Reviewerly/exHarmony huggingface dataset:
Effective Document Image Rectification via a Deep Learning Framework

International Journal of Pattern Recognition and Artificial IntelligenceAccepted Papers No AccessEffective Document Image Rectification via a Deep Learning FrameworkHsiau-Wen Lin, Hwei Jen Lin, Ph. D.Yihjia Tsai, Yoshimasa Tokuyama, and Chou-Wei KongHsiau-Wen Lin, Hwei Jen Linhttps://orcid.org/0000-0002-1539-233X Search for more papers by this author , Yihjia Tsai Search for more papers by this author , Yoshimasa Tokuyama Search for more papers by this author , and Chou-Wei Kong Search for more papers by this author https://doi.org/10.1142/S0218001423510230Cited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 14 November 2023 Accepted 2 December 2023 PDF download
TCN-GAWO: Genetic Algorithm Enhanced Weight Optimization for Temporal Convolutional Network

Abstract This paper proposes TCN-GAWO, a Genetic Algorithm enhanced Weight Optimization method for Temporal Convolutional Network (TCN). TCN-GAWO combines the evolutionary process of the genetic algorithm with the gradient-based training, and can achieve higher predication/fitting accuracy than traditional TCN. Performances of TCN-GAWO are also more stable. In TCN-GAWO, multiple TCNs are generated with random initial weights first, then these TCNs are trained individually for given epochs, next the selection-crossover-mutation procedure is applied among TCNs to get the evolved offspring. Gradient-based training and selection-crossover-mutation are taken in turns until convergence. The TCN with the optimal performance is then selected. Performances of TCN-GAWO are thoroughly evaluated using realistic engineering data, including CMPASS dataset provided by NASA and jet engine lubrication oil dataset provided by airlines. Experimental results show that TCN-GAWO outperforms existing methods for both datasets, demonstrating the effectiveness and the wide range applicability of the proposed method in solving time series problems.
Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction

With the continuous development of new wearable devices, sensor-based human activity recognition is enjoying enormous popularity in research and industry. The signals from inertial sensors allow for the detection, classification, and analysis of human activities such as jogging, cycling, or swimming. However, human activity recognition is often limited to basic activities that occur in short, predetermined periods of time (sliding windows). Complex macro-activities, such as multi-step sports exercises or multi-step cooking recipes, are still only considered to a limited extent, while some works have investigated the classification of macro-activities, the automated understanding of how the underlying micro-activities interact remains an open challenge. This study addresses this gap through the application of graph link prediction, a well-known concept in graph theory and graph neural networks (GNNs). To this end, the presented approach transforms micro-activity sequences into micro-activity graphs that are then processed with a GNN. The evaluation on two derived real-world data sets shows that graph link prediction enables the accurate identification of interactions between micro-activities and the precise validation of composite macro-activities based on learned graph embeddings. Furthermore, this work shows that GNNs can benefit from positional encodings in sequence recognition tasks.
Design and application of transport planning analysis and data service platform based on flexible architecture technology

In order to solve the problems of insufficient sharing and multiplexing of integrated transportation planning services, weak ability of multi-user large-scale concurrent services of high-frequency data and spatial data, lack of customized sub-scene collaborative application functions, design and develop Transport planning analysis and data service platform(i.e., “the platform”) based on flexible architecture technology. The platform bases on SpringCloud mature distributed micro-service technology framework, integrates advanced digital and intelligent technologies of comprehensive transportation planning space-time big data governance, land space resource elements and visualization, initially builds a basic data center for transportation planning, a technical support experimental platform for transportation space planning and an electronic map for space planning that integrates data analysis, platform support, and model application. The construction and application of the platform comprehensively improved the scientific nature of the evaluation of comprehensive transportation spatial planning, and has initially played an important role in assisting the connection of transportation sector planning and territorial spatial planning.
Underwater Turbid Media Stokes-Based Polarimetric Recovery

Underwater optical imaging for information acquisition has always been an innovative and crucial research direction. Unlike imaging in the air medium, the underwater optical environment is more intricate. From an optical perspective, natural factors such as turbulence and suspended particles in the water cause issues like light scattering and attenuation, leading to color distortion, loss of details, decreased contrast, and overall blurriness. These challenges significantly impact the acquisition of underwater image information, rendering subsequent algorithms reliant on such data unable to function properly. Therefore, this paper proposes a method for underwater image restoration using Stokes linearly polarized light, specifically tailored to the challenges of underwater complex optical imaging environments. This method effectively utilizes linear polarization information and designs a system that uses the information of the first few frames to calculate the enhanced images of the later frames. By doing so, it achieves real-time underwater Stokes linear polarized imaging while minimizing human interference during the imaging process. Furthermore, the paper provides a comprehensive analysis of the deficiencies observed during the testing of the method and proposes improvement perspectives, along with offering insights into potential future research directions.
FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization

In recent years, a multitude of self-supervised anomaly detection algorithms have been proposed. Among them, PatchCore has emerged as one of the state-of-the-art methods on the widely used MVTec AD benchmark due to its efficient detection capabilities and cost-saving advantages in terms of labeled data. However, we have identified that the PatchCore similarity principal approach faces significant limitations in accurately locating anomalies when there are positional relationships between similar samples, such as rotation, flipping, or misaligned pixels. In real-world industrial scenarios, it is common for samples of the same class to be found in different positions. To address this challenge comprehensively, we introduce Feature-Level Registration PatchCore (FR-PatchCore), which serves as an extension of the PatchCore method. FR-PatchCore constructs a feature matrix that is extracted into the memory bank and continually updated using the optimal negative cosine similarity loss. Extensive evaluations conducted on the MVTec AD benchmark demonstrate that FR-PatchCore achieves an impressive image-level anomaly detection AUROC score of up to 98.81%. Additionally, we propose a novel method for computing the mask threshold that enables the model to scientifically determine the optimal threshold and accurately partition anomalous masks. Our results highlight not only the high generalizability but also substantial potential for industrial anomaly detection offered by FR-PatchCore.
Application of Metaverse Technology in Entrepreneurship Education: A Systematic Review

A systematic review of the application of metaverse technology in entrepreneurship education can help relevant teaching and research personnel better grasp the current development trends, and better utilize cutting-edge technologies to support the development of entrepreneurship education in teaching and practice. Therefore, this review aims to review the latest trends and research with the question "What issues can metaverse technology be applied in entrepreneurship education?". This system review adopts Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) , and collects data based on two core journal databases, Scopus and Web of Science, while using Note Express and Mendeley as supporting databases. A total of 27 articles were identified through a systematic search of ("metaverse*" OR "cyberspace*" OR "metaverse technology*") AND ("entrepreneurship education*" OR "enterprise education*" OR "entrepreneurial education*"). The results shown that metaverse related technologies are changing entrepreneurial intentions, make it easier for entrepreneurs to access entrepreneurial resources, and improving the efficiency of entrepreneurship education. The exploration of these issues also lays the foundation for subsequent researchers to study how to apply cutting-edge technologies to entrepreneurship education.
Recent Advances in Group-Based Trajectory Modeling for Clinical Research

Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
The temporally-topological defect modes in photonic time crystals

In this paper, we investigate the properties of temporally-topological defect modes (TTDMs) (or temporally-topological interface states) in the topological photonic time crystal (PTC) systems. The PTC systems are constructed by the cascade of multiple sub-PTCs that possess temporal inversion symmetries and different topologies. The cases of two-, three-, and multiple-sub-PTC for the topological PTC system are studied. By transfer matrix method, we find that the TTDMs appear when the topological signs of the corresponding gaps in the sub-PTCs are different. The positions of TTDMs can be adjusted by changing the modulation strength of the refractive index, the time duration, and the period of the sub-PTCs. Moreover, the number of TTDMs is one less than the number of sub-PTCs. In addition, the robustness of the systems is also studied. We find that the topological PTC systems have good robustness, especially on the random configuration of the refractive index and time duration for the temporal slabs in the systems. Such research may provide a new degree of freedom for PTC applications, such as novel PTC lasers, tunable band-stop or band-suppression PTC filters, and many others, in the field of integrated photonic circuits for optical communications.
AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

Abstract To address the issue of tracking drift and failures in thermal infrared (TIR) tracking tasks caused by target occlusion, this study proposes an anti‐occlusion TIR target tracker named AODiMP‐TIR. This approach involves an anti‐occlusion strategy that relies on target occlusion status determination and trajectory prediction. This enables the prediction of the target's current position when it is identified as occluded, ensuring swift recapture upon reappearance. A criterion is introduced for occlusion status determination based on the classification response map of SuperDiMP. Additionally, a trajectory mapping module designed to decouple target motion from camera motion is presented, enhancing the precision of trajectory prediction. Comparative experiments with other state‐of‐the‐art trackers are conducted on the large‐scale high‐diversity thermal infrared object tracking benchmark (LSOTB‐TIR), LSOTB‐TIR100, and thermal infrared pedestrian tracking benchmark (PTB‐TIR) datasets. The results indicate that the AODiMP‐TIR performs well across all three datasets, particularly exhibiting outstanding performance in occlusion sequences. Furthermore, ablation study experiments confirm the effectiveness of the anti‐occlusion strategy, occlusion determination criterion and trajectory mapping module.
Mutual learning with reliable pseudo label for semi-supervised medical image segmentation

Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels is crucial in this regard. Unreliable pseudo-labeling can result in the introduction of noise, leading the model to converge to suboptimal solutions. To address this issue, we propose learning from reliable pseudo-labels. In this paper, we tackle two critical questions in learning from reliable pseudo-labels: which pseudo-labels are reliable and how reliable are they? Specifically, we conduct a comparative analysis of two subnetworks to address both challenges. Initially, we compare the prediction confidence of the two subnetworks. A higher confidence score indicates a more reliable pseudo-label. Subsequently, we utilize intra-class similarity to assess the reliability of the pseudo-labels to address the second challenge. The greater the intra-class similarity of the predicted classes, the more reliable the pseudo-label. The subnetwork selectively incorporates knowledge imparted by the other subnetwork model, contingent on the reliability of the pseudo labels. By reducing the introduction of noise from unreliable pseudo-labels, we are able to improve the performance of segmentation. To demonstrate the superiority of our approach, we conducted an extensive set of experiments on three datasets: Left Atrium, Pancreas-CT and Brats-2019. The experimental results demonstrate that our approach achieves state-of-the-art performance. Code is available at: https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels.
The c-Differential-Linear Connectivity Table of Vectorial Boolean Functions

Vectorial Boolean functions and codes are closely related and interconnected. On the one hand, various requirements of binary linear codes are needed for their theoretical interests but, more importantly, for their practical applications (such as few-weight codes or minimal codes for secret sharing, locally recoverable codes for storage, etc.). On the other hand, various criteria and tables have been introduced to analyse the security of S-boxes that are related to vectorial Boolean functions, such as the Differential Distribution Table (DDT), the Boomerang Connectivity Table (BCT), and the Differential-Linear Connectivity Table (DLCT). In previous years, two new tables have been proposed for which the literature was pretty abundant: the c-DDT to extend the DDT and the c-BCT to extend the BCT. In the same vein, we propose extended concepts to study further the security of vectorial Boolean functions, especially the c-Walsh transform, the c-autocorrelation, and the c-differential-linear uniformity and its accompanying table, the c-Differential-Linear Connectivity Table (c-DLCT). We study the properties of these novel functions at their optimal level concerning these concepts and describe the c-DLCT of the crucial inverse vectorial (Boolean) function case. Finally, we draw new ideas for future research toward linear code designs.
The Impact of Artificial Intelligence on Students in the First Three Grades in Basic Schools in the City of Amman-Jordan from The Perspective of Their Teachers

Nowadays Artificial Intelligence (AI) affects most of our lives; it is positive in some ways, but there are limitations in others. Teachers' perceptions of AI are still poorly studied, despite ongoing debate and growing research in the field. One of its main effects is on students in the first grades; however, this study aimed to investigate the effect of AI on the first three grades of students in basic schools in the governorate of the capital Amman Jordan, from the perspective of its teachers as a case study. The study was performed using a scientific questionnaire, and 125 participants completed this survey. The findings of an analysis of teachers' beliefs regarding using AI technologies to teach the first three grades in elementary schools in Jordan's capital, Amman Governorate, are presented in detail. As a result of the significance of these axes in educational curricula and strategies, as well as related sustainability, the focus was on five axes in the questionnaire questions: the validity of information derived from artificial intelligence, supporting students' technical knowledge and conceptual knowledge, focusing on development practices for students' skills, and finally achieving educational outcomes. The findings show that in-service teachers need to be trained to use current AI-based tools more effectively. To integrate AI into regular education, teachers must participate in the process of co-designing materials while taking into account contextual circumstances and, most importantly, curricula. Teachers' input during the development can help put AI in perspective, resulting in tangible effects and significant educational advances.
Impact of Employee Engagement On Service Quality in Nigerian Public Agencies

This study investigates the connection between employee engagement and service quality in the Asset Management Company of Nigeria (AMCON), which is a key player in Nigeria's financial sector. This study examines three key dimensions of employee engagement – absorption, vigor, and dedication – and their effects on service quality, specifically focusing on the responsiveness dimension of the SERVQUAL model. This study is important because there is a lack of representation of AMCON and similar institutions in the existing literature, particularly within the context of Nigeria's unique financial landscape. This study utilized a mixed-methods approach that included quantitative surveys and qualitative interviews with AMCON employees. The analysis employed the SERVQUAL model to evaluate service quality and multiple regression analysis to explore the influence of each engagement dimension. The findings of this study are expected to contribute to a theoretical understanding of the impact of employee engagement on service quality within the asset management sector. Additionally, this study aims to provide practical insights for AMCON, potentially improving service delivery strategies and effectiveness in fulfilling its mandate. The broader implications of this study extend to similar asset management entities in emerging economies, providing a comparative perspective, and understanding of these dynamics in diverse economic and regulatory environments.
Cloud-based Metabase GIS Data Analysis Platform Quality Management According to ISO 9126 Indicators

Platform metabase GIS data analysis based on the cloud that has been successfully developed is an alternative solution for spatial-text data analysis. The output of this cloud-based platform not only provides accurate textual information but also precise location representation of the objects. This research examines the quality of the developed platform based on ISO 9126, which consists of six main indicators: functionality, reliability, feasibility, efficiency, maintainability, and portability. Each indicator has different sub-indicators, totaling 22 sub-indicators. The quality assessment results indicate that the platform for metabase GIS data analysis based on the cloud exhibits excellent quality, with an average test result based on the ISO 9126 indicators reaching 93%.
Deep Learning-Based Semantic Segmentation in Autonomous Driving

&lt;p&gt;Perception is a fundamental task of autonomous driving systems, which gathers all the necessary information about the surrounding environment of the moving vehicle. Then a decision-making system takes the perception data as input and provides the optimum decision given a scenario, which maximizes the safety of the passengers. In this project, we have developed variants of the U-Net model to perform semantic segmentation on urban scene images to understand the surroundings of an autonomous vehicle. The U-Net model and its variants are adopted for semantic segmentation in this project to account for the power of the U-Net in handling large and small datasets. We have also compared the best-performing variant with other commonly used semantic segmentation models. The comparative analysis was performed using three well-known models, including FCN-16, FCN-8, and SegNet. After conducting sensitivity and comparative analysis, it is concluded that the U-Net variants performed the best in terms of the Intersection over Union (IoU) evaluation metric and other quality metrics.&lt;/p&gt;
Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques.
LatCrit

Abstract Ever since its 1995 inception at a Puerto Rico colloquium on Critical Race Theory and Latina/o communities, Latina and Latino Critical Legal Theory (LatCrit) agendas have prioritized students and sustainability. This chapter highlights those priorities in the context of the times and ambitions that have helped to activate and shape LatCrit theory, community, and praxis as a form of organized academic activism. The authors describe LatCrit’s origins and history since 1995, its collective present, and their hopes for the future. Understanding the emergence, circumstances, and purposes of LatCrit’s development as a still thriving school of critical outsider jurisprudence and its programmatic expression of anti-subordination interventions during these past few decades brings into focus how and why LatCrit remains an “advocacy project” that might inspire other individuals or collectives, whether in academia or other parts of society, and whether focused on students or other populations. Specifically for students, we suggest a variety of ways to connect with LatCrit scholarship, events, and networks to build your own critical knowledge, professional capacity, and bottom-up advocacy projects.