Reviewerly/exHarmony huggingface dataset:
Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test

Background Generative adversarial network (GAN)–based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. Objective The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. Methods We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with &lt;5 years of experience [Group I], 4 radiologists with 5-10 years of experience [Group II], and 2 radiologists with &gt;10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image. Results The mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4% vs 1500/3000, 50.0%, respectively; P&lt;.001). However, in terms of identifying synthetic images as fake, there was no significant difference in the specificity between the visual Turing test and random guessing (779/1500, 51.9% vs 750/1500, 50.0%, respectively; P=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0%; Group II, 726/1200, 60.5%; and Group III, 359/600, 59.8%; P=.36). Interreader agreements were poor (κ=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details. Conclusions The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details.
Effectiveness modelling of digital contact-tracing solutions for tackling the COVID-19 pandemic

Abstract Since the beginning of the coronavirus (COVID-19) global pandemic, digital contact-tracing applications (apps) have been at the centre of attention as a digital tool to enable citizens to monitor their social distancing, which appears to be one of the leading practices for mitigating the spread of airborne infectious diseases. Many countries have been working towards developing suitable digital contact-tracing apps to allow the measurement of the physical distance between citizens and to alert them when contact with an infected individual has occurred. However, the adoption of digital contact-tracing apps has faced several challenges so far, including interoperability between mobile devices and users’ privacy concerns. There is a need to reach a trade-off between the achievable technical performance of new technology, false-positive rates, and social and behavioural factors. This paper reviews a wide range of factors and classifies them into three categories of technical, epidemiological and social ones, and incorporates these into a compact mathematical model. The paper evaluates the effectiveness of digital contact-tracing apps based on received signal strength measurements. The results highlight the limitations, potential and challenges of the adoption of digital contact-tracing apps.
App Use and Usability of a Barcode-Based Digital Platform to Augment COVID-19 Contact Tracing: Postpilot Survey and Paradata Analysis

Background The COVID-19 pandemic has drastically changed life in the United States, as the country has recorded over 23 million cases and 383,000 deaths to date. In the leadup to widespread vaccine deployment, testing and surveillance are critical for detecting and stopping possible routes of transmission. Contact tracing has become an important surveillance measure to control COVID-19 in the United States, and mobile health interventions have found increased prominence in this space. Objective The aim of this study was to investigate the use and usability of MyCOVIDKey, a mobile-based web app to assist COVID-19 contact tracing efforts, during the 6-week pilot period. Methods A 6-week study was conducted on the Vanderbilt University campus in Nashville, Tennessee. The study participants, consisting primarily of graduate students, postdoctoral researchers, and faculty in the Chemistry Department at Vanderbilt University, were asked to use the MyCOVIDKey web app during the course of the study period. Paradata were collected as users engaged with the MyCOVIDKey web app. At the end of the study, all participants were asked to report on their user experience in a survey, and the results were analyzed in the context of the user paradata. Results During the pilot period, 45 users enrolled in MyCOVIDKey. An analysis of their enrollment suggests that initial recruiting efforts were effective; however, participant recruitment and engagement efforts at the midpoint of the study were less effective. App use paralleled the number of users, indicating that incentives were useful for recruiting new users to sign up but did not result in users attempting to artificially inflate their use as a result of prize offers. Times to completion of key tasks were low, indicating that the main features of the app could be used quickly. Of the 45 users, 30 provided feedback through a postpilot survey, with 26 (58%) completing it in its entirety. The MyCOVIDKey app as a whole was rated 70.0 on the System Usability Scale, indicating that it performed above the accepted threshold for usability. When the key-in and self-assessment features were examined on their own, it was found that they individually crossed the same thresholds for acceptable usability but that the key-in feature had a higher margin for improvement. Conclusions The MyCOVIDKey app was found overall to be a useful tool for COVID-19 contact tracing in a university setting. Most users suggested simple-to-implement improvements, such as replacing the web app framework with a native app format or changing the placement of the scanner within the app workflow. After these updates, this tool could be readily deployed and easily adapted to other settings across the country. The need for digital contact tracing tools is becoming increasingly apparent, particularly as COVID-19 case numbers continue to increase while more businesses begin to reopen.
General Differential Fault Attack on PRESENT and GIFT Cipher With Nibble

Lightweight block cipher PRESENT is an algorithm with SPN structure.Due to its excellent hardware performance and simple round function design, it can be well applied to Internet of things terminals with limited computing resources.As an improved cipher of PRESENT, GIFT is similar in structure to PRESENT and has been widely concerned by academia and industry.This article studies the P permutation law of PRESENT and GIFT, and presents a general differential fault attack(DFA) method with their differential characteristics.For PRESENT, this article chooses to inject a nibble fault before the 30 th and 31 st rounds of S-box operation.A total of 32 nibble fault ciphertexts are needed to recover the original key.The computational complexity and data complexity are 2 10.94 and 2 8 , respectively.For GIFT, this article chooses to inject a nibble fault before the 25 th , 26 th , 27 th and 28 th rounds of S-box operation.A total of 64 nibble fault ciphertexts are needed to recover the original key.The computational complexity and data complexity are 2 11.91 and 2 9 , respectively.Compared with other public cryptoanalysis results of PRESENT and GIFT, this general attack method has great advantages.In this article, the DFA of GIFT is experimentally verified and the effectiveness is proved.These experiments have been done on a personal computer and run in a very reasonable time(around 500ms).
PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation

Gaze estimation methods learn eye gaze from facial features. However, among rich information in the facial image, real gaze-relevant features only correspond to subtle changes in eye region, while other gaze-irrelevant features like illumination, personal appearance and even facial expression may affect the learning in an unexpected way. This is a major reason why existing methods show significant performance degradation in cross-domain/dataset evaluation. In this paper, we tackle the cross-domain problem in gaze estimation. Different from common domain adaption methods, we propose a domain generalization method to improve the cross-domain performance without touching target samples. The domain generalization is realized by gaze feature purification. We eliminate gaze-irrelevant factors such as illumination and identity to improve the cross-domain performance. We design a plug-and-play self-adversarial framework for the gaze feature purification. The framework enhances not only our baseline but also existing gaze estimation methods directly and significantly. To the best of our knowledge, we are the first to propose domain generalization methods in gaze estimation. Our method achieves not only state-of-the-art performance among typical gaze estimation methods but also competitive results among domain adaption methods. The code is released in https://github.com/yihuacheng/PureGaze.
Multi-label learning with missing and completely unobserved labels

Abstract Multi-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approaches to multi-label learning, there is still a problem neglected by researchers, i.e., not only are some of the values of observed labels missing, but also some of the labels are completely unobserved for the training data. We refer to the problem as multi-label learning with missing and completely unobserved labels , and argue that it is necessary to discover these completely unobserved labels in order to mine useful knowledge and make a deeper understanding of what is behind the data. In this paper, we propose a new approach named MCUL to solve multi-label learning with Missing and Completely Unobserved Labels. We try to discover the unobserved labels of a multi-label data set with a clustering based regularization term and describe the semantic meanings of them based on the label-specific features learned by MCUL, and overcome the problem of missing labels by exploiting label correlations. The proposed method MCUL can predict both the observed and newly discovered labels simultaneously for unseen data examples. Experimental results validated over ten benchmark datasets demonstrate that the proposed method can outperform other state-of-the-art approaches on observed labels and obtain an acceptable performance on the new discovered labels as well.
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining.However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages.To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas.We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches.Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models.We find that XLM-R's zero-shot performance is poor for all 10 languages, with an average performance of 38.48%.Continued pretraining offers improvements, with an average accuracy of 43.85%.Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12%.
In or out? A field observational study on the placement of entertaining robots in retailing

Purpose The purpose of this paper is to empirically investigate the role of the placement (i.e. location) of humanoid service robots (HSRs) for entertainment applications in retailing by inspecting a multitude of performance metrics along the point-of-sale conversion funnel. Design/methodology/approach The study was conducted using unobtrusive observations at a Belgian chocolate store. In total, 42 h of video observation material was collected and analyzed, with an even spread over three conditions: (1) an HSR placed outside, (2) an HSR inside the store and (3) a control condition (no robot stimuli). All passersby and their interactions with the robot and the store were systematically coded and compared. Findings The study found that the better placement of HSRs (inside or outside the store) is contingent on the goals the retailer prioritizes. When the goal is to create awareness and interest toward the store, the HSR should be placed outside, as it has double the stopping power. To induce consumers to enter the store, placement of the HSR inside the store is the better option. Ultimately, however, in terms of the number of transactions and total amount spent, outside placement of the HSR outperforms inside placement. Research limitations/implications This study was not able to verify the internal emotional/cognitive state of the passersby, as the method relied on unobtrusive camera observations. A longitudinal research design would be desirable to exclude potential bias due to the novelty effect. Originality/value While research on robots in retail services is emerging, this study is the first to provide insights on how retailers can decide on the placement of robots inside or outside the store, depending on the particular goals they are aiming to reach at the point of purchase.
Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression

Solar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of solar power. In this study, a novel K-means time series clustering (K-MTSC) algorithm is first proposed to cluster solar radiation intensity and compared with astronomy method and K-means. Then, different feature inputs for different categories of solar radiation intensity are screened. Afterwards, the different kernel functions of Gaussian process regression (GPR) are compared and optimal kernel function is selected in terms of deterministic forecasting and probabilistic forecasting for different categories. Finally, the case study in Tibet province, China are performed to verify the validity and practicability of this research model and method. In this experiment, the average accuracy of GPR is 44% higher than that of Artificial Neural Network ANN, and 17% higher than that of Support Vector Regression. The experiments show that (1) the clustering results obtained by the K-MTSC algorithm have a larger inter-group distance and a smaller intra-group distance, and at the same time, it will not destroy the continuity of the time series. (2) The probability forecast results obtained by GPR are reliable and high-accuracy.
Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Abstract Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.
ACT: an Attentive Convolutional Transformer for Efficient Text Classification

Recently, Transformer has been demonstrating promising performance in many NLP tasks and showing a trend of replacing Recurrent Neural Network (RNN). Meanwhile, less attention is drawn to Convolutional Neural Network (CNN) due to its weak ability in capturing sequential and long-distance dependencies, although it has excellent local feature extraction capability. In this paper, we introduce an Attentive Convolutional Transformer (ACT) that takes the advantages of both Transformer and CNN for efficient text classification. Specifically, we propose a novel attentive convolution mechanism that utilizes the semantic meaning of convolutional filters attentively to transform text from complex word space to a more informative convolutional filter space where important n-grams are captured. ACT is able to capture both local and global dependencies effectively while preserving sequential information. Experiments on various text classification tasks and detailed analyses show that ACT is a lightweight, fast, and effective universal text classifier, outperforming CNNs, RNNs, and attentive models including Transformer.
Determination of quantum toric error correction code threshold using convolutional neural network decoders

Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers. In order to find the best syndrome of the stabilizer code in quantum error correction, we need to find a fast and close to the optimal threshold decoder. In this work, we build a convolutional neural network (CNN) decoder to correct errors in the toric code based on the system research of machine learning. We analyze and optimize various conditions that affect CNN, and use the RestNet network architecture to reduce the running time. It is shortened by 30%–40%, and we finally design an optimized algorithm for CNN decoder. In this way, the threshold accuracy of the neural network decoder is made to reach 10.8%, which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%–10.3% has been slightly improved, and there is no need to verify the basic noise.
Re-Transformer: A Self-Attention Based Model for Machine Translation

Machine translation is one of the most popular and hardest tasks in Natural Language Processing. This paper proposes a self-attention based model for machine translation, named Re-Transformer, by transforming the Transformer [1]. Different from prevailing approach through module or GPU stacking to improve the system performance, Re-Transformer modifies the basic architecture; there are four refinements as follows. First, Re-Transformer adopts sub-word Tokenization in corpus preprocessing to overcome rare words. In the encoder layer, dual Self-Attention stacks and less Point Wise Feed Forward layer are adopted to obtain better comprehension of an input sentence. In decoder, reduced the stack of “Decoder” are used to speedup training and inferring speed meanwhile keeps the same level of BLEU. The experiment results show that Re-Transformer with BLEU metric score 31.36, 38.45 (four-layer decoder) and 32.14, 55.62 (two-layer decoder) improves around 4 and 17 points of BLEU metric against the Transformer over the WMT 2014 English-German and English-French Translation Corpus.
MagicChem: a MR system based on needs theory for chemical experiments

Real chemical experiments may be dangerous or pollute the environment; meanwhile, the preparation of drugs and reagents is time-consuming. Due to the above-mentioned reasons, few experiments can be actually operated by students, which is not conducive to the chemistry learning and the phenomena principle understanding. Recently, due to the impact of Covid-19, many schools adopt online teaching, which is even more detrimental to students' learning of chemistry. Fortunately, MR(mixed reality) technology provides us with the possibility of solving the safety issues and breaking the space-time constraints, while the theory of human needs (Maslow's hierarchical needs) provides us with a way to design a comfortable and stimulant MR system with realistic visual presentation and interaction. The paper combines with the theory of human needs to propose a new needs model for virtual experiment. Based on this needs model, we design and develop a comprehensive MR system called MagicChem, which offers a robust 6-DoF interactive and illumination consistent experimental space with virtual-real occlusion, supporting realistic visual interaction, tangible interaction, gesture interaction with touching, voice interaction, temperature interaction, olfactory interaction and virtual human interaction. User study shows that MagicChem satisfies the needs model better than other MR experimental environments that partially meet the needs model. In addition, we explore the application of the needs model in VR environment.The online version contains supplementary material available at 10.1007/s10055-021-00560-z.