Reviewerly/exHarmony huggingface dataset:
AMP-IT and WISDOM: Improving 3D Manipulation for High-Precision Tasks in Virtual Reality

Precise 3D manipulation in virtual reality (VR) is essential for effectively aligning virtual objects. However, state-of-the-art VR manipulation techniques have limitations when high levels of precision are required, including the unnaturalness caused by scaled rotations and the increase in time due to degree-of-freedom (DoF) separation in complex tasks. We designed two novel techniques to address these issues: AMP-IT, which offers direct manipulation with an adaptive scaled mapping for implicit DoF separation, and WISDOM, which offers a combination of Simple Virtual Hand and scaled indirect manipulation with explicit DoF separation. We compared these two techniques against baseline and state-of-the-art manipulation techniques in a controlled experiment. Results indicate that WISDOM and AMP-IT have significant advantages over best-practice techniques regarding task performance, usability, and user preference.
Affordance-Guided User Elicitation of Interaction Concepts for Unimodal Gaze Control of Potential Holographic 3D UIs in Automotive Applications

Identifying usable and intuitive in¬teraction methods for novel display technologies, in settings where multiple interaction modes and devices can be used simultaneously (such as the car), remains a challenge for developers and user interface designers. The process can become even more complex when the target hardware is still in prototype stages of development and does not support usability tests in early design iterations. Using an affordance-guided user-centered elicitation survey with non-expert participants, we researched intuitive unimodal gaze interaction concepts to complete a series of interaction tasks with a 3D UI in a HoloLens 2 emulation of a 3D holographic passenger display inside a car.
Data-driven Storytelling in Hybrid Immersive Display Environments

Data-driven stories seek to inform and persuade audiences through the use of data visualisations and engaging narratives. These stories have now been highly optimised to be viewed on desktop and mobile computers. In contrast, while immersive virtual and augmented reality (VR/AR) technologies have been shown to be more persuasive, no clear standard has yet emerged for such immersive stories. With this in mind, we propose that a hybrid data-driven storytelling approach can leverage the familiarity of 2D display devices with the immersiveness and presence afforded by VR/AR headsets. In this position paper, we characterise hybrid data-driven stories by describing its design opportunities, considerations, and challenges. In particular, we describe how both 2D and 3D display environments can play either complementary or symbiotic roles with each other for the purposes of storytelling. We hope that this work inspires researchers to investigate how hybrid user interfaces may be used for storytelling.
CADC++: Advanced Consensus-Aware Dynamic Convolution for Co-Salient Object Detection

When given a group of relevant images for co-salient object detection (Co-SOD), humans first summarize consensus cues from the whole group and then search for co-salient objects in each image. Most previous methods do not consider robustness, scalability, or stability in the summarization stage and adopt a simple fusion strategy to fuse consensus and image features in the searching stage. Our work presents a novel consensus-aware dynamic convolution (CADC) model directly from the “summarize and search” perspective to explicitly and effectively perform Co-SOD. For the summarization stage, we extract robust individual image features by a pooling method and integrate them to generate consensus features via self-attention, thus modeling the scalability and stability. Then, we simultaneously learn two types of consensus-aware dynamic kernels, i.e., a common kernel to capture group-wise common knowledge and adaptive kernels to mine image-specific consensus cues. For the second stage, we adopt dynamic convolution to perform object searching. A novel data synthesis strategy is also developed for model training. Although CADC has obtained competitive performance, we argue that incrementally learning dynamic kernels and representations is more intuitive and natural instead of using a simultaneous scheme, thus presenting our CADC <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</monospace> , an extension of CADC. Concretely, we first adopt the common kernel based dynamic convolution to capture coarse common cues as priors and then use the adaptive kernel based dynamic convolution for mining image-specific details. We also propose a recursive guidance strategy to further explore deep interactions among the two kinds of kernels and image features. Besides, we annotate several challenging attributes for Co-SOD datasets and perform attribute-based evaluation and robustness analysis to promote thorough model evaluation for the Co-SOD field. Extensive experimental results on four benchmark datasets verify both the effectiveness and robustness of our proposed method.
Noisy Evolutionary Optimization With Application to Grid-Based Persistent Monitoring

This work concerns evolutionary approaches to black-box noisy optimization where the problem is accessed via noisy function evaluations. An evolution strategy (ES) algorithm is proposed, which uses a Gaussian distribution to guide the search and requires only the comparisons among solutions. The new method achieves the similar convergence rate as finite-difference based gradient methods on non-convex landscapes, while being adaptive in the sense that the convergence is ensured with any initial step-size. We further improve the method with a variance adaptation mechanisms that alleviates the need for hyper-parameter tunning and an asynchronous parallelization implementation that enables linear speedup. The persistent monitoring task is chosen as an application to investigate the effectiveness of the proposed method. The task is to schedule a team of agents to minimize the uncertainties of some targets in a changing environment defined on a grid map. We show in the single-agent case that the task can be cast into a sequence of pathfinding subproblems of which the sequential order can be modeled as a Markov decision procedure and solved by the proposed ES method. In the multi-agent case, we show that solving the single-agent subproblems using the ES method in a round-robin way can provide collision-free solutions. Numerical studies demonstrate the reduction in convergence time and the robustness against complicated environments relative to several existing evolutionary algorithms and zeroth-order gradient methods.
Computer Vision-Based QR Code Detection for Manipulation of a DC Motor in Semi-Controlled Environments

The present research work carries out the design and implementation of a computer vision algorithm supported by image pre-processing techniques to improve the QR (Quick Response) code detection rate efficiency in a semi-controlled environment. The proposed algorithm is implemented in an embedded system using a Raspberry Pi model4B+ microcomputer to manipulate a DC motor that emulates the change in direction in a mobile robot. For greater precision and simplicity, the encoded data of the QR code are 1 and 0, which allow turning to the right or left, respectively, on the DC motor. To complement the embedded system, a Raspberry camera, and a control module (current regulator) for the DC motor are used. Finally, efficiency tests are carried out with the proposed algorithm, where the idea is to obtain a lower error factor compared to the tests carried out in classical algorithms that do not have image pre- processing stages. These tests are carried out in a semi-controlled environment, varying the luminosity and distance to detect the QR code. Once the objective is reached, the best light and distance conditions are proposed for detection, allowing the general system to be versatile and fast.
Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders

Inspired by recent progress in text-conditioned image generation, we propose a model for the problem of text-conditioned graph generation. We introduce the Vector Quantized Text to Graph generator (VQ-T2G), a discrete graph variational autoencoder and autoregressive transformer for generating general graphs conditioned on text. We curate two multimodal datasets of graph-text pairs, a real-world dataset of subgraphs from the Wikipedia link network and a dataset of diverse synthetic graphs. Experimental results on these datasets demonstrate that VQ-T2G synthesises novel graphs with structure aligned with the text conditioning. Additional experiments in the unconditioned graph generation setting show VQ-T2G is competitive with existing unconditioned graph generation methods across a range of metrics.
An Improved Traffic Sign Recognition and Road Lane Detection for Self-Driving Cars using YOLO-v8

This paper presents a unique way for improving the efficiency and efficacy of self-driving cars through enhanced traffic sign recognition and road lane detection using the deep learning framework. The selected solution demonstrates good performance across a wide range of environmental challenges by thoroughly training the YOLO model on a massive dataset including various lighting factors, weather scenarios, and other aspects. The model's effectiveness at recognising and categorising multiple traffic signs in a range of circumstances, including low-light and severe weather, has been enhanced. Considering YOLOv8's versatility, the model is trained on a wide range of road lane video datasets, ensuring exact vehicle localization between lanes.Deep convolutional neural network (DCNN) based on residual network 50 (resnet) architecture for sign and lane identification, as well as you only look once (YOLOv8), an advanced CNN technique for real-time object detection, were used to accomplish the proposed model.
The Effect Of English Video Podcast For Developing Students’ English Skill In Listening Comprehension

Teaching and learning process in the classroom will run well when the learning process uses the good method. One of the method that can be applied in the classroom is using technology such as podcast. The aim of this research is to discover the effectiveness of using English Video Podcast in improving the students’ listening comprehension of eight-ninth students of SMP Advent Makassar. This research is applied by using a mix method approach, specifically the researcher applied quasi-experimental research design, and the data analysis was done by statistical description. And the based on the researcher findings, the use of English video podcast can be effective learning media to improve students’ listening comprehension. It can be seen there was increasing from the data of Pre-Test to the Post-test after the teacher gave the treatment to the experimental group. Before the treatment was given the students’ mean score for the experimental group on the pre test is 56.50 and after the treatment was given the students’ mean score is 78.50. It means that the using of podcast has improved students’ listening comprehension. The result was improved. It is supported by some previous study that also has a positive effect by using a podcast in teaching listening. And based on the questionnaire and the interview given the result showed positive impact of using English video podcast in improving listening comprehension.
The effect of using electronic practicum worksheets on students' understanding of digital literacy

This research aims to determine the effect of implementing the Electronic Practicum Worksheet with Google Sites on the Digital Literacy Abilities of Class X High School Students in Palembang City on Virus Material. This research was carried out at Senior High School in Palembang. This research method is quasi-experimental with a non-equivalent control group design. Sampling in this study used a purposive sampling technique so that class X.IPA.6 was selected as the experimental class and X.IPA.10 as the control class. Instrument analysis in this research used the IBM SPSS Statistics 25 for Windows program and the Microsoft Excel application. The instrument used in this research was multiple choice questions on a digital literacy test. The research results show that the implementation of an Electronic Practicum Worksheet with Google Site has a significant effect on students' digital literacy test results. Apart from that, the implementation of electronic practice worksheets with Google Site can improve students' digital literacy skills.
Compacting Singleshot Multi-Plane Image via Scale Adjustment

A recent singleshot multiplane image (MPI) generation enables to copy an observed reality within a camera frame into other reality domains via view synthesis. While the scene scale is unknown due to the nature of singleshot MPI processing, camera tracking algorithms can estimate depth within the application world coordinate system. Given such depth information, we propose to adjust the scale of singleshot MPI to that of the currently observed scene. We find the individual scales of the MPI layers by minimizing the differences between the depth of MPI rendering and that of camera tracking. We eventually found that many layers fall within a close depth. Therefore, we merge such layers into one to compact the MPI representation. We compared our method with baselines using real and synthetic datasets with dense and sparse depth inputs. Our results demonstrate that our algorithm achieves higher scores in image metrics and reduces MPI data amount by up to 78%.
CLensRimVision: A Novel Computer Vision Algorithm for Detecting Rim Defects in Contact Lenses

Automated optical inspection (AOI) plays a pivotal role in the quality control of contact lenses, safeguarding the safety and integrity of lenses intended for both medical and cosmetic applications. As the role of computer vision in defect detection expands, our study probes its effectiveness relative to traditional methods, particularly concerning subtle and irregular defects on the lens rim. In this research study, we propose a novel algorithm designed for the precise and automated detection of rim defects in contact lenses called “CLensRimVision”. This algorithm integrates a series of procedures, including image preprocessing, circle detection for identifying lens rims, polar coordinate transformation, setting defect criteria and their subsequent detection, and, finally, visualization. The method based on these criteria can be adapted either to thickness-based or area-based approaches, suiting various characteristics of the contact lens. This approach achieves an exemplary performance with a 0.937 AP score. Our results offer a richer understanding of defect detection strategies, guiding manufacturers and researchers towards optimal techniques for ensuring quality in the contact lens domain.
Textual Information Processing Based on Multi-Dimensional Indicator Weights

With the rapid advancement of artificial intelligence and wireless communication technologies, the abundance of textual information has grown significantly, accompanied by a plethora of multidimensional metrics such as innovation, application prospects, key technologies, and expected outcomes. Extracting valuable insights from these multifaceted indicators and establishing an effective composite evaluation weighting framework poses a pivotal challenge in text information processing. In response, we propose a novel approach in this paper to textual information processing, leveraging multi-dimensional indicator weights (MDIWs). Our method involves extracting semantic information from text and inputting it into an LSTM-based textual information processor (TIP) to generate MDIWs. These MDIWs are then processed to create a judgment matrix following by eigenvalue decomposition and normalization, capturing intricate semantic relationships. Our framework enhances the comprehension of multi-dimensional aspects within textual data, offering potential benefits in various applications such as sentiment analysis, information retrieval, and content summarization. Experimental results underscore the effectiveness of our approach in refining and utilizing MDIWs for improved understanding and decision-making. This work contributes to the enhancement of text information processing by offering a structured approach to address the complexity of multidimensional metric evaluation, thus enabling more accurate and informed decision-making in various domains.
A Study on Childcare Teachers’ Perception and Practice of Media Literacy Education

Purpose: The purpose of this study is to investigate the perception and practice of media literacy education among childcare teachers. Accordingly, the research questions were estalished to include childcare teachers' awareness of media literacy education, educational practice, and perception of educational revitalization.&#x0D; Methods: The subject of the study involved two hundred questionnaires collected from childcare teachers currently working in metropolitan areas such as Seoul, Gyeonggi-do, and Incheon. Of these, 171 copies, excluding twenty-nine inappropriate ones, were collected using the SPSS 26.0 Window program to determine the mean, standard deviation, frequency, and percentages, multiple responses, and other areas were calculated.&#x0D; Results: According to the research question, the research results indicated that the majority of childcare teachers have never been exposed to media literacy, and answered that YouTube is the medium that has the greatest influence on infants and toddlers. They recognized that violent scenes in the media have a negative impact on infants’aggression levels. As for the status of media literacy education for childcare teachers, most teachers conducted it once a week, and the content of the education was to learn about promises that must be kept when using media. Regarding the revitalization of media literacy education, they responded that media literacy education is necessary for infants and toddler sto have diverse experiences, and that if there is media literacy-related training in the future, they will ‘participate if possible’. They also said that environmental support, such as media and educational materials that can be utilized in educational settings, is required.&#x0D; Conclusion: Media literacy requires continuous education from infancy, and the implication is that it is necessary to develop good media literacy education materials and programs so that media literacy can be achieved through connections not only with educational institution but also with families.
Mobip: a lightweight model for driving perception using MobileNet

The visual perception model is critical to autonomous driving systems. It provides the information necessary for self-driving cars to make decisions in traffic scenes. We propose a lightweight multi-task network (Mobip) to simultaneously perform traffic object detection, drivable area segmentation, and lane line detection. The network consists of a shared encoder for feature extraction and two decoders for handling detection and segmentation tasks collectively. By using MobileNetV2 as the backbone and an extremely efficient multi-task architecture to implement the perception model, our network has great advantages in inference speed. The performance of the multi-task network is verified on a challenging public Berkeley Deep Drive(BDD100K) dataset. The model achieves an inference speed of 58 FPS on NVIDIA Tesla V100 while still maintaining competitive performance on all three tasks compared to other multi-task networks. Besides, the effectiveness and efficiency of the multi-task architecture are verified via ablative studies.
A Hybrid DWT-SVD Based Adaptive Image Watermarking Scheme

In the digital age, protecting the ownership and data veracity of digital documents is a major challenge. To address the issues concerning copyright protection and data verification of digital media, digital watermarking has emerged as a solution. In this paper, we aspire to make a modest contribution to this emerging and exciting field by presenting our proposed adaptive hybrid image watermarking approach that combines Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Our method involves applying DWT to both the host image and watermark, followed by singular decomposition using SVD on the Low-Low (LL) component of both images. Now modify the singular values of the host image by the singular values of the watermark, and then inverse SVD is applied, followed by inverse DWT, to obtain the watermarked image. After that, the reverse process is applied to obtain the watermark image. Finally, we evaluate our approach’s performance by measuring the Peak Signal-to-Noise Ratio (PSNR) between the original and watermarked image as well as the Normalized Cross-Correlation (NCC) between the original and extracted watermark. Simulation results indicate that the proposed method is rich in terms of robustness, imperceptibility and capacity than the previously presented schemes.
Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression

Symbolic regression (SR) on high-dimensional data is a challenging problem, often leading to poor generalization performance. While feature selection can improve the generalization ability and efficiency of learning methods, it is still a hard problem for genetic programming (GP) for high-dimensional SR. Shapley value has been used in an additive feature attribution method to attribute the difference between the output of the model and an average baseline to the input features. Owing to its solid game-theoretic principles, Shapley value has the ability to fairly compute each feature importance. In this paper, we propose a novel feature selection algorithm based on the Shapley value to select informative features in GP for high-dimensional SR. A set of experiments on ten high-dimensional regression datasets show that, compared with standard GP, the proposed algorithm has better learning and generalization performance on most of the datasets. A further analysis shows that the proposed method evolves more compact models containing highly informative features.
LEVERAGING CHATGPT FOR EDUCATIONAL TEXT ANALYSIS AIMING ASSESSMENT GENERATION

With the rise of new technologies, especially tools using Large Language Models (LLM), many areas of our lives are seeing exciting changes. In education, LLM can dramatically help to improve both learning and testing processes, benefiting everyone involve
Creation of a Virtual Reality Environment for Teaching Applied to a Catamaran-Type Ferry, Detailing the Engine Room

This work focused on implementing virtual reality technology on a ferry with the aim of creating an immersive experience for training and training in the naval industry, the experience allows users to explore the interior of the ship and learn about the different components and processes that make up its operation