Reviewerly/exHarmony huggingface dataset:
Risk preferences and risk perception affect the acceptance of digital contact tracing

Abstract Digital contact-tracing applications (DCTAs) can help control the spread of epidemics, such as the coronavirus disease 2019 pandemic. But people in Western societies fail to install DCTAs. Understanding the low use rate is key for policy makers who support DCTAs as a way to avoid harsh nationwide lockdowns. In a preregistered study in a representative German-speaking Swiss sample ( N = 757), the roles of individual risk perceptions, risk preferences, social preferences, and social values in the acceptance of and compliance with DCTA were compared. The results show a high compliance with the measures recommended by DCTAs but a comparatively low acceptance of DCTAs. Risk preferences and perceptions, but not social preferences, influenced accepting DCTAs; a high health-risk perception and a low data-security-risk perception increased acceptance. Additionally, support of political measures, technical abilities, and understanding the DCTA functionality had large effects on accepting DCTAs. Therefore, we recommend highlighting personal health risks and clearly explaining DCTAs, focusing on data security, to enhance DCTA acceptance.
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis

Abstract Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast tomosynthesis (DBT), where less than 1% of cases contain cancer. In this study, we propose a method to train an inpainting generative adversarial network to be used for cancer detection using only images that do not contain cancer. During inference, we removed a part of the image and used the network to complete the removed part. A significant error in completing an image part was considered an indication that such location is unexpected and thus abnormal. A large dataset of DBT images used in this study was collected at Duke University. It consisted of 19,230 reconstructed volumes from 4348 patients. Cancerous masses and architectural distortions were marked with bounding boxes by radiologists. Our experiments showed that the locations containing cancer were associated with a notably higher completion error than the non-cancer locations (mean error ratio of 2.77). All data used in this study has been made publicly available by the authors.
Towards Equity and Algorithmic Fairness in Student Grade Prediction

Equity of educational outcome and fairness of AI with respect to race have been topics of increasing importance in education. In this work, we address both with empirical evaluations of grade prediction in higher education, an important task to improve curriculum design, plan interventions for academic support, and offer course guidance to students. With fairness as the aim, we trial several strategies for both label and instance balancing to attempt to minimize differences in algorithm performance with respect to race. We find that an adversarial learning approach, combined with grade label balancing, achieved by far the fairest results. With equity of educational outcome as the aim, we trial strategies for boosting predictive performance on historically underserved groups and find success in sampling those groups in inverse proportion to their historic outcomes. With AI-infused technology supports increasingly prevalent on campuses, our methodologies fill a need for frameworks to consider performance trade-offs with respect to sensitive student attributes and allow institutions to instrument their AI resources in ways that are attentive to equity and fairness.
Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging

Automatic fall detection is a vital technology for ensuring the health and safety of people. Home-based camera systems for fall detection often put people's privacy at risk. Thermal cameras can partially or fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to the normal activities of daily living. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. To handle these problems, we formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging. We present a novel adversarial network that comprises of two-channel 3D convolutional autoencoders which reconstructs the thermal data and the optical flow input sequences respectively. We introduce a technique to track the region of interest, a region-based difference constraint, and a joint discriminator to compute the reconstruction error. A larger reconstruction error indicates the occurrence of a fall. The experiments on a publicly available thermal fall dataset show the superior results obtained compared to the standard baseline.
How Quantum Evolution with Memory is Generated in a Time-Local Way

Two widely used but distinct approaches to the dynamics of open quantum systems are the Nakajima-Zwanzig and time-convolutionless quantum master equation, respectively. Although both describe identical quantum evolutions with strong memory effects, the first uses a time-nonlocal memory kernel K, whereas the second achieves the same using a time-local generator G. Here we show that the two are connected by a simple yet general fixed-point relation: G=K^[G]. This allows one to extract nontrivial relations between the two completely different ways of computing the time evolution and combine their strengths. We first discuss the stationary generator, which enables a Markov approximation that is both nonperturbative and completely positive for a large class of evolutions. We show that this generator is not equal to the low-frequency limit of the memory kernel, but additionally “samples” it at nonzero characteristic frequencies. This clarifies the subtle roles of frequency dependence and semigroup factorization in existing Markov approximation strategies. Second, we prove that the fixed-point equation sums up the time-domain gradient or Moyal expansion for the time-nonlocal quantum master equation, providing nonperturbative insight into the generation of memory effects. Finally, we show that the fixed-point relation enables a direct iterative numerical computation of both the stationary and the transient generator from a given memory kernel. For the transient generator this produces nonsemigroup approximations which are constrained to be both initially and asymptotically accurate at each iteration step.Received 11 February 2020Revised 17 December 2020Accepted 23 March 2021DOI:https://doi.org/10.1103/PhysRevX.11.021041Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systemsTechniquesDensity matrix methodsNon-Markovian processesQuantum master equationStatistical PhysicsQuantum Information
The effects of blockchain technology on the accounting and assurance profession in the UAE: an exploratory study

Purpose Blockchain looks to be the next step of technology transformation and would redesign the business landscape. It is expected to have an impact on business methods in the next few years; which add new challenges and complexity to the accounting and assurance profession. This paper aims to analyze the perceptions of accountants and auditors toward the implementation of blockchain technology in the UAE after the government decided to transform 50% of government transactions into the blockchain platform by 2021. Design/methodology/approach A qualitative approach has been used in this study. A semi-structural interview has been conducted with 19 accountants, internal auditors, auditors and risk managers on the potential opportunities and challenges of blockchain technology on accounting and auditing practices in the UAE. Findings The findings show that the blockchain impacts on the accounting profession in terms of recording of transactions, storing evidence and providing a secured environment for conducting business transactions. For the auditors, the results indicate that the blockchain changes their audit process and strategy. The blockchain has great potential to supplement traditional auditing by providing a low-cost and decentralized audit process and automated audit evidence. The accounting of the companies will not be changed but it will be automated with the development of cryptocurrencies and blockchain activities. The blockchain will be developed in assurance services through the awareness and involvement of accounts and auditors. Originality/value The study contributes to the existing literature as follows. First, the research is one of the very few studies that discussed empirically the effect of blockchain on the accounting and assurance profession, which contributes to improved knowledge about the potential of blockchain technology to accounting and assurance services. Second, our research is the first to explore the accountants and financial auditors’ perceptions regarding the potential effect of blockchain technology on their profession in the UAE context after the government decided to transform 50% of government transactions into the blockchain platform by 2021. Third, the research identifies the pending challenges for blockchain and possible factors for its effectiveness.
Contrast-Enhanced Mammographic Features of In Situ and Invasive Ductal Carcinoma Manifesting Microcalcifications Only: Help to Predict Underestimation?

Background: The contrast-enhanced mammographic features of ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) manifesting microcalcifications only on mammograms were evaluated to determine whether they could predict IDC underestimation. Methods: We reviewed patients who underwent mammography-guided biopsy on suspicious breast microcalcifications only and received contrast-enhanced spectral mammography (CESM) within 2 weeks before the biopsy. Those patients who were proven to have cancers (DCIS or IDC) by biopsy and subsequently had surgical treatment in our hospital were included for analysis. The presence or absence, size, morphology and texture of enhancement on contrast-enhanced spectral mammography were reviewed by consensus of two radiologists. Results: A total of 49 patients were included for analysis. Forty patients (81.6%) showed enhancement, including 18 (45%) DCIS and 22 (55%) IDC patients. All nine unenhanced cancers were pure DCIS. Pure DCIS showed 72.22% nonmass enhancement and 83.33% pure ground glass enhancement. IDC showed more mass (72.2% vs. 27.8%) and solid enhancements (83.33% vs. 16.67%). The cancer and texture of enhancement were significantly different between pure DCIS and IDC, with moderate diagnostic performance for the former (p-value &lt; 0.01, AUC = 0.66, sensitivity = 93%, specificity = 39%) and the latter (p-value &lt; 0.01, AUC = 0.74, sensitivity = 65%, specificity = 83%). Otherwise, pure DCIS showed a significant difference in enhanced texture compared with upgraded IDC and IDC (p = 0.0226 and 0.0018, respectively). Conclusions: Nonmass and pure ground glass enhancements were closely related to pure DCIS, and cases showing mass and unpurified solid enhancements should be suspected as IDC. Unenhanced DCIS with microcalcifications only has a low DCIS upgrade rate. The CESM-enhanced features could feasibly predict IDC underestimation.
Analysis of the Use of Videoconferencing in the Learning Process During the Pandemic at a University in Lima

Due to the health emergency situation, which forced universities to stop using their centers as a means of teaching, many of them opted for virtual education.Affecting the learning process of students, which has predisposed many of them to become familiar with this new learning process, making the use of virtual platforms more common.Many educational centers have come to rely on digital tools such as: Discord, Google Meet, Microsoft Team, Skype and Zoom.The objective of the research is to report on the impact of student learning through the use of the aforementioned videoconferencing tools.Surveys were conducted with teachers and students who stated that 66% were not affected in their educational development.Most of them became familiar with the platforms; however, less than 24% qualified that their academic performance has improved, some teachers still have difficulties at a psychological level due to this new teaching modality.In conclusion, teachers and students agree that these tools are a great help for virtual classes.
Sentiment analysis and sarcasm detection from social network to train health-care professionals

Purpose Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments. Design/methodology/approach This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set. Findings This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively. Originality/value This research work is original.
Robust Learning by Self-Transition for Handling Noisy Labels

Real-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize false-labeled samples after a certain point of training. Thus, to counter the label noise challenge, we propose a novel self-transitional learning method called MORPH, which automatically switches its learning phase at the transition point from seeding to evolution. In the seeding phase, the network is updated using all the samples to collect a seed of clean samples. Then, in the evolution phase, the network is updated using only the set of arguably clean samples, which precisely keeps expanding by the updated network. Thus, MORPH effectively avoids the overfitting to false-labeled samples throughout the entire training period. Extensive experiments using five real-world or synthetic benchmark datasets demonstrate substantial improvements over state-of-the-art methods in terms of robustness and efficiency.
Improved upper bounds on the stabilizer rank of magic states

In this work we improve the runtime of recent classical algorithms for strong simulation of quantum circuits composed of Clifford and T gates. The improvement is obtained by establishing a new upper bound on the stabilizer rank of $m$ copies of the magic state $|T\rangle=\sqrt{2}^{-1}(|0\rangle+e^{i\pi/4}|1\rangle)$ in the limit of large $m$. In particular, we show that $|T\rangle^{\otimes m}$ can be exactly expressed as a superposition of at most $O(2^{\alpha m})$ stabilizer states, where $\alpha\leq 0.3963$, improving on the best previously known bound $\alpha \leq 0.463$. This furnishes, via known techniques, a classical algorithm which approximates output probabilities of an $n$-qubit Clifford + T circuit $U$ with $m$ uses of the T gate to within a given inverse polynomial relative error using a runtime $\mathrm{poly}(n,m)2^{\alpha m}$. We also provide improved upper bounds on the stabilizer rank of symmetric product states $|\psi\rangle^{\otimes m}$ more generally; as a consequence we obtain a strong simulation algorithm for circuits consisting of Clifford gates and $m$ instances of any (fixed) single-qubit $Z$-rotation gate with runtime $\text{poly}(n,m) 2^{m/2}$. We suggest a method to further improve the upper bounds by constructing linear codes with certain properties.
Chaotic switching for quantum coin Parrondo's games with application to encryption

Quantum game theory has stimulated some interest in recent years with the advancement of quantum information theory. This interest has led to a resurgence of quantum Parrondo's games. With two losing games combining to give a winning game, this paradoxical idea is known as Parrondo's paradox. By using chaotic switching between the two losing quantum games, we show that it is possible to achieve Parrondo's paradox involving a quantum walker playing two-sided quantum coin tossing games. Furthermore, we show that the framework of chaotic switching in quantum coin tosses can be applied to encryption. This is a proposal to deploy a quantum coin toss with chaotic switching for semiclassical encryption.Received 24 February 2021Accepted 18 April 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L022019Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasControl & applications of chaosQuantum walksTechniquesChaos & nonlinear dynamicsStatistical PhysicsNonlinear Dynamics
Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring

Despite recent progress, learning new tasks through language instructions remains an extremely challenging problem.On the AL-FRED benchmark for task learning, the published state-of-the-art system only achieves a task success rate of less than 10% in an unseen environment, compared to the human performance of over 90%.To address this issue, this paper takes a closer look at task learning.In a departure from a widely applied end-toend architecture, we decomposed task learning into three sub-problems: sub-goal planning, scene navigation, and object manipulation; and developed a model HiTUT 1 (stands for Hierarchical Tasks via Unified Transformers) that addresses each sub-problem in a unified manner to learn a hierarchical task structure.On the ALFRED benchmark, HiTUT has achieved the best performance with a remarkably higher generalization ability.In the unseen environment, HiTUT achieves over 160% performance gain in success rate compared to the previous state of the art.The explicit representation of task structures also enables an in-depth understanding of the nature of the problem and the ability of the agent, which provides insight for future benchmark development and evaluation.
Design of a compact varifocal panoramic system based on the mechanical zoom method

A compact varifocal panoramic annular lens (PAL) system based on the four-component mechanical zoom method is proposed, which solves the problem that the traditional PAL system cannot zoom in to the region of interest. By moving the zoom group and the compensation group, our design achieves continuous zooming, in which the focal length changes from 3.8 to 6 mm. It can keep the position of the image surface unchanged while maintaining a compact structure. The system has a field of view (FoV) of 25°–100° in wide-angle mode and an FoV of 25°–65° in telephoto mode. The modulation transfer function of the wide-angle view is higher than 0.22 at 147 lp/mm. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>F</mml:mi> </mml:math> -theta distortion is less than 3%, and the relative illuminance is higher than 0.9 in the zoom process. Compared with the zoom PAL system with multiple free-form aspheric surfaces, the proposed system uses multiple spherical lenses and only one <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> -type asphere lens to achieve outstanding panoramic zoom imaging results. It is practical and straightforward, easy to manufacture, detect, and mass produce.
Multi-scale Graph Fusion for Co-saliency Detection

The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment each image to semantic superpixel clusters as well as generate different scales/sizes of images for each input image by the VGG-16 model. Different scales capture different patterns of the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features, aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed GCN method jointly conducts multi-scale feature fine-tune, graph learning, and feature learning in a unified framework. We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics.
skweak: Weak Supervision Made Easy for NLP

We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks.Weak supervision is an emerging machine learning paradigm based on a simple idea: instead of labelling data points by hand, we use labelling functions derived from domain knowledge to automatically obtain annotations for a given dataset.The resulting labels are then aggregated with a generative model that estimates the accuracy (and possible confusions) of each labelling function.The skweak toolkit makes it easy to implement a large spectrum of labelling functions (such as heuristics, gazetteers, neural models or linguistic constraints) on text data, apply them on a corpus, and aggregate their results in a fully unsupervised fashion.skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling.We illustrate the use of skweak for NER and sentiment analysis.