Reviewerly/exHarmony huggingface dataset:
Feature Dual Supervision Model for the Searches of Online Advertising Audiences

Online advertising has become one of the most important strategies used by companies. They get the valuable results from Internet marketing and communication strategies. Therefore, it is necessary to study the click-through rate (CTR) model to search the potential audiences in online advertising. The advertisers desire to search for potential candidates through a large number of queries for audiences in programmatic advertising. Facing such a large corpus, the most common method is that using two-tower model to learn user’s queries and ad representations, and then the similarity function is applied to match the feature representation to get the potential audiences related to the ad. However, in the process of feature extraction, there is a lack of information interaction between the two towers, resulting in the loss of details in the representation. In order to alleviate the lack of information interaction between the networks in the two-tower model during feature extraction. In this paper, we propose a novel model named Feature Dual Supervision Model (FDSM), which integrates by Feature Expression Unit (FEU) and Feature Supervision Unit (FSU). The FEU is used to extract ads or users features, and FSU generates a weight vector to supervise the working process of the FEU. In addition, we propose a feature cross-layer with bridge connections in FDSM to achieve effective feature interaction between ad and user representations. Finally, we conduct experiments on the Tencent Lookalike and MovieLens datasets. The experimental results indicate that the FDSM model outperforms other state-of-the-art CTR prediction models in audience expansion.
Research on the Deployment Strategy of Big Data Visualization Platform by the Internet of Things Technology

INTRODUCTION: To improve the big data visualization platform's performance and task scheduling capability, a big data visualization platform is constructed based on Field Programmable Gate Array (FPGA) chip application power equipment.OBJECTIVES: This study proposes to combine a genetic algorithm and an ant colony scheduling (ACOS) algorithm to design a big data visualization platform deployment strategy based on an improved ACOS algorithm.METHODS: Firstly, big data technology is analyzed. Then, the basic theory of the ant colony algorithm is studied. According to the basic theory of ACOS and genetic algorithm, an improved ACOS algorithm model is constructed. The improved ACOS algorithm scheduler is compared with the other three schedulers. Under the same environment, the completion time of scheduling the same job and different task amounts are analyzed. The Central Processing Unit (CPU) utilization is analyzed when different schedulers have entirely different workloads. RESULTS: The results show that the constructed big data visualization platform based on the improved ACOS algorithm model has higher task scheduling efficiency than other schedulers and can greatly shorten the data processing time. The experimental results show that under the homogeneous cluster, the completion time of the improved ACOS algorithm generally lags the capacity scheduler and the fair scheduler. Under the heterogeneous cluster, the improved ACOS algorithm scheduler can reasonably allocate tasks to nodes with different performances, reducing the task completion time. When the number of completed tasks increases from 50 to 200, the time increases by 45s, and the completion time is shorter than other schedulers. The CPU utilization of different task volumes is the highest, and the utilization rate increases from 81% to 95%. CONCLUSION: The improved ACOS algorithm scheduler has the shortest data processing time and the highest efficiency. This work provides a specific reference value for optimizing the big data visualization platform's deployment strategy and improving the platform's performance.
Financial Fraud: Identifying Corporate Tax Report Fraud Under the Xgboost Algorithm

INTRODUCTION: With the development of economy, the phenomenon of financial fraud has become more and more frequent. OBJECTIVES: This paper aims to study the identification of corporate tax report falsification. METHODS: Firstly, financial fraud was briefly introduced; then, samples were selected from CSMAR database, 18 indicators related to fraud were selected from corporate tax reports, and 13 indicators were retained after information screening; finally, the XGBoost algorithm was used to recognize tax report falsification. RESULTS: The XGBoost algorithm had the highest accuracy rate (94.55%) when identifying corporate tax statement falsification, and the accuracy of the other algorithms such as the Logistic regressive algorithm were below 90%; the F1 value of the XGBoost algorithm was also high, reaching 90.1%; it also had the shortest running time (55 s). CONCLUSION: The results prove the reliability of the XGBoost algorithm in the identification of corporate tax report falsification. It can be applied in practice.
Attention and self-attention in random forests

New models of random forests jointly using the attention and self-attention mechanisms are proposed for solving the regression problem. The models can be regarded as extensions of the attention-based random forest whose idea stems from applying a combination of the Nadaraya–Watson kernel regression and the Huber’s contamination model to random forests. The self-attention aims to capture dependencies of the tree predictions and to remove noise or anomalous predictions in the random forest. The self-attention module is trained jointly with the attention module for computing weights. It is shown that the training process of attention weights is reduced to solving a single quadratic or linear optimization problem. Three modifications of the self-attention are proposed and compared. A specific multi-head self-attention for the random forest is also considered. Heads of the self-attention are obtained by changing its tuning parameters including the kernel parameters and the contamination parameter of models. The proposed modifications of the attention and self-attention combinations are verified and compared with other random forest models by using several datasets. The code implementing the corresponding algorithms is publicly available.
Lexicon-based probabilistic indexing of handwritten text images

Abstract Keyword Spotting (KWS) is here considered as a basic technology for Probabilistic Indexing (PrIx) of large collections of handwritten text images to allow fast textual access to the contents of these collections. Under this perspective, a probabilistic framework for lexicon-based KWS in text images is presented. The presentation aims at providing formal insights which help understanding classical statements of KWS (from which PrIx borrows fundamental concepts), as well as the relative challenges entailed by these statements. The development of the proposed framework makes it clear that word recognition or classification implicitly or explicitly underlies any formulation of KWS. Moreover, it suggests that the same statistical models and training methods successfully used for handwriting text recognition can advantageously be used also for PrIx, even though PrIx does not generally require or rely on any kind of previously produced image transcripts. Experiments carried out using these approaches support the consistency and the general interest of the proposed framework. Results on three datasets traditionally used for KWS benchmarking are significantly better than those previously published for these datasets. In addition, good results are also reported on two new, larger handwritten text image datasets (B entham and P lantas ), showing the great potential of the methods proposed in this paper for indexing and textual search in large collections of untranscribed handwritten documents. Specifically, we achieved the following Average Precision values: IAMDB: 0.89, G eorge W ashington : 0.91, P arzival : 0.95, B entham : 0.91 and P lantas : 0.92.
Training People to Think in Opposites Facilitates the Falsification Process in Wason’s Rule Discovery Task

With reference to Wason's 2-4-6 rule discovery task, this study investigated the effects of a simple training session that prompted participants to "think in opposites". The results showed a significant improvement in performance under the training condition when compared to the control condition, both in terms of the proportion of participants who discovered the correct rule and how quickly it was discovered. An analysis of whether or not participant submitted test triples formed of descending numbers showed that fewer participants under the control condition considered ascending/descending to represent a critical dimension and, in any case, this occurred later (that is, after more test triples) than in the training condition. These results are discussed in relation to previous literature showing improvements in performance that were prompted by strategies involving "contrast" as a critical factor. The limitations of the study are discussed, as well as the benefits of a training program like this, which is non-content related.
Analysis on the Young Age of Roblox Platform Audience Targeting

The rapid development and construction of metaverse games have gradually blurred the virtual world and the real world, and it is found that its impact on young people is particularly significant through literature research and data surveys. This article takes the phenomenon of the young age as the background and, based on data analysis, takes the Roblox platform as an example. Through the research, it is found that the related theoretical and practical research of metaverse games is on the rise in general, the current situation of younger users is not optimistic, and the narrow range of audience positioning on the platform is the main reason for the problem. Based on this, the strategic thinking of enhancing the fineness of game screens, providing personalized game recommendations, expanding the Roblox platform ecosystem, and accelerating platform transformation is proposed, providing certain references for the future development of Roblox and other game industry companies.
Digital Transformation in the FinTech Sector

This paper mainly analyzed the specific application of fintech technology in different aspects, and the changes this emerging technology can bring to people's lives. In terms of the payment system, the formation of oligopoly may lead to serious uneven market competition. Although clients with personal advisers are less likely to trust the services of robots, robo-advice can provide better investment advice to ordinary people and expand the benefit group. For P2P lending, this is a new lending model, eliminating the tedious steps and high fees of the middleman. Virtual currency as an original application of blockchain is the financial derivative that threatened the financial money investment market. The author explained how to benefit more consumers through fintech technology, and the progress of technology enables customers of different classes to have better service experience. The digitalization process of financial services promotes economic development and can provide customers with more inclusive and professional financial services. Fintech has gradually changed the pattern of the financial industry, gradually weakened the role of financial intermediaries, and created a new operating system.
Classify Glassy Antiques Based on the Chemical Components

As one sort of cultural relic, glassy antiques can effectively convey the historical information of a certain era and reveal the cultural exchanges in different regions as a symbol of foreign trade. However, due to long-term weathering and corrosion when buried in soil, the shape, color, and chemical components of a glassy antique can change considerably, and hence identifying it and recognizing its category is particularly difficult. Clustering is a popular technique of data analysis and data mining. K-means is one of the most popular data mining algorithms, as it is simple, scalable, and easy to modify in different contexts and fields of application. This paper uses k-means to find the clustering center showing the characteristics of the chemical components of different categories of glassy antiques. Then the particle swarm optimization algorithm (PSO) that offers a globalized detailed search methodology is utilized to improve the k-means clustering. The new result compared to the previous one of traditional k-means clusterin shows better classification capacity. Finally, it compares the results of k-means with that of PSO-k-means and analysis the advantages and disadvantages of PSO-k-means.
Implicit Emotion Analysis Based on Improved Supervised Contrastive Learning

Implicit sentiment analysis (ISA) can be distinguished from traditional text sentiment analysis by the fact that it does not rely on emotional words as emotional clues, and the expression is usually vaguer. Identifying implicit emotions is more difficult, as it requires a deeper understanding of the context, even when emotional words are absent. Researchers have focused on context feature modeling and developing sophisticated feature extraction mechanisms instead of starting from the emotional perspective. Enhancing the difference in emotional features of text samples is an intuitive method to address this challenge. We proposed a supervised contrastive learning (SCL) method during training that enables the model to conduct contrastive learning based on emotion labels even while training on weak emotion features. SCL training can strengthen the average embedding distance between texts with different emotion labels and enhance implicit emotion discrimination. Moreover, research indicates that contextual information can improve implicit emotion classification ability. Therefore, we applied a straightforward context feature fusion method (bi-affine) over a more complicated context feature modeling approach. To evaluate the effectiveness of our proposed method, we conducted experiments on the SMP2019-ECISA (Chinese implicit sentiment analysis) dataset. The results show a 2.13% enhancement in the F1 value compared to the BERT baseline, proving the effectiveness of our methods.
Design of Enhanced Document HTML and the Reliable Electronic Document Distribution Service

Electronic documents are becoming increasingly popular in various industries and sectors as they provide greater convenience and cost-efficiency than physical documents. PDF is a widely used format for creating and sharing electronic documents, while HTML is commonly used in mobile environments as the foundation for creating web pages displayed on mobile devices, such as smartphones and tablets. HTML is becoming a more critical document format as mobile environments have been raised as the primary communication channel nowadays. However, HTML does not have the standard content integrity feature, and an electronic document based on HTML consists of a set of related files. Therefore, it has a vulnerability in terms of reliable electronic documents. We have proposed Document HTML, a single independent file with extended meta tags, to be a reliable electronic document and Chained Document, a single independent file with a blockchain network to secure content integrity and delivery assurance. In this paper, we improved the definition of Document HTML and researched certified electronic document intermediaries. Additionally, we designed and validated the electronic document distribution service using Enhanced Document HTML for real usability. Moreover, we conducted experimental verification using a tax notification electronic document, which has one of the top distribution volumes in Korea, to confirm how Document HTML provides a content integrity verification feature. Document HTML can be used in an enterprise that must send a reliable electronic document to a customer with an electronic document delivery service provider.
Inequality and the structure of countries’ external liabilities

Abstract In this paper, we present empirical evidence that higher income inequality is associated with a greater equity share in countries’ external liabilities, and we develop a theoretical model that can explain this observation: In a small open economy with traded and non-traded goods, entry barriers depress entrepreneurial activity in non-traded industries and raise income inequality. The small number of domestic non-traded goods firms leaves room for foreign firms to operate on the domestic market, and it reduces external borrowing. The model thus suggests that barriers to entrepreneurial activity raise both inequality and the equity share in foreign liabilities. Our empirical results lend some support to this conjecture.
OISE: Optimized Input Sampling Explanation with a Saliency Map Based on the Black-Box Model

With the development of artificial intelligence technology, machine learning models are becoming more complex and accurate. However, the explainability of the models is decreasing, and much of the decision process is still unclear and difficult to explain to users. Therefore, we now often use Explainable Artificial Intelligence (XAI) techniques to make models transparent and explainable. For an image, the ability to recognize its content is one of the major contributions of XAI techniques to image recognition. Visual methods for describing classification decisions within an image are usually expressed in terms of salience to indicate the importance of each pixel. In some approaches, explainability is achieved by deforming and integrating white-box models, which limits the use of specific network architectures. Therefore, in contrast to white-box model-based approaches that use weights or other internal network states to estimate pixel saliency, we propose the Optimized Input Sampling Explanation (OISE) technique based on black-box models. OISE uses masks to generate saliency maps that reflect the importance of each pixel to the model predictions, and employs black-box models to empirically infer the importance of each pixel. We evaluate our method using deleted/inserted pixels, and extensive experiments on several basic datasets show that OISE achieves better visual performance and fairness in explaining the decision process compared to the performance of other methods. This approach makes the decision process clearly visible, makes the model transparent and explainable, and serves to explain it to users.
Virtual Laboratory for the Full-Field Goniophotometer of Luminaires

In order to solve the problem that students have insufficient understanding of the full-field goniophotometer instrumentation due to the expensive equipment and the high requirements of the experimental environment, virtual reality technology is used to complete the virtual laboratory of a light intensity distribution measurement of the full-field goniophotometer through Unity 3D. The virtual laboratory is designed for near-field measurement, mid-field measurement, and far-field measurement of light intensity. It combines 3D MAX and C# to realize the model building of laboratory, luminaire and full-field goniophotometer, the dynamic drawing of light distribution curves, and the simulation system can also be run on mobile phones. The experimental process is mainly through the manipulation of the virtual experimenter and each module to generate collision detection, pop-up buttons, and click on the button triggers an event and the operation prompt page appears. The light distribution curve is generated dynamically by reading and processing the IES file data to generate 3D coordinates. This virtual simulation laboratory is demonstrated on multiple platforms. The results proved that the virtual simulation restored the experimental process vividly and can complete the experimental teaching remotely.
Welcome, How Can I Help You? Design Considerations for a Virtual Reality Environment to Support the Orientation of Online Initial Teacher Education Students

Alongside the rapid and broad uptake of online learning in higher education, fully online students report feeling isolated and disconnected from their institutions. Although formal course content may be expertly designed to engage online learners, much of the information provided to support higher education students’ orientation to the institution and to study is presented online in a written static form. Such presentations may not be accessible and engaging and may contribute to feelings of disconnection. Technologies such as virtual reality (VR) are being used in higher education to engage, motivate and connect students in their learning. This paper reports on the early design stages for a VR that aims to support initial teacher education students to connect and engage with key orienting information. The design of the VR was achieved by following a user-centred, iterative engineering design process and design principles of spatiality, interaction and narrative. The VR environment emulates the School of Education’s physical, on-campus reception area to provide an immersive experience where students have a choice in the types and format of key study information they receive. This experience was designed to be utilised in online orientation but also throughout students’ first year of study. Future research directions include collecting student responses to the VR to inform how students can be involved in enhancing the VR so that it supports their learning and sense of connection. Furthermore, future research can aim for the expansion of the VR inclusive of additional information, rooms and buildings and increased capabilities such as gamification and mobile access. This will enable the creation of a valuable teaching resource for online programs.
A New Way of Cataloging Research through Grounded Theory

Grounded theory (GT) has been extensively used in social studies through surveys and interviews. However, its application in software development has not been appropriately categorized, limiting its in-depth study in this field. Additionally, the qualitative analysis provided by GT is in increasing demand in software engineering, presenting a significant opportunity to further investigate this topic. This article discusses the identification and analysis of key GT elements beyond traditional data sources, such as research results, engineering artifacts, and written documents, and introduces the role of basic coding, master core category, and the theory emerging, thus showing a way to present the results of GT studies in software development. The study provides valuable insights for researchers and practitioners interested in applying GT in software development. The article also explores the crucial role of constant comparison until saturation and the challenges it presents. Additionally, the integration of Glaserian grounded theory (GGT) with systematic mapping study (SMS) is examined, resulting in a novel approach called Glaserian systematic mapping study (GSMS), which defines saturation through three equations, providing a set of components that satisfactorily categorize GT in software development. This article discusses the identification and analysis of key grounded theory (GT) elements beyond traditional data sources in the context of software development.