Reviewerly/exHarmony huggingface dataset:
Point Cloud Geometry and Color Coding in a Learning-Based Ecosystem for JPEG Coding Standards

Despite its novelty, learning-based coding for images and point clouds is already outperforming some of the best long-standing conventional codecs. In addition to its rising compression performance, learning-based coding has opened new opportunities, notably the use of a single compressed domain representation to provide both high fidelity reconstructions for human visualization as well as effective performance for computer vision tasks, effectively unifying the visual language for man and machine. This paper proposes a new double learning-based static point cloud geometry and color coding solution, which targets point cloud component scalability, rate control flexibility at coding time, and a unified compressed domain representation. The proposed solution exploits the synergies between learning-based coding for images and point clouds, through the current JPEG PCC standard for geometry coding and the JPEG AI standard for image/color coding, establishing a learning-based ecosystem for JPEG coding standards. The proposed solution is able to overcome some design limitations of the current JPEG PCC Verification Model, and significantly improve its RD performance, becoming competitive with MPEG PCC standards.
Deep Active Learning Based on Saliency-Guided Data Augmentation for Image Classification

Image classification based on Deep learning usually requires a large amount of labeled data for model training. However, the labeling cost of data is expensive. The aim of active learning is to reduce the cost of manual labeling by selecting lesser data for labeling through a query sampling strategy. In this paper, the framework of active learning is improved based on saliency guided data augmentation, while enhancing the generality of the algorithm and reducing labeling cost. Firstly, data augmentation based on a saliency map is used to expand the sample data, which can retain the most important information in the data. In addition, a low-computation neural network, i.e., SpinalNet, is incorporated into the proposed algorithm to optimize the classification network in the active learning algorithm to compensate for the increased computational cost by data augmentation. Finally, the experiments show that the proposed framework can improve the performance of the baseline.
An Adjustable Fast Decision Method for Affine Motion Estimation in VVC

The Affine motion estimation (AME) in Versatile Video Coding (VVC) can predict complex non-translational motions such as rotation, zoom, or shearing more effectively than the translational motion estimation (TME) tools, at the cost of greatly increased computational complexity. In this paper, to reduce encoding complexity, we propose a novel adjustable fast decision method for AME in VVC. Our method skips the AME process for blocks with low TME rate-distortion (RD) cost as we observed that skipping them reduces the encoding time without significantly affecting the compression performance. A distinctive feature of the proposed method is that it can be progressively adjusted to provide different compromises between speed-up and compression performance. First, a default TME RD cost threshold is estimated using a Multiple Linear Regression (MLR) model and then, adjusted to achieve the desired trade-off between speed-up and coding performance. Experimental results show that, with the default threshold, the proposed method can reduce the VTM encoding time by 8% on average, on classes B, C, and D, with a Bjøntegaard-Delta bitrate (BD-BR) of 0.44%. For the same classes and using 1.5 times the default threshold, it can reach 11% with a BD-BR of 0.82%.
Analog Programmable‐Photonic Computation

Abstract Digital electronics is a technological cornerstone in this modern society that has covered the increasing demand for computing power during the last decades thanks to a periodic doubling of transistor density in integrated circuits. Currently, such scaling law is reaching its fundamental limit, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real‐time multi‐data processing, e.g., medical diagnostic imaging, robotic control, and autonomous driving, among others. In this scenario, an analog computing approach implemented in a real‐time reconfigurable nonelectronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the unique benefits of PIP (and integrated photonics in general). Here, the foundations of a new computation theory are presented, termed Analog Programmable‐Photonic Computation (APC), explicitly designed to unleash the full potential of PIP technology. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models and can be implemented in other technologies (e.g., in electronics, acoustics or using metamaterials), consequently exhibiting the potential to spark a ground‐breaking impact on the information society.
Augmented-Reality-Based Human Memory Enhancement Using Artificial Intelligence

This work presents a human memory augmentation system that uses augmented reality (AR), computer vision (CV), and artificial intelligence to replace the internal mental representation of objects in the environment with an external augmented representation. The system consists of two components: 1) an AR headset; and 2) a computing station. The AR headset runs an application that senses the indoor environment, sends data to the computing station for processing, receives the processed data, and updates the external representation of objects using a virtual 3-D object projected into the real environment in front of the user's eyes. The computing station performs CV-based indoor environment self-localization, object detection, and object-to-location binding using first-person view data received from the AR headset. We designed a behavioral study to evaluate the usability of the system. In a pilot study with 26 participants (12 females and 14 males), we investigated human performance in an experimental task that involved remembering the positions of objects in a physical space and displaying the positions of the learned objects on the 2-D map of the space. We conducted the studies under two conditions—that is, with and without using the AR system. We investigated the usability of the system, subjective workload, and performance variables under both conditions. The results showed that the AR-based augmentation of the mental representation of objects indoors reduced cognitive load and increased performance accuracy.
Deep Semi-Supervised Ultrasound Image Segmentation by Using a Shadow Aware Network with Boundary Refinement

Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.
A Survey of CNN-Based Techniques for Scene Flow Estimation

The analysis of 3D motion information is the key to solve various computer vision tasks. Scene flow estimation tackles the problem of obtaining the 3D motion field. In this paper, we review the recent scene flow estimation papers with a focus on learning-based methods. The problem formulation, challenges and applications are introduced. The existing datasets and performance metrics are presented. The reason behind learning-based methods replacing the traditional variational methods are discussed. CNN-based scene flow estimation methods are then categorized with respect to the level of supervision, data-availability and the number of steps involved in obtaining the results. The performance of different methods on the well known KITTI and FlyingThings3D datasets are tabulated. Their relative advantages and limitations are then analysed. Future trends and some open problems in the estimation of scene flow are discussed with special focus on the self-supervised methods that does not require labelled training data.
Human-Based Interaction Analysis via Automated Key Point Detection and Neural Network Model

The human interaction with an object is one of the most challenging domains in real-life applications, such as smart homes, surveillance, medical, education, safety-based application of computer vision, and artificial intelligence. In this research article, we have proposed a framework for human and object interaction in real-life examples such as sports and other activities. Initially, we reviewed video-based data by considering the three state-of-the-art data sets. Preprocessing steps have been followed to avoid extra costs, such as video-to-frame conversion, noise reduction and background subtraction. Human silhouette extraction has been performed via the Gaussian mixture model (GMM) and supper pixel model. Next, human body points and object location detection were performed. Finally, human and object-based features have been extracted. To minimize the features replication and to achieve optimized results, we have applied stochastic gradient descent and Restricted Boltzmann Machine; As a result, we have achieved an accuracy of 88.46%, 82.00%, and 88.30% on human body parts recognition over the MPII dataset, UCF_aerial dataset, and wild Dataset respectively. The classification accuracy for the MPII dataset is 92.71%, for the UCF_aerial dataset is 90.60%, and for sports video in the wild Dataset is 92.42%. We have achieved a high accuracy rate compared to other state-of-the-art methods and frameworks due to the complex feature extraction and optimization approach.
Intelligent Index Classification Method Based on Machine Learning for Detection of Reference Signal in 5G Networks

In order to maintain stable communication in 5G wireless networks, the link between a 5G base station and user equipment (UE) should be constantly monitored and adapted to the time-varying wireless channel. The use of UE for seamless information exchange is based on obtaining a target reference signal. The method used to obtain the reference signal involves identifying the index of the reference signal received from the 5G base stations. However, the existing index identification method employed in commercial 5G networks is based on the blind detection method, which is inefficient in terms of time and can cause misdetections. On the other hand, machine learning (ML), which is statistically predictable through data accumulation, can be robust in practical network environments. Taking this into account, we build a dataset consisting of reference signal data collected in a real-world 5G network environment to obtain an optimal machine learning model that is applicable to practical 5G networks. We evaluate a total of 23 index classification models by combining six ML models and three data pre-processing methods. The results of the study represent optimized combinations of ML-based index classifiers and data pre-processing methods. Performance differences between neural network (NN) models and non-NN models are also revealed.
Trigonometric-Coded Refined Detector for High Precision Oriented Object Detection

Oriented object detection in aerial images is a crucial link in earth observation. As a special parameter in oriented object representation, angle is the key to achieving high-precision detection. However, the widely-used regression-based methods suffer from boundary discontinuity problem due to the periodicity of angle. To address this issue, we proposed a novel angle prediction method called Fixed Step Trigonometric Coder (FSTC). Exploiting the innate periodicity of trigonometric functions, FSTC can encode angles cyclically in a succinct, continuous, and uniform manner. Based on FSTC, we designed a single-shot oriented object detector, namely, Trigonometric-coded Refined Detector (TRDet), for high-precision object detection in real-time. TRDet consists of two modules: the Angle Optimization Module (AOM) and the Object Detection Module (ODM). AOM employs FSTC to generate high-quality rotated anchors. In ODM, a Dynamically Weighted Loss (DWL) was proposed to make the model focus on hard samples with higher angle deviation. Extensive experiments on DOTA and HRSC2016 show that both FSTC and TRDet can achieve competitive performance compared with peer works.
Scale-Aware Monocular Visual Odometry and Extrinsic Calibration Using Vehicle Kinematics

This paper proposes a new approach to scale-aware monocular visual odometry (VO) and extrinsic calibration using constraints on camera motion by vehicle kinematics. Main idea is to utilize the Ackermann steering model to observe absolute metric scale in turning motion. To describe motion of the camera attached to the vehicle, we first estimate unknown camera-vehicle relative pose by the proposed extrinsic calibration method. To stably observe scale, we detect turn regions and design an observer to estimate the absolute scale as a function of the camera rotation and direction of translational motion during turning. Using the observed scale, we propose an absolute scale recovery to estimate the unknown scale between turns. Because the proposed scale observer becomes singular near zero rotation, we conduct sensitivity analysis on the scale observer, and investigate appropriate conditions for stable scale estimation. For quantitative evaluation of the extrinsic calibration and the absolute scale recovery, we randomly generate synthetic driving datasets with various noise conditions, and evaluate the performance of each module statistically by Monte-Carlo simulations. To evaluate the overall performance, we implement our method and state-of-the-art monocular and stereo VO methods in the public outdoor driving KITTI dataset, and our method shows competitive scale recovery performance with no external sensor and no assumption on surroundings such as planar ground landmarks. To show promising applicability, we collect real-world driving datasets in two multi-floor underground parking lots, and demonstrate the accurate absolute scale recovery performance of our method in indoor driving situations.
Efficacy of educational platforms in developing the skills of employing augmented reality in teaching mathematics

The aim of the current study is to reveal the effectiveness of a training program using an educational platform on developing the skills of employing augmented reality apps to enhance the teaching of mathematics among elementary school teachers. The experimental approach with a quasi-experimental design, a one-group pre-/post-test design, was used in this study. The study sample consisted of 36 elementary school mathematics teachers in Saudi Arabia, and an achievement test and an observation card were used as measurement tools. The study identified a statistically noteworthy distinction, with a significance level of 0.05, in the mean scores of the study group before and after the administration of the achievement test and the observation card, favoring the post-application results. The study suggested the importance of directing those in charge of developing training programs within the Ministry of Education to incorporate educational platforms as a key component of distance education methods.
Development and validation of the secondary mathematics teachers’ TPACK scale: A study in the Chinese context

This study aimed to develop and validate the secondary mathematics teachers’ technological pedagogical and content knowledge (TPACK) scale (SMTTS) to assess the knowledge domains of TPACK framework among secondary mathematics teachers in China. SMTTS was designed to be subject-oriented and culturally relevant, addressing the specific needs and context of mathematics education in China. Data were collected using a web-based questionnaire from secondary mathematics teachers in Chongqing Jiulongpo District. Exploratory and confirmatory factor analyses were conducted to assess the reliability and validity of the scale. The findings demonstrated that SMTTS exhibited strong reliability and validity, supporting its use as a robust measurement tool for assessing secondary mathematics teachers’ TPACK. The scale demonstrated good psychometric properties, including satisfactory factor loadings, internal consistency, and model fit indices. The development of SMTTS contributes to the field by providing a precise and reliable instrument that can inform the design of targeted professional development programs and guide policy decisions regarding technology integration in mathematics education. This study has theoretical and practical implications. SMTTS addresses the need for a subject-specific and culturally relevant assessment tool for measuring TPACK in mathematics education. It acknowledges mathematics teachers’ unique challenges in integrating digital technologies into their instructional practices. The scale’s development and validation process incorporated considerations of the Chinese educational context, enhancing its relevance for practitioners and researchers in China. SMTTS can facilitate the identification of areas for improvement in teachers’ TPACK and guide the implementation of tailored interventions and support initiatives.
Framework for Localization of Forgery Regions in Image

With the development of computer technologies, manipulations are made on digital images without leaving a clear trace thanks to image processing software. There is a great need for applications to detect forgered images made with malicious intent in many fields such as politics, law, medicine and military. Many studies have been carried out and various algorithms have been developed to detect forgered regions by detecting forgered images. Today, superior methods are developed by combining traditional image forgery techniques with deep learning techniques. In this study, the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) were used together with Convolutional Neural Networks (CNN) to locate the forgered regions of the forgered images. Three different methods were made to locate the forgered region. In the first method, DWT and CNN were used together. In the second method, DCT and CNN were used together. In the last method, DCT and DWT were combined in parallel and used together with CNN.
Knowledge Sharing for Contextual Performance: An Empirical Study in the Ministry of Culture, Tourism and Antiquities in Iraq

Knowledge and knowledge sharing are the main drivers of economic development and the main key to enhancing performance. This research aims to examine the impact of the knowledge sharing (donating and collecting) on the contextual performance at Ministry of Culture, Tourism and Antiquities in Iraq. To achieve the objectives of the research, the descriptive and analytical approach was used through the distribution of (278) questionnaires to the employees at the Ministry. Structural equation modeling with AMOS v.28 was used to build the model and analyse the relationships between variables. The research found that knowledge sharing can create a performance culture for the Ministry employees by building and encouraging knowledge collecting and donating inside and outside their departments, which in turn, increase their skills and experience in developing the performance of their tasks and duties. The implications of the findings and suggestions for future research are discussed.
Classification of firing pin impressions using <scp>HOG‐SVM</scp>

Crimes, such as robbery and murder, often involve firearms. In order to assist with the investigation into the crime, firearm examiners are asked to determine whether cartridge cases found at a crime scene had been fired from a suspect's firearm. This examination is based on a comparison of the marks left on the surfaces of cartridge cases. Firing pin impressions can be one of the most commonly used of these marks. In this study, a total of nine Ruger model 10/22 semiautomatic rifles were used. Fifty cartridges were fired from each rifle. The cartridge cases were collected, and each firing pin impression was then cast and photographed using a comparison microscope. In this paper, we will describe how one may use a computer vision algorithm, the Histogram of Orientated Gradient (HOG), and a machine learning method, Support Vector Machines (SVMs), to classify images of firing pin impressions. Our method achieved a reasonably high accuracy at 93%. This can be used to associate a firearm with a cartridge case recovered from a scene. We also compared our method with other feature extraction algorithms. The comparison results showed that the HOG-SVM method had the highest performance in this classification task.
<scp>TestDimorph</scp>: An R package for analysis of interpopulation sexual dimorphism differences using summary statistics

Abstract Objective The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics . Materials and Methods We provide two approaches of analysis of inter‐sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within‐sex size variability between samples is compared using one‐way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non‐overlapping area using the dissimilarity index as well as Hedges' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples. Results We demonstrate various results from applying TestDimorph functions to data supplied with the package. Discussion The package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.