Reviewerly/exHarmony huggingface dataset:
Research on the current situation and countermeasures of cultivating talents in recreational sports under the perspective of artificial intelligence

Abstract With the change of the fourth generation of information technology represented by artificial intelligence, higher education is gradually stepping into the stage of intelligent development, and leisure sports majors as one of the majors that are booming, which puts forward higher requirements on the teaching power of teachers of leisure sports majors. Therefore, studying the current situation of talent cultivation of leisure sports majors under artificial intelligence plays a good role in promoting the overall improvement of the teaching quality of leisure sports majors. In this paper, the current situation of talent training and classroom teaching of leisure sports majors is studied based on a convolutional neural network in the artificial intelligence perspective, and the framework of artificial intelligence technology for leisure sports education is constructed. It can be seen that the attitude of leisure sports students towards theory class is better; 40.93% think theory class is very important, 38.13% think theory class is more important, and 19.27% think the importance of theory class is average. Students’ attitudes toward practical classes were better than those toward theoretical classes, with 63.71% considering practical classes very important and 26.85% considering them more important. From the analysis results, we can see that there are still problems of weak basic theory research, confusion of cultivation goals, lack of science in curriculum setting, and lack of teachers in cultivating talents in leisure sports majors. In summary, this paper argues that colleges and universities and college teachers should take the initiative to apply the technical framework of artificial intelligence in leisure sports education, strengthen theoretical research, make up for the shortcomings of teaching materials, as well as integrate training objectives, improve curriculum settings, enhance faculty strength, investigate the talent specification needs of the market in-depth, proactively meet the arrival of the era of artificial intelligence, and contribute to the cultivation of talents in leisure sports majors.
Research on personalized recommendation method of preschool e-learning resources

Abstract E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8. In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.
Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

Particle swarm optimization (PSO) is a stochastic computation technique that has become an increasingly important branch of swarm intelligence optimization. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems. Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization (abbreviated as AMS-PSO). To start with, the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO. Subsequently, according to the current iteration, different update schemes are used to regulate the particle search process at different evolution stages. To be specific, two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage. Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity. In addition, an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method. Finally, extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.
Data and Algorithms for End-to-End Thermal Spectrum Face Verification

Despite recent advances in deep convolutional neural networks (DCNNs), low-light and nighttime face verification remains challenging. Although state-of-the-art visible-spectrum face verification methods are robust to small changes in illumination, low-light conditions make it difficult to extract discriminative features required for accurate authentication. In contrast, thermal face imagery, which captures body heat emissions, captures discriminative facial features that are invariant to lighting conditions, enabling low-light or nighttime recognition performance. However, due to the increased cost and difficulty of obtaining diverse thermal-spectrum data, directly training face verification systems on small thermal-spectrum datasets results in poor verification performance. This paper presents a synthesis-based algorithm that adapts thermal spectrum face images to the visible spectrum, allowing us to repurpose off-the-shelf visible-spectrum feature extractors without fine-tuning. Our proposed approach achieves state-of-the-art performance on the ARL-VTF dataset. Importantly, we study the impact of face alignment, pixel-level correspondence, identity classification with label smoothing, and synthetic data augmentation for multi-spectral face synthesis and verification. We show that our proposed method is widely applicable, robust, and highly effective on the ARL-VTF dataset. Finally, we present MILAB-VTF(B), a multi-distance, unconstrained thermal-visible dataset. To the best of our knowledge, it is the largest, most diverse dataset of its kind, collected in realistic conditions. We show that our end-to-end thermal-to-visible face verification system serves as a strong baseline for the MILAB-VTF(B) dataset.
World Wide Web Consortium (W3C) Progress Report

The World Wide Web Consortium (W3C) continues its efforts to bring support for rich audio-visual experiences to the web platform. The increase in performance and breadth of the web platform makes it increasingly relevant to professional media workflows, many of which happen to also be migrating to the cloud. Below are recent highlights.
Obstacle-Avoidance Path Planning of Robot Arm Based on Improved RRT Algorithm

In the application of RRT (Rapidly-exploring Random Trees) algorithm in obstacle avoidance path planning of redundant robot arms in high-dimensional space, the sampling area of random sampling points is large, the search time is long, the path twists and turns, and the redundant points are too many. In this paper, an efficient sampling RRT algorithm with adaptive step size and fast automatic convergence is proposed. Firstly, by adding the optimized gravity function, the step size of the extended tree is changed adaptively and the convergence is stronger. Secondly, the limited sampling of parent node area expansion is proposed to avoid useless point sampling and repeated point sampling, so that the utilization of sampling points is greatly improved. Finally, by changing the way of parent node reconnection and re-selection, the path tortuous degree is lower, the path planning cost and redundancy points are reduced, and MATLAB software is used to carry out the path planning simulation experiment. By comparing RRT, gravitational field RTT and RRT* algorithm, the improved RRT algorithm has significantly improved in distance, time and number of nodes. It has important engineering application value.
Exploring the development of intercultural communication skills of language students in universities based on big data

Abstract With the development of globalization, people’s communication becomes more and more frequent. China urgently needs to cultivate talents at the international level and strong foreign language communicative ability to participate in international affairs and follow the international development trend. Based on the data analysis method and machine learning method in big data analysis, this article explores the current situation of intercultural communication ability and competence cultivation of language students in colleges and universities. The article summarizes the existing problems of language students’ intercultural communication ability in Chinese universities based on questionnaires conducted in four universities in southwest, northeast, east, and South China. Secondly, based on the current problems in the intercultural communication competence of language students in Chinese universities, the article further proposes teaching inspirations for the cultivation of intercultural communication competence of language students in Chinese universities. The survey results show that the highest total number of respondents who chose A and B answers, i.e., they think they have strong intercultural communication ability, was 689 from Zhongshan University, accounting for 33.62%, followed by 615 from Shanghai University, accounting for 30.1%; the lowest was 240 from Guizhou University, accounting for 11.7% only, and the level of intercultural communication ability of language students in these four universities can be ranked as follows. Zhongshan University &gt; Shanghai University &gt; Heilongjiang University &gt; Guizhou University. This study can suggest highly rigorous and practical strategies for cultivating language students’ intercultural communication ability in Chinese universities.
The Implementation of Glasses-free 3D Display in ICL Surgery: A Pilot Study

To compare the efficacy and safety of the glasses-free three-dimensional (3D) display and conventional optical microscope in Implantable Collamer Lens (ICL; STAAR Surgical) surgery.This randomized controlled trial enrolled 51 eyes of 26 patients who received ICL surgery. After random allocation, patients received surgery under either a glasses-free 3D display (16 eyes) or a conventional optical microscope (35 eyes). The surgical duration, uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), intraocular pressure, vault, postoperative manifest refraction spherical equivalent (MRSE), and complications were evaluated.Mean surgical time was 5.04 ± 2.74 minutes for the glasses-free 3D group and 4.65 ± 2.63 minutes for the conventional microscope group (P = .639). Postoperative UDVA was -0.02 ± 0.04 and -0.04 ± 0.05 logMAR (P = .169), CDVA was -0.03 ± 0.04 and -0.02 ± 0.05 logMAR (P = .434), and IOP was 17.01 ± 3.15 and 14.82 ± 2.20 mm Hg (P = .055) at 1 month of follow-up, respectively. Vault was 562.86 ± 192.89 and 520.18 ± 215.19 µm, and MRSE was +0.25 ± 0.21 and +0.10 ± 0.51 diopters, respectively, at 1 month postoperatively; all were comparable between the glasses-free 3D group and conventional microscope group (all P > .05). No complication occurred in both groups.The glasses-free 3D group achieved similar efficacy and safety compared to the conventional microscope group, and glasses-free 3D surgery is expected to show a significant advantage in clinical and medical education. [J Refract Surg. 2023;39(9):612-619.].
The Round Complexity of Local Operations and Classical Communication (LOCC) in Random-Party Entanglement Distillation

A powerful operational paradigm for distributed quantum information processing involves manipulating pre-shared entanglement by local operations and classical communication (LOCC). The LOCC round complexity of a given task describes how many rounds of classical communication are needed to complete the task. Despite some results separating one-round versus two-round protocols, very little is known about higher round complexities. In this paper, we revisit the task of one-shot random-party entanglement distillation as a way to highlight some interesting features of LOCC round complexity. We first show that for random-party distillation in three qubits, the number of communication rounds needed in an optimal protocol depends on the entanglement measure used; for the same fixed state some entanglement measures need only two rounds to maximize whereas others need an unbounded number of rounds. In doing so, we construct a family of LOCC instruments that require an unbounded number of rounds to implement. We then prove explicit tight lower bounds on the LOCC round number as a function of distillation success probability. Our calculations show that the original W-state random distillation protocol by Fortescue and Lo is essentially optimal in terms of round complexity.
Innovative Strategies for the Development of International Chinese Language Education Based on Deep Learning Models

Abstract The development of international Chinese language education greatly impacts the building of a global Chinese cultural environment. This paper constructs a model structure based on a convolutional neural network with a deep learning model and explains it in detail, and uses it to analyze the current development situation of international Chinese language education. The paper also presents the performance evaluation of the convolutional neural network model and discusses the problems in the development of international Chinese language education and the innovative development direction of online teaching. From the characteristics of innovative online teaching materials, rich media, mobility, interactivity, personalization, timeliness, and open sharing become the main melodies of the development of international Chinese language education, and the five-year average values of each characteristic are 53.59%, 51.17%, 49.77%, 47.84%, 45.94%, and 42.19%, respectively. The convolutional neural network model based on deep learning can effectively analyze the problems of international Chinese language education and the direction of innovation, providing an effective technology to help the development of Chinese culture in China.
Research on the blended teaching path of water conservancy engineering in universities based on big data analysis

Abstract The rapid development of big data technology brings opportunities and challenges to traditional teaching methods, and online teaching mode is gradually carried out widely and deeply in colleges and universities. This paper proposes a framework for constructing students’ online learning evaluation process by using big data methods. The focus is on data analysis of the evaluation model of water resources engineering in colleges and universities through the BP algorithm. Under the accuracy index, the BP algorithm 93% performs better than the other three algorithms. The higher the accuracy rate, the more it reflects the feasibility and correctness of the model. In the recall index, the recall rate of the BP algorithm fluctuates from 80% to 93%, the fluctuation range of 13% is more stable, and the overall performance is better than the other three algorithms. The objective evaluation of learning obtained through big data will greatly reduce teachers’ teaching burden and produce more hydraulic engineering professionals for China.
Study on the aesthetic laws of VR digital media art on the reception of film art

Abstract To improve the level of film creation, pull up the technology of VR technology applied in film creation, enhance the aesthetic satisfaction of film viewers to film art, and promote the peaceful development of the film market. In this paper, we study the aesthetics of film art from the viewers’ evaluation of the film after the application of VR digital media art to film and the change of film expression in the film industry to summarize the aesthetic law of audience acceptance of film art. According to the study, 85.90% of the audiences are very fond of VR digital media movie art and are willing to go to the cinema to experience the audio-visual enjoyment of VR movies. 79.65% of the audiences think that VR digital application in movies enhances the impact of movie images and the aesthetic pleasure of watching movies. Among all the survey groups, the college student group has the highest love for the combination of VR technology and movies, at 92.60%. It can be seen that VR digital media art has won unanimous praise in the current movie market, and grasping the aesthetic rules of movie watchers can better create good movies that meet the aesthetic needs of the public, stimulate the vitality of the movie market, and enrich the spiritual and cultural life of the people.
Students' Knowledge, Attitude, and Behavior Towards Flood Preparedness: A Case Study at a Junior High School in Bekasi, Indonesia

Preparedness in dealing with the coming of floods is very important for the community because, with preparedness, the community can prepare themselves and do first aid after a disaster. knowledge, attitude, and behavior are factors that influence preparedness. This study aims to determine knowledge, attitude, and behavior about flood preparedness at a junior high school in Bekasi, Indonesia. This research method uses quantitative method. Based on data processing and analysis of research conducted regarding the influence of knowledge, attitude, and behavior about flood disaster preparedness in a junior high school in Bekasi, it is concluded that the disaster actions of junior high school in Bekasi positively influence flood disaster preparedness. In this study, it was also found that the variables of knowledge, attitudes, and actions together influence 9% of flood preparedness, while other factors influence the rest.
Research on Influencing Factors of Second-Hand Housing Price in Haidian District, Beijing

The housing issue is an economic issue as well as an issue of people's livelihood, especially in megacities like Beijing, where housing prices in the second-hand housing market fluctuate greatly, so its influencing factors have become the focus of attention. Studying the influencing factors of housing prices in the second-hand housing market can provide reference for the government to formulate appropriate real estate regulation policies and predict the development trend of housing prices in the future. This article collects nearly 3,000 groups of public second-hand housing individual transaction data from the Lianjia website in the domestic second-hand housing market. The time span of the data is from June 2020 to June 2021. After data cleaning and descriptive statistical analysis of the acquired data, we set the total price of the house as the explained variable, and the number of rooms, the area of the house, the decoration, the floor, and the unit price of the house are used as the explanatory variables. We establish a multiple linear regression model, then test and optimize it. The results are as follows: in the second-hand housing market of Haidian District, if the second-hand house has a large construction area, a large number of rooms, a high unit price and has been renovated, the total price of the house will be high.
Causes and Improvement Strategies of Students' "Pretending to Study" Phenomenon

Emphasizing the learning barriers and psychological transformations of students is imperative in educational research and practice. The high school phase holds paramount importance in the students' educational journey, as it is during this period that emotional and self-cognitive challenges stemming from academics and personal life can significantly impact academic performance. This paper delves into the phenomena of "pretending to study" among high school students, utilizing surveys involving teachers, students, and parents. The study sheds light on the widespread occurrence of this behavior, where students simulate efforts during self-study solely to meet the expectations of their teachers and parents. They perceive themselves as diligent workers without putting in actual effort, resulting in learning driven by self-pity, mindless studying with little yield, and dependency on others devoid of critical thinking. Attribution analysis implicates several factors contributing to this behavior, namely divergent self-perceptions, lack of determination, pressure to appease parents and teachers, fear, aversion to acknowledging personal limitations, and tendencies towards laziness. In conclusion, this study proposes recommendations for all stakeholders involved, including students, parents, and teachers, aimed at fostering improvement in this regard.
Robust pedestrian tracking in video sequences using an improved STF module

Abstract Object tracking technology based on image processing has made great progress recently. Based on the track-by-detection framework, the tracking algorithms are often combined with deep neural networks to perform online target tracking. However, existing motion models assume linearity and are sensitive to sudden changes in trajectories due to occlusion, overlap, or other detection issues. In this paper, we modified the existing object tracking algorithms and introduced a strong tracking filter (STF) module to the track-by-detection framework for solving the sudden change problem of the target. The STF module is designed to have a strong ability to track sudden changes by orthogonalizing the residual sequence. When the trajectory of the target is stable, the STF module returns to the inactive state, behaving similarly to tracking algorithms that follow conventional linear models. Experimental results on a public pedestrian tracking dataset show that the proposed method improves tracking performance on various metrics, including the ability to rematch missed trajectories. Moreover, compared with existing algorithms, the proposed method exhibits strong stability under noisy conditions.
Guidance Models for Designing Big Data Cyber Security Analytics Systems

Big Data Cyber Security Analytics (BDCA) systems leverage big data technologies to collect, store, and analyze a large volume of security event data for detecting cyber-attacks. Architecting BDCA systems is a complex design activity, which involves critical decisions about the selection of architectural tactics for the satisfaction of various quality goals. Software architects need to consider associated dependencies, constraints, and impact on quality goals, which makes the design process quite challenging. To facilitate the design process, we propose guidance models for supporting the systematic design of BDCA systems. The guidance models facilitate architects to map functional and non-functional requirements for BDCA systems to a set of architectural tactics.