Reviewerly/exHarmony huggingface dataset:
Role of gamification in classroom teaching: Pre-service teachers’ view

Planning a creative learning environment is not an easy task. Teachers prefer to teach traditionally in chalk-and-talk approach during language teaching. However, this does not fit learners of the 21st Century as they prefer game-like activities instead of pen-and-paper lessons. Hence, the introduction of gamification in classroom would result in immediate enthusiasm and curiosity, which leads to the learners’ willingness to learn. Gamification prepares learners to be active and take responsibility upon their own learning. Lessons filled with fun games are believed to be more effective in producing a positive outcome as learners are motivated to play more although they do not realise the fact that they are learning subconsciously. This study aimed to explore views of 33 pre-service teachers in using gamification in language teaching during their 16-week internship at local primary schools around Selangor, Malaysia. A quantitative survey research method was used by administering a set of questionnaires at the end of their internship. The data collected was then analysed and presented in the form of tables. The results showed that integrating gamification into language learning improved learners’ creative, critical and problem-solving skills. Thus, interesting and engaging activities spark learners’ interest in language learning subconsciously.
Two-Digit Phase-Coding Strategy for Fringe Projection Profilometry

Phase-coding methods have been widely used for three-dimensional (3-D) shape measurement, which use sinusoidal phase-shifting patterns to recover wrapped phase and the stair phase-coding patterns to determine the fringe order. However, due to the system nonlinearity, random noises, and image blurring, the total number of codewords that can be used is limited. In order to increase the number of codewords, this article presents a two-digit phase-coding (TDPC) strategy, in which each fringe of the sinusoidal phase-shifting patterns corresponds to a two-digit codeword encoded in the phase-coding patterns. In this way, more codewords can be generated without increasing additional patterns. Experiments of the cup and sculptures have been carried out, and the results confirm that the TDPC method can recover the 3-D shape of objects more robustly. Meanwhile, the precision experiment of a standard ball proves the high accuracy of the proposed method.
Structure Aware Negative Sampling in Knowledge Graphs

Learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns.A crucial aspect of contrastive learning approaches is the choice of corruption distribution that generates hard negative samples, which force the embedding model to learn discriminative representations and find critical characteristics of observed data.While earlier methods either employ too simple corruption distributions, i.e. uniform, yielding easy uninformative negatives or sophisticated adversarial distributions with challenging optimization schemes, they do not explicitly incorporate known graph structure resulting in suboptimal negatives.In this paper, we propose Structure Aware Negative Sampling (SANS), an inexpensive negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node's k-hop neighborhood.Empirically, we demonstrate that SANS finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization.
Accelerating copolymer inverse design using monte carlo tree search

There exists a broad class of sequencing problems in soft materials such as proteins and polymers that can be formulated as a heuristic search that involves decision making akin to a computer game.
Attitudes and Perceptions Toward COVID-19 Digital Surveillance: Survey of Young Adults in the United States

Background COVID-19 is an international health crisis of particular concern in the United States, which saw surges of infections with the lifting of lockdowns and relaxed social distancing. Young adults have proven to be a critical factor for COVID-19 transmission and are an important target of the efforts to contain the pandemic. Scalable digital public health technologies could be deployed to reduce COVID-19 transmission, but their use depends on the willingness of young adults to participate in surveillance. Objective The aim of this study is to determine the attitudes of young adults regarding COVID-19 digital surveillance, including which aspects they would accept and which they would not, as well as to determine factors that may be associated with their willingness to participate in digital surveillance. Methods We conducted an anonymous online survey of young adults aged 18-24 years throughout the United States in June 2020. The questionnaire contained predominantly closed-ended response options with one open-ended question. Descriptive statistics were applied to the data. Results Of 513 young adult respondents, 383 (74.7%) agreed that COVID-19 represents a public health crisis. However, only 231 (45.1%) agreed to actively share their COVID-19 status or symptoms for monitoring and only 171 (33.4%) reported a willingness to allow access to their cell phone for passive location tracking or contact tracing. Conclusions Despite largely agreeing that COVID-19 represents a serious public health risk, the majority of young adults sampled were reluctant to participate in digital monitoring to manage the pandemic. This was true for both commonly used methods of public health surveillance (such as contact tracing) and novel methods designed to facilitate a return to normal (such as frequent symptom checking through digital apps). This is a potential obstacle to ongoing containment measures (many of which rely on widespread surveillance) and may reflect a need for greater education on the benefits of public health digital surveillance for young adults.
Few-shot Learning with Generative Adversarial Networks Based on WOA13 Data

In recent years, extreme weather events accompanying the global warming have occurred frequently, which brought significant impact on national economic and social development. The ocean is an important member of the climate system and plays an important role in the occurrence of climate anomalies. With continuous improvement of sensor technology, we use sensors to acquire the ocean data for the study on resource detection and disaster prevention, etc. However, the data acquired by the sensor is not enough to be used directly by researchers, so we use the Generative Adversarial Network (GAN) to enhance the ocean data. We use GAN to process WOA13 dataset and use ResNet to determine if there is a thermocline layer in a sea area. We compare the classification results of the enhanced datasets of different orders of magnitude with the classification results of the original datasets. The experimental result shows that the dataset processed by GAN has a higher accuracy. GAN has a certain enhancement effect to marine data. Gan increased the accuracy of the WOA dataset from 0.91 to 0.93. At the same time, the experimental results also show that too much data cannot continue to enhance the accuracy of WOA in ResNet.
The Invisible Work of Maintenance in Community Health

Frontline health workers are the first and often the only access point to basic health care services in low-and-middle income countries. However, the work and the issues frontline health workers face are often invisible to the healthcare system, with limited resources to assist them. This study explores the work practices, challenges and roles of frontline health workers in community health with particular focus on pregnancy care in South India. Drawing on the notion of maintenance and articulation work, we describe the maintenance work of frontline health workers maintaining, anticipating, reconciling, and supporting care infrastructures beyond data collection practices. Our findings highlight how socio-cultural practices, perceptions, status, and existing systems influence maintenance work practices. Based on our findings, we suggest moving beyond the focus on training and performance to design CSCW tools to support the maintenance work of frontline health workers as 'system-builders' to make healthcare infrastructures work in community health.
MLBO: Mixed Leader Based Optimizer for Solving Optimization Problems

There are numerous optimization problems in various sciences that need to be solved using the appropriate technique.One of the most widely used techniques for solving optimization problems are population-based optimization algorithms.The innovation and contribution of this paper is to design a new optimizer called Mixed Leader Based Optimizer (MLBO) to solve optimization problems.The main idea in the proposed MLBO is to create a new member as a leader by mixing the best population member and a random member to guide the algorithm population.The main advantage and feature of the proposed MLBO is that it has no control parameters and therefore no need to adjust the parameter.The proposed MLBO algorithm is mathematically formulated to implement in solving various optimization problems.The capability of the proposed optimizer in optimizing and providing appropriate solutions has been tested on a set of twenty-three standard objective functions.These objective functions are selected from three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal in order to analyze different aspects of optimization algorithms.Also, in order to analyze the obtained optimization results, the performance of the MLBO is compared with eight other well-known algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching Learning-Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO).The obtained optimization results from the MLBO show the proper performance of the proposed algorithm in solving various optimization problems.On the other hand, comparing the performance of the MLBO with the other eight optimization algorithms indicates the superiority of the proposed optimizer over the compared algorithms.
Tempered Sigmoid Activations for Deep Learning with Differential Privacy.

Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using the model architectures that already performed well in a non-privacy-preserving setting. This approach leads to less than ideal privacy/utility tradeoffs, as we show here. Instead, we propose that model architectures are chosen ab initio explicitly for privacy-preserving training. To provide guarantees under the gold standard of differential privacy, one must bound as strictly as possible how individual training points can possibly affect model updates. In this paper, we are the first to observe that the choice of activation function is central to bounding the sensitivity of privacy-preserving deep learning. We demonstrate analytically and experimentally how a general family of bounded activation functions, the tempered sigmoids, consistently outperform unbounded activation functions like ReLU. Using this paradigm, we achieve new state-of-the-art accuracy on MNIST, FashionMNIST, and CIFAR10 without any modification of the learning procedure fundamentals or differential privacy analysis.
How anthropomorphism affects trust in intelligent personal assistants

Purpose With the continuous improvement of artificial intelligence (AI) technology, intelligent personal assistants (IPAs) based on AI have seen unprecedented growth. The present study investigates the effect of anthropomorphism on cognitive and emotional trust and the role of interpersonal attraction in the relationship between anthropomorphism and trust. Design/methodology/approach A structural equation modeling technique with a sample of 263 consumers was used to analyze the data and test the conceptual model. Findings The findings illustrate that the anthropomorphism of IPAs did not directly induce trust. Anthropomorphism led users to assign greater social attraction and task attraction to IPAs, which in turn reinforced cognitive or emotional trust in these assistants. Compared with task attraction, social attraction was more powerful in strengthening both cognitive trust and emotional trust. The present study broadens the current knowledge about interpersonal attraction and its role in AI usage by examining two types of interpersonal attraction of IPAs. Originality/value As trust plays an important role in the rapid development of human–computer interaction, it is imperative to understand how consumers perceive these intelligent agents and build or improve trust. Prior studies focused on the impact of anthropomorphism on overall trust in AI, and its underlying mechanism was underexplored. The findings can help marketers and designers better understand how to enhance users' trust in their anthropomorphic products, especially by increasing social interactive elements or promoting communication.
Multi-Task SE-Network for Image Splicing Localization

Image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets.
Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps

More than 78 countries have developed COVID contact-tracing apps to limit the spread of coronavirus. However, many experts and scientists cast doubt on the effectiveness of those apps. For each app, a large number of reviews have been entered by end-users in app stores. Our goal is to gain insights into the user reviews of those apps, and to find out the main problems that users have reported. Our focus is to assess the “software in society” aspects of the apps, based on user reviews. We selected nine European national apps for our analysis and used a commercial app-review analytics tool to extract and mine the user reviews. For all the apps combined, our dataset includes 39,425 user reviews. Results show that users are generally dissatisfied with the nine apps under study, except the Scottish (“Protect Scotland”) app. Some of the major issues that users have complained about are high battery drainage and doubts on whether apps are really working. Our results show that more work is needed by the stakeholders behind the apps (e.g., app developers, decision-makers, public health experts) to improve the public adoption, software quality and public perception of these apps.
Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.
COSTA

Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA on various downstream tasks. The graph augmentation step is a vital but scarcely studied step of GCL. In this paper, we show that the node embedding obtained via the graph augmentations is highly biased, somewhat limiting contrastive models from learning discriminative features for downstream tasks. Thus, instead of investigating graph augmentation in the input space, we alternatively propose to perform augmentations on the hidden features (feature augmentation). Inspired by so-called matrix sketching, we propose COSTA, a novel COvariance-preServing feaTure space Augmentation framework for GCL, which generates augmented features by maintaining a "good sketch" of original features. To highlight the superiority of feature augmentation with COSTA, we investigate a single-view setting (in addition to multi-view one) which conserves memory and computations. We show that the feature augmentation with COSTA achieves comparable/better results than graph augmentation based models.
SearchBuddies: Bringing Search Engines into the Conversation

Although people receive trusted, personalized recommendations and auxiliary social benefits when they ask questions of their friends, using a search engine is often a more effective way to find an answer. Attempts to integrate social and algorithmic search have thus far focused on bringing social content into algorithmic search results. However, more of the benefits of social search can be preserved by reversing this approach and bringing algorithmic content into natural question-based conversations. To do this successfully, it is necessary to adapt search engine interaction to a social context. In this paper, we present SearchBuddies, a system that responds to Facebook status message questions with algorithmic search results. Via a three-month deployment of the system to 122 social network users, we explore how people responded to search content in a highly social environment. Our experience deploying SearchBuddies shows that a socially embedded search engine can successfully provide users with unique and highly relevant information in a social context and can be integrated into conversations around an information need. The deployment also illuminates specific challenges of embedding a search engine in a social environment and provides guidance toward solutions.
Boosting Applied to Classification of Mass Spectral Data

Boosting is a machine learning algorithm that is not well known in chemometrics.We apply boosting tree to the classification of mass spectral data.In the experiment, recognition of 15 chemical substructures from mass spectral data have been taken into account.The performance of boosting is very encouraging.Compared with previous result, boosting significantly improves the accuracy of classifiers based on mass spectra.
Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$100{\times}$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Speed-Up

Image restoration methods aim to recover the underlying clean image from corrupted observations. The expected patch log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes the EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fastEPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems, such as denoising, deblurring, super-resolution, inpainting, and devignetting. To the best of our knowledge, the FEPLL is the first algorithm that can competitively restore a 512 × 512 pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation.