Reviewerly/exHarmony huggingface dataset:
Fast and Accurate Image Stitching for High-Resolution Images

The purpose of image stitching is to generate a panoramic image with a large viewing angle from a series of images. Panoramic images can be applied to many practical scenes, such as military, simulation, augmented reality, and virtual reality. At present, the accuracy and efficiency of existing image stitching methods need to be improved for generating high-quality panoramic images from high-resolution images. To solve the above problems, in this paper, we propose a novel image stitching method for processing high-resolution images, to quickly generate high-quality panoramic images. The core of the proposed method includes real-time image matching, image space optimization, and panoramic image post-processing. Based on the proposed method, a high-precision, and fast image stitching system, namely, MetaPano, is developed. Experimental results show that the MetaPano system can quickly generate high-quality panoramic images on some challenging image data sets.
Research on Regional Water Resources Classification Algorithm Based on Improved Deep Residual Network

In order to realize the rapid and accurate classification of regional water resources, this paper designs the regional water resources classification algorithm based on the improved deep residual network. This paper analyzes the overall situation of regional water resources distribution in China, and takes the remote sensing image of Gaf-2 satellite as the basis of algorithm design, and preprocesses the remote sensing image of regional water resources, such as enhancement, marking and segmentation. On this basis, the pyramid network is used to improve the depth residual network, and the improved depth residual network is combined with the pre-processing results of remote sensing image to obtain the regional water resources classification results. The experimental results show that the proposed algorithm has short preprocessing time, low classification error rate, short classification time and better practical application effect.
A Real-time 3D Perception Method with Multi-resolution Voxel Space

3D perception is the key technology of intelligent equipment to understand the world. We propose an online 3D perception method from RGBD sequences which consists of 3D instance segmentation, camera pose optimization, 3D reconstruction, and refinement modules. Firstly, we perform 3D instance segmentation by mapping and fusing 2D instance segmentation in 3D space. Secondly, we obtain accurate camera poses by adopting a frame-to-frame optimization method and local-to-global strategy. Thirdly, we reconstruct 3D scenes by a multi-resolution implicit structure based on voxels with semantic information. Finally, we render refined implicit surfaces by an improved rendering method. Experiments show that our method has better 3D perception results in a variety of complex environments compared with similar methods. Our code is available: https://github.com/3D-Vision-Lab-of-JUST/Real-time-3D-Perception.
A Pre-game Item Recommendation Method Based on Self-Supervised Learning

With the development of e-sports, Multiplayer Online Battle Arena (MOBA) has become the most popular game type in the world. In addition to bringing huge commercial value, it also produces a large amount of rich public game data, which attracts researchers to deeply combine deep learning technology with MOBA. Among them, item recommendation is the core task, including pre-game item recommendation and in-game item recommendation. Compared with in-game item recommendation, pre-game recommendation faces serious data sparsity and missing data labels, which makes pre-game item recommendation a hard challenge. Existing methods pay more attention to conventional indicators such as precision and recall, but ignore the usability of results in MOBA, and have not fully considered the relationship between items. In this paper, we propose a pre-game item recommendation method based on self-supervised learning. While repairing the missing labels, the Transformer encoder is used to learn the relationship between items. Experimental results on a public dataset show that our method outperforms previous methods in terms of precision and usability indicators.
A Deepfake Face Video Authentication Method Based on Spatio-temporal Fusion Features

With the wide spread of deepfake face videos, it has brought huge hidden dangers of trust to national security and social stability. In this paper, the authentication model framework of deepfake face video with spatio-temporal fusion features is proposed. Through three improvements including collecting mixed training samples, training two 2D deep convolutional neural networks with face center clipping images and using 3D deep convolutional neural networks to utilize the inter-frame consistency information, the authentication success rate of deepfake face video is improved. In the experiment two video forgery methods FaceSwap and Deepfakes were selected in the Faceforences ++ dataset to identify the deepfake video of facial feature area and facial edge area, which obtained certain results. Further breakthroughs are expected in the future through the integration of multi-modal data features and the use of large-scale pre-trained models.
Spatial-temporal Correlation Constraint based Siamese Network for Visual Object Tracking

Visual object tracking with siamese networks has recently attracted much attention because of its favourable performances. Although spatial and temporal constraints have been widely utilized under other networks, the existing siamese networks usually ignore the spatial-temporal correlation in tracking process. So as these models are usually sensitive to complex background interference and target deformation, which will lead to tracking deviations. To this end, we propose a novel measure of similarity metric into siamese network which integrates both spatial and temporal correlations simultaneously. In spatial domain, a distractor-aware module is introduced to generate strong semantic based hard negative samples in each frame according to object detection and segmentation result. In temporal domain, an online target templates updated strategy is devised to capture the appearance and scale variations of the target in consecutive frames. Qualitative and quantitative results on VOT2016, OTB50 and OTB100 datasets demonstrate that our proposed method is able to achieve superior tracking results.
3D HOG-Aided Convolutional Neural Network for Prostate Segmentation in Biomedical Image

Accurate and automatic segmentation of prostate magnetic resonance (MR) images is essential for the diagnosis, prediction, treatment and prognosis of prostate diseases. In this paper, we propose a three-dimensional (3D) histogram of oriented gradient (HOG)-aided convolutional neural network method to segment prostate MR images. Our model consists of three modules: 3D HOG feature extraction stage for prostate boundary information, 3D convolutional neural network (CNN) module and feature information fusion module. We extend the traditional HOG feature from two to three dimensions to provide the boundary information of prostate in MR images. To overcome the shortcomings of the traditional skip connection in the 3D-CNN module, which uses 3D-Unet as the baseline, we improved the skip connection of 3D-Unet such that each decoder layer merges the smaller and same scale feature map of the encoder with the larger scale feature map of the decoder. Finally, we perform a fusion mechanism on the two features to obtain a segmentation probability map. We evaluate our model on MR images of prostate acquired from a local hospital. The average relative volume difference (RVD) and Dice coefficient (DSC) of the prostate volume area are 1.90% and 90.83%, respectively. Quantitative experiments show that, compared with other methods, the results of our method are highly consistent with expert manual segmentation.
Acute Stress Classification with a Stroop task and in-office biophilic Relaxation in Virtual Reality based on Behavioral and Physiological data

Acute stress in Virtual Reality (VR) requires more experimental inputs to determine how VR affects human psychophysiology, how to alleviate stress, and whether visual fatigue can impact its detection with machine learning. We randomly assigned 93 participants to three experimental conditions: Control, Stressful, and Stereoscopy. Participants fulfilled a rest-baseline period, a Stroop task, an STAI-6 questionnaire, and a relaxation period. We recorded behavioral and physiological data from eye-tracking (ET), electrocardiogram (ECG), and electrodermal activity (EDA). We identified three acute stress levels based on STAI-6 scores and behavioral data. We deployed linear models penalized with L1 constraints to select the most relevant features correlated with stress levels. The EDA sensor provided the most selected features compared to ECG and ET. We compared SVM, Logistic Regression, and Gradient boosting classifiers. The Logistic regression with ElasticNet constraint provides the best feature selection. The Gradient boosting method is the most performant Acute stress classifier, with 89.86% accuracy and an 89.8% F1 score.
A Modular Approach for 3D Reconstruction with Point Cloud Overlay

Integrating the real-world through the power of 3D reconstruction plays an essential role in extended reality teleconsultation. Video streams and voxel-based representations are common techniques for teleconsultation visualization. However, video streaming applications miss 3D content information, and voxel-based representation can only provide visual details with increasing computational costs. To enhance the classical voxel-based representation and address the timeliness of voxels, we present a modular approach which provides a point cloud overlay to highlight the latest update in the current camera frustum. Our approach applies state-of-the-art algorithms and custom packages within ROS to enable 3D reconstruction with dynamic cameras. We present a modular approach allowing the flexible exchange of the individual part, i.e. the camera or SLAM algorithm. This work presents results from a pilot study involving five participants to gain an impression of what kind of visualization type would be preferred and whether the point cloud overlay would assist the user in recognizing changes in the surroundings. The point cloud overlay enabled the participants to perceive more changes. The pilot study revealed that 60% of the participants showed a preference for the point cloud overlay over the pure mesh representation.
Interactive Registration Methods for Augmented Reality in Robotics: A Comparative Evaluation

Augmented Reality (AR) visualization has shown potential for supporting intuitive and efficient human-robot interaction in a range of tasks. Since all these tasks are spatially related to the robot, the precise positioning of the AR content is critical to the applicability. However, most research has primarily focused on developing visualizations rather than exploring methods for aligning AR content in the robotic workspace. This paper aims to bridge this gap by implementing and comparing different interactive registration methods, including two point-based and one manual approach. We comparatively evaluated these registration methods in a user study (n=21), measuring registration accuracy, duration, and subjective user feedback. Our results indicate that the point-based methods outperform the manual approach in terms of both accuracy and perceived workload. Furthermore, participants achieved significantly faster performance with a point-based approach using physically defined registration points compared to a point-based approach using markers attached to the robot.
Empowering Education through EERP: A Customizable Educational VR Escape Room Platform

Efficient programming learning necessitates practical knowledge, creativity, and problem-solving skills. However, current methods often disregard these crucial elements, focusing solely on coding principles. As a result, student engagement is compromised. To mitigate this, game-based learning, especially through virtual reality, proves effective in enhancing outcomes and motivation. Addressing this gap, we have developed a pioneering VR-based escape room platform for learning programming. This innovation empowers educators to tailor rooms and puzzles to their curriculum, while its multiplayer feature fosters collaboration, intensifying the learning journey. By seamlessly integrating immersive learning with personalized content, our platform can revolutionize programming education.
Eye Detection in Image of Face with Mask from Various Viewpoints by Haar-based Cascade Technique

Existing research on facial mask-worn face detection has limitations when applied to non-frontal face images. To improve the performance, side-view faces must be analyzed. The eye detection process is to analyze side-view face images. Therefore, we proposed an eye detection method from face images by a Haar-based cascade technique. Input data were images of frontal faces, non-frontal faces, face with mask, and face without mask. The face angle varied from negative 90 to positive 90 degrees. The proposed method consisted of the following process: 1) Database preparation, 2) Identification of head area and Center of the Head Calculation method installation, 3) a Haar-based Cascade Object Detector for Eye detection in angled faces preparation, 4) Eye side identification conditioning and 5) Implement of eye detection. To test the performance of the proposed method, 250 face images were used to measure the accuracy of each viewpoint of test images. In eye detection, the accuracy reached 86% for -90°, 90% for -45°, 88% for 0°, 90% for 45°, and 78% for 90°. The performance of the proposed method was satisfactory.
A Novel Approach for Virtual Locomotion Gesture Classification: Self-Teaching Vision Transformer for a Carpet-Type Tactile Sensor

Locomotion gesture classification in virtual reality (VR) is the process of analyzing and identifying specific user movements in the real world to navigate virtual environments. However, existing methods often necessitate the use of wearable sensors, which present limitations. To address this, we utilize a high-resolution carpet-type tactile sensor as a foot action recognition interface, which is previously unexplored in the context of locomotion gesture classification. This interface can capture the user’s foot pressure data in detail to distinguish similar actions. In this paper, to efficiently process captured user’s foot tactile data and classify nuanced actions, we propose a vision transformer (ViT) architecture and a novel self-teaching vision transformer (STViT) model integrating elements of the shifted window vision transformer (SwinViT) and data-efficient image transformer (DeiT).
Android-based e-module in improving student learning outcomes on virus material in high school

The increasingly rapid development of technology causes students to be more interested in using digital-based teaching media. one of which is Android-based E-modules. This study aims to examine the effect of using an Android-based E-module on student learning outcomes in class X virus material SMA Negeri 2 Barru. The examination technique utilized was semi-trial utilizing a non-identical benchmark group plan. The example in this review added up to 60 understudies and was separated into two gatherings. in particular the exploratory class and the control class. utilizing a purposive testing procedure. The instrument utilized is a trial of learning results and documentation. The information examination strategy utilized is expressive measurable investigation and inferential factual examination. Inferential measurable examination acquired sig. of 0.000. demonstrating that sig. α = 0.000 &lt;0.05. so Ho is dismissed and H1 is acknowledged. These outcomes demonstrate that there is a critical impact from the utilization of Android-put together E-modules concerning understudy learning results in class X class X SMA Negeri 2 Barru material. The ramifications of this exploration are that the Android-based E-module can be applied by understudies to make more obvious the topic.
"ROLE OF NAMED ENTITIES IN UNDERSTANDING SEMANTIC SIMILARITY OF ENGLISH TEXT"

Understanding semantic similarities between documents is challenging but have enormous benefits, like plagiarismdetection and information retrieval. Various techniques are available in Natural language processing, which help inunderstanding
"CONSTRUCTING A STUDENT ENGAGEMENT AND LEARNING DEVELOPMENT MODEL IN MOBILE LEARNING BY SEM"

This study aimed to build a model to detect the factors to enhance student engagement and learning development in mobilelearning during the COVID-19 Pandemic. Data from a total of 400 junior-high-school students were collected in China inthe fall
EMOTIONAL EVALUATION OF MOVIE POSTERS

This study examines the impressions that movie posters give people and analyzes movie poster trends. The primaryobjective was to determine whether movie posters have any characteristics, which was done through clustering usingarousal-valence maps.
"FRAMEWORK OF BEST PRACTICES FOR UNIVERSITY STUDENTS' MOBILE LEARNING IN STEM SUBJECTS"

This study investigated the students' experience and impact of using Science, Technology, Engineering, and Mathematics(STEM) related subjects for mobile learning or mLearning. In this study, mobile learning student-generated activities(SGA)
Exploring high school students' major and career recognition through Early Childhood Education major experiential learning -Focused on movement education for young children-

Purpose: This study was conducted to explore high school students’ career and major recognition through major experiential learning.&#x0D; Methods: S high school students who participated in an “Open University” program at C university in Chungcheongnamdo were targeted. Participants were asked to write a journal about their departmental and major experiential learning. The collected data were categorized, and content analysis wasconducted.&#x0D; Results: First, the high school students who participated in the major experiential learning expressed both astonishment and perplexity while re-evaluating Childhood Education. Second, high school students who participated in the study examined their propensities and careers in earnest. Although Childhood Education was not their current desired career path, students discovered their interests and the recognition that it could fit with their individual propensities emerged. Furthermore, student sreflected on their past inappropriate discipline and education in Physical Education classes and recognized the value and importance of appropriate movement education.&#x0D; Conclusion: First, opportunities for experiential majors linked to high schools and universities should be continuously provided to help high school students. Second, to develop high school students' career awareness, confidence enhancement and appropriate guidance at school are required.
Solutions to Political Problems and Their Meaning as Seen Through Editorials of Media Outlets

This study divides past settlement into two types (criminal prosecution model - reconciliation model) and integrates the 5 principles of solving the “Gwangju problem” (punishment of responsible persons, compensation for victims, investigation of the truth, restoration of victims' honor, reconciliation and integration). pursuit) was analyzed. To this end, by analyzing 139 editorials of the Dong-A Ilbo, one of the major daily newspapers, from 1993 to 2022, it is diachronic how the tasks of liquidation of the May 18th over the past 30 years have been recorded and expressed through the ways and aspects of change. It was analyzed and evaluated from this point of view. As a result of the analysis, Dong-A Ilbo editorials related to the settlement of the past posted the most editorials related to truth finding among the 5 principles, followed by editorials on punishment of those responsible (perpetrators), followed by demands for restoration of the victim's honor, demand for compensation for victims, and reconciliation and integration. It was confirmed that the amount of publication was classified in the order of pursuit. It is interpreted as a reflection of the will that, in order to truly settle the past, first of all, it is essential to find out the truth and fair punishment for those responsible. In particular, the Dong-A Ilbo's editorials were identified as focusing on the original role of the media as a public communication medium rather than adhering to value-oriented discourses according to political tendencies regarding changes and trends that are receiving great attention both socially and politically. Through this analysis, this study aims to help our society and community reexamine the democratic and historical meaning of May 18 in the history of Korean democracy, gain knowledge about social issues that have arisen in the public sphere, and further increase awareness of the universal values pursued by human society. We wanted to provide a foundation for rediscovery. In addition, for this purpose, after the discourse analysis discussion process, the reasons for emphasizing the ‘necessity of democratic citizenship education’ were briefly presented.
Preliminary Action Recognition

In this paper, we propose preliminary action recognition which is an action anticipation method that can be used in real-time. Preliminary action recognition as a solution to action anticipation, which is the task of detecting the action of a person before the person performs the action. We define preliminary action class before action class that is anticipated by recognizing the preliminary actions specific to the action before it occurs, just as humans do. Conventional action anticipation methods have two problems for real-time use. First, it requires 10 to 20 seconds of video to anticipate 1 second ahead, and it can only anticipate every 10 seconds. Second, the video input in training always switches actions 1 second after the end of the video. This problem setting can be a leakage because there is no action break in real-time operation. Proposed method solves this problem and makes it possible to operate action prediction in real-time. In addition, since action classes and preliminary action classes tend to share a lot of similarities, it is difficult to distinguish between them. We propose a model that can discriminate the subtle differences in motion by increasing the latent distance between adjacent classes in terms of time.
監視カメラ映像における物体情報を付与した骨格ベースの異常行動検知

This paper proposes an object-aware skeleton-based anomaly detection method for surveillance videos. The previous skeleton-based anomaly detection approaches learn to reconstruct normal skeleton patterns solely from the skeleton information. However, such methods suffer from detecting object-related abnormal behavior, which has a similar skeleton pose to normal behavior (e.g., riding bicycles/motorcycles). To improve the detection accuracy of such anomalies, we propose incorporating the information of objects (bounding boxes and class labels) around humans. The object and skeleton information are jointly processed through an encoder-decoder RNN to reconstruct the information. We evaluate the proposed method on the HR-ShanghaiTech dataset and achieve an accuracy improvement of 3.1%, reaching 78.2% in the best model.