Reviewerly/exHarmony huggingface dataset:
A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation

The combination of Augmented Reality (AR) and Digital Twin (DT) has begun to show its potential nowadays, leading to a growing research interest in both academia and industry. Especially under the current human-centric trend, AR embraces the potential to integrate operators into the new generation of Human Cyber–Physical System (HCPS), in which DT is a pillar component. Some review articles have focused on this topic and discussed the benefits of combining AR and DT, but all of them are limited to a specific domain. To fill the gap, this research conducts a state-of-the-art survey (till 17-July-2022) from the AR-assisted DT perspective across different sectors of the industrial field, covering a total of 118 selected publications. Firstly, application scenarios and functions of AR-assisted DT are summarized by following the engineering lifecycle, among which production process, service design, and Human–Machine Interaction (HMI) are hot topics. Then, improvements specifically brought by AR are analyzed according to three dimensions, namely virtual twin, hybrid twin, and cognitive twin, respectively. Finally, challenges and future perspectives of AR-assisted DT for futuristic human-centric industry transformation are proposed, including promoting product design, robotic-related works, cyber–physical interaction, and human ergonomics.
Diffusion models in medical imaging: A comprehensive survey

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical imaging. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including image-to-image translation, reconstruction, registration, classification, segmentation, denoising, 2/3D generation, anomaly detection, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub.1 We aim to update the relevant latest papers within it regularly.
Research collaboration in Large Scale Research Infrastructures: Collaboration types and policy implications

Over the past decades, Large Scale Research Infrastructures (LSRIs) have come to play a central role in providing scientist-users access to highly specialised scientific instrumentation and experimental conditions. Collaborations between (permanent) instrument scientists and users are at the core of these organisations, yet knowledge about the nature of such collaborations and their development over time is surprisingly scarce. In particular, we know very little about the interrelation between the individual and organisational drivers of collaboration. Based on a qualitative study of scientists and their collaborations at Institut Laue-Langevin, a world-leading neutron source, we identify four typical collaboration patterns, which reflect particular configurations of (dis)similarity between instrument scientists and users in terms of perceived expertise gap and co-development focus. Our findings suggest that the co-existence of multiple collaboration types within the same organisation plays an important role in the long-term success of LSRIs. In addition, we contend that dissimilarity can generate productive collaboration to the same extent as partner similarity; not only at the organisational level – co-existence of different types of collaborations across the LSRI, favouring the co-existence of a broad range of instrumentation – but also at the individual level – where instrument scientists benefit in terms of more productive collaborations over time despite the cost of learning involved.
Decision Making in IoT Environment through Unsupervised Learning

Nowadays, unsupervised learning can provide new perspectives to identify hidden patterns and classes inside the huge amount of data coming from the Internet of Things (IoT) world. Analyzing IoT data through machine learning techniques requires the use of mathematical algorithms, computational techniques, and an accurate tuning of the input parameters. In this article, we present a study of unsupervised learning techniques applied on IoT data to support decision-making processes inside intelligent environments. To assess the proposed approach, we discuss two case studies in which behavioral IoT data have been collected, also in a noninvasive way, in order to achieve an unsupervised classification that can be adopted during a decision-making process. The use of unsupervised learning techniques is acquiring a key role to complement the more traditional services with new decision-making ones supporting the needs of companies, stakeholders, and consumers.
Several Classes of Minimal Linear Codes With Few Weights From Weakly Regular Plateaued Functions

Minimal linear codes have significant applications in secret sharing schemes and secure two-party computation. There are several methods to construct linear codes, one of which is based on functions over finite fields. Recently, many construction methods for linear codes from functions have been proposed in the literature. In this paper, we generalize the recent construction methods given by Tang et al. in [IEEE Transactions on Information Theory, 62(3), 1166-1176, 2016] to weakly regular plateaued functions over finite fields of odd characteristic. We first construct three-weight linear codes from weakly regular plateaued functions based on the second generic construction and then determine their weight distributions. We also give a punctured version and subcode of each constructed code. We note that they may be (almost) optimal codes and can be directly employed to obtain (democratic) secret sharing schemes, which have diverse applications in the industry. We next observe that the constructed codes are minimal for almost all cases and finally describe the access structures of the secret sharing schemes based on their dual codes.
Spatially guided nonlocal mean approach for denoising of PET images

Purpose Nonlocal mean (NLM) filtering proved to be an effective tool for noise reduction in natural and medical imaging. The technique relies on existing redundant information in the input image to discriminate the genuine signal from noise. However, due to the prohibitively long computation time, the search for finding similar information is confined by a predefined search window, which may hamper the performance of this filter. In this work, a spatially guided non local mean (SG‐NLM) approach was proposed to overcome this issue. The proposed method was evaluated on whole‐body positron emission tomography images presenting with high noise levels, which adversely affect lesion detectability and quantitative accuracy. Methods In the SG‐NLM method, as opposed to the conventional NLM method, where a predefined search window is defined to confine exhaustive search for finding similar patterns, the information about similar patterns is extracted from the clustered version (created based on signal intensity levels) of the input image as well as information about prominent edges. The performance of the SG‐NLM was evaluated against post‐reconstruction NLM, Gaussian, bilateral and BayesShrink Wavelet denoising approaches. A digital phantom containing three small inserts mimicking lesions in the lung, experimental study using the Jaszczak phantom and whole‐body PET/CT clinical studies were utilized to assess the performance of abovementioned denoising approaches. Results The SG‐NLM method led to a signal‐to‐noise (SNR) increase from 21.3 (unfiltered PET image) to 30.1 in computer simulations of small lesions while the NLM mean filer resulted in an SNR of 29.4 ( P &lt; 0.05). The experimental Jaszczak phantom study demonstrated that the contrast‐to‐noise ratio (CNR) increased from 11.3 when using the Gaussian filter to 18.6 and 19.5 when using NLM and SG‐NLM filters ( P &lt; 0.05), respectively. The superior performance of the SG‐NLM approach was confirmed by clinical studies where the bias in malignant lesions decreased to −2.3 ± 1.1% compared to −11.7 ± 2.4 and −2.9 ± 1.1 achieved using the Gaussian and NLM methods ( P &lt; 0.05), respectively. Conclusions The proposed SG‐NLM achieves promising compromise between noise reduction and signal preservation compared to the conventional NLM method. The superior performance of the SG‐NLM method was accomplished without adding extra burden to the computational complexity of the conventional NLM filter, which makes it attractive for denoising PET images.
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another.However, the ability of NLI models to make pragmatic inferences remains understudied.We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types.We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences.Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences.It reliably treats scalar implicatures triggered by "some" as entailments.For some presupposition triggers like only, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation.BOW and InferSent show weaker evidence of pragmatic reasoning.We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.1 github.com/facebookresearch/ImpPres
Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect

The Azure Kinect, the successor of Kinect v1 and Kinect v2, is a depth sensor. In this paper we evaluate the skeleton tracking abilities of the new sensor, namely accuracy and precision (repeatability). Firstly, we state the technical features of all three sensors, since we want to put the new Azure Kinect in the context of its previous versions. Then, we present the experimental results of general accuracy and precision obtained by measuring a plate mounted to a robotic manipulator end effector which was moved along the depth axis of each sensor and compare them. In the second experiment, we mounted a human-sized figurine to the end effector and placed it in the same positions as the test plate. Positions were located 400 mm from each other. In each position, we measured relative accuracy and precision (repeatability) of the detected figurine body joints. We compared the results and concluded that the Azure Kinect surpasses its discontinued predecessors, both in accuracy and precision. It is a suitable sensor for human–robot interaction, body-motion analysis, and other gesture-based applications. Our analysis serves as a pilot study for future HMI (human–machine interaction) designs and applications using the new Kinect Azure and puts it in the context of its successful predecessors.
Hyperspectral super-resolution via coupled tensor ring factorization

Hyperspectral super-resolution (HSR) fuses a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) to obtain a high-resolution HSI (HR-HSI). In this paper, we propose a new model called coupled tensor ring factorization (CTRF) for HSR. The proposed CTRF approach simultaneously learns the tensor ring core tensors of the HR-HSI from a pair of HSI and MSI. The CTRF model can separately exploit the low-rank property of each class (Section 3.3), which has not been explored in previous coupled tensor models. Meanwhile, the model inherits the simple representation of coupled matrix/canonical polyadic factorization and flexible low-rank exploration of coupled Tucker factorization. We further introduce spectral nuclear norm regularization to explore the global spectral low-rank property. The experiments demonstrated the advantage of the proposed nuclear norm regularized CTRF model compared to previous matrix/tensor and deep learning methods.
Security measurement of a medical communication scheme based on chaos and DNA coding

To encrypt sensitive information existing in a color DICOM images, a medical privacy protection scheme (called as MPPS) based on chaos and DNA coding was proposed by using two coupled chaotic systems to produce cryptographic primitives. Relying on some empirical analyses and experimental results, the designers of MPPS claimed that it can withstand a chosen-plaintext attack and some other classic attacking models. However, this statement is groundless. In this paper, we investigate the essential properties of MPPS and DNA coding, and we then propose an efficient chosen-plaintext attack to disclose its equivalent secret-key. The attack only needs $\lceil \log_{256}(3\cdot M\cdot N)\rceil+4$ pair of chosen plain-images and the corresponding cipher-images, where $M \times N$ and ``3" are the size of the RGB color image and the number of color channels, respectively. In addition, the other claimed superiorities are questioned from the perspective of modern cryptography. Both theoretical and experimental results are presented to support the efficiency of the proposed attack and the other reported security faults. The proposed cryptanalysis results will promote the proper application of DNA encoding to protect multimedia privacy data, especially that in a DICOM image.
Silicon microring synapses enable photonic deep learning beyond 9-bit precision

Deep neural networks (DNN) consist of layers of neurons interconnected by synaptic weights. A high bit-precision in weights is generally required to guarantee high accuracy in many applications. Minimizing error accumulation between layers is also essential when building large-scale networks. Recent demonstrations of photonic neural networks are limited in bit-precision due to crosstalk and the high sensitivity of optical components (e.g., resonators). Here, we experimentally demonstrate a record-high precision of 9 bits with a dithering control scheme for photonic synapses. We then numerically simulated the impact with increased synaptic precision on a wireless signal classification application. This work could help realize the potential of photonic neural networks for many practical, real-world tasks.
Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach

Artificial intelligence (AI)-based chatbots have received considerable attention during the last few years. However, little is known concerning what affects their use for educational purposes. This research, therefore, develops a theoretical model based on extracting constructs from the expectation confirmation model (ECM) (expectation confirmation, perceived usefulness, and satisfaction), combined with the knowledge management (KM) factors (knowledge sharing, knowledge acquisition, and knowledge application) to understand the sustainable use of chatbots. The developed model was then tested based on data collected through an online survey from 448 university students who used chatbots for learning purposes. Contrary to the prior literature that mainly relied on structural equation modeling (SEM) techniques, the empirical data were analyzed using a hybrid SEM-artificial neural network (SEM-ANN) approach. The hypotheses testing results reinforced all the suggested hypotheses in the developed model. The sensitivity analysis results revealed that knowledge application has the most considerable effect on the sustainable use of chatbots with 96.9% normalized importance, followed by perceived usefulness (70.7%), knowledge acquisition (69.3%), satisfaction (61%), and knowledge sharing (19.6%). Deriving from these results, the study highlighted a number of practical implications that benefit developers, designers, service providers, and instructors.
Metaverse phenomenon and its impact on health: A scoping review

Metaverse is a hypothesis of the next generation of the Internet, which consists of a decentralized and stable online 3D virtual environment and can be widely used in health. Due to the growth of Metaverse technology in various fields and the importance of using it in health, the present study has been conducted to review and identify the application areas of this emerging technology in the health field. In this scoping review, an electronic search was done via scientific databases such as Web of Science, PubMed, and Scopus by combining topic-related keywords without time limits. Two authors independently gathered the data by a data extraction table. Of 46 articles found through the initial search, nine studies were eligible for review inclusion. Most of the studies (55.55%) were published in 2021, and South Korea was the country with the most published studies (44.44%). The main services for using Metaverse include educational services, interventional services, and communication services. Also, medical imaging was the most practical aspect of Metaverse used in healthcare. The use of Metaverse in various healthcare fields is expanding rapidly. It is now used widely in other areas, such as medical interventions and the provision of health services; in medical images, it has many applications due to the fundamental change in the nature and quality of imaging.
A scoping review of metaverse in emergency medicine

Interest in the metaverse has been growing worldwide as the virtual environment provides opportunities for highly immersive and interactive experiences. Metaverse has gradually gained acceptance in the medical field with the advancement of technologies such as big data, the Internet of Things, and 5 G mobile networks. The demand for and development of metaverse are different in diverse subspecialties owing to patients with varying degrees of clinical disease. Hence, we aim to explore the application of metaverse in acute medicine by reviewing published studies and the clinical management of patients.Our review examined the published articles about the concept of metaverse roadmap, and four additional domains were extracted: education, prehospital and disaster medicine, diagnosis and treatment application, and administrative affairs.Augmented reality (AR) and virtual reality (VR) integration have broad applications in education and clinical training. VR-related studies surpassed AR-related studies in the emergency medicine field. The metaverse roadmap revealed that lifelogging and mirror world are still developing fields of the metaverse.Our findings provide insight into the features, application, development, and potential of a metaverse in emergency medicine. This study will enable emergency care systems to be better equipped to face future challenges.
RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval

Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-aided diagnosis has been well developed to assist pathologists in decision-making. Content-based WSI retrieval can be a new approach to find highly correlated WSIs in a historically diagnosed WSI archive, which has the potential usages for assisted clinical diagnosis, medical research, and trainee education. During WSI retrieval, it is particularly challenging to encode the semantic content of histopathological images and to measure the similarity between images for interpretable results due to the gigapixel size of WSIs. In this work, we propose a Retrieval with Clustering-guided Contrastive Learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance. The proposed feature learning method makes use of existing large-scale unlabeled histopathological image data, which helps learn universal features that could be used directly for subsequent WSI retrieval tasks without extra fine-tuning. The proposed WSI retrieval method not only returns a set of WSIs similar to a query WSI, but also highlights patches or sub-regions of each WSI that share high similarity with patches of the query WSI, which helps pathologists interpret the searching results. Our WSI retrieval framework has been evaluated on the tasks of anatomical site retrieval and cancer subtype retrieval using over 22,000 slides, and the performance exceeds other state-of-the-art methods significantly (around 10% for the anatomic site retrieval in terms of average mMV@10). Besides, the patch retrieval using our learned feature representation offers a performance improvement of 24% on the TissueNet dataset in terms of mMV@5 compared with using ImageNet pre-trained features, which further demonstrates the effectiveness of the proposed CCL feature learning method.
Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution

It has been reported that quantum generative adversarial networks have a potential exponential advantage over classical generative adversarial networks. However, quantum machine learning is difficult to find real applications in the near future due to the limitation of quantum devices. The structure of quantum generator is optimized to reduce the required parameters and make use of quantum devices to a greater extent. And an image generation scheme is designed based on quantum generative adversarial networks. Two structures of quantum generative adversarial networks are simulated on Bars and Stripes dataset, and the results corroborate that the quantum generator with reduced parameters has no visible performance loss. The original complex multimodal distribution of an image can be converted into a simple unimodal distribution by the remapping method. The MNIST images and the Fashion-MNIST images are successfully generated by the optimized quantum generator with the remapping method, which verified the feasibility of the proposed image generation scheme.