Reviewerly/exHarmony huggingface dataset:
Older Adults’ Experiences and Perceptions of Immersive Virtual Reality: Systematic Review and Thematic Synthesis

Immersive virtual reality (IVR) can be defined as a fully computer-generated environment shown on a head-mounted display. Existing research suggests that key features of IVR can assist older adults in their everyday lives, providing opportunities for health promotion and tackling social isolation and loneliness. There has been a surge in qualitative studies exploring older adults' experiences and perceptions of IVR. However, there has been no systematic synthesis of these studies to inform the design of new, more accessible IVR technologies.This study aimed to systematically review and synthesize qualitative studies exploring older adults' experiences and perceptions of IVR.A systematic review and thematic synthesis were conducted following the ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) guidelines. In total, 2 reviewers completed title and abstract screening, full-text screening, data extraction, and quality appraisal. Thematic synthesis is derived from the qualitative method, thematic analysis. It involves 3 key steps: initial coding and grouping of these codes, the formation of descriptive themes from these codes, and going beyond these data to form analytical themes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach.Overall, 13 studies were included in the final synthesis, including 224 participants across 9 countries and 5 continents. Confidence in the evidence ranged from high to moderate. Three descriptive themes were generated: practical aspects of IVR use, experiencing unique features of IVR, and perceptions of IVR. The findings from the descriptive themes suggested that there are several improvements that need to be made to existing IVR devices to facilitate older adults' use of this technology. However, older adults' responses to IVR were generally positive. Three analytical themes were generated: tolerating the bad to experience the good, buying in to IVR (don't judge a book by its cover), and "it proves to me I can do it." The analytical themes illustrated that older adults were willing to tolerate discomforts that accompany existing IVR technologies to experience features such as immersive social networking. There was a discrepancy between older adults' perceptions of IVR before use-which were generally negative-and after use-which were generally positive-and IVR provided a platform for older adults to access certain activities and environments more easily than in the real world because of limitations caused by aging.This review offers insights into older adults' experiences and perceptions of IVR and suggests how a few improvements to its existing hardware and software as well as how it is first presented could offer new opportunities for older adults to take part in meaningful activities tailored to their needs and preferences.PROSPERO CRD42020200774; https://tinyurl.com/8f48w2vt.RR2-10.1177/16094069211009682.
Interactive Somatosensory Games in Rehabilitation Training for Older Adults With Mild Cognitive Impairment: Usability Study

In aging societies, dementia risk increases with advancing age, increasing the incidence of dementia-related degenerative diseases and other complications, especially fall risk. Dementia also escalates the care burden, impacting patients, their families, social welfare institutions, and the social structure and medical system.In elderly dementia, traditional card recognition rehabilitation (TCRR) does not effectively increase one's autonomy. Therefore, from the usability perspective, we used the Tetris game as a reference to develop an interactive somatosensory game rehabilitation (ISGR) with nostalgic style for elders with mild cognitive impairment (MCI). Through intuitive gesture-controlled interactive games, we evaluated subjective feelings concerning somatosensory game integration into rehabilitation to explore whether the ISGR could improve the willingness to use and motivation for rehabilitation among elders with MCI.A total of 15 elders with MCI (7 males and 8 females with an average age of 78.4 years) underwent 2 experiments for 15 minutes. During experiment 1, TCRR was performed, followed by completing the questionnaire of the System Usability Scale (SUS). After 3-5 minutes, the second experiment (the ISGR) was conducted, followed by completing another SUS. We used SUS to explore differences in impacts of TCRR and ISGR on willingness to use among elders with MCI. In addition, we further investigated whether the factor of gender or prior rehabilitation experience would affect the rehabilitation willingness or not.The novel ISGR made the elderly feel interested and improved their willingness for continuous rehabilitation. According to the overall SUS score, the ISGR had better overall usability performance (73.7) than the TCRR (58.0) (t28=-4.62, P<.001). Furthermore, the ISGR individual item scores of "Willingness to Use" (t28=-8.27, P<.001), "Easy to Use" (t28=-3.17, P<.001), "System Integration" (t28=-5.07, P<.001), and "Easy to Learn" (t28=-2.81, P<.001) were better than TCRR. The somatosensory game was easier to learn and master for females than for males (t13=2.71, P=.02). Besides, the ISGR was easier to use (t12=-2.50, P=.02) and learn (t14=-3.33, P<.001) for those without prior rehabilitation experience. The result indicates that for elders with no rehabilitation experience ISGR was easier to use and simpler to learn than TCRR.Regardless of prior rehabilitation experience, the ISGR developed in this study was easy to learn and effective in continuously improving willingness to use. Furthermore, the adoption of a nostalgic game design style served the function of cognitive training and escalated interest in rehabilitation. The ISGR also improved user stickiness by introducing different game scenarios and difficulties, increasing long-term interest and motivation for rehabilitation. For future research on the adoption of interactive somatosensory games in rehabilitation, additional rehabilitation movements can be developed to benefit the elderly with MCI.
MODY probability calculator utility in individuals' selection for genetic testing: Its accuracy and performance

MODY probability calculator (MPC) represents an easy-to-use tool developed by Exeter University to help clinicians prioritize which individuals should be oriented to genetic testing. We aimed to assess the utility of MPC in a Portuguese cohort with early-onset monogenic diabetes.This single-centre retrospective study enrolled 132 participants submitted to genetic testing between 2015 and 2020. Automatic sequencing and, in case of initial negative results, generation sequencing were performed. MODY probability was calculated using the probability calculator available online. Positive and negative predictive values (PPV and NPV, respectively), accuracy, sensitivity and specificity of the calculator were determined for this cohort.Seventy-three individuals were included according to inclusion criteria: 20 glucokinase (GCK-MODY); 16 hepatocyte nuclear factor 1A (HNF1A-MODY); 2 hepatocyte nuclear factor 4A (HNF4A-MODY) and 35 DM individuals with no monogenic mutations found. The median probability score of MODY was significantly higher in monogenic diabetes-positive subgroup (75.5% vs. 24.2%, p < .001). The discriminative accuracy of the calculator, as expressed by area under the curve, was 75% (95% CI: 64%-85%). In our cohort, the best cut-off value for the MODY calculator was found to be 36%, with a PPV of 74.4%, NPV of 73.5% and corresponding sensitivity and specificity of 76.2% and 71.4%, respectively.In a highly pre-selected group of probands qualified for genetic testing, the Exeter MODY probability calculator provided a useful tool in individuals' selection for genetic testing, with good discrimination ability under an optimal probability cut-off of 36%. Further geographical and population adjustments are warranted for general use.
Chemical signatures to identify the origin of solid ashes for efficient recycling using machine learning

Solid ashes constitute the world's leading environmental problem and their efficient recycling requires specific origin information. Chemical signatures encode the physicochemical and structural characteristics may reveal their origins. In this study, we collected 310 solid ash samples from various continents, countries, and origins and combined particle swarm optimization , random forest (RF), and novel interpretation methods to build an accurate ash origin detection system. The RF model took major oxides as inputs, without further feature-engineering, and automatically classified the solid ash into four origins. Our model predicted solid ash origins with 98.5% accuracy on a training set and with 91.5% accuracy on an independent testing set, even without expert knowledge of the operating conditions. Our approach also demonstrated feature importance and interpreted the decision-making mechanisms underlying optimum RF models. These results demonstrated the feasibility of the proposed modeling framework as a blueprint for automated machine-learning origin detection of solid ashes. • Chemical signatures were proposed for the origin identification of solid ashes. • Origin detection can promote the recovery of solid ashes. • A global solid ash dataset was collected by an extensive literature review. • The RF-PSO modeling framework can quickly and accurately estimate ash origin. • Interpretation techniques were used to reveal the decision-making of RF-PSO.
Towards A Lightweight Identity Management and Secure Authentication for IoT Using Blockchain

Handling nodes identities and authentication is one of the current critical security challenges in an Internet of Things (IoT) environment, which consists of numerous devices with limited computation, communication, storage, and power capabilities. Motivated by the need to maintain trustworthiness in IoT networks to secure node-to-node or user-to-node communication, a blockchain-based identity management and secure authentication mechanism for a Wireless Sensor Network (WSN) scenario is proposed in this paper. The considered WSN is assumed to have three types of nodes: base station, cluster heads, and monitor nodes. The WSN is connected through the base station to the IoT cloud. The proposed system employs a private blockchain for internal authentication of cluster heads and monitor nodes, while a public blockchain is deployed between the base station and the IoT cloud to authenticate communication across different WSNs and end-users. Furthermore, a machine learning-based detection module is utilized to mitigate possible denial-of-service (DoS) attacks that may target cluster head nodes, raising the registration and authentication costs for monitor nodes within its vicinity and amplifying other blockchain attacks.
Small object detection in remote sensing images based on attention mechanism and multi-scale feature fusion

Due to the influence of dense distribution of detection objects and complex background, there are many small objects, which are difficult to detect in remote sensing images. In order to solve the difficult problem of small object detection in remote sensing images, we propose an object detection algorithm named CotYOLO-v3 in this paper. First, we redesign the residual blocks in the backbone Darknet-53, and we replace it with Contextual Transformer (Cot) blocks with contextual information in the backbone Darknet-53 to extract contextual information for small objects and enhance visual representation; Second, we introduce the shallow information with attention mechanism before the feature fusion of YOLO-v3 to reduce the influence of background interference factors and improve the expression ability of the network. Then, we optimize the feature fusion process, we replace the up-sampling method with sub-pixel convolution, and we replace the first convolution layer of the prediction branch with a residual block. Finally, we use K-Medians clustering algorithm to regenerate the anchors suitable for the remote sensing image datasets. In this paper, we set up a comparative experiment of CotYOLO-v3 and commonly used object detection algorithms to detect small objects in DIOR datasets. The experimental results show that, compared with other commonly used object detection algorithms, CotYOLO-v3 object detection algorithm has obvious advantages in detecting small objects in remote sensing images. Compared with the original object detection algorithm YOLO-v3, the mean Average Precision (mAP) of CotYOLO-v3 improved by 5.07%.
StrainNet-3D: Real-time and robust 3-dimensional speckle image correlation using deep learning

This paper proposes a methodology of applying convolutional neural network (CNN) in solving 3D-DIC tasks. First, a multi-configuration stereo speckle dataset generation algorithm is designed with labels to train the networks. Then, an affine-transformation-based disparity calculation method and a light-weight CNN used for subpixel correlation are proposed. The three-dimensional displacement is calculated using the disparities and time-wise optical flow calculated by CNN, guided by stereo-vision theory and through an optional refiner network. After training, numerical experiments are carried out to verify the accuracy and the speed. Finally, real time high-resolution film bulging experiments are carried out which indicates the CNN-based method can achieve real-time and high-precision calculation with a comparable accuracy to DIC and an excellent robustness to intensity changes, assisted by the proposed gray adjustment technique. This method, named StrainNet-3D, may play an important role in experimental measurement tasks requiring real-time calculation.
Function and Application of Flavonoids in the Breast Cancer

Breast cancer is one of the top causes of death, particularly among women, and it affects many women. Cancer can also be caused by various factors, including acquiring genetic alteration. Doctors use radiation to detect and treat breast cancer. As a result, breast cancer becomes radiation-resistant, necessitating a new strategy for its treatment. The approach discovered by the researchers is a flavonoid, which is being researched to see if it might help treat radiation-resistant breast cancer more safely than an approved medicine already being used in the field. As a result, this study focuses on the role of flavonoids in breast cancer suppression, breast cancer gene anomalies, and the resulting apoptotic mechanism.
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style

Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-grained control over the diverse appearance aspects of individual objects. Gaining control over the image generation process is fundamental to build practical applications with a user-friendly interface. In this paper, we propose a method for attribute controlled image synthesis from layout which allows to specify the appearance of individual objects without affecting the rest of the image. We extend a state-of-the-art approach for layout-to-image generation to additionally condition individual objects on attributes. We create and experiment on a synthetic, as well as the challenging Visual Genome dataset. Our qualitative and quantitative results show that our method can successfully control the fine-grained details of individual objects when modelling complex scenes with multiple objects. Source code, dataset and pre-trained models are publicly available (https://github.com/stanifrolov/AttrLostGAN).
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?

The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distribution (OoD) Generalization, i.e. reliably make predictions on out-of-distribution data. Yet in practice, models usually fail to generalize well when facing a shift in distribution. Several methods were thereby designed to improve the robustness of the features learned by a model through Regularization- or Domain-Prediction-based schemes. Segmenting medical images such as MRIs of the hippocampus is essential for the diagnosis and treatment of neuropsychiatric disorders. But these brain images often suffer from distribution shift due to the patient’s age and various pathologies affecting the shape of the organ. In this work, we evaluate OoD Generalization solutions for the problem of hippocampus segmentation in MR data using both fully- and semi-supervised training. We find that no method performs reliably in all experiments. Only the V-REx loss stands out as it remains easy to tune, while it outperforms a standard U-Net in most cases.
Federated knowledge graph completion via embedding-contrastive learning

Recently, the research about knowledge graphs (KGs) which contain a large number of triples, has gained massive attention. Many knowledge graph embedding methods are proposed to tackle the knowledge graph completion task. In real applications, knowledge graphs are applied not only in a centralized way but also in a decentralized manner. We study the problem of learning knowledge graph embeddings for a set of federated knowledge graphs, where their raw triples are not allowed to be collected together. We propose a federated learning framework FedEC. In our framework, a local training procedure is responsible for learning knowledge graph embeddings on each client based on a specific embedding learner. We apply embedding-contrastive learning to limit the embedding update for tackling data heterogeneity. Moreover, a global update procedure is used for sharing and averaging entity embeddings on the master server. Furthermore, we design embedding ensemble procedures to take full advantage of knowledge learned from different aspects. Finally, we conduct extensive experiments on datasets derived from KGE benchmark datasets, and the results show the effectiveness of our proposed model.
Evaluation of security metrics in AIoT and blockchain-based supply chain by Neutrosophic decision-making method

Today, smart supply chains are one of the main and growing applications of evolving technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, which bring many benefits. IoT and blockchain security is a subset of cybersecurity that protects against IoT-related threats. Therefore, the simultaneous combination of these technologies and the creation of a supply chain based on the artificial intelligence of things (AIoT) and blockchain, in addition to increasing the performance of activities in supply chain processes, can also bring many security challenges to this smart supply chain. Because the data collected by IoT devices and stored in the blockchain-based systems that manage them are much more sensitive than traditional network security strategies.Due to the importance of this issue, in this study, an attempt has been made to analyse these security indicators in the AIoT and blockchain-based intelligent supply chain using a combined decision-making method in a fuzzy neutrosophic environment. Understanding these security measures will definitely help to implement more secure and powerful smart supply chains. The results showed that customer privacy criteria have the highest priority. The following priorities are authentication, Security challenges based on infrastructure, and network integrity.
Sentiment Analysis of Chinese E-Commerce Product Reviews Using ERNIE Word Embedding and Attention Mechanism

The development of e-commerce has ushered in a golden age. E-commerce product reviews are remarks initiated by online shopping users to evaluate the quality and service of the products they purchase; these reviews help consumers learn the reality of the product. The sentiment polarity of e-commerce product reviews is the best way to obtain customer feedback on products. Therefore, sentiment analysis of product reviews on e-commerce platforms is greatly significant. However, the challenges of sentiment analysis of Chinese e-commerce product reviews lie in dimension mapping, disambiguation of sentiment words, and polysemy of Chinese words. To solve the above problems, this paper proposes a sentiment analysis model ERNIE-BiLSTM-Att (EBLA). Here, the dynamic word vector generated using the Enhanced Representation through Knowledge Integration (ERNIE) word embedding model is input into the Bidirectional Long Short-term Memory (BiLSTM) to extract text features. Then, the Attention Mechanism (Att) is used to optimize the weight of the hidden layer. Finally, softmax is used as the output layer for sentiment classification. The experimental results on the JD.com Chinese e-commerce product review dataset show that the proposed model achieves more than 0.87 in precision, recall, and F1 values, which is superior to classic deep learning models proposed by other researchers; it has strong practicability in sentiment analysis of Chinese e-commerce product reviews.
Approximating gradients for differentiable quality diversity in reinforcement learning

Consider the problem of training robustly capable agents. One approach is to generate a diverse collection of agent polices. Training can then be viewed as a quality diversity (QD) optimization problem, where we search for a collection of performant policies that are diverse with respect to quantified behavior. Recent work shows that differentiable quality diversity (DQD) algorithms greatly accelerate QD optimization when exact gradients are available. However, agent policies typically assume that the environment is not differentiable. To apply DQD algorithms to training agent policies, we must approximate gradients for performance and behavior. We propose two variants of the current state-of-the-art DQD algorithm that compute gradients via approximation methods common in reinforcement learning (RL). We evaluate our approach on four simulated locomotion tasks. One variant achieves results comparable to the current state-of-the-art in combining QD and RL, while the other performs comparably in two locomotion tasks. These results provide insight into the limitations of current DQD algorithms in domains where gradients must be approximated. Source code is available at https://github.com/icaros-usc/dqd-rl
Coevolutionary generative adversarial networks for medical image augumentation at scale

Medical image processing can lack images for diagnosis. Generative Adversarial Networks (GANs) provide a method to train generative models for data augmentation. Synthesized images can be used to improve the robustness of computer-aided diagnosis systems. However, GANs are difficult to train due to unstable training dynamics that may arise during the learning process, e.g., mode collapse and vanishing gradients. This paper focuses on Lipizzaner, a GAN training framework that combines spatial coevolution with gradient-based learning, which has been used to mitigate GAN training pathologies. Lipizzaner improves performance by taking advantage of its distributed nature and running at scale. Thus, the Lipizzaner algorithm and implementation robustness can be scaled to high-performance computing (HPC) systems to provide more accurate generative models. We address medical imaging data augmentation to create chest X-Ray images by using Lipizzaner on the HPC infrastructure provided by Oak Ridge National Labs' Summit Supercomputer. The experimental analysis shows improved performance by increasing the scale of the Lipizzaner GAN training. We also demonstrate that distributed coevolutionary learning improves performance even when using suboptimal neural network architectures due to hardware constraints.