Reviewerly/exHarmony huggingface dataset:
The Implementation of English Learning Activity Concept to English Reading Teaching in Senior High School

General Senior High School Curriculum Standards (2017 Edition) attach great importance to the cultivation of core competencies of the English subject and advocate English learning activity concept which points out the new direction for English learning and teaching. As an essential part of English teaching, the effectiveness of reading teaching has an extremely critical impact on English language teaching. However, in recent years, the effectiveness of English reading teaching has not been significantly improved and students’ reading ability and comprehensive competencies have not been effectively developed because of the outstanding problems such as lacking of real context and situation-based practical activities, only taking vocabulary and grammar learning as the main line and so on. The introduction to English learning activity concept provides new theoretical guidance and practical ways to advance English reading teaching and cultivate students’ core competencies of the English subject.
Design and Development of High School English Elective Courses based on the Reform of New College Entrance Examination

In the new round of college entrance examination English reform, the design and development of high school English elective courses has become a trend in basic education in the new era. The updating of teaching objectives, the formulation of teaching plans, the creation of teaching atmosphere, and the diversification of teaching evaluation have all become the important factors for the effective teaching of high school English elective courses. Based on the reform of the new college entrance examination, this paper analyzes the current status and problems of the opening of high school English elective courses in China, and points out that it is necessary to design and develop high school English elective courses around the new teaching objectives to meet the different needs of English learning, stimulate students' enthusiasm for learning, and promote the continuous improvement of students' comprehensive English ability.
<scp>VLSI</scp> implementation of image compressor using probabilistic run length coding

Abstract Multimedia applications, such as image processing including image and video transfer, heavily rely on reduction. The traditional device methods to picture reduction use more space, energy, and processing time. The majority of current efforts use the Golomb‐Rice encoding, due to its larger memory requirement and higher computing difficulty. So, this research concentrated on hardware design‐oriented probability run length (PRL) coding technique based on lossless colour image compression. The block truncation coding (BTC) features of the compression process are used by the suggested PRL method. The proposed image compression hardware consists of various modules such as a Parameter calculator, fuzzy table, bitmap generator, BTC parameters training, prediction, and error control, and PRL‐based finite state machine (PRL‐FSM). The proposed image compressor utilizes the parameter calculator block, which estimates the block type based on the image pixel intensities for each sub‐block. Thus, each block of the image is compressed by using a new block type and generates a variable block size. The proposed method utilizes the PRL‐BTC encoding method, which also calculates the probability of error between the compressed image to the test image. The process is iterated until the performance trade‐off between hardware cost and compression ratio (CR) is achieved. Hence, both smooth regions and non‐smooth regions of images are perfectly compressed by the probability‐based block selection. The simulation results show that the proposed method resulted in a better area, power, delay metrics, peak signal‐to‐noise ratio (PSNR), and CR compared to the state of art approaches.
360-degree video-based body-ownership illusion for inducing embodiment: development and feasibility results

Abstract Illusions that create a sense of ownership over a virtual body have been widely used to investigate the characteristics of our bodily experience. Despite the great potential of 360-degree videos to implement full-body ownership illusion, research is in its early stages, and no validated tools—neither commercial nor free—are available for the scientific and clinical community. In the current study, we present and discuss the development and feasibility results of a free 360-degree video-based body ownership illusion that researchers and scholars can experience using a cardboard headset with their smartphones. Forty-six participants underwent the 360-degree video-based full-body ownership illusion, visualizing in a first-person perspective (1PP) or in a mirror view the pre-recorded body of a young female performer. All participants were exposed to a congruent visuo-tactile condition (embodiment condition) and to an incongruent visuo-tactile condition (control condition). Participants completed the Embodiment Questionnaire and the Objectified Body Consciousness (OBC) scale. Results revealed that in the congruent visuo-tactile condition (compared to the control one), participants experienced a strong illusion in terms of body ownership, self-location, and agency. In terms of visual perspective, there was no difference in embodiment feelings between participants who experienced the illusion in 1PP and those who underwent a mirror perspective. Lastly, the control beliefs subscale (i.e., OBC scale) displayed a positive correlation with the self-location illusion susceptibility. Overall, these results point to the feasibility of this novel tool as immersive 360-degree video-based scenarios to deliver bodily illusions, and they open new avenues for future clinical interventions.
An anti- speckle multiplicative noise SAR and optical image registration method based on Zernike matrix-phase features

ABSTRACTAs SAR adopts an oblique distance imaging mode, due to obvious speckle multiplicative noise, the registration accuracy of SAR and optical images is low. This study proposes using Zernike polynomials to fit the front of the complex SAR echo data to obtain its phase feature, simultaneously extracting the SAR Zernike matrix feature from the complex echo phase and optical image, and using it to construct the registration descriptor to realize the registration of SAR and optical images. Experimental results show that the proposed method avoids the problem of obvious coherent speckle noise, the target characteristics are more prominent, the registration accuracy is higher, and the ability to recognize SAR targets and scenes is improved.KEYWORDS: Multisource image registrationregistration descriptorphase featurespeckle noiseZernike matrix Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported in part by the National Key Laboratory Foundation(Contract No. 6142411203416). We would also like to convey our heartfelt gratitude to the reviewers and members of the editorial team for their comments and contributions.
Patients’ behavioral intentions toward robotic adoption in healthcare: An approach on apprehension of embedding robotics

The ubiquitous healthcare sector requires a variegated set of emerging innovations and advanced technologies in the healthcare sector. Healthcare stakeholders in a turbulent marketplace continuously strive to maintain relevance; thus, the healthcare robotics notion has emerged to become a game-changer in the human realm. These emerging systems provide new possibilities to create a core value for stakeholders, specifically care providers and care receivers, in the turbulent environment. Although robotics is moving forward in healthcare, human inertia and apprehension are still anchors exerting robotics. Human behaviors and attitudes may change with time and in different circumstances. This paper aims to contribute a new perspective to the patients’ degree of apprehensions—trust, ethical, privacy, and legal concerns—which affect their behavioral intentions toward robotics adoption in healthcare by considering the moderating effect of social crises, including the aging and COVID-19 pandemic. The brink of a robotic era is encountered with challenges, including the aging population and COVID-19 for public and private healthcare stakeholders worldwide. Hence, this study’s essential support is to investigate emerging technology adoption and hurdles to ascertain how the patients’ behavioral intentions might affect robotic adoption in healthcare. Furthermore, this paper examines the anchors’ factors that affect individual behavioral intentions to successfully propose remedies to embed robotics in the human realm. The results indicate that trust, privacy, and ethical concerns are direct predictors of behavioral intentions toward robotic adoption. Moreover, behavioral intentions toward robotics adoption significantly affect the actual use of robotics in the healthcare industry in the future. Furthermore, social inertia, including aging and pandemics, significantly and positively affects behavioral intentions toward healthcare robotic adoption and use. Finally, both aging and pandemic positive interactions affect actual robotic use in the future of the healthcare industry.
Sequential ensemble learning for next item recommendation

Predicting the next item that users may engage in is a key task of recommender systems, and many methods have been proposed to deal with this task from different aspects. Theoretically, the proper ensemble of multiple different methods (a.k.a. base models) can make more accurate and stable recommendations. However, most of the existing ensemble methods rely on static aggregation strategies, which fail to capture base models’ dynamic predictive abilities for each user over time. In addition, most of the existing diversity measures used in regression or classification ensemble methods rely on a distance metric of base models’ outputs, which makes it intractably apply for next-item recommendation whose base models output sequential ranking lists. To solve the above problems, we propose a Sequential Ensemble Method, named SEM, to aggregate different base models for next-item recommendation. We assume users’ concentration and base models’ expertise can be inferred from users’ sequential behaviors and base models’ prediction results. Therefore, we propose to explicitly model base models’ dynamic predictive abilities on different users over time based on users’ concentration and base models’ expertise. In addition, we propose a new diversity measure for sequential ranking ensemble, which can perform diversity-based learning over time for better performance of next-item recommendation. Extensive experiments on six real-world data sets show that our method consistently outperforms state-of-the-art methods.
Maximum-margin polyhedral separation for binary Multiple Instance Learning

Multiple Instance Learning (MIL) is a kind of weak supervised learning, where each sample is represented by a bag of instances. The main characteristic of such problems resides in the training phase, since the class labels are provided only for each bag, whereas the instance labels are unknown. We focus on binary MIL problems characterized by two types of instances (positive and negative): based on the standard MIL assumption, a bag is considered positive if at least one of its instances is positive and it is considered negative otherwise. Then our idea is to generate a maximum-margin polyhedral separation surface such that, for each positive bag, at least one of its instances is inside the polyhedron and all the instances of the negative bags are outside. The resulting optimization problem is a nonlinear, nonconvex and nonsmooth mixed integer program, that we heuristically solve by a Block Coordinate Descent type method, based on repeatedly applying the DC (Difference of Convex) Algorithm. Numerical results are presented on a set of benchmark datasets.
Study the correlation between the readme file of GitHub projects and their popularity

A readme file plays an important role in a GitHub repository to provide a starting point for developers to reuse and make contributions. A good readme could provide sufficient information for users to learn and start a GitHub repository and might be correlated to the popularity of a repository. Given the importance of the role that a readme file plays, we aim to study to understand the correlation between the readme file of GitHub repositories and their popularity. We analyze readme files of 5,000 GitHub repositories across more than 20 languages. We study the relationship between readme file related factors and the popularity of GitHub repositories. We observe that: (1) Most of the studied readme file related factors (e.g., the number of lists, the number and frequency of updates on the readme file) are statistically significantly different between popular and non-popular repositories with non-negligible effect size. (2) After controlling repository-specific factors (e.g., repository topics and license information), the number of lists and the frequency of updates are the most significantly important factors that discriminate between popular and non-popular repositories. (3) The most of updates were made to update references in popular repositories, while in non-popular repositories most updates are for the content of how to use the repository. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Towards sample efficient deep reinforcement learning in collectible card games

Collectible card games (CCGs) are widely-played games in which players build a deck from a set of custom cards and use it to battle each other. They are notoriously more challenging than games such as Go and Texas Hold’em Poker, the protagonists of recent breakthroughs in game-playing AI. Deep reinforcement learning approaches have recently become state-of-the-art in CCGs, although requiring huge amounts of computational power to train. In this paper, we propose a collection of deep reinforcement learning approaches to battling in CCGs that are trainable on a single desktop computer. Each approach tries different mechanisms to increase sample efficiency. We use Legends of Code and Magic, a CCG designed for AI research, as a testbed and compare our approaches to each other, considering their win rate and other metrics. Then, we discuss the position of our approaches regarding the current literature, their limitations, directions of improvement, and extension to commercial CCGs.
MWCapsNet: A novel Multi-level Wavelet Capsule Network for insider threat detection using image representations

The detection of insider threats is a significant challenge for many organizations due to the complexities of the data, the potential for changes in user behavior, and limited ground truth. Current techniques for detecting insider threats, including balancing datasets, anomaly detection, and image classification, have challenges such as lower precision and a high False Positive Rate (FPR). This is due to complex and heterogeneous data and the disregard of the spatial arrangement and relationships between features in image classification techniques that are essential to understanding the detailed behavior pattern of a user. To address these issues, a novel image-based insider threat detection framework is proposed, which integrates Multi-level Wavelet decomposition into a Capsule Network (MWCapsNet). The image generator for tabular data (IGTD) framework is used to generate image representations by identifying the correlation between the features which depict user behavior. The multi-level wavelet decomposition helps extract spectral and spatial features, whereas the capsule network captures contextual relationships between features from the generated image representations. Thus, improving the accuracy and precision of the MWCapsNet model with much lower false alarms. The proposed MWCapsNet model outperforms existing state-of-the-art techniques, achieving 98.88% accuracy and 99.21% precision with a lower rate of false positives when evaluated using the CERT insider threat datasets.
Checked corecursive streams: Expressivity and completeness

Checked corecursive streams are a novel approach to stream definitions relying on a semantics of function application detecting cyclic calls, and a well-definedness check ensuring that access to an arbitrary index will always return a result. We extend the technique beyond the simple stream operators considered in previous work, notably by adding an interleaving combinator which has a non-trivial recursion scheme. We show that this actually enhances expressive power, since the interleaving operator cannot be expressed by the others, and that it is still possible to perform a sound and complete well-definedness check, through a symbolic computation which mimics access to an arbitrary index.
Design of progressive fuzzy polynomial neural networks through gated recurrent unit structure and correlation/probabilistic selection strategies

This study focuses on two critical design aspects of a progressive fuzzy polynomial neural network (PFPNN): the influence of the gated recurrent unit (GRU) structure and the implementation of fitness-based candidate neuron selection (FCNS) through two probabilistic strategies. The primary objectives are to enhance modeling accuracy and to reduce the computational load associated with nonlinear regression tasks. Compared with the existing fuzzy rule-based modeling architecture, the proposed dynamic model consists of the GRU structure and the hybrid fuzzy polynomial architecture. In the initial two layers of the PFPNN, we introduce three types of polynomial and fuzzy rules into the GRU neurons (GNs) and fuzzy polynomial neurons (FPNs), which can effectively reveal potential complex relationships in the data space. The synergy of the FCNS strategies and the ℓ2 regularization learning method is to design a progressive regression model adept at melding the GRU structure with a self-organizing architecture. The proposed GRU structure and polynomial-based neurons significantly improve the modeling accuracy for time-series datasets. The rational utilization of FCNS strategies can reinforce the network structure and discover the potential performance of neurons of the network. Furthermore, the inclusion of ℓ2 norm regularization provides additional stability to the proposed model and mitigates the overfitting issue commonly encountered in many existing learning methods. We validated the proposed neural networks using six time-series, four machine learning, and two real-world datasets. The PFPNN outperformed other models in the comparison studies in 83.3% of the datasets, emphasizing its superiority in terms of developing a stable deep structure from diverse candidate neurons and reducing computational overhead.
Investigating the impact of structural and temporal behaviors in ethereum phishing users detection

The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto-crime. Phishing scams, for example, are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user's crypto wallet. This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network. We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data. The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4% in Recall, and 5% in F1-score.
Quantum alternating operator ansatz for solving the minimum exact cover problem

The Quantum Alternating Operator Ansatz (QAOA+) is an extension of the Quantum Approximate Optimization Algorithm (QAOA), where the search space is smaller in solving constrained combinatorial optimization problems. However, QAOA+ requires a trivial feasible solution as the initial state, so it cannot be used for problems that are difficult to find a trivial feasible solution. For simplicity, we call them as Non-Trivial-Feasible-Solution Problems (NTFSP). In this paper, we take the Minimum Exact Cover (MEC) problem as an example, studying how to apply QAOA+ to NTFSP. As we know, exact covering (EC) is the feasible space of MEC problem, which has no trivial solutions. To overcome the above problem, the EC problem is divided into two steps to solve. First, disjoint sets are obtained, which is equivalent to solving independent sets. Second, on this basis, the sets covering all elements (i.e., EC) are solved. In other words, we transform MEC into a multi-objective constrained optimization problem, where feasible space consists of independent sets that are easy to find. Finally, we also verify the feasibility of the algorithm from numerical experiments. Our method provides a feasible way for applying QAOA+ to NTFSP, and is expected to expand its application significantly.
Harnessing generative grammars and genetic algorithms for immersive 2D maps

Modern games typically demand varied, detailed, and wide maps. In particular, 2D game maps are typically designed as a combination of smaller pieces called tiles, so positioning each tile on a map layer is analogous to colorizing a pixel using a palette. However, building a map is an exhaustive process that consumes a lot of planning and maintenance time from a multidisciplinary team, including artists and designers. Consequently, PCG techniques emerge as an attractive solution to this challenge. We propose a novel PCG technique to assist designers in writing rules applied to a balanced combination of genetic algorithms and generative grammar. Our implementation supports generating two-dimensional pathways and places detailed objects (e.g., plants, flowers, forest, etc.) along the path while following a desired distribution given beforehand by the designer. Notably, our PCG method allows the generation of variable transitions of biomes around a road and these results are compatible with real-world technologies for game development and prototyping. Our method can aid designers in various specific modeling tasks regarding environments with two or more dimensions since the results are sufficiently interpretable to undergo subsequent human intervention.