Detail Information Research Articles

Experimental Work Of Development Of Information Culture Of Secondary Special Islamic Colleges

This article explains how information culture and its specific features in the modern era of information culture. The social factors that determine the lack of a critical approach to the selection of necessary information among students of a special Islamic educational college have been studied. Also, based on the fact that the acquisition of information culture is a vital necessity for future imams, the author made a number of proposals and recommendations in this regard. In the article in the information society, ensuring fast and high-quality circulation of information in the education system becomes the main criterion for the progress and development of the country, and also analyzes in detail the need for the effective use of information in the field of education. The main factors determining the level of formation of the information culture of students of secondary specialized Islamic education are explained. In the XXI century, every student, every teenager must meet the basic criteria of information culture; the problems of its use in the educational process and, of course, improving the culture of using the Internet are highlighted. The scientific article presents an analysis of studies conducted on the culture of using the Internet and social networks among students studying in secondary specialized educational institutions. This is a scientific study not only in Uzbekistan, but also in all countries of Central Asia; the formation of the information culture of students in secondary specialized Islamic educational colleges has not been studied at all. Formation criteria have been developed and explained in detail information culture of students studying in secondary specialized Islamic educational colleges.

Descriptive Study of Teachers’ Perceptions in Using Merdeka Curriculum in Teaching English

This recent research aimed to describe teachers’ perceptions in using Merdeka Curriculum in teaching English in SMA Negeri 3 Gorontalo Utara and SMA Negeri 5 Gorontalo Utara. The method of this research was qualitative design that is hoped to give detail and in-depth information about certain phenomenon. the result of research showed teachers positively perceive the Merdeka Curriculum's structure, commending its flexibility in organizing intracurricular content to cater to diverse student needs through innovative approaches. The division of teaching hours into intracurricular and Pancasila strengthening is also acknowledged. Implementation strengths include flexible teaching models, student-centered learning, and school autonomy. Challenges include effective teaching hour allocation, adapting to new terminology, and the need for continuous support, especially in English learning. Regarding the Pancasila Students' Profile, teachers appreciate the multifaceted approach to character education, emphasizing practical application over theoretical understanding. The early stages of the P5 program show promise in integrating Pancasila values, yet challenges persist in students' character attitudes and the call for enhanced collaboration.

Failure analysis of S30408 pipe cracking and preventive measures

BACKGROUND: The feed pipeline made from 30408 stainless steel of a new unit leaked during the air pressure test. OBJECTIVE: The present work aims to examine the specific cause of pipeline cracking, and providing effective approaches to avoid similar failures. METHODS: Macroscopic inspections of the cracked pipe defects were made on site immediately after leakage. Mechanical properties and hardness of specimens machined from the failed pipe were tested. In addition, microscopic analyses including material composition, microstructure observation and crack morphologies of the failed part were performed to get detail information. Composition of the feed raw material was also analyzed to identify whether it had been contaminated by corrosive elements or not. RESULTS: No impurity composition was found in the feed raw material. The element constituents, yield strength, tensile strength and hardness of the cracked pipe fulfill standard requirements. A number of scratches and defects with a size of several microns were found on the inner wall of the leaked pipe, and they were believed to be formed at the perforation step during pipeline processing. Liquation cracks were found at the pipeline butt weld joint, and they laid hidden dangers for the safety and steady operation of the pipeline. CONCLUSION: The overall analysis results indicated the pipeline leakage during air pressure test was caused by cracks initiated around inner wall defects, which sabotaged the bearing capacity of the pipe by wall thickness reduction and stress concentration. Therefore, improving the inner wall surface quality at the perforation step may help to avoid such failure. The metallurgical effect and weld stress caused during the welding process promoted the initiation and propagation of liquation cracks. The tendency of welding hot crack formation could be reduced by taking strict composition control of the welding rod and adopting reasonable welding parameters.

MDEformer: Mixed Difference Equation Inspired Transformer for Compressed Video Quality Enhancement.

Deep learning methods have achieved impressive performance in compressed video quality enhancement tasks. However, these methods rely excessively on practical experience by manually designing the network structure and do not fully exploit the potential of the feature information contained in the video sequences, i.e., not taking full advantage of the multiscale similarity of the compressed artifact information and not seriously considering the impact of the partition boundaries in the compressed video on the overall video quality. In this article, we propose a novel Mixed Difference Equation inspired Transformer (MDEformer) for compressed video quality enhancement, which provides a relatively reliable principle to guide the network design and yields a new insight into the interpretable transformer. Specifically, drawing on the graphical concept of the mixed difference equation (MDE), we utilize multiple cross-layer cross-attention aggregation (CCA) modules to establish long-range dependencies between encoders and decoders of the transformer, where partition boundary smoothing (PBS) modules are inserted as feedforward networks. The CCA module can make full use of the multiscale similarity of compression artifacts to effectively remove compression artifacts, and recover the texture and detail information of the frame. The PBS module leverages the sensitivity of smoothing convolution to partition boundaries to eliminate the impact of partition boundaries on the quality of compressed video and improve its overall quality, while not having too much impacts on non-boundary pixels. Extensive experiments on the MFQE 2.0 dataset demonstrate that the proposed MDEformer can eliminate compression artifacts for improving the quality of the compressed video, and surpasses the state-of-the-arts (SOTAs) in terms of both objective metrics and visual quality.

Automatic reconstruction of geological reservoir models based on conditioning data constraints and BicycleGAN

For geological reservoir units with different sizes of pore spaces and relatively stronger anisotropic heterogeneities, Generative-Adversarial-Network-based (GAN) modeling methods can overcome limitation of numerical-simulation-based ones and support finely representation of nonstationary models. However, due to conditioning data weak constraints, GANs might ignore local detail features, with artifacts and noise during simulation. Some pre-processing strategies are strictly limited by the conditioning data distribution patterns, and corresponding processes should be updated frequently when new data are introduced, with relatively complicated operation. Therefore, the BicycleGAN framework was introduced in this paper. Based on bijective consistency between the latent vector and output, an image-to-image translation task with different dimensions is established, and multiple networks were coupled to realize finely representation of spatial attributes. Specifically, the mapping between conditioning data and output is established through the U-Net architecture, which not only local detail information is considered, but also reduce the impact of conditioning data distribution patterns. Meanwhile, the mapping between latent and actual test time distribution is also established by an encoding function to ensure simulation authenticities. In addition, a joint loss function combined with conditioning loss and prior loss is defined to ensure reconstruction accuracy of different facies types. Four kinds of categorical and continuous training images were selected to verify the network simulation performances. Results show that reconstruction accuracy for facies types of conditioning data is almost consistent with those of the references, keeping similarities in terms of spatial variability, connectivity, structural similarity, and facies type reproductions. Meanwhile, for the saved BicycleGAN, inputting different kinds of conditioning data once only, and a variety of simulations can be obtained rapidly, thereby realizing conditioning reconstruction of geological reservoir models.

A novel dual-pooling attention module for UAV vehicle re-identification

Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

CNB Net: A Two-Stage Approach for Effective Image Deblurring

Image blur, often caused by camera shake and object movement, poses a significant challenge in computer vision. Image deblurring strives to restore clarity to these images. Traditional single-stage methods, while effective in detail enhancement, often neglect global context in favor of local information. Yet, both aspects are crucial, especially in real-life scenarios where images are typically large and subject to various blurs. Addressing this, we introduce CNB Net, an innovative deblurring network adept at integrating global and local insights for enhanced image restoration. The network operates in two stages, utilizing our specially designed Convolution and Normalization-Based Block (CNB Block) and Convolution and Normalization-Based Plus Block (CNBP Block) for multi-scale information extraction. A progressive learning approach is adopted with a Feature Active Selection (FAS) module at the end of each stage that captures spatial detail information under the guidance of real images. The Two-Stage Feature Fusion (TSFF) module reduces information loss caused by downsampling operations while enriching features across stages for increased robustness. We conduct experiments on the GoPro dataset and the HIDE dataset. On the GoPro dataset, our Peak Signal-to-Noise Ratio (PSNR) result is 32.21 and the Structural Similarity (SSIM) result is 0.950; and on the HIDE dataset, our PSNR result is 30.38 and the SSIM result is 0.932. Our results exceed other similar algorithms. By comparing the generated feature maps, we find that our model takes into account both global and local information well.

Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour

The rejoining of bone sticks holds significant importance in studying the historical and cultural aspects of the Han Dynasty. Currently, the rejoining work of bone inscriptions heavily relies on manual efforts by experts, demanding a considerable amount of time and energy. This paper introduces a multi-scale feature fusion Siamese network guided by edge contour (MFS-GC) model. Constructing a Siamese network framework, it first uses a residual network to extract features of bone sticks, which is followed by computing the L2 distance for similarity measurement. During the extraction of feature vectors using the residual network, the BN layer tends to lose contour detail information, resulting in less conspicuous feature extraction, especially along fractured edges. To address this issue, the Spatially Adaptive DEnormalization (SPADE) model is employed to guide the normalization of contour images of bone sticks. This ensures that the network can learn multi-scale boundary contour features at each layer. Finally, the extracted multi-scale fused features undergo similarity measurement for local matching of bone stick fragment images. Additionally, a Conjugable Bone Stick Dataset (CBSD) is constructed. In the experimental validation phase, the MFS-GC algorithm is compared with classical similarity calculation methods in terms of precision, recall, and miss detection rate. The experiments demonstrate that the MFS-GC algorithm achieves an average accuracy of 95.5% in the Top-15 on the CBSD. The findings of this research can contribute to solving the rejoining issues of bone sticks.

Minimally invasive techniques in quest of Holy Grail of surgical management of enlarged prostates: a narrative review.

Past decade has seen a renewed interest in minimally invasive surgical techniques (MISTs) for management of enlarged prostate. This narrative review aims to explore newer MIST for benign prostatic hyperplasia (BPH) which are not yet integrated into established societal guidelines. We conducted a literature search across PubMed, Google Scholar, and FDA ClinicalTrials.gov databases on June 1st, 2023, to identify studies published within the past decade exploring various MISTs for BPH. Additionally, we gathered insights from abstracts presented in meetings of professional associations and corporate websites. We broadly classified these procedures into three distinct categories: energy-based, balloon dilation, and implant/stent treatments. We collected detail information about the device, procedure details, its inclusion and exclusion criteria, and outcome. Our review reveals that newer energy-based MISTs include Transperineal Laser Ablation, Transurethral Ultrasound Ablation, and High-Intensity Focused Ultrasound. In the sphere of balloon dilation, Transurethral Columnar Balloon Dilation and the Optilume BPH Catheter System were gaining momentum. The noteworthy implants/stents that are on horizon include Butterfly Prostatic Retraction Device, Urocross Expander System, Zenflow Spring System, and ProVee Urethral Expander System. The exploration of various MISTs reflects ongoing efforts to enhance patient care and address limitations of existing treatments. This review provides a bird-eye view and valuable insights for urologists and researchers seeking to navigate the dynamic landscape of MISTs in the quest for effective and minimally invasive solutions for enlarged prostates.

Local to global purification strategy to realize collaborative camouflaged object detection

The purpose of camouflaged object detection is to detect objects in images that are not easily perceived by human eyes. Aiming at the problems of low recognition performance and unsatisfied texture information extraction in the complex environment in the current camouflaged object detection algorithms, we propose to improve the accuracy by simultaneously detecting a group of images containing the same camouflaged category. Therefore, we put forward a novel method termed local to global purification network (LGPNet) for collaborative camouflaged object detection. Our method comprises two main modules: the Local Detail Mining module (LDM) and the Global Intra-group Feature Extraction module (GIFE). The LDM is designed to exploit diversified detail information via different adaptive kernels and receptive field mechanisms locally, and the GIFE module is invented for feature enhancement and multi-level information aggregation. Specifically, the GIFE first utilizes channel attention and spatial attention mechanisms to enhance high-level semantic information and then aggregates the intra-group characteristics by level. Extensive experiments on CoCOD8K dataset and 4 COD benchmark datasets illustrate the effectiveness and superiority of our method compared to SOTAs.

Adaptively Enhancing Facial Expression Crucial Regions via a Local Non-local Joint Network

AbstractFacial expression recognition (FER) is still challenging due to the small interclass discrepancy in facial expression data. In view of the significance of facial crucial regions for FER, many existing studies utilize the prior information from some annotated crucial points to improve the performance of FER. However, it is complicated and time-consuming to manually annotate facial crucial points, especially for vast wild expression images. Based on this, a local non-local joint network is proposed to adaptively enhance the facial crucial regions in feature learning of FER in this paper. In the proposed method, two parts are constructed based on facial local and non-local information, where an ensemble of multiple local networks is proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region. In particular, the attention weights obtained by the non-local network are fed into the local part to achieve interactive feedback between the facial global and local information. Interestingly, the non-local weights corresponding to local regions are gradually updated and higher weights are given to more crucial regions. Moreover, U-Net is employed to extract the integrated features of deep semantic information and low hierarchical detail information of expression images. Finally, experimental results illustrate that the proposed method achieves more competitive performance than several state-of-the-art methods on five benchmark datasets.

Infrared and visible image fusion method based on full convolutional network (FCN)

In the IP sector, the combination of visible image fusion (VIF) with infrared (IR) gives a more comprehensive and accurate description of a target image. To get over the problems of detail and energy loss during the fusion process caused by current deep learning fusion approaches, it is proposed to use a fusion strategy of IR and visible pictures based on full convolutional network (FCN) applying transfer learning. FCN model can take any size of the input and generate constant size of the output with desired rules. Through effective inference and learning procedure, the ability of features extraction and energy conservation can be enhanced a lot. Experimental results demonstrate that the suggested method succeeds in improving IF quality over the other two comparable methods by preserving high light intensity and retrieving detail information. This also confirms its dominance across five different objective assessment indices: mutual information (MI), entropy (EN), edge-based similarity measure (Qabf), sum of correlations of differences (SCD), and multi-scale structural similarity for image (MS-SSIM).

ACTUAL PEDAGOGICAL ISSUES RELATED TO YOUTH INFORMATION CULTURE

This article explains how information culture and its specific features in the modern era of information culture. The social factors that determine the lack of a critical approach to the selection of necessary information among students of a special Islamic educational college have been studied. Also, based on the fact that the acquisition of information culture is a vital necessity for future imams, the author made a number of proposals and recommendations in this regard.
 In the article in the information society, ensuring fast and high-quality circulation of information in the education system becomes the main criterion for the progress and development of the country, and also analyzes in detail the need for the effective use of information in the field of education. 
 The scientific article presents an analysis of studies conducted on the culture of using the Internet and social networks among students studying in secondary specialized educational institutions. This is a scientific study not only in Uzbekistan, but also in all countries of Central Asia; the formation of the information culture of students in secondary specialized Islamic educational colleges has not been studied at all. Formation criteria have been developed and explained in detail information culture of students studying in secondary specialized Islamic educational colleges

Unsharp Masking of Medical Images using Rolling Guidance Filter

Medical Images consists of both high frequency and low frequency information needed by Physician for diagnostics and treatment purpose. Since, human eye is sensitive to high frequency information, the enhancement of high frequency information of medical image is very important. Most image enhancement techniques use the traditional unsharp mask filter to enhance the detail and edge information of medical image. While enhancing the detail and edge information, Gaussian Filter in traditional Unsharp Masking Filter introduces halo effects around edges. To remove this halo effects in enhanced images, this paper proposes a novel image enhancement filter in which Gaussian Filter is replaced by rolling guidance filter. This method provides satisfying results over Traditional Unsharp Mask Filter, Amended Unsharp Mask Filter, Anisotropic Diffusion based Unsharp Mask Filter and Guided Filtering based Unsharp Mask Filter in terms of high frequency information measures like mean gradient, spatial frequency and sharpness.

Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network.

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.

Iterative Edge Enhancing Framework for Building Change Detection

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Building Change Detection</i> (BCD) task serves urban planning through monitoring the landuse. However, due to the complexity of remote sensing images and high foreground-background similarity, it leads to inaccurate detection of building edge regions. Existing methods deal with this problem by fusing features of different layers. But the fusing operation cannot separate details information from overall information of buildings, resulting in inaccurate detection of building edge area. To address the above challenges, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iterative Edge Enhancing Framework</i> (IEEF). The IEEF alleviates the building edge detection difficulty by densely implementing Detail Semantic Enhancement Module in decoding part. This module takes differential features between adjacent scales to explicitly represent the building edge information. Simultaneously, to deal with the classes imbalance problem, a Density-Guided Sampling method dedicated for change detection is proposed to increase the proportion of positive samples during training. Our proposed method achieves state-of-the-art performance on the LEVIR-CD dataset and the WHU dataset, and obtains accurate changed building edges.

Multi-scale local-global transformer with contrastive learning for biomarkers segmentation in retinal OCT images

Quantitative analysis of biomarkers in Optical Coherence Tomography (OCT) images plays an import role in the diagnosis and treatment of retinal diseases. However, biomarker segmentation in retinal OCT images is very hard due to the large variations in size and shape of retinal biomarkers, blurred boundaries, low contrast, and speckle interference. We proposed a novel Multi-scale Local-Global Transformer network (MsLGT-Net) for biomarker segmentation in retinal OCT images. The network combines the proposed Multi-scale Fusion Attention (MFA) module, Local-Global Transformer (LGT) module, and Contrastive Learning Enhancement (CLE) module to tackle the challenges of biomarker segmentation. Specifically, the proposed MFA module aims to enhance the network’s ability to learn multi-scale features of retinal biomarkers by effectively combining the local detail information and contextual semantic information of biomarkers at different scales, and improve the representation ability for different classes of biomarkers. The LGT module is designed to learn local and global information adaptively from multi-scale fused features to address the challenge of small biomarker segmentation. In addition, to distinguish features between different types of retinal biomarkers, we propose the CLE module to enhance the feature representation of different biomarkers. Our proposed method is validated on one public dataset and one local dataset. The experimental results show that the proposed method is more effective than other state-of-the-art methods.

A Multidimensional Tensor Low Rank Method for Magnetic Resonance Image Denoising.

In this paper, we present the Magnetic Resonance Image (MRI) denoising method via nonlocal multidimensional low rank tensor transformation constraint (NLRT). We first design a nonlocal MRI denoising method by non-local low rank tensor recovery framework. Furthermore, a multidimensional low rank tensor constraint is used to obtain low-rank prior information combined with 3-dimensional structure feature of MRI image cubes. Our NLRT can achieve denoising by retaining more image detail information. The optimization and updating process of the model is solved via the alternating direction method of multipliers (ADMM) algorithm. Several state-of-the-art denoising methods are selected for comparative experiments. In order to reflect the performance of the denoising method, Rician noise with different levels is added to the experiment to analyze the results. The experimental results prove that our NLTR has more outstanding denoising ability and can obtain better MRI images.

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

Abstract This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.