Reviewerly/exHarmony huggingface dataset:
Exploration of college teacher education path based on cognitive multi-method integration for innovation and entrepreneurship ability cultivation

Abstract In this paper, a cognitive multi-method integration-based approach combined with a genetic algorithm for optimization is used to set the random variables as Bayesian network nodes, and the joint probability distribution is obtained by analyzing the strength of association between the nodes. An independently distributed observation data set with random variables is used to determine the log-likelihood function. After generating the random data, the roulette wheel is used to select the individuals with higher fitness values for transformation, and the excellence of the individuals in the population is improved through cyclic iteration. 20 points improved the educational practice’s 4th test score compared to the pre-training score after the innovation and entrepreneurship training. Therefore, the method of cultivating innovation and entrepreneurial abilities based on cognitive multi-method integration has a positive effect on improving the education level and talent cultivation quality of college teachers.
The Integration of Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Context of Internet Plus

Abstract Firstly, the signal extraction in the teaching of vocal music and traditional music in the background of Internet+ is explained in the research. Considering that the initial signal is an analog signal, it is necessary to carry out quantization, pre-emphasis, and windowing and framing processing of the music signal, thus realizing the conversion from analog signals to digital signals. Secondly, the features of the music signal are extracted from the fundamental frequency perturbation and resonance peak perturbation so as to obtain the eigenvalues of the music digital signal. Then, the evaluation model of teaching fusion of vocal music and traditional music culture in colleges and universities is constructed, the input and network parameters of the model are determined according to the music features and variables, and the evaluation model of teaching fusion of vocal music and traditional music based on the Internet is empirically analyzed by means of simulation analysis. The results show that on the evaluation model instance analysis, the average scoring results obtained from the network based on the evaluation model network for the integration of vocal music and traditional music teaching are 0.729, an error of 0.05%, and compared with the subjective scoring of 0.741 (scored by the experts), the two evaluation results are similar, and the output of the evaluation model basically is able to meet the requirements. Through this study, the main content of classroom teaching can be enhanced, and students can be guided to treat traditional music and culture correctly.
A Corpus-Based Socio-Onomastic Analysis On Turkish And American Horror Film Naming

Both fear and horror have been extensively explored as universal unpleasant emotions with significant effects on psychological well-being. It is believed that horror is the feeling aroused when watching a horror film, and a sense of suspense and resolution is behind it. The present study explores how cultural, social, interactive, and cognitive contexts influence Turkish and American horror film naming. The present study aims to analyze Turkish and American horror film titles based on a socio-onomastic approach. So, 223 Turkish (1949-2021) and 2840 American (1898-2023) horror movie titles were investigated. The hypothesis was that Turkish horror film naming uses religious elements due to religious background. But according to the present study's findings, religious elements are also available in USA horror film naming. Yet, while making and naming their films, American horror film producers pay more attention to cultural events than Turkish filmmakers. On the other hand, the keywords used in naming Turkish and American horror films, regardless of their languages, were the same.
Research on the Design of Sports Park Based on the Theory of Urban Double Cultivation

Abstract: In general, every sports park in the urban space environment is a collection of cultural essence of a city and a region, which reflects the cultural imprint of the historical era, is the cultural fusion substance of a specific period, and has distinctive characteristics and cultural context that distinguish itself from other regions. Sports parks not only witness the history, but also foresee the future. The rebirth of sports parks is mainly carried out in the two dimensions of protection and innovation. It is necessary to take its essence, eliminate its dross, develop in protection, and innovate in development. Methods: GIS, bibliometrics, knowledge atlas, human factors and efficacy and other tools were introduced into the research of sports park design based on the theory of urban double training to enhance the scientific, persuasive and logical nature of sports park design. Conclusion: The research on the design of sports parks based on the theory of urban double repair is guided by the specific content and requirements of urban double repair, and properly handles the relationship between the past and the present, the present and the future with appropriate scale and scale. It achieves the integrity of the development of each piece of land and each piece of urban area through urban organic renewal, and achieves the organic unity of sports parks and the city as a whole, to realize the improvement of urban planning quality and the sustainability of urban renewal, and finally move towards a new organic order.
Numeri per argomentare: strategie di dispositio nelle produzioni scritte degli studenti universitari

This paper concerns text organization and the signalling of its arrangement in parts. Drawing on text linguistics and combining a qualitative and a quantitative description, it explores the use of enumerative markers as linguistic devices of discursive structure, adopted by Italian second-year university students while fulfilling a timed writing task. More precisely, the paper presents a corpus-based analysis, focusing on textual segments opened by two types of enumerative markers: in primo luogo (in the first place) and in primis. The occurrences are extracted from UniverS-ITA, a corpus made by a total of 2016 students’ written texts (813,000 words). The paper aims at documenting and characterizing, on a qualitative level, the functioning of numerical sequencers (or ordinatori numerici del discorso), mainly drawing on textual scope, i.e. their effect on the textual segments involved. Findings suggest that the strategy most applied by students in the process of writing is an additive one, also emphasized by the frequent use of inoltre (moreover), whereas the homogenous series introduced by a marker acting as a trigger are rather scarce. Nevertheless, some attempts to give a linear ordering to the contents occur. Besides their descriptive value, these findings present relevant implications for teaching and learning academic writing at universities.
Mood without Modality: An outline of an 'amodal' approach to the Italian subjunctive

This paper aims to propose a novel approach to the Italian subjunctive that surpasses traditional modalinterpretations. Rather than conveying a given type of modality (such as irrealis), this paper argues that thesubjunctive is better understood as designating two semantic types: a so-called 'State-of-Affairs' or a'proposition'. By shifting the focus from modality to semantic types, this approach can account for a widerrange of the subjunctive's usages, including complements of implicatives, such as fare 'make' and capitare'happen', that modal approaches have failed to fully account for.
I colori della lingua di una semicolta siciliana

The aim of the paper is to examine the linguistic varieties present in the autobiographical diary of an illiterateSicilian peasant woman, Carolina Drago, who has chosen to entrust her memoirs to the written page. The diary,composed since 2007, when Carolina Drago was more than 70 years old, contains recipes of gastronomicpreparations, prayers, songs as well as the narration of the most significant episodes of her existence seenthrough the eyes and heart of a mother who writes the diary in order to deliver her memories to her childrenand grandchildren. The facts described within only partially intertwine the "great history" (e.g. the Spanishepidemic of the twenties, the Second World War) and dwell rather on the family environment, offering a crosssectionof peasant and popular life that crosses the twentieth century. From a linguistic point of view, CarolinaDrago uses the variety of the repertoire at her disposal: basically, the dialect and popular regional Italian, mixingthe linguistic colours of her poor palette with the enthusiasm of those who want to tell and tell themselves. Alinguistic analysis will also be presented, aimed at demonstrating how the writings of illiterate people allow usto see that Italian defined as "hidden" by Testa (2014), almost in a karstic way, has continued to evolvealongside the normative codification anticipating innovative trends.
Analysis of the Teaching Mode Based on the Integration and Mutual Promotion of Cultural and Creative Professional Construction and Curriculum Civics in the Context of Deep Learning

Abstract In this paper, an intelligent fusion teaching system is constructed in the context of deep learning. Firstly, a convolutional neural network is utilized to extract multi-level knowledge point features, and a nonlinear function is combined to accelerate the fusion of knowledge point features. Then, semantic features are integrated into keyword extraction, and the SKE algorithm calculates the semantic contribution value of words, and word centering density, and the words are matched according to the keyness. Finally, multiple regression analysis is used for the dynamic mining of online learning behavior data to get the frequent dynamic metric distribution set of the data so as to realize intelligent recommendations. The results show that 75% of the students believe that the integration of curriculum Civics in the cultural and creative majors can enhance the values and is conducive to the integration and development of Civics education and the construction of cultural and creative majors.
Virtual Currencies: Estimating the Revenue from Taxing Bitcoin in Light of the US Experience and Proposing a Method of Taxation

The aim of this study is to estimate the tax revenue of Bitcoin that could have been generated between 2014 and 2017, in the United States, which is the first state in terms of trading volume on online exchanges of Bitcoin in 2020. The authors hereby respond to the question whether the taxation of such a currency can constitute a supplementary budget resource for states whose public expenditures are rising especially after the COVID-19 pandemic. This study is the first using Bitcoin’s exchange rate to assess taxable revenue based on the supply-and-demand law of Bitcoin and USD. The authors made estimates based on the observation and analysis of the Bitcoin/USD exchange rate. The resulting estimates show that taxing Bitcoin can generate an important tax resource. Given that no consensus has been reached by worldwide authorities on the taxation of such a resource, the authors set forth a possible global solution.
Fundamental Elements of a Blockchain-Based Tax System: Governance, Legal and Technology Aspects

This article, which builds on an earlier one by the same authors, aims to make a further step in the definition of the fundamental elements of a blockchain-based tax system by developing a comprehensive theoretical framework for the use of blockchain technology in the tax domain. Based on literature review and conceptual legal analysis, the authors zoom in on three building blocks respectively focusing on the governance, legal and technology aspects. Each building block is composed of a series of conceptual items that require the attention of legislators, founding entities and developers while starting to approach the design of a blockchain solution for tax. In the authors’ view, only by first solving the issues related to each of these items in a coordinated manner will it be finally possible to enable governments and tax authorities across the globe to promote a gradual shift towards a blockchain-based tax system.
Image Dehazing Assessment: A Real-World Dataset and A Haze Density-Aware Criteria

Full-reference image dehazing quality assessment (FR-IDQA) evaluates the visual quality of a dehazed image by measuring its differences with a clear reference. The existing FR-IDQA methods are not convincing due to the lack of well-aligned datasets of hazy and clear image pairs and the limited hand-crafted features make it difficult to simulate the complicated perception by the human visual system (HVS). In this work, we build a real-world image dataset, namely RW-Haze, which comprises natural hazy images and their well-aligned clear references. Each clear image is paired with several hazy images with diverse haze levels from slight to heavy. Meanwhile, the existing FR-IDQA works evaluate the dehazed image quality in a global manner, without considering local haze distributions in the original hazy image. Actually, the perceived haze in a natural hazy image is not uniformly distributed, and the haze density varies with scene depth. Based on this priori observation, we design a haze density-aware convolutional neural network (CNN), namely DehIQA, for FR-IDQA. It adopts transfer learning to alleviate the issue of lacking sufficient labeled data. Specifically, we divide image dehazing assessment into two tasks. The source task is to classify unpaired clear and hazy images, which enforces the deep network to learn haze-related features. The target task is image quality assessment, which is achieved by transferring the trained model for the source task to the target task. Considering the fact that the perceived distortion in a dehazed image is also not uniform, we present a haze density-aware mechanism into DehIQA, which assigns different weights for different local regions in a dehazed image in terms of the dark channel of the original hazy image. Extensive experimental results show that DehIQA outperforms the state-of-the-art (SOTA) works on the benchmark dataset and achieves better consistency with human perceptions.
Supporting Engineering Education with Augmented Reality: A Systematic Review from 2000 to 2022

Augmented reality (AR) has been widely used to support teaching and learning in various disciplinary domains. One discipline merits our special attention is engineering education due to the increasing demand of quality engineers in the Information Age. This study provides a systematic review of the literature on AR-supported higher engineering education that were published between 2000 and 2022. After an initial literature search and manual screening, a total of 45 articles were selected as the study sample for this systematic review. Each article was analyzed through five related aspects of AR-supported instruction: basic information, instructional context, technology features, instructional design, and research results. The study results revealed the characteristics of AR-supported engineering education over the past 23 years in terms of publishing trends, research types, applied disciplines, instructional design, technical features, and research design. Finally, the implications and suggestions for the application of AR in engineering education are put forward.
Projection Ensemble: Visualizing the Robust Structures of Multidimensional Projections

We introduce Projection Ensemble, a novel approach for identifying and visualizing robust structures across multidimensional projections. Although multidimensional projections, such as t-Stochastic Neighbor Embedding (t-SNE), have gained popularity, their stochastic nature often leads the user to interpret the structures that arise by chance and make erroneous findings. To overcome this limitation, we present a frequent subgraph mining algorithm and a visualization interface to extract and visualize the consistent structures across multiple projections. We demonstrate that our system not only identifies trustworthy structures but also detects accidental clustering or separation of data points.
Research on the application strategy of chemistry teaching based on digital science argument mapping

In 2022, a new version of the Compulsory Education Chemistry Curriculum Standards was released, and its core literacy shift places more emphasis on the cultivation and development of higher-order competencies, yet how to effectively promote the development of chemistry subject competencies in the learning process is a challenge that has been constantly explored. With the continuous development of information technology in education, various tools dedicated to help learners learn have been introduced and have played an important role in the transformation of education. In this paper, we take the perspective of digital scientific argumentation diagrams and build a preliminary application model for chemistry teaching and learning, and its role in the development of chemistry subject competence. The paper also aims to promote the educational use of digital scientific argumentation diagrams.
An Evaluation of Table Detection Methods in Document Images

Document understanding is based on the analysis of individual document modalities (i.e., document tables, charts, diagrams and images). Document tables are of particular importance for the readers and convey important information about the contents and results of a study in close synergy with natural language text. Document analysis for the extraction of tabular information involves three steps, namely table detection, recognition and understanding. This paper presents a critical review of table detection methods with application document images. The published works are categorized into (a) rule-based approaches, (b) machine learning approaches and (c) hybrid approaches which leverage a combination of both aforementioned techniques. For each category of methods, a reference diagram showing the key processing tasks and their interaction is presented and discussed. The most representative methodologies of each category are tested and evaluated in terms of a set of features associated main with their level of maturity and performance.
Visualizing Query Traversals Over Bounding Volume Hierarchies Using Treemaps

Bounding volume hierarchies (BVHs) are one of the most common spatial data structures in computer graphics. Visualizing ray intersections in these data structures is challenging due to the large number of queries in typical image rendering workloads, the spatial clutter induced by superimposing the tree in a 3D viewport, and the strong tendency of these queries to visit several tree leaves, all of which add a very high dimensionality to the data being visualized. We present a new technique for visualizing ray intersection traversals on BVHs over triangle meshes. Unlike previous approaches which display aggregate traversal costs using a heatmap over the rendered image, we display detailed traversal information about individual queries, using a 3D view of the mesh, a treemap of the BVH, and synchronized highlighting between the two views, along with a pixel grid to select a ray intersection query to view. We demonstrate how this technique elucidates traversal dynamics and tree construction properties, which makes it possible to easily spot algorithmic improvements in these two categories.
Few-shot Object Detection with Refined Contrastive Learning

Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to the difficulty in distinguishing confusable classes. We also notice that the high standard deviation of average precision reveals the inconsistent detection performance. To this end, we propose a novel FSOD method with Refined Contrastive Learning (FSRC). A pre-determination component is introduced to find out the Resemblance Group from novel classes which contains confusable classes. Afterwards, Refined Contrastive Learning (RCL) is pointedly performed on this group of classes in order to increase the inter-class distances among them. In the meantime, the detection results distribute more uniformly which further improve the performance. Experimental results based on PASCAL VOC and COCO datasets demonstrate our proposed method outperforms the current state-of-the-art research.
A cross-attention and Siamese network based model for off-topic detection

Off-topic detection analyzes the closeness between a user’s response and a specific topic in a given context or question. The development of off-topic detection techniques stems from the real-world needs of automated scoring systems, as feature-based machine learning methods are only based on shallow features of the text. For example, sentence length, number of words, etc., cannot reflect the relevance of the user’s answer to the topic from the semantic level, resulting in the content of the user’s answer containing some answers that do not have grammatical errors but are not relevant to the topic can also get a higher score, which significantly affects the impartiality and fairness of the automatic scoring system. Therefore, this paper proposes a deep learning model based on the combination of Siamese network and a cross-attention mechanism for the off-topic detection task, which is suitable for automatic short-answer off-topic detection scenarios. By conducting experiments using the publicly available dataset Mohler, the experimental results show that our proposed model is adequate. The experimental results in this paper achieve 91.9% F1 value, 92.5% recall, and 94.1% precision. The F1 value and precision are improved by 3.7% and 3%, respectively, compared to the best-performing baseline model, the Bert-based deep neural network model.
Deep Interaction Behavioral Feature Network for Click-Through Rate Prediction

Click-through rate prediction(CTR) is a critical task in recommendation system. Most previous work on modelling the interaction features between user history behavior and target item focused only on mining the single interaction feature between user’s single historical behavior and the target item, or only modeling the user behavior sequence to capture user interest, which may lead to lower prediction accuracy. To solve the above problem, we propose the Deep Interaction Behavioral Feature Network(DIBFN), which is a dual-module approach that focuses on both the interaction features of user interest and the target item and on the interaction features of user’s single historical behavior and the target item. First, the Evolving Interaction Module (EIM), which processes user behavior sequence to explore the user interest over time, captures the interaction features of the user interest with the target item. Then, the Behavioral Feature Module (BFM) is proposed to learn behavior representations from multi-space through multi-head self-attention and use a two-layer attention mechanism to mine the interaction features between user’s single historical behavior and the target item. Experimental results on real datasets show that the proposed DIBFN model outperforms previous main-stream approaches.