Reviewerly/exHarmony huggingface dataset:
Low-Asymmetry Interface for Multiuser VR Experiences with Both HMD and Non-HMD Users

We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the point-of-view asymmetry and behavior asymmetry between HMD and non-HMD users are reduced. Our system comprises a portable mobile device as a visual display to provide a changing PoV for the non-HMD user and a walking simulator as an in-place walking detection sensor to enable the same level of realistic and unrestricted physical-walking-based locomotion for all users. Because this allows non-HMD users to experience the same level of visualization and free movement as HMD users, both of them can engage as the main actors in movement scenarios. Our user study revealed that the low-asymmetry interface enables non-HMD users to feel a presence similar to that of the HMD users when performing equivalent locomotion tasks in a virtual environment. Furthermore, our system can enable one HMD user and multiple non-HMD users to participate together in a virtual world; moreover, our experiments show that the non-HMD user satisfaction increases with the number of non-HMD participants owing to increased presence and enjoyment.
Efficient Role of Machine Learning Classifiers in the Prediction and Detection of Breast Cancer

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Effects of Virtual Reality Head-mounted Displays on Oculomotor Functions

Purpose: Three-dimensional (3D) video games played using virtual reality head-mounted displays (VR-HMDs) are becoming increasingly popular.However, the use of this technology may lead to visual symptoms and discomfort by disrupting the normal linkage between the accommodation and vergence systems.This study aimed to investigate the effect of playing 3D video games using VR-HMDs on different oculomotor functions (i.e., accommodation and/or vergence system parameters) and to quantify symptoms associated with playing games using VR-HMD technology.Methods: Twenty -six male and female young adults (age range 19-27 years) with normal binocular vision completed this study.Different clinical accommodative and vergence measurements were collected before and after playing 3D video games using a VR-HMD system for 45 minutes.In addition, visual-and non-visual-related symptoms were measured before and after the video game sessions using the standardized Simulator Sickness Questionnaire.Results: The majority of accommodative parameters-including negative relative accommodation, accommodative accuracy, and monocular and binocular accommodative facilities-were significantly affected after playing 3D video games.With respect to vergence system measurements, the horizontal negative fusional vergence range at near and vergence facility test outcomes were affected significantly after the 3D video game sessions.Significant increments in different types of symptoms (i.e., visual and nonvisual) were also observed after playing 3D video games using the VR-HMD system.Conclusions: Playing 3D video games using VR-HMD systems can lead to a deterioration of certain oculomotor functions (i.e., accommodative and vergence systems).Players can be expected to experience eyestrain and discomfort after just 45 minutes of playing.
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models

Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language. While it has proven effective for learning generalisable representations, the training of such models often requires a large amount of multimodal data that shares commonality, which can be expensive to come by. To mitigate this, we develop a novel contrastive framework for generative model learning, allowing us to train the model not just by the commonality between modalities, but by the distinction between related and multimodal data. We show in experiments that our method enables data-efficient multimodal learning on challenging datasets for various multimodal VAE models. We also show that under our proposed framework, the generative model can accurately identify samples from unrelated ones, making it possible to make use of the plentiful unlabeled, unpaired multimodal data.
CODA: Improving Resource Utilization by Slimming and Co-locating DNN and CPU Jobs

While deep neural network (DNN) models are often trained on GPUs, many companies and research institutes build GPU clusters that are shared by different groups. On such GPU cluster, DNN training jobs also require CPU cores to run pre-processing, gradient synchronization. Our investigation shows that the number of cores allocated to a training job significantly impact its performance. To this end, we characterize representative deep learning models on their requirement for CPU cores under different GPU resource configurations, and study the sensitivity of these models to other CPU-side shared resources. Based on the characterization, we propose CODA, a scheduling system that is comprised of an adaptive CPU allocator, a real-time contention eliminator, and a multi-array job scheduler. Experimental results show that CODA improves GPU utilization by 20.8% on average without increasing the queuing time of CPU jobs.
Proposing a Recognition System of Gestures Using MobilenetV2 Combining Single Shot Detector Network for Smart-Home Applications

The paper proposes a system for identifying gestures and actions in smart homes. The proposed method is based on MobilenetV2 feature extraction combining with single shot detector (SSD) network. We used eleven types of gestures of walking, sitting down, falling back, wearing shoes, waving hands, falling down, smoking, baby crawling, standing up, reading, and typing for recognizing the gestures. In this system, the data are captured from the camera of mobile devices that are used to detect the object. The results are obtained objects on the frame by a bounding box. The results show that the system meets the requirements with an accuracy of over 90% that is suitable for real application.
Despeckling and enhancement of ultrasound images using non-local variational framework

Speckles are introduced in the ultrasound data due to constructive and destructive interference of the probing signals that are used for capturing the characteristics of the tissue being imaged. There are a plethora of models discussed in the literature to improve the contrast and resolution of the ultrasound images by despeckling them. There is a class of models that assumes that the noise is multiplicative in its original form, and transforming the model to a log domain makes it an additive one. Nevertheless, such a transformation duly oversimplifies the scenario and does not capture the inherent properties of the data-correlated nature of speckles. Therefore, it results in poor reconstruction. This problem is addressed to a considerable extent in the subsequent works by adopting various models to address the data-correlated nature of the noise and its distributions. This work introduces a weberized non-local total bounded variational model based on the noise distribution built on the Retinex theory. This perceptually inspired model apparently restores and improves the contrast of the images without compromising much on the details inherently present in the data. The numerical implementation of the model is carried out using the Bregman formulation to improve the convergence rate and reduce the parameter sensitivity. The experimental results are highlighted and compared to demonstrate the efficiency of the model.
Attention-Mechanism-Based Real-Time Gaze Tracking in Natural Scenes With Residual Blocks

Gaze tracking is widely used in fatigue driving detection, eye disease diagnosis, mental illness diagnosis, website or advertising design, virtual reality, gaze-control devices and human-computer interaction.However, the influence of light, specular reflection and occlusion, the change of head pose, especially the ever-changing human pose in natural scenes, have brought great challenges to the accurate gaze tracking.In this paper, gaze tracking in natural scenes is studied, and a method based on Convolutional Neural Network (CNN) with residual blocks is proposed, in which attention mechanism is integrated into the network to improve the accuracy of gaze tracking.Furthermore, it is tested on the GazeFollow database which contains six kinds of databases.The results show that the performance of proposed method outperforms that of other state-of-the-art methods in natural scenes.Moreover, the proposed method has better real-time performance and is more suitable for practical applications.
Can Vision Transformers Learn without Natural Images?

Is it possible to complete Vision Transformer (ViT) pre-training without natural images and human-annotated labels? This question has become increasingly relevant in recent months because while current ViT pre-training tends to rely heavily on a large number of natural images and human-annotated labels, the recent use of natural images has resulted in problems related to privacy violation, inadequate fairness protection, and the need for labor-intensive annotations. In this paper, we experimentally verify that the results of formula-driven supervised learning (FDSL) framework are comparable with, and can even partially outperform, sophisticated self-supervised learning (SSL) methods like SimCLRv2 and MoCov2 without using any natural images in the pre-training phase. We also consider ways to reorganize FractalDB generation based on our tentative conclusion that there is room for configuration improvements in the iterated function system (IFS) parameter settings of such databases. Moreover, we show that while ViTs pre-trained without natural images produce visualizations that are somewhat different from ImageNet pre-trained ViTs, they can still interpret natural image datasets to a large extent. Finally, in experiments using the CIFAR-10 dataset, we show that our model achieved a performance rate of 97.8, which is comparable to the rate of 97.4 achieved with SimCLRv2 and 98.0 achieved with ImageNet.
Design of Lingnan Cultural Gene Implantation Cultural and Creative Products Based on Virtual Reality Technology

With the development of virtual reality technology, people are increasingly aware that the combination of virtual reality technology and product design can enable companies to obtain stable profits and maintain long-term competitive advantages. And design evaluation plays a pivotal role in a large number of important decisions in product development. This article takes Lingnan culture and cultural products as an example, combining virtual reality technology with the design of Lingnan cultural and creative products, verifies the effectiveness and rationality of the virtual evaluation system of Lingnan cultural products through the evaluation of examples of Lingnan cultural products, and proposes amendments to the evaluation case. This paper constructs a product design evaluation system based on virtual reality technology and designs a theoretical model VR, which is the application of virtual reality technology in product design evaluation. The PDES system expounds the idea and method of constructing the system model. This paper studies the evaluation object, evaluation content, evaluation method, evaluation platform, and manifestation of evaluation results of the Lingnan cultural product virtual evaluation system and builds the framework of the Lingnan cultural product virtual evaluation system. This paper studies the product display method based on virtual reality technology and realizes the three-dimensional display of products on the Internet and the design of user interaction in virtual reality. In this paper, the two algorithms BRSK and SURF are used together, and the multiscale expression characteristics of BRISK in space and the rotation invariant characteristics of SURF are used. Experimental research shows that, compared with the pure BRISK algorithm, the rotation performance of the method in this paper can be seen through the experimental results in this paper to have better accuracy. The method in this paper ensures the accuracy and accuracy of matching as much as possible.
Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.
Resilient design of distribution grid automation system against cyber-physical attacks using blockchain and smart contract

The current Distribution Grid Automation (DGA) Systems are being heavily dependent on the Information and Communication Technologies (ICT) infrastructure for its proper operation. The DGA architectures are predominantly centralized and usually deployed on a dedicated hardware. This increases the risk of blackouts under a coordinated cyber-physical attack. The compromise of the dedicated hardware that hosts the central coordinator of the DGA automation results in a blackout. Though many countermeasures have already been proposed for tackling different types cyber and physical attacks on the ICT infrastructure, very few measures have been proposed to ensure the availability of the grid operation functions, even when it is compromised. This study proposes an automatic, distributed approach based on Blockchain and Smart Contract that ensures the availability of the core DGA functions even if the central coordinator that operates the grid is compromised. This is done by virtualizing and migrating/re-initialising these functions from the dedicated hardware that was compromised to another. Additionally, a Multi-Attribute Decision Making based method is incorporated into the Smart Contract that helps in selection of the optimal hardware that can host the function considering its limitations (hardware and software). Finally, a proof of concept implementation of the proposed solution is presented that utilizes the Calvin IoT (Internet of Things) platform, Flow programming tool and Hyperledger fabric and its performance is evaluated.
Virtual reality as a versatile tool for research, dissemination and mediation in the humanities

&lt;p class="VARAbstract"&gt;The VESPACE project aims to revive an evening of theatre at the &lt;em&gt;Foire Saint-Germain&lt;/em&gt; in Paris in the 18&lt;sup&gt;th&lt;/sup&gt; century, by recreating spaces, atmospheres and theatrical entertainment in virtual reality. The venues of this fair have disappeared without leaving any archaeological traces, so their digital reconstruction requires the use of many different sources, including the expertise of historians, historians of theatre and literature. In this article, we present how we have used video game creation tools to enable the use of virtual reality in three key stages of research in the human sciences and particularly in history or archaeology: preliminary research, scientific dissemination and mediation with the general public. In particular, we detail the methodology used to design a three-dimensional (3D) model that is suitable for both research and virtual reality visualization, meets the standards of scientific work regarding precision and accuracy, and the requirements of a real-time display. This model becomes an environment in which experts can be immersed within their fields of research and expertise, and thus extract knowledge reinforcing the model created –through comments, serendipity and new perspectives– while enabling a multidisciplinary workflow. We also present our tool for annotating and consulting sources, relationships and hypotheses in immersion, called PROUVÉ. This tool is designed to make the virtual reality experience go beyond a simple image and to convey scientific information and theories in the same way an article or a monograph does. Finally, this article offers preliminary feedback on the use of our solutions with three target audiences: the researchers from our team, the broader theatre expert community and the general public.&lt;/p&gt;&lt;p class="VARAbstract"&gt;Highlights:&lt;/p&gt;&lt;p&gt;• Immersive Virtual Reality is used to enhance the digital reconstruction of an 18th-century theatre, by allowing experts to dive into their research topic.&lt;/p&gt;&lt;p&gt;• Virtual Reality (VR) can also be used to disseminate the digital model through the scientific community and beyond while giving access to all kinds of sources that were used to build it.&lt;/p&gt;&lt;p&gt;• A quick survey shows that VR is a powerful tool to share theories and interpretations related to archaeological or historical tri-dimensional data.&lt;/p&gt;
Optical and electrical programmable computing energy use comparison

Optical computing has been proposed as a replacement for electrical computing to reduce energy use of math intensive programmable applications like machine learning. Objective energy use comparison requires that data transfer is separated from computing and made constant, with only computing variable. Three operations compared in this manner are multiplication, addition and inner product. For each, it is found that energy use is dominated by data transfer, and that computing energy use is a small fraction of the total. Switching to optical from electrical programmable computing does not reduce energy use.
Visual Transformer Pruning.

Visual transformer has achieved competitive performance on a variety of computer vision applications. However, their storage, run-time memory, and computational demands are hindering the deployment on mobile devices. Here we present an visual transformer pruning approach, which identifies the impacts of channels in each layer and then executes pruning accordingly. By encouraging channel-wise sparsity in the Transformer, important channels automatically emerge. A great number of channels with small coefficients can be discarded to achieve a high pruning ratio without significantly compromising accuracy. The pipeline for visual transformer pruning is as follows: 1) training with sparsity regularization; 2) pruning channels; 3) finetuning. The reduced parameters and FLOPs ratios of the proposed algorithm are well evaluated and analyzed on ImageNet dataset to demonstrate its effectiveness.
Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.
Commercially Successful Blockchain Healthcare Projects: A Scoping Review

Background: The healthcare industry is the new frontier for blockchain technology. Given its properties of immutability and decentralization, blockchain represents an opportunity for unprecedented level of privacy and security for all stakeholders by ensuring data integrity while giving patients control over their own health data. On a backdrop of rising interest in blockchain in general and blockchain healthcare applications in particular, there has been a proliferation of blockchain healthcare projects over the past few years. The aim of this review is to identify and understand real-world blockchain healthcare projects that have attained commercial success in the highly competitive blockchain market. Methods and findings: A scoping review was performed in January 2021 on all projects in the CoinMarketCap database. Following a pre-defined inclusion and exclusion criteria, eligible projects were selected. A single reviewer then reviewed each project’s official website and whitepaper (where available) and performed data abstraction; 10 blockchain healthcare projects fulfilled the selection criteria. The review found that these projects made up 0.24% of the total number of actively tracked projects on CoinMarketCap. In terms of market capitalization, the total market capitalization for the projects was US$65,078,849, comprising less than 0.01% of the total market capitalization of all projects. Among the projects, the most frequent type was for personal health tracking. Conclusions: This review revealed that blockchain health projects currently comprise a small fraction of the overall number of commercially successful blockchain projects. However, because this sub-industry is still in its early stages, there are reasons to be optimistic that many more blockchain health projects will emerge and attain commercial success in future. Findings from this review done from an entrepreneurial perspective should help with the identification of future projects most likely to succeed.