Reviewerly/exHarmony huggingface dataset:
Foreign Language Teaching and Learning Behaviour with a Big Data Corpus

Abstract This paper firstly constructs a foreign language subject system according to the foreign language teaching objectives and students’ learning situation in colleges and universities, puts forward a policy of informatization of foreign language teaching, and summarizes the ways in which college students’ foreign language learning behavior in the era of big data. Secondly, on the basis of corpus technology, the word vectorization representation of foreign language utterances is carried out, followed by similarity calculation of the word vectorization representation, judging the type of foreign language learning according to the results of foreign language semantic similarity calculation, and calculating the maximum weight path of the word vector sequence by using dynamic planning algorithm. Then, according to the demand analysis of the foreign language teaching corpus, the foreign language teaching corpus is constructed, and the application analysis of the corpus of foreign language teaching is carried out. The results indicate that the students in both classes have a similar understanding of the meaning and lexical properties of vocabulary. However, there is a certain gap in the collocation and utilization of vocabulary, and the corpus-based vocabulary teaching method is more conducive to students’ mastery of the target vocabulary than the traditional vocabulary teaching method, and the level of vocabulary learning is comparatively higher and more effective. The quality of foreign language teaching in colleges and universities can be improved by reference to this study.
Analysis of factors affecting the transformation of scientific and technological achievements in universities based on structural equation modeling

Abstract Based on structural equation modeling, this paper measures and evaluates the transformation of scientific and technological achievements of colleges and universities in terms of implicit and explicit variables. The PLS algorithm is used to measure the intrinsic factor, which is designed to follow the Gaussian distribution and reach the expected value through iterative loops. The indicators and parameters of the explicit variables are evaluated by combining the principal component analysis and the great likelihood method. According to the results, the factors that are influenced by the policy environment account for 16.5%, while the problem of capital investment accounts for 36.3%. The number of invention patents increased by 8.93% after measuring and assessing them using the method presented in this paper. It demonstrates that universities can explore their technological achievements in depth and enhance their scientific research level.
Research on the development path of higher education model innovation based on quadratic planning algorithm

Abstract According to the internal and external constraints of higher education model innovation and development as well as the innovation power, this paper proposes the higher education model development path based on the quadratic planning algorithm and neural network. Using the establishment of a quadratic planning algorithm and neural network model, the EM-AGA-BP algorithm is selected to construct the quality prediction model of higher education model innovation path of deep neural network based on the establishment of the quality evaluation system. The proposed algorithmic path is analyzed for development prediction, and the algorithm is recommended for 2020-level learners, which are classified into different types based on their learning and the relationship between the achievement of course objectives and the similarity of educational paths is explored separately for different types of learners. In type 4 learners, in addition to the similarity of 20%-40%, the total course goal attainment decreases by 0.0085. For the same type of learners, the more similar the educational path is to the recommended path, the better the total course goal attainment is.
Assessing Digital Competence Among Pre-Service English Language Teachers: Strengths and Weakenesses

This study aimed to examine the digital competence of 102 pre-service English language teachers and identified areas of strength and weakness. A questionnaire aligned with the European Framework for Educators' Digital Competence (DigCompEdu) was used to assess self-perceived competence in various digital areas. The findings revealed strengths in organizational communication, professional collaboration, managing and sharing digital resources, guidance, collaborative and self-regulated learning, accessibility and inclusion, and digital content creation. However, reflective practice and analyzing evidence were identified as areas of relative weakness. The study highlighted the need for targeted training programs and curriculum enhancements to address these gaps. Enhancing the digital competence of pre-service teachers is crucial for effective integration of technology in language instruction. By addressing strengths and weaknesses, the study emphasized the importance of comprehensive digital education programs that go beyond assuming digital natives possess advanced skills. Improving digital competence among pre-service teachers can enhance the quality of language education and provide better learning experiences for students.
PENGARUH METODE PROBLEM BASED LEARNING DAN PENGGUNAAN BUKU TEKS UNTUK PENINGKATAN KEMAMPUAN KOSA KATA BAHASA INGGRIS BAGI SISWA KELAS VIII DI SMPN 1 GENTENG

Mastery of English requires a lot of vocabulary and adequate grammatical forms. A large vocabulary without being based on strong grammar makes it impossible for us to properly understand a reading and vice versa, good grammar without having a vocabulary. To simplify and overcome this, it is necessary to use the Problem Based Learning (PBL) learning model and learning media, Problem In this study, the method used was the Pre-Experimental Design method with the One Group Pretest-Posttest Design research design, where in this design a causal relationship will be revealed by viewing one group of subjects. The data analysis used in this study used descriptive statistics. and inferential statistics. Analysis of data on test results for students' vocabulary comprehension abilities was carried out quantitatively. The statistical test used in this study is the inferential test of the independent t test. Daa analysis using SPSS version 24. The results showed that there was a positive and significant effect of the use of picture story books on the speaking ability and English vocabulary of junior high school students. These results indicate that junior high school students who are taught with picture story books as a medium have an increased speaking ability using English vocabulary compared to before learning.&#x0D;
Improvement of vehicles accident detection using object tracking with U-Net

Over the past decade, researchers have suggested many methods to find anomalies. However, none of the studies has applied frame reconstruction with Object Tracking (OT) to detect anomalies. Therefore, this study focuses on road accident detection using a combination of OT and U-Net associated with variants such as skip, skip residual and attention connections. The U-Net algorithm is developed for reconstructing the images using the UFC-Crime dataset. Furthermore, YOLOV4 and DeepSort are used for object detection and tracking within the frames. Finally, the Mahalanobis distance and the reconstruction error (RCE) are determined using a Kalman filter and the U-Net model.
Generative adversarial networks (GANs) and object tracking (OT) for vehicle accident detection

Accident detection is one of the biggest challenges as there are various anomalies, occlusions, and objects in the image at different times. Therefore, this paper focuses on detecting traffic accidents through a combination of Object Tracking (OT) and image generation using GAN with variants such as skip connection, residual, and attention connection. The background removal techniques will be applied to reduce the background variation in the frame. Later, YOLO-R is used to detect objects, followed by DeepSort tracking of objects in the frame. Finally, the distance error metric and the adversarial error are determined using the Kalman filter and the GAN approach and help to decide accidents in videos.
Song-to-Video Translation: Writing a Video from Song Lyrics Based on Multimodal Pre-training

Writing a video from text script (i.e., video editing) is an important but challenging multimedia-related task. Although a number of recent works have started to develop deep learning models for video editing, they mainly focus on writing a video from generic text script, not suitable for some specific domains (e.g., song lyrics). In this paper, we thus introduce a novel video editing task called song-to-video translation (S2VT), which aims to write a video from song lyrics based on multimodal pre-training. Similar to generic video editing, this S2VT task also has three main steps: lyric-to-shot retrieval, shot selection, and shot stitching. However, it has a large difference from generic video editing in that: the song lyrics are often more abstract to understand than the common text script, and thus a large-scale multimodal pre-training model is needed for lyric-to-shot retrieval. To facilitate the research on S2VT, we construct a benchmark dataset with human annotations according to three evaluation metrics (i.e., semantic-consistence, content-coherence, and rhythm-matching). Further, a baseline method for S2VT is proposed by training three classifiers (each for a metric) and developing a beam shot-selection algorithm based on the trained classifiers. Extensive experiments are conducted to show the effectiveness of the proposed baseline method in the S2VT task.
MFMGC: A Multi-modal Data Fusion Model for Movie Genre Classification

Movie Genre Classification (MGC) is a classic multi-label task that aims to classify movies into different genres. Existing studies have proposed many approaches for this task based on multi-modal data (e.g., synopsis, posters, and trailer). Despite the significant contributions made by them, they usually fuse multi-modal information based on simple operations, e.g., concatenation or weighted sum, failing to effectively capture the interactive information between multi-modal data. In addition, movies with significant overlap in directors and actors tend to own the same genres. This information could potentially improve the performance of MGC, which has been ignored by previous studies. Having observed the shortcomings of existing work, we propose a Multi-modal data Fusion Model for MGC (MFMGC), including two modules: Multi-modal Data Fusion (MDF) and Movie Graph Representation Learning (MGRL). In MDF, we carefully design the fusion layer based on the attention mechanism to effectively capture the modalities’ interactive information. In MGRL, we construct a movie graph to extract the structural information between movies. Specifically, the graph is constructed based on the overlap of movies’ directors, screenwriters, and actors, and each node in the graph has multi-modal attributes. The experiments conducted on datasets Moviescope and MovieBricks demonstrate the superiority of the proposed model MFMGC over the state-of-the-art approaches.
TED-CS: Textual Enhanced Sensitive Video Detection with Common Sense Knowledge

In the era of short videos, the task of sensitive video detection faces new challenges with the increasing diversity and quantity of videos in the network. Aiming at the problem that existing research methods are constrained by missing video comments and user subjectivity, a novel text enhancement method is proposed for sensitive video detection. Firstly, based on the CLIP pre-training model, image caption are generated for video frames. Then, through the integration of external common sense knowledge, the method extracts deep contextual information from the generated captions, including the underlying intentions and purposes conveyed by the text. Besides, considering the complementarity and redundancy between different sources of information, a multi-source data collaborative encoding mechanism and a multi-modal feature fusion mechanism are designed to achieve semantic feature alignment. Finally, state-of-the-art was achieved on the two public datasets NPDI-800 and Pornography-2k, and a large number of detailed comparison and ablation experiments were performed to verify the effectiveness of the method.
Difficulty-Controlled Question Generation in Adaptive Education for Few-Shot Learning

Adaptive education aims to achieve common educational goals by implementing targeted education based on differences in student status. However, existing intelligent teaching methods cannot be applied in data-limited environments. This paper aims to address a difficulty-controlled question generation task in adaptive education in data-limited settings, where a knowledge tracing model is used to judge the difficulty of new questions and generate problems adaptively based on the student’s mastery of knowledge. Based on this, a knowledge tracing and question generation model based on few-shot learning is proposed to predict the difficulty of new questions and generate adaptive questions as needed. The proposed method improves the performance of few-shot knowledge tracing by adjusting the design of input and output and adding a self-control loss function during fine-tuning. In terms of difficulty-controllable question generation, the controlled generation is achieved by splitting the Bayesian formulation into a probabilistic model for generation model and control sampling. The knowledge tracking model is used as a probabilistic model for control sampling thus solving the problem of the inability to generate questions with difficulty differentiation in the few-shot case. Experimental results show that the proposed method can generate questions with difficulty distinctions in a few-shot setting based on the given difficulty. In this way, the proposed method provides a broader application range for modern intelligent personalized education.
Duet Representation Learning with Entity Multi-attribute Information in Knowledge Graphs

Representation Learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimension space. Most methods concentrate on learning entities’ representations with structure information indicating the relations between entities (Trans- methods), while the utilization of entity multi-attribute information is insufficient for some scenarios, such as cold start issues or zero-shot problems. How to utilize the complex and diverse multi-attribute information for RL is still a challenging problem for enhancing knowledge graph embedding research. In this paper, we propose a novel RL model Duet Entity Representation Learning (DERL) for knowledge graphs, which takes advantage of entity multi-attribute information. Specifically, we devise a novel encoder Entity Attribute Encoder (EAE), which encodes both entity attribute types and values to generate the entities’ attribute-based representations. We further learn the entities’ representations with both structure information and multi-attribute information in DERL. We evaluate our method on two tasks: the knowledge graph completion task and the zero-shot task. Experimental results on real-world datasets show that our method outperforms other baselines on two downstream tasks by building effective representations for entities from their multi-attribute information. The source code of this paper can be obtained from https://anonymous.4open.science/r/DUET-adma2023/ .
DetOH: An Anchor-Free Object Detector with Only Heatmaps

In object detection, the anchor-based method relies on too much manual design, and the training and prediction process is too inefficient. In recent years, one-stage anchor-free methods such as Fully Convolutional One-stage Object Detector (FCOS) and CenterNet have made a splash in object detection. They not only have a simple structural design, but also demonstrate competitive performance. They have exceeded many two-stage or anchor-based approaches. However, in industrial applications, the design of its multiple output heads hinders the installation of the model. At the same time, different output heads mean a combination of multiple loss functions. This introduces problems in training. Here, we propose an anchor-free object Detector with Only Heatmaps (DetOH) to solve object detection. Bounding box parameters are calculated by post-processing. The design of the single output head allows object detection to use a semantic segmentation network, realizing the unification of the two frameworks. In addition, compared with CenterNet, we have greatly improved the speed of object detection (6 vs. 32 Frames Per Second) with 3.2% Average Precision boost. The proposed DetOH framework can be applied to multi-target tracking, key point detection and other tasks.
Research on Image Recognition Methods Based on Deep Learning

Abstract In this paper, deep learning is used to study image recognition techniques. Firstly, the image recognition process is structured, the YOLOv4 network framework is constructed, the features are extracted using the PANet reinforcement network, and the image overlap is extracted using the loss function. Then, we make an improved architecture ACDNet algorithm based on YOLOv4 and set the main function of the ACDNet model. Finally, the accuracy of image recognition under different algorithms and the recognition effect evaluation of the ACDNet algorithm are tested, respectively. The study shows that the image recognition accuracy of the ACDNet algorithm is located in the first of the three algorithms, with the highest accuracy of 98.16%, which is good and effective for image recognition and classification. The accuracy of ACDNet in the training set of plant image recognition is 99.34%, which is good for classification and recognition performance.
The Embedding of Higher Education Innovation Theory in the Management of College Students in the Context of Informatization

Abstract This paper uses the BTM algorithm, which has been improved by word embedding and self-attention mechanisms, to analyze the degree of embedding of innovation theory in college students’ management. By analyzing the principle of attention mechanism and self-attention mechanism, it constructs the embedding analysis model for higher education innovation theory. By analyzing the cw2vec and word2vec models, the expression of innovation theory vocabulary is derived. Based on the improved BTM algorithm, the WESA-BTM model is applied to give higher weights to the subject words with higher subject contributions. Parameter solving, as well as derivation, were used to determine the impact of innovation theory on college students’ management follow-up. The results show that the improved BTM algorithm can effectively analyze the embeddedness of innovation theory in college students’ management, and the WESA-BTM model in Web Snippets is evaluated to be −573.26 at the number of topics of 20 and −561.71 at the number of topics of 40. This study improves the efficiency of college students’ management in colleges and universities to a certain extent.
Semantic pattern recognition of special English words based on data corpus technology

Abstract This paper first researches special English vocabulary teaching defines special English vocabulary definitions and characteristics based on special English usage, and proposes strategies for learning special English vocabulary. The study involves studying the semantic recognition of special English using data corpus technology. Based on data corpus technology, the semantic structure of English vocabulary is analyzed according to Chinese analysis and word vectors, and to facilitate the extraction of English semantic features, IOB 2 labeled training corpus is needed. Then, the CRFs network algorithm in the data corpus technology is used to construct the special English semantic recognition model, the lexical annotation set is refined on the preprocessing, and at the same time, the semantic features are added to the recognition model of CRFs mainly used to recognize the noun phrases of gerund class and the experimental analysis is carried out on the recognition of the semantic patterns of the special English vocabulary. The results show that among high school students with high English semantic recognition ability, the correlation coefficient between their aggregates and reading level is 0.753, the correlation coefficient between collocated phrases and reading level is 0.772, and there is a high correlation between the ability to recognize aggregates, collocated phrases, and sentence framing phrases and reading level. This study is designed to improve the efficiency of English reading and communication and has a facilitatory effect on recognizing English lexical semantic patterns.
Analysis of the Coupling Development Mechanism of Vocational Education and Open Education in the Information Age

Abstract This paper analyzes the mutual influence mechanism between vocational education and open education based on the development of the information age. Secondly, it constructs the coupling evaluation mode, uses the comprehensive development level test model to measure the value of the comprehensive development level of the regional system, and quantitatively analyzes the interactions between multiple systems through the coupling degree model. Then, the gray correlation model and entropy value method are used to test and analyze vocational education and open education. Finally, the empirical analysis of the coupling development of vocational education and open education. From the time analysis of the coupling coordination degree, the average value of the coupling coordination degree of vocational education and open education across the country was 0.18432 in 2010 and 0.35384 in 2020, with an overall increase of 0.16952, 91.97%, which is a significant increase. As a result, the coupling development mechanism of vocational education and open education adapts to the current needs of economic development and individual enhancement, prompting self-improvement and sustainable development of education.
Systematic integration of information big data and physics teaching based on deep learning algorithms

Abstract This paper first investigates the main performance of the shackles of traditional physics teaching on students and its negative consequences and then designs a physics teaching process model based on the teaching environment of information big data and constructs a framework of the physics teaching system by utilizing data mining technology. Then, the physics teaching system is modeled based on the artificial neuron model and recurrent neural network, and the attention mechanism is used to improve the overall generalization performance of the model. Finally, the application effect of the physics teaching system is compared through empirical evidence. The results show that there is a deviation in the mean value of the two classes; the average score of the experimental class is 75.552 points, and the average score of the control class is 65.910 points, and there is a difference of 9.642 points between the two classes’ mean values. The data show that applying a physics teaching system can improve the learning status quo of students’ mechanical imitation, help students better master knowledge, enhance students’ ability to use information technology and achieve better teaching results than traditional teaching.