Reviewerly/exHarmony huggingface dataset:
Improving the Resolution and SNR of Diffusion Magnetic Resonance Images From a Low-Field Scanner

Spatial resolution, signal-to-noise ratio (SNR) and acquisition time are interconnected in magnetic resonance imaging (MRI). Trade-offs are made to keep the SNR at the acceptable level, maximizing the resolution, minimizing the acquisition time and maintaining radiologically useful images. In low-field MRI scanners and especially in diffusion imaging, these trade-offs are even more crucial due to a generally lower image quality. Image post-processing is necessary in such cases to improve image quality. In this work, we alleviate the challenges of low SNR in dMRI at low magnetic fields by performing super-resolution reconstruction (SRR). Our approach combines multiple low-resolution images acquired at different image slice rotations and employs a convolutional neural network to perform the SRR. Training is performed on noisy images. The network learns to extract and compose complementary image details into a super-resolution output image. Because of the properties of noise and the training process, the super-resolution images are less noisy than the directly acquired high-resolution ones, contain more high-resolution details than the input low-resolution images and the total acquisition time is decreased.
A Validity and Reliability Analysis of the Pre-service Teachers' Attitudes towards Virtual Classroom Environment Scale

This studyaims to develop a measurement tool to be used to determine the attitudes of pre-service teachers towards virtual classroom environments. For this purpose, the literature on the subject was examined, and student opinions were taken. As a result of these processes, a 50-item piloting form of the scale was prepared. The prepared scale items were presented to the expert opinion in terms of content validity, and after the feedback obtained from the experts, some items were rearranged with minor corrections and transformed into a 50-item scale. The scale was administered to 282 students, 234 female and 48 male, studying in different teaching programs and different grade levels at Gazi University Gazi Faculty of Education in the spring term of the 2021-2022 academic year. Validity and reliability analyses of the scale were made based on the data set obtained as a result of the application. Exploratory Factor Analysis (EFA) was performed for construct validity, and as a result of the Exploratory Factor Analysis (EFA), a scale consisting of 46 items and 2 sub-dimensions was obtained. The total variance explanation rate of the two factors in the structure of the scale was 62.79%. The reliability analysis results obtained for the scale revealed that the Cronbach's alpha reliability coefficient for the overall scale was .98, and the reliability coefficients for the sub-factors were .98 and .96, respectively.
Upgrading Vocabulary by Crossword Puzzles Games to Improve Descriptive Text Writing Ability

The aim of this research is to increase students' vocabulary. The previous students' vocabulary was low when measured during random drilling. Only a few students know the meaning of vocabulary words. In class 7C there are 35 students, but only 8 students can answer orally, which is around 23%. The remaining 27 students still do not understand vocabulary, namely 77% of students don’t understand. So that the use of Crossword Puzzles Game is expected to be able to upgrade the vocabularies that students have. When students have many vocabularies, students will be able to create descriptive sentence at least in accordance with the vocabulary were found in the Crossword puzzles game. In the value of Crossword Puzzles Game students get extraordinary scores. Only 1 student or 3% below the KKM 75 and 34 students or 97% of students above the KKM 75. Meanwhile, the score of the descriptive text writing made by the students scored below the KKM 75 was 4 students or 11 % and above KKM 75 there are 31 students or 86%. Thus Upgrading Vocabularies By Crossword Puzzles Game To Improve Descriptive Text Writing Ability in Class 7C SMP Negeri 2 Sukodono is proven to be true.
PENGEMBANGAN E-MODUL BERBASIS PJBL DALAM PEMBUATAN MULTIMEDIA INTERAKTIF

This study aimed to develop PJBL-based e-modules in the interactive multimedia created in the context of developing media that can used to make learning more effective. This study uses the Research and Development (R&amp;D) method with the ADDIE model through the stages of Analyze, Design, Develop, Implement, and Evaluate. The subjects of this study were students of the multimedia expertise program at SMK. The PJBL-based e-module development was carried by validating media experts, design and materials, measured with a Likert scale of 1-4 and calculated with the Gregory index. Obtaining measurements from media experts a validity coefficient is 0.89, from design and media experts obtained a validity coefficient of 1. So it can concluded that PJBL-based e-module media is suitable for use as material for making interactive multimedia. The results of student responsess as measured by percentages, obtained an average student interest response of 96.34%, student motivation in learning reached an average of 93.26%. The effectiveness test through the pretest and posttest involved 30 participants with a significance of less than 0.05, showed that there was a significant difference between the pretest and posttest. Based on this, it concluded that PJBL-based e-module learning media was effective in increasing learning in making interactive multimedia in multimedia expertise competencies.
Understanding Encryption &amp; SSH Cipher Benchmarking

With the advancement in technology, cybersecurity is becoming more important these days. The United States should have the most advanced cybersecurity to protect itself and its citizens from foreign hackers because other nations are building more sophisticated cyberinfrastructure. At the Texas Advanced Computing Center, maintaining security of personal information and data transfers is very important. Benchmarking scripts created in this project can test ciphers to find ones that are stronger but more efficient than most. Bash scripts were created in this project to test the efficiency of ciphers’ encryption time in an automatic way. Ciphers were studied to see which encrypted and decrypted a 2-gigabyte file the quickest.
Boosting Robust Multi-focus Image Fusion with Frequency Mask and Hyperdimensional Computing

Multi-focus image fusion (MFIF) creates an image from different source images with various sensors or optical settings as the devices can’t focus all objects at different distances. Most of the MFIF methods have several limitations in encoder enough features from the images and the result are not robust. To overcome the primary issue, we present a robust fusion algorithm based on the Frequency mask and the Hyperdimensional computing. We propose the Frequency Mask Filter (FMF) to get the narrow-band signals by encoding the frequency domain vector through the mask filter in the frequency domain. The Hyperdimensional encoder uses monogenic mapping, in which the multi-modulation features (MMF) such as the frequency, phase and amplitude are dynamically selected to obtain robust focus maps. Generated by multiscale monogenic representations of each image, the narrow-band image are mapped to hypervector encoding. Hyperdimensional encoder shows the energetic and structural information and leads to robust fusion results. Our proposed method is far superior to the existing MFIF method in terms of both objective evaluation metrics and visual effects on three publicly available datasets.Additionally, our proposed method requires only 0.88 seconds and has a parameter count of 0.13 million for multi-focus image fusion.
PV power probability density prediction based on the long short-term memory network with quantile regression

Considering the chaotic nature of atmospheric systems and the random behavior of electricity users, it is difficult to precisely forecast the targets in the new energy power system. Using single point values as output predictions inevitably results in irreducible errors, leading to a high level of uncertainty in the forecasting process. I kindly request that you improve and correct any errors in my language and provide a more literary and eloquent expression of the same meaning. Thank you in advance. Diverging from traditional point forecasting models, we present a short-term solar photovoltaic (PV) power probability density forecasting model based on Long Short-Term Memory Quantile Regression (LSTMQR). First, LSTMQR is employed to forecast future PV power across various quantiles. Subsequently, a Gaussian kernel function is integrated with LSTMQR and Kernel Density Estimation (KDE) to provide short-term PV power probability forecasting. Through this approach, a probability density function of future PV power predictions can be obtained. Our power system probability forecasting is outputted in the form of probability distribution, quantiles, and prediction intervals for the predicted targets, which effectively quantify prediction uncertainty. These outputs provide power system decision-makers with comprehensive supply, demand, and transaction information.
Multi-Sensors System and Deep Learning Models for Object Tracking

Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities.
The Integration of OpenCV and Unity for the Development of Interactive Educational Game

In recent years, online and interactive educational games have become increasingly popular as a tool to improve and accelerate the learning experience for students. Computer vision libraries in python such as OpenCV provide a wide range of tools for image processing, object detection, and face recognition that can be used to create interactive and engaging games. This paper explores the use of OpenCV in Unity 3D for the development of interactive online educational games. Specifically, we discuss how OpenCV can be used to create interactive games that respond to user actions in real-time, such as tracking the movement of a user’s hand or face to control in-game actions. It focuses on an unconventional approach to interactive game development using OpenCV combined with a computer network based protocol namely User Datagram Protocol (UDP) as communication channel to transfer real-time data to Unity Game Engine from Python Server. Though being the conventional approach for object and face tracking based games Augmented Reality (AR) requires specialized hardware and high camera quality for accurate tracking of object position and orientation. Given the lack of technical infrastructure and computational resources in majority government schools under the Indian academic education system this approach is not feasible for large scale implementation. This research paper aims to contribute to the development of innovative and effective approaches for interactive and online educational games suitable for large scale implementation in the form of a Vlab (Virtual Lab).
Machine Learning Could be Easier if All Data Were MNIST

MNIST is a famous image dataset; several researchers evaluated their algorithms using MNIST and provided high accuracy. However, the accuracies were degraded on other datasets. Such an aspect raises the assumption that accuracy can be improved if all data were MNIST. Accordingly, this study proposes a preprocessing algorithm to transform all data into MNIST. In the proposal, an autoencoder (AE) is trained from MNIST, where the hypothesis lies that all decoder outputs are MNIST. Then, decoders are transferred to process feature vectors extracted from arbitrary input datasets. In the experiment, transformed data are compared with the original data in supervised classification. Although the accuracy is not improved, the proposed transformation method shows an advantage regarding privacy protection.
Multi-Layer Transformer for Video Classification

Video classification is a challenging task because of the intricate spatiotemporal information present within videos. Current models often rely on 2D or 3D convolutional neural networks. However, convolutional neural networks are difficult to solve the long-range dependency problem. In addition, they are computationally expensive and memory-intensive. To address the challenges, a Multi-layer Transformer is proposed for video classification. The proposed method takes advantage of the high correlation between adjacent frames by grouping them and learning local and global information with a multi-layer structure based on Transformer. First, different frame sampling rates and grouping strategies are tested in the experiments, then comparing the method with state-of-the-art models. The results demonstrate that the proposed method has advanced performance with TOP1 accuracy of 77.8% on the Kinetics-400 dataset and 64.9% on the Something-Something v2 dataset.
Recruitment of TikTok Live Hosts for Selling Clothes Using the MOORA Method

Mistakes in recruiting Hosts for the business of selling clothes on TikTok live streaming can have a negative impact on business owners, because Host failure can cause the products offered not to sell to potential customers. To overcome this problem, a decision support system is needed as a support tool for clothing selling entrepreneurs in recruiting TikTok Live Hosts that are relevant and can contribute to the development of their business. In this study the authors used Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) for the TikTok Live Host recruitment process with 5 (five) alternatives and 5 assessment criteria in subjective decision making. The results of this study indicate that the alternative that gets the first ranking position and is recommended to be accepted as a TikTok Live Host is A5 (Rina) with a value of 0.2752. As for the ranking order of other alternatives, namely A1 (Janses) with a value of 0.2738 at rank 2, A3 (Aisyah) with a value of 0.2582 at rank 3, A4 (Kylen) with a value of 0.2460 at rank 4, and A2 (Delima) with a value of 0.2190 at rank 5.
Development of GPS Track and Trace System in Dewantara Smart City Application to Realise Mobile-based Good Governance and Clean Government

Smart cities with GPS track and trace implementation have great potential to achieve good and clean governance. This technology enables the government to gather and analyse real-time data concerning mobility, public assets, and the allocation of funds. With precise and transparent information, the government can enhance efficiency, accountability, and responsiveness to the needs of the public. The primary goals of this application are to promote transparency, improve the management of community activities, and encourage community engagement. The approach employed in the creation and development of this smart city application follows the waterfall method. This methodology simplifies the system's initial development and its potential for future enhancements. The results from implementing and testing the smart city system, utilizing usability testing, yield scores exceeding 3, specifically 3.56. This indicates that both government officials and the community find the system and application highly user-friendly
Screen recapture detection based on color-texture analysis of document boundary regions

This paper examines a presentation attack detection when a document recaptured from a screen is presented instead of the original document. We propose an algorithm based on analyzing a moiré pattern within document boundary regions as a distinctive feature of the recaptured image. It is assumed that the pattern overlapping the document boundaries is a recapture artifact, not a match between document and background textures. To detect such a pattern, we propose an algorithm that employs the result of the fast Hough transform of the document boundary regions with enhanced pattern contrast. The algorithm performance was measured for the open dataset DLC-2021, which contains images of mock documents as originals and their screen recaptures. The precision of the proposed solution was evaluated as 95.4 %, and the recall as 90.5 %.
The Child-Friendly School Literacy Movement at The Indonesian Daan Haag School, The Netherlands

The child-friendly literacy movement is a movement that must be implemented by all educational units. Components of child-friendly schools include: 1) SRA Policy; 2) Educators and education personnel trained on child rights; 3) Implementation of a child-friendly learning process with the application of non-violent discipline; 4) Facilities and infrastructure that are child friendly do not endanger children, and prevent children from getting hurt; 5) Child participation; 6) Participation of parents, community organizations, business world, other stakeholders, and alumni. Through the child-friendly literacy movement, it is hoped that students will be comfortable at school. Because the school is a place for studying knowledge, but if the school is not able to make children excited, it is necessary to evaluate the things that are lacking. The characteristics of this type of child-friendly school are being fair to students, comfortable learning methods, to a comfortable and beautiful school and classroom environment. Friendly schools build a new paradigm in educating and teaching students to create a new generation without violence, fostering adult concern and fulfilling rights and protecting children from things that harm their future.
Efficient and Automatic Breast Cancer Early Diagnosis System Based on the Hierarchical Extreme Learning Machine

Breast cancer is the leading type of cancer in women, causing nearly 600,000 deaths every year, globally. Although the tumors can be localized within the breast, they can spread to other body parts, causing more harm. Therefore, early diagnosis can help reduce the risks of this cancer. However, a breast cancer diagnosis is complicated, requiring biopsy by various methods, such as MRI, ultrasound, BI-RADS, or even needle aspiration and cytology with the suggestions of specialists. On certain occasions, such as body examinations of a large number of people, it is also a large workload to check the images. Therefore, in this work, we present an efficient and automatic diagnosis system based on the hierarchical extreme learning machine (H-ELM) for breast cancer ultrasound results with high efficiency and make a primary diagnosis of the images. To make it compatible to use, this system consists of PNG images and general medical software within the H-ELM framework, which is easily trained and applied. Furthermore, this system only requires ultrasound images on a small scale, of 28×28 pixels, reducing the resources and fulfilling the application with low-resolution images. The experimental results show that the system can achieve 86.13% in the classification of breast cancer based on ultrasound images from the public breast ultrasound images (BUSI) dataset, without other relative information and supervision, which is higher than the conventional deep learning methods on the same dataset. Moreover, the training time is highly reduced, to only 5.31 s, and consumes few resources. The experimental results indicate that this system could be helpful for precise and efficient early diagnosis of breast cancers with primary examination results.
VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints

Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. Ownership testing techniques are designed to identify model fingerprints for verifying plagiarism. However, previous works often rely on overfitting or robustness features as fingerprints, lacking theoretical guarantees and exhibiting under-performance on generalized models. In this paper, we propose a novel ownership testing method called VeriDIP, which <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">veri</u> fies a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> NN model's <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> ntellectual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> roperty. VeriDIP makes two major contributions. (1) It utilizes membership inference attacks to estimate the lower bound of privacy leakage, which reflects the fingerprint of a given model. The privacy leakage fingerprints highlight the unique patterns through which the models memorize sensitive training datasets. (2) We introduce a novel approach using less private samples to enhance the performance of ownership testing. Extensive experimental results confirm that VeriDIP is effective and efficient in validating the ownership of deep learning models trained on both image and tabular datasets. VeriDIP achieves comparable performance to state-of-the-art methods on image datasets while significantly reducing computation and communication costs. Enhanced VeriDIP demonstrates superior verification performance on generalized deep learning models, particularly on table-trained models. Additionally, VeriDIP exhibits similar effectiveness on utility-preserving differentially private models compared to non-differentially private baselines.
Morph-GCNX: A Universal Architecture for High-Performance and Energy-Efficient Graph Convolutional Network Acceleration

While current Graph Convolutional Networks (GCNs) accelerators have achieved notable success in a wide range of application domains, these GCN accelerators can not support various intra- and inter- GCN dataflows or adapt to diverse GCN applications. In this paper, we propose Morph-GCNX, a flexible GCN accelerator architecture for high-performance and energy-efficient GCN execution. The proposed design consists of a flexible Processing Element (PE) array that can be partitioned at runtime and adapt to the computational needs of different layers within a GCN or multiple concurrent GCNs. The proposed Morph-GCNX also consists of a morphable interconnection design to support a wide range of GCN dataflows with various parallelization and data reuse strategies for GCN execution. We also propose a hardware-application co-exploration technique that explores the GCN and hardware design spaces to identify the best PE partition, workload allocation, dataflow, and interconnection configurations, with the goal of improving overall performance and energy. Simulation results show that the proposed Morph-GCNX architecture achieves 18.8×, 2.9×, 1.9×, 1.8×, and 2.5× better performance, reduces DRAM accesses by a factor of 10.8×, 3.7×, 2.2×, 2.5×, and 1.3×, and improves energy consumption by 13.2×, 5.6×, 2.1×, 2.5×, and 1.3×, as compared to prior designs including HyGCN, AWB-GCN, LW-GCN, GCoD, and GCNAX, respectively.