Reviewerly/exHarmony huggingface dataset:
Pengaruh media kartu kata bergambar dalam meningkatkan kemampuan membaca permulaan di sekolah dasar

This Study alims: 1) to find out how the use of picture word calrds medial in improving the albility to start reading in clalss 1, 2) to find out how the effect of picture word calrds medial in improving the albility to start reading in clalss 1 students. The resealrch method used in this resealrch is alqualntitaltivealpproalch with Qualsi Experiment resealrch type. The independent valrialble in this resealrch is the use of illustralted word calrds while the dependent valrialble is the albility to start reading. The populaltionused is 20 experimentallclalss students alnd 20 control clalss students. The resealrchsalmpleused is salturaltedsalmple. In this study, the daltal collection technique used walsobservaltion. Tests, alnddocumentaltiondaltalwals collected by pretest, trealtment, alndpost test. Alnd the daltalalnallysis techniques calrriedout in this study were descriptive staltalticall tests, normallity tests, wilcoxon signed hypothesis tests, alnd homogeneity tests with the SPSS system. The result of this study indicaltethalt the alveralgevallue of students before the alpplicaltion of the medialwals 79,5%, while alfter the alpplicaltionvallue of students increalsed to 92%. It is alccepted. So it caln be concluded thalt this picture word walrdcalrd medialhalsal very significalnt influence in improvingalloudrealding skills in gralde 1 elementalry school, so this picture word calrd medial is very suitalble for use in gralde 1 to improve students’ start reading skills.
Classification of eye diseases in fundus images using Convolutional Neural Network (CNN) method with EfficientNet architecture

This research is designed to classify eye disease conditions into three classes, namely normal, cataract, and glaucoma using a system. The system will use Convolutional Neural Network (CNN) with EfficientNet architecture. The EfficientNet that will be used is EfficientNet-B0. The dataset in this paper is obtained from Kaggle totaling 300 images. From this data, augmentation is carried out so that 3.600 images are obtained consisting of "normal" (1200 images), "cataract" (1200 images), and "glaucoma" (1200 images). This data is processed into 4 different datasets, namely the original dataset, augmentation dataset, augmentation dataset that has been preprocessed grayscale, and augmentation dataset that has been preprocessed thresholding. The best results are obtained using the Adam optimizer, learning rate 0.00001, and batch size 32, and iterations of 20 epochs. The best dataset is the augmentation dataset that has been preprocessed grayscale with an accuracy of 79.22%, precision value of 80.3%, recall value of 79.22%, F1-Score of 78.87%.
Improving students' reading comprehension skills through the survey, question, read, recite, review (SQ3R) method

&lt;span&gt;This research describes the development of the ability to understand reading content and student activity when learning using the SQ3R method at XII APT SMK Negeri 1 Panai Hulu T.P 2022/2023. They used two cycles for 16 weeks in Odd Semester. The results of 2 rounds of research obtained the following: 1. Reading Skills Students' comprehension improves by using the SQ3R method. A mean of 65 was obtained during the first ability test, with a final classical percent of 45%. And in the second ability test, a mean result of 85 was obtained with a final class percentage of 87%, and 31% progress was obtained. The result was an increase, and a minimum requirement of the Indonesian language was achieved. 2. There is an increase in students' activities in absorbing teaching materials by applying the SQ3R method.&lt;/span&gt;
Pengaruh Bimbingan Kelompok dengan Media Audio Visual terhadap Motivasi Belajar Siswa di MTs Negeri 4 Jakarta

This research investigated the impact of group guidance with audio-visual media on learning motivation at MTs Negeri 4 Jakarta. A quantitative approach with a One Group Pretest Posttest experimental design was employed. Purposive sampling was used to select eighth-grade students as participants. Validity testing of the research instrument involved 20 statements, with 15 deemed valid and 5 invalid. The instrument demonstrated reliable validity with a Cronbach's Alpha coefficient of 0.675, surpassing the 0.60 threshold. Data analysis using the Wilcoxon test showed that 6 students experienced improved learning motivation, with an average increase of 3.50. This highlights the significant influence of group guidance with audio-visual media on students' learning motivation. Hypothesis testing revealed a significant effect, with an Asymp.Sig (2-tailed) value of 0.027 below the 0.05 significance level. Thus, the null hypothesis was rejected, indicating an impact of group guidance with audio-visual media on students learning motivation at MTs Negeri 4 Jakarta.
Navigating GDPR Compliance in AI: A Deep Dive into OpenAI’s ChatGPT — A Perspective from Multimedia Design Architecture

IntroductionIn an era increasingly dominated by artificial intelligence, the matter of informed consent has never been more crucial. A study conducted by Ipsos indicates a significant 11-point drop in internet trust since 2019[1]. Particularly in the European Union, there is growing concern about the handling of personal data. This article aims to shed light on the ways in which AI platforms, like OpenAI’s ChatGPT, fall short of meeting key guidelines established by the European Union’s General Data Protection Regulation (GDPR).Utilizing data from Enforcement Tracker , we present a graph illustrating the distribution of GDPR fines across various sectors. The Media, Telecoms, and Broadcasting sectors are particularly noteworthy, both for the number of violations and the scale of the fines imposed, signaling serious continuos non-compliance .
Povreda autorskog prava na internetu – naknada ili prevencija štete?

The availability of technology and the spread of the Internet has led to a huge transition: people have gone from being consumers of content to being creators of content. However, the speed of technological progress, the ease of publishing and sharing content with a wide audience raises the question of whether the existing norms in the field of copyright keep pace with the development of technology, as well as whether they adequately protect the author from the damage that may be caused to him by infringing the copyrighted work. In the first part of the paper, the author analyzes the difference between continental and Anglo-Saxon law in terms of copyright protection and limitations. Furthermore, the author analyzes the issue of compensation for damages in the case of copyright infringement on the Internet through the presentation of cases from the practice of the United States of America and the European Union. In addition to the above, special attention is paid to Article 17 of the Directive on Copyright and Related Rights in The Single Digital Market, which eliminates the shortcomings of existing systems and enables prevention in the protection of copyright. Finally, the author analyzes the provisions of the legislation of the Republic of Serbia regarding the regulation of the mentioned issue.
Efficient Per-Shot Transformer-Based Bitrate Ladder Prediction for Adaptive Video Streaming

Recently, HTTP adaptive streaming (HAS) has become a standard approach for over-the-top (OTT)-based video streaming services due to its ability to provide smooth streaming. In HAS, stream representations are encoded to target a specific bitrate providing a wide range of operating bitrates known as the bitrate ladder. In the past, a fixed bitrate ladder approach for all videos has been widely used. However, such a method does not consider video content, which can vary considerably in motion, texture, and scene complexity. Moreover, building a per-title bitrate ladder based on an exhaustive encoding is quite expensive due to the large encoding parameter space. Thus, alternative solutions allowing accurate and efficient per-title bitrate ladder prediction are in great demand. On the other hand, self-attention-based architectures have achieved tremendous performance in large language models (LLMs) and particularly vision transformers (ViTs) in computer vision tasks. Therefore, this paper investigates ViT’s capabilities in building an efficient bitrate ladder without performing any encoding process. We provide the first in-depth analysis of the prediction accuracy and the complexity overhead induced by the ViTs model in predicting the bitrate ladder on a large and diverse video dataset. The source code of the proposed solution and the dataset will be made publicly available.
JPEG Information Regularized Deep Image Prior for Denoising

Image denoising is a representative image restoration task in computer vision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful image denoising from only a noisy image by inductive bias of convolutional neural network architectures without any pre-training. The major challenge of DIP based image denoising is that DIP would completely recover the original noisy image unless applying early stopping. For early stopping without a ground-truth clean image, we propose to monitor JPEG file size of the recovered image during optimization as a proxy metric of noise levels in the recovered image. Our experiments show that the compressed image file size works as an effective metric for early stopping.
TRG-DQA: Texture Residual-Guided Dehazed Image Quality Assessment

Image dehazing algorithms have emerged to solve the visual impairment caused by haze. It is important to establish dehazed image quality assessment (DQA) methods that can accurately evaluate the dehazed image quality and the performance of dehazing algorithms. However, classical image quality assessment (IQA) and most hand-crafted feature based DQA methods may not be able to adequately measure complex distortions of dehazed images. To address this issue, this paper proposes a Texture Residual-Guided Dehazed image Quality Assessment (TRG-DQA) method. Specifically, we first introduce a global and local feature extraction module employing a combination of the Transformer and convolutional neural networks (CNN) for extracting the comprehensive features. Considering that texture residual maps represent haze density and artifact distortion information, we propose a residual-guided module to guide the model for efficient learning. Additionally, to mitigate the information loss issue that occurs in deeper networks, a distortion-aware feature enhancement module is proposed. Extensive experiments on six DQA databases demonstrate the proposed TRG-DQA achieves superior performance among all the state-of-the-art methods.
External Parameters Calibration for Binocular Camera based on Linear Solution and Nonlinear Optimization

In the binocular stereo vision image processing, generally the pose information between the camera coordinate systems of the right eye and the left eye is widely employed, and the inaccurate pose information directly affects the validity of image processing. However, conventional algorithms to calibrate binocular cameras equipped with depth estimation function often need to collect multiple frames of binocular images to calculate and obtain external parameters, resulting in a long calibration time. Aiming at this problem, this paper proposes a novel external parameters calibration algorithm for binocular cameras based on linear solution and nonlinear optimization, which makes full use of the depth information of binocular camera. Compared with the open-source algorithm Kalibr, this method needs fewer frames of images for calibration and significantly shortens the calibration time while taking into account the calibration accuracy. The method simplifies the process of binocular external parameters calibration and improves the efficiency.
Attribute-based Searchable Encryption with Policy Hidden in Edge Computing

Edge computing bring storage, computing, processing, and networking closer to the devices that generate or consume data than traditional models based entirely on the cloud, which is a set of edge computing technologies. Edge nodes take on some of the encryption and decryption work for users, reducing the network latency and computational burden on lightweight end devices to a certain extent. Attribute-based encryption algorithm combined with searchable encryption algorithm, which achieve the flexible access and search to encrypted data. Access policies are usually published in plain text, which can cause leakage of user information and data privacy. Traditional single-keyword search generates massive data that is irrelevant to the search results. An attribute-based searchable encryption scheme with policy hiding in edge computing is proposed to access and retrieve encrypted data through attributes and keywords, with the edge nodes sharing the computational burden of data users. We utilize a novel approach to anonymize access policies and protect attribute privacy information from disclosure. Our scheme is proven resistant to Chosen Plaintext Attack (CPA) and Chosen Keyword Attack (CKA).
Multi-vision Based 3D Reconstruction System for Robotic Grinding

High-precision grinding process often requires to plan the grinding paths based on the reconstructed models of the workpieces. This paper presents a multi-vision based 3D reconstruction system which uses four industrial cameras to capture RGB images from multiple angles to reconstruct the 3D models of the workpieces to be ground. Feature extraction, feature matching and loop closure correction were applied for reconstructing the rough outline of the workpiece. Then we used depth map estimation and fusion to expand sparse point clouds into denser point clouds to improve the reconstruction performance. Poisson reconstruction was finally used to repair the voids on the surface to get a smooth model. The experimental results show that this system can obtain a complete and accurate 3D point cloud model of the workpiece in preparation for the path planning of the robotic grinding.
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement

Over the recent years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains, including robotics, self-driving cars, and finance. In this paper, we are introducing Reinforcement Learning (RL) to label placement, a complex task in data visualization that seeks optimal positioning for labels to avoid overlap and ensure legibility. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy, the first machine-learning-driven labeling method, in contrast to the existing hand-crafted algorithms designed by human experts. To facilitate RL learning, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualization. Our results show that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and the compared methods designed by human experts in terms of completeness (i.e., the number of placed labels). The trade-off is increased computation time, making the proposed method slower than the compared methods. Nevertheless, our method is ideal for scenarios where the labeling can be computed in advance, and completeness is essential, such as cartographic maps, technical drawings, and medical atlases. Additionally, we conducted a user study to assess the perceived performance. The outcomes revealed that the participants considered the proposed method to be significantly better than the other examined methods. This indicates that the improved completeness is not just reflected in the quantitative metrics but also in the subjective evaluation by the participants.
Real-Time Supermarket Product Recognition on Mobile Devices Using Scalable Pipelines

The recognition of supermarket products on mobile devices is gaining importance as more and more consumers seek to make informed decisions about their purchases in real time. However, the realization is often difficult due to the vast product assortments of modern supermarkets and the limited computational resources available on mobile devices. In this work, we propose a real-time on-device product recognition pipeline, based on the Global Trade Item Number (GTIN) system, that is both robust to dynamic changes in the product assortment and scalable to tens of thousands of products. We evaluate detection performance on SKU110k and R6k datasets and demonstrate the scalability of our pipeline with 5974 different products, using synthetic data. Furthermore, the proposed product recognition pipeline is deployed on a Google Pixel 6 mobile phone, where it achieves an inference time of 121ms (8.3fps), demonstrating its real-time capabilities in practice.
Improvement of Image Segmentation Model for Handwritten Notebook Analytics

The main objective of this paper is to improve the image segmentation model for handwritten notebook analytics. We conducted a considerable amount of research in this area to increase the accuracy and efficiency of segmentation. To address the issues with traditional methods, we introduced attention mechanism and recursive residual convolutional neural network in the multi-task U-Net model. Through training and testing the model on handwritten notebook dataset and compared it with other existing technologies, we demonstrated the effectiveness of this method. The results showed that the model had a significant improvement in accuracy. Therefore, the research findings in this paper are important for improving the technology of handwritten notebook analytics.
Scene Text Recognition Models Explainability Using Local Features

Explainable AI (XAI) is the study on how humans can be able to understand the cause of a model’s prediction. In this work, the problem of interest is Scene Text Recognition (STR) Explainability, using XAI to understand the cause of an STR model’s prediction. Recent XAI literatures on STR only provide a simple analysis and do not fully explore other XAI methods. In this study, we specifically work on data explainability frameworks, called attribution-based methods, that explains the important parts of an input data in deep learning models. However, integrating them into STR produces inconsistent and ineffective explanations, because they only explain the model in the global context. To solve this problem, we propose a new method, STRExp, to take into consideration the local explanations, i.e. the individual character prediction explanations. This is then benchmarked across different attribution-based methods on different STR datasets and evaluated across different STR models.
A 3D Label Stereo Matching Method Using Underwater Energy Function

Stereo vision is one of the most important ways to explore the underwater world. However, due to the irregularities of underwater scenes and significant distortion in the images taken underwater which prevents accurate matching of corresponding points by using one-dimensional disparity value between images, acquiring three-dimensional information in the complex underwater environment is very challenging. In this paper, we propose an underwater stereo matching method to estimate dense disparity maps in the underwater environment and obtain three-dimensional information about the target object. First, the 3D label underwater energy function is proposed to fit the inclined and curved planes of irregular underwater objects under the condition of underwater image distortion. Then the underwater disparity refinement method is proposed to remove the mismatch disparities caused by noise underwater. Experimental results show that the proposed method outperforms existing methods in terms of subjective and objective quality. The dataset is available at: https://github.com/lv-wj/underwater-datasets.
A Baseline on Continual Learning Methods for Video Action Recognition

Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervised-trained models. However, most research on this subject has tackled continual learning in simple image classification scenarios. In this paper, we present a benchmark of state-of-the-art continual learning methods on video action recognition. Besides the increased complexity due to the temporal dimension, the video setting imposes stronger requirements on computing resources for top-performing rehearsal methods. To counteract the increased memory requirements, we present two method-agnostic variants for rehearsal methods, exploiting measures of either model confidence or data information to select memorable samples. Our experiments show that, as expected from the literature, rehearsal methods outperform other approaches; moreover, the proposed memory-efficient variants are shown to be effective at retaining a certain level of performance with a smaller buffer size.
Algorithm-Aware Qubit Mapping

&lt;p&gt;Algorithm-aware (AlgAw) qubit mapping aims at directly providing the solutions to qubit mapping of algorithms with regular structures based on the algorithm's features. Although exact methods can provide high quality solutions, their compilation time grows exponentially with circuit size. To improve scalability, we propose the AlgAw qubit mapping approach. The main idea is to first identify subcircuits in an algorithm to be mapped, then analyze optimal solutions for small scale subcircuits found by exact methods, extend these solutions to large scale subcircuits, reconstruct the entire circuit and assign parameters, and finally map pretranspiled circuits onto the target quantum devices. Applying AlgAw to the quantum approximate optimization algorithm (QAOA) on linear and T-shaped subtopologies produces optimal and scalable solutions for arbitrary numbers of qubits and depths, which is critical to the algorithm's performance on near-term quantum devices. Compared to other methods including Qiskit, Tket, and SWAP-network, AlgAw produces the least number of CNOT gates and the lowest circuit depth for the pretranspiled circuits. Furthermore, AlgAw takes only a few seconds to construct a resulting circuit having a hundred qubits. The benchmarking results on IBM quantum devices show that AlgAw qubit mapping yields better performance than others. AlgAw is applicable to other variational quantum algorithms (VQAs) and can also be applied to other algorithms.&lt;/p&gt;
Software Development Life Cycle (SDLC) Methodologies for Information Systems Project Management

The software development life cycle (SDLC) is a framework for planning, analyzing, designing, developing, testing, and deploying software. There are many different SDLC methodologies available, each with its advantages and disadvantages. The best methodology for a particular project will depend on factors such as the size and complexity of the project, the availability of resources, and the preferences of the project team. This paper provides an in-depth overview of the different SDLC methodologies used in the industry for IS project management. The paper discusses five significant SDLC methodologies: Waterfall, V-Model, Iterative, Agile, and Hybrid. It also compares and contrasts the traditional SDLC and Agile methodology and discusses using decision support matrices for selecting SDLC methodologies. By providing this comprehensive overview, the paper will help IS practitioners and project managers make informed decisions about the best approach for their specific projects.
Crypto Funds and Hybrid Currency: Digital Wallets for the Future Generation

In recent years, the world of finance and currency has witnessed a significant evolution with the advent of crypto currencies and block chain technology. Among the many innovations that have emerged, "crypto funds" have emerged as a revolutionary concept, promising to reshape how individuals manage, invest, and transact with digital assets. This article explores the concept of crypto funds and their role as digital wallets for the future generation.