Reviewerly/exHarmony huggingface dataset:
The Improvement Path of Primary School Students' Chinese Literacy under Diversified Reading

Reading is the key content of primary school Chinese teaching, so teachers should stimulate students 'interest in reading, deepen students' reading comprehension, optimize students 'reading effect, and finally improve students' core quality of reading. However, there are still many outstanding problems in the current reading teaching, such as students 'weak interest in reading, inappropriate reading strategies, superficial reading and teachers' disordered reading guidance. This requires primary school Chinese teachers to combine the basic situation of reading teaching, and actively explore measures to better optimize reading teaching driven by problems, so as to lay a solid foundation for the comprehensive improvement of students' reading ability and reading literacy. This article explores the path of improving primary students' Chinese literacy under diversified reading, including blank reading, stimulating reading interest, group reading, improving the quality of thinking; theme reading, light up the spiritual world; group book reading, rich life background; speculative reading, sound personality charm, and fashion reading, enriching life connotation.
Generative Model-Based Simulation of Driver Behavior When Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains

Unmanned ground vehicles (UGVs) in unstructured environments mostly operate through teleoperation. To enable stable teleoperated driving in unstructured environments, some research has suggested driver assistance and evaluation methods that involve user studies, which can be costly and require lots of time and effort. A simulation model-based approach has been proposed to complement the user study; however, the models on teleoperated driving do not account for unstructured environments. Our proposed solution involves simulation models of teleoperated driving for drivers that utilize a deep generative model. Initially, we build a teleoperated driving simulator to imitate unstructured environments based on previous research and collect driving data from drivers. Then, we design and implement the simulation models based on a conditional variational autoencoder (CVAE). Our evaluation results demonstrate that the proposed teleoperated driving model can generate data by simulating the driver appropriately in unstructured canyon terrains.
The Study of Online Public Opinion Method Based on Emotional Analysis and CNN

The study of online public opinion has received widespread attention both domestically and internationally. By analyzing the research and development trends of emotional online public opinion, the aim is to provide research hotspots and directions for scholars in related fields, and to provide theoretical knowledge support and reference for future in-depth research and development. This article first uses statistics and CiteSpace to visually analyze the number of domestic and foreign literature publications, topic hotspots, and research keywords in this research field, and uses literature analysis to summarize and analyze research technology and methods at home and abroad. Then, by organizing relevant research literature at home and abroad, the future research and development directions in this field were summarized. Finally, from an emotional perspective, the domestic and international research on online public opinion is described, and the research points of relevant scholars are analyzed, providing reference and assistance for future researchers moving towards this field.
Discourse-Aware Prompt for Argument Impact Classification

Discourse information behind the arguments attracts a lot of attention from the field of Natural Language Processing (NLP) and computational argumentation. Durmus et al. [10] launched a new study on the influence of discourse contexts on determining argument impact. Argument Impact Classification is an intriguing but challenging task to classify whether the argumentative unit or an argument is impactful in a conversation. This paper empirically demonstrates that the discourse marker (e.g., "for example," "in other words") can be represented by the learnable continuous prompt to align with discourse information existing in Pre-trained Language Model (PLM). This discourse information helps the Pre-trained Language Model understand the input template and elicit the discourse information to improve the performance on this task. Therefore, based on this intuition, we propose a prompt model DAPA and surpass the previous state-of-the-art model with a 2.5% F1 score.
Two‐stage personalized federated learning based on sparse pretraining

Abstract Aiming at solving the performance degradation of federated learning (FL) under heterogeneous data distribution, personalized FL (PFL) was proposed. It is designed to produce a dedicated model for each client. However, the existing PFL solution only focuses on the performance of personalized model, ignoring the performance of global model, which will affect the willingness of new clients to participate. In order to solve this problem, this paper proposes a new PFL solution, a two‐stage PFL based on sparse pretraining, which can not only train a sparse personalized model for each client, but also obtain a sparse global model. The whole training process is divided into sparse pretraining and sparse personalized training, which focus on the performance of global model and personalized model respectively. Also, we propose a mask sparse aggregation technique to maintain the sparsity of the global model in the sparse personalized training stage. Experimental results show that compared with existing algorithms, our proposed algorithm can improve the accuracy of the global model while maintaining advanced personalized model accuracy, and has higher communication efficiency.
An optimized extreme learning machine‐based novel model for bearing fault classification

Abstract This work addresses the rolling element bearing (REB) fault classification problem by tackling the issue of identifying the appropriate parameters for the extreme learning machine (ELM) and enhancing its effectiveness. This study introduces a memetic algorithm (MA) to identify the optimal ELM parameter set for compact ELM architecture alongside better ELM performance. The goal of using MA is to investigate the promising solution space and systematically exploit the facts in the viable solution space. In the proposed method, the local search method is proposed along with link‐based and node‐based genetic operators to provide a tight ELM structure. A vibration data set simulated from the bearing of rotating machinery has been used to assess the performance of the optimized ELM with the REB fault categorization problem. The complexity involved in choosing a promising feature set is eliminated because the vibration data has been transformed into kurtograms to reflect the input of the model. The experimental results demonstrate that MA efficiently optimizes the ELM to improve the fault classification accuracy by around 99.0% and reduces the requirement of hidden nodes by 17.0% for both data sets. As a result, the proposed scheme is demonstrated to be a practically acceptable and well‐organized solution that offers a compact ELM architecture in comparison to the state‐of‐the‐art methods for the fault classification problem.
Research on Knee Injuries and Preventive Measures for University Students in The Context of Healthy China

In recent years, with the continuous promotion of the Healthy China Initiative, the whole society has begun to pay general attention to and pay attention to health problems, and people's health awareness has gradually increased. Scientific and reasonable sports can improve physical fitness, but wrong sports posture and unreasonable sports intensity and methods will cause sports injury. On the basis of analyzing the anatomical structure and function of the knee joint, this study investigates the knee injuries that often occur in university students' sports through questionnaire survey method, comparative analysis method and interview method to find out the causes and characteristics of the injuries and put forward the relevant preventive countermeasures.
A Decentralized Information Marketplace Preserving Input and Output Privacy

Data-driven applications are engines of economic growth and essential for progress in many domains.The data involved is often of a personal nature.We propose a decentralized information marketplace where data held by data providers, such as individual users can be made available for computation to data consumers, such as government agencies, research institutes, or companies who want to derive actionable insights or train machine learning models with the data while (1) protecting input privacy, (2) protecting output privacy, and (3) compensating data providers for making their sensitive information available for secure computation.We enable this privacy-preserving data exchange through a novel and carefully designed combination of a blockchain that supports smart contracts and two privacy-enhancing technologies: (1) secure multi-party computations, and (2) robust differential privacy guarantees.
金子大栄における『華厳経』の善知識論

Kaneko Daiei 金子大栄 （1881-1976）, a Shin Buddhist priest and scholar of the Huayan sūtra 華厳経, presented innovative views on the samantabhadracaryā, which he saw as the core teaching of the Huayan sūtra. This paper examines his ideas on the ultimate embodi­ment of the samantabhadracaryā, which he discussed through his analyses of the story of the young practitioner Sudhana’s pilgrimage with various mentors （kalyāṇamitra）, described in the Huayan sūtra.
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning

Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself. In this work, we perform a set of experiments to assess to which extent, and in which way, such bounds can predict and improve the performance of real-world distributed (namely, federated) learning tasks. We find that, as can be expected given the way they are obtained, bounds are quite loose and their relative magnitude reflects the training rather than the testing loss. More unexpectedly, we find that some of the quantities appearing in the bounds turn out to be very useful to identify the clients that are most likely to contribute to the learning process, without requiring the disclosure of any information about the quality or size of their datasets. This suggests that further research is warranted on the ways – often counter-intuitive – in which convergence bounds can be exploited to improve the performance of real-world distributed learning tasks.
Analysis on Digital Literacy, Self-Efficacy and Scaffolding Techniques among EFL Chinese Teachers

With the rising of China’s national strength, more and more Chinese people start to do business with foreign clients. As the bridge of communication, the role of English has gained increased importance year by year. The educators in China started to shift their attention to EFL teaching. This paper aims to investigate the current situation of digital literacy, self-efficacy, and scaffolding teaching techniques for EFL Chinese teachers, and propose corresponding plans for its improvement and enhancement.
A Hybrid Binary Neural Tree For Malicious URL Detection

Criminals use malicious URLs to distribute unsolicited content as a primary distribution method. Aiming at the problems of low detection accuracy caused by insufficient representation of URL character-level local features in the character-level embedded malicious URL detection, an algorithm based on a Hybrid Binary Neural Tree (HBNT) is proposed. HBNT combines the decision tree and neural networks to form an interpretable model of character features. It performs branching decisions and local semantic enhancement from top to bottom on nodes and connected edges of the tree. The URL is judged to have malicious attributes by merging the output results of each leaf node detection function. Experimental results on URL datasets obtained from public websites show that the detection accuracy of HBNT is higher than that of both traditional machine learning methods and deep learning methods. In addition, HBNT eliminates the unsupervised language modelling step for URL datasets and achieves comparable detection accuracy of over 99% when compared to advanced large-scale pre-trained language models. Finally, the effectiveness of each node module of HBNT is evaluated by ablation experiments.
Construction of Sports Parks in The Context of The Wisdom of National Fitness

The purpose of this paper is to study the construction of sports parks in the context of the wisdom of national fitness in order to create a wisdom fitness service system and to enrich the theoretical study of wisdom fitness in sports parks, which is important for improving the quality of sports park service construction and enhancing the satisfaction of residents.
Map Optimization of Path Planning in Q-Learning

Path planning is significant in the field of artificial intelligence and robotics. This paper proposes a unique map optimization of path planning relying on Q-learning to overcome the shortcomings of classic Q-learning, such as delayed convergence or low efficiency. First, improvements were made to the setup environment, turning a simple environment into a more complex one. Secondly, rewards were set to ensure that each step is optimal exploration. The optimal path is the globally optimal path by setting up, down, left, and right directions simultaneously. Finally, MATLAB simulation was used for verification. As compared to the original training environment, the improved map enhances learning efficiency in a more complicated environment, increases the algorithm's convergence rate, and enables the robot to swiftly discover the collection-free path and finish the job in a complex environment. The rationality of the improvement is verified, which provides important data and a theoretical basis for the subsequent research on Q-learning.
Building the Waray-waray Neural Language Model using Recurrent Neural Network

In the Philippines, language modeling is challenging since most of its languages are low-resourced. Tagalog and Cebuano are the only languages present in machine translation platforms like Google Translate; Winaray, a language spoken in the Eastern Visayas region, is inexistent. Hence, this study developed a Winaray language model that could be used in any natural language processing-related tasks. The text corpus used in creating the model was scrapped from the web (religious and local news websites, and Wikipedia) containing Winaray sentences. The model was trained using an encoder-decoder recurrent neural network with four sequential layers and 100 hidden neurons. The text prediction accuracy of the model reached 76.17%. The model was manually evaluated based on its text-generated sentences using linguistic quality dimensions such as grammaticality, non-redundancy, focus, structure and coherence. Results of manual evaluation showed a promising result as the linguistic quality reached 3.66 (acceptable); however, training data must be improved in terms of size with the addition of texts in various text genres.
Exploring User Experience Design in Computer-Based New Media Art

New Media Art is a modern interdisciplinary art, It integrates fine arts, psychology, sociology and computer science.With the rapid development of information society and computer science, in the information age of big data, the rapid development of artificial intelligence technology has raised computer science to a higher level, while art still stays in aesthetics, acoustics, vision, psychology and other aspects, lacking logic, intelligence and integration.Therefore, the new media art urgently needs to be further developed with the support of the new computer technology.The project uses big data and artificial intelligence technology to realize the basic model of new media art, which greatly improves the quality of art classification, reduces the labor production costs, and increases the operating income of enterprises, produces good economic benefits.To strengthen user experience design as the core, improve user participation and sense of gains.
Prompt-WNQA: A prompt-based complex question answering for wireless network over knowledge graph

With the rapid development of wireless networks, the scale of network devices is constantly expanding. Experts spend vast amounts of time consulting and dealing with various network problems of the same nature during the course of daily network operation and maintenance service, and there is no time to collect statistics and sort their information, which affects the overall operation and maintenance efficiency. This paper investigates complex Knowledge Graph Question Answering (KGQA) in the wireless network domain in order to improve operation and maintenance efficiency. Accordingly, we propose Wireless Network QA over the KG using Prompt learning (Prompt-WNQA), a novel method that deals with both constraint-based and multi-hop complex questions making use of prompt learning. Our method is also helpful in performing complex KGQA over incomplete KGs, which can complete missing relations between unconnected entities. Compared to state-of-the-art approaches, our Prompt-WNQA achieves significant improvement over extensive experiments on both the wireless network QA dataset and two public complex QA datasets.
The Application of Insulation and Decoration Integrated Board in The Construction of High-rise Residential Exterior Walls

Insulation and decoration integrated board is a widely used material in the construction of high-rise residential exterior walls. This material has various advantages, including insulation, waterproofing, fire prevention, environmental protection, and other characteristics, which can improve the overall performance and energy-saving effect of buildings. In addition, the integrated insulation and decoration board has advantages such as easy installation, fast construction speed, and low maintenance costs, which can save construction time and costs. In the construction of high-rise residential exterior walls, the use of insulation and decoration integrated panels can achieve fast, efficient, and safe construction, while ensuring the appearance and decoration effect of the building. Therefore, the application of insulation and decoration integrated panels in the construction of high-rise residential exterior walls has important significance and broad development prospects.
Single Image De-Rain Algorithm Based on Multi-Scale Attention Residual Structure

Rain in nature is affected by wind, gravity, atmospheric refraction, and temperature as it falls. As a result of these factors, rain trails in images often appear to have multiple angles, streaks, and lengths. Currently, available methods are often based on specific assumptions, but this hardly covers all cases in reality. In this paper, we consider rain streaks in images as a kind of noise with a specific pattern and based on this idea we follow the idea of image denoising. In this paper, we propose a new efficient module for rain trace extraction and design a multi-scale fusion attention network that is more suitable for the image de-noising task, obtaining significant improvements in the extraction of rain traces and greatly enhancing the robustness of the model.
Image Watermarking for Machine Learning Datasets

Machine learning has received increasing attention for the last decade due to its significant success in classification problems in almost every application domain. For its success, the amount of available data for training plays a crucial role in the creation of a machine-learning model. However, the data-gathering process for machine learning algorithms is a tedious and time-consuming task. In many cases, the developers rely on publicly available datasets, which are not always of high quality. Recently, we are witnessing a data market paradigm where valuable datasets are sold. Thus, once the dataset is created or bought, protecting the dataset against illegal use or (re)sale and establishing intellectual property rights is necessary. In this paper, we investigate the question of deploying well-studied image watermarking techniques to be applied to classification algorithm datasets, without degrading the quality of the dataset. We investigate whether Singular Value Decomposition (SVD)-based techniques from image watermarking could be deployed on machine learning datasets or not. To this end, we chose the watermarking technique described in [8] and applied it to a machine-learning dataset. We provide experimental results on the robustness of the scheme. Our results show that the watermark embedding scheme provides decent imperceptibility and robustness against update, zero-out, and insertion attacks but, it is not successful against deletion attacks. We believe our work can inspire researchers who might want to consider applying well-studied image watermarking techniques to machine learning datasets.
DeepMTP: A Python-based deep learning framework for multi-target prediction

DeepMTP is a python framework designed to be compatible with the majority of machine learning sub-areas that fall under the umbrella of multi-target prediction (MTP). Multi-target prediction includes problem settings like multi-label classification, multivariate regression, multi-task learning, matrix completion, dyadic prediction, and zero-shot learning. Instead of using separate methodologies for the different problem settings, the proposed framework employs a single flexible two-branch neural network architecture that has been proven to be effective across the majority of MTP problem settings. To our knowledge, this is the first attempt at providing a framework that is compatible with more than two MTP problem settings. The source code of the framework is available at https://github.com/diliadis/DeepMTP and an extension with a graphical user-interface is available at https://github.com/diliadis/DeepMTP_gui.
Multiclass Classification of Software Vulnerabilities with Deep Learning

Detecting software vulnerabilities has been a challenge for decades. Many techniques have been developed to detect vulnerabilities by reporting whether a vulnerability exists in the code of software. But few of them have the capability to categorize the types of detected vulnerabilities, which is crucial for human developers or other tools to analyze and address vulnerabilities. In this paper, we present our work on identifying the types of vulnerabilities using deep learning. Our data consists of code slices parsed in a manner that captures the syntax and semantics of a vulnerability, sourced from prior work. We train deep neural networks on these features to perform multiclass classification of software vulnerabilities in the dataset. Our experiments show that our models can effectively identify the vulnerability classes of the vulnerable functions in our dataset.