Extract Accurate Information Research Articles

FVI-BD: Multiple File Extraction using Fusion Vector Investigation (FVI) in Big Data Hadoop Environment

— The Information Extraction (IE) approach extracts useful data from unstructured and semi-structured data. Big Data, with its rising volume of multidimensional unstructured data, provides new tools for IE. Traditional Information Extraction (IE) systems are incapable of appropriately handling this massive flood of unstructured data. The processing capability of current IE systems must be enhanced because to the amount and variety of Big Data. Existing IE techniques for data preparation, extraction, and transformation, as well as representations of massive amounts of multidimensional, unstructured data, must be evaluated in terms of their capabilities and limits. The proposed FVI-BD Framework for IOT device Information Extraction in Big Data. The unstructured data has cleaned and integration using POS tagging and similarity finding using LTA method. The features are extracted using TF and IDF. The Information extracted using NLP with WordNet. The classification has done with FVI algorithm. This research paper discovered that vast data analytics may be enhanced by extracting document feature terms with synonymous similarity and increasing IE accuracy.

Experimental study on the influence of explosive stress wave on the dynamic crack propagation characteristics under biaxial confining pressure

The effect of biaxial confining pressure on dynamic crack and the interaction between explosive stress wave and dynamic crack are studied based on dynamic photoelastic method in this experiment. The experiment consists of five groups, primarily focusing on the angle at which the stress wave encounters the crack. The experimental results reveal two main fractures: mode I fracture and mixed-mode fracture. The influence of biaxial confining pressure on stress waves and dynamic cracks differs between these two fracture modes. Additionally, the Newton-Raphson iteration method is employed to analyze and calculate the fringe pattern of the crack tip at various stages. Subsequently, the calculation results are used to reconstruct the photoelastic fringe. To validate the accuracy of the calculation method, the reconstructed photoelastic fringe pattern is compared with the experimental results. The results show that the presence of confining biaxial pressure enhances the crack initiation toughness, propagation toughness and propagation speed. It also improves the crack's resistance to the impact of the incident stress wave from all directions. When the stress wave interacts with the crack, the photoelastic fringe patterns at the crack tip can be categorized into two main types. One is primarily influenced by the stress wave, while the other is primarily influenced by the stress field at the crack tip. To extract accurate crack tip information, it is essential to analyze the fringe pattern dominated by the stress field at the crack tip.

A comprehensive analysis of different geometric correction methods for the Pleiades -1A and Spot-6 satellite images

Satellite images have been widely used in the production of geospatial information such as land use and land cover mapping and the generation of several thematic layers via image processing techniques. The systematic sensor and platform-induced geometry errors influence images acquired by sensors onboard various satellite platforms. Thus, geometric correction of satellite images is essential for image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values could be achieved with the model that uses RPC from data providers for PHR and SPOT, followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracy values, were observed with the PHR image except for a single test.

Exploring a radically new exponential Retinex model for multi-task environments

The Retinex Theory (RT) and its adaptations have gained significant popularity in the field of image processing. Nevertheless, traditional Retinex algorithms are generally customized to specific tasks. Besides, their use of logarithmic transformation (LT) to convert the multiplicative model to an additive one often results in the loss of texture information in the reflectance layer. In contrast to conventional methods, our approach involves direct decomposition of the observed image. This approach circumvents the necessity of intermediate transformations, thereby preserving essential texture features. In this study, we introduce a weight-aware ℓ1-ℓ2 technique based on the assumption that the reflectance layer is discontinuous and the illumination layer is spatially smooth. To preserve texture and structural information in the illumination layer, we introduce a weight-aware illumination component coefficient, ℓ1-norm, and estimate the reflectance component using ℓ2-norm. By utilizing weight-aware coefficients, the proposed technique is highly effective in addressing the issue of texture loss in the reflectance layer. Additionally, we employ ℓ2-norm to extract accurate information from the reflectance layer and implement a bright channel prior to prevent ambiguity during the decomposition process. We utilize an alternating minimization approach to obtain the optimal objective function solution and modify the illumination layer using gamma and non-linear stretching algorithms. Our proposed technique not only tackles the problem of texture duplication but also improves the quality of low-light images, and can be seamlessly integrated with various image and vision-based tasks. Our evaluation of eight benchmark datasets using 15 quality metrics, along with a variety of 22 conventional and modern algorithms, shows that the proposed algorithm is capable of delivering competitive qualitative and quantitative results without compromising its flexibility and scalability. Besides, the proposed model is evaluated on retinal images, and the results demonstrate a substantial improvement in the accuracy of learning-based models.

Local maximum multisynchrosqueezing transform and its application

The synchrosqueezing technique is widely used for analyzing the time variations of non-stationary signals. Recently, the local maximum synchrosqueezing transform (LMSST) method has been presented to effectively improve the time-frequency (TF) representation, which seems to be a promising tool. However, the LMSST is unable to accurately characterize the amplitude of strongly non-stationary signals, making it rather challenging to extract accurate TF information for signals with strong frequency modulation. Therefore, there is a significant barrier preventing the development of more precise and sharper results from the micro-Doppler signals with strong frequency modulation and multi-component characteristics. To overcome this problem, we coined a novel LMSST method, namely local maximum multisynchrosqueezing transform (LMMSST). The LMMSST is on the basis of local maximizing TF reassignment and multiple synchrosqueezing operators in which an iterated redistribution program is used to concentrate the ambiguous TF energy with a stepwise approach. As a result, the LMMSST significantly enhances the concentration in terms of energy of TF results and performs better in addressing strong frequency modulation and multi-component signals. In addition, it also allows perfect signal reconstruction and accurate estimating of the instantaneous frequency. Numerical results and application cases verify the effectiveness of the LMMSST method.

Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology.

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.

The comparison of algorithms of audio fingerprinting

With the increasing demand for song recognition on music platforms, the Audio fingerprint system is being used more and more frequently. How to improve the speed and accuracy of the Audio fingerprint system is a problem that is worth studying. Finding a better algorithm is crucial if people who use this system want to improve their efficiency of it. This paper, it is compared the accuracy and speed of extracting music information by the Multiple Hashing Algorithm and the Philips Robust Hashing Algorithm. The Multiple Hashing Algorithm can improve the accuracy of information extraction by increasing the sub-fingerprint bands, but with the increase of sub-fingerprint bands, the algorithm run time is much longer. Therefore, the Philips Robust Hashing Algorithm should be used when processing simple audio information to ensure the speed of the system. The Multiple Hashing Algorithm can improve the accuracy of information extraction by increasing the number of sub-fingerprint bands.

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.

Hospital-wide natural language processing summarising the health data of 1 million patients.

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task.

Analytical Tensor Voting in ND Space and its Properties.

This article aims to propose a novel Analytical Tensor Voting (ATV) mechanism, which enables robust perceptual grouping and salient information extraction for noisy N-dimensional (ND) data. Firstly, the approximation of the decaying function is investigated and adopted based on the idea of penalizing the 1-tensor votes by distance and curvature, respectively, followed by the derivation of analytical solution to the 1-tensor voting in ND space from the geometric view. Secondly, a novel spherical representation mechanism is proposed to facilitate the representation of the elementary tensors in various dimensional spaces, where the high dimensional spherical coordinate system is utilized to construct the controllable unit vectors and corresponding 1-tensors. Accordingly, any elementary K-tensor is represented by the surface integration of the constructed 1-tensors over the unit K-sphere. Thirdly, the ATV mechanism is constructed using the adopted decaying function and proposed spherical representation mechanism, where the analytical solution to tensor voting in ND space is derived, which enables the robust and accurate salient information extraction from noisy ND data. Finally, several interesting properties of the proposed ATV mechanism are investigated. Experimental results on synthetic and real data validate the effectiveness, efficiency and robustness of the proposed method in perceptual grouping tasks in 3D,10D or higher dimensional spaces.

AF-OSD: An Anchor-Free Oriented Ship Detector Based on Multi-Scale Dense-Point Rotation Gaussian Heatmap

Due to the complexity of airborne remote sensing scenes, strong background and noise interference, positive and negative sample imbalance, and multiple ship scales, ship detection is a critical and challenging task in remote sensing. This work proposes an end-to-end anchor-free oriented ship detector (AF-OSD) framework based on a multi-scale dense-point rotation Gaussian heatmap (MDP-RGH) to tackle these aforementioned challenges. First, to solve the sample imbalance problem and suppress the interference of negative samples such as background and noise, the oriented ship is modeled via the proposed MDP-RGH according to its shape and direction to generate ship labels with more accurate information, while the imbalance between positive and negative samples is adaptively learned for the ships with different scales. Then, the AF-OSD based on MDP-RGH is further devised to detect the multi-scale oriented ship, which is the accurate identification and information extraction for multi-scale vessels. Finally, a multi-task object size adaptive loss function is designed to guide the training process, improving its detection quality and performance for multi-scale oriented ships. Simulation results show that extensive experiments on HRSC2016 and DOTA ship datasets reveal that the proposed method achieves significantly outperforms the compared state-of-the-art methods.

Inception Convolution and Feature Fusion for Person Search.

With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), which considerably enhanced the accuracy of pedestrian retrieval by combining global and fine-grained information. Experiments on CHUK-SYSU and PRW datasets showed that our method has higher accuracy than Seq-Net. In addition, our method is simpler and can be easily integrated into existing two-stage frameworks.

Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception.

When moving a piano or dancing tango with a partner, how should I control my arm muscles to sense their movements and follow or guide them smoothly? Here we observe how physically connected pairs tracking a moving target with the arm modify muscle coactivation with their visual acuity and the partner's performance. They coactivate muscles to stiffen the arm when the partner's performance is worse and relax with blurry visual feedback. Computational modeling shows that this adaptive sensing property cannot be explained by the minimization of movement error hypothesis that has previously explained adaptation in dynamic environments. Instead, individuals skillfully control the stiffness to guide the arm toward the planned motion while minimizing effort and extracting useful information from the partner's movement. The central nervous system regulates muscle activation to guide motion with accurate task information from vision and haptics while minimizing the metabolic cost. As a consequence, the partner with the most accurate target information leads the movement.NEW & NOTEWORTHY Our results reveal that interacting humans inconspicuously modulate muscle activation to extract accurate information about the common target while considering their own and the partner's sensorimotor noise. A novel computational model was developed to decipher the underlying mechanism: muscle coactivation is adapted to combine haptic information from the interaction with the partner and own visual information in a stochastically optimal manner. This improves the prediction of the target position with minimal metabolic cost in each partner, resulting in the lead of the partner with the most accurate visual information.

The influence of Sb doping on the local structure and disorder in thermoelectric ZnO:Sb thin films

Thermoelectric transparent ZnO:Sb thin films were deposited by magnetron sputtering, with Sb content varying between 2 and 14 at%. As evidenced by X-ray diffraction analysis, the films crystallize in the ZnO wurtzite structure for lower levels of Sb-doping, developing a degree of amorphization for higher levels of Sb-doping. Temperature-dependent (10–300 K) X-ray absorption spectroscopy studies of the produced thin films were performed at the Zn and Sb K-edges to shed light on the influence of Sb doping on the local atomic structure and disorder in the ZnO:Sb thin films. The analysis of the Zn K-edge EXAFS spectra by the reverse Monte Carlo method allowed to extract detailed and accurate structural information in terms of the radial and bond angle distribution functions. The obtained results suggest that the introduction of antimony to the ZnO matrix promotes static disorder, which leads to partial amorphization with very small crystallites (∼3 nm) for large (12–14 at%) Sb content. Rutherford backscattering spectrometry (RBS) experiments enabled the determination of the in-depth atomic composition profiles of the films. The film composition at the surfaces determined by X-ray photoelectron spectroscopy (XPS) matches that of the bulk determined by RBS, except for higher Sb-doping in ZnO films, where the concentration of oxygen determined by XPS is smaller near the surface, possibly due to the formation of oxygen vacancies that lead to an increase in electrical conductivity. Traces of Sb–Sb metal bonds were found by XPS for the sample with the highest level of Sb-doping. Time-of-flight secondary ion mass spectrometry obtained an Sb/Zn ratio that follows that of the film bulk determined by RBS, although Sb is not always homogeneous, with samples with smaller Sb content (2 and 4 at% of Sb) showing a larger Sb content closer to the film/substrate interface. From the optical transmittance and reflectance curves, it was determined that the films with the lower amount of Sb doping have larger optical band-gaps, in the range of 2.9–3.2 eV, while the partially amorphous films with higher Sb content have smaller band-gaps in the range of 1.6–2.1 eV. Albeit the short-range crystalline order (∼3 nm), the film with 12 at% of Sb has the highest absolute Seebeck coefficient (∼56 μV/K) and a corresponding thermoelectric power factor of ∼0.2 μW·K−2·m−1.

Contrast U-Net driven by sufficient texture extraction for carotid plaque detection.

Ischemic heart disease or stroke caused by the rupture or dislodgement of a carotid plaque poses a huge risk to human health. To obtain accurate information on the carotid plaque characteristics of patients and to assist clinicians in the determination and identification of atherosclerotic areas, which is one significant foundation work. Existing work in this field has not deliberately extracted texture information of carotid from the ultrasound images. However, texture information is a very important part of carotid ultrasound images. To make full use of the texture information in carotid ultrasound images, a novel network based on U-Net called Contrast U-Net is designed in this paper. First, the proposed network mainly relies on a contrast block to extract accurate texture information. Moreover, to make the network better learn the texture information of each channel, the squeeze-and-excitation block is introduced to assist in the jump connection from encoding to decoding. Experimental results from intravascular ultrasound image datasets show that the proposed network can achieve superior performance compared with other popular models in carotid plaque detection.

Generative Steganography via Auto-Generation of Semantic Object Contours

As a promising technique of resisting steganalysis detection, generative steganography usually generates a new image driven by secret information as the stego-image. However, it generally encodes secret information as entangled features in a non-distribution-preserving manner for the stego-image generation, which leads to two common issues: 1) limited accuracy of information extraction, and 2) low security in feature-domain. To address the above issues, we propose a generative steganographic framework via auto-generation of semantic object contours, in which a given secret message is encoded as the disentangled features, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., object-contours, in a distribution-preserving manner for the stego-image generation. In this framework, we propose a contour generative adversarial nets (CtrGAN) consisting of a contour-generator and a contour-discriminator, which are adversarially trained with reinforcement learning. To realize the generative steganography, by using the contour-generator of the trained CtrGAN, a contour point selection (CPS)-based encoding strategy is designed to encode the secret message as the contours. Then, the BicycleGAN is employed to transform the generated contours to the corresponding stego-image. Extensive experiments demonstrate the proposed steganographic approach achieves superior performance in the aspects of information extraction accuracy, especially under common image attacks, and feature-domain security, compared to the state-of-the-arts.

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Geodesy is the science of accurately measuring the topography of the earth (geometric shape and size), its orientation in space, and its gravity field. With the advances in our knowledge and technology, this scientific field has extended to the understanding of geodynamical phenomena such as crustal motion, tides, and polar motion. This Special Issue is dedicated to the recent advances in modelling geodetic time series recorded using various instruments. Due to the stochastic noise properties inherent in each of the time series, careful modelling is necessary in order to extract accurate geophysical information with realistic associated uncertainties (statistically sufficient). The analyzed data have been recorded with various space missions or ground-based instruments. It is impossible to be comprehensive in the vast and dynamic field that is Geodesy, particularly so-called “Environmental Geodesy”, which intends to understand the Earth’s geodynamics by monitoring any changes in our environment. This field has gained much attention in the past two decades due to the need by the international community to understand how climate change modifies our environment. Therefore, this Special Issue collects some articles which emphasize the recent development of specific algorithms or methodologies to study particular natural phenomena related to the geodynamics of the earth’s crust and climate change.

Lossless Image Steganography Based on Invertible Neural Networks.

Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy of secret information. In this paper, the steganography method based on invertible neural networks is proposed, which can generate stego images with high invisibility and security and can achieve lossless recovery for secret information. In addition, this paper introduces a mapping module that can compress information actually embedded to improve the quality of the stego image and its antidetection ability. In order to restore message and prevent loss, the secret information is converted into a binary sequence and then embedded in the cover image through the forward operation of the invertible neural networks. This information will then be recovered from the stego image through the inverse operation of the invertible neural networks. Experimental results show that the proposed method in this paper has achieved competitive results in the visual quality and safety of stego images and achieved 100% accuracy in information extraction.

MLM-based typographical error correction of unstructured medical texts for named entity recognition

BackgroundUnstructured text in medical records, such as Electronic Health Records, contain an enormous amount of valuable information for research; however, it is difficult to extract and structure important information because of frequent typographical errors. Therefore, improving the quality of data with errors for text analysis is an essential task. To date, few prior studies have been conducted addressing this. Here, we propose a new methodology for extracting important information from unstructured medical texts by overcoming the typographical problem in surgical pathology records related to lung cancer.MethodsWe propose a typo correction model that considers context, based on the Masked Language Model, to solve the problem of typographical errors in real-world medical data. In addition, a word dictionary was used for the typo correction model based on PubMed abstracts. After refining the data through typo correction, fine tuning was performed on pre-trained BERT model. Next, deep learning-based Named Entity Recognition (NER) was performed. By solving the quality problem of medical data, we sought to improve the accuracy of information extraction in unstructured text data.ResultsWe compared the performance of the proposed typo correction model based on contextual information with an existing SymSpell model. We confirmed that our proposed model outperformed the existing model in a typographical correction task. The F1-score of the model improved by approximately 5% and 9% when compared with the model without contextual information in the NCBI-disease and surgical pathology record datasets, respectively. In addition, the F1-score of NER after typo correction increased by 2% in the NCBI-disease dataset. There was a significant performance difference of approximately 25% between the before and after typo correction in the Surgical pathology record dataset. This confirmed that typos influenced the information extraction of the unstructured text.ConclusionWe verified that typographical errors in unstructured text negatively affect the performance of natural language processing tasks. The proposed method of a typo correction model outperformed the existing SymSpell model. This study shows that the proposed model is robust and can be applied in real-world environments by focusing on the typos that cause difficulties in analyzing unstructured medical text.

Feature representation under crowding in macaque V1 and V4 neuronal populations

Visual perception depends strongly on spatial context. A profound example is visual crowding, whereby the presence of nearby stimuli impairs the discriminability of object features. Despite extensive work on perceptual crowding and the spatial integrative properties of visual cortical neurons, the link between these two aspects of visual processing remains unclear. To understand better the neural basis of crowding, we recorded activity simultaneously from neuronal populations in V1 and V4 of fixating macaque monkeys. We assessed the information available from the measured responses about the orientation of a visual target both for targets presented in isolation and amid distractors. Both single neuron and population responses had less information about target orientation when distractors were present. Information loss was moderate in V1 and more substantial in V4. Information loss could be traced to systematic divisive and additive changes in neuronal tuning. Additive and multiplicative changes in tuning were more severe in V4; in addition, tuning exhibited other, non-affine transformations that were greater in V4, further restricting the ability of a fixed sensory readout strategy to extract accurate feature information across displays. Our results provide a direct test of crowding effects at different stages of the visual hierarchy. They reveal how crowded visual environments alter the spiking activity of cortical populations by which sensory stimuli are encoded and connect these changes to established mechanisms of neuronal spatial integration.