Reviewerly/exHarmony huggingface dataset:
Are non‐fungible tokens the barrier to realizing the Dermoverse?

Journal of the European Academy of Dermatology and VenereologyVolume 37, Issue 7 p. e860-e861 LETTER TO THE EDITOR Are non-fungible tokens the barrier to realizing the Dermoverse? This article relates to: Ethics in the Dermoverse: The challenge of a new technological era comment on 'Are non-fungible tokens the barrier to realizing the Dermoverse?' Miriam Fernández-Parrado, Héctor Perandones-González, Journal of the European Academy of Dermatology and Venereology First Published online: June 19, 2023 D. du Crest, Corresponding Author D. du Crest [email protected] SkinAid SAS, Levallois Perret, France Correspondence Dominique du Crest, SkinAid SAS, 50, Rue Danton - 92300, Levallois Perret, France. Email: [email protected]Search for more papers by this authorC. Tequi, C. Tequi Ternoa, Biarritz, FranceSearch for more papers by this authorF. Dessi, F. Dessi Ternoa, Biarritz, FranceSearch for more papers by this author D. du Crest, Corresponding Author D. du Crest [email protected] SkinAid SAS, Levallois Perret, France Correspondence Dominique du Crest, SkinAid SAS, 50, Rue Danton - 92300, Levallois Perret, France. Email: [email protected]Search for more papers by this authorC. Tequi, C. Tequi Ternoa, Biarritz, FranceSearch for more papers by this authorF. Dessi, F. Dessi Ternoa, Biarritz, FranceSearch for more papers by this author First published: 14 February 2023 https://doi.org/10.1111/jdv.18962Citations: 1 Linked article: https://doi.org/10.1111/jdv.18525. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Open Research DATA AVAILABILITY STATEMENT Data derived from public domain resources. REFERENCES 1Fernández-Parrado M, Perandones-González H. A new universe in dermatology: from metaverse to Dermoverse. J Eur Acad Dermatol Venereol. 2023; 37: e470–1. https://doi.org/10.1111/jdv.18525 2 Non-fungible Token. https://en.wikipedia.org/wiki/Non-fungible_token 3Sitaru S, Kaczmarczyk R, Zink A. Nonfungible tokens (NFTs) in dermatology and health care: a guide for health care professionals. JEADV Clin Pract. 2023; 2: 32–7. https://doi.org/10.1002/jvc2.95 4 Cambridge Bitcoin Electricity Consumption Index, 2017/2018 est. https://ccaf.io/cbeci/index/comparisons 5 Yearly electricity consumption for bitcoin, Ethereum, proof of stake networks Polkadot, Tezos, avalanche, Algorand, Cardano and Solana, and an average US household in kWh. Logarithmic Scale; 2022. carbon-ratings.com Citing Literature Volume37, Issue7July 2023Pages e860-e861 ReferencesRelatedInformation
The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review

Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.
Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Anomaly detection (AD) is a challenging problem in computer vision. Particularly in the field of medical imaging, AD poses even more challenges due to a number of reasons, including insufficient availability of ground truth (annotated) data. In recent years, AD models based on generative adversarial networks (GANs) have made significant progress. However, their effectiveness in biomedical imaging remains underexplored. In this paper, we present an overview of using GANs for AD, as well as an investigation of state-of-the-art GAN-based AD methods for biomedical imaging and the challenges encountered in detail. We have also specifically investigated the advantages and limitations of AD methods on medical image datasets, conducting experiments using 3 AD methods on 7 medical imaging datasets from different modalities and organs/tissues. Given the highly different findings achieved across these experiments, we further analyzed the results from both data-centric and model-centric points of view. The results showed that none of the methods had reliable performance for detecting abnormalities in medical images. Factors such as the number of training samples, the subtlety of the anomaly, and the dispersion of the anomaly in the images are among the phenomena that highly impact the performance of the AD models. The obtained results were highly variable (AUC: 0.475-0.991; Sensitivity: 0.17-0.98; Specificity: 0.14-0.97). In addition, we provide recommendations for deployment of AD models in medical imaging and foresee important research directions.
One-Sided Measurement-Device-Independent Practical Quantum Secure Direct Communication

Measurement-device-independent quantum secure direct communication (MDI-QSDC) protocol is capable of defending against attacks on detectors by illegal parties, and its security relies on the trustworthy preparation of quantum states. In this paper, we propose the one-sided measurement-device-independent quantum secure direct communication (1SMDI-QSDC) protocol, which not only has the advantage of being independent of detection loopholes but also weakens the requirement of MDI-QSDC for state preparation. We analyze the real-life secrecy capacity of this proposed protocol by using realistic experimental settings that contain the weak coherent pulse source, parametric down-conversion source, and customized decoy-state method. The results indicate that our protocols can achieve secure communication over a few hundred kilometers. Furthermore, we find that the coding technology of increasing capacity using masking (INCUM) can mitigate the degradation of the maximum distance of secure communication when the state preparation is highly untrustworthy.
Sequence as a Whole: A Unified Framework for Video Action Localization With Long-Range Text Query

Comprehensive understanding of video content requires both spatial and temporal localization. However, there lacks a unified video action localization framework, which hinders the coordinated development of this field. Existing 3D CNN methods take fixed and limited input length at the cost of ignoring temporally long-range cross-modal interaction. On the other hand, despite having large temporal context, existing sequential methods often avoid dense cross-modal interactions for complexity reasons. To address this issue, in this paper, we propose a unified framework which handles the whole video in sequential manner with long-range and dense visual-linguistic interaction in an end-to-end manner. Specifically, a lightweight relevance filtering based transformer (Ref-Transformer) is designed, which is composed of relevance filtering based attention and temporally expanded MLP. The text-relevant spatial regions and temporal clips in video can be efficiently highlighted through the relevance filtering and then propagated among the whole video sequence with the temporally expanded MLP. Extensive experiments on three sub-tasks of referring video action localization, i.e., referring video segmentation, temporal sentence grounding, and spatiotemporal video grounding, show that the proposed framework achieves the state-of-the-art performance in all referring video action localization tasks.
Illumination-insensitive Line Binary Descriptor Based on Hierarchical Band Difference

Similar to points, lines are also the basis for many vision applications. Line segments provide more structured information and are more robust than point features, especially for those scenarios with low textures. There are increasing works to tackle the line matching problem, but few of them are designed to match lines under drastic illumination changes, which is critical for long-term vision tasks. In this brief, an illumination-insensitive line binary (IILB) descriptor is proposed to tackle the above issue based on the band differences among multi-spatial granularities of the line region of support. The proposed IILB descriptor has a low computation cost due to the usage of integral images, which could satisfy the requirements of many realistic platforms with limited computation and storage abilities. Numerical experiments in natural and synthetic datasets show that the proposed IILB descriptor has remarkable performance. The code of the IILB descriptor is publicly available1.
Deep HyFeat Based Attention in Attention Model for Face Super-Resolution

Face super-resolution (SR) is the task of generating high-resolution (HR) face images from the low-resolution (LR) inputs. Recently, deep learning-based methods have shown remarkable progress in the SR field. Most of the methods perform additional tasks such as face parsing, landmark, and attention to generate the HR images. However, parsing maps and landmark-guided models require the supplementary labeled dataset, which is difficult to obtain in real life. The attention mechanism does not require the datasets extra labeling and is also beneficial for face SR. These methods focus on a few critical features and ignore the remaining ones, which sometime causes to ignore the valuable features. Therefore, this article proposes a novel deep hybrid feature (HyFeat)-based Attention in Attention model for face SR. Moreover, the proposed model uses the coarse SR network and deep convolutional neural network (CNN) to generate the HR image. A coarse SR network is applied to upsample the LR image and generate the coarse super-resolved image, which is further sent to the deep CNN model. The proposed work incorporates the HyFeat attention in attention unit (HyFA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{U}$ </tex-math></inline-formula> ), which consists of HyFeat block and attention in attention block (A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{B}$ </tex-math></inline-formula> ) in the deep CNN model to improve the visual quality of the output face images. HyFeat block assists the model in extracting the coarse features and learning the enriched contextual information to enhance the details of coarse features. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{B}$ </tex-math></inline-formula> preserves the attentive and non-attentive beneficial features while suppressing the unwanted features. The attention branch focuses on specific facial features and ignores the rest of the features. The non-attention branch aims to learn the informative features that the attention branch ignores. The proposed model repeats the HyFA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{U}$ </tex-math></inline-formula> units to focus on different facial components and enhance the features to improve the quality of resultant faces. Experimental outcomes exhibit that the proposed model gains state-of-the-art performance on the standard datasets, namely CelebAHQ, Helen, FFHQ, and LFW face. The proposed method achieves an improvement of more than 0.35 dB in peak signal-to-noise ratio (PSNR) and 0.012 in structure similarity (SSIM) on different datasets over the best models available in the literature.
Effects of an article-structure strategy-based spherical video-based virtual reality approach on EFL learners’ English reading comprehension and learning conceptions

Reading comprehension is critical and challenging for the English learning process. EFL (English as Foreign Language) learners often do not understand the meaning of the text due to the lack of an authentic learning context. This easily leads learners to read literally without deeply understanding. To address this issue, an article-structure strategy-based SVVR (short for ASS-SVVR) approach was proposed in the study, in which spherical video-based virtual reality (SVVR) was employed to immerse EFL learners in a real environment, and the article-structure strategy was introduced as step-by-step reading scaffolding to improve reading efficiency, accuracy, and comprehension. To verify the effectiveness of the proposed approach, a quasi-experiment was conducted in an English reading class at a university with two classes of 63 EFL learners. Among them, 33 learners were randomly distributed to the experimental group, conducting the ASS-SVVR approach, while the remaining 30 were assigned to the control group, conducting the conventional SVVR (C-SVVR) approach. The research findings showed that EFL students conducting the ASS-SVVR approach had positive performance in reading comprehension, extrinsic learning motivation, and metacognitive awareness of reading strategy. Moreover, cognitive load and sense of presence were not shown to have a significant difference between the two groups.
A simple and effective static gesture recognition method based on attention mechanism

To solve the problem of low sign language recognition rate under the condition of small samples, a simple and effective static gesture recognition method based on an attention mechanism is proposed. The method proposed in this paper can enhance the features of both the details and the subject of the gesture image. The input of the proposed method depends on the intermediate feature map generated by the original network. Also, the proposed convolutional model is a lightweight general module, which can be seamlessly integrated into any CNN(Convolutional Neural Network) architecture and achieve significant performance gains with minimal overhead. Experiments on two different datasets show that the proposed method is effective and can improve the accuracy of sign language recognition of the benchmark model, making its performance better than the existing methods.
Is democracy pro poor in Sub-Saharan Africa?

This study examines if democracy reduces poverty in 40 Sub-Saharan African countries for the period 1999–2018. For this purpose, we employ the Generalised Method of Moments. The results show that democracy is not directly associated with poverty reduction in sub-Saharan Africa. However, this observation hides important non-linearities and an interesting pattern of policy complementarities. Indeed, democracy is associated with poverty reduction in countries where economic growth is strong and human capital high. The robustness tests carried out do not change these results. This means that poor economic growth and weak human capital not only have a direct negative effect on the well-being of SSA countries, but also prevent the poor in those countries from benefitting the gains of democracy. Therefore, in order to reduce poverty in SSA, policy makers should continue the process of democratization while simultaneously adopting policies of economic development and human capital building. Democracy in isolation is useless for the poor in SSA.
Augmented Reality in Business and Economics: Bibliometric and Topics Analysis

Augmented reality enhances the sensory experience of the real world, often across multiple senses such as visual, hearing, tactile and sensorimotor, with technology using computer-generated sensory input.Current literature reviews of augmented reality mainly focus on its generic usage or specific topics, such as medicine or tourism.However, augmented reality has become one of the prevalent topics in business and economics since it is one of the main growth drivers of disruptive companies.Since many companies are considering its usage in their business models, there is a lack of literature review in business and economics.Therefore, this paper aims to present a literature review of the scientific research that investigates the broad range of usage of augmented reality in business and economics.Web of Knowledge has been searched with the keywords "augmented reality" within the research area of business and economics for 2017-2021.Bibliometric analysis has been conducted to investigate the main journals, conferences, authors and countries.Finally, text mining with VosViewer has been conducted to extract the main topics, which are: (i): Technologies; Education; (ii) e-Commerce; Retailing; (iii) Tourism; User Experience; (iv) Consumers; Purchase.The results indicate that the research of augmented reality in business and economics focused on various applications, among which education is one of the emerging ones.
IC-LDPC Polar codes-based reconciliation for continuous-variable quantum key distribution at low signal-to-noise ratio

Abstract The error correction of information reconciliation affects the performance of the continuous-variable quantum key distribution (CV-QKD). Polar codes can be strictly proven to reach the Shannon-limit. However, due to the insufficient polarization of finite code-length, partial subchannels are neither completely noise-free nor completely noisy. In this paper, an intermediate channel low-density parity check code concatenated polar code (IC-LDPC Polar codes)-based reconciliation for CV-QKD is proposed for the above shortcomings. The experimental results show that the reconciliation efficiency of IC-LDPC Polar code can be over 98% when the signal-to-noise ratio is from −13.2 dB to −20.8 dB, the secret keys can be extracted, and the minimum frame error rate (FER) is 0.19. Therefore, the proposed scheme can improve the reconciliation efficiency and reduce the FER at a very low signal-to-noise ratio range, and it is more useful for a practical long-distance CV-QKD system.
HoMeNL: A Homogeneity Measure-Based NonLocal Filtering Framework for Detail-Enhanced (Pol)(In)SAR Image Denoising

As an inherent problem in coherent imaging systems, the existence of speckle noise results in SAR images with strong signal-dependent variance and seriously hinders the related properties estimation and the image interpretation. Among many filtering methods, nonlocal means (NLM) have been proven to be effective in reducing noise while preserving details. However, traditional NLM filters still face two core problems: 1) it is difficult for homogeneous pixels selection to construct a patch adaptive to local structure for preventing the omission phenomenon; 2) most central pixel value estimators are still at the stage of suppressing the blurring effect rather than eliminating the wrongly selected heterogeneous pixels. To overcome these two problems in the (Pol)(In)SAR image denoising, this paper proposes a homogeneity measure-based nonlocal (HoMeNL) filtering framework based on the following three innovations: 1) to sufficiently select homogeneous pixels in the patch-wise matching processing, the shape-adaptive (SA) patch can be selected from multiple preset patches with the one-to-many matching strategy; 2) as a general extension of the Lee estimator in (Pol)(In)SAR image denoising, the homogeneity measure (HoMe)-based estimator can achieve an optimal bias-variance tradeoff for the central pixel value; 3) the highlight of the proposed method is that the iterative re-weighted (IRW) estimation combines the residuals statistics and the homogeneity measure to adaptively locate and remove the wrongly selected heterogeneous pixels. Simulated and real experimental results show that the proposed filtering framework owns a superior performance than most state-of-art filters in three aspects of noise reduction, detail enhancement, and coherence magnitude estimation.
An improved hybrid quantum-classical convolutional neural network for multi-class brain tumor MRI classification

The efficiency of quantum computing has recently been extended to machine learning, which has made a significant impact on quantum machine learning. The hybrid structure of quantum and classical ones has developed into the most successful application mode currently due to noisy intermediate scale quantum limitations. In this paper, an improved hybrid quantum-classic convolutional neural network (HQC-CNN) with fast training speed, lightweight, and high performance is proposed. Its convolution layer realizes feature mapping through parameterized quantum circuit, while other layers keep classic operation and finally complete the task of four classifications of brain tumors. The experiment in this paper is based on kaggle brain tumor magnetic resonance imaging public dataset. The final experimental results show that HQC-CNN can effectively classify meningioma, glioma, pituitary, and no tumor with a classification accuracy of 97.8%. When compared to numerous well-known landmark models, HQC-CNN has obvious advantages.
An Improved Multilabel k-Nearest Neighbor Algorithm Based on Value and Weight

Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address this problem, this study proposes an improved ML-kNN algorithm based on value and weight. In this improved algorithm, labels are divided into minority and majority, and different strategies are adopted for different labels. By considering the label of latent information carried by the nearest neighbors, a value calculation method is proposed and used to directly classify majority labels. Additionally, to address the misclassification problem caused by a lack of nearest neighbor information for minority labels, weight calculation is proposed. The proposed weight calculation converts distance information with and without label sets in the nearest neighbors into weights. The experimental results on multilabel datasets from different benchmarks demonstrate the performance of the algorithm, especially for datasets with high imbalance. Different evaluation metrics show that the results are improved by approximately 2–10%. The verified algorithm could be applied to a multilabel classification of various fields involving label imbalance, such as drug molecule identification, building identification, and text categorization.