Reviewerly/exHarmony huggingface dataset:
Data Crawler Data Collection and QoS Optimization Path of Smart Course Online + Offline Evaluation Platform

Based on the development background of "Internet +", this paper introduces the advantages of the application of the smart teaching mode, analyzes the problems existing in the smart teaching of vocational education courses, and explores the countermeasures for the application of the smart teaching mode in vocational education. The influence of internal factors such as educational emotion and information teaching technology and external factors such as schools and social environment, web page analysis technology, database technology, Web technology and GIS technology for extraction, storage, and spatial analysis of real estate data, "smart learning platform", etc. The core link, the routing strategy is mainly implemented by the QoS routing control framework, and the physical topology discovery module in the framework, in order to analyze the internal elements of the teaching paradigm change from the perspective of technical support.
Front Matter for Geo-Congress 2023: Geotechnics of Natural Hazards

Front matter pages come before the papers or chapters in a published work. Front matter includes the title page, copyright and notices page, and table of contents. It can also include a foreword, preface, or introduction; series information; lists of contributors, sponsors or reviewers; lists of abbreviations and notations; and conversion tables.
Digital Documents Verification System

In our day to day life everything becomes digital. Now every manual works are converted into machine work. Early days storing documents was a big problem. At first documents are stored in hard disk then in CD. and now in pendrive which is small in size and light weighted. Now documents can be saved in cloud by which user can get the documents anytime at anywhere. The rapid growth in technology sharing of information among individuals drive the people towards the digital documentation. Here with this rapid growth in information technology faking of documents arises. Verification and authentication of important documents like education documents, bank documents etc.are needed. The verification ofthis document is complex and time consuming process. Using cloud storage the web applications are stored. And the each document is given unique hash value. So the verification of the documents is much faster and more convenient than traditional method. Our system successfully passes all the criterias for document verification by using live face verification.
Exceptions

If an error occurs in our program, we want to be able to handle it in some way. One way to do this is through exceptions. Exceptions are mechanisms where we try to execute some code in the try{} block, and if an error occurs, an exception is thrown. The control is then transferred to a catch clause, which handles that exception. A structure of a try/catch block would be:
Balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems

The recommender systems have been widely applied in numerous applications that support online retailers, video sharing websites, medical systems, etc. Similar measures are essential in providing valuable recommendations to users in such systems. This work presents a novel approach, namely Ball-Sim, with a new similarity metric using a balltree structure for recommender systems. Furthermore, we want to leverage the tree structure to determine the closest k nearby users to improve the recommender systems’ efficiency. The work’s experimental scenarios outlined the steps of building a balltree and identifying nearby users based on the tree structure. Besides, the work also evaluates the implemented recommender system by comparing the recommender system’s results based on the balltree-based spatial partitioning with the recommender system using the default parameters. The data used in the experiments is the Movielens dataset, a web-based film recommender system, and an important data source for evaluating the studies, with 100,000 samples, including ratings from 943 users for 1,664 movies. The results show that the recommender system with a balltree-based similarity metric can improve the accuracy compared to a commonly-used measure such as the cosine metric.
Constants

When we want to have a read-only object or promise not to change the value of some object in the current scope, we make it a constant. C++ uses the const type qualifier to mark the object as a read-only. We say that our object is now immutable. To define an integer constant with a value of five, for example, we would write
COMMERCIALIZATION OF PROFESSIONAL EDUCATION IN RUSSIA FROM THE PERSPECTIVE OF WORKFORCE TRAINING

The subject of the research is changes in the training of the workforce in the field of higher and secondary vocational education due to the growth of its commercialization. The purpose of the work is to analysis of quantitative and structural changes occurring in these conditions, identification of their causes and evaluation of the results. The relevance of the study is due to the need for a comprehensive analysis of the impact of commercialization on the development of the professional aspect of education. The scientific novelty consists in the theoretical substantiation of the social essence and approaches to the development of education in a mixed economy, determining the role of commercialization among modern factors influencing professional education, and identifying its positive and negative results. Methods of factor and statistical analysis, sociological surveys, comparative logic, systematization, normative (prescriptive) modeling, etc. are used in the study. Based on the results of the study, the authors came to the following conclusions: current professional education in Russia is a mixed public good that has two forms of expression: free, state-guaranteed, as well as private, commercial; the terminological identification of a good that forms and develops a person with one of the services acquired by a person distorts the meaning of this relationship. The nature of the relationship between property relations, opportunities, and real trends in the financing of professional education in Russia is revealed. The assessment of the role and contribution of the commercial sector by levels and types of professional training of the workforce is given. All the pros and cons of the commercialization of professional education in the country are analyzed, and its impact on the level of its development and changes in its structure is revealed. The main problems in the area under consideration are systematized, and approaches to their solution are outlined.
Logics. Mathematics. Rationalism: From Parmenides to Heraclitus

The modern crisis of scientific knowledge is manifested in the overdifferentiation of knowledge, the loss of a unified picture of the world, dead ends in the development of various sciences. A way out of the crisis is possible when scientists master the synergetic style of thinking, i.e. thinking in “moving thought forms”. The paradigm of Parmenides, accepted in scientific thinking, is gradually being replaced by the paradigm of Heraclitus. The thesis “The One is essentially motionless” is replaced by the thesis “Everything is movement”. The new rationalism will require a revision of many outdated traditions of scientific knowledge. We consider these problems in the context of the development of logic and applied mathematics. Applied mathematics and its transformation into an industry of strategic importance are playing a key role in changing the paradigm of science.
The Development of Spondee Word in Garhwali Language: A Preliminary Study

Background: Garhwali is spoken by people in Uttarkashi, Chamoli, Tehri Garhwal, Pauri Garhwal, Dehradun, Rudraprayag, and Haridwar districts of Uttarakhand, India. Aims and Objectives: The present study focused on the development of spondee words in Garhwali language. Materials and Methods: The final spondee word list included 50 words. A total of 30 adults were participated in the present study. The test started with obtaining thresholds of AC and BC between the frequencies 250 and 8 KHz of the individual. Results: The mean pure tone average for frequencies 500 Hz, 1 KHz, 2 KHz, and 4 KHz was 10.46 dBHL, then the spondee word list was presented at the level of 20 dBSL. The speech recognition threshold (SRT) mean threshold was 11 dBHL. Conclusion: This study provided evidence on the quality of being especially suitable for paired words for testing SRT in Garhwali people. This test material can be used in selecting appropriate rehabilitative options and also to measure the efficacy of different rehabilitative devices.
Personalized Recommendation Model Based on Improved GRU Network in Big Data Environment

To address the diversity of user preferences and dynamic changes of interests in the personalized recommendation scenario, a personalized recommendation model based on the improved gated recurrent unit (GRU) network in a big data environment is proposed. First, in order to deal with outliers in sequence recommendation, context awareness sequence recommendation is introduced, and the dynamic changes of users’ interests are modeled by redefining the update gate and the reset gate of the GRU. Then, the duration information about how long users browse each item is processed and transformed to obtain the duration attention factor of each recommended item. And the duration attention factors and the item information are together used as the input of the proposed model for training and prediction. Finally, the auxiliary loss function is introduced to make up for the shortcomings of the traditional negative logarithmic likelihood function, and a super-parameter is applied to combine the auxiliary loss function with the negative logarithmic likelihood function so as to enhance the relationship between the interest representation and the accuracy of recommendation. Experiments show that the root mean square error (RMSE) of the proposed method in the Criteo dataset and MovieLens-1M dataset is 0.7257 and 0.7869, respectively, and the mean absolute error (MAE) is 0.5147 and 0.5893, respectively, which are better than those of the comparison methods. Therefore, the proposed method significantly outperforms the comparison methods in improving the accuracy of personalized recommendation in the system.
Multi-domain authorization and decision-making method of access control in the edge environment

The problem of mutual trust between users and edge nodes in an edge computing environment is a sufficient guarantee for the double security of edge computing service users and service providers. How to achieve the user's security and credibility and ensure the trust of the edge service nodes is a serious challenge facing the security of edge computing. Therefore, the research target of this paper is to establish the mutual trust relationship between users and edge service nodes and to formulate dynamic access control strategies. While realizing user security and trustworthiness in the edge environment, it also ensures the trustworthiness of service nodes. The research content of this paper is the access control model of mutual trust between nodes and users in the edge computing environment. This model closely combines trust management with the RBAC (Role Based Access Control) model. Given the multi-domain characteristics of the edge computing environment, the intra-domain access control and cross-domain access control strategies based on mutual trust are implemented, which realizes the improvement and expansion of the RBAC model in the edge computing environment. The performance of EUSMTM is compared and analyzed through simulation experiments, and the validity of the trust model between users and edge servers is verified. Then through the comparative experiment of two-way trust and one-way trust between edge users and edge servers, the advantages of access control based on two-way trust in the EUSMTM model are analyzed.
Who Owns Bibliographic Metadata Created by Libraries?

AbstractThe ownership of MARC bibliographic data has been an issue between OCLC and other companies in the marketplace. Two lawsuits are discussed between OCLC and Clarivate and SkyRiver. The “WorldCat Rights and Responsibilities for the OCLC Cooperative” provides a basis for many library contracts and how OCLC lays claim to bibliographic data. The “ICOLC Statement on the Metadata Rights of Libraries” is used as a general framework for discussing issues surrounding who owns MARC records and how they can be used or re-used. Most librarians are interested in free and open use of MARC records whether or not they may have passed through the OCLC system at some point.Keywords: MARC21MARCmachine readable catalogingbibliographic descriptionMARC record ownershiplawsuitsOCLCclarivateSkyRiverICOLC
Conditional Cross-Platform User Engagement Prediction

The bursting of media sharing platforms like TikTok, YouTube, and Kwai enables normal users to create and share content with worldwide audiences. The most popular YouTuber can attract up to 100 million followers. Since there are multiple popular platforms, it’s quite common that a YouTuber publishes the same media to multiple platforms, or replicates all media from one platform to another. However, the users of different platforms have different tastes. The media that is popular on one platform may not be a great vogue on other platforms. Observing such cross-platform variance, we propose a new task: estimating the user engagement score of a media on one platform given its popularity on other platforms. This task can benefit both the YouTubers and the platform. On one hand, YouTubers can use the predicted engagement to guide the media reworking; on the other hand, the platform can use the predicted engagement to establish promotion and advertising plans. Therefore, this task is of great practical value. To tackle this task, we propose a disentangled neural network that can separate the general media adorability from platform inclinations. In this manner, by substituting the inclination from the source platform to the target platform, we are able to predict the user engagement in the target platform. To validate the proposed model, we manage to build a dataset of micro-videos which are published on four platforms TikTok, Kwai, Bilibili, and WESEE. The experimental results prove the effectiveness of the proposed model.
EMOTION RECOGNITION BY TEXTUAL TWEETS USING VOTING CLASSIFIER (LR-SGD)

The analysis of social media posts is a grueling task, particularly the recognition of stoner feelings.Text is one of the most common mediums used by humans to express emotion, particularly on social media platforms.Twitter is being used to collect views about products, trends, and politics.Sentiment analysis is a fashion used to dissect the station, feelings and opinions of different people towards anything, and it can be carried out on tweets to dissect public opinion on news, programs, social movements, and personalities.By employing Machine literacy models, opinion mining can be performed without reading tweets manually.Emotion plays a vital part in mortal day-to-day life conveying their passions and situation in their life.feelings can be expressed using speech, gestures facial expression, or textbook.happy or joy, sad, nausea, wrathfulness, fear and surprise as introductory emotion.Emotion Discovery from textual source can be done using Logistic Retrogression-Stochastic grade Decent.
Anonymous Proxy signature scheme based on multivariate polynomials over finite field

Proxy signature schemes can be used in all the scenarios where a signer is not able to sign an important message. In some cases which involve a group of proxy signers, the anonymity of the proxy signer is required. We propose the first multivariate anonymous proxy signature scheme over finite field, and discuss all its security properties such as anonymity, unforgeability, verifiability, distinguishability, and undeniability. Additionally, we give a toy example to demonstrate the mathematical functioning of the proposed scheme. We compare our scheme with the existing post-quantum anonymous proxy signature scheme.
A Fuzzy Comprehensive Evaluation Method of Regional Economic Development Quality Based on a Convolutional Neural Network

This paper presents an in-depth research analysis on the evaluation of the development quality of regional economy through an improved convolutional neural network algorithm, and uses it to design a fuzzy comprehensive evaluation model for the practical process. Based on the measured indices of different variables, a spatial econometric model is constructed and provincial panel data are selected to empirically analyze the impact and spatial spillover effects of financial agglomeration and technological innovation on regional economic quality development from both static and dynamic aspects and to examine the spatial correlation of the factors. A new serial data flow model is adopted, which optimizes the control of data flow in convolutional computation, reduces the percentage of clock cycles used to read memory data, and increases the computational efficiency. At the same time, with dynamic data caching, a convolutional computation can be completed in one clock cycle, reducing the memory capacity required for caching intermediate data. The effectiveness of the evaluation system constructed in this paper is further tested. Most of the indicators have a significant positive or negative impact on the quality level of economic development, and the direction of the impact is consistent with the positive and negative attributes of the indicators in this study, which verifies the validity of the evaluation indicator system constructed in this paper. In summary of the study, effective suggestions are made in terms of human capital investment, reasonable allocation of fiscal expenditure, enhancing regional greening development and improving risk prevention measures.
Report of Alternative ☆ Energy Contest

The Alternative ☆ Energy Contest was launched in 2008 as the new energy utilization contest for students of universities and National Institute of Technologies, which was held in collaboration with Technology and Society Division, The Japan Society of Mechanical Engineers. In this report, the number of applications, participants and sponsoring companies were investigated in the 14th Alternative ☆ Energy Contest held at College of Engineering, Nihon University on Saturday, October 16, 2021. In addition, we classified the energy sources and energy conversions in the presentation.
UBRMTC: User Behavior Recognition Model With Transaction Character

Behavior analysis has been used widely in antifraud transactions. However, existing methods of behavior analysis mainly focus on behavior patterns and do not fully consider the behavior psychology of users in the transaction process, which affects the precise recognition of fraudulent behaviors. It is difficult to recognize fraudulent transactions precisely how to describe the user’s behavioral psychology and integrate the behavioral psychology into the transaction behavior. Thus, this article first proposes the transaction character model based on the user’s cautiousness to reflect the user’s behavioral psychology. This model is built from the user’s historical normal interaction behavior data. Then, a user behavior benchmark is established to reflect the user’s behavior pattern from the user’s historical normal transaction behavior data. To integrate the user’s transaction character and user behavior, the mapping relationship model is built by using the least-squares generalized inverse method. This model is the core of the fraudulent behavior recognition method with transaction characters. Experiments in fraud detection scenarios show that the new method improved the average recognition performance of four fraud detection indicators (recall rate, precision rate, accuracy rate, and F1 value) by 23%. The method also shows that individual psychological character has a great influence on user behavior.
Online Recommendation System Using Collaborative Deep Learning

Recommendations engines are information filtering technologies that employ various frameworks as well as data to inform a specific user/viewer of the most important aspects. Recommendation systems for content streaming platforms or OTT platforms are critical for responding to the changing demands of a large audience with high-quality, error-free content. Collaborative filtering and content-based filtering are two main ways of implementing a recommendation system that has been presented. Both strategies have advantages, yet they are ineffective in many situations. To boost performance, a variety of hybrid solutions are being examined. This research provides Hybrid User Profiling recommender systems for OTT platforms based on the Movie Lens Dataset and uses Machine Learning techniques as Collaborative learning. The result shows that the proposed method has an MSE of 0.67, a root mean squared error of 0.82, and a training time of 52.46 s. The proposed algorithms have better outcomes in terms of performance parameters like MSE, RMSE, and training time, indicating that they may assist individuals in discovering useful and relevant content as compared to other algorithms.
CERTIFICATE VERIFICATION AND VALIDATION USING BLOCKCHAIN

Everything is digitalized in the digital age, including the SSLC, HSC, and academic certificates, which are sent to students and digitalized at educational establishments.Students have difficulty maintaining their degree credentials.Organizations and institutions find it difficult and time-consuming to verify certificates.By storing the certificate in the Blockchain system, our proposal will aid in its security.Advanced certificates are first made from the paper certificates.The certificate's hash code esteem is made utilizing the turbulent calculation.From that point onward, the confirmations are saved in Blockchain.Moreover, these certificates are confirmed through the versatile application.
DIPA : An Image Dataset with Cross-cultural Privacy Concern Annotations

Image privacy protection is an important topic in Human-Computer Interaction and usable security. Researchers have examined different aspects of image privacy by collecting samples by themselves. However, there does not exist a publicly-available dataset on image privacy, which prevents these efforts from sharing common technical foundations. We introduce DIPA, an open source dataset that provides content-level annotations that specifically focus on image privacy. We include 1,495 images from two existing datasets in DIPA, and augment them with 5,671 annotations. Each annotation includes reasons why the associated visual content can be privacy-threatening, a rating of how informative annotators thought the associated content is to threaten privacy, and another rating of how broadly the image could be shared. We also collected annotations from people living in Japan and UK to enable researchers and developers to perform analysis from the perspective of cultural differences. In this paper, we present the construction procedure of DIPA and report high-level statistics of the data we obtained. We hope that DIPA would accelerate various future research, including quantitative understandings of cultural differences on perceptions of image privacy and the development of robust recognition models for image privacy protection.