Reviewerly/exHarmony huggingface dataset:
Framework&amp;nbsp;Development&amp;nbsp;for the&amp;nbsp;Selection of&amp;nbsp;Cloud Services for Construction Industry&amp;nbsp;– A Case of Conversational-BIM Chatbot

This study is to present a structural framework for selecting cloud services for implementing Conversational AI system in the construction industry using Design Thinking Methodology. A focus group discussion approach was used to get user requirements from construction workers to implement the Conversational AI for BIM. This resulted in five factors: cost, speed of operation, security, functionality, and visualisation as the requirements for implementing Conversational AI for the construction industries. The user specifications were mapped into technical modules which were used to select cloud services employed to implement the virtual assistant for the construction industry. Furthermore, the study presented the architecture of ConvBIM using AWS.
Full-scene fault research of primary and secondary fusion equipment based on data platform

A full-scenario fault analysis method for primary and secondary fusion equipment based on data platform is proposed. The types and methods of low-voltage distribution network fault scenarios are described, and the factors that affect distribution network fault simulation are analyzed. The single-phase grounding fault model of low-voltage distribution network is described, the small resistance grounding system model and the arc suppression coil grounding system model are established, and the influencing factors of the two when simulating faults are analyzed. Based on the data platform, the convolutional neural network fault diagnosis method is proposed, and the process analysis is carried out in combination with the functional characteristics of the primary and secondary equipment fusion device. The simulation analysis verifies the effectiveness of the fault scenario simulation proposed in this paper in terms of data accuracy and training data results.
Exploration of Paperless Intelligent Accounting Management Based on Financial Middle Platform

With the rapid development of economic globalization, the widespread application of computer technology, and the rapid progress of Internet information technology, enterprises of all levels of society are constantly exploring new management concepts and methods in various fields such as production, operation, and management. With the vigorous development of economic informatization, networking, digitization, and intelligence, the informatization of enterprise project management and the digitization of data materials have become an important prerequisite for modern enterprises to achieve better and faster development. This article aims to study the exploration of paperless accounting management based on financial middle platform, based on the analysis of the advantages of accounting paperless, the requirements of financial middle platform mode for accounting file management and the applicability of block chain in electronic invoice management. Designed an electronic accounting file management system, in order to verify the feasibility and effectiveness of the designed system, this article carried out a pilot business to verify. The results showed that after the electronic invoice and paperless accounting went online, the reporting time for a single employee reimbursement process was shortened to 5 minutes, and the reimbursement efficiency increased by more than 50%.
A Mathematical Framework for Learning Probability Distributions

The modeling of probability distributions, specifically generative modeling and density estimation, has become an immensely popular subject in recent years by virtue of its outstanding performance on sophisticated data such as images and texts. Nevertheless, a theoretical understanding of its success is still incomplete. One mystery is the paradox between memorization and generalization: In theory, the model is trained to be exactly the same as the empirical distribution of the finite samples, whereas in practice, the trained model can generate new samples or estimate the likelihood of unseen samples. Likewise, the overwhelming diversity of distribution learning models calls for a unified perspective on this subject. This paper provides a mathematical framework such that all the well-known models can be derived based on simple principles. To demonstrate its efficacy, we present a survey of our results on the approximation error, training error and generalization error of these models, which can all be established based on this framework. In particular, the aforementioned paradox is resolved by proving that these models enjoy implicit regularization during training, so that the generalization error at early-stopping avoids the curse of dimensionality. Furthermore, we provide some new results on landscape analysis and the mode collapse phenomenon.
Multi-Programming Implementation of Online Education Platform for Postgraduates based on Cyclic Graph Analysis

Build an online learning platform for postgraduates in colleges and universities based on the cyclic graph analysis big data network layered algorithm. Through online and offline services, the identification and conversion of various levels of learning achievements such as adult, TV university, higher vocational, and community education can be established to meet the individualization of students, diverse learning and development needs. The design principles of the platform, the components of the system and the key technologies of platform development were analyzed, and the development and realization process of personalized educational resources were discussed in order to promote the research and construction of educational resources and continuously improve the training of master of education. the quality of.
Post-Effects of the COVID-19 Pandemic: Satisfaction of Foreign Students with Telelearning in Russian Universities

Introduction. The influx of foreign students into domestic universities makes it possible to attract foreign intellectual resources, develop international cooperation and diplomacy, and tools for the principles of “soft power”. In this regard, the urgent task is to study the satisfaction of foreign students of Russian universities with the parameters of distance technologies in the environment of a digital alternative to the educational system of higher education in the context of the COVID-19 pandemic. Materials and Methods. Qualitative and quantitative methods of scientific knowledge were used as a tool for multi-stage research. At the first stage, a general theoretical method of analyzing the results of domestic and foreign studies was used. At the second, empirical stage of the study, the authors reasonably used methods of sociological survey of foreign students (N = 760 people) studying at universities located in various constituent entities of the Russian Federation, as well as an induction method for the purpose of interpreting and summarizing the results of the survey. Results. The assessment of the level of satisfaction with foreign students with the parameters of telelearning at Russian universities made it possible to develop practical-oriented recommendations in the field of digital competencies, technological and resource opportunities for better conduct of the educational process in remote digital format for foreign students in Russian universities. Discussion and Conclusion. The conclusions made by the authors make a theoretical and practical contribution to the development of scientifically based approaches and methodological tools for studying the level of satisfaction of foreign students with the parameters of distance learning in Russian universities. The materials of the article will be useful to scientists and researchers involved in such problems, as well as federal and regional governments, ministries of education, universities.
THE ROLE OF INNOVATIVE TECHNOLOGIES IN THE PROCESS OF TRAINING PROJECT MANAGERS

The article substantiates that the modern world does not keep up with the changes that aretaking place. Because of this, the problems of society become the subject of theoreticalresearch already after they have become quite acute and require immediate solutions. The authorresearched the concept and necessity of implementing project-based learning under theconditions of the digital transformation of education. The innovative technologies thatcan be effectively used to spread systemic thinking are presented. The considered methodsto form systemic thinking, characteristic features, and stages of problem-solving. It has beenproven that the solution to the problem is covered in the most detail through the developmentof a project plan. Therefore, in order to form systemic thinking, it is necessary to use theproject-based learning method in the educational process. Based on the research, directionsfor improving the educational environment for the formation of integration competencein the digital transformation of society are determined.
Natural Language Processing

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from an artificial intelligence perspective. In several domains, the importance of natural language processing is paramount and ever growing, as digital information in the form of language is ubiquitous. Applications of natural language processing include language translation, sentiment analysis, web search applications, customer service automation, text classification, topic detection from text, language modeling, and so forth. Traditional methods of natural language processing relied on the bag of word models, the vector space of word model, and on-hand coded knowledge bases and ontologies. One of the key areas for natural language processing is the syntactic and semantic analysis of language. Syntactic analysis refers to how words are grouped and connected in a sentence. The main tasks in syntactic analysis are tagging parts of speech, detecting syntactic classes (such as verbs, nouns, noun phrases, etc.), and assembling sentences by constructing syntax trees. Semantic analysis refers to complex tasks such as finding synonyms, performing word-verb disambiguation, and so on. As part of this chapter, we will begin with a brief introduction of the traditional natural language processing techniques and then move on to deep learning-based methods involving recurrent neural networks, attention, and transformers.
Image Colourization using Clustering Technique

Colorization of grayscale images has become a more researched area in the recent years, thanks to the advent of K-mean clustering networks. We attempt to apply this concept to colorization of real images obtained from video sequences. Previous similar research focused mainly colorization of natural images, while colorization of real is traditionally done by leveraging manual scribble methods. Our proposed method is a fully automated process. To implement it, we propose and compare two distinct K-mean clustering architectures trained under various loss functions. We aim to compare each variant based on results obtained as individual images.
PDE: A Real-Time Object Detection and Enhancing Model under Low Visibility Conditions

Deep object detection models are important tools that can accurately detect objects and frame them for the user in real time.However, in low visibility conditions, such as fog or low light conditions, the captured images are underexposed and blurred, which negatively affects the recognition accuracy and is not well visible to humans.In addition, the image enhancement model is complex and time-consuming.Using the image enhancement model before the object recognition model cannot meet the real-time requirements.Therefore, we propose the Parallel Detection and Enhancement model (PDE), which detects objects and enhances poorly visible images in parallel and in real time.Specifically, we introduce the specially designed tiny prediction head along with coordinated attention and multistage concatenation modules to better detect underexposed and blurred objects.For the parallel image enhancement model, we adaptively develop improved weighting evaluation models for each "3D Lookup Table" module.As a result, PDE achieves better detection accuracy for poorly visible objects and more userfriendly reference in real time.Experimental results show that PDE has significantly better object recognition performance than the state-of-the-art on real foggy (8.9%) and low-light (20.6%) datasets.
Facial Gesture Recognition Based Real Time Gaming for Physically Impairment

Technology has refined online gaming by introducing new engagement modes that are being created at a rapid pace in today’s society. Eye blinking and facial movement is a basic nonverbal interaction that is increasingly evolving in the next generation. This eye-gazing technology and facial feature movement create a communication bridge for individuals with impairments. With the aid of a highly efficient library, OpenCV intended to handle Computer Vision issues and PyAutoGUI, which enables the Python scripts to manage the mouse and keyboard to automate interactions with other programs, developers can automate the game by blinking the eyes and other facial gestures. This paper is written to illustrate a Python alternative to commercial systems like Tobii and Kinect, so that the code may be used in mobile devices and apps with limited CPU power. Our research focuses on the virtual keyboard, which not only triggers keys through facial gestures but may also give help to those who are physically impaired. To do this, we have used the built-in camera on our computer/laptop, which identifies the face and features of the face. This makes detecting the face far more straightforward than before. A mouse click on the virtual interface can be replaced by an eye blink or lip movement. The ultimate goal is to enable a computer-human connection based solely on the movement of facial features, allowing physically handicapped persons to communicate with the computer and play games easily using a facial based interface.
Deep Learning and Sign Language Models Based Enhanced Accessibility of e-governance Services for Speech and Hearing-Impaired

Sign Language is the basic building block to communicate with the hearing and speech impaired. This can be made easy by developing a robust system to transcribe real-life spoken language sentences into its sign language sequence video and contra wise. Such a system is built with both a sign recognition unit and a sign translation unit. In this paper we provide an in-depth analysis of the existing proposed models to develop such a robust system, discussing their pros and cons. In addition to that, we evaluate the performance of those models based on the quality outcome from the video generation unit. We also brief the future scope in establishing real-life SLP communication models build with advanced deep learning architectures for the hearing and speech disabled thus paving the way to impart education and employment among the hearing and speech impaired.
Hierarchical Graph Representation Learning with Structural Attention for Graph Classification

Recently, graph neural networks (GNNs) exhibit strong expressive power in modeling graph structured data and have been shown to work effectively for graph classification tasks. However, existing GNN models for predicting graph categories frequently neglect the graph hierarchy information or fail to accurately capture the graph substructure, resulting in significant performance decreases. In this paper, we propose Hierarchical Graph Representation Learning (HGRL), a multi-level framework for capturing hierarchical local and global topological structures to enrich graph representations. In specific, we utilize a structural coarsening module that generates a series of coarsened graphs for an input graph instance, followed by a graph encoder to preserve the local graph structure information. Furthermore, graph convolutional networks are layered to capture high dimensional proximity in graphs, and we incorporate the attention mechanism for entire graph embedding, which enables our framework to focus on critical nodes with significant contribution to the learned low-dimensional graph representations for subsequent graph classification tasks. Experimental results on multiple benchmark datasets demonstrate that the proposed HGRL can substantially improve the classification accuracy and outperform the existing state-of-the-art graph classification approaches.
The Influence of Social Media, Brand Image and Price on Purchase Decisions on Crypto Asset Exchange Platform Applications

This research study investigates the influence of social media, brand image, and price on cryptocurrency consumer purchasing decisions within the context of Tokocrypto applications. The study employs a quantitative approach, utilizing a non-probability sampling method with purposive sampling techniques to gather data from 100 respondents through online questionnaires distributed via Google Forms. The collected data is analyzed using multiple linear regression through the SPSS Statistics program (version 18). The results reveal significant relationships between social media, brand image, and price with purchasing decisions. Specifically, social media and brand image positively impact purchasing decisions, while price exerts a negative influence. Furthermore, when considered together, social media, brand image, and price collectively affect consumer purchasing decisions. These findings provide valuable insights for businesses operating in the cryptocurrency market, enabling them to devise effective marketing strategies and enhance customer engagement to drive purchasing decisions.&#x0D; Highlights:&#x0D; &#x0D; The study examines the influence of social media, brand image, and price on cryptocurrency consumer purchasing decisions.&#x0D; Quantitative methods are utilized, with a sample of 100 respondents and data collected through online questionnaires.&#x0D; The results demonstrate the significant impact of social media, brand image, and price on consumer purchasing decisions in the context of Tokocrypto applications.&#x0D; &#x0D; Keywords: Social Media, Brand Image, Price, Cryptocurrency, Purchasing Decisions.
A Mixed Study on the Student Agency of College Students Participated in Basic Subject Class of Major Based on Flip Learning

The purpose of this study is to analyze the impact of flipped learning classes on college students and to examine the meaning of their experience of participating in such classes. To this end, a survey of 29 college students who took the A University Culture and Education course was conducted on self-directed learning ability and academic self-efficacy before and after learning. In addition, in order to examine the learners’ flipped learning experiences from the perspective of student agency, an FGI interview was conducted with six college students, and their reflection journals were analyzed. As a result, we found that college students had more significantly higher self-directed learning ability and academic self-efficacy scores after taking the flipped learning class than before taking the class. In addition, as a result of analyzing the contents of the FGI interview and reflection diary, we derived the following: ‘reflection on the class experience’, ‘interest and responsibility for the surroundings’, and ‘change through flipped learning classes’. Their diaries also expressed the level of agency the students felt. In this study, quantitative research was conducted on a single group, and there were limitations in the interpretation of research results. Thus, systematic experimental research is needed in future studies. In addition, in the FGI interview, there were regrets expressed about the ‘burden of the role within the group’, the ‘passive attitude of the group’, and the ‘lack of understanding of the concepts presented in online classes’. Accordingly, in future studies, it was suggested that it is necessary to develop a flipped learning class that better reflects the learner’s experience.
Exploring question generation in medical intelligent system using entailment

The concept of a medical intelligent system has steadily garnered attention as modern technology advances. An intelligent medical system is a medical system that develops a certain amount of intelligence and performs a task without the assistance of a human. A chatbot can be thought of as a medical intelligent consultation system. The chatbot's question generation quality can be improved by creating more relevant questions depending on the patient's demands. Question generation, in addition to chatbots, is used to assess a learner's comprehension. This paper proposes a two-step approach to question generation. The first stage generates the entailment for the sentence that the question should be generated for. The generated entailed sentences are used to create questions in the second step. By generating questions from the original sentence, one can discover relevant information about the sentence. Furthermore, to increase the size of the entailment dataset, a data augmentation approach is used in this paper. The proposed work in this paper focuses on the importance of entailment in question generation and also studies the influence of entailment on the questions generated. Since data augmentation is employed, the overall effectiveness of data augmentation on the model is also investigated.
The professionalisation of teaching staff in a different paradigm

The professionalisation of teachers requires certain innovations, which also mean certain paradigmatic ingredients. The professionalisation of teachers implies an activity of training and efficient application of professional competences by becoming an integral teacher based on contextual integrity that is being structured in a new direction, motivated and determined by a new scientific, reflective, professional and educational framework.
EFL Students’ Difficulties in Answering Listening TOEFL of Short Dialogue Test

This study investigates the most difficult parts in listening comprehension test faced by undergraduate students from Universitas Teuku Umar. The subject was 30 students who have taken the TOEFL test more than three times. The study used descriptive qualitative research. The data were taken from students’ TOEFL score and interview. This study focused on 13 aspects of short dialogues (synonym, similar sound, Who, What and Where, Passive, Negative Expression, Double Negative, Negative with Comparative, “Almost Negative”, Expression of Surprise, Expression of Agreement, Uncertainty and Suggestion, Wishes and Untrue Condition, Idiomatic Expression) which adopted from Longman Complete Course for the TOEFL Test. The result found that the most difficult aspects in listening comprehension of short dialogue test were similar sound, uncertainty and suggestion, idiom words, and synonym. It was revealed that the most difficult aspect in listening Part A (short dialogue) is similar sound that was proven from students’ incorrect answer (97%), only 3% of them answered correctly, followed by Uncertainty and Suggestion (94%) and idiom words (94%), only 2% of students answered correctly from each of the aspect, and synonym (90%), 3% of them chose the right answers. The result of interview found that most of them got unfamiliar with the sound, lack of practice and low motivation in learning a foreign language. It can be stated that the most difficult aspect in listening short dialogue is similar sound and the factors that cause them hard to answer short dialogue part come from external (unfamiliar sound) and internal problems (lack of practice and low motivation). Keywords: Listening Comprehension, Short Dialogue Problems, TOEFL Test.