Reviewerly/exHarmony huggingface dataset:
An Experimental Study on Online and Offline Blended Teaching Model Based on Factor Analysis

As a new, effective and multi-dimensional teaching model, blended teaching model combines offline teaching of traditional classroom with online teaching of the Internet, which is the mainstream of information-based blended teaching.This paper is intended to conduct an experimental study on task-based online and offline blended police English teaching model.SPSS software is used to conduct independent sample T-test on test data.Factor analysis is used to analyze the collected five types of score data.The scoring formula of comprehensive ability level is obtained, and the corresponding score of 41 units is calculated.After forward indexing and sequencing, the score data of the two groups are compared and visualized finally.The teaching experiment shows that blended teaching model is helpful to stimulate students' interest in learning and improve students' comprehensive ability level of police English.
On the Applicability of Quantum Machine Learning

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field. To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.
Implementation and Exploration of Process Assessment of Undergraduate Courses Based on Digital Platform

Based on the analysis and comparison of the differences between Chinese and Western university curriculum assessment, this paper proposes the urgency and necessity of strengthening the process assessment of undergraduate courses.In view of the bottlenecks encountered in the process assessment of domestic university education in terms of syllabus, teaching methods, teaching contents, assessment methods and examination frequency, various signs indicate that the traditional assessment method has obvious disadvantages and the process assessment effect is significant.Therefore, the teaching reform, It is urgent.The crux of the problem is that there are so many process assessment methods.Each course needs to adopt appropriate process consideration methods based on its own characteristics.In addition, how to formulate assessment standards for a specific assessment method can ensure fairness and justice.This paper puts forward my own simple suggestions based on the results of the professional questionnaire survey, It is expected to provide reference for the reform and innovation of undergraduate curriculum assessment in China.
Accelerating Content-Defined Chunking for Data Deduplication Based on Speculative Jump

In data deduplication systems, chunking has a significant impact on the deduplication ratio and throughput. Existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Content-Defined Chunking</i> (CDC) approaches exploit a sliding window to calculate rolling hashes of the input data stream byte-by-byte, and then determine chunk cut-points if the rolling hash satisfies a given cut-condition. Since previous CDC approaches are extremely costly, it often significantly degrades the throughput of data deduplication systems. In this paper, we argue that calculating and checking the rolling hashes byte-by-byte is unnecessary. To reduce the CPU overhead of CDC, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jump-based chunking</i> (JC) approach. The key idea is to introduce a jump-condition, and the sliding window can jump over a specific length of the input data stream if the rolling hashes satisfy the jump-condition. Moreover, we also explore the impact of the cut-condition and the jump-condition on the chunk size. Our theoretic studies demonstrate the effectiveness and efficiency of JC, without compromising the deduplication ratio. Experimental results show that JC improves the throughput of chunking by about 2× on average compared with the state-of-the-art CDC approaches while still guaranteeing high deduplication ratio.
Three dimensional reconstruction and measurement of underwater spent fuel assemblies

It is an important work to measure the dimensions of underwater spent fuel assemblies in the nuclear power industry during the overhaul, to judging whether the spent fuel assemblies can continue to be used. In this paper, a three dimensional reconstruction method for underwater spent fuel assemblies of nuclear reactor based on linear structured light is proposed, and the topography and size measurement was carried out based on the reconstructed 3D model. Multiple linear structured light sensors are used to obtain contour size data, and the shape data of the whole spent fuel assembly can be collected by one-dimensional scanning motion. In this paper, we also presented a corrected model to correct the measurement error introduced by lead-glass and water is corrected. Then, we set up an underwater measurement system for spent fuel assembly based on this method. Finally, an underwater measurement experiment is carried out to verify the 3D reconstruction ability and measurement ability of the system, and the measurement error is less than ±0.05 mm.
Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

Digital imaging is a technology that is extensively employed in diverse diagnostic examinations such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging, among other modalities. Transferring a patient’s diagnostic images and medical data to a specialist physician in a distinct geographical location is conducted to facilitate an accurate diagnosis. The safeguarding of patient data privacy and confidentiality is ensured through the utilisation of smart hospital applications for medical data security. The current research presents the effective utilisation of lifting wavelet transform (LWT) and Hessenberg-based particle swarm optimization in order to generate resilient and safeguarded watermarks on ultrasound images. The empirical evidence suggests that our innovative approach outperforms our prior methodology, established through extensive testing. The watermark’s imperceptibility and accuracy are exemplified by its capacity to sustain a superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), even amidst diverse image processing assaults.
Path-guided intelligent switching over knowledge graphs with deep reinforcement learning for recommendation

Abstract Online recommendation systems process large amounts of information to make personalized recommendations. There has been some progress in research on incorporating knowledge graphs in reinforcement learning for recommendation; however, some challenges still remain. First, in these approaches, an agent cannot switch paths intelligently, because of which, the agent cannot cope with multi-entities and multi-relations in knowledge graphs. Second, these methods do not have predefined targets and thus cannot discover items that are closely related to user-interacted items and latent rich semantic relationships. Third, contemporary methods do not consider long rational paths in knowledge graphs. To address these problems, we propose a deep knowledge reinforcement learning (DKRL) framework, in which path-guided intelligent switching was implemented over knowledge graphs incorporating reinforcement learning; this model integrates predefined target and long logic paths over knowledge graphs for recommendation systems. Specifically, the designed novel path-based intelligent switching algorithm with predefined target enables an agent to switch paths intelligently among multi-entities and multi-relations over knowledge graphs. In addition, the weight of each path is calculated, and the agent switches paths between multiple entities according to path weights. Furthermore, the long logic path has better recommendation performance and interpretability. Extensive experiments with actual data demonstrate that our work improves upon existing methods.The experimental results indicated that DKRL improved the baselines of NDCG@10 by 3.7%, 9.3%, and 4.7%; of HR@10 by 12.39%, 20.8%, and 13.86%; of Prec@10 by 5.17%, 3.57%, 6.2%; of Recall@10 by 3.01%, 4.2%, and 3.37%. The DKRL model achieved more effective recommendation performance using several large benchmark data sets compared with other advanced methods.
Hot Spot Perspective and Trend Analysis of Blended Teaching Research

This paper attempts to investigate the hot spots, cutting-edge trends of China's mixed teaching research so as to clarify and address the focus of their improvement in the post epidemic era.The information visualization software Cite Space was explored to visualize and analyze the relevant journals and literatures of mixed teaching research including in the Web of Science Core Collection database from 2018 to 2022, and has drawn the corresponding map of scientific knowledge.The research reveals that mixed teaching research concentrates on blended learning, technology acceptance model, learning, community of inquiry, etc. in which institutional driver, distance learning, and student perception will be the focus of mixed teaching research in the future from the perspective of research trend.Based on the research results, five suggestions for improvement are proposed regarding the establishment of the system and mechanism, the creation of the teaching platform, and the construction of the training system.
A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications.
Virtual Training System for Vocational Skill Appraisal

The purpose of this paper is to provide a virtual assessment and training system for vocational skill identification, which can lay a solid foundation for the theory, basic skill operation and practical assessment of professional skill operation.This paper presents a virtual assessment and training system for vocational skill identification, including: theoretical knowledge training and assessment platform, which is used to provide independent training mode and simulated examination mode for basic theoretical knowledge and professional theoretical knowledge; The virtual training and assessment platform of basic skills is used to provide independent training and simulated examination of basic skills for all types of workers in ordnance specialty.Operators can conduct virtual interactive operations of basic skills experiments in the virtual experimental platform to improve their basic skills; The virtual training and assessment platform of professional skills is used to provide independent training and simulation examination of professional skills for all types of workers in ordnance.Operators can perform virtual interactive operations of professional skills in virtual equipment and components to improve their professional skills; It also includes a question bank management platform.This paper can lay a solid foundation for the theory, basic skills operation and practical assessment of professional skills operation of personnel.
Secure and Efficient Hierarchical Decentralized Learning for Internet of Vehicles

Decentralized machine learning enables multiple devices to train a global model collaboratively and is a promising paradigm to realize ubiquitous intelligence for the Internet of Vehicles (IoV). Existing work mainly focused on either the data privacy protection techniques or efficient topology orchestration of decentralized machine learning. However, these techniques cannot be directly applied to IoV due to possible accuracy degradations and insufficient topology adaptability, not to mention the joint secure and efficient decentralized learning designs. This paper proposes a secure and efficient hierarchical decentralized learning framework for IoV networks with multiple fog nodes and mobile vehicles. The proposed framework combines federated learning and distributed consensus for vehicle-fog and inter-fog collaborative learning, respectively, and integrates masking with local training to protect data privacy. We propose the network-level masking mechanism and consensus matrix optimization for signaling-efficient implementations in IoV. The network-level masking can eliminate the masking pairing requirements of inter-fog handover of mobile vehicles and is proved to be canceled via distributed consensus. Experimental results on two popular datasets validate the superiority of the proposed framework in terms of learning accuracy, data protection, and signaling efficiency, compared to the existing approaches.
Sentiment Analysis of Movies Based on Natural Language Processing

Movies are an important spiritual food for people to satisfy their spiritual needs in contemporary society.Each person's evaluation of one movie is different.The data mining of movie-related data can make a certain accuracy prediction of the final rating of a movie.This paper shows a new movie rating prediction model is proposed by using nlp technology.It analyzes the data recorded in Movielens for nearly 45,000 movies.Some of the selected data include final rating, movie duration, title, budget, story overview, genre, and so on.This includes a large amount of textual data that needs to be converted into vector data using nlp techniques.In this paper, random forest and neural network are chosen as the main models and a lot of tuning is done to obtain a relatively accurate model, i.e., a random forest movie rating prediction model using natural language processing.This research can be used for upcoming or newly released movies that lack ratings, which is a good reference for audiences' viewing choices and movie producers' promotion and investment decisions.
Transitioning to Individual Learning Paths in the Opinions of Students and Teachers: the Case of the University of Tyumen

The aim of this study is to measure and evaluate the application of personalized learning technology based on the system of individual educational paths at the University of Tyumen. In order to collect and analyze the data, the strategy of mix-methods was applied, which allows both for identifying the most common trends in the opinions of the subjects of educational space, and for obtaining detailed interpretations of the ongoing changes. The purpose of the study is to identify the opinions held by the key subjects of education when it comes to educational activity content and technologies, the benefits and risks of individual learning paths and the traditional way of teaching. Interviews with teachers demonstrate that in assessing the positive aspects of the transition to individual learning paths, such aspects have been taken note of as the possibility of focusing on one’s own interests, increased motivation and desire for professional development in a competitive environment, as well as simplification of reporting as a result of digitization. What teachers are apprehensive about is a decline in the fundamental nature of training and the de-professionalization of students, the collapse of stable social ties and identities of teachers, subpar living conditions due to continuous spatial mobility, and uncertainty when it comes to workload. The study showed that a fifth of students are not satisfied with the learning outcomes under new conditions and experience difficulty adapting in mixed groups with other students of different specialties. The variability and individualization of education is not yet a priority for students enrolled in traditional programs. Students pursuing individual study paths do not realize the many advantages the new system provides when it comes to forming the personal qualities necessary for successful selfrealization, due to them lacking the opportunity to compare their form of education to what is considered traditional.
Improving the speed and quality of cancer segmentation using lower resolution pathology images

Abstract In this paper, we propose a pipeline to investigate the performance of semantic segmentation model that employs an encoder-decoder architecture with atrous separable convolution and spatial pyramid pooling, trained on multi-resolution whole slide breast pathological images with different patch sizes. Our segmentation model obtains the best performance on zoom level 2 (10 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> magnification) with AUC score 0.974 in terms of slide-level classification. This outperforms both the performance of the pathologist and other semantic segmentation models on the Camelyon16 dataset. By offering a larger field of view and reducing noise and detail, training a semantic segmentation model on the properly selected lower resolution pathology images can further improve the precision of pixel-wise cancer region segmentation. By contrast, the corresponding inference time is 14 times shorter than the inference time trained on the highest resolution patches, and it is also shorter than the time required by a pathologist with time constraints. Moreover, we prove that the model trained on lower resolution patches can still generate refined external polygons of cancer region on the highest resolution image. This study provides new insights into efficient gigapixel histopathology analysis that will make clinical adoption more likely.
Reinforcement Learning Recommendation Algorithm Based on Label Value Distribution

Reinforcement learning is an important machine learning method and has become a hot popular research direction topic at present in recent years. The combination of reinforcement learning and a recommendation system, is a very important application scenario and application, and has always received close attention from researchers in all sectors of society. In this paper, we first propose a feature engineering method based on label distribution learning, which analyzes historical behavior is analyzed and constructs, whereby feature vectors are constructed for users and products via label distribution learning. Then, a recommendation algorithm based on value distribution reinforcement learning is proposed. We first designed the stochastic process of the recommendation process, described the user’s state in the interaction process (by including the information on their explicit state and implicit state), and dynamically generated product recommendations through user feedback. Next, by studying hybrid recommendation strategies, we combined the user’s dynamic and static information to fully utilize their information and achieve high-quality recommendation algorithms. Finally, the algorithm was designed and validated, and various relevant baseline models were compared to demonstrate the effectiveness of the algorithm in this study. With this study, we actually tested the remarkable advantages of relevant design models based on nonlinear expectations compared to other homogeneous individual models. The use of recommendation systems with nonlinear expectations has considerably increased the accuracy, data utilization, robustness, model convergence speed, and stability of the systems. In this study, we incorporated the idea of nonlinear expectations into the design and implementation process of recommendation systems. The main practical value of the improved recommendation model is that its performance is more accurate than that of other recommendation models at the same level of computing power level. Moreover, due to the higher amount of information that the enhanced model contains, it provides theoretical support and the basis for an algorithm that can be used to achieve high-quality recommendation services, and it has many application prospects.