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The article analyzes the legal support for electronic document management in Ukraine. It is determined that the introduction of innovative transformations in Ukraine in the context of information technology development involves the use of modern forms and methods of work based on electronic information exchange. Numerous forms and methods that have been used for decades in the field of information and communication relations for information processing have proven to be unjustified and ineffective in the new social and information and innovative realities. Electronic document management has replaced paper document management. The study found that the electronic document management system in Ukraine is undergoing a long path of development and formation. The introduction of electronic document management contributes to the digital modernization of Ukraine. The availability of reformed legislation is important for carrying out qualitative changes, which generates legal certainty of the status of subjects of the process of significant transformations. The electronic form of documents provides the necessary level of information security for participants in legal relations, since the exchange of documents occurs in encrypted form using a qualified electronic signature. It has been established that there is a saving of human and material resources that were previously used for printing, sending and storing documents. Remote advantages provide mobility and convenience of document transfer via electronic document management platforms or e-mail. The implementation of this document management system allows creating electronic archives for more efficient document storage. It has been established that the main effect of introducing electronic document management is resource-based. At the same time, it is necessary to take into account the risks of introducing electronic document management by both state and local government bodies a nd business entities. The state of electronic document management in Ukraine in the field of public administration has been analyzed and ways of its further development have been identified. It has been established that the legal support for electronic document management requires further development, updating, balance, harmonization with international legal norms and standards. Implementation of high-quality legal, institutional, organizational, and managerial transformations in the sphere of public administration in the context of European integration requirements based on the recognition of a person as the highest social value is an urgent vital necessity, which will contribute to the development of Ukraine as a competitive state with a strong economy and advanced technologi es. [ABSTRACT FROM AUTHOR]

Handwriting is a natural way to communicate and exchange ideas, but converting handwritten diagrams to application‐specific digital formats requires skill and time. Automatic handwritten document conversion can save time, but diagrams and text require different recognition engines. Since accurate segmentation of handwritten diagrams can improve the accuracy of later diagram recognition steps, the authors propose to solve the problem of segmentation of text and non‐text elements of handwritten diagrams using deep semantic segmentation. The model, DeepDP is a flexible U‐net style architecture that can be tuned in complexity to a level appropriate for a particular dataset and diagram type. Experiments on a public hand‐drawn flowchart dataset and a business process diagram dataset show excellent performance, with a per pixel accuracy of 98.6% on the public flowchart datasets and improvement over the 99.3% text stroke accuracy and 96.6% non‐text stroke accuracy obtained by state of the art methods that use online stroke information. On the smaller offline business process diagram dataset, the method obtains a per‐pixel accuracy of 96.9%. [ABSTRACT FROM AUTHOR]

The Inner Northern Apennines (Italy) are a region with a dominant N-S to NNW-SSE fault system, but dissected and offset by several E-W to NE-SW trending structures and lineaments. The knowledge about the nature of these transverse structures, their origin, activity and role in current tectonic motions is limited and debated. To better establish the location, subsurface shape, and kinematics of faults related to the Livorno-Empoli lineament, one of the major transverse structures in the Northern Apennines, we analysed the seismicity in western Tuscany. In the Viareggio Basin we identified and relocated two distinct earthquake clusters as well as calculated 12 new focal mechanisms. The results show that the clusters consisted of several swarms from the years 2006, 2015, 2016 and 2021. The events had a depth between 2 and 15 km and were located along a NE-SW oriented, SE dipping fault system dissecting the Viareggio Basin. Focal mechanisms show oblique normal slip. We interpret the fault system to form a connection between the Viareggio Basin and the Lucca Basin to the east as well as continuing offshore. The results show that the transversal faults of the Inner Northern Apennines are seismogenic, with the length, position and onshore to offshore nature of the fault suggesting reactivation of pre-existing structures. [ABSTRACT FROM AUTHOR]

The construction sector's pivotal role in the global economy faces challenges due to its dynamic nature. Inaccurate documentation impacts project cost management, underscoring the need for effective document management systems (DMS), including electronic document management systems (EDMS). This study conducts a systematic literature review to comprehensively examine EDMS implementation, utilization, and effectiveness across sectors. Analyzing peer-reviewed articles and scholarly sources reveals key themes, trends, and findings, providing insights into successful EDMS adoption and best practices. The review contributes evidence-based insights for practitioners, researchers, and policymakers, addressing gaps in knowledge and advancing understanding of EDMS in modern information management. Additionally, it presents a detailed breakdown of publication distribution across sectors, highlighting significant research areas like companies and businesses, education, and information technology and software. Furthermore, analysis of factors influencing employee behavior, including technical factors, employee's personal characteristics, organizational factors, and trust, offers valuable insights into workplace dynamics. Overall, the study offers comprehensive insights into EDMS implementation, guiding future research, and organizational strategies. [ABSTRACT FROM AUTHOR]

The invasive colonial tunicate, Didemnum vexillum Kott, 2022 was initially observed in Atlantic Canada in Nova Scotia in 2013 and has since been expected to spread to the western side of the Canadian Bay of Fundy due to colonies known to be present in nearby Eastport, Maine. Since 2018, we collected water environmental DNA (eDNA) samples at eight sites in the Quoddy Region in the western side of the Canadian Bay of Fundy. In 2021 and 2022 we used diver-based visual surveys and sample collection, as well as a surface-deployed near-seafloor optical imaging system to document the presence and extent of D. vexillum in the Head Harbour/West Isles/Passages Ecologically and Biologically Significant Area (EBSA) within the Quoddy Region. Forty-one dive sites were surveyed via SCUBA, and seven near-seafloor camera transects were conducted at depths deeper than dive limits (~ 30 m), collecting continuous highdefinition video and periodic high-resolution still images. Didemnum vexillum was detected at two sites from eDNA metabarcoding and quantitative PCR in 2018, 2020 and 2021, and observed by divers at 11 sites, two of which exhibited extensive tunicate coverage. Of the 1945 m² area surveyed by the near-seafloor drift camera system, D. vexillum occurred at depths to 118 m and across a spatial extent of 858 m², of which 170 m2 contained numerous tunicate patches and/or a homogenous mat. Didemnum vexillum was observed extensively overgrowing benthic substrates and fauna, possibly threatening the diversity of natural benthic habitats in the EBSA and adjacent areas supporting numerous commercial fisheries. This study is the first to report D. vexillum presence in the Canadian western Bay of Fundy, and the first to observe colonies at depths exceeding previous records of 81 m. We conclude by providing advice on how to improve coastal invasive species surveys from the combination of biodiversity metrics. [ABSTRACT FROM AUTHOR]

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body. [ABSTRACT FROM AUTHOR]

Background: The Diagnosis-Intervention Packet (DIP) payment system, initiated by China's National Healthcare Security Administration, is designed to enhance healthcare efficiency and manage rising healthcare costs. This study aims to evaluate the impact of the DIP payment reform on inpatient care in a specialized obstetrics and gynecology hospital, with a focus on its implications for various patient groups. Methods: To assess the DIP policy's effects, we employed the Difference-in-Differences (DID) approach. This method was used to analyze changes in total hospital costs and Length of Stay (LOS) across different patient groups, particularly within select DIP categories. The study involved a comprehensive examination of the DIP policy's influence pre- and post-implementation. Results: Our findings indicate that the implementation of the DIP policy led to a significant increase in both total costs and LOS for the insured group relative to the self-paying group. The study further identified variations within DIP groups both before and after the reform. In-depth analysis of specific disease groups revealed that the insured group experienced notably higher total costs and LOS compared to the self-paying group. Conclusions: The DIP reform has led to several challenges, including upcoding and diagnostic ambiguity, because of the pursuit of higher reimbursements. These findings underscore the necessity for continuous improvement of the DIP payment system to effectively tackle these challenges and optimize healthcare delivery and cost management. [ABSTRACT FROM AUTHOR]

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media, particularly for measurements relevant to near-infrared spectroscopy. The three temporal domains, continuous wave, frequency domain, and time domain, each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient. Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change. Therefore, spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains. The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media. These works are experimental and theoretical, presenting one-, two-, and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods, domains, and data types. Following this history, we present a compendium of sensitivity maps organized by temporal domain and then data type. This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document. Methods for one to generate these maps are provided in Appendix A, including the code. This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize, investigate, compare, and generate sensitivity to localized absorption change maps. [ABSTRACT FROM AUTHOR]

Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%. [ABSTRACT FROM AUTHOR]

Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents. [ABSTRACT FROM AUTHOR]

In public procurement, a multitude of data is used, produced, and exchanged in various ways and formats. Laws and regulations often require that the exchange of information takes place through signed and recorded documents, following specific authorization processes. On the other hand, many modern Project Management techniques rely on accurate knowledge of project data, which requires information flow to occur in a structured and digital manner. The concept of an Electronic Document and Records Management System was created for this purpose, to combine document flow and structured information flow. However, in practice, it is difficult to find ready-to-use commercial solutions, and many administrations choose to use traditional document management systems. This work aims to investigate the two requirements independently and then jointly, proposing a clean and simple approach to align them. The chosen approach envisions a two-tier system with defined functional specifications, where the first tier coincides with an existing document management system. The outcome demonstrates how to implement an Electronic Document and Records Management System by retrofitting a traditional document engine. The described approach will be tested through a case study related to a large public construction contract. [ABSTRACT FROM AUTHOR]

Conventional techniques for sharing paper documents in teleconferencing tend to introduce two inconsistencies: 1) media inconsistency: a paper document is converted into a digital image on the remote site; 2) space inconsistency: a workspace deliberately inverts the partner's handwriting to make a document easy to read. In this paper, we present a novel system that eliminates these inconsistencies. The media and space inconsistencies are resolved by reproducing a real paper document on a remote site and allowing a user to handover the paper document to a remote partner across a videoconferencing display. From a series of experiments, we found that reproducing a real paper document contributes to a higher sense of information sharing. We also found that handing over a document enhances a sense of space sharing, regardless of whether the document is digital or paper-based. These findings provide insights into designing systems for sharing paper documents across distances. [ABSTRACT FROM AUTHOR]

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling. [ABSTRACT FROM AUTHOR]

The article is aimed at studying the current state of the digitalization of customs procedures in Ukraine and determining the prospects for the development of this process in Ukraine. Improving the electronic document management system at customs is consistent with Ukraine's European integration aspirations. Electronic declarations can not only minimize the time of customs procedures, but also fight corruption, develop international cooperation, and interact with the information systems of government organizations in the country. The purpose of the study is to determine the impact of the digital economy on the transformation of customs in Ukraine and to analyze the existing shortcomings of this system. The article describes the features and current state of digitalization of customs procedures in the context of simplification of customs control. The state of implementation of modern electronic services for the automation of customs procedures in Ukraine is analyzed. The authors emphasize the need for the development of world trade and the implementation of customs control procedures using communication technologies and electronic communications. The positive aspects of the introduction of information technology are identified. The achievements and problems of the process of reforming modern information technologies in the work of the State Customs Service of Ukraine within the framework of the strategic course of the state development are outlined. The reduction of customs clearance times can be achieved by introducing such new customs methods: 1) modern risk management processes using a multifunctional declaration system; 2) the widest possible use of post-clearance control methods; 3) gradual transition from transactional control to control based on audit methods; 4) independent control by participants of foreign economic activity over compliance with the law through the introduction of the institute of authorized economic operators. It is emphasized that the introduction of modern digital service technologies into the practice of the State Customs Service of Ukraine will improve the image of this state body both in Ukraine and in the international arena. [ABSTRACT FROM AUTHOR]

A logotype is a rectangular region defined by a set of characteristics, which come from the pixel information and region shape, that differ from those of the text. In this paper, a new method for automatic logo detection is proposed and tested using the public Tobacco800 database. Our method outputs a set of regions from an official document with a high probability to contain a logo using a new approach based on the variation of the feature rectangles method available in the literature. Candidate regions were computed using the longest increasing run algorithm over the document blank lines' indices. Those regions were further refined by using a feature-rectangle-expansion method with forward checking, where the rectangle expansion can occur in parallel in each region. Finally, a C4.5 decision tree was trained and tested against a set of 1291 official documents to evaluate its performance. The strategic combination of the three previous steps offers a precision and recall for logo detention of 98.9% and 89.9%, respectively, being also resistant to noise and low-quality documents. The method is also able to reduce the processing area of the document while maintaining a low percentage of false negatives. [ABSTRACT FROM AUTHOR]

The growing integration of converter‐interfaced renewable energy sources (RESs) utilizing Grid‐Following (GFL) converters has displaced conventional synchronous generators (SGs) in central generation units. This shift presents challenges, including diminished system inertia, lower fault levels, and implications for system strength and network resilience. The propagation of voltage dips, particularly during disturbances like system Short Circuit (SC) faults, is adversely affected by the increased penetration of such RESs. This is attributed to the limited support capability of these sources and their distinct SC response compared to SGs. In response to these challenges, Grid‐Forming (GFM) converters emerge as a promising technology equipped with advanced functionalities that emulate SG operation. Consequently, they hold potential for mitigating the effects of voltage dip propagation in renewable‐rich power systems. This study aims to assess the impact of employing GFM converters in renewable‐rich power systems on voltage dip propagation across the network. The authors' investigation begins by examining the SC response of GFM converters and comparing it with the responses of traditional GFL converters and SGs. The paper proceeds to analyze voltage dip propagation, considering various penetration scenarios involving RESs based on GFL and GFM converters. The IEEE 9‐BUS test system, implemented in the DIgSILENT PowerFactory software, serves as the basis for these evaluations. Through extensive simulations and analysis, the authors' research provides valuable insights into the effectiveness of GFM converters in enhancing the network's response to voltage dips. [ABSTRACT FROM AUTHOR]

Shadows cause a reduction of text legibility and contrast of the overall document, leading to poor recognition rates by Optical Character Recognition (OCR). Shadows were cast during photo capturing because of object interferences due to projection angle from illumination sources and are challenging to handle by OCR. This work proposes an automated technique to detect the shadows caused by object interferences and eliminate them from document images. The proposed algorithm generates two reference masks of shadow and non-shadow regions using a global thresholding method to detect and remove shadow. Initially, the document image is pre-processed, followed by mask generation for shadow and non-shadow regions, and finally, a global thresholding strategy that removes the shadow using an image mapping technique. To evaluate the effectiveness of the proposed approach, a dataset of real-world smartphone-captured document images of about 300 is considered. The datasets encompass Mobile Dataset 3 and Mobile Dataset 4 from DIBCO datasets. Experiments are conducted to quantify the efficiency of the proposed approach using performance measures such as recognition rate by OCR, presentation score, legibility score, skew, and overall document quality score. From the results, the proposed approach collectively produces an average recognition rate of 87.15% and 81.50% towards word-level recognition of Mobile Datasets 3 and 4. The text quality of the document image post shadow removal procedure achieves a higher performance score with an average presentation score of 0.35, legibility score of 0.78, skew as 73.45 degrees from the horizontal axis, and overall document quality score above six towards datasets considered. Based on qualitative analysis, comparative analysis proves that the proposed is simple yet more proficient than existing methods. [ABSTRACT FROM AUTHOR]

The restoration of degraded document images is a critical aspect of document image processing. In this study, we present a rigorous comparative analysis of two denoising techniques applied to degraded document images within the HSV and UV color spaces. The primary objective is to quantitatively assess the efficacy of these methods in enhancing the quality of degraded document images. Experiments were conducted using an extensive dataset of degraded document images, and a comprehensive evaluation was performed employing established metrics, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean square error (MSE). Results unequivocally demonstrate the superiority of the UV-based approach over the HSV-based method across all evaluation criteria. Specifically, the UV-based approach consistently outperforms the HSV-based approach in PSNR, SSIM, and MSE assessments. Qualitative analysis further underscores the remarkable effectiveness of both techniques in elevating the quality of degraded document images. This study addresses a pressing problem in document image processing by delivering invaluable insights into the comparative efficacy of these denoising techniques. It offers a useful tool for academics and professionals who want to improve the clarity and legibility of deteriorated document images. [ABSTRACT FROM AUTHOR]

Purpose: Develop an efficient, interactive, and instructive checklist document for the management of implanted electronic medical devices in a multimodality radiotherapy clinic. Methods: The built-in scripting and interactivity of a popular commercial word processor was used to develop an interactive document that changes the information presented to the user based on drop-down selections. The interactivity and scripting were compatible with the radiation oncology information system (ROIS) which allows the document to be accessible by all team members and serve as a permanent record in a patient's electronic chart. Results: The final interactive document,which was clinically deployed after beta testing with a group consisting of nurses and medical physicists, presents information and action plans to the user based on multiple departmental medical device decision trees that are specific to the combination of device, treatment modality, rhythm-pacing dependence for cardiac devices, and distance from the device to the treatment volume. Conclusion: A script-enabled interactive document was developed for a busy multimodality clinic, condensing multiple comprehensive departmental guidelines spanning multiple device types and treatment modalities into a single interactive checklist accessible within the ROIS. Given the wide accessibility of the commercial word processor, this approach could be adopted by other clinics to streamline their own respective workflows. [ABSTRACT FROM AUTHOR]

DC microgrids (DC MGs) offer advantages such as efficiency, control, cost, reliability, and size compared to AC MGs. However, they often operate with numerous constant power loads (CPLs), exhibiting a negative incremental impedance characteristic that can lead to instability. This instability weakens stability boundaries and reduces system damping, especially when dealing with pulsed power loads (PPLs) on electric aircraft, ships, and cars. Linear controllers may not ensure stability across various operations, causing voltage dips and potential system instability. To secure DC/DC converter functionality and comply with impedance specifications, it is crucial to consider minor loop gain in control strategies and stabilization techniques. Employing diverse methods to decrease minor loop gain in DC/DC converters is essential. A comprehensive evaluation, including strengths, weaknesses, opportunities, and threats (SWOT) analysis, is conducted to assess control strategies, stabilization techniques, and stability standards for different DC/DC converters, identifying SWOT. [ABSTRACT FROM AUTHOR]

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learningbased backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%). [ABSTRACT FROM AUTHOR]

Shadow removal for document images is an essential task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large, diverse dataset for document shadow removal takes time and effort. Thus, only small real datasets are available. Graphic renderers have been used to synthesize shadows to create relatively large datasets. However, the limited number of unique documents and the limited lighting environments adversely affect the network performance. This paper presents a large-scale, diverse dataset called the Synthetic Document with Diverse Shadows (SynDocDS) dataset. The SynDocDS comprises rendered images with diverse shadows augmented by a physics-based illumination model, which can be utilized to obtain a more robust and high-performance deep shadow removal network. In this paper, we further propose a Dual Shadow Fusion Network (DSFN). Unlike natural images, document images often have constant background colors requiring a high understanding of global color features for training a deep shadow removal network. The DSFN has a high global color comprehension and understanding of shadow regions and merges shadow attentions and features efficiently. We conduct experiments on three publicly available datasets, the OSR, Kligler's, and Jung's datasets, to validate our proposed method's effectiveness. In comparison to training on existing synthetic datasets, our model training on the SynDocDS dataset achieves an enhancement in the PSNR and SSIM, increasing them from 23.00 dB to 25.70 dB and 0.959 to 0.971 on average. In addition, the experiments demonstrated that our DSFN clearly outperformed other networks across multiple metrics, including the PSNR, the SSIM, and its impact on OCR performance. [ABSTRACT FROM AUTHOR]

Document image retrieval (DIR) systems simplify access to digital data within printed documents by capturing images. These systems act as bridges between print and digital realms, with demand in organizations handling both formats. In education, students use DIR to access online materials, clarify topics, and find solutions in printed textbooks by photographing content with their phones. DIR excels in handling complex figures and formulas. We propose using ensembles of DIR systems instead of single-feature models to enhance DIR's efficacy. We introduce "Vote-Based DIR" and "The Strong Decision-Based DIR". These ensembles combine various techniques, like optical code reading, spatial analysis, and image features, improving document retrieval. Our study, using a dataset of university exam preparation materials, shows that ensemble DIR systems outperform individual ones, promising better accuracy and efficiency in digitizing printed content, which is especially beneficial in education. [ABSTRACT FROM AUTHOR]

In document forensics, identifying ink mismatches is crucial for detecting forgeries and determining document authenticity. However, identifying and separating specific inks from paper can be challenging. Hyperspectral imaging can capture unique spectral patterns exhibited by inks composed of different materials, even when their colors appear identical. Hyperspectral document analysis (HSDI) can be employed to authenticate documents by analyzing the ink. Earlier studies had insufficiently accurate black ink detection, so this paper proposes a novel approach using deep learning and pre-processing methods to improve black ink detection accuracy. The proposed approach uses supervised deep learning to identify ink mismatches in hyperspectral document images. The study evaluated the performance of the proposed model using a hyperspectral image dataset consisting of UWA (University of Western Australia) writing ink in both blue and black colors and different types of artificially identical color inks mixed in various ratios to find ink mismatches. The results showed that the proposed system performed better than other systems reported in the literature, improving the average accuracy by up to 0.18% for blue and 0.36% for black. The proposed method accurately detects ink mismatches and identifies various inks based on their unique spectral response, rendering it highly beneficial for applications in document forensics. [ABSTRACT FROM AUTHOR]

Deletion/insertion polymorphism (DIP) is one of the more promising genetic markers in the field of forensic genetics for personal identification and biogeographic ancestry inference. In this research, we used an in-house developed ancestry-informative marker-DIP system, including 56 autosomal diallelic DIPs, three Y-chromosomal DIPs, and an Amelogenin gene, to analyze the genetic polymorphism and ancestral composition of the Chinese Korean group, as well as to explore its genetic relationships with the 26 reference populations. The results showed that this novel panel exhibited high genetic polymorphism in the studied Korean group and could be effectively applied for forensic individual identification in the Korean group. In addition, the results of multiple population genetic analyses indicated that the ancestral component of the Korean group was dominated by northern East Asia. Moreover, the Korean group was more closely related to the East Asian populations, especially to the Japanese population in Tokyo. This study enriched the genetic data of the Korean ethnic group in China and provided information on the ancestry of the Korean group from the perspective of population genetics. [ABSTRACT FROM AUTHOR]

The digital image processing has major attention towards the automatic optical character recognition system. The image analysis of ancient images is mainly focused on printed and inscription images. In printed image analysis the text is printed by using machine and in the inscription images text is written by different persons. Presently most of the research highlighted challenges related to the inscription image analysis. In the printed document images font style, variation, size, and writing direction is standard, but in case of inscription font style, size, variation and writing direction vary by person to person. Image enhancement deals with the improvement of the quality of a digital image. The quality is achieved by highlighting useful details and removing the redundant details. In this paper, image enhancement based on the Binarization technique is discussed for the improvement of ancient inscription. The binary image is an essential part of document analysis. The proposed method is composed using a colour threshold and median filter. The proposed method is tested on different sample images. To evaluate the performance of the proposed method, the MSE, PSNR, F-Measure, NRM, Accuracy is calculated. These results are compared with manual results obtained by archeologist. It is also compared with the Nick method in the literature. The results of the proposed method are effective than manual and nick method. The enhancement of ancient Marathi scripts plays an important role in the identification, classification and the recognition of ancient Marathi characters. [ABSTRACT FROM AUTHOR]

Multimodal Relation Extraction (MRE) is a core task for constructing Multimodal Knowledge images (MKGs). Most current research is based on fine-tuning small-scale single-modal image and text pre-trained models, but we find that image-text datasets from network media suffer from data scarcity, simple text data, and abstract image information, which requires a lot of external knowledge for supplementation and reasoning. We use Multimodal Relation Data augmentation (MRDA) to address the data scarcity problem in MRE, and propose a Flexible Threshold Loss (FTL) to handle the imbalanced entity pair distribution and long-tailed classes. After obtaining prompt information from the small model as a guide model, we employ a Large Language Model (LLM) as a knowledge engine to acquire common sense and reasoning abilities. Notably, both stages of our framework are flexibly replaceable, with the first stage adapting to multimodal related classification tasks for small models, and the second stage replaceable by more powerful LLMs. Through experiments, our EMRE2llm model framework achieves state-of-the-art performance on the challenging MNRE dataset, reaching an 82.95% F1 score on the test set. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval. [ABSTRACT FROM AUTHOR]

Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and noncontact image capture, which enables many new applications and breathes new life into existing ones, but cameracaptured documents may suffer from distortions caused by a nonplanar document shape and perspective projection, which lead to the failure of current optical character recognition (OCR) technologies. This paper presents a new CNN model for script identification from cameracaptured Indian multilingual document images. To evaluate the performance of the proposed model 9 regional languages, one national language and one international Roman languages are considered. Two languages, Hindi national language, and Roman English language are taken as the common languages with regional language for the study. The proposed method is applied on Bi-script, Tri-script, and Multiscript combinations. The average recognition accuracy for three script combinations is 92.92%, for bi-script 91.33%, and for tri-script 87.33%. is achieved. The proposed method is the unified approach used for identifying the script from bi-script, tri-script and multi-script cameracaptured document images and is the novelty of this paper. The proposed model is compared with the Alexnet pretrained CNN model, and it achieved the highest recognition accuracy. [ABSTRACT FROM AUTHOR]

In the digital world, a wide range of handwritten and printed documents should be converted to digital format using a variety of tools, including mobile phones and scanners. Unfortunately, this is not an optimal procedure, and the entire document image might be degraded. Imperfect conversion effects due to noise, motion blur, and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition (OCR) systems. In Document Image Analysis Systems (DIAS), skew estimation of images is a crucial step. In this paper, a novel, fast, and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function (CLF), so-called Radon CLF, was proposed. The Radon CLF model aims to take advantage of the properties of Radon spaces. The Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon space. Several significant performance indicators, including Mean Square Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Measure (SSIM), Accuracy, and run-time, were taken into consideration when assessing the performance of our model. In addition, a new dataset named DSI5000 was constructed to assess the accuracy of the CLF model. Both two-dimensional image signal and the Radon space have been used in our simulations to compare the noise effect. Obtained results show that the proposed method is more effective than other approaches already in use, with an accuracy of roughly 99.87% and a run-time of 0.048 (s). The introduced model is far more accurate and timeefficient than current approaches in detecting image skew. [ABSTRACT FROM AUTHOR]

This research introduces a robust and reliable technique for structuring document image pages hierarchically, harnessing the power of Delaunay triangulation. Central to our approach is the formation of a cluster tree, which encapsulates the page's content through strategically exploiting layout elements arrangements and their relative distances. By applying our technique, we proficiently categorize the page into distinct clusters encompassing images, titles, and paragraphs. The consequent hierarchical framework, founded on the cluster tree, establishes a durable and trustworthy blueprint of the document layout, thereby accelerating document comprehension and examination. [ABSTRACT FROM AUTHOR]

Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain. [ABSTRACT FROM AUTHOR]

Protecting electronic documents, especially those containing sensitive data, is a major challenge in an open web. The data security industry has long struggled to manage the security of e-books, data shows that information security issues can cause significant economic losses after three years Although cryptographic methods have been proposed as a solution in of these challenges Focusing on speed and efficiency The shortcomings of traditional encryption methods have been thoroughly examined Although a number of network management techniques assure retention privacy and integrity though, when it comes to encryption. Digital signature algorithms, although effective in detecting unauthorized changes and limiting the scope for copyright protection, do not ensure the confidentiality of shared electronic documents When addressing research gaps addressing this issue, the paper proposes a security framework for electronic documents that combines three important security mechanisms: encryption, digital signatures, and watermark algorithms Dhishu By matching their strengths, constraints are overcome. The combination of encryption and digital signatures is explored as a promising approach to protecting electronic documents, ensuring authenticity and confidentiality Importantly, the need to explore security mechanisms such as digital is highlighted emphasis on handwriting, encryption, and watermarking systems in depth. [ABSTRACT FROM AUTHOR]

The paper discusses the design and development of an electronic document management system (EDMS) that prepares documents for sharing and information dissemination. EDMS has to do with capturing, storing, indexing, retrieval, and disposal of documents. The electronic document management system process starts by converting paper document into digital record to efficiently store and organize document in standardized file structure and format, promoting a paper waste reduction in reproducing the document. The EDMS provided an easier way of sharing information with different stakeholders and securing documents according to standardized compliance rules. The developed system has also improved the accessing of vital documents by users since there is real-time information distribution of information and easy searching and retrieving of needed documents. [ABSTRACT FROM AUTHOR]

Taktic, an Amitraz-based insecticide, is commonly used in sub-Saharan Africa to treat cattle for ticks. Due to misuse in rural dipping pools, some ticks are showing resistance to Taktic. This work presents a low-cost e-nose with commercial sensors to monitor Taktic levels in dipping pool water. The device shows distinctly different measurements for the odours of air, distilled water, farm water, and four levels of Taktic insecticide in farm water. A naive Bayes algorithm with a Gaussian distribution is trained on the data and a validation set achieves a 96.5% accuracy. This work also compares two sol-gel ZnO nanoparticle solutions with an off-the-shelf ZnO nanoparticle ink for use as active material in chemiresistive gas sensors to be employed in an e-nose array. The ZnO solutions are screen-printed onto gold electrodes, auto-sintered with a built in heater, and used with UV illumination to operate as low-power, room temperature gas sensors. All of the screen-printed ZnO sensors show distinct changes in resistance when exposed to Taktic vapours under room temperature and humidity conditions. The custom room temperature ZnO gas sensors fabricated via facile and low-cost processes are suitable for future integration in a point-of-need microsystem for the detection of Taktic in water. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technologies are now widely employed to overcome human-induced faults in a variety of systems used in our daily lives, thanks to the digital transformation.One example of such systems is online document tracking systems (DTS). The DTS's reliability and preferability are enhanced by automatic document classification and understanding features. Although automatic document classification systems can assist humans in document understanding tasks, most of of them are not designed to function with Portable Document Format (PDF), which contains text, tables or figures. In this study, we investigate separate ways to efficiently classify student documents that are uploaded in PDF format and are required for university education. We propose three possible techniques for this issue. The first approach is based on Optical Character Recognition (OCR) and traditional machine learning methods. The second is purely on deep learning. The third one is based on fusion of deep learning methods based on entropy. The proposed techniques can classify twelve distinct types of digital documents. The validity of the proposed methods has been verified by student affairs department of Kocaeli University in Turkey. The system has not only increased the efficiency of online document uploading steps for students, but also reduced the human cost for tracking the documents. The highest F-score (94.45%) is obtained by the ensemble of EfficientNetB3 and ExtraTree. [ABSTRACT FROM AUTHOR]

Technology has made significant strides in recent years, which accounts for how pervasive it is in our daily lives. In order to address the fundamental issue with historical document preservation, namely their degeneration, this work suggests using new technology. The method is built on pieces of artificial intelligence that can read the writing from a page and recognize the useful information, converting it into a digital version. Contrary to photographing or scanning, binarizing a document is a considerably more effective method, both in terms of quality--the legibility of the writing--and quantity--the amount of memory needed to retain the resulting image. According to common assessment measures, the suggested fully convolutional network manages to deliver results that are comparable to those of other solutions of a similar nature. [ABSTRACT FROM AUTHOR]

Technology and digital communications have advanced so that digital photos, videos, or text may be easily manipulated by those not authorized to do so. In addition, the availability of specialized picture editing programs like Photoshop has simplified the process of altering photographs. At first glance, there may seem to be no problem, especially when an image editing method is necessary to delete or add a certain scene that increases the picture's beauty. But what about personal images or images with copyright? Attempts are constantly made to spoof these images using different approaches. Therefore, measures to reduce the likelihood of counterfeiting in digital and printed forms of media are required. The proposed approach aims to detect a counterfeit in images using a unique generator that conceals the data represented by the embedded watermark utilizing modern visual cryptography and hash algorithms. Image extractions may easily be analyzed for signs of forgery. As a result, our approach will detect and validate phony documents and images. [ABSTRACT FROM AUTHOR]

This document provides a simplified solution to the problem of calculation of laser hazard distances defined in the Advisory Circular 70-1B by the U.S. Federal Aviation Administration regarding atmospheric attenuation (assuming its constant value) and measurement uncertainties. The calculation approaches and examples presented in this document do not specify the procedure that should be followed in the case of atmospheric attenuation, nor do they take into account the uncertainties associated with the measured parameters. The analysis presented in the article complements to some extent AC 70-1B and can be used by those who need such a simplified solution regarding illumination of landing or taking off aircrafts. The article presents a sample analysis for a typical laser pointer, where the necessary parameters of the laser beam along with the appropriate uncertainties were determined in accordance with the methods accredited by the Polish Centre for Accreditation while the appropriate laser hazard distances were calculated taking into account different atmospheric attenuation coefficients. [ABSTRACT FROM AUTHOR]

The removal of staff lines is the most significant step to separate notes from the score images in optical music recognition (OMR). However, musical images are often affected by different deformations, and it is difficult to delete the staff lines completely without affecting the integrity of the notes. A novel multi-layer image operator learning algorithm based on sample structure is proposed in this paper to solve the problem of staff lines deletion. Our algorithm is dedicated to obtain the structural characteristics of staff lines via image operator learning. Firstly, an iterative strategy is proposed to update the distribution of the samples for learning multiple image operators with different sample structure features. Further, based on the learned image operators, a multi-layer image operators network is designed to obtain the optimal combination of multiple operators. Finally, we have verified the feasibility of our algorithm on the data set 2013 ICDAR/GREC staff lines removal competition. The experiment shows that the proposed algorithm is robust against many kinds of deformation. Moreover, our algorithm is more competitive by comparing with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents' matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use. [ABSTRACT FROM AUTHOR]

The aim of the paper is a spectral study of a mural fresco from Serbesti wooden church, Dambovita county, partially painted, a historical monument from 1828 (or 1804 after some other documents). Spectral techniques as X-ray Fluorescence wavelengths dispersive (WDXRF), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) or Raman spectroscopy, have been used in order to highlight the presence of the degradation processes. Also, the chromatic parameters have been used as additional data in order to observe the discoloring aspect of these mural fresco. [ABSTRACT FROM AUTHOR]

Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation. [ABSTRACT FROM AUTHOR]

Microscopic images in material science documents have increased in number due to the growth and common use of electron microscopy instruments. Through the use of data mining techniques, they are easily accessible and can be obtained from documents published online. As data-driven approaches are becoming increasingly common in the material science field, massively acquired experimental images through microscopy play important roles in terms of developing an artificial intelligence (AI) model for the purposes of automatically diagnosing crucial material structures. However, irrelevant objects (e.g., letters, scale bars, and arrows) that are often present inside original microscopic photos should be removed for the purposes of improving the AI models. To avoid the issue above, we applied four image inpainting algorithms (i.e., shift-net, global and local, contextual attention, and gated convolution) to a learning approach, with the aim of recovering microscopic images in journal papers. We estimated the structural similarity index measure (SSIM) and ℓ 1 / ℓ 2 errors, which are often used as measures of image quality. Lastly, we observed that gated convolution possessed the best performance for inpainting the microscopic images. [ABSTRACT FROM AUTHOR]

The effects of a compound that contains a xanthate group named DIP on the radical reactions and structural evolution of the ethylene propylene diene monomer (EPDM) were investigated. It was found that the structural evolution and long-term stability of the EPDM can be realized by controlling the radical reaction in the matrixes of EPDM during γ-ray irradiation. The results show that the DIP can prevent EPDM deterioration, when the γ-ray irradiation dose reaches 450 kGy, the retention rate of the elongation at break and tensile strength of the EPDM samples containing 1 wt% DIP could remain about 90% and 160%, while those of additive-free EPDM are only about 33% and 70%. On the other hand, the thermal stability of the EPDM samples decreases markedly with the dose increasing under γ-ray irradiation. However, the thermal decomposition temperature of the EPDM samples containing DIP remains basically unchanged after irradiation, and DIP can also improve the oxidation resistance of the EPDM. Electron spin resonance studies reveal that DIP can effectively control the radical reactions inside the EPDM during γ-ray irradiation. Rheology results show that the structural evolution of the EPDM can be well controlled under γ-ray irradiation owing to the presence of DIP in the EPDM matrixes. Density functional theory calculations indicate that the reversible radical reactions inside EPDM/DIP systems are crucial in realizing the long-term stability and controllable structural evolution of the EPDM under γ-ray irradiation. [ABSTRACT FROM AUTHOR]

With the advent of the screen‐reading era, the confidential documents displayed on the screen can be easily captured by a camera without leaving any traces. Thus, this paper proposes a novel screen‐shooting resilient watermarking scheme for document image using deep neural network. By applying this scheme, when the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the captured photographs. Specifically, the scheme is an end‐to‐end neural network with an encoder to embed watermark and a decoder to extract watermark. During the training process, a distortion layer between encoder and decoder is added to simulate the distortions introduced by screen‐shooting process in real scenes, such as camera distortion, shooting distortion, and light source distortion. Furthermore, a background sensitive loss and a lpips loss are used to improve visual quality of the watermarked document images in the training process. Besides, a strength factor adjustment strategy is also designed to improve the visual quality with little loss of bit extraction accuracy. The experimental results show that the proposed scheme has higher visual quality and robustness than the other two recent state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]