Reviewerly/exHarmony huggingface dataset:
A Study on the Utilization of UI/UX Design Education: Focusing on Figma

This study focused on research the utilization of UI/UX design education through Figma, a prototyping tool. Through theoretical background research, the parts to be considered in user experience and interface were organized. The difference between the two factors was confirmed as well. In addition, the current status of NCS-based UI/UX design education currently operated by university institutions was identified, and a framework for performance standards was prepared. Afterwards, the functions supported by Figma were examined centering on the curriculum of the NCS learning module, and through this, implications for improving the completeness of classes for students were discovered. Finally, positive feedback and opinions of the students were confirmed through Figma in design education as the results. It is considered that UI/UX design education using Figma will be effective.
Robust Image Denoising of No-Flash Images Guided by Consistent Flash Images

Images taken in low light conditions typically contain distracting noise, and eliminating such noise is a crucial computer vision problem. Additional photos captured with a camera flash can guide an image denoiser to preserve edges since the flash images often contain fine details with reduced noise. Nonetheless, a denoiser can be misled by inconsistent flash images, which have image structures (e.g., edges) that do not exist in no-flash images. Unfortunately, this disparity frequently occurs as the flash/no-flash pairs are taken in different light conditions. We propose a learning-based technique that robustly fuses the image pairs while considering their inconsistency. Our framework infers consistent flash image patches locally, which have similar image structures with the ground truth, and denoises no-flash images using the inferred ones via a combination model. We demonstrate that our technique can produce more robust results than state-of-the-art methods, given various flash/no-flash pairs with inconsistent image structures. The source code is available at https://github.com/CGLab-GIST/RIDFnF.
Improving Dynamic HDR Imaging with Fusion Transformer

Reconstructing a High Dynamic Range (HDR) image from several Low Dynamic Range (LDR) images with different exposures is a challenging task, especially in the presence of camera and object motion. Though existing models using convolutional neural networks (CNNs) have made great progress, challenges still exist, e.g., ghosting artifacts. Transformers, originating from the field of natural language processing, have shown success in computer vision tasks, due to their ability to address a large receptive field even within a single layer. In this paper, we propose a transformer model for HDR imaging. Our pipeline includes three steps: alignment, fusion, and reconstruction. The key component is the HDR transformer module. Through experiments and ablation studies, we demonstrate that our model outperforms the state-of-the-art by large margins on several popular public datasets.
Sistem Informasi Kearsipan Surat dan Proposal Berbasis Website

The development of technology increases rapidly along with the times. Archiving is something that cannot&#x0D; be separated from an organization or institution, archives have such an important role. the number of&#x0D; letters created and received administratively, so that the search data is inefficient in terms of time and&#x0D; energy using a manual system. So at this time needed a letter management administration system that is&#x0D; more structured to speed up the search for existing data and make reports, So the author makes an archive&#x0D; application that is considered necessary and helps Baznas. This application is designed using PHP and&#x0D; Mysql. Because it is web-based, this archive application has the ability, can be accessed anywhere, the&#x0D; admin can manage with user logins so that it can facilitate, install, save and save letters and proposals in&#x0D; or out, print reports. Archiving letters and proposals that used to be manual are now digital.
Learning Compact Features via In-Training Representation Alignment

Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i.e., last hidden layer) and a linear classifier (i.e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e.g., cross-entropy). In each epoch, the true gradient of the loss function is estimated using a mini-batch sampled from the training set and model parameters are then updated with the mini-batch gradients. Although the latter provides an unbiased estimation of the former, they are subject to substantial variances derived from the size and number of sampled mini-batches, leading to noisy and jumpy updates. To stabilize such undesirable variance in estimating the true gradients, we propose In-Training Representation Alignment (ITRA) that explicitly aligns feature distributions of two different mini-batches with a matching loss in the SGD training process. We also provide a rigorous analysis of the desirable effects of the matching loss on feature representation learning: (1) extracting compact feature representation; (2) reducing over-adaption on mini-batches via an adaptively weighting mechanism; and (3) accommodating to multi-modalities. Finally, we conduct large-scale experiments on both image and text classifications to demonstrate its superior performance to the strong baselines.
Scene-Level Sketch-Based Image Retrieval with Minimal Pairwise Supervision

The sketch-based image retrieval (SBIR) task has long been researched at the instance level, where both query sketches and candidate images are assumed to contain only one dominant object. This strong assumption constrains its application, especially with the increasingly popular intelligent terminals and human-computer interaction technology. In this work, a more general scene-level SBIR task is explored, where sketches and images can both contain multiple object instances. The new general task is extremely challenging due to several factors: (i) scene-level SBIR inherently shares sketch-specific difficulties with instance-level SBIR (e.g., sparsity, abstractness, and diversity), (ii) the cross-modal similarity is measured between two partially aligned domains (i.e., not all objects in images are drawn in scene sketches), and (iii) besides instance-level visual similarity, a more complex multi-dimensional scene-level feature matching problem is imposed (including appearance, semantics, layout, etc.). Addressing these challenges, a novel Conditional Graph Autoencoder model is proposed to deal with scene-level sketch-images retrieval. More importantly, the model can be trained with only pairwise supervision, which distinguishes our study from others in that elaborate instance-level annotations (for example, bounding boxes) are no longer required. Extensive experiments confirm the ability of our model to robustly retrieve multiple related objects at the scene level and exhibit superior performance beyond strong competitors.
Linking People across Text and Images Based on Social Relation Reasoning

As a sub-task of visual grounding, linking people across text and images aims to localize target people in images with corresponding sentences. Existing approaches tend to capture superficial features of people (e.g., dress and location) that suffer from the incompleteness information across text and images. We observe that humans are adept at exploring social relations to assist identifying people. Therefore, we propose a Social Relation Reasoning (SRR) model to address the aforementioned issues. Firstly, we design a Social Relation Extraction (SRE) module to extract social relations between people in the input sentence. Specially, the SRE module based on zero-shot learning is able to extract social relations even though they are not defined in the existing datasets. A Reasoning based Cross-modal Matching (RCM) module is further used to generate matching matrices by reasoning on the social relations and visual features. Experimental results show that the accuracy of our proposed SRR model outperforms the state-of-the-art models on the challenging datasets Who's Waldo and FL: MSRE, by more than 5\% and 7\%, respectively. Our source code is available at https://github.com/VILAN-Lab/SRR.
Learning Semantic Alignment with Global Modality Reconstruction for Video-Language Pre-training towards Retrieval

Video-language pre-training for text-based video retrieval tasks is vitally important. Previous pre-training methods suffer from the semantic misalignments. The reason is that these methods ignore sequence alignments but focusing on critical token alignment. To alleviate the problem, we propose a video-language pre-training framework, termed videolanguage pre-training For lEarning sEmantic aLignments (FEEL), to learn semantic alignments at the sequence level. Specifically, the global modality reconstruction and the cross- modal self-contrasting method is utilized to learn the alignments at the sequence level better. Extensive experimental results demonstrate the effectiveness of FEEL on text-based video retrieval and text-based video corpus moment retrieval.
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild

In this work, we tackle the challenging task of jointly tracking hand object poses and reconstructing their shapes from depth point cloud sequences in the wild, given the initial poses at frame 0. We for the first time propose a point cloud-based hand joint tracking network, HandTrackNet, to estimate the inter-frame hand joint motion. Our HandTrackNet proposes a novel hand pose canonicalization module to ease the tracking task, yielding accurate and robust hand joint tracking. Our pipeline then reconstructs the full hand via converting the predicted hand joints into a MANO hand. For object tracking, we devise a simple yet effective module that estimates the object SDF from the first frame and performs optimization-based tracking. Finally, a joint optimization step is adopted to perform joint hand and object reasoning, which alleviates the occlusion-induced ambiguity and further refines the hand pose. During training, the whole pipeline only sees purely synthetic data, which are synthesized with sufficient variations and by depth simulation for the ease of generalization. The whole pipeline is pertinent to the generalization gaps and thus directly transferable to real in-the-wild data. We evaluate our method on two real hand object interaction datasets, e.g. HO3D and DexYCB, without any fine-tuning. Our experiments demonstrate that the proposed method significantly outperforms the previous state-of-the-art depth-based hand and object pose estimation and tracking methods, running at a frame rate of 9 FPS. We have released our code on https://github.com/PKU-EPIC/HOTrack.
Evaluating a New Auto-ML Approach for Sentiment Analysis and Intent Recognition Tasks

Automated Machine Learning (AutoML) is a research area that aims to help humans solve Machine Learning (ML) problems by automatically discovering good ML pipelines (algorithms and their hyperparameters for every stage of a machine learning process) for a given dataset. Since we have a combinatorial optimization problem for which it is impossible to evaluate all possible pipelines, most AutoML systems use a Genetic Algorithm (GA) or Bayesian Optimization (BO) to find a good solution. These systems usually evaluate the performance of the pipelines using the K-fold cross-validation method, for which the more pipelines are evaluated, the higher the chance of finding an overfitted solution. To avoid the aforementioned issue, we propose a system named Auto-ML System for Text Classification (ASTeC), that uses the Bootstrap Bias Corrected CV (BBC-CV) method to evaluate the performance of the pipelines. More specifically, the proposed system combines GA, BO, and BBC-CV to find a good ML pipeline for the text classification task. We evaluated our approach by comparing it with state-of-the-art systems: in the the Sentiment Analysis (SA) task, we compared our approach to TPOT (Tree-based Pipeline Optimization Tool) and Google Cloud AutoML service, and for the Intent Recognition (IR) task, we compared with TPOT and MLJAR AutoML. Concerning the data, we analysed seven public datasets from the SA domain and sixteen from the IR domain. Four out of those sixteen are composed by written English text, while all of the others are in Brazilian Portuguese. Statistical tests show that, in 21 out of 23 datasets, our system's performance is equivalent to or better than the others.
Critical analysis of admissibility of forensic evidence and reports in the criminal justice system of India

We have needs to use scientific methods in investigation for collection of evidence and to ensure that evidence is collected and retained without being contaminated and altered, packed and sent in a scientific and safe manner to the lab is challenge before the investigating agency where the advance techniques are used and applied to extract evidence that will link the evidence to the scene of crime and finally identify criminal so that he or she may be successfully behind the bar of justice.In spite of the increased attention paid to forensic evidence over the past decade, there is published empirical data identifying the types of evidence routinely collected, and the extent to which this evidence is submitted to and examined in forensic crime laboratories.There is even less research that describes the role and impact of such evidence on criminal justice outcomes.While the current study shows that forensic evidence can affect case processing decisions, it is not uniform across all crimes and all evidence types; The effects of evidence vary depending upon criminal offense, variety of forensic evidence, the criminal decision level, and other characteristics of the case.
[Non-rigid registration for medical images based on deformable convolution and multi-scale feature focusing modules].

Non-rigid registration plays an important role in medical image analysis. U-Net has been proven to be a hot research topic in medical image analysis and is widely used in medical image registration. However, existing registration models based on U-Net and its variants lack sufficient learning ability when dealing with complex deformations, and do not fully utilize multi-scale contextual information, resulting insufficient registration accuracy. To address this issue, a non-rigid registration algorithm for X-ray images based on deformable convolution and multi-scale feature focusing module was proposed. First, it used residual deformable convolution to replace the standard convolution of the original U-Net to enhance the expression ability of registration network for image geometric deformations. Then, stride convolution was used to replace the pooling operation of the downsampling operation to alleviate feature loss caused by continuous pooling. In addition, a multi-scale feature focusing module was introduced to the bridging layer in the encoding and decoding structure to improve the network model's ability of integrating global contextual information. Theoretical analysis and experimental results both showed that the proposed registration algorithm could focus on multi-scale contextual information, handle medical images with complex deformations, and improve the registration accuracy. It is suitable for non-rigid registration of chest X-ray images.非刚性配准在医学图像分析中有着重要的作用。U-Net被证明是医学图像分析的研究热点，被广泛应用于医学图像配准中，然而现有的基于U-Net及其变体的配准模型在处理复杂形变时，学习能力不足，且没有充分利用多尺度上下文信息，导致配准精度不够理想。针对该问题，本文提出了一种基于可变形卷积和多尺度特征聚焦模块的X线图像非刚性配准算法。该算法首先使用残差可变形卷积替代原U-Net的标准卷积，以增强配准网络对图像几何形变的表达能力；然后使用步长卷积代替下采样操作的池化运算，以缓解连续池化导致的特征丢失问题；此外在编、解码结构的桥接层中引入多尺度特征聚焦模块，以提高网络模型集成全局上下文信息的能力。理论分析和实验结果均表明提出的配准算法能聚焦多尺度上下文信息，能够处理具有复杂形变的医学图像，配准精度有一定提高，适合胸部X线片的非刚性配准。.
The Development of Curriculum and Experiential Device for Internet of Things Education in Elementary School and Application to Classroom

This study aims to develop a curriculum (draft) for the education of contents related to the Internet of Things (IoT) in elementary school setting and a device for such students to experience the IoT, and test the appropriateness of the device by applying it to the actual educational field. The results of the study are as follows.&#x0D; First, a curriculum (draft) for IoT education in elementary schools was proposed as the revision of the 2022 curriculum.&#x0D; Second, 4-basic and 3-advanced IoT devices for IoT education in elementary schools were developed as well as a guide for assembling and operating them.&#x0D; Third, although elementary school students had a certain understanding of what the IoT is and where it is used in our lives, they did not understand the components and operation process of it well.&#x0D; Fourth, elementary school students of the present analysis claimed that it was effortless to assemble and operate the developed IoT device, its user experience was interesting, and they were able to understand the components and operation process of the IoT device well.&#x0D; Fifth, students of this experiment declared their interest to assemble and operate more types of devices other than the ones developed in this research and revealed their opinion that the IoT would be used more in our lives in the future.&#x0D; Sixth, it was thought that it was urgently necessary to introduce IoT education in elementary schools so that students could understand more in detail and experience more. These results suggest that the hereby developed IoT experience device would be appropriate for further testing as an effective IoT educational material for elementary school students.
Development and application of software education-linked artificial intelligence education program

Artificial intelligence (AI) software technology is a key innovation that will change human society and lead future industries. Accordingly, the government is developing various educational policies to revitalize artificial intelligence education. However, AI education is not organized as a subject or unit in the current elementary school curriculum, making it difficult to learn. The present study developed an AI image recognition technology education program for elementary school students in connection with a software unit of a practical course subject and aimed to confirm the effectiveness of the program to create an artificial intelligence class that can utilize the curriculum time.&#x0D; For the present analysis, 50 students in two classes of the 6th grade of an elementary school were divided into an experimental group and a control group. The experimental group was subjected to the AI image recognition technology education program linked with the software education in the 20th session, while the control group underwent the software class using the textbook in the 20th session to analyze the improvement of computing thinking ability, STEAM Literacy and software education satisfaction. Results of the study revealed that the experimental group showed statistically significant differences in computing thinking. Furthermore, the experimental group showed statistically significant differences in the convergence competency of STEAM Literacy compared to the control group. Moreover, the experimental group’s software education satisfaction test result was higher than that of the control group. The hereby interpreted results indicate that it is possible to apply AI education to elementary school curriculum through this program. Furthermore, it might have a positive effect on students' learning competency, as the hereby performed experiment indicates.
Analysis of Agricultural Literacy Education Programs in Korea and the United States

The purpose of this study was to investigate the implications for practical arts education in Korea through the analysis of the contents of agricultural literacy education programs in Korea and the United States. Comparative criteria were set to achieve the purpose of the study, wherein the system and contents of agricultural literacy education programs were compared and analyzed according to these criteria. As a result of the analysis, implications for practical arts education in Korea are as follows.&#x0D; First, it is necessary to present the educational contents of the agricultural literacy education programs in two grades in terms of formality and to address the level in detail. In the case of elementary schools, it is necessary to present content standards by grade group, while it would be effortless to present middle and high schools in general. Second, in the development of practical arts curriculum in Korea, an integrated area approach is needed rather than one in a specific area. Rather than emphasizing the practical arts and content areas, such an integrated approach to areas (e.g., integrating agriculture and diet) will be essential in the long run.&#x0D; Based on the hereby demonstrated results, the present paper offers the following suggestions.&#x0D; First, research on the curriculum of various countries that might offer new ideas in elementary agricultural education is continuously needed.&#x0D; Second, it is rather difficult to explore the characteristics of current elementary agricultural education being conducted in the curriculum document. Therefore, studies on the actual cases of elementary agricultural education in these countries are needed.
NILSIC-BERT4Rec: Sequential Recommendation with Non-Invasive and Interest Capturing Self-Attention Mechanism

Recently, recommendation models based on Transformer have become a hot research topic. Despite the large number of recommendation models proposed in recent years, we argue that the use of invasive fusion when using heterogeneous auxiliary information and mixing multiple different vector spaces results in side effects in existing self-attention models. Additionally, the self-attention layer in Transformer overly focuses on distant items in user interaction sequence, lacking the ability to capture local dependencies of items. Thus, we propose a novel sequential recommendation model, Non-Invasive Long-term Short-term Interest Capturing BERT4Rec (NILSIC-BERT4Rec). NILSIC-BERT4Rec retains the ability of the representation layer and self-attention layer to output in the same vector space. We also introduce a local encoder in the self-attention layer to strengthen the role of item context in the attention function, enhancing the self-attention layer’s ability to capture local dependencies. Experiment results show that NILSIC-BERT4Rec can learn a better attention distribution and achieves the best experiment results on all datasets, with an average of 7.36% improvement on evaluation metrics.
Artificial Intelligence Education in an Elementary School and Its Evaluation by Career Development, Motivation and Rubrics

Artificial intelligence (AI) has the potential to contribute to solving various problems spreading through society today. To that end, students should acquire knowledge of AI from an early age and be sensitized to careers in this field. Therefore, this study implemented an educational practice using AI for career development in an elementary school. Four items were used to verify the educational effectiveness of the practice: intrinsic value, attainment utility value, expectancies for success, and career development. The results of the analysis confirmed an increase in the observed items. Thus, the educational practice proposed in this study is considered valid in terms of educational effectiveness from the perspectives of motivation, career development, and so on.
Chapter 6. Discrete and Continuous

The distinction between discrete and continuous mathematics is complex and sometimes hard to pin down, but very important. I think this gulf is as big as the one between pure and applied, and in my case it may be bigger, for I consider myself qualified to have opinions about pure continuous mathematics, but not about discrete mathematics, pure or applied.