Reviewerly/exHarmony huggingface dataset:
Global Warming Analysis Based on Long Short-Term Memory and Extreme Gradient Boosting Feature Engineering Models

Global warming brings negative impacts to people’s lives. In order to explore the factors that contribute to global warming and predict temperature changes, engineering models using Long Short-Term Memory and Extreme Gradient Boosting algorithms were built. With the global year-average temperature data after preprocessing by Python, the mathematical models were computed to discover the factors that influence the global temperature change with temperature parameters and fluctuations of anomalies, including the global average temperature, carbon dioxide concentration, solar activity, etc. With the multi-variable linear differential equation, we conclude that the global climate is determined by a variety of factors. The results implied that a high concentration of greenhouse gases had a very limited impact on climate change, and solar activity had the greatest impact on global temperature.
Bayesian Updating Using DBSCAN-Based Hybrid Importance Sampling and Subset Simulation

A novel methodology for Bayesian updating is proposed by seamlessly integrating the power of DBSCAN, an unsupervised learning algorithm renowned for its ability to identify dense clusters in data, with the concept of importance sampling. While traditional Bayesian updating methods heavily rely on supervised learning and labeled data, our approach unleashes the true potential of unsupervised updating. Through the utilization of DBSCAN, we obtain remarkable insights by effectively extracting similarities and patterns within the data, allowing for a comprehensive understanding of the underlying structure. By incorporating importance sampling, we intelligently select representative samples, thus enhancing the accuracy and efficiency of the Bayesian updating process. Empirical validation reinforces the profound impact of our approach, demonstrating its ability to achieve unprecedented levels of accuracy and efficiency in Bayesian updating, all while negating the necessity of labeled data. Consequently, our pioneering method enriches the field of unsupervised learning in the context of Bayesian updating, offering novel avenues for exploratory data analysis and informed decision-making.
Learning of Bounded Treewidth Bayesian Networks via A-kg

Bounded-treewidth Bayesian networks can reduce overfitting and exact inference complexity. Several known methods learn bounded treewidth Bayesian networks by learning from k-trees. However, they adopt an approximate method instead of an accurate method. This work presents an accurate algorithm called A-kg for learning bounded treewidth Bayesian networks. Our approach consists of two parts. The first part is an accurate algorithm that learns Bayesian networks with high BIC scores, which measures the Bayesian network’s quality. In the second part, we adopt the greedy strategy to perform parent set selection efficiently. A-kg achieves better performance compared to some approximate solutions in small domains.
The Impact of Sarcasm Contained in Online Media Disinformation Headlines on Enterprise Value: A Study of a Leading Internet Company

Disinformation in online media has the profound impact on enterprise value. And faced with massive online information, online readers usually only browse through headlines. Therefore, even few negative words including sarcasm within headlines can still impact companies. Given the increasing importance of headlines in online media, it is crucial to examine sarcasm contained in disinformation headlines impact on enterprise value. However, that has received comparatively less attention in the existing literature. Therefore, the primary aim of this article is to address this gap by examining the impact of sarcasm contained in online media disinformation headlines on enterprise value. This article takes a leading Internet company as an example, and combines enterprise value related theory, machine learning, deep learning, pre-training model to mine sarcasm contained in disinformation headlines. Moreover, it uses regression analysis methods to study whether sarcasm contained in online media disinformation headlines will have an impact on the closing price, and further study whether it will exert enterprise value. The results indicate that sarcasm contained in disinformation headlines has a significant impact on enterprise value. This study enriches the relevant research on the impact of sarcasm contained in disinformation headlines on enterprise value, and provides support for companies to reduce enterprise value losses caused by online disinformation.
Fast-Growing Field: Bibliometrics of Cryptocurrency and Blockchain Research

Cryptocurrency and blockchain technology have experienced rapid advancement in recent years. Bitcoin, as a representative cryptocurrency, has been acknowledged as a legitimate payment method by numerous countries and institutions, while blockchain technology has significantly matured, particularly in the realm of distributed algorithms and decentralized systems. In this paper, we conduct a bibliometric analysis of relevant studies by using CiteSpace and Pajek, where literature data sourced from the Web of Science (WoS). Through our examination of this literature, we identify that the focal points of research in this technology are the innovation within blockchain platforms and the protection of user privacy. Moreover, in the field of application, we concentrate our analysis on the legal compliance associated with Bitcoin mining.
Understanding the Challenges Surrounding Decentralized Applications: An Empirical Study

As the Blockchain gains prominence as a disruptive technology, there has been a rapid growth in the number of decentralized applications (dApps) built on them. Despite the growing number and importance of dApps, we still know very little about the success factors surrounding such applications. While the existing research is primarily focused on the creation and development of dApps, very little research is dedicated to the challenges from dApps developers’ perspective. We addressed these issues in the paper by collecting data by means of individual discussions with technical people. The purpose of this collection is to map, classify, and describe the challenges surrounding dApps to help organizations make adequate decisions regarding decentralized applications’ development and promotion. We identified five dimensions of challenges and proposed a framework to describe dApps management and highlight how identified factors influence such applications’ adoption.
An Efficient Matching Algorithm for Question Answering System

Wheat and corn are the two most important grain crops in northern China, and at this stage, it is difficult to obtain professional information in the field of wheat and corn, the acquisition efficiency is low, and the accuracy is poor, which seriously restricts the production efficiency. The intelligent question answering system can efficiently and accurately automatically screen out professional information, to effectively solve the above problems. However, the existing intelligent question answering system for wheat and maize has too low matching accuracy and slow retrieval speed to be widely promoted. Therefore, an efficient and accurate two-stage matching algorithm is designed, which uses the BM25 algorithm to recall the candidate set, and then uses the BERT model to screen out the optimal solution based on the candidate set. Experimental results show that the algorithm has high retrieval accuracy and retrieval efficiency.
Fully 2D Convolutional Network for Continuous Sign Language Recognition

In continuous sign language recognition (CSLR) models based on deep learning, hierarchical models are more favored by researchers compared to non-hierarchical models due to their simple model, fewer parameters, and clear hierarchy. In the hierarchical model, spatial feature extraction and temporal feature extraction are separated and there is a sequential order. This paper proposes an end-to-end fully 2D convolutional network (F2DCNet) for CSLR to explore a novel spatial-temporal feature extraction method in hierarchical models. The network consists of 2D-CNN only. After extracting frame level features from sign language videos, the frame level features are concatenated by temporal dimension to form new 2D features, which are later fed into a custom 2D-CNN for spatial-temporal feature extraction, and finally the network is trained using the multi-level connectionist temporal classification (CTC) loss proposed in our previous study. We conduct experiments on two large-scale publicly available continuous sign language datasets, and the experimental results demonstrate the effectiveness of the F2DCNet, achieving highly competitive results against other advanced methods. And the proposed F2DCNet feature extraction method can be applied to other video feature extraction to extract spatial-temporal features.
A Multimodal Multi-Objective Evolutionary Particle Swarm Algorithm Based on Grey Prediction Selection Strategy

In view of the fact that the diversity of the objective space of multimodal multi-objective optimization will decrease with the increase of the diversity of the decision space, and it is unable to identify all pareto optimal solution sets, this paper proposes an evolutionary particle swarm optimization algorithm based on the gray prediction selection strategy to solve this problem. First, the historical optimal individuals and the neighborhood optimal individuals are selected by using multivariable gray prediction to improve the diversity of the target space; Secondly, the index based ring topology is used to update the neighborhood optimal archive, induce a stable ecological niche, and identify more pareto optimal solution sets; Finally, the non dominated special crowding distance is used as a density measure in the decision space and objective space to identify more pareto optimal solutions. In the experimental stage, 11 MMO test functions are selected and compared with four classic multimodal optimization algorithms. The results show that the proposed algorithm can identify all pareto optimal solution sets in the decision space and maintain good convergence and diversity in the objective space.
Application of AI Technology in Online Platforms Based on Cognitive Emotion Regulation

In order to cater to the reform of online platforms and improve online users’ learning efficiency, this study conducted a design and readjustment of the function of online platforms through application of AI Technology based on cognitive emotion regulation. The results show that AI Facial Recognition Technology can not only identify and regulate users’ emotional states, but also improve the learning states in the online learning process. It is effective in cultivating online users’ planning and adjusting the content of online platforms. The research put forward improving strategies of online platforms. The results mark a new direction for the improvement of learning state and lay a theoretical foundation for the design of online platforms.
Robust Convolutional Neural Network for Image Classification with Gaussian Noise

A Convolutional Neural Network (CNN) is one branch of Deep Learning widely used for image classification. CNN have complex architectures and capable of achieving high accuracy and producing good results. However, CNN have limitations, especially when dealing with noisy images. Image noise can decrease classification performance and increase network training time. This research was tested the robustness of two CNN methods, namely VGG16 and ResNet50, in processing images with added Gaussian noise at various levels, without any preprocessing. The dataset used is the Rice Image Dataset, which consists of images of 5 different types of rice. The data is divided into three parts: 70% for training, 20% for testing, and 10% for validation. The results of this study show that as the variability in generating Gaussian noise in the images increases, the loss function value consistently increases, and the accuracy decreases. However, the increase in the loss function value and the decrease in accuracy are not significantly different among the different levels of noise variability.
Investigating the factors of blockchain technology influencing food retail supply chain management: a study using TOE framework

In history, the food sector has remained the most vulnerable and is accountable for many crises and food scandals, so to avoid this in the near future, it is essential to have better control over the flow of products and the usage of blockchain technology can achieve this control in the supply chain, which can add trust, transparency, and traceability in the entire system. This paper investigates the factors affecting blockchain adoption in the food retail supply chain and creates awareness among retail managers for its adoption in their operations. A structured literature review is performed to identify the factors and a TOE framework is adopted for the research. Factors of technology, organization and environment are taken as independent variables; the intention to adopt the technology is taken as a mediating variable and blockchain adoption is considered a dependent variable. The findings contributed that TOE factors contribute to blockchain adoption by keeping the intention to adopt the technology as a mediating variable.
A future already past?

The article argues that blockchain-based games should be conceptualized as an emerging social practice that attracts financial speculators under the guise of online games. The article first outlines the blockchain-gaming discourse, which promises ownership and benefits to players, while it encourages financiers and publishers to exploit players. The article presents the performative discourse of blockchain advocates as well as the counterarguments presented by journalist, players, and developers, in order to demonstrate that arguments against cryptogaming are not anticapitalist and politicized, but mostly based on common sense. Then, the article investigates game studies concepts for their capacity to further explicate cryptogames, and finds that neither gamification nor playbor are completely fitting. Instead, the article turns to the game research fundamentals of Huizinga and Caillois to cast blockchain gaming in a new light. From this perspective, games like CryptoKitties and Axie Infinity emerge as nested activities that can be approached as play of financial speculation, with the latter approach being significantly privileged in existing games.
SYNCHRONOUS IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE AND PHARMACOLOGICAL COGNITIVE ENHANCEMENTS IN EDUCATION: A SOCIO-PHILOSOPHICAL ANALYSIS

The article analyzes the prospects and risks of hypothetical synchronous application of AI technologies and pharmacological cognitive enhancements in the educational system through the exampleof transfor-mation of the system of educational results assessment in mass school. The paper shows that despite the presence of a great potential for improving education, some features of the mass school’s functioning and the social visibility of the results of educational efficiency can become a serious obstacle to gaining posi-tive results of the introduction of these technologies, for example in connection with the introduction of biometrics technologies in mass school. It is shown that these technologiesthemselves do not necessarily lead to negative consequences and are seen to have a high positive potential, but there arise difficulties when they are implemented in an environment where there have previously occurred problematic admin-istrative and communication practices. The article substantiates that, similarly to biometrics, there is a risk of embedding AI technologies in depersonalized and formalized educational environment of mass school, which will strengthen the orientation toward quantitative measurement of educational effective-ness in the form of a permanent assessment system, which, in turn, will lead to distortion of the essence of the educational process due to the effects of Campbell’s Law. It is also shown that there are high risks of strengthening the neoliberal approach to education in case of legalization of pharmacological cognitive enhancements, which will also lead to epistemological difficulties in interpreting the educational situation (correct understanding of students’ behavior). Theconclusion emphasizes the importance of both having a correct strategy for the introduction of AI technologies and pharmacological cognitive enhancements and ensuring the return of a humanistic-sense-based approach to the goals and objectives of education, which only together can lead to significant positive results when using these technologies in education.
PROSPECTS FOR IMPLEMENTING MACHINE LEARNING IN THE SPHERE OF LABOR RELATIONS: THE PROBLEM OF ROBOTICS

The article examines the prospects of implementing robotics in the sphere of labor, identifying key trends and challenges facing society. The authors analyze the impact of automation on productivity, provide an overview of new opportunities for the workforce, and highlight emerging industries and professions. Additionally, the article addresses ethical issues, job retention, and social aspects, proposing strategies to address challenges associated with the growth of robotics.
ROUND-TRIP TRANSLATION AS A WRITING TOOL FOR ENGLISH AS A SECOND LANGUAGE

The potential of the technique known as round-trip translation to detect errors in language use has been exploited in the design of programs for automatic error detection (Hermet &amp; Désilets, 2009; Madnani et al., 2012), but to my knowledge, no study has explored the potential of translators as a tool that learners themselves can use to correct their writing in a second language. Consequently, there is no information as to how many of the transformations introduced by round-trip translation are useful for learners, how many simply rephrase the original text, or how many actually make it worse. Hermet and Désilets (2009) report a “repair rate” of 66.4% working with prepositions in French, while Madnani et al. (2012) report 36% successful changes, 33% paraphrasing and 31% changes for the worse in 200 sentences in English. The present study found a significant improvement in the number of corrections in texts written in English by Spanish students (97%) at the cost of generating an excessive number of false positives (34%). The most reliable transformations are those affecting spelling or word morphology, which correct errors in 88.33% and 78.57% of cases, respectively. These results show the progress made in machine translation and the reliability of the round-trip translation technique for correcting errors and inform which transformations are most useful.
Catalysing Democracy: Exploring Security and Performance in Blockchain-Based Electronic Voting Systems and Centralized Solutions

Abstract: This research paper conducts a comprehensive comparative study between blockchain-based electronic voting systems and centralized solutions, focusing on their security and performance attributes. Acknowledging the significance of electronic voting in modern democracies, the study delves into blockchain's role in enhancing security and transparency while addressing potential challenges. Through an in-depth literature review, the paper examines the existing research landscape, highlighting security aspects, performance evaluations, and the impact of decentralization. The subsequent analyses dissect the security features, enhancements, vulnerabilities, and potential attacks in each system, followed by a thorough performance assessment encompassing transaction speed, scalability, and resource consumption. The interplay between security and performance tradeoffs is explored, culminating in a discussion of the research's implications, future directions, and ethical considerations. The study's significance lies in guiding decision-making, promoting balance, and fostering ongoing innovation in the pursuit of secure and efficient electronic voting systems.
Analysis of Tonality of Text Using Machine Learning

Abstract: Tonality analysis using machine learning algorithms refers to the computational examination of written text with the objective of discerning and categorizing the emotional or subjective tone embedded within the content. In the realm of natural language processing (NLP), this analytical approach seeks to automate the process of understanding and classifying the sentiment expressed in textual data, facilitating a nuanced interpretation of language that extends beyond mere lexical meaning. At its core, the analysis of tonality involves the utilization of sophisticated machine learning models to distinguish and categorize the emotional underpinnings of text. This is particularly valuable in scenarios where vast amounts of unstructured textual data need to be processed, such as customer reviews, social media interactions, or news articles. The overarching goal is to automatically assign predefined tonal labels, such as positive, negative, or neutral, to segments of text, thereby enabling a more quantitative and systematic investigation of the subjective aspects of language. The process typically commences with the acquisition of raw textual data, which undergoes a series of preprocessing steps. These steps involve cleaning the text, breaking it into individual units (tokenization), and extracting relevant linguistic features. Feature extraction is a critical aspect, as it involves transforming the raw text into a format suitable for machine learning algorithms. This may encompass capturing word frequencies, utilizing n-grams to identify contextual patterns, and incorporating sentiment lexicon scores to gauge the emotions.
National Financial Switch Simulator Using Mobile and Backend SDK

Abstract: In the realm of digital finance, the Unified Payments Interface (UPI), managed by the National Payments Corporation of India (NPCI), revolutionizes real-time, boundary less payments between bank accounts. However, Transaction Service Providers (TSPs) encounter challenges in NPCI's testing infrastructure. To resolve this, an independent National Financial Switch (NFS) Simulator for UPI is proposed. This simulator will empower TSPs to conduct autonomous UPI transaction testing, reducing reliance on external resources. By replicating UPI processes, it will facilitate early issue identification, expedite development, and strengthen security. The NFS Simulator's technical implementation involves Android and iOS app creation, NPCI's Common Library integration, and backend simulation, streamlining development and ensuring robustness in the dynamic digital finance landscape.