Reviewerly/exHarmony huggingface dataset:
High-accuracy and efficient method for calibrating spatial laser beam based on optimized PSD

The laser sensing technique with visual laser beam has been an increasing interest in large-scale 3D measurement. However, a major limitation has been the high-accuracy positioning of laser beam in 3D space. An efficient calibration method of spatial laser beam in large-scale measurement is proposed in this paper. A novel PSD-based laser detecting device is presented to accurately collect the laser beam data. The 3D coordinates of points on the laser beam can be transformed from the PSD coordinate system to the world coordinate system by the pre-calibrated reference points and spatial transformation analysis. Furthermore, the optimized method of PSD data processing is proposed to reduce the influence of ambient light on detecting laser point coordinates. Finally, the spatial posture of laser beam can be obtained by linear fitting from the high-accuracy acquired points. The calibration process is greatly simplified compared with the existing methods. The experimental results show that the average deviation between the obtained spatial points and the fitted line is less than 0.015 mm within 5 m. The proposed method is applied to non-orthogonal shafting theodolites, and the point measurement average error is less than 0.17 mm, and the distance measurement average error is less than 0.16 mm.
Work-in-Progress—Towards an AR Materials Library for Design and Engineering Education

Materials play an essential role in product design and affect many design aspects. Materials libraries are built by universities to provide resources and inspire design concept generation and decision-making. Augmented reality (AR) is a technology overlaying digital content onto the physical world and brings a new perspective on material library through increased engagement and interactivity. This work-in-progress paper aims to enhance materials education and foster disciplinary communication by establishing an AR materials library. The proposed library will contribute to design and engineering education by serving as a practical platform with material resources and a novel tool engaged in learning.
SAVR – Design and Evaluation of an Immersive Virtual Reality Serious Game on Hazard Perception in Technical and Vocational Education

Technical and vocational secondary school students take internships in enterprises as part of their training. As such, they are often confronted with hazardous situations, such as contact with chemical substances, operating specialist machinery or in potentially dangerous working conditions. Students therefore need to be prepared in the school setting, prior to their internship experience. To address this challenge an immersive virtual reality serious game was developed. Students’ perceptions of the effectiveness of the game were assessed using a survey. Targeted variables were presence, design, interest and usefulness. Students positively evaluated all measures, suggesting an immersive virtual reality serious game is a useful instrument in teaching hazard perception in technical and vocational secondary education.
Benchmarking Self-Supervised Representation Learning from a million Cardiac Ultrasound images

Supervised deep learning has become defacto standard for most computer vision and machine learning problems including medical imaging. However, the requirement of having high quality annotations on large number of datasets places a huge overhead during model development. Self-supervised learning(SSL) is a paradigm which leverages unlabelled data to derive common-sense knowledge relying on signals present in the data itself for the learning rather than external supervisory signals. Recent times have seen the emergence of state-of-the-art SSL methods that have shown performance very close to supervised methods with minimal to no supervision on natural image settings. In this paper, we perform a thorough comparison of the performance of the state-of-the-art SSL methods for medical image setting, particularly for the challenging Cardiac view classification from Ultrasound acquisitions. We analyze the effect of data size in both phases of training - pre-text task training and main task training. We compare the performance with a task specific SSL technique based on simple image features and transfer learning ImageNet pre-training.
Breast Masses Detection and Segmentation in Full-Field Digital Mammograms using Unified Convolution Neural Network

Breast Cancer has been the primary reason for mortality in women of age between twenties and sixties worldwide; moreover early detection and treatment provides patients to get absolute treatment and decrease the mortality rate. Furthermore, recent research indicates that most experienced physicians have plenty of limitations, hence the plethora of work has been carried out to develop an automated mechanism of segmentation and classification of affected area and type of cancer; however, it is still considered to be highly challenging due to the variability of tumor in shape, low signal to noise ratio, shape, size and location of tumor. Furthermore, mammographic mass segmentation and detection are performed as a separate task and a convolution neural network is a highly adopted architecture for the same. In this research, we have designed and developed unified CNN architecture to perform the segmentation and detection of a breast mass. The unified-CNN architecture comprises a novel module for convolution which is combined through additional offset. Further RRS aka Random Region Selection mechanism is applied for data augmentation approach and high-level feature map is implied to achieve the high prediction. Furthermore, unified-CNN is evaluated using the metrics like true positive Rate at FPI (False Positive per Image) and Dice Index on INBreast dataset, also comparative analysis is out carried with various existing methodology. Unified-CNN is developed through improvising CNN. It introduces a novel module at the convolution layer to aim for a high-level feature map in order to get a high prediction. RRS (Random Region Selection) algorithm is used as the data augmentation approach to select the boundary region of the affected area; further robust model training is designed and optimized for process to make optimal. Unified-CNN introduces novel module at the convolution layer to aim for high level feature map in order to get high prediction; further ROI pooling is utilized for boundary detection in images.
Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening

Intelligent computer-aided algorithms analyzing photographs of various mouth regions can help in reducing the high subjectivity in human assessment of oral lesions. Very often, in the images, a ruler is placed near a suspected lesion to indicate its location and as a physical size reference. In this paper, we compared two deep-learning networks: ResNeSt and ViT, to automatically identify ruler images. Even though the ImageN et 1K dataset contains a "ruler" class label, the pre-trained models showed low sensitivity. After fine-tuning with our data, the two networks achieved high performance on our test set as well as a hold-out test set from a different provider. Heatmaps generated using three saliency methods: GradCam and XRAI for ResNeSt model, and Attention Rollout for ViT model, demonstrate the effectiveness of our technique. Clinical Relevance- This is a pre-processing step in automated visual evaluation for oral cancer screening.
Synthetic Generation of Cardiac MR Images Combining Convolutional Variational Autoencoders and Style Transfer

The number of studies in the medical field that uses machine learning and deep learning techniques has been increasing in the last years. However, these techniques require a huge amount of data that can be difficult and expensive to obtain. This specially happens with cardiac magnetic resonance (MR) images. One solution to the problem is raise the dataset size by generating synthetic data. Convolutional Variational Autoencoder (CVAe) is a deep learning technique which allows to generate synthetic images, but sometimes the synthetic images can be slightly blurred. We propose the combination of the CVAe technique combined with Style Transfer technique to generate synthetic realistic cardiac MR images. Clinical Relevance-The current work presents a tool to increase in a simple easy and fast way the cardiac magnetic resonance images dataset with which perform machine learning and deep learning studies.
Investigating the Requirement of Building Blockchain Simulator for IoT Applications

The pervasiveness of the Internet of Things (IoT) has enabled the administration of a large number of intelligent devices. However, IoT is based on centralised models, which introduce a number of problems, such as a single point of failure and security risks. Blockchain may offer a viable option for addressing these concerns. Practically, both blockchain and IoT are complex technologies posing further challenges in assessing application performance. The availability of a reliable simulation environment for Blockchain based IoT applications would be a major aid in the development and evaluation of such applications. Our research has found that currently there are no simulators with a comprehensive set of features, for the development and evaluation of blockchain based IoT applications, which is the main motivation for our work. The purpose of this study is to gather the opinions of experts regarding the creation of a simulation environment for IoT based blockchain applications. To do this, we utilise two separate investigations. First, a questionnaire is developed to ensure that the development of such simulation software would be of significant use. Second, interviews with participants are performed to gain their perspectives on the primary issues they face with blockchain-based IoT applications. In addition, the interviews focused on collecting the perspectives of participants on how blockchain may improve IoT and how to identify blockchain's applicability in IoT. Our findings demonstrate that the participants had a great deal of confidence in blockchain to resolve IoT issues. However, they lack the tools necessary to assess this concept. This highlights their requirement for a simulator to analyse the integration of blockchain and IoT.
A Class-Imbalanced Study with Feature Extraction via PCA and Convolutional Autoencoder

It is inherently challenging to train a machine learning algorithm on a class-imbalanced dataset. Under conditions of high dimensionality, this training process can become even more difficult due to the large number of features in the dataset. During preprocessing, data sampling is commonly used to address class imbalance and feature extraction is frequently used to reduce the number of dataset features. In this study, we explore the use of these two preprocessing activities before passing on the data to four ensemble classifiers (Random Forest, CatBoost, LightGBM, and XGBoost). With reference to feature extraction, the Principal Component Analysis (PCA) and Convolutional Autoencoder (CAE) methods are evaluated. With regard to data sampling, the Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE) methods are evaluated. Classification performance is measured with the Area Under the Receiver Operating Characteristic Curve (AUC) metric. Our results indicate that the implementation of the RUS method followed by the CAE method leads to the best classification performance.
Sustaining the High Performance of AI-Based Network Traffic Classification Models

Network traffic classification plays an essential role in network measurement and management. Emerging Artificial Intelligence (AI) algorithms have become a viable solution to encrypted network traffic classification. Nonetheless, the classification performance of existing AI-based traffic classifiers is restricted to a limited number of network applications depending on the coverage of the knowledge database. Such AI-based traffic classifiers cannot maintain high performance to provide accurate traffic classification when dealing with updated or new network applications. To tackle the issues, we present an autonomous model update mechanism to sustain the high performance of AI-based traffic classifiers. Specifically, an instability check algorithm is derived to evaluate if the current classifier requires an update. A filtering algorithm is proposed to extract unknown traffic and build a new knowledge database based on a new metric, i.e., familiarity, defined based on the prediction confidence and instability. Extensive experiment results demonstrate that our proposed updating mechanism can provide prompt model updates and establish a proper new knowledge base to maintain high accuracy in various experimental scenarios. Moreover, the comparison is conducted and the results show the proposed familiarity-based filtering algorithm can filter about 7 and 3 times more true positive packets in the two considered scenarios, respectively.
V-PCC Projection Based Blind Point Cloud Quality Assessment for Compression Distortion

In immersive media communication with point cloud (PC), PC compression will inevitably cause distortion, which may affect quality of users’ visual experience. To monitor the visual quality of PCs, point cloud quality assessment (PCQA) metrics are highly desired. Inspired by the video-based point cloud compression (V-PCC) standard to project a PC to planes and quantify the corresponding projection maps, a new blind PCQA metric based on V-PCC texture projection map and geometry projection map of PC is proposed for evaluating PC with compression distortion. Specifically, considering the combination of texture and geometry features of the projection maps to characterize distorted PC, a dual-stream convolutional network is designed from the perspective of global and local feature description to extract texture and geometry features of the distorted PC. The network mainly consists of two modules, that is, global texture and geometry fusion (GTGF) module and local geometry weighted local texture (LGWLT) module. The GTGF module is designed to realize the fusion of global texture and geometry features, which can describe PC's combination effect of texture and geometry distortions. Aiming at the relationship between texture and geometry distortions, the LGWLT module is designed to extract the features of local texture distortion caused by local geometry distortion. Furthermore, visual perception is combined to take distortions of the PC's salient regions into account in the process of PCQA. The experimental results on three PC databases show that the proposed PCQA metric is more consistent with visual perception, thus has greater potential than the representative PCQA metrics.
Multi-scale attention guided network for end-to-end face alignment and recognition

Attention modules embedded in deep networks mediate the selection of informative regions for object recognition. In addition, the combination of features learned from different branches of a network can enhance the discriminative power of these features. However, fusing features with inconsistent scales is a less-studied problem. In this paper, we first propose a multi-scale channel attention network with an adaptive feature fusion strategy (MSCAN-AFF) for face recognition (FR), which fuses the relevant feature channels and improves the network’s representational power. In FR, face alignment is performed independently prior to recognition, which requires the efficient localization of facial landmarks, which might be unavailable in uncontrolled scenarios such as low-resolution and occlusion. Therefore, we propose utilizing our MSCAN-AFF to guide the Spatial Transformer Network (MSCAN-STN) to align feature maps learned from an unaligned training set in an end-to-end manner. Experiments on benchmark datasets demonstrate the effectiveness of our proposed MSCAN-AFF and MSCAN-STN.
基于两光子偏振-空间模超纠缠态的高容量的量子秘密共享协议

<p indent="0mm">This paper proposes a feasible high-capacity tripartite quantum secret sharing protocol based on the two-photon polarization-spatial-mode hyperentangled state. Alice, the secret owner, prepares polarization-spatial-mode hyperentangled photon pairs and distributes them to two agents, Bob and Charlie. Using nonlocal complete Bell state analysis technology assisted by spatial mode entanglement, the two agents can collaborate to distinguish Alice’s Bell state in polarization degree of freedom. Only linear optical elements are needed to construct the agents’ measuring devices, which makes the protocol more feasible in experimental implementation. Compared with the existing quantum secret sharing protocols based on two-photon entanglement, this scheme does not need two-way quantum communication and has high capacity because it can transmit two bits of information with a polarization-spatial-mode hyperentangled photon pair. The proposed quantum secret sharing protocol is secure and reliable due to error correction and privacy amplification technology.
Learning diverse and deep clues for person reidentification

Extracting robust features has been the core of person re-identification(ReID). The existing convolutional neural network-based methods pay more attention to local features rather than the connection between local features. Given that human bodies possess certain structural information, it is absolutely necessary to strengthen the connection of local features for the ReID task. This paper proposes a two-stage attention network termed Width and Depth Channel Attention Network (WDC-Net) for ReID. Unlike conventional attention-based methods, which only focus on the single local features, our network exploits diverse feature representations to alleviate the missing information problem caused by occlusion. Precisely, for the first stage, it splits the local associations of the feature map through a multi-scale perspective to extract relatively independent multi-level local features of the human body. For the second stage, the correlation of multi-level local features is reconstructed through grouped pyramid structure to obtain a more robust global feature representation. We also propose an adaptive margin weight adjustment strategy to enhance the adaptability of the attention weights. Large-scale ReID datasets are tested to evaluate our method. On Market1501 and DukeMTMC, the proposed method achieves 90.7 % /96.4% mAP/R-1 and 81.8 % /90.8% mAP/R-1, respectively. It is worth highlighting that the proposed method also achieves 55.3 % /65.3% mAP/R-1 on the challenging Occluded-Duke dataset. Extensive experimental results demonstrate the superiority of our method, which achieves state-of-the-art performance on ReID. • An efficient framework is used to handle both historic ReID and occluded ReID. • Exploring diverse local features through the width channel attention. • Constructing global context-aware features with pyramid attention. • Enhancing the adaptability of the attention weights.
Sensitivity to Hand Offsets and Related Behavior in Virtual Environments over Time

This work explored how users’ sensitivity to offsets in their avatars’ virtual hands changes as they gain exposure to virtual reality. We conducted an experiment using a two-alternative forced choice (2-AFC) design over the course of 4 weeks, split into four sessions. The trials in each session had a variety of eight offset distances paired with eight offset directions (across a two-dimensional plane). While we did not find evidence that users became more sensitive to the offsets over time, we did find evidence of behavioral changes. Specifically, participants’ head–hand coordination and completion time varied significantly as the sessions went on. We discuss the implications of both results and how they could influence our understanding of long-term calibration for perception-action coordination in virtual environments.
Aegis: Privacy-Preserving Market for Non-Fungible Tokens

Non-fungible tokens (NFTs) are unique non-interchangeable digital assets verified and stored using blockchain technology. Quite recently, there has been a surging interest and adoption of NFTs, with sales exceeding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> 10 billion in the third quarter of 2021. Given the public state of Blockchain, NFTs owners face a privacy problem. More precisely, an observer can trivially learn the whole NFT collections owned by an address. For some categories of NFTs like arts and game collectibles, owners can sell them for a profit. However, popular marketplaces trade NFTs using public auctions and direct offers. Hence, an observer can learn about the new owner and the NFT purchase price. To tackle those problems, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aegis</monospace> ,(Aegis is a shield carried by Zeus and Athena. It is a symbol of protection.) a protocol that allows users to add privacy to their NFTs ownership. In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aegis</monospace> users can swap NFTs for payment amounts in fungible tokens while hiding the details (i.e., involved parties, the NFTs, and the payment amounts). One of the main properties of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aegis</monospace> is its complete compatibility with existing NFT standards. We design <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aegis</monospace> by leveraging zkSNARK proof system and smart contracts. We build an open-source prototype and perform experiments to evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aegis</monospace> 's performance.
Face Recognition in Multiple Variations Using Deep Learning and Convolutional Neural Networks

Face Recognition (FR) has been widely used in the tracking and identification of individuals. However, because face images vary depending on expressions, ages, individual locations, and lighting conditions, the facial photographs of the same sample may appear to be distinct, making face recognition more difficult. Deep learning (DL) is now a suitable solution for face recognition and computer vision. In this study, features and traits were extracted from images of a large data set (called FERET) consisting of 14,126 images that were divided into 80% for training data and 20% for testing data using a Convolutional Neural Network (CNN). The CNN is first pre-trained using supplementary data for the purpose of obtaining updated weights, and then trained with the target dataset in order to uncover more hidden facial characteristics. Three different deep learning models are implemented: AlexNet, Resnet18, and DenseNet-161. The performance of these models is compared experimentally in terms of their classification accuracy. The obtained results showed that the DenseNet-161 has the highest accuracy of 98.6%, while the accuracies of the Resnet18 and AlexNet are 96.3% and 93.3%, respectively.