Reviewerly/exHarmony huggingface dataset:
Romancing the Algorithm

Many romance novelists have shifted to self-publishing mediated through online technologies, such as online retailer platforms for selling novels and social media for marketing. However, engagement with such complex algorithmic systems has posed challenges, including understanding continually changing algorithms, frequently changing silently, impacting novelists' successful professionalization and monetization. We conducted surveys and interviews with romance novelists to examine how they experience, interpret, and navigate algorithms. Our findings detail interviewees' efforts to comprehend algorithms, both individually and collectively, and leverage that comprehension to navigate and manipulate algorithms. We discuss how our interviewees constructed literacy of precarious algorithms on their work platforms, suggesting implications for designing algorithmic systems supporting digital work.
PatchMix Augmentation to Identify Causal Features in Few-Shot Learning

The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It is currently an important research question and has great practical values in the real-world applications. Despite extensive previous efforts are made on few-shot learning tasks, we emphasize that most existing methods did not take into account the distributional shift caused by sample selection bias in the FSL scenario. Such a selection bias can induce spurious correlation between the semantic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">causal</i> features, that are causally and semantically related to the class label, and the other <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-causal</i> features. Critically, the former ones should be invariant across changes in distributions, highly related to the classes of interest, and thus well generalizable to novel classes, while the latter ones are not stable to changes in the distribution. To resolve this problem, we propose a novel data augmentation strategy dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PatchMix</i> that can break this spurious dependency by replacing the patch-level information and supervision of the query images with random gallery images from different classes from the query ones. We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features. To further make these features to be discriminative enough for classification, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Correlation-guided Reconstruction</i> (CGR) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hardness-Aware</i> module for instance discrimination and easier discrimination between similar classes. Moreover, such a framework can be adapted to the unsupervised FSL scenario. The utility of our method is demonstrated on the state-of-the-art results consistently achieved on several benchmarks including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> ImageNet, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tiered</i> ImageNet, CIFAR-FS, CUB, Cars, Places and Plantae, in all settings of single-domain, cross-domain and unsupervised FSL. By studying the intra-variance property of learned features and visualizing the learned features, we further quantitatively and qualitatively show that such a promising result is due to the effectiveness in learning causal features.
Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning

Magnetically manipulated medical robots are a promising alternative to current robotic platforms, allowing for miniaturization and tetherless actuation. Controlling such systems autonomously may enable safe, accurate operation. However, classical control methods require rigorous models of magnetic fields, robot dynamics, and robot environments, which can be difficult to generate. Model-free reinforcement learning (RL) offers an alternative that can bypass these requirements. We apply RL to a robotic magnetic needle manipulation system. Reinforcement learning algorithms often require long runtimes, making them impractical for many surgical robotics applications, most of which require careful, constant monitoring. Our approach first constructs a model-based simulation (MBS) on guided real-world exploration, learning the dynamics of the environment. After intensive MBS environment training, we transfer the learned behavior from the MBS environment to the real-world. Our MBS method applies RL roughly 200 times faster than doing so in the real world, and achieves a 6 mm root-mean-square (RMS) error for a square reference trajectory. In comparison, pure simulation-based approaches fail to transfer, producing a 31 mm RMS error. These results demonstrate that MBS environments are a good solution for domains where running model-free RL is impractical, especially if an accurate simulation is not available.
Technical methods of national security supervision: Grain storage security as an example

Grain security guarantees national security. China has many widely distributed grain depots to supervise grain storage security. However, this has led to a lack of regulatory capacity and manpower. Amid the development of reserve-level information technology, big data supervision of grain storage security should be improved. This study proposes big data research architecture and an analysis model for grain storage security; as an example, it illustrates the supervision of the grain loss problem in storage security. The statistical analysis model and the prediction and clustering-based model for grain loss supervision were used to mine abnormal data. A combination of feature extraction and feature selection reduction methods were chosen for dimensionality. A comparative analysis showed that the nonlinear prediction model performed better on the grain loss data set, with R2 of 87.21%, 87.83%, 91.97%, and 89.40% for Gradient Boosting Regressor (GBR), Random Forest, Decision Tree, XGBoost regression on test sets, respectively. Nineteen abnormal data were filtered out by GBR combined with residuals as an example. The deep learning model had the best performance on the mean absolute error, with an R2 of 85.14% on the test set and only one abnormal data identified. This is contrary to the original intention of finding as many anomalies as possible for supervisory purposes. Five classes were generated using principal component analysis dimensionality reduction combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering, with 11 anomalous data points screened by adding the amount of normalized grain loss. Based on the existing grain information system, this paper provides a supervision model for grain storage that can help mine abnormal data. Unlike the current post-event supervision model, this study proposes a pre-event supervision model. This study provides a framework of ideas for subsequent scholarly research; the addition of big data technology will help improve efficient supervisory capacity in the field of grain supervision.
Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction

Since the onset of civilization, sketches have been used to portray our visual world, and they continue to do so in many different disciplines today. As in specific government agencies, establishing similarities between sketches is a crucial aspect of gathering forensic evidence in crimes, in addition to satisfying the user’s subjective requirements in searching and browsing for specific sorts of images (i.e., clip art images), especially with the proliferation of smartphones with touchscreens. With such a kind of search, quickly and effectively drawing and retrieving sketches from databases can occasionally be challenging, when using keywords or categories. Drawing some simple forms and searching for the image in that way could be simpler in some situations than attempting to put the vision into words, which is not always possible. Modern techniques, such as Content-Based Image Retrieval (CBIR), may offer a more useful solution. The key engine of such techniques that poses various challenges might be dealt with using effective visual feature representation. Object edge feature detectors are commonly used to extract features from different image sorts. However, they are inconvenient as they consume time due to their complexity in computation. In addition, they are complicated to implement with real-time responses. Therefore, assessing and identifying alternative solutions from the vast array of methods is essential. Scale Invariant Feature Transform (SIFT) is a typical solution that has been used by most prevalent research studies. Even for learning-based methods, SIFT is frequently used for comparison and assessment. However, SIFT has several downsides. Hence, this research is directed to the utilization of handcrafted-feature-based Oriented FAST and Rotated BRIEF (ORB) to capture visual features of sketched images to overcome SIFT limitations on small datasets. However, handcrafted-feature-based algorithms are generally unsuitable for large-scale sets of images. Efficient sketched image retrieval is achieved based on content and separation of the features of the black line drawings from the background into precisely-defined variables. Each variable is encoded as a distinct dimension in this disentangled representation. For representation of sketched images, this paper presents a Sketch-Based Image Retrieval (SBIR) system, which uses the information-maximizing GAN (InfoGAN) model. The establishment of such a retrieval system is based on features acquired by the unsupervised learning InfoGAN model to satisfy users’ expectations for large-scale datasets. The challenges with the matching and retrieval systems of such kinds of images develop when drawing clarity declines. Finally, the ORB-based matching system is introduced and compared to the SIFT-based system. Additionally, the InfoGAN-based system is compared with state-of-the-art solutions, including SIFT, ORB, and Convolutional Neural Network (CNN).
Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers

Breast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the death rate. The identification and classification of breast cancer are challenging tasks. The most commonly known procedure of breast cancer detection is performed by using mammographic images. Recently implemented algorithms suffer from generating accuracy below expectations, and their computational complexity is high. To resolve these issues, this paper proposes a fully automated biomedical diagnosis system of breast cancer using an AlexNet, a type of Convolutional Neural Network (CNN), and multiple classifiers to identify and classify breast cancer. This system utilizes a neuro-fuzzy method, a segmentation algorithm, and various classifiers to reach a higher accuracy than other systems have achieved. Numerous features are extracted to detect and categorize breast cancer. Three datasets from Kaggle were tested to validate the proposed system. The performance evaluation is performed with quantitative and qualitative accuracy, precision, recall, specificity, and F-score. In addition, a comparative assessment is performed between the proposed system and some works of literature. This assessment shows that the presented algorithm provides better classification results and outperforms other systems in all parameters. Its average accuracy is over 98.6%, while other metrics are more than 98%. This research indicates that this approach can be applied to assist doctors in diagnosing breast cancer correctly.
Quantum capsule networks

Abstract Capsule networks (CapsNets), which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence (AI). The capsule, as the building block of CapsNets, is a group of neurons represented by a vector to encode different features of an entity. The information is extracted hierarchically through capsule layers via routing algorithms. Here, we introduce a quantum capsule network (dubbed QCapsNet) together with an efficient quantum dynamic routing algorithm. To benchmark the performance of the QCapsNet, we carry out extensive numerical simulations on the classification of handwritten digits and symmetry-protected topological phases, and show that the QCapsNet can achieve an enhanced accuracy and outperform conventional quantum classifiers evidently. We further unpack the output capsule state and find that a particular subspace may correspond to a human-understandable feature of the input data, which indicates the potential explainability of such networks. Our work reveals an intriguing prospect of QCapsNets in quantum machine learning, which may provide a valuable guide towards explainable quantum AI.
Transcendental Avatar: Experiencing Bioresponsive Avatar of the Self for Improved Cognition

Transcendental Avatar is a virtual reality (VR) system focused on stress relief to support relaxation techniques, using biofeedback, a well-known therapy technique to improve physical and mental health. Biofeedback as a method uses visual and audio feedback of one’s physiology to reflect, recognize, and help one gain awareness of many physiological functions, improving their cognitive and emotional state. Yet, there has been little work on how this can be appropriately leveraged in virtual reality (VR). In Transcendental Avatar, we proposed a system that shows the personification of the avatar, reflecting in real-time user’s biofeedback to enhance their cognitive and emotional state. In this study, we examine whether stress, and self-reported anxiety symptoms can be relieved or reduced by the proposed immersive experience.
Reform of Physical Education Teaching in Colleges from the Perspective of Lifelong Physical Education: A Novel Perspective

Reform of physical education teaching in colleges from the perspective of lifelong physical education is studied in the paper. Information technology in education has become a major trend in the education industry, and physical education has become a trend in the education industry, and sports teaching is becoming more convenient due to the popularity of information technology convenient, whether students consult learning materials or teachers search. Under this policy background, update the educational concept, re-examine the core literacy of physical education, discover and explore the problems existing in the education reform and put forward countermeasures. This paper gives the novel suggestions and discussions.
Feature Map Augmentation to Improve Scale Invariance in Convolutional Neural Networks

Abstract Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any convolution layer within a CNN. However, its maximum effectiveness is shown when placed at the output end of final convolution layer. We test RiT in the application of scale-invariance where we attempt to classify scaled images from benchmark datasets. Our results show promising improvements in the networks ability to be scale invariant whilst keeping the model computation cost low.
Realizing multiple-qubit entangling gate in Rydberg atoms via soft quantum control

Abstract Entangling gates are important for the generation of entanglement in quantum communicational and computational tasks. In this work, we propose an efficient protocol to realize the multi-qubit entangling gates with high fidelity in Rydberg atoms. Particularly, we apply the technique of soft quantum control to design the off-resonant pulses such that the atoms are driven to the ground-state subspace via unconventional Rydberg pumping. Thus, our scheme is insensitive to the decay effect as all atoms are only virtually excited. Moreover, Gaussian temporal modulation is further adopted to improve its robustness against the model uncertainty, such as operating time and environment noise. Finally, we perform numerical simulation to validate the effectiveness of our scheme. Hence, our work has potential applications in quantum information processing based on Rydberg atoms.
Environment, Social and Governance Reporting and Firm Performance: Evidence from GCC Countries

The aim of this research is to investigate the impact of ESG reporting on firm performance, Environment, Social and Governance (ESG) are a triple-bottom-line approach that combines financial gains with adhering to social, governance and environmental norms. In addition, the study's objective is to determine the relationship between ESG disclosure and firm performance in Gulf Cooperation Council (GCC) listed companies. ESG scores and other samples for 91 firms from 6 GCC countries were collected for this purpose over a three-year period from 2019, 2020 and 2021. The sample comprised nine diverse industries. The dependent variables are Return on Assets (ROA) and market capitalization , experimental variables are environmental pillar score, social pillar score, governance pillar score and overall ESG score and the control variables: size, leverage. The study found that ESG scores and governance pillar scores have a positive impact on a firm’s market value but environmental and social pillar scores were not significant. In addition, there is a strong relationship between all ESG disclosures and ECG scores.
Constructing a personalized recommender system for life insurance products with machine‐learning techniques

Summary The collaborative filtering (CF) recommendation algorithm predicts the purchases of specific users based on their characteristics and purchase history. This study empirically analyzes the possibility of applying CF to the insurance industry using real customer data from South Korea. Using three different CF models, we examined the relevance of applying the CF model to insurance products under various situations by comparing them with logistic‐regression‐based recommendation models. Through experiments, we empirically show that CF models apply to the insurance industry, especially when customer purchase information is added to the model.
Work in Progress: Using Virtual Reality Technology for Remote Students and Students with Disabilities in an Electrical Engineering Lab

This Innovative Practice Work in Progress presents a step toward using virtual reality technology in the undergraduate electrical engineering classroom to offer remote students and students with disabilities access to ‘hands-on’ engineering labs. In this paper, we describe the VR pilot labs created for our freshman-level electronic circuits course and present initial feedback from students concerning the effectiveness of the VR environment for learning their way around a basic electronic circuits laboratory.
Conceptual framework for students’ social psychological monitoring

The article states and grounds the task to develop and introduce the system of university students monitoring as a tool to manage education process that enables to solve the problem of lack of knowledge about the object under control. The article describes theoretical and methodological reasons for the monitoring system. It analyses comprehension of competency profile, taken as an integrative index of the education result: this index enables to evaluate the learning process efficiency from the point of view of the requirements applied to the subject-matter specialist. The authors describe the monitoring conceptual framework and the possibility of its adaptation to reveal students readiness to maintain business activities and research. The conceptual model of the students competence profile is based on understanding the concept of readiness for any activity as personality integrative characteristic including needs, motivations, values and social attitudes. The article proves the procedure of students motivation diagnostics, the diagnostic procedure of their value system and intentions, and social attitudes. It articulates the way of developing and introducing the social psychological monitoring system, associated with sociological and psychological methods being included into the complex evaluation technique. These methods comprise: 1) estimation technique for the personnel involvement into the company activity; 2) Likert scale; 3) adopted motive classifications by P.M. Lapin and T.V. Razina; 4) M. Rokichs methodology for value orientation study; 5) S. Schwartzs methodology for measuring value orientations; 6) methods for IAT and VAAST attitudes indirect diagnostic.
A New 4D Chaos System with an Encryption Algorithm for Color and Grayscale Images

In this paper, a new 4D chaos system is introduced based on the mathematical structure through composing and transferring. The behavior of the proposed chaotic system is studied using different tests, such as Lyapunov exponent, bifurcation diagram and histogram. In order to show the advantage of the new chaos system, the proposed system is used for image encryption. In the proposed algorithm, in addition to the new chaos system, cellular automata is also used to improve the security of the algorithm. The proposed algorithm has a separate structure for encryption key, which increases the security of the proposed algorithm. The security of the proposed environment which is evaluated using different types of security tests shows the efficiency of the proposed algorithm.