Reviewerly/exHarmony huggingface dataset:
A Study of the Relationship between Grit, Mindset, Fear of Failure, and Career Belief from Participation in Project-Based lLearning (PBL) for Sports College Students

Purpose: The purpose of this study is to investigate whether the grit, mindset, coping with the fear of failure and career belief that students develop through practice in field education activities of the Project-Based Learning (PBL) class would be changed.BR Method: A total of 52 students from University A located in Cheonan, Chungcheongnam-do majoring in sports participated in this study. Questionnaires distributed to 52 students who participated in Therapeutic Recreation PBL were collected and processed for analysis. The frequency analyses were performed to identify the research subject, and Cronbach’s α coefficient was calculated to confirm the reliability of the research tools. To confirm the research question, the mean and standard deviation of the pre- and post-scores of factors related to grit, mindset, fear of failure, and career belief were calculated and a t-test was performed.BR Results: The results were as follows. First, as a result of examining the changes related to grit before and after the PBL participating in the therapeutic recreation class of college students, significant differences were found in the post-test, but not in the pre-test. Second, as a result of examining changes related to mindset before and after the PBL participating in the therapeutic recreation class of college students, the growth mindset was slightly increased, while the mindset was slightly decreased in the post-test compared to the pre-test. Third, as a result of examining the changes related to fear of failure before and after the PBL participating in the therapeutic recreation class of college students, it was found to be decreased in the post-test compared to the pre-test. Fourth, as a result of examining changes related to career belief before and after the PBL participating in the therapeutic recreation class of college students, it was found to be decreased in the post-test compared to the pre-test.BR Conclusion: In summary, the findings in this study suggest that participation in the PBL class for sports college students has an effect on grit, mindset, fear of failure, and career belief.
Text Classification of International Relations Based on Deep Learning

With the rapid development of global information and the popularization of the Internet, this new method of information dissemination has changed people's lifestyles, but Internet data as an information carrier has also shown explosive growth. Effectively organizing and extracting these data to find the information that users need faster, more accurately, and more comprehensively is a major challenge facing the field of informatics today. Text mining mainly includes methods such as text sorting, text grouping, and document summarization. Text sorting is a very important way for people to acquire knowledge and recognize things. It plays an important role in natural language editing, language understanding, machine learning, and topic recognition. Therefore, this article focuses on the deep learning of international relations text classification. The learned international relations text classification model is constructed, and the constructed model is tested. The test results show that the classification model proposed in this paper has an average classification accuracy of 90%, which is better than other classification models.
Research on Intelligent Online System of Chinese Training based on Visual Operating System

A prototype of a visual programming environment based on the operating system structure and programming model is designed and realized by using the Eclipse platform, and the theory of intelligent teaching system and the learning strategies and teaching theories of Chinese as a foreign language are explored. To understand the English-Chinese intelligent teaching system that is widely used, according to the teaching content, make a comparative analysis according to the classification of language element teaching, language skills teaching and multimedia comprehensive teaching, investigate the limitations of the current intelligent teaching system of Chinese as a foreign language, and explore the application of intelligent teaching system in English. The advantages of optimizing the intelligent teaching system of Chinese as a foreign language are possible.
Overview of Mobile Cloud Computing

Mobile Cloud Computing (MCC) is a combination of three main parts; they are mobile device, cloud computing and mobile internet. With the help of MCC, a mobile user gets a rich application delivered over the Internet. The capabilities of mobile devices have been improving quickly than computers. Many researchers focus on the area of mobile computing and cloud computing. Cloud computing is the trend in which resources are provided to a local client on an on-demand basis, usually by means of the internet. Mobile cloud computing (MCC) is simply cloud computing in which at least some of the devices involved are mobile. This paper goes over multiple techniques and methods for mobile cloud computing. It explores both general-purpose mobile cloud computing solutions and application-specific solutions. It also discusses instances of mobile cloud computing where mobile devices serve as the cloud rather than the client. Finally it discusses some issues raised by this technology such as privacy and data ownership.
Knowledge graph representation learning and graph neural networks for language understanding

As AI technologies become mature in natural language processing, speech recognition and computer vision, "intelligent" user interfaces emerge to handle complex and diverse tasks that require human-like knowledge and reasoning capability. In Part 1, I will present our recent work on knowledge graph representation learning using Graph Neural Networks (GNNs): the first approach is called orthogonal transform embedding (OTE), which integrates graph context into the embedding distance scoring function and improves prediction accuracy on complex relations such as the difficult N-to-1, 1-to-N and N-to-N cases; the second approach is called multi-hop attention GNN (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. Experimental results on knowledge graph completion as well as node classification benchmarks show that MAGNA achieves state-of-the-art results. In Part 2, I will present how we take advantage of GNNs for language understanding and reasoning tasks. We show that combined with large pre-trained language models and knowledge graph embeddings, GNNs are proven effective in multi-hop reading comprehension across documents, improving time sensitivity for question answering over temporal knowledge graphs, and constructing robust syntactic information for aspect-level sentiment analysis.
Design and implementation of high-performance instant messaging system and personalized aggregate content service

Nowadays, instant messaging software is widely used in people's lives, and software usage scenarios have a tendency to transform from PC to mobile. At this time, the software needs to meet the conditions of the mobile device's network environment and device configuration. Therefore, it is necessary to focus on issues such as the immediacy, reliability, orderliness, and weak network optimization of the mobile terminal's instant messaging program to send and receive messages. In the process of system design and implementation, the server side mainly uses Netty and Spring Boot frameworks, and the data storage mainly uses relational database MySQL, key-value pair database Redis, object storage MinIO, and uses the JavaMail framework to write mail services and the open-source RSSHub to provide aggregate content services. The client-side program is designed and developed based on the Android system. The program is characterized by fast message sending and receiving, high delivery rate, low traffic consumption, and low power consumption, to make sure that messages can still be sent and received successfully even when the network is weak. The main network protocols used for communication between the client and the server are TCP and HTTP.
Threat to the Current Blockchain Cryptosystems Due to the Advancement of Quantum Computers

In this note, we’ve discussed how there is a constant threat to the current cryptographic systems of Blockchain by the advancement of Quantum Computers. As the security and privacy of Blockchain solely depends on the hashing and digital signatures, quantum algorithms like Shor’s algorithm and Grover’s algorithm can prove to be a real problem if developed in the coming future. Majorly, ECDSA is used as a way to sign digitally. It uses the logarithmic problem for security which cannot be solved by the classical computers, making it secure from classical computers. But this can be compensated for by Shor’s algorithm. Furthermore, we have discussed the Post-Quantum Cryptographic schemes which are considered to be Quantum Resistant, i.e., secure from future quantum attacks.
Students` Activities in the Digital Environment

This paper aims to highlight the key role of the information and communication technologies (ICT) in the activities of Bulgarian students, and to establish the mastery of their digital literacy skills. In its empirical part, the study turns to some areas of digital competence of students such as application of knowledge, skills and attitudes. The survey was conducted among 100 students during 2014 – 2015 by the method of direct individual survey through pre-designed questionnaire comprising of 40 questions. The data of the survey were analyzed using statistical methods – frequency, mean value and confidence level – 95%. The importance of the current research is not only in a theoretical, but also in a practical-application plane, aiming to highlight the crucial role of the digital literacy for students to adapt to the dynamic labour market and the need of outlining a d igital library as a significant mediator in the university educational environment.
1 • Un antico <i>usus emendandi</i>: integrazione e correzione con parola-segnale

A description of the corrective method of the ‘signal-word’ used by copyists, who, either in the margin, or in or between the lines of manuscripts, not only replaced or rectified mistaken or omitted terms, but also repeated one or more of the preceding or succeeding words, to indicate the location of the lacuna or error.
Indian Sign Language: Effective way to give voice to the voiceless using Artificial Intelligence

Although Sign Language is one of the classical and most natural means of communication, most people are unaware of it, and interpreters are less in number. The aim of this paper is to develop an interface between dumb/deaf and the normal people. Motivation behind this is to introduce an application that will not only facilitate users to learn sign language but also will help them to convert the hand sign gestures to corresponding alphabets or numbers. The system will also help to convert text to hand sign gestures. This paper contributes an approach for users to communicate with dumb and deaf individuals effectively and efficiently by removing the need of translation facility. Convolution Neural Networks (CNN) is used in translating the hand sign gestures to text. Natural Language Processing (NLP) helps in converting given input text to their corresponding hand sign gestures.
The coherence function and its information content for optical metrology

Abstract The coherence function offers new possibilities for optical metrology that are not available with conventional wave field sensing. Its measurement involves a spatio-temporal sampling of the wave fields modulated by the object under investigation. Temporal sampling is well known e. g. by means of White Light Interferometry (WLI) and spatial sampling can e. g. performed by Computational Shear Interferometry (CoSI). The present paper describes an approach that combines both temporal and spatial sampling using a robust common-path setup. While the evaluation of the coherence function is more elaborate than approaches that either sample the temporal or the spatial domain, an information theoretical treatment shows that it also delivers more information about the object under investigation. Our approach is based on the mutual information that represents the reduction of uncertainty about the object as a consequence of the measurements performed. Using a simplified measurement case, we calculate the mutual information for different measurement situations and demonstrate that spatio-temporal sampling of the coherence function results in a higher mutual information as compared to classical approaches. Based on the proposed approach, we identify further open research tasks for an efficient information extraction from the coherence function to surpass current limitations of optical metrology.
Deepfake Detection

In recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning-based approaches it is now possible to generate data with a high level of realism. While this opens up new opportunities for the entertainment industry, it simultaneously undermines the reliability of multimedia content and supports the spread of false or manipulated information on the Internet. This is especially true for human faces, allowing to easily create new identities or change only some specific attributes of a real face in a video, so-called deepfakes. In this context, it is important to develop automated tools to detect manipulated media in a reliable and timely manner. This talk will describe the most reliable deep learning-based approaches for detecting deepfakes, with a focus on those that enable domain generalization [1]. The results will be presented on challenging datasets [2,3] with reference to realistic scenarios, such as the dissemination of manipulated images and videos on social networks. Finally, new possible directions will be outlined.
Research on robot path planning based on machine vision

With the development of artificial intelligence, intelligent robot has gradually become one of the current research hotspots. Among them, path planning is the key technology of intelligent mobile robot. The planning to enable the mobile robot with a safe and effective path efficiently has always been a challenging research field. This paper designs a mobile robot path planning system based on machine vision, establishes the overall framework of the path planning system, and focuses on the global path search algorithm of ant colony algorithm to realize the optimal path in mobile robot space. According to the basic principle of ant colony algorithm, the workspace environment of mobile robot is established, and the relationship curves between the core parameters and ant colony algorithm performance are obtained by software simulation. On this basis and under the condition of the optimal combination of parameters, the efficient path planning based on ant colony algorithm is realized. The path planning algorithm can provide a corresponding solution for the path searching problem of mobile robot.
Intelligent Application of Tensor Analysis and Edge Information in Supply Chain Optimization

This article first analyzes and optimizes the current problems in supply chain management through algorithmic model analysis, and then combines the characteristics of tensor analysis and edge information to build a problem-solving business operation system (including information sharing mechanism and management mode under information sharing), and then Perform performance impact analysis on the proposed business operation system, and finally design an example. Establish a supply chain management algorithm model based on tensor analysis. Based on the edge information, an edge model of the credit behavior of supply chain business entities was constructed, and the application value of multi-source data in supply chain management was analyzed. The system dynamics model was used to simulate the credit game process, and the result was increased by 7.6%.
A Study on the Impact of AI of Consumer Behaviour With Reference to Commercial, Industrial, and Liability Insurance in New India Assurance in Mumbai

As the development of artificial intelligence continues, its impact is being felt in several areas of life, even though the method by which it is used is not always apparent. The insurance sector is shifting to incorporate artificial intelligence in response to customer demands. The impact of technology on business is growing for all industries around the world. Digital operational models have seen increasing popularity over the last decade. The concepts “AI” and “machine learning” are hardly new in the tech world and we will continue to see their rising adoption and implementation across businesses and industries.
Decentralization in Blockchain and its Impact on Identity

Data security has been an important aspect in the digital world. One of the technologies which has addressed this issue is Blockchain. Digital payments were the first application of Blockchain. The cryptocurrency Bitcoin was introduced for digital currency transactions. Blockchain introduced a method of interaction between two or more entities without a third party, thus eliminating the risk of trust issues. With the ever-increasing use of internet and activities on digital platforms, the trust issues among the interacting entities have been facing challenges due to third-party interruptions in authentication processes. The federated systems have made the authentication of users more dependent on certain domains, rather than giving users the ownership of their authenticity. Introducing Blockchain in this identity management of users has proved efficient and reduced the third-party dependency, too. This chapter focuses on the use of Blockchain and its underlying concepts for de-centralization in an identity management system. The chapter begins with an introduction to Blockchain and then sheds light on the de-centralization solution for identity management systems. The next part explains the concepts of self-sovereign identity, which are the base of a de-centralized identity management system. A case study explores the distributed nature of Blockchain for identity management.
Excel data processing based on FileReader in grade management system

In the era of big data and Internet, teaching informatization and grade management informatization are more and more widely used, How to quickly and efficiently import various grades generated in the teaching platform into the grade management platform has become a concern of many schools and teachers. The FileReader object in front-end development can quickly read various file contents into web pages, and after that, you can complete various operations in the web page according to the read file data. Based on the function of FileReader object, a score import function is designed to import Excel data into web pages, which can be displayed, sorted and modified in tabular form. The design and development of score import function effectively improves the efficiency of score entry, ensures the correctness of input data, and provides convenience for teachers.
Blockchain for Healthcare Systems

Currently existing traditional healthcare systems have issues concerning privacy, integrity, security, and ease of use. The features of blockchain such as decentralization, immutability, accessibility, privacy, security, transparency, and integrity can help resolve these concerns and create a more patient-centric healthcare system. Blockchain technology helps eliminate third-party vendors and reduce dependency on a central authority in a distributed environment. The integrity and validity of patients’ records is maintained due to immutability, and privacy is ensured as it is cryptographically secure. Data exchanges between stakeholders can be brought under a single roof using blockchain. Some applications of blockchain in the healthcare sector are EHR (Electronic Health Record), monitoring of patients and pharmaceutical supply chains, health insurance claims, daily health data collection, and healthcare research. This chapter details the application of blockchain in healthcare systems. The following sections elaborate on the integration of blockchain technology in healthcare, the issues and challenges faced by the current traditional healthcare systems, the workflow, architecture and practical use cases of these systems, and some challenges to build these systems. Also, a case study of a decentralized application (DApp) for a blockchain-based medical data management system built using Ethereum Blockchain and IPFS (Interplanetary File System) is also explained.
Tourism Data Clustering Analysis Based on Random Forest Algorithm

Due to the rapid development of the computer and the rapid popularization of the network, people produce a large number of product information on the digital Internet and show an explosive increase, and is moving towards the era of big data analysis. How to use computers to do this, in today&#x2019;s data age, the effective organization, management and knowledge discovery of seemingly unrelated data is very important, is an important and urgent problem in today&#x2019;s data age. In order to solve the tourism data clustering of the random forest algorithm, this paper adopts to verify the effect of the improved sample similarity calculation method by using the data set in the UCI database. The results show that, Compared with traditional methods, this method has better fitting and prediction effect.
Spam Comment Recognition of E-commerce Products Based on Collaborative Filtering Algorithm

In recent years, the way people express their opinions and communicate with each other has also changed. In the field of e-commerce product reviews, people increasingly prefer to post their opinions on products on shopping sites, and these user reviews contain a lot of useful information. At the same time, these views are full of useless and untrue spam, and the presence of such spam can affect the quality of product review mining. This paper aims to study a method for identifying spam reviews of e-commerce products based on collaborative filtering algorithm. This paper analyzes the definition of collaborative filtering algorithm, the advantages and disadvantages of collaborative filtering algorithm and the definition of spam in the field of product reviews, and also analyzes feature extraction methods and experimental dataset sources and obtained experimental results. The empirical analysis results show that there are various types of spam comments in e-commerce product reviews, and the existence of these spam comments affects people's important interests and affects the performance of automatic opinion mining systems. Therefore, analyzing and identifying spam comments is a very meaningful study. After adopting the collaborative filtering algorithm, the performance of the traditional e-commerce product spam comment identification method has been greatly improved, and we conducted a survey and found that more than 87% of people felt that the design of the e-commerce product spam comment identification method applied the collaborative filtering algorithm, and found that the design efficiency has become higher.