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Enhanced interpretation of novel datasets by summarizing clustering results using deep-learning based linguistic models

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APPLIED INTELLIGENCE 2025年第4期55卷 1-23页

作者： Natarajan, K. Verma, Srikar Kumar, Dheeraj Natl Inst Technol Tiruchirappalli Dept Met & Mat Engn Tanjore Main RdNH67Near BHEL Tiruchirappalli Tamil Nadu India Indian Inst Technol BHU Dept Mech Engn Varanasi Campus Varanasi Uttar Pradesh India Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee Uttarakhand India

In today's technology-driven era, the proliferation of data is inevitable across various domains. Within engineering, sciences, and business domains, particularly in the context of big data, it can extract actionable insights that can revolutionize the field. Amid data management and analysis, patterns or groups of interconnected data points, commonly referred to as clusters, frequently emerge. These clusters represent distinct subsets containing closely related data points, showcasing unique characteristics compared to other clusters within the same dataset. Spanning across disciplines such as physics, biology, business, and sales, clustering is important in understanding these novel datasets' essential characteristics, developing complex statistical models, and testing various hypotheses. However, interpreting the characteristics and physical implications of generated clusters by different clustering algorithms is challenging for researchers unfamiliar with these algorithms' inner workings. This research addresses the intricacies of comprehending data clustering, cluster attributes, and evaluation metrics, especially for individuals lacking proficiency in clustering or related disciplines like statistics. The primary objective of this study is to simplify cluster analysis by furnishing users or analysts from diverse domains with succinct linguistic synopses of clustering results, circumventing the necessity for intricate numerical or mathematical terms. Deep learning techniques based on large language models, such as encoder-decoders (for example, the T5 model) and generative pre-trained transformers (GPTs), are employed to achieve this. This study aims to construct a summarization model capable of ingesting data clusters, producing a condensed overview of the contained insights in a simplified, easily understandable linguistic format. The evaluation process revealed a clear preference among evaluators for the summaries generated by GPT, with T5 summaries followin

关键词： Data clusters Linguistic summarization Deep learning encoder-decoder Generative pre-trained transformers (GPT)

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Bidirectional trained tree-structured decoder for Handwritten Mathematical Expression Recognition

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PATTERN RECOGNITION 2025年 165卷

作者： Cheng, Hanbo Liu, Chenyu Hu, Pengfei Zhang, Zhenrong Ma, Jiefeng Du, Jun Univ Sci & Technol China Natl Engn Res Ctr Speech & Language Informat Proc 96 JinZhai Rd Hefei Anhui Peoples R China IFLYTEK Res Hefei Anhui Peoples R China

The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of Optical Character Recognition (OCR). Recent studies have demonstrated that incorporating bidirectional context information significantly improves the performance of HMER models. However, existing methods fail to effectively utilize bidirectional context information during the inference stage. Furthermore, current bidirectional training methods are primarily designed for string decoders and cannot adequately generalize to tree decoders, which offer superior generalization capabilities and structural analysis capacity. To overcome these limitations, we propose the Mirror-Flipped Symbol Layout Tree (MF-SLT) and Bidirectional Asynchronous Training (BAT) structure. Our method extends the bidirectional training strategy to the tree decoder, enabling more effective training by leveraging bidirectional information. Additionally, we analyze the impact of the visual and linguistic perception of the HMER model separately and introduce the Shared Language Modeling (SLM) mechanism. Through the SLM, we enhance the model's robustness and generalization when dealing with visual ambiguity, especially in scenarios with abundant training data. Our approach has been validated through extensive experiments, demonstrating its ability to achieve new state-of-the-art results on the CROHME 2014, 2016, and 2019 datasets, as well as the HME100K dataset. The code used in our experiments will be publicly available at https://***/Hanbo-Cheng/***.

关键词： Handwritten Mathematical Expression Recognition Bidirectional training Symbol Layout Tree encoder-decoder

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An adaptive multi-head self-attention coupled with attention filtered LSTM for advanced scene text recognition

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2025年 1-20页

作者： Selvam, Prabu Kumar, S. N. Kannadhasan, S. SRM Inst Sci & Technol Sch Comp Tiruchirapalli Campus Tiruchirapalli 621105 India Amal Jyothi Coll Engn Dept Elect & Elect Engn Kanjirappally 686518 Kerala India Study World Coll Engn Dept Elect & Commun Engn Coimbatore India

Recognizing text embedded within a scene plays a critical role in computer vision by aiming to extract textual information from images. This task proves to be more challenging than optical character recognition due to the diverse nature of scenes and the varied shapes of the text. Scene Text Recognition (STR) algorithms have demonstrated substantial progress using an encoder-decoder framework. The integration of diverse attention mechanisms has significantly boosted the performance of recognition models. Enhancing individual segments of the framework or employing more efficient attention mechanisms could dramatically improve overall performance. In line with this approach, we propose an advanced STR model based on the encoder-decoder architecture designed to elevate the performance of STR tasks. We introduced an adaptive multi-head self-attention mechanism that guides the model in learning when and where to utilize image features, resulting in more precise character generation. Secondly, a novel Attention-Filtered LSTM module (AFLSTM) in the decoder block presents dynamic attention into the language model. This module integrates visual attention into the LSTM output gate and tracks the sequential cell status as it changes. As a result, AFLSTM's attention takes on dynamic characteristics, enabling the currently focused visual region to direct future states. Finally, an Attention on Attention mechanism in the encoder and decoder introduces an additional attention mechanism to focus better on most contributive text regions. These three mechanisms significantly enhance the model's performance and accelerate training speed. The performance of the proposed method is benchmarked against seven established approaches, showcasing its superior performance over the majority of existing methods.

关键词： Attention mechanism Convolutional neural network Deep learning encoder-decoder Scene text recognition

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CrackCLF: Automatic Pavement Crack Detection Based on Closed-Loop Feedback

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 5965-5980页

作者： Li, Chong Fan, Zhun Chen, Ying Lin, Huibiao Moretti, Laura Loprencipe, Giuseppe Sheng, Weihua Wang, Kelvin C. P. Shantou Univ Coll Engn Key Lab Digital Signal & Image Proc Guangdong Prov Shantou 515063 Peoples R China Sapienza Univ Rome Dept Civil Construct & Environm Engn I-00184 Rome Italy Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Oklahoma State Univ Sch Civil & Environm Engn Stillwater OK 74078 USA

Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances.

关键词： Automatic pavement crack detection generative adversarial network encoder-decoder deep learning closed-loop feedback

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An expandable machine learning-optimization framework to sequential decision-making

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期314卷 280-296页

作者： Yilmaz, Dogacan Buyuktahtakin, I. Esra New Jersey Inst Technol Dept Mech & Ind Engn Newark NJ 07102 USA Grad Dept Ind & Syst Engn Virginia Tech Blacksburg VA 24061 USA

We present an integrated prediction-optimization (PredOpt) framework to efficiently solve sequential decision making problems by predicting the values of binary decision variables in an optimal solution. We address the key issues of sequential dependence, infeasibility, and generalization in machine learning (ML) to make predictions for optimal solutions to combinatorial problems. The sequential nature of the combinatorial optimization problems considered is captured with recurrent neural networks and a sliding-attention window. We integrate an attention-based encoder-decoder neural network architecture with an infeasibility-elimination and generalization framework to learn high-quality feasible solutions to time-dependent optimization problems. In this framework, the required level of predictions is optimized to eliminate the infeasibility of the ML predictions. These predictions are then fixed in mixed-integer programming (MIP) problems to solve them quickly with the aid of a commercial solver. We demonstrate our approach to tackling the two well-known dynamic NP-Hard optimization problems: multi-item capacitated lot-sizing (MCLSP) and multi-dimensional knapsack (MSMK). Our results show that models trained on shorter and smaller-dimensional instances can be successfully used to predict longer and larger-dimensional problems. The solution time can be reduced by three orders of magnitude with an average optimality gap below 0.1%. We compare PredOpt with various specially designed heuristics and show that our framework outperforms them. PredOpt can be advantageous for solving dynamic MIP problems that need to be solved instantly and repetitively.

关键词： (R) Machine learning encoder-decoder Capacitated lot-sizing Knapsack Combinatorial optimization

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TopologyFormer: structure transformer assisted topology reconstruction for point cloud completion

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第26期83卷 68743-68771页

作者： Jiang, Zhenwei Gao, Chenqiang Li, Pengcheng Liu, Chuandong Liu, Fangcen Zhu, Lijie Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

Point cloud completion is a fundamental task to enhance the completeness and authenticity of point cloud data captured in the real world. Existing mainstream methods tend to obtain the complete point cloud by refining or up-sampling the coarse point cloud in a coarse-to-fine manner. However, these methods ingore the long-distance correlations between point clouds and obtain blurred topological structures, leading to potential challenges for downstream tasks. In this paper, we present a novel point cloud completion model, called TopologyFormer. After obtaining a coarse point cloud, our model restores the missing topology structure by directly reconstructing the coarse point cloud itself instead of relying on point displacements for refining or up-sampling the coarse point cloud. Specifically, the proposed method contains three main components, a Structure Completion Network, a topology reconstruction module and a feature enhancement module, the latter two of which compose a Topology-Aware Reconstruction Network. The structure completion network considers long-range correlations among different parts of the point cloud and emphasizes the global geometric structure of the incomplete point cloud, which completes the missing point cloud into an intermediate representation. The topology reconstruction module receives the intermediate representation from the structure completion network to recover the local shapes and even point distribution, aiming to generate a point cloud with a higher-quality topology. Furthermore, the feature enhancement module ensures more reliable topology information and allows further use of the global feature obtained by the structure completion network. Extensive experiments on public benchmarks including ShapeNet and real-scanned dataset KITTI demonstrate that our method is superior to the existing state-of-the-art methods.

关键词： Point cloud Point cloud completion Topology reconstruction encoder-decoder

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DTAAD: Dual Tcn-attention networks for anomaly detection in multivariate time series data

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KNOWLEDGE-BASED SYSTEMS 2024年 295卷

作者： Yu, Ling-rui Lu, Qiu-hong Xue, Yang Shanghai Univ Elect Power Shanghai 200090 Peoples R China Hrstek Co Ltd Shanghai Peoples R China

Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-variate time series data, which are of major significance for today's industrial applications. However, establishing an anomaly detection system that can be rapidly and accurately located is a challenging problem due to the lack of anomaly labels, the high dimensional complexity of the data, memory bottlenecks in actual hardware, and the need for fast reasoning. In this paper, we propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN). Our overall model is an integrated design in which an autoregressive model (AR) combines with an autoencoder (AE) structure. Scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Constructed by us, the Dual TCN-Attention Network (DTA) uses only a single layer of Transformer encoder in our baseline experiment, belonging to an ultra -lightweight model. Our extensive experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance. Specifically, DTAAD improved F1 scores by 8 . 38 % and reduced training time by 99 % compared to the baseline. The code and training scripts are publicly available on GitHub at https://***/Yu-Lingrui/DTAAD.

关键词： Index terms Anomaly detection Anomaly Deep learning encoder-decoder Autoregressive Unsupervised learning

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DAS Up- and Downgoing Wavefield Separation via Radon Transform Combined With Parallel U-Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Sun, Decheng Yao, Guijin Li, Yue Jilin Univ Coll Commun Engn Dept Informat Changchun 130012 Peoples R China

After mitigating noise pollution, the primary challenge in processing downhole distributed acoustic sensing (DAS) data is the effective separation of its wavefield. Wavefield separation networks specifically for DAS data are scarce. Existing vertical seismic profiling (VSP) wavefield separation methods include traditional techniques, establishing propagation models applied to neural networks, and using the results of traditional methods as labels. Traditional methods can lead to issues of spatial aliasing and artifacts. Using such "not-so-clean" data for network training results in suboptimal performance. In addition, constructing models under simplified conditions results in training data that are overly simplistic, leading to a loss of detailed information in modern, higher sampling frequency, and more densely sampled complex DAS data. Inspired by the Radon transform and neural networks, we propose a DAS wavefield separation framework that combines the Radon transform with parallel U-Net (RTPU-Net) to address the issue of spatial aliasing in the Radon transform. We identified two key features in wavefield separation based on Radon transform: phase-reversed spatial aliasing and high amplitude preservation. In addition to constraining the network with loss functions, we also used reconstruction loss (MSE) to associate these two features. Using the Radon transform as a preprocessing method, our approach can also synthesize a large amount of training data automatically from raw data. Applications to both synthetic and field DAS data demonstrate that RTPU-Net can be widely used for high-precision DAS wavefield separation. When comparing the MSE metric of the overlapped wavefield from separated field data, our method also consistently achieves the lowest value among all the tested methods.

关键词： Transforms Training Noise reduction Signal to noise ratio Reflection Energy loss Training data Radon Geoscience and remote sensing Data models Distributed acoustic sensing (DAS) encoder-decoder multiscale radon transform wave field separation

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Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第3期47卷 1958-1974页

作者： Tang, Hao Li, Zechao Zhang, Dong He, Shengfeng Tang, Jinhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore

RGB-Thermal Salient Object Detection (RGB-T SOD) aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. A key challenge lies in bridging the inherent disparities between RGB and Thermal modalities for effective saliency map prediction. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities, thereby leading to suboptimal performance in complex scenarios. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a "Divide-and-Conquer" strategy. This framework utilizes a unified encoder with specialized decoders, each addressing different subtasks of exploring modality-specific and modality-complementary information for RGB-T SOD, thereby enhancing the final saliency map prediction. Specifically, ConTriNet comprises three flows: two modality-specific flows explore cues from RGB and Thermal modalities, and a third modality-complementary flow integrates cues from both modalities. ConTriNet presents several notable advantages. It incorporates a Modality-induced Feature Modulator (MFM) in the modality-shared union encoder to minimize inter-modality discrepancies and mitigate the impact of defective samples. Additionally, a foundational Residual Atrous Spatial Pyramid Module (RASPM) in the separated flows enlarges the receptive field, allowing for the capture of multi-scale contextual information. Furthermore, a Modality-aware Dynamic Aggregation Module (MDAM) in the modality-complementary flow dynamically aggregates saliency-related cues from both modality-specific flows. Leveraging the proposed parallel triple-flow framework, we further refine saliency maps derived from different flows through a flow-cooperative fusion strategy, yielding a high-quality, full-resolution saliency map for the final prediction. To evaluate the ro

关键词： Decoding Robustness Benchmark testing Object detection Noise Lighting Accuracy Visualization Thermal noise Modulation encoder-decoder multi-modal fusion RGB- thermal salient object detection

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Dual-path decoder architecture for semantic segmentation of wheat ears

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APPLIED INTELLIGENCE 2025年第2期55卷 1-13页

作者： Wang, Lihui Chen, Yu Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Technol Minist Educ Nanjing 210096 Peoples R China

In this study, a dual-path decoder segmentation network (DPDS) is presented, which innovatively introduces a dual-path structure into a semantic segmentation network incorporating atrous spatial pyramid pooling (ASPP). A novel loss function, boundary focal loss (BFLoss), is designed specifically for wheat ears segmentation scenarios, which adaptively adjusts weights for different pixel points through the binarization of boundary information, focusing the training on the edges of wheat ears. It is suggested to apply the DPDS network in conjunction with BFLoss to the semantic segmentation of wheat ears. The experimental results demonstrated that BFLoss possesses advantages over commonly used binary cross entropy loss (BCELoss) and focal loss in semantic segmentation. Additionally, the dual-path decoder architecture was proved to reach higher precision than activating only one of the pathways. In comparative experiments with established semantic segmentation networks, the DPDS model achieved the best performance on several evaluation metrics, and attained a balance between precision and recall. Notably, the combination of DPDS and BFLoss achieved a 91.86% F1 score on the wheat ears semantic segmentation test dataset. Therefore, the DPDS model can be effectively applied to semantic segmentation scenarios of crops like wheat, and also provides new insights for the improvement of existing networks. Code is available at https://***/awesome-pythoner/dual-path-decoder-segment.

关键词： Semantic segmentation Dual-path network encoder-decoder Loss function Wheat ears UAV remote sensing

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