Reviewerly/exHarmony huggingface dataset:
The Impact of the Linus Literacy Programme on Reading Ability Skills in Malaysia

This study was conducted to identify factors contributing to reading proficiency among primary school students who attended the Literacy and Numeracy Screening (LINUS) Programme in Malaysia. A total of 108 students were selected to participate in this study, which used a mixed method. The findings show no significant difference in the proficiency of reading aspects between students in terms of gender. However, the findings proved that students whose parents work in the government sector show better achievement than students from families where their parents working in the private sector. This study also found that students obtained a better command in reading during the post-test was higher than during the pre-test. A qualitative approach is applied to obtain desired data by using observation, interview and document review techniques. The study found that the LINUS teachers used various methods such as demonstrations, instructions, drills and fun learning during teaching sessions while incorporating learning aids; for instance, scan cards and reading kits help students recognise syllables and increase reading ability. Low-income parents, type of occupation, passive LINUS students and frequent absenteeism are among the constraints identified in this study. Keywords: education, LINUS programme, literacy, primary schools
Correspondence between entangled states and entangled bases under local transformations

We investigate whether pure entangled states can be associated to a measurement basis in which all vectors are local unitary transformations of the original state. We prove that for bipartite states with a local dimension that is either 2, 4, or 8, every state corresponds to a basis. Via numerics, we strongly evidence the same conclusion for two qutrits and three qubits also. However, for some states of four qubits, we are unable to find a basis, leading us to conjecture that not all quantum states admit a corresponding measurement. Furthermore, we investigate whether there can exist a set of local unitaries that transform any state into a basis. While we show that such a state-independent construction cannot exist for general quantum states, we prove that it does exist for real-valued $n$-qubit states if and only if $n=2,3$, and that such constructions are impossible for any multipartite system of an odd local dimension. Our results suggest a rich relationship between entangled states and iso-entangled measurements with a strong dependence on both particle numbers and dimension.
Analisis Kesalahan Ejaan dalam Teks Deskripsi Siswa Kelas VII SMP Negeri 14 Surakarta

Language errors are the symptoms that cannot be separated from language learning. Language errors are often found in the description text of students' works. This research aims to describe the errors in writing Indonesian language spelling in a collection of descriptive text works by grade VII students of SMP Negeri 14 Surakarta. This research is a descriptive research which uses a qualitative approach. The data sources for this research are 12 descriptive texts by grade VII students of SMP Negeri 14 Surakarta. The data in this research are spelling errors in 12 student description texts. Data collection uses documentation techniques. This research is carried out with the steps of (1) collecting sample data sources; (2) analyzing data and finding spelling errors in data sources; (3) classifying errors; (4) describing spelling errors in the data; (5) evaluating errors and finding corrections. The results show that there are 108 spelling errors in the descriptive text of the students' works at SMP Negeri 14 Surakarta. These spelling errors include errors in writing capital letters, writing prepositions, writing abbreviations, writing punctuation, writing repeated words, and writing affixes.
Word self-update contrastive adversarial networks for text-to-image synthesis

Synthesizing realistic fine-grained images from text descriptions is a significant computer vision task. Although many GANs-based methods have been proposed to solve this task, generating high-quality images consistent with text information remains a difficult problem. These existing GANs-based methods ignore important words due to the use of fixed initial word features in generator, and neglect to learn semantic consistency between images and texts for discriminators. In this article, we propose a novel attentional generation and contrastive adversarial framework for fine-grained text-to-image synthesis, termed as Word Self-Update Contrastive Adversarial Networks (WSC-GAN). Specifically, we introduce a dual attention module for modeling color details and semantic information. With a new designed word self-update module, the generator can leverage visually important words to compute attention maps in the feature synthesis module. Furthermore, we contrive multi-branch contrastive discriminators to maintain better consistency between the generated image and text description. Two novel contrastive losses are proposed for our discriminators to impose image-sentence and image-word consistency constraints. Extensive experiments on CUB and MS-COCO datasets demonstrate that our method achieves better performance compared with state-of-the-art methods.
A Method for Generating Error-caused Scenarios

Abstract In recent years, with the development of artificial intelligence, Unmanned Autonomous Systems (UAS) have also developed rapidly. It is necessary to conduct sufficient testing and evaluation of UAS before using it. This paper proposes a method for generating error-caused scenarios that lead to incorrect behavior in UAS, and how to evaluate the level of incorrect behavior in UAS caused by the scenario. Based on the method proposed in this paper, error-caused scenarios can be generated in the simulation platform, and the dataset constructed from these scenarios can be input into the target detection model to verify the effectiveness of error-caused scenarios. Our experiments have shown that the scenarios generated by the method can easily lead to errors in UAS, and the method can play an important role in the field of model-based UAS testing and validation.
Object Detection in Security Inspection Scenarios Based on YOLOv5s: Exploring Experiments

Abstract At present, there are many problems with the common methods of manual safety checks. Applying object detection technology to X-ray security inspection can effectively improve efficiency and reduce operating costs. The training parameters of deep neural networks directly affect the performance of the network. Based on YOLOv5s network model, this paper uses the method of control variables to explore the impact of training strategies, data enhancement strategies, and loss function selection on network performance. The experimental results show that a suitable batch size and learning rate can achieve significant performance improvements in the model without changing the network structure. With the increase in strength of the data enhancement strategy, the model performance shows a trend of first improving and then decreasing. The selection of the loss function has a great impact on the model’s performance. The research methods and ideas in this article can provide inspiration for subsequent related research.
PIMedSeg: Progressive interactive medical image segmentation

Accurate object segmentation in medical images is a crucial step in medical diagnosis and other applications. Despite years of research on automatic segmentation approaches, achieving clinically acceptable image quality remains challenging. Interactive segmentation is seen as a promising alternative; thus, we propose a new interactive segmentation framework based on a progressive workflow to reduce user effort and provide high-quality results.First, our approach encodes user-provided region clicks and edge scribbles using our proposed disk and curve transform. Then, it is followed by refinement with a transformer-based module that extracts effective features from the outputs of the convolutional neural network (CNN) and the extra input maps.Extensive experiments conducted on various medical images, including ultrasound (US), computerized tomography (CT), and magnetic resonance images (MRI), have demonstrated the effectiveness of our new approach over the state-of-the-art alternatives.The proposed framework can achieve high-quality segmentation using minimal interactions without the substantial cost of manual segmentation.
Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network

Objective Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images. Methods We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning. Results MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance. Conclusion The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions. Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images. We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning. MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance. The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.
Joint Feature Aggregation for Stereo Image Super-resolution

Stereo image super-resolution (Stereo SR) has been a newly rising and challenging problem with the popular application of dual cameras, which can be used to promote the SR performance by adding auxiliary information from another viewpoint. Most of the existing excellent works have concentrated on leveraging the intrinsic feature correlation of two view images via exploring the non-local attention mechanism. However, they only perform interaction once for feature registration and fusion accompanied by the complex view transition constraint, which cannot fully take advantage of the information in the stereo image pairs. In this paper, we propose a joint feature aggregation network for Stereo SR to calibrate single-view features and integrate cross-view knowledge effectively. Specifically, we introduce a self-calibrated feature extractor to excavate multi-scale and multi-direction features within the single-view image. What’s more, we design an adaptive fusion module with the cross-view attention mechanism, which is utilized to mine and fuse the long-range dependencies between the stereo image pairs so as to get rid of the inflexible cycle constraints. Extensive experimental results demonstrate that our proposal successfully achieves superior performance against the state-of-the-art methods on four datasets.
Scale-Aware Task Message Transferring for Multi-Task Learning

Exploring cross-task interaction has been the mainstream in recent multi-task learning for dense predictions. However, existing works that focus on excavating cross-task contextual information are briefly based on hierarchical all-scale features, ignoring the complementary information from task relationships in multi-scale features. Meanwhile, recent research advances rarely pay attention to efficient information transformation between tasks. In this paper, we propose a novel end-to-end multi-task learning network termed as Scale-Aware Task Message Transferring Network (SATMTN) to explore the task relationships in multi-scale features to enrich the contextual cross-task information in the all-scale features. Specifically, we design Multi-Scale Message Passing Decoder (MSMP) to model interaction information between tasks in the multi-scale features, which is characterized by stacked bi-directional fully connected graph neural networks. Further, we devise another All-Scale Message Passing decoder (ASMP) to extract rich contextual information between tasks in the all-scale features, which is based on adaptive message transformation in the graph. Extensive experiments are implemented to show that our method surpasses the current state-of-the-art works on public multi-task benchmarks.
The development of rowing performance evaluation technique using high-speed imaging

Rowing as an on-river sports has a long history dating back to the 16th century. The deeper understanding of fluid dynamics theory and the development of advanced material and manufacturing science and technology have inevitably fueled the continuous improvement on the world record of the rowing sports. The current paper focuses on the accurate extraction of the kinematics information including the boats location, velocity, and acceleration, resulting from the athletes rowing motion. A consumer-level digital camera is used for imaging the rowing motion at 50 frames per second from a distance, corresponding to a specific field of view ranging from 12 m 36 m. Furthermore, due to large-scale imaging lens distortion, the physical resolution is varying significantly due to imaging location change. A video-specific calibration method using fourth order polynomials are developed to calibrate the physical resolution of each pixel based on the physical length of the boat. The resulting data is pixel-level accurate and provides detailed record for athletes training. The acquired information is extremely useful in gauging athletes performance and can be further analyzed to provide concrete technical improvement advice.
Research on quantum computing and its development

Based on the rapid development of quantum computing and quantum communication all over the world, this paper studies the latest development of quantum computing on the basis of consulting a large number of literatures. By analyzing literature and summarizing, the basic knowledge and concept of quantum computing, the theory of time and data complexity, quantum computer, and the technical route of quantum computing are studied. Through the research, it is found that quantum computing is still in the primary stage, and the hardware of quantum computing, quantum error correction, anti-noise quantum computing and quantum simulation all need further research to improve quantum theory.
Towards global recognition: Chinese recognition with non-local attention

As a part of image recognition, Chinese character recognition has a great application market in China, such as license plate recognition, logistics information recognition and so on. In recent years, the application of CNN has set off a frenzy of computer vision. Especially in the task of image recognition, CNN is widely used because of its high accuracy and few calculation parameters. However, CNNs local operation of using repeated filters to process images also has its shortcomings, for example, it cant pay attention to the relationship between distant pixels in the image. To solve this problem, we tried to add non-local operation to CNN to improve its performance. We chose the classic model of ResNetV2-50 as the foundation, and added non-local blocks to it. We compared the results of the two models and found that the accuracy increased by 4%.
3D-DIC for Large Displacement With Laser-Based Speckle Patterns

To overcome the limitation caused by the production of surface speckles, Digital Image Correlation (DIC) with speckles generated by the light interference of lasers reflected from rough surfaces has been developed by researchers. However, such speckle patterns are highly dependent on the light path that is determined by the locations of the laser, the target surface, and the cameras, which leads to two problems: 1. the speckle distributions of the same subset are different for cameras of different locations, thus 3D measurements based on stereo cameras are unavailable; 2. the speckle distributions are changing when the target is going through large displacements, thus measurements of large displacement are not available. In this paper, a method to align the laser speckle subsets in the left and the right cameras using printed or projected speckles are proposed to solve the first aforementioned problem, and a speckle updating method is proposed to solve the second problem. An experiment is performed to evaluate the feasibility and the accuracy of the proposed methods with standard DIC measurement results as benchmarks. The result suggests that both methods provide accurate measurement result. The extra error caused by the method proposed for left-right alignment is minor and that by the speckle updating method is much better than the other existing solution.
A COMPARATIVE STUDY ON THE EFFECT OF NHT AND RSQC2 TECHNIQUES

The aims of this research are to investigate whether there is any significant difference on reading comprehension between students who were taught using NHT and those who were taught using RSQC2 techniques and, to determine which one of the two techniques is more effective in improving students’ reading comprehension. The research design used in this study was Pre-test and Post-test Comparison Group Design. The samples were three classes of the tenth grade students at SMKN 6 Pekanbaru of the 2015 academic year. The samples were selected using the technique of cluster sampling. The total samples were 97 students. The forms of the test were multiple choices. Finally, first the writer found that significant probabilities was higher than 0.05 (sig &gt; 0.05) in multiple comparison one way ANOVA. It means that there is no significant difference on students’ reading comprehension between using NHT and RSQC2 techniques are good in comprehending reading text at SMKN 6 Pekanbaru. The second, found that significant probabilities was lower than 0.05 (sig &gt; 0.05) in both paired sample t-test and independent sample t-test. It means that there is a significant difference of students’ reading comprehension pre-test and post-test of experimental classes
Multilevel Explainable Artificial Intelligence: Visual and Linguistic Bonded Explanations

Applications of deep neural networks (DNNs) are booming in more and more fields but lack transparency due to their black-box nature. Explainable artificial intelligence (XAI) is therefore of paramount importance, where strategies are proposed to understand how these black-box models function. The research so far mainly focuses on producing, for example, class-wise saliency maps, highlighting parts of a given image that affect the prediction the most. However, this method does not fully represent the way humans explain their reasoning and, awkwardly, validating these maps is quite complex and generally requires subjective interpretation. In this article, we conduct XAI differently by proposing a new XAI methodology in a multilevel (i.e., visual and linguistic) manner. By leveraging the interplay between the learned representations, i.e., image features and linguistic attributes, the proposed approach can provide salient attributes and attribute-wise saliency maps, which are far more intuitive than the class-wise maps, without requiring per-image ground-truth human explanations. It introduces self-interpretable attributes to overcome the current limitations in XAI and bring the XAI closer to a human-like explanation. The proposed architecture is simple in use and can reach surprisingly good performance in both prediction and explainability for deep neural networks thanks to the low-cost per-class attributes.
Prof. Nevzat Tarhan: "Fear of earthquakes triggers 'mean world syndrome'"

President of Üsküdar University Prof. Nevzat Tarhan touched upon the effects of the great Istanbul earthquake discussions.
PENGEMBANGAN ALAT PERMAINAN EDUKATIF ULAR TANGGA DALAM PEMBELAJARAN MATEMATIKA KELAS II SDN 9 NEGERIKATON

ABSTRACT&#x0D; This research uses development research. This research is motivated by the unavailability of snakes and ladders educational game tools as learning media in mathematics. Snakes and ladders educational game tool media is good to use because it can make it easier for educators to carry out learning, students can also learn while playing so that the process of learning activities feels fun and not boring. Based on the research results, it was obtained that the material validation was 90.38% with the criteria of "very feasible". The media expert validation results were 100% with the "very feasible" criteria. The results of the educator's response were 91.17% included in the "very interesting" category. The results of student responses to product trials with a percentage of 96% fall into the "good" category and can already be said to be "interesting". The average learning outcomes of mathematics using the snake and ladder educational game tool with a score of 85 from 37 students who took part in the product trial, and obtained an effectiveness of 100%. All students have achieved the minimum score and everything is complete.
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN ONLINE TERHADAP PRESTASI BELAJAR SISWA MELALUI MINAT BELAJAR

This type of research was quantitative with an experimental method. The procedure employed in this research was the class was given a pre-test and post-test as well as the results of the validity test, reliability test, normality test, and hypothesis test. Reliability was used to measure an instrument with two or more answer choices and for an instrument with more than two answer choices. It is said to be reliable if the Cronbach Alpha reliability coefficient is between 0.70 - 0.90 and the results obtained from the results of the questionnaire validity test are 0.721. The test results showed that the use of learning media influences students' achievement.