture Domain adaptation Crop classification Attention mechanism

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A comprehensive survey on image captioning: from handcrafted to deep learning-based techniques, a taxonomy and open research issues

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第11期56卷 13619-13661页

作者： Sharma, Himanshu Padha, Devanand Cent Univ Jammu Dept Comp Sci & Informat Technol Jammu & Kashmir Jammu 181124 India

Image captioning is a pretty modern area of the convergence of computer vision and natural language processing and is widely used in a range of applications such as multi-modal search, robotics, security, remote sensing, medical, and visual aid. The image captioning techniques have witnessed a paradigm shift from classical machine-learning-based approaches to the most contemporary deep learning-based techniques. We present an in-depth investigation of image captioning methodologies in this survey using our proposed taxonomy. Furthermore, the study investigates several eras of image captioning advancements, including template-based, retrieval-based, and encoder-decoder-based models. We also explore captioning in languages other than English. A thorough investigation of benchmark image captioning datasets and assessment measures is also discussed. The effectiveness of real-time image captioning is a severe barrier that prevents its use in sensitive applications such as visual aid, security, and medicine. Another observation from our research is the scarcity of personalized domain datasets that limits its adoption into more advanced issues. Despite influential contributions from several academics, further efforts are required to construct substantially robust and reliable image captioning models.

关键词： Attention-based image captioning encoder-decoder architecture Image captioning Multimodal embedding

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M²: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 4033-4046页

作者： Peng, Bo Ren, Zhiyun Parthasarathy, Srinivasan Ning, Xia Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Dept Comp Sci & Engn Columbus OH 43210 USA Ohio State Univ Translat Data Analyt Inst Columbus OH 43210 USA

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M-2 ) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M-2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans ) to better model the transition patterns among items. We compared M-2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M-2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.

关键词： Recurrent neural networks Task analysis Benchmark testing Adaptation models Decoding Protocols Markov processes Recommender systems next-basket recommendation encoder-decoder architecture mixed models

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Mathematical expression recognition using a new deep neural model

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NEURAL NETWORKS 2023年 167卷 865-874页

作者： Mirkazemy, Abolfazl Adibi, Peyman Ehsani, Seyed Mohhamad Saied Darvishy, Alireza Hutter, Hans-Peter Univ Isfahan Fac Comp Engn Artificial Intelligence Dept Esfahan Iran Zurich Univ Appl Sci ZHAW Sch Engn Winterthur Switzerland

In this paper, we propose a novel deep neural model for Mathematical Expression Recognition (MER). The proposed model uses encoder-decoder transformer architecture that is supported by additional pre/post-processing modules, to recognize the image of mathematical formula and convert it to a well-formed language. A novel pre-processing module based on domain prior knowledge is proposed to generate random pads around the formula's image to create more efficient feature maps and keeps all the encoder neurons active during the training process. Also, a new post-processing module is developed which uses a sliding window to extract additional position-based information from the feature map, that is proved to be useful in the recognition process. The recurrent decoder module uses the combination of feature maps and the additional position-based information, which takes advantage of a soft attention mechanism, to extract the formula context into the LaTeX well-formed language. Finally, a novel Reinforcement Learning (RL) module processes the decoder output and tunes its results by sending proper feedbacks to the previous steps. The experimental results on im2latex100k benchmark dataset indicate that each devised pre/post-processing as well as the RL refinement module has a positive effect on the performance of the proposed model. The results also demonstrate the higher accuracy of the proposed model compared to the state-of-the-art methods. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Mathematical expression recognition Deep learning encoder-decoder architecture Attention Scientific documents accessibility

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TransFlowLog: Log Anomaly Detection Based on Transformer encoder and Interflow decoder 29

TransFlowLog: Log Anomaly Detection Based on Transformer Enc...

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

作者： Lin, Zaichao Lu, Siyang Han, Ningning Wang, Dongdong Wei, Xiang Wang, Mingquan Beijing Jiaotong University School of Computer and Information Technology Beijing China University of Central Florida Department of Civil Environmental and Construction Engineering OrlandoFL United States Beijing Jiaotong University School of Software Engineering Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9798350330717

Logs are valuable resources that record the health status of systems. Analyzing logs to uncover and investigate abnormal behaviors has become an essential approach of ensuring system security. However, some potential anomalies may be missed when performing log anomaly detection, and even a seemingly insignificant abnormal behavior can lead to a series of severe anomalies or continuous negative impact on the performance of systems. Therefore, the reliability and security of systems are facing significant challenges. To address this issue, in this study, we propose TransFlowLog, an encoder-decoder architecture-based approach for log anomaly detection. It utilizes the Transformer encoder, a state-of-the-art sequence modeling technique, to comprehensively understand contextual relationships using self-attention mechanism. Moreover, we introduce the Interflow decoder, which considers information exchange between channels in embedded log sequences. The Interflow decoder enhances the features encoded by the Transformer encoder in both sequence and channel dimensions, thereby capturing interdependence between different channels. Comparative experiments conducted on three real-world datasets demonstrate the effectiveness of the proposed method, as it achieves higher F1-score and reduces the number of false negatives. © 2023 IEEE.

关键词： anomaly detection encoder-decoder architecture self-attention systems reliability

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Adversarial learning-based skeleton synthesis with spatial-channel attention for robust gait recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期82卷 1489-1504页

作者： Chen, Ying Xia, Shixiong Zhao, Jiaqi Zhou, Yong Niu, Qiang Yao, Rui Zhu, Dongjun Chen, Hao China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Engn Res Ctr Mine Digitizat Xuzhou 221116 Jiangsu Peoples R China Xuzhou Guanglian Technol Co Ltd Xuzhou 221116 Jiangsu Peoples R China

Person re-identification (ReID) aims to identify the same person across multiple cameras. Gait recognition is the person ReID using human gait to identify a walking person, which is an effective identification technology with many advantages, such as remote identification and without invasion. State-of-the-art solutions solve the problem of extensive annotation skeleton information and labeling process by employing encoder-decoders to reconstruct skeleton data, which encodes gait feature with a fixed-length vector limiting the performance of this architecture. In this paper, we propose an end-to-end pipeline dubbed as SCA-GAN, which assembles spatial-channel attention with GAN-like framework to synthesize skeleton sequences reversely without labeled skeleton data. A disadvantage of traditional encoder-decoder architecture is that, because of the fixed-length latent vector encoded from the encoder, the decoder fails to learn the reasonable standard to generate imperfect samples. Therefore, we design a GAN-like framework for discriminative gait feature extraction via adversarial learning. In addition, for learning the rich global information of skeleton data, the information of skeleton is extracted via convolutional block embedding locality-aware attention mechanism. Specifically, a contrastive feature loss is constructed between the gait encoder and the gait decoder to minimize their pixel-wise distance explicitly. The proof-of-principle experiments and ablation study on several benchmarks prove that the proposed method significantly outperforms gait recognition counterparts in precision.

关键词： Person re-identification Gait recognition encoder-decoder architecture Adversarial learning

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