Extracting Result Schema Based on Query Instances in the Deep Web

Deep Web sources contain a large of high-quality and query-related structured date. One of the challenges in the Deep Web is extracting result schemas of Deep Web sources. To address this challenge, this paper describes a novel approach that extracts both result data and the result schema of a Web database. The approach first models the query interface of a Deep Web source and fills in it with a specifically query instance. Then the result pages of the Deep Web sources are formatted in the tree structure to retrieve subtrees that contain elements of the query instance, Next, result schema of the Deep Web source is extracted by matching the subtree＇ nodes with the query instance, in which, a two-phase schema extraction method is adopted for obtaining more accurate result schema. Finally, experiments on real Deep Web sources show the utility of our approach, which provides a high precision and recall.

A Genetic Approach to Analyze Algorithm Performance Based on the Worst-Case Instances

Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.

Combining rough set theory and instance selection in ontology mapping

This paper presents a novel ontology mapping approach based on rough set theory and instance selection.In this appoach the construction approach of a rough set-based inference instance base in which the instance selection(involving similarity distance,clustering set and redundancy degree)and discernibility matrix-based feature reduction are introduced respectively;and an ontology mapping approach based on multi-dimensional attribute value joint distribution is proposed.The core of this mapping aI overlapping of the inference instance space.Only valuable instances and important attributes can be selected into the ontology mapping based on the multi-dimensional attribute value joint distribution,so the sequently mapping efficiency is improved.The time complexity of the discernibility matrix-based method and the accuracy of the mapping approach are evaluated by an application example and a series of analyses and comparisons.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition&#174. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

Dynamic SLAM Visual Odometry Based on Instance Segmentation:A Comprehensive Review

Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Real-time instance segmentation based on contour learning

Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

Research on the Preservation Method of Traditional Village Roof Information:A Case Study of Gubeikou Village

Traditional Chinese villages serve as crucial repositories of traditional culture.However,In China,the urgent task of preserving information about traditional village architecture has arisen due to the degradation of these villages’appearance caused by rapid urbanization in recent years.This paper proposes a method for preserving information about traditional village rooftops based on high spatial resolution remote sensing imagery.Leveraging an improved Mask R-CNN model,the method conducts target recognition on the rooftops of traditional village buildings and generates vectorized representations of these rooftops.The precision rate,recall rate,and F1-score achieved in the experimental results are 93.26%,86.33%,and 92.02%,respectively.These findings indicate the effectiveness of the proposed method in preserving information about traditional village architecture and providing a viable approach to support the sustainable development of traditional villages in China.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

First-principles calculation of the structural,electronic,and magnetic properties of cubic perovskite RbXF3(X = Mn,V,Co,Fe)

First-principles calculations by means of the full-potential linearized augmented plane wave method using the generalized gradient approximation with correlation effect correction（GGA＋U） within the framework of spin polarized density functional theory（DFT＋U） are used to study the structural,electronic,and magnetic properties of cubic perovskite compounds RbXF3（X = Mn,V,Co,and Fe）.It is found that the calculated structural parameters,i.e.,lattice constant,bulk modulus,and its pressure derivative are in good agreement with the previous results.Our results reveal that the strong spin polarization of the 3d states of the X atoms is the origin of ferromagnetism in RbXF3.Cohesive energies and the magnetic moments of RbXF3 have also been calculated.The calculated electronic properties show the half-metallic nature of RbCoF3 and RbFeF3,making these materials suitable for spintronic applications.

The First Charity Law Formulated in China Opens an Age of Governing “The Good” According to Law

The 4th Session of the Twelfth National People＇s Congress of the People＇s Republic of China held a plenary meeting on March 16,2016,and adopted the Charity Law of the People＇s Republic of China,as the first charity law in China,it shall come into force on September 1,2016,

现实主义、现代主义、后现代主义的继往共生——读“A Modern Instance”《一个现代的例证》之联想

小说《一个现代的例证》是威廉·迪安·豪威尔斯 ( William Dean Howells,1837—192 0 )的杰作之一。在后现代主义空前繁荣和发展的时代 ,重读 Howells的小说 ,发现它依然余音绕梁 ,意味犹存。纵观文学发展的历史 ,不难发现文学创作始终处于不断演变与进化之中 ,各阶段的发展相互渗透 。

从认知理论看存现句There-be的话题性

存现句there-be是英语中一个特殊结构范畴。从认知语法的视点分析该范畴的原型例与非中心例在语用方面的差异,可见后者具有话题性功能。

SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation

Mature soybean phenotyping is an important process in soybean breeding;however, the manual process is time-consuming and labor-intensive. Therefore, a novel approach that is rapid, accurate and highly precise is required to obtain the phenotypic data of soybean stems, pods and seeds. In this research, we propose a mature soybean phenotype measurement algorithm called Soybean Phenotype Measure-instance Segmentation(SPM-IS). SPM-IS is based on a feature pyramid network, Principal Component Analysis(PCA) and instance segmentation. We also propose a new method that uses PCA to locate and measure the length and width of a target object via image instance segmentation. After 60,000 iterations, the maximum mean Average Precision(m AP) of the mask and box was able to reach 95.7%. The correlation coefficients R^(2) of the manual measurement and SPM-IS measurement of the pod length, pod width, stem length, complete main stem length, seed length and seed width were 0.9755, 0.9872, 0.9692, 0.9803,0.9656, and 0.9716, respectively. The correlation coefficients R^(2) of the manual counting and SPM-IS counting of pods, stems and seeds were 0.9733, 0.9872, and 0.9851, respectively. The above results show that SPM-IS is a robust measurement and counting algorithm that can reduce labor intensity, improve efficiency and speed up the soybean breeding process.

Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.

Modeling and understanding of the thermal failure induced by high power microwave in CMOS inverter

The thermal failure induced by high power microwave（HPM） in a complementary metal oxide semiconductor（CMOS） inverter is investigated and its dependence on microwave parameters is discussed in detail. An analytical model of the temperature distribution is established and the relationships between hotspot temperature and pulse width and between hotspot temperature and frequency are predicted, which reveals a more severe rise in temperature under the influence of microwave with longer width and lower frequency. The temperature variation mechanism and the theoretical temperature model are validated and explained by the simulation. Furthermore, variation trend of damage threshold with microwave parameters is derived theoretically, and the conclusions are consistent with simulation results and reported data.

Australian Hajj pilgrims' knowledge about MERS-CoV and other respiratory infections

Dear Editor,With the intense crowding in mass gatherings such as Hajj,there is a high risk of acquisition of airborne in-fections with the potential for its transmission in the pilgrims’country of origin(Memish Z A,et al.,2014).The risk of importing serious infections from Hajj has escalated since the emergence of the Middle East respiratory syndrome coronavirus(MERS-CoV)in Saudi Arabia and other neighbouring countries from September2012.Active surveillance of Hajj pilgrims in 2012 and 2013