Reviewerly/exHarmony huggingface dataset:
Evidence Exchange Under the EIO: Technological Challenges

The European Investigation Order instrument offers a legal basis for exchanging evidence, in a digital manner, between competent authorities in different Member States of the European Union. The European directive explicitly sets out that the transfer of the evidence obtained by the executing authority shall be transferred to the issuing authority, without undue delay. The article illustrates how this goal can be accomplished using standards for the representation and the exchange of the Evidence in a cross-border scenario, having at disposal a secure platform for guaranteeing the confidentiality and the integrity of its content during the transfer.
Alignment and joint recovery of multi-slice free-breathing cardiac cine using manifold learning

Free-breathing cardiac cine methods are needed for pediatric and chronic obstructive pulmonary disease (COPD) subjects. Multi-slice acquisitions can offer good blood-myocardium contrast, and hence preferred over 3D methods. Current approaches independently recover the slices, followed by post-processing to combine data from different slices. In this work, a deep manifold learning scheme is introduced for the joint alignment and reconstruction of multi-slice dynamic MRI. The proposed scheme jointly learns the parameters of the deep network as well as the latent vectors for each slice, which captures the motion induced dynamic variations, from the k-t space data of the specific subject.
Deep generative MRI reconstruction for unsupervised Gibbs ringing correction

MRI reconstruction is formulated as the retrieval of the parameters of a deep decoder network fitted to the observations by an image formation model including the truncation of high-frequency information. Solutions without Gibbs ringing at any prescribed image grid can be obtained naturally by the model without training or ad-hoc post-corrections. We present quantitative and visual results for spectral extrapolation of magnitude images at different scales in an in-silico experiment and a high resolution ex-vivo brain MRI scan. After minor modifications to deal with complex data, the architecture is applied to 2D parallel imaging showing promising visual results.
An unsupervised deep learning method for affine registration of multi-contrast brain MR images

Image registration is a crucial preprocessing step for many downstream analysis tasks. Existing iterative methods for affine registration are accurate but time consuming. We propose a deep learning (DL) based unsupervised affine registration algorithm that executes orders of magnitude faster when compared to conventional registration toolkits. The proposed algorithm aligns 3D volumes from the same modality (e.g. T1 vs T1-CE) as well as different modalities (e.g. T1 vs T2). We train the model and perform quantitative evaluation using a pre-registered brain MRI public dataset.
Adaptive convolution kernels for breast tumor segmentation in multi-parametric MR images

Multi-parametric magnetic resonance imaging (mpMRI) provides high sensitivity and specificity for breast cancer diagnosis. Accurate breast tumor segmentation in mpMRI can help physicians achieve better clinical managements. Existing deep learning models have presented promising performances. However, the effective exploitation and fusion of information provided in mpMRI still need further investigation. In this study, we propose a convolutional neural network (CNN) with adaptive convolution kernels (AdaCNN) to automatically extract and absorb the useful information from multiple MRI sequences. Extensive experiments are conducted, and the proposed method can generate better breast tumor segmentation results than those obtained by CNNs with normal convolutions.
Evaluation of the Combined Deep Learning Networks Using Mask R-CNN and ResNet50 Classification for Detection and Diagnosis of Breast Cancer on MRI

We developed two deep learning methods for breast MRI evaluation, first using Mask R-CNN for detection of suspicious areas, and then using ResNet50 for estimating the malignancy probability. These two networks were combined to test its diagnostic validity in two datasets. In Dataset-1, sensitivity=96.1% and specificity=78.1%. In Dataset-2, sensitivity=81.1% and specificity= 80.6%. We further characterized all false positives (FPs), and found other than confirmed benign lesions, FPs may come from vessels and asymmetric parenchymal enhancements, which can be further eliminated by other algorithms. The results suggest the potential of combined deep learning networks as a fully-automatic breast MRI CAD.
Optimize and Evaluate the Efficacy of Pairwise AE Model in Predicting the Prognosis of Concurrent Chemo-radiotherapy for Cervical Cancer

To overcome the small data set problems in clinical situations, this paper proposed a pairwise auto encoder (AE) model, which can learn more relationship information among samples to enhance the generalization ability, in predicting the prognosis of concurrent chemo-radiotherapy for LACC and demonstrated potential in this field.
Accurate Prostate Segmentation in MR Images Guided by Semantic Flow

To enlarge the receptive field, downsampling is frequently utilized in deep learning (DL) models. Consequently, there exists one common issue for DL-based image segmentation &amp;ndash; the misalignment between high-resolution features and high-semantic features. To this end, decoding or upsampling has been proposed and promising performances have been achieved. However, upsamling without explicit pixel-wise localization guidance may introduce errors. To address this issue, we propose a semantic flow-guided prostate segmentation method. By guiding the upsampling process with semantic flow calculated from both high-resolution and high-semantic features, more accurate segmentation results are generated.
Inclusion of B0 mapping images in motion correction for improved quality of CEST maps

The inhomogeneity of the main magnetic field (B0) often results in degraded quality of CEST images. Currently, various B0 mapping methods have been used to correct the B0 inhomogeneity of CEST images. Motion inevitably happens between B0 mapping and CEST scans, which, however, is usually ignored in the current CEST processing pipeline. Here, we show that the inclusion of B0 mapping images into the motion correction process improved the quality of the CEST maps, and statistically significantly improved the uniformity indices. Thus, we suggest adding the motion correction of B0 mapping data step into the standard CEST processing pipeline.
Implementation of Streaming Video Information Systems for Web-Based Computer Science Learning

Information systems are an example of implementing the use of technology at this time where systems are built on a computerized basis so as to facilitate or simplify human work in the economic, social, political, health and even in the education sector, there are many information systems built to support educational development or more precisely in development learning, as is the case with many learning-based streaming videos or tutorials, both tutorials for soft skill development and hard skill development, but some problems can be seen from video streaming information systems that are widely spread on internet media that it is still relatively rare to get streaming videos that focus on learning in certain fields so that it is difficult to carry out self-development to concentrate on certain fields such as focusing on the field of computer science learning. Therefore, to facilitate video-based learning, researchers have developed a computer science-based video streaming information system.
Enhancing Security Measures for Mobile Banking Applications: A Comprehensive Analysis of Threats, Vulnerabilities, and Countermeasures in Kenya Banking Industry

This study focused on the risks and vulnerabilities associated with mobile banking apps in the Kenyan banking sector.While these apps offer convenience and accessibility, they are also exposed to various threats that can compromise user information and financial transactions.The study aimed to identify these risks, vulnerabilities, and attack routes specific to Kenyan mobile banking apps and provide remedies to enhance their security.The research had three main goals.Firstly, it seek to examine and evaluate the primary vulnerabilities and attack vectors affecting Kenyan mobile banking apps and understand their impact on user data and financial transactions.Secondly, it aimed to assess the common flaws in these apps, considering both technological issues and user behavior patterns.Lastly, it aimed to propose appropriate security measures customized for the Kenyan banking sector, considering user behavior and technological advancements.This included exploring encryption, multi-factor authentication, and user awareness programs to promote secure banking habits.The research intended to assist financial institutions and customers in enhancing the security of their mobile banking experiences by conducting a thorough assessment of risks and vulnerabilities in Kenyan mobile banking apps and offering practical advice.It emphasized the importance of understanding and addressing the specific challenges faced by the Kenyan banking sector to ensure secure and reliable financial transactions through mobile platforms.
A Study Regarding the Improvement of Counseling Activity Classes in the Elementary School Curriculum

Purpose: This study aimed to identify the operation status of counseling activity classes operatedin the elementary school curriculum (creative experience activities) and to find ways to smoothly operatecounseling activity classes in the curriculum.&#x0D; Methods: An online survey was conducted with 144 elementary school counselors in Seoul, andtwo focus group interviews with 17 participants including eight school counselors, four schooladministrators, and five elementary school teachers.&#x0D; Results: As a result of the analysis, elementary school counseling activity classes were 1) activitiesthat helped students understand; 2) Counseling activity classes, which are mostly operated aslecture-style classes, put a burden on professional counselors, making it difficult to “consult” theiroriginal work; and 3) In order for counseling activities classes to operate smoothly in the curriculum,institutional supplementation is required, such as improving the treatment of professional counselorsand preparing a school counseling curriculum.&#x0D; Conclusion: Based on the results of the study, a proposed policy plan for the smooth operation ofcounseling activities classes in the elementary school curriculum (creative experience activities) wasdetailed.
Digital Forensics Investigation Using Cloud(AWS)

In this paper, a novel AWS-based DF investigation framework and social systems environment is proposed, which can provide security and privacy preservation for forensic & medical report.To implement such features, we present symmetric algorithm & AWS S3 service framework, which can offer forensic investigation with good authenticity, immutability, traceability, resilience, and distributed trust between crime departments.The digital forensics (DF) can deliver a guarantee of traceability and track evidence items & criminals.The digital forensics (DF) can increase trust of both crime departments by providing transparency of the audit train.In propose system, the police station register crime details based on the crime and the forensic staff collects data/evidence in crime place & generate forensic report.The Forensic report write to pdf file, encrypt the file using symmetric algorithm & store the encrypted file in AWS S3 service.Based on the crime, medical report generate by doctor & these report also write to pdf file, encrypt the file using symmetric algorithm & store the encrypted file in AWS S3 service.
Locally low-rank denoising of multi-echo fMRI data preserves detection of resting-state networks following scan truncation

Functional magnetic resonance imaging (fMRI) has inherent limitations of fast acquisitions due to low signal-to-noise ratio (SNR) and artifacts. Multi-echo fMRI acquires images at multiple TEs, increasing robustness to off-resonance based signal loss and improving sensitivity to neural activity. However, noise is substantial given limitations of EPI, although consistent across TEs contributing to prolonged scan times required for sufficient statistical power. Reduction of acquisition time with reconstruction and processing techniques is of interest. This study extends preliminary work on locally low-rank denoising of multi-echo fMRI data to explore scan time reduction through processing with retrospective truncation.
A 3D Dense-U-Net for fully automated 5D flow MRI segmentation

Recently, a free-running 5D flow framework was introduced and validated. However, some 5D flow MRI is based on 3D radial imaging, which is limited by reduced SNR that can result in challenges with 3D segmentation. A number of previous studies have investigated automatic segmentation for 4D flow MRI, however these have been traditionally optimized for Cartesian datasets, which are typically acquired over much smaller spatial matrices and cover only one respiratory position. The purpose of this study was thus to adapt and expand a deep-learning framework to cardiac 5D flow MRI data for automatic segmentation of the thoracic aorta.
Dual-domain self-supervised network for removing motion artifact related to Gadoxetic acid-enhanced MRI

We proposed a dual-domain self-supervised motion artifacts disentanglement network (DSMAD-Net) for the liver's gadoxetic acid-enhanced arterial phase images. The motion correction is converted to the image-to-image translation problem by assuming that motion-free images and motion-corrupted images belong to different domains. Specifically, image-to-image translation within the same domain is designed to constrain auto-encoders to learn the feature representation by utilizing the input images as supervision information. Moreover, the cross-domain translation explores the cycle consistency in the absence of paired motion-free and motion-corrupted images. Experimental results demonstrate that our method remarkably removes artifacts in the gadoxetic acid-enhanced arterial phase images.
Reconsidering Multi-Branch Aggregation for Semantic Segmentation

For semantic segmentation tasks, there are problems in using multi-branch structures to enrich feature maps and aggregate different branches of feature maps in a certain network depth, such as the insufficient richness of feature maps and the incomplete aggregation of feature maps. Given multi-branch feature maps and branch aggregation, this study proposes a lightweight method, called multi-branch aggregation atrous spatial pyramidal pooling, by introducing an attention mechanism and CARAFE to enrich the features of the feature maps, giving the feature maps adaptive parameters, periodically adjusting the adaptive parameters, and aggregating the feature maps in both the vertical and horizontal directions. First, the atrous pyramid is retained and the attention mechanism and CARAFE are used to handle the pooling features to obtain 10 different feature maps. Secondly, giving each feature map a cascaded adaptive parameter and periodically adjusting the adaptive parameter to promote or suppress certain feature maps prevents the model from being at a local minimum or saddle point for long periods due to aleatory uncertainty. Finally, the feature maps are vertically aggregated, horizontally aggregated, and weighted for summation. This work demonstrates a competitive performance on the benchmark dataset, with an improvement of 1.88% in MPA, 0.66% in FWIoU, and 1.29% in MIoU compared to atrous spatial pyramid pooling. The benchmark datasets include PASCAL VOC 2012 and CIFAR-100.
Machine Learning and Deep Learning Methods on Breast Cancer Metastases Detection

Abstract There is a lot of room for research in how to detect breast cancer metastases in whole slide images more quickly and precisely. This work contrasts several machine learning and deep learning models on the breast cancer metastases detection problem and experiments the impacts of feature extraction, dimension reduction and the sizes of the datasets on these models. This work also establishes a breast cancer metastases detection pipeline based on the most accurate model in the experiments. Among the models, Dense Model using features trained from DenseNet201 with weights trained by ImageNet is of the best performance. This work develops a computer aided method to examine the cancerous regions in the whole slide images of breast cancer more quickly and precisely than an experienced doctor does.
The Current Status of Korean Studies in France and the Revitalization of Korean Independence Movement

Korean Studies research in France has achieved remarkable growth both quantitatively and qualitatively due to Korea's economic growth and increasing cultural influence. Currently, in-depth research is being conducted across all fields, expanding the scope to Korean studies as a whole, not just Korean language education. Unfortunately, however, the field of research on the history of the independence movement is far from the interest of institutions, researchers, and students. In this situation, the emergence of the Korean-French Independence Movement History Research Association (Libertas), founded in 2017, is encouraging. Members held several international academic conferences related to the history of the Korean independence movement, and achieved significant results through active activities such as excavation of a large number of materials and posthumous writings of independence activists. Although the framework for research and dissemination of the history of the Korean independence movement in France has been established, it is still in a basic state due to realistic limitations. Financial and academic support, as well as the interest and active participation of many researchers in Korea, are needed to vitalize research on the history of the independence movement.
INTERVENTION PROGRAM FOR THE DEVELOPMENT AND CULTIVATION OF THE SOFT SKILL OF RESPECT IN STUDENTS WITH DISABILITIES STUDYING IN A SPECIAL CONTEXT

The area of Special Education and Education is an educational system whose tracing and traversal constitutes a difficult undertaking and action. In this sense, the essential, efficient and effective teaching of soft social skills and, above all, respect, to students with disabilities who study in special contexts and structures is very laborious. With this specific scientific and critical review, the design, implementation and evaluation of an intervention program for the development and cultivation of the soft skill of respect for students with disabilities is investigated and discussed. Based on the review findings of this research, it is demonstrated that intervention programs, in the context of respect, should aim at the development of a specific soft skill and not, in a general and vague way, at the improvement of all soft skills.
Analysis of Research Trends in Domestic Entrepreneurship Education

This study aims to analyze research trends and suggest future research directions based on a total of 73 papers published in the field of domestic entrepreneurship education. To achieve this, the research on entrepreneurship education was classified into research methods and research topics according to the temporal flow. Furthermore, discussions were conducted on subsequent research tasks for the advancement of domestic entrepreneurship education. The following results were derived from this study. First, examining the research periods, it was found that research on entrepreneurship education has been actively conducted since 2010, following the first paper in 2002, with a total of 52 papers published after 2017. This indicates that the research topics of entrepreneurship education are expected to become more vibrant over time. Second, the analysis results revealed that although research on entrepreneurship education has been conducted using various methods, there is a relatively higher proportion of quantitative studies compared to qualitative studies. Third, the specific research topics were classified into seven categories, including educational methods and effects, research subjects, entrepreneurship education, social entrepreneurship, international cases, career education, and research trends. Based on these findings, the study suggests future research directions and implications such as establishing the concept of entrepreneurship education, expanding research variables, and activating qualitative research. By emphasizing the significance of domestic entrepreneurship research, this study aims to contribute to the effective development of entrepreneurship education and the cultivation of creative talents.
The Mediating Role of Customer Satisfaction in the Relationship Between Product Trust, Perceived Value, Price Fairness, and Purchase Decisions : an Empirical Study of Telecomcel Company in Timor-Leste

This study aims to investigate the relationships between product trust, perceived value, price fairness, customer satisfaction, and purchasing decisions based on the existing marketing literature. The study develops 10 hypotheses that propose direct relationships between product trust, perceived value, price fairness, customer satisfaction, and purchasing decision. Additionally, the study proposes a mediating role for customer satisfaction in the relationships between the other variables. The study collected data from 178 randomly selected Telkomcel customers in Timor-Leste through a survey. Smart-PLS was used to assess the validity, reliability, and test the hypotheses. The results strongly support all the hypotheses, highlighting the importance of product trust, perceived value, and price fairness in shaping customer satisfaction and purchasing decisions. This study provides insights for future research in this area.
Design of an Instructional Framework to Deepen Teaching and Learning Experience in Regular AI Education for Middle/High School Levels

In our digital era, Artificial Intelligence (AI) has been assisting people from even daily tasks to other bigger categories, such as business, education, or entertainment. This incredible development of AI technologies requires the young generation to gain a basic understanding of AI as soon as possible, as they are future AI developers and users. Realizing the importance of AI education for young students, in this study, we propose an education framework to support middle and high school teachers in customizing their AI lessons to fit students’ competencies, and it can suggest that teachers introduce actual AI products before teaching about complex AI concepts. This instructional methodology becomes feasible by our newly developed application, which provides educators with data on national (or international) standards of K-12 AI education, a detailed description and social influence of exemplified AI products, and knowledge of how AI technology has been implemented in these actual products. This research is an evolved version of our previous work, which specified significant adjustment points in the arrangement of AI lesson content and the application design.