Reviewerly/exHarmony huggingface dataset:
Network Security of University Financial Sharing Platform under the Background of Smart Campus

With the continuous advancement of the construction of smart campuses, the rapid development based on big data technology has greatly promoted the construction of college financial sharing platforms. In the context of smart campuses, the construction of the network security system of the financial sharing platform for colleges at this stage has taken a step forward. With the rapid development of information technology, the great data technology, artificial intelligence technology and other technologies shall be applied for the construction of a platform for the exchange of resources with a view to improving the security of the platform network. This paper takes smart campus as the theoretical basis of the research, uses big data technology and artificial intelligence technology as auxiliary research, and integrates its main content to analyze and research the improvement of the network security construction of the university's financial sharing platform. This document sets out as a research subject the Financial Distribution Platform of the University of Science and Technology, collects the data of various applications in the school financial sharing platform system, and uses the K-means clustering algorithm in the clustering algorithm to characterize the network security level of the university financial sharing platform Classification, and then use machine learning algorithms to perform correlation analysis on network security. Recognizing the important role of the early warning system in maintaining network security and the ideological security of university finance, system, the results of the experimental research show that this research is of great importance for maintaining the safety performance of the university platform network; economic distribution.
Data and Image Classification of Haematococcus pluvialis Based on SVM Algorithm

The growth process of Haematococcus pluvialis includes cell proliferation stage and astaxanthin accumulation stage. There are differences in cell radius, cell color and cell number between the two stages. In this paper, cell images and cell data of two growth stages were collected, and SVM algorithm was used to establish two classification models of data and image of Haematococcus pluvialis cell. 30% of the data in the samples were selected as the test set. In cell data binary classification problem, SVM classification algorithm was used, and the final classification result was 0.855; In the problem of image binary classification, the accuracy of the model is tested by the method of ten-fold cross validation, and the ROC curve and AUC area are used to evaluate the model. Finally, the classification result of the model for Haematococcus pluvialis cell image is 0.90, and the performance is good.
Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.
Exploring BYOD Usage in the Classroom and Policies

This paper presents results of an exploratory study that investigated instructor and student perceptions of in-class use of personal mobile devices and policies for appropriate practices. The study is based on an undergraduate course offered at a higher education institution in the United Arab Emirates. Eighteen students and one instructor participated in the study. A mix methods approach was adopted. Data collection consisted of a survey, focus group and individual interviews. Quantitative results suggested an overall tendency to use the devices rarely for content and non-content related activities. Qualitative results, however, indicated more usage of the devices, but not to the extent to cause disruptions in class. The study furthers the discussion on a bring your own device (BYOD) policy development. When developing BYOD policies for appropriate practices in the classroom, educational institutions should take into account students' context, culture and main stakeholders' opinion. Policy implementation should be complemented with training to support effective BYOD integration in the classroom.
Classification

Classification is a supervised machine learning method used to understand the internal structure and relationships between a previously known and labeled dataset and new, unseen instances. Classification has been a popular data science technique in a variety of disciplines for a century, and, especially since the advancement of computing power and the emergence of novel deep learning methods, it has even been (misleadingly) considered synonymous with data science and machine learning. This chapter will introduce the basic concept of classification, present multiple examples, and discuss potential applications for cryptocurrency blockchain data.
Approaches for M-Health Environment

It is a well-known fact that when a camera or other imaging system captures an image, often, the vision system for which it is captured cannot implement it directly. There may be several reasons behind this fact such as there can exist random intensity variation in the image. There can also be illumination variation in the image or poor contrast. These drawbacks must be tackled at the primitive stages for optimum vision processing. This chapter will discuss different filtering approaches for this purpose. The chapter begins with the Gaussian filter, followed by a brief review of different often used approaches. Moreover, this chapter will also render different filtering approaches including their hardware architectures.
Real Deep K-means with Multiple Auto-Encoders

K-means clustering separates a set of samples into several groups based on the similarities between samples. To further assess the nonlinear correlation between high-dimensional samples, existing deep K-means algorithms just exploit an auto-encoder to extract the inherent features of samples, and then perform K-means on it. From this letter, we present the real deep K-means clustering model with K auto-encoders where K is the number of clusters, which is named as DKMA. Specifically, the centroid of each cluster is acted by one auto-encoder, rather than the constant vector in the traditional K-means. Each sample decides its category by choosing one auto-encoder which reconstructs the sample point best. The extensive experimental results indicate that the our present approach performs better than the other clustering algorithms.
An Update on Bitcoin as a Digital Currency

The chapter “The Evolution of Virtual Currencies: Analyzing the Case of Bitcoin” by Manrique and Manrique was recently published in the book Information and Communication Technologies in Public Administration. It was written at the height of the bitcoin controversy when its value skyrocketed and waned. More than a year has passed since research on that was undertaken, and it seems that the issues with regards to bitcoin as a virtual acceptable currency has calmed down. It is the purpose of this chapter to give an update on the status of bitcoin as a currency and to determine its stability and ability to become a real currency.
Preface

Nowadays, the situation of COVID-19 pandemic is unpredictable around the world. Due to COVID-19 pandemic all over the world, almost countries have travel restrictions. Most of countries are still fighting the COVID-19 pandemic, so it is inadvisable to postpone the conference Taking all special situations into consideration, conference committees made a decision to change physical conference in to virtual format conference. It is feasible to hold virtual format conference, otherwise conference participants can’s go to the conference venue ane do oral presentations. We use zoom application which makes virtual format conference come true. List of Conference Committee, Statement of Peer Review and this titles are available in this pdf.
Introduction

In this chapter, an introduction to the essence of digital image forensics has been given, including its differences with more widely known multimedia security techniques such as watermarking and steganography. Using illustrative examples, several image forensics techniques have been discussed. This chapter aims to make the reader interested in this budding research domain.
Cloud Orchestration

The objective of this chapter is to introduce the reader to the basics of cloud orchestrationCloud orchestration in a multi-cloud environment. By the end of the chapter the reader should have a fair understanding about why we need a multi-cloud environment and hybrid IT, its challenges, and how cloud orchestrationCloud orchestration helps to resolve some of the challenges. The reader should also gain an insight into the currently available tools used in cloud orchestrationCloud orchestration .
The Internet of Things and Cognitive Computing

The Internet of Things (IoT) and Cognitive Computing are concepts that have been developing since the 1950s with varying nomenclature. Enabled by advances in technology such as the development of lower cost, lower power, smaller microprocessors and microcontrollers, cellular/wireless chips, and the enablement of more widespread machine-to-machine communications, IoT technologies are now more widely deployed. Advances in Artificial Intelligence (AI) and question-answering systems (Ferrucci et al., 2010; Simmons, 1970) have enabled development of cognitive computing systems which can be applied to numerous use cases. There is an opportunity to leverage IoT and cognitive computing technologies together with data analytics to develop deeper insights from the vast amount of data being created by IoT to create value for people, cities, and industry. In this chapter, we explore the state of the art and future opportunities to apply IoT and cognitive computing together in IoT uses cases for smart cities and connected healthcare, to improve efficiencies, outcomes, and the human experience.
Quantum Internet and E-Governance

We are leaving in the era where almost everyone in the world uses internet for the communication over social media site, shopping, E-commerce, online transaction and many more. The exponential growth in usage of internet resulted in security related challenges. Since last several years, traditional cryptography algorithms are found working well. Evolution of quantum computer and its high computing capability can break existing cryptography algorithms. To handle the security constraints, this chapter provides details on evolution of quantum cryptography, components involved to design network architecture for quantum internet, quantum key exchange mechanism and functionality wise stages for quantum internet. This chapter also includes challenges involved in evolution of quantum internet. Further, chapter also contains the details on e-governance, challenges in e-governance and solution using quantum cryptography.
Pengaruh People, Process dan Physical Evidence Terhadap Keputusan Mahasiswa Memilih STIBANKS Al Ma’soem (Tahun Akademik 2017/2018)

This research aimed to analyze the influence of People, Process and Physical evidence on the students' decision choice STIBANKS Al Ma’soem. This research uses quantitative associative method. The research data was obtained from primary data by distributing questionnaires to students in STIBANKS Al Ma'soem and then processed with IBM SPSS Statistic 23. The results showed that correlation is weak and there are not significant People, Process and Physical evidence on the students' decision choice STIBANKS Al Ma’soem. F test results showed that the factors of People, Process and Physical evidence does not significantly affect the students' decision choice STIBANKS Al Ma’soem. The R-square value obtained amounted to 0.1615, or 16.15%, that meaning simultaneous People, Process and Physical evidence contributes 16.15% to the effect on the students choice STIBANKS Al Ma’soem, and while the rest (1-R²) of 83.85% is the number of contributions made by other factors not examined. The study reveals that students expect fast response from the officer while giving information to students, such as academic information. Students also expect a simple procedure in order for their needs, such as to make academic letters. The management of STIBANKS Al Ma’soem must improve on other factors that can increase the number of students in the next period, such as service quality and promotion mix.
Predictive Analytics in Changing Environments: Trade-Offs in Response to Changes in Data Patterns

Despite advances in analytics techniques and access to abundant data, making accurate predictions is still challenging due to changing data environments as we lack sufficient information to adjust prediction models in a timely manner. Data analysts face two trade-offs – 1) whether or not they should use the large but potentially less relevant historical source data, and 2) whether they should adjust the prediction model immediately when change in data is suspected or later when more relevant post-change source data becomes available. These trade-offs are related to the fundamental bias-variance and exploration-exploitation trade- offs. To aid data analysts’ deal with these trade-offs, we develop theoretical insights by adopting a sample selection perspective to represent changes in data patterns. Based on the sample selection model, we propose a transfer learning framework that leverages the large historical source data. The framework allows us to theoretically study the two fundamental trade-offs. Our analysis shows that using historical source data offers relatively larger benefits when the prediction model has higher complexity, and when the extent of change is not too large. Moreover, the benefit also varies with the scarcity of post-change source data thus with the timing of adjusting the prediction model. Our theoretical analysis shows that the decisions on the two trade-offs interact with each other. Our analysis is further corroborated by Monte Carlo simulations in a data change detection and model adjustment context. Overall, this study provides theoretical insights and practical guidelines for the application of predictive analytics in changing data environments.
The Future of Digitized Materials: Where We Have Been and Where We’re Going

The E Text Center at the University of Virginia deals exclusively in humanities and social sciences texts and images, with a focus on owning rather than licensing content. After ten years as the founding director of the E Text Center, David Seaman is leaving at the end of July to become the new director of the Digital Library Federation. In his introduction, Seaman paraphrased the title of his talk, indicating that he would discuss the future of electronic publishing, or where e-publishing is going from a full-text perspective. The world of books has changed during the last two or three years, with an enormous growth in digital publishing and the advent of a delivery media other than the Web. Based on this study, he believes people will buy e-books for pleasure reading, but will not pay more than print equivalents.
Smart Services

Imagine a working scenario...On the envisioned new Silk Road we need efficient means of transport on the route between China and Europe. On the first stage the community agents shall recognize the needs and expectations of customers. After completing this task, planning the production and distribution across a network of intelligent agents would then provide for the best solution. In the process service agents of the involved customers, producers and consignors are cooperating as fair competitors to fulfill the required tasks. By such a scenario the citizens get the best product, price, time and so forth, according to their demands.
Dealing with Large Volumes of Data

This chapter is concerned with issues relating to large volumes of data, in particular the ability of classification algorithms to scale up to be usable for such volumes. Some of the ways in which a classification task could be distributed over a local area network of personal computers are described and a case study using an extended version of the Prism rule induction algorithm known as PMCRI is presented. Techniques for evaluating a distributed system of this kind are then illustrated. The issue of streaming data is also considered, leading to a discussion of a classification algorithm that lends itself well to an incremental approach: the Naïve Bayes classifier.
Identification of 3D Conformations of Alkanes Using Simple Augmented Reality App

An augmented reality app was prepared to present the 3D conformations of Alkanes. These 3D conformations are presented in the form of video. This app is prepared with a few free user-friendly gaming software that provides a platform to create links between the target image and the related video of the conformations. This app is useful to visualize the 3D molecule in space at different view angles, whose 2D image is printed on paper. The method of preparation of the app can be used for the representation of 3D conformation of any kind of molecules like all hydrocarbons, hetero compounds, macromolecules, biomolecules, complex compounds and supramolecules.
Research on information management innovation of graduation project (Thesis) in Universities

The graduation design (Thesis) of Application- oriented Colleges and universities in Shaanxi Province generally has the problems of poor quality and formality of practical activities. The main reason is that the research on management innovation lags behind the actual teaching needs. The author investigates and analyzes the existing management model of Applied Undergraduate Colleges and universities in Shaanxi Province. In order to solve the existing problems, it is proposed to refine specialty classification, strengthen specialty classification practice and innovate the diversified management of graduation design (Thesis) form in the information environment. Continuously improve the information management level of graduation project (Thesis) and improve the quality of graduation project (Thesis).
Forensic Endodontics

Each human tooth is distinct in itself, which makes dental identification in forensic odontology an integral part of forensic studies. Identification of charred bodies or persons burnt beyond recognition, is next to impossible and endodontics has a very important role to play here, as, in such cases, identification is done with the help of the distinctive nature of human teeth and the study of roots, root canal anatomy and biomaterials with the help of advances in endodontic imaging, researches and practice.
In-Memory Analytics

Analytics is the processing of data for information discovery. In-memory implementation of machine learning and statistical algorithms enable the fast processing of data for descriptive, diagnostic, predictive, and prescriptive analytics. In this chapter, the authors first present some concepts and challenges for fast analytics, then they discuss some of the most relevant proposals and data management structures for in-memory data analytics in centralized, parallel, and distributed settings. Finally, the authors offer further research directions and some concluding remarks.
Profiling Idioms:

This paper introduces a novel approach to the study of lexical and pragmatic meaning called ‘sociolexical profiling’, which aims at correlating the use of lexical items with author-attributed demographic features, such as gender, age, profession, and education. The approach was applied to a case study of a set of English idioms derived from the Pattern Dictionary of English Verbs (PDEV), a corpus-driven lexical resource which defines verb senses in terms of the phraseological patterns in which a verb typically occurs. For each selected idiom, a gender profile was generated based on data extracted from the Blog Authorship Corpus (BAC) in order to establish whether any statistically significant differences can be detected in the way men and women use idioms in every-day communication. A quantitative and qualitative analysis of the gender profiles was subsequently performed, enabling us to test the validity of the proposed approach. If performed on a large scale, we believe that sociolexical profiling will have important implications for several areas of research, including corpus lexicography, translation, creative writing, forensic linguistics, and natural language processing.
Artificial Neural Networks and Their Applications in Business

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.
Smart Contracts

OneSmart contract of the most exciting topics to emerge in the blockchainBlockchain ecosystem is the smart contractSmart contract. A smart contract is a computer program whose code is stored in a distributed blockchainBlockchain structure and that directly controls digital assets without relying on a third-party intermediary.
Research on Tibetan Word Segmentation Method Based on SOM

research-article Research on Tibetan Word Segmentation Method Based on SOM Share on Authors: Zejun Liu Tibet University, China Tibet University, ChinaSearch about this author , Jiao Wang Tibet University, China Tibet University, ChinaSearch about this author , Shutao Shen Tibet University, China Tibet University, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 376–379https://doi.org/10.1145/3495018.3495083Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Board 78: ILL Communication: Analyzing Five Years of Iowa State University's Print Interlibrary Loan Requests

not own the item, the library may own a copy but it is already checked out to another patron, or the assignment of a required but expensive textbook spurs high