Reviewerly/exHarmony huggingface dataset:
FairCrowd: Fair Human Face Dataset Sampling via Batch-Level Crowdsourcing Bias Inference

Human face image is a large category of visual information utilized by various human facial data services (e.g., face recognition, face generation, face attribute prediction). However, the quality of data services (QoDS) on human face datasets is usually biased towards the majority demographic group due to the data imbalance issue. In this paper, we focus on a fair human face dataset sampling problem where the goal is to sample a sub-dataset from the original dataset to reduce its bias by leveraging crowd intelligence to infer the demographic labels of face images (e.g., male or female, old or young). Our problem is motivated by the limitations of current fair data sampling solutions that require pre-annotated demographic labels to sample a fair dataset. Two important challenges exist in solving our problem: 1) it is extremely time-consuming and expensive to assign crowd workers to annotate demographic labels of all images in a large-scale facial dataset; 2) it is not a trivial task to improve the fairness of the sampled sub-dataset (with fewer data samples) without sacrificing the accuracy performance of data services on such dataset. To address the above challenges, we develop FairCrowd, a fair crowdsourcing-based data sampling framework that leverages an efficient batch-level demographic label inference model and a joint fair-accuracy-aware data shuffling method. We evaluate the performance of FairCrowd through a large-scale real-world face image dataset that consists of celebrity faces from a diversified set of demographic groups. The results show that FairCrowd not only reduces demographic bias but also improves the accuracy of data services trained on the sub-dataset generated by FairCrowd, leading to a more desirable QoDS of the application.
Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification

Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.
Deep Regional Metastases Segmentation for Patient-Level Lymph Node Status Classification

Generally, automatic diagnosis of the presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Detection and classification of breast cancer metastases have high clinical relevance, especially in whole-slide images of histological lymph node sections. Fast early detection leads to huge improvement of patient’s survival rate. However, currently pathologists mainly detect the metastases with microscopic assessments. This diagnosis procedure is extremely laborious and prone to inevitable missed diagnoses. Therefore, automated, accurate patient-level classification would hold great promise to reduce the pathologist’s workload while also reduce the subjectivity of diagnosis. In this paper, we provide a novel deep regional metastases segmentation (DRMS) framework for the patient-level lymph node status classification. First, a deep segmentation network (DSNet) is proposed to detect the regional metastases in patch-level. Then, we adopt the density-based spatial clustering of applications with noise (DBSCAN) to predict the whole metastases from individual slides. Finally, we determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection in clinical trials.
Impact of digital nudging on information security behavior: an experimental study on framing and priming in cybersecurity

Purpose Phishing attacks are the most common cyber threats targeted at users. Digital nudging in the form of framing and priming may reduce user susceptibility to phishing. This research focuses on two types of digital nudging, framing and priming, and examines the impact of framing and priming on users' behavior (i.e. action) in a cybersecurity setting. It draws on prospect theory, instance-based learning theory and dual-process theory to generate the research hypotheses. Design/methodology/approach A 3 × 2 experimental study was carried out to test the hypotheses. The experiment consisted of three levels for framing (i.e. no framing, negative framing and positive framing) and two levels for priming (i.e. with and without priming). Findings The findings suggest that priming users to information security risks reduces their risk-taking behavior, whereas positive and negative framing of information security messages regarding potential consequences of the available choices do not change users' behavior. The results also indicate that risk-averse cybersecurity behavior is associated with greater confidence with the action, greater perceived severity of cybersecurity risks, lower perceived susceptibility to cybersecurity risks resulting from the action and lower trust in the download link. Originality/value This research shows that digital nudging in the form of priming is an effective way to reduce users' exposure to cybersecurity risks.
Eye Movement Prediction Based on Adaptive BP Neural Network

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.
Meta-reweighted Regularization for Unsupervised Domain Adaptation

Unsupervised domain adaptation enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy, and the adversarial learning based paradigm has achieved remarkable success. On top of this, recent works seeks to further regularize the classification decision boundary via self-training to learn target adaptive classifier with pseudo-labeled target samples. However, since pseudo labels are inevitably noisy, most of prior methods focus on manually designing elaborate target selection algorithms or optimization objectives. Different from them, we propose a meta-learning based target-reweighting regularization algorithm called MetaReg. Specifically, MetaReg is motivated by the intuition that an ideal target classifier trained on correct target pseudo labels should make small classification errors on target-like source samples. Therefore, we explicitly define a meta reweighting problem that aims to find optimal weights for different samples by minimizing the classification loss on a class-balanced set consisting of source samples that are most similar to target ones. The optimization problem is solved efficiently with a simplified approximation technique. As a result, the automatically learned optimal weights are utilized to reweight pseudo-labeled target samples and regularize the model learning. Comprehensive experiments verify that MetaReg outperforms the non-regularized UDA counterparts with state-of-the-art performance.
How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?

As NLP models are increasingly deployed in socially situated settings such as online abusive content detection, it is crucial to ensure that these models are robust.One way of improving model robustness is to generate counterfactually augmented data (CAD) for training models that can better learn to distinguish between core features and data artifacts.While models trained on this type of data have shown promising out-of-domain generalizability, it is still unclear what the sources of such improvements are.We investigate the benefits of CAD for social NLP models by focusing on three social computing constructs -sentiment, sexism, and hate speech.Assessing the performance of models trained with and without CAD across different types of datasets, we find that while models trained on CAD show lower in-domain performance, they generalize better out-of-domain.We unpack this apparent discrepancy using machine explanations and find that CAD reduces model reliance on spurious features.Leveraging a novel typology of CAD to analyze their relationship with model performance, we find that CAD which acts on the construct directly or a diverse set of CAD leads to higher performance.
Estimation of soil copper content in mining area using ZY1-02D satellite hyperspectral data

The content of soil heavy metal elements such as copper (Cu) is a crucial indicator for soil environmental monitoring and evaluation. However, traditional field sampling and laboratory chemical analysis methods are time-consuming and labor-intensive. The newly launched ZY1-02D satellite has a large imaging width, nanometer-level spectral resolution, and almost continuous spectral curve, providing a promising means for large-scale rapid estimation of soil heavy metal content. For the first time, our study explores the application potential of ZY1-02D satellite hyperspectral data in estimating soil Cu content in mining areas. A comprehensive approach combining correlation analysis, feature selection, and a regression model is proposed to predict the soil Cu content. First, the correlations between sampling soil Cu content and spectral features (including dozens of spectral indices and different transformed spectra calculated based on the pixel spectrum) were analyzed. Then the random frog algorithm was chosen to reduce the spectral features’ dimensionality and select the proper predictors. Several regression models of soil Cu content, including Gaussian process regression (GPR), boosted trees, bagged trees, and support vector machine, were established using these predictors. The model comparison shows that the GPR model has the best agreement with the sampling data. Furthermore, the accuracy of the GPR model with different numbers of predictors was also analyzed. The results show that the proposed estimation approach based on correlation analysis, random frog algorithm, and GPR model has a good performance compared to sampling data (R2 = 0.83, adjusted R2 = 0.81, RMSE = 364.61 mg / kg, and RPD = 1.83). And the results further indicate the great potential of AHSI/ZY1-02D hyperspectral data in predicting soil Cu content in mining areas.
SHAPE: Shifted Absolute Position Embedding for Transformers

Position representation is crucial for building position-aware representations in Transformers.Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost.We investigate shifted absolute position embedding (SHAPE) to address both issues.The basic idea of SHAPE is to achieve shift invariance, which is a key property of recent successful position representations, by randomly shifting absolute positions during training.We demonstrate that SHAPE is empirically comparable to its counterpart while being simpler and faster 1 .
An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning

As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).
Low-depth amplitude estimation on a trapped-ion quantum computer

Amplitude estimation (AE) is a fundamental quantum algorithmic primitive that enables quantum computers to achieve quadratic speedups for a large class of statistical estimation problems, including Monte Carlo methods. Recent works have succeeded in somewhat reducing the necessary resources for AE by trading off some of the speedup for lower depth circuits, but high quality qubits are still needed for demonstrating such algorithms. Here, we report the results of an experimental demonstration of AE on a state-of-the-art trapped ion quantum computer. AE was used to estimate the inner product of randomly chosen four-dimensional unit vectors, and the low-depth AE algorithms were based on the maximum likelihood estimation (MLE) and the Chinese remainder theorem (CRT) techniques. Significant improvements in accuracy were observed for the MLE based approach when deeper quantum circuits were taken into account, including circuits with more than 90 two-qubit gates and depth 60, achieving a mean additive estimation error on the order of ${10}^{\ensuremath{-}2}$. The CRT based approach was found to provided accurate estimates for many of the data points but was less robust against noise on average. Last, we analyze two more AE algorithms that take into account the specifics of the hardware noise to further improve the results.
Mood variation with belief predicates: Modal comparison and the raisability of questions

This paper proposes that subjunctive in the complement of belief sentences in Italian expresses a relation between the attitude holder’s beliefs and the common ground. In contrast to most other Romance languages, ‘believe’ commonly and prescriptively takes subjunctive in Italian, though indicative is found as well, and as has been observed in the literature, the choice of indicative or subjunctive has semantic effects. We show that the indicative with ‘believe’ is used when the belief statement describes the personal mental state of the holder of the attitude, an interpretation that follows from the traditional Hintikkean semantics. In contrast, we show that subjunctive with ‘believe’ is used to mark a relation between the content of belief and the discourse context. To analyze these facts, we propose that the modal quantification present in attitude reports comes not from the attitude verb, but instead from the embedded verbal mood. What differentiates Italian from related languages where ‘believe’ only takes indicative, is that Italian allows the subjunctive to access the com- mon ground as a modal base, utilizing the verb’s doxastic background as an ordering source. The fact that subjunctive relates the common ground to the subject’s beliefs explains the discourse oriented meaning of this combination. We extend our analysis to several other predicates that show mood variation in Italian.
Creating a Metamodel for Predicting Learners’ Satisfaction by Utilizing an Educational Information System During COVID-19 Pandemic

Faced with the disruption generated by the COVID-19 pandemic, the advent of enforced and exclusive online learning presented a challenging opportunity for researchers worldwide, to quickly adapt curricula to this new reality and gather electronic data by tracking students’ satisfaction after attending online modules. Many researchers have looked into the subject of student satisfaction to discover if there is a link between personal satisfaction and academic achievement. Using a set of data, filtered out of a statistical analysis applied on an online survey, with 129 variables, this study investigates students’ satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms. A hybrid machine learning model that has been created, initially consists of a Generalized Linear Model (GLAR), based on critical variables that have been filtered out after a correlation analysis. Its fitting errors are utilized as an extra predictor, that is used as an input to an artificial neural network. The model has been trained using as a basis the 70% of the population (consisting of 165 observations) to predict the satisfaction of the remaining 30%. After several trials and gradual improvement, the metamodel’s architecture is produced. The trained hybrid model’s final form had a coefficient of determination equal to 1 (R = 1). This indicates that the data fitting method was successful in linking the independent variables with the dependent variable 100 percent of the time (satisfaction prediction).
Wasserstein Patch Prior for Image Superresolution

Many recent superresolution methods are based on supervised learning. That means, that they require a large database of pairs of high- and low-resolution images as training data. However, for many applications, acquiring registered pairs of high and low resolution data or even imaging a large area with a high resolution is unrealistic. To overcome this problem, we introduce a Wasserstein patch prior for unsupervised superresolution of two- and three-dimensional images. In addition to the low-resolution observation, our method only requires one, possibly small, reference image which has a similar patch distribution as the high resolution ground truth. This assumption can e.g. be fulfilled when working with texture images or images of homogeneous material microstructures. The proposed regularizer penalizes the Wasserstein-2-distance of the patch distributions within the reconstruction and the reference image at different scales. We demonstrate the performance of the proposed method by applying it to two- and three-dimensional images of materials&#x0027; microstructures.
Control by infrastructure: Political ambitions meet technical implementations in RuNet

Discourse about sovereignty and Internet in Russia is predominantly focused on control of harmful content and information and its negative influence on the political regime and society. However, content control is not the only way to exercise sovereignty over digital media and the Web. Recently, the Russian government started to realize that without controlling Internet infrastructure, most strategies to filter and block Web sites and services are wasted. In the past five to seven years, Russia invested a lot of efforts in the development and adoption of new laws and regulations that deal with RuNet infrastructure, where the aim of centralized Internet traffic control was a real novelty, albeit a very ambitious political goal. This article tries to address the pitfalls of the control-by-infrastructure endeavor of the Russian government through four emblematic cases: the implementation of the “Revizor” system to control ISPs’ compliance to filter Internet resources from the blacklist; the battle to block Telegram messenger in Russia; the implementation of law FZ-90 (popularly referred to as the law “on Sovereign RuNet”); and finally, the ongoing experiment with free access to ‘socially significant Web sites’, which may have serious consequences in the future if used as a ‘white list’ of permitted Web resources. These four cases were chosen because they are deeply interconnected and show how the government has been gradually implementing infrastructure control in connection to content control.
Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments

In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions.A challenge in this task is how to handle 'off the path' scenarios where an agent veers from a reference path.Prior work supervises the agent with actions based on the shortest path from the agent's location to the goal, but such goal-oriented supervision is often not in alignment with the instruction.Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow.In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.