Reviewerly/exHarmony huggingface dataset:
The Oxymoron of Free Semester Program (FSP) in Korea

This chapter discusses the Free Semester Program (FSP), an educational policy in South Korea, which aims to incorporate whole-person development agenda in grades 7–9 curricula. First, we introduce the contents of the policy and its history, focusing on how this policy was borrowed, transplanted, and expanded in the Korean educational discourse. Further, we provide an analysis of the result of the policy implementation based on previous literature, focusing on the policy’s intended and unintended effects. The results show that while the idea of whole-person development is desirable, the policy might produce unintended side effects. Discussion about adopting a whole-person development approach in education follows.
Data Cleansing for Salt Dome Dataset With Noise Robust Network on Segmentation Task

Noisy labels seriously degrade the performance of the deep learning models. Especially on segmentation tasks, labels are represented at pixel level, and therefore, it is easier to generate them as noisy labels. In this letter, we aim to cleanse the dataset which contains noisy labels using Kullback–Leibler (KL) divergence algorithm and noise robust loss function. Here, we regard the whole dataset as noisy labels and separate noisy labels into two different parts. One is the strong noisy labels where there are the most noisy labels, which is referred to as incorrect labels, and the other is The weak noisy labels where pixels are incorrect in some parts of the image. The KL algorithm is more efficient in removing the labels which contain the most noise. At the same time, we use noise robust model to remove the labels where pixels are inaccurate only in small parts of the image. Noise robust loss function is robust to noisy labels, and thus, we consider noisy labels when intersection over union (IoU) is lower than a constant threshold despite using noise robust loss function. The effectiveness of our proposed method is assessed using Kaggle’s TGS Salt Identification Challenge dataset. We have demonstrated that using our method, segmentation performance is increased without heavy noisy labels.
Iterative query selection for opaque search engines with pseudo relevance feedback

Retrieving information from an online search engine is the first and most important step in many data mining tasks, such as fake news detection. Most of the search engines currently available on the web, including all social media platforms, are black-boxes (i.e., opaque) supporting short keyword queries. In these settings, it is challenging to retrieve all posts and comments discussing a particular news item automatically and on a large scale. In this paper, we propose a method for generating short keyword queries given a prototype document. The proposed iterative query selection (IQS) algorithm interacts with the opaque search engine to iteratively improve the query, by maximizing the number of relevant results retrieved. Our evaluation of IQS was performed on the Twitter TREC Microblog 2012 and TREC-COVID 2019 datasets and demonstrated the algorithm’s superior performance compared to state-of-the-art. In addition, we implemented IQS algorithm to automatically collect a large-scale dataset for fake news detection task of about 70K true and fake news items. The dataset, which we have made publicly available to the research community, includes over 22M accounts and 61M tweets. We demonstrate the usefulness of the dataset for fake news detection task achieving state-of-the-art performance. • An automated mechanism for optimizing short keyword queries named IQS. • A large-scale Fake News dataset on 70K news items discussed by 61M tweets. • Demonstrating the dataset quality through the application of fake news detection.
Turned Away and at Risk: Denial of Family Planning Services to Women in Malawi

Abstract Family planning (FP) has been a development priority since the mid‐1990s, yet barriers to access persist globally, including women being turned away from facilities without a method. This study aimed to assess the extent of, and reasons for, FP turnaway in three districts of Malawi. In 2019, data collectors screened women exiting 30 health facilities and surveyed those who had been denied a method. Follow‐up surveys were conducted via telephone with turned away clients at six and 12 weeks postvisit. Of the 2,246 women who were screened, 562 were new or restarting users. Of these, 15% (83/562) reported having been turned away from the health facility without an FP method. Women cited 14 different reasons for turnaway; the top three were unavailability of method (34%), unavailability of a provider (17%), or a requirement to return on the scheduled FP day (15%). The multiple reasons cited for leaving the health facility without an FP method indicate that reducing turnaway will not be achieved easily. The top reasons for turnaway are related to health systems or management issues within health facilities. Facilities need additional support for staffing, training on long‐acting and permanent methods, and a consistent supply of methods.
Multifactor IoT Authentication System for Smart Homes Using Visual Cryptography, Digital Memory, and Blockchain Technologies

In this era of smartness, the existing access control of things and objects in homes utilizes personal identification number (PIN)- or password-based authentication scheme, which seems more difficult and confusing since users need to accurately recall intricate information. Besides, other existing schemes suffer susceptibility to cyber threats and are non-scalable. Thus, this study overcomes the aforementioned limitations by leveraging the emerging users’ digital memories to recollect attributes or specific events rather than memorize a plethora of different PINs and passwords. Digital memories preserve the user’s memories into physical formats (images, audios, videos). These are usually with embedded metadata stored in the cloud, which may be used for user authentication. Arising from the foregoing, a multifactor authentication mechanism that is based on digital memory, visual cryptography, and blockchain technologies is proposed, with a view to securing the digital memories by incorporating an efficient (2, 2) visual cryptography scheme (VCS) in the digital memory authentication service (DMAS) in smart homes. The blockchain technology ensures a decentralized identity storage and sensitive data management. While this authentication system prevents unauthorized access to smart doors, its mobile application developed permits authorized by the user’s remote access to the smart door(s) in smart homes. The proposed strategy offers about 50% improvement in speed over the conventional public-key crypto-based authentication systems. Scyther tool was used to evaluate the registration as well as login protocols of the proposed system. The results obtained show that there is no assault present. Future explorations can consider alternatives to power supply and biometrics as a multifactor authentication.
Image Style Transfer Using Deep Learning Methods

Image style transfer is an increasingly popular technology that can learn the style of an existing picture through neural network algorithms and apply this style to another picture. It is widely used in the field of art, such as oil painting, cartoon animation production, image season conversion and text style conversion. Meanwhile, deep learning methods are attracting more and more attention both in research and applications in various areas. In this paper, we give an overview on current research progress and results of image style transfer using deep learning methods. The deep learning methods are categorized into Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). As for CNN methods, we mainly talk about models based on VGG; and in terms of GAN methods, conditional GAN, Cycle GAN, and cartoon-GAN methods are contained. Finally, we summarized the shortcomings of the current results and the future study direction.
Dynamical Representation Learning for Ethereum Transaction Network via Non-negative Adaptive Latent Factorization of Tensors

As a common cryptocurrency platform, Ethereum involves massive accounts and numerous real-time transactions. Moreover, as the involved accounts increase drastically, it is impossible to have transactions among all accounts at one time slot, which results in a high-dimensional and incomplete (HDI) dynamic transaction network. In spite of its HDI nature, such HDI dynamic transaction network contains much useful knowledge regarding involved accounts' behavior patterns like potential transaction links. To extract such knowledge from an HDI dynamic transaction network, this paper proposes a Non-negative Adaptive Latent Factorization of Tensors (NAL) model with two interesting ideas: a) adopting an HDI tensor to describe an HDI dynamic transaction network and building a non-negative learning objective based on the principle of data density-oriented, and b) implementing model hyper-parameter self-adaptive via using a particle swarm optimization (PSO) algorithm in the training process. Empirical studies on three real Ethereum transaction networks show that compared with state-of-the-art methods, the proposed NAL model achieves superior performance in terms of accuracy and computational efficiency in predicting potential transaction links.
Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model

In this paper, we propose a relative pose estimation algorithm for micro-lens array (MLA)-based conventional light field (LF) cameras. First, by employing the matched LF-point pairs, we establish the LF-point-LF-point correspondence model to represent the correlation between LF features of the same 3D scene point in a pair of LFs. Then, we employ the proposed correspondence model to estimate the relative camera pose, which includes a linear solution and a non-linear optimization on manifold. Unlike prior related algorithms, which estimated relative poses based on the recovered depths of scene points, we adopt the estimated disparities to avoid the inaccuracy in recovering depths due to the ultra-small baseline between sub-aperture images of LF cameras. Experimental results on both simulated and real scene data have demonstrated the effectiveness of the proposed algorithm compared with classical as well as state-of-art relative pose estimation algorithms.
Assistive IoT-centric Robotics for Senior Living

Senior citizens consider the most significant part of aging to be the changes that occur to their bodies. For this reason, they avoid doing body exercises and therefore often prone to heart diseases, muscle disorders and many other ailments. Although weakening of bones and muscles is inevitable as we grow old, we can still maintain our health by doing regular physical exercise. Encouraging elderly people to do physical exercise can be challenging, therefore the Robot-Trainer is introduced to make their exercising journey exciting accompanied by a friendly humanoid robot. The main goal of the system is to program an IoT-Assistive Robot to interact and conduct physical exercise training sessions with elderly people and maintain their physical fitness and mental health.
A proposed hybrid algorithm for mining frequent patterns on Spark

Frequent itemset mining is one of the most important data mining techniques applied to discover frequent itemset, interesting information, and correlation from data. Many algorithms such as Apriori, Fp-growth and Eclat have been adjusted and implemented to deal with big data. Those algorithms are implemented on big data processing engines such as MapReduce and Spark. However, the existing implementations have limitations. Consequently, this paper proposes a hybrid algorithm to mine frequent patterns on sparse big dataset over Spark platform. The proposed hybrid algorithm uses Apriori in the first few levels then switches to use Eclat for the rest of levels. The proposed hybrid algorithm consists of four phases. Experiments for testing the performance of the proposed algorithm are conducted, and the elapsed time of the proposed hybrid algorithm is compared with parallel fp-growth, YAFIM and Eclat-Spark. The proposed algorithm outperforms YAFIM, Eclat, and fp-growth with a high degree of minimum support.
Neural hybrid recommendation based on GMF and hybrid MLP

Recently, recommender systems have become exceptionally widespread, providing users with personalized appropriate items or service recommendations. On the other hand, deep learning techniques achieved great success in many research areas including computer vision, image processing, pattern recognition, and natural language processing. The application of deep learning in recommender systems have been explored with promising results. This chapter focuses on the use of deep neural networks for learning the interaction function from data. We propose a hybrid recommendation approach that combines collaborative filtering (CF) and content-based filtering (CBF) in an architecture based on two models: (1) generalized matrix factorization (GMF) and (2) hybrid multilayer perceptron (HybMLP). Extensive experiments on two well-known datasets MovieLens-1M and Yelp show significant improvements in our approach compared to the existing methods, especially for the cold start situation. These empirical evaluations show that the use of deeper layers of neural networks offers better recommendation accuracy.
A Computational Interaction Model for a Virtual Medical Assistant Using Situational Leadership

In a medical emergency in which an amateur caregiver is separated from medical experts by space and/or time, a virtual assistant could be useful in order to guide the caregiver through the required procedure successfully. A successful procedure is one that preserves and improves the health of the patient and maintains a positive interaction between the caregiver and the assistant. Regardless of whether the caregiver is experienced in the tasks in the procedure, the assistant must be able to guide them through each step. To manage the interaction between the assistant and caregiver, we employ situational leadership. We propose an agent system for (1) obtaining caregiver behavior during the procedure, (2) assigning a follower style to the caregiver, (3) determining the appropriate leadership style for the assistant, and (4) generating appropriate agent behavior to progress the procedure and maintain a positive interaction with the caregiver.
Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. The main challenge of closed-domain QA is to develop a model that only requires small datasets for training since large-scale corpora may not be available. One approach is a flexible QA model that can adapt to different closed domains and train on their corpora. In this paper, we present a novel versatile reading comprehension style approach for closed-domain QA (called CA-AcdQA). The approach is based on pre-trained contextualized language models, Convolutional Neural Network (CNN), and a self-attention mechanism. The model captures the relevance between the question and context sentences at different levels of granularity by exploring the dependencies between the features extracted by the CNN. Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. The model can be tuned to different domains with a small training dataset for sentence-level QA. The approach is tested on four publicly-available closed-domain QA datasets: Tesla (person), California (region), EU-law (system), and COVID-QA (biomedical) against nine other QA approaches. Results show that the ALBERT model variant outperforms all approaches on all datasets with a significant increase in Exact Match and F1 score. Furthermore, for the Covid-19 QA in which the text is complicated and specialized, the model is improved considerably with additional biomedical training resources (an F1 increase of 15.9 over the next highest baseline).
A Brave New World: Virtual Reality and Augmented Reality in Systems Biology

How we interact with computer graphics has not changed significantly from viewing 2D text and images on a flatscreen since their invention. Yet, recent advances in computing technology, internetworked devices and gaming are driving the design and development of new ideas in other modes of human-computer interfaces (HCIs). Virtual Reality (VR) technology uses computers and HCIs to create the feeling of immersion in a three-dimensional (3D) environment that contains interactive objects with a sense of spatial presence, where objects have a spatial location relative to, and independent of the users. While this virtual environment does not necessarily match the real world, by creating the illusion of reality, it helps users leverage the full range of human sensory capabilities. Similarly, Augmented Reality (AR), superimposes virtual images to the real world. Because humans learn the physical world through a gradual sensory familiarization, these immersive visualizations enable gaining familiarity with biological systems not realizable in the physical world (e.g., allosteric regulatory networks within a protein or biomolecular pathways inside a cell). As VR/AR interfaces are anticipated to be explosive in consumer markets, systems biologists will be more immersed into their world. Here we introduce a brief history of VR/AR, their current roles in systems biology, and advantages and disadvantages in augmenting user abilities. We next argue that in systems biology, VR/AR technologies will be most useful in visually exploring and communicating data; performing virtual experiments; and education/teaching. Finally, we discuss our perspective on future directions for VR/AR in systems biology.
LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications

Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem, but suffer from two drawbacks. First, it takes a long time (high overhead) to collect training samples. Second, the optimal configuration for one input data size of the same application might not be optimal for others. To address these issues, we propose a novel Bayesian Optimization (BO) based approach named LOCAT to automatically tune the configurations of Spark SQL applications online. LOCAT innovates three techniques. The first technique, named QCSA, eliminates the configuration-insensitive queries by Query Configuration Sensitivity Analysis (QCSA) when collecting training samples. The second technique, dubbed DAGP, is a Datasize-Aware Gaussian Process (DAGP) which models the performance of an application as a distribution of functions of configuration parameters as well as input data size. The third technique, called IICP, Identifies Important Configuration Parameters (IICP) with respect to performance and only tunes the important ones. As such, LOCAT can tune the configurations of a Spark SQL application with low overhead and adapt to different input data sizes. We employ Spark SQL applications from benchmark suites TPC-DS, TPC-H, and HiBench running on two significantly different clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates the optimization procedures of the state-of-the-art approaches by at least 4.1x and up to 9.7x; moreover, LOCAT improves the application performance by at least 1.9x and up to 2.4x. On the x86 cluster, LOCAT shows similar results to those on the ARM cluster.
Masked Face Recognition using Deep Learning Model

Face Masks have become part of our day-to-day activities. But these create a problem with the existing face recognition techniques. Existing face recognition techniques vary from simple ML techniques such as SVM, PCA, etc. to state-of-the-art models such as ResNet, VGG, etc. In this paper, we are studying the effects of masked faces on the performance of face recognition techniques. Face recognition itself is divided into face verification and face identification. This study is performed for the face identification task using different deep learning models. In particular, we are studying the pre-trained deep learning models that are trained using transfer learning for face identification tasks. In this study, a custom dataset is used consisting of 65 subjects. The custom dataset is a part of the VGGFace2 dataset. Subject faces are augmented with masks. The dataset doesn’t consist the faces without masks. In this study, we are using different popular pre-trained models such as VGG16, InceptionV3, etc. We are re-training those pre-trained models on a custom dataset and analyzing the gathered results. Apart from the pre-trained models, a new model has been proposed for the masked face identification task.
Radial shearing dynamic wavefront sensor based on a geometric phase lens pair

A radial shearing dynamic wavefront sensor is theorized and experimentally verified. The proposed sensor is based on a geometric phase lens pair that generates two radially sheared wavefronts. A polarization pixelated camera instantaneously obtains polarization-multiplexed phase maps from a single acquired image using a spatial phase-shifting technique. Experimental tests applied several wavefront shapes with a deformable mirror. The results were compared with a Shack-Hartmann wavefront sensor to evaluate the performance.
Counteracting Adversarial Attacks in Autonomous Driving

This article studies the robust deep stereo vision in autonomous driving systems and counteracting adversarial attacks. The autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract the practical attacks in autonomous systems, novel methods based on simulated attacks are proposed in this article. Univariate and multivariate functions are adopted to represent the relationships between the left and right input images and the deep stereo model. A stereo regularizer is proposed to guide the model to learn the implicit relationship between the images and characterize the loss function’s local smoothness. The attacks are generated by maximizing the regularizer term to break the linearity and smoothness. The model then defends the attacks by minimizing the loss and regularization terms. Two techniques are developed in this article. The first technique, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereo</monospace> , explores the basic knowledge from the physical world and smoothness, while the second technique, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereoV2</monospace> , improves <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereo</monospace> through leveraging the smooth activation functions during the defense. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereoV2</monospace> can learn and utilize the gradient information concerning the attacks. The gradients of the smooth activation functions can handle attacks for improving the model robustness. Numerical experiments on KITTI datasets demonstrate that the proposed methods offer superior performance.