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FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization

APPLIED SCIENCES-BASEL 2024年第17期14卷 7548页

作者： Zhang, Xin Wei, Qiyi Zheng, Bin Liu, Jiefeng Zhang, Pengzhou Commun Univ China Sch Comp & Cyber Sci Beijing 100024 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

Framing theory is a widely accepted theoretical framework in the field of news communication studies, frequently employed to analyze the content of news reports. This paper innovatively introduces framing theory into the text summarization task and proposes a news text summarization method based on framing theory to address the global context of rapidly increasing speed and scale of information dissemination. Traditional text summarization methods often overlook the implicit deep-level semantic content and situational frames in news texts, and the method proposed in this paper aims to fill this gap. Our deep learning-based news frame identification module can automatically identify frame elements in the text and predict the dominant frame of the text. The frame-aware summarization generation model (FrameSum) can incorporate the identified frame feature into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report while maintaining high information coverage, readability, and objectivity. Through empirical studies on the standard CNN/Daily Mail dataset, we found that this method performs significantly better in improving summary quality and maintaining the accuracy of news facts.

关键词： news text summarization framing theory deep learning framework recognition encoder-decoder model

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Unsupervised feature selection using orthogonal encoder-decoder factorization

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INFORMATION SCIENCES 2024年 663卷

作者： Mozafari, Maryam Seyedi, Seyed Amjad Mohammadiani, Rojiar Pir Tab, Fardin Akhlaghian Univ Kurdistan Dept Comp Engn Sanandaj Iran

Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non -redundant and relevant features from a high -dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more efficient and interpretable models. Current embedded UFS methods primarily rely on self -representation or pseudo -supervised feature selection approaches to address redundancy and irrelevant feature issues, respectively. Nevertheless, there is currently a lack of research showcasing the fusion of these two approaches. This paper proposes the Orthogonal encoderdecoder factorization for unsupervised Feature Selection (OEDFS) model, combining the strengths of self -representation and pseudo -supervised approaches. This method draws inspiration from the self -representation properties of autoencoder architectures and leverages encoder and decoder factorizations to simulate a pseudo -supervised feature selection approach. To further enhance the part -based characteristics of factorization, orthogonality constraints and local structure preservation restrictions are incorporated into the objective function. The optimization process is based on the multiplicative update rule, ensuring efficient convergence. To assess the effectiveness of the proposed method, comprehensive experiments are conducted on 14 datasets and compare the results with eight state-of-the-art methods. The experimental results demonstrate the superior performance of the proposed approach in terms of UFS efficiency.

关键词： Unsupervised feature selection encoder-decoder model Self-representation learning Pseudo-supervised learning Nonnegative matrix factorization

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encoder-decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

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SENSORS 2021年第20期21卷 6839页

作者： Al-Mohannadi, Aisha Al-Maadeed, Somaya Elharrouss, Omar Sadasivuni, Kishor Kumar Qatar Univ Dept Comp Sci & Engn POB 2713 Doha Qatar Qatar Univ Ctr Adv Mat POB 2713 Doha Qatar

Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

关键词： carotid intima-media thickness IMT CCA segmentation deep learning encoder-decoder model

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Weather Radar Echo Extrapolation Method Based on Deep Learning

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ATMOSPHERE 2022年第5期13卷 815-815页

作者： Zhang, Fugui Lai, Can Chen, Wanjun Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China Chengdu Univ Informat Technol Sch Elect Engn Chengdu 610225 Peoples R China

In order to forecast some high intensity and rapidly changing phenomena, such as thunderstorms, heavy rain, and hail within 2 h, and reduce the influence brought by destructive weathers, this paper proposes a weather radar echo extrapolation method based on deep learning. The proposed method includes the design and combination of the data preprocessing, convolutional long short-term memory (Conv-LSTM) neuron and encoder-decoder model. We collect eleven thousand weather radar echo data in high spatiotemporal resolution, these data are then preprocessed before they enter the neural network for training to improve the data's quality and make the training better. Next, the neuron integrates the structure and the advantages of convolutional neural network (CNN) and long short-term memory (LSTM), called Conv-LSTM, is applied to solve the problem that the full-connection LSTM (FC-LSTM) cannot extract the spatial information of input data. This operation replaced the full-connection structure in the input-to-state and state-to-state parts so that the Conv-LSTM can extract the information from other dimensions. Meanwhile, the encoder-decoder model is adopted due to the size difference of the input and output data to combine with the Conv-LSTM neuron. In the neural network training, mean square error (MSE) loss function weighted according to the rate of rainfall is added. Finally, the matrix "point-to-point" test method, including the probability of detection (POD), critical success index (CSI), false alarm ratio (FAR) and spatial test method contiguous rain areas (CRA), is used to examine the radar echo extrapolation's results. Under the threshold of 30 dBZ, at the time of 1 h, we achieved 0.60 (POD), 0.42 (CSI) and 0.51 (FAR), compared with 0.42, 0.28 and 0.58 for the CTREC algorithm, and 0.30, 0.24 and 0.71 for the TITAN algorithm. Meanwhile, at the time of 1 h, we achieved 1.35 (total MSE ) compared with 3.26 for the CTREC algorithm and 3.05 for the TITAN algorithm. The r

关键词： weather radar echo extrapolation deep learning Conv-LSTM encoder-decoder model loss function

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A parallel workflow framework using encoder-decoder LSTMs for uncertainty quantification in contaminant source identification in groundwater

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JOURNAL OF HYDROLOGY 2023年第1期619卷

作者： Anshuman, Aatish Eldho, T. I. Indian Inst Technol Dept Civil Engn Mumbai 400076 India

Groundwater contamination is a major problem and it can be contaminated by natural or manmade activities. Contaminant sources cannot be identified promptly after a contamination event due to slow groundwater movement in aquifers. To limit the further spread of the plume and to take appropriate remedial measures, it is essential to determine the release rates associated with source locations as soon as a contaminant is detected in one of the observation wells in the aquifer. Conventional techniques for contaminant source identification such as the simulation-optimization (SO) uses a simulation model, which is based on estimated or, measured hydrogeological parameters, for replicating the aquifer response to contamination at source locations. However, the associated intrinsic uncertainty in the parameters can affect the estimated release histories at the source locations. To assess confidence in the results, it is important to consider stochasticity in the hydrogeological parameters for contaminant release history estimation requiring numerous runs of the SO model for corresponding breakthrough curves. The present study proposes a novel surrogate simulation optimization (SSO) framework which utilizes an encoder-decoder Long Short-Term Memory (ED-LSTM) based surrogate model along with Multiverse Optimization (MVO) for contaminant source identification. The comparison of ED-LSTM with other surrogate models shows its better accuracy and significantly low computational time requirement compared to the simulation model. To enhance the computational efficiency further, the SSO model is embedded in a parallel workflow for the quantification of uncertainty associated with the inverse modelling. The study shows that the proposed workflow can be efficiently used for uncertainty quantification in contaminant source identification due to stochastic parameters with a reduced computational cost.

关键词： Contaminant Source identification Uncertainty quantification Surrogate modelling encoder-decoder model Long Short-Term Memory (LSTM) Parallel Computing

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Saliency and ballness driven deep learning framework for cell segmentation in bright field microscopic images

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 118卷

作者： Asha, S. B. Gopakumar, G. Subrahmanyam, Gorthi R. K. Sai Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Vallikavu India Indian Inst Technol Tirupati Dept Elect Engn Tirupati India

Cell segmentation is the most significant task in microscopic image analysis as it facilitates differential cell counting and analysis of sub-cellular structures for diagnosing cytopathological diseases. Bright-field microscopy is considered the gold standard among different types of optical microscopes used for cell analysis due to its simplicity and cost-effectiveness. However, automatic cell segmentation in bright field microscopy is challenging due to imaging artifacts, poor contrast, overlapping cells, and wide variability of cells. Also, the availability of labeled bright-field images is limited, further constraining the research in developing supervised models for automated cell segmentation. In this research, we propose a novel cell segmentation framework termed Saliency and Ballness driven U-shaped Network (SBU-net) to overcome these challenges. The proposed architecture comprises a novel data-driven feature fusion module that enhances the perceivable structure of cells using its saliency and ballness features. This, together with an encoder-decoder model having dilated convolutions and a novel combination loss function, captured the global context of cell structures and produced accurate cell segmentation results. SBU-net is evaluated using two publicly available bright-field datasets of T cells and pancreatic cancer cells. The model is subjected to 5-fold cross-validation and outperformed state-of-the-art models by producing mean Intersection over Union (IoU) scores of 0.804, 0.829, and mean Dice of 0.891, 0.906, respectively. The architecture was also tested on a fluorescent dataset to see how well it could generalize, and it came out with a mean IoU of 0.892 and a mean Dice of 0.948, outperforming other models reported in the literature.

关键词： Deep learning based semantic segmentation encoder-decoder model Microscopic image analysis Bright-field microscopy

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A deep reinforcement learning-based approach for the home delivery and installation routing problem

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2022年第0期244卷 108362-108362页

作者： Qiu, Huaxin Wang, Sutong Yin, Yunqiang Wang, Dujuan Wang, Yanzhang Dalian Univ Technol Sch Econ & Management Dalian 116023 Peoples R China Univ Elect Sci & Technol China Sch Econ & Management Chengdu 610064 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

This paper investigates a home delivery and installation routing problem with synchronization constraints stemming from a home industry company in China who provides the last-mile delivery of home decoration and furniture. The company first arranges for products to be delivered from door to door, and later the technicians come to perform the installation service for the customers. The products for each customer must be firstly delivered to the customer by a vehicle and then installed by technicians. The objective is to identify the optimal delivery routes of the vehicles and optimal service routes of the technicians so as to minimize the total travel distance of the delivery and service routes. A deep reinforcement learning method in an encoder-decoder fashion with multi-head attention mechanism and beam search strategy is developed to solve the problem. To evaluate the designed method, extensive numerical experiments based on real service networks provided by the company are conducted. The results show that the proposed method can effectively solve the problem, which outperforms some classical strategies, and some meaningful management implications are provided.

关键词： Delivery and installation routing problem Deep reinforcement learning encoder-decoder model Attention mechanism

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Pedestrian Trajectory Prediction in Extremely Crowded Scenarios

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SENSORS 2019年第5期19卷 1223-1223页

作者： Shi, Xiaodan Shao, Xiaowei Guo, Zhiling Wu, Guangming Zhang, Haoran Shibasaki, Ryosuke Univ Tokyo Ctr Spatial Informat Sci Kashiwa Chiba 2778568 Japan Univ Tokyo Earth Observat Data Integrat & Fus Res Initiat Tokyo 1538505 Japan

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians' trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.

关键词： trajectory prediction human interaction LSTM crowded scenarios encoder-decoder model deep learning

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Recognition of printed Urdu script in Nastaleeq font by using CNN-BiGRU-GRU Based encoder-decoder Framework

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 18卷

作者： Zia, Sohail Azhar, Muhammad Lee, Bumshik Tahir, Adnan Ferzund, Javed Murtaza, Fozia Ali, Moazam Comsats Univ Islamabad Dept Comp Sci Sahiwal Campus Sahiwal 57000 Pakistan Chosun Univ Dept Informat & Commun Engn Gwangju 61452 South Korea Islamia Univ Bahalwapur Dept Comp Sci & IT Rahim Yar Khan Campus Punjab 64200 Pakistan

RNN based Deep learning models has shown tremendous success in sequential and temporal data where the order is critical to achieve higher accuracy in context understanding. RNN family like LSTM, BLSTM, GRU, BiGRU etc. are the mainly used models in these kind of sequential tasks. RNN family based encoder-decoder frameworks are widely used for the recognition of various languages scripts. However, in Urdu, very less research has been done especially with the deep learning models. The existing research work for printed Urdu recognition have shown that the current models only work for very basic sentences of Urdu but in case of complex words and sentences, these algorithms totally fail in terms of accuracy and the time complexity in identification of the Nastaleeq font writing. To identify printed Urdu text in images, we have proposed an encoder-decoder based hybrid deep learning approach with Convolutional Neural Network (CNN) for feature extraction part, bi-directional Gated Recurrent Unit network (BiGRU) as encoder and Gated Recurrent Unit network (GRU) as decoder. The CNN layer of the algorithm is used to obtain ligature features in Urdu, which are subsequently utilized by encoder (BiGRU) and decoder (GRU) to recognize the sentences by accurately distinguishing the characters and joiners. Experimental results have shown that our proposed CNN-BiGRU-GRU hybrid technique with specific hyper-parameter tuning performs well as compared to other state-of-the-art algorithms in terms of epochs (70 epochs as compared to 100 with BLSTM-LSTM based encoder decoder), 6 percent increase of Character Recognition Accuracy (86.95 percent as compared to 81.08 percent of BLSTM-LSTM), 10 percent increase of Word Recognition Accuracy (WRA) (89.48% as compared to 79.06 percent of BLSTM-LSTM) and less time complexity (18 seconds less than BLSTM-LSTM with same system configuration).

关键词： OCR Deep learning Sequence learning GRU BiGRU encoder-decoder model

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Attractive and repulsive effects of sensory history concurrently shape visual perception (vol 20, 247, 2022)

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BMC BIOLOGY 2022年第1期20卷 247-247页

作者： Moon, Jongmin Kwon, Oh-Sang Ulsan Natl Inst Sci & Technol Dept Biomed Engn 50 UNIST Gil Ulsan 44919 South Korea

Sequential effects of environmental stimuli are ubiquitous in most behavioral tasks involving magnitude estimation, memory, decision making, and emotion. The human visual system exploits continuity in the visual environment, which induces two contrasting perceptual phenomena shaping visual perception. Previous work reported that perceptual estimation of a stimulus may be influenced either by attractive serial dependencies or repulsive aftereffects, with a number of experimental variables suggested as factors determining the direction and magnitude of sequential effects. Recent studies have theorized that these two effects concurrently arise in perceptual processing, but empirical evidence that directly supports this hypothesis is lacking, and it remains unclear whether and how attractive and repulsive sequential effects interact in a trial. Here we show that the two effects concurrently modulate estimation behavior in a typical sequence of perceptual tasks. We first demonstrate that observers’ estimation error as a function of both the previous stimulus and response cannot be fully described by either attractive or repulsive bias but is instead well captured by a summation of repulsion from the previous stimulus and attraction toward the previous response. We then reveal that the repulsive bias is centered on the observer’s sensory encoding of the previous stimulus, which is again repelled away from its own preceding trial, whereas the attractive bias is centered precisely on the previous response, which is the observer’s best prediction about the incoming stimuli. Our findings provide strong evidence that sensory encoding is shaped by dynamic tuning of the system to the past stimuli, inducing repulsive aftereffects, and followed by inference incorporating the prediction from the past estimation, leading to attractive serial dependence.

关键词： Vision Perception Perceptual bias Sensory adaptation Aftereffect Serial dependence Bayesian inference Ideal observer encoder-decoder model

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