Reviewerly/exHarmony huggingface dataset:
Case Study: Instance Segmentation

Computer vision is a field that automates tasks such as ascribing highlevel descriptions to images and videos. It has been applied to a wide variety of domains ranging from the highly technical such as automatic tagging of satellite images or analysis of medical imaging [1] [2], to the more mundane such as categorising pictures in your phone or creating an emoji from a picture of your face. This field has seen tremendous progress since 2012, when A. Krizhevsky et al. first used deep learning in computer vision and crushed their opponents in the ImageNet challenge [3]. We have already discussed the image recognition task in the previous chapter. Here we introduce another classical computer vision task, Instance Segmentation, which labels objects within an image. We will discuss its connection with other similar applications, how the deep neural networks are constructed in OCaml, and how such a network when loaded with pre-trained weights, can be used to process users’ input images.
Application of Data Mining Technology in Abnormal Financial Data Identification

In order to better identify abnormal financial data through data mining technology, ensure the quality of financial data, this paper will carry out relevant research, mainly discusses the basic concept of data mining technology, and then build abnormal financial data identification system around this technology. The system can give full play to the role of data mining technology and accurately identify abnormal financial data.
SEGP: Stance-Emotion Joint Data Augmentation with Gradual Prompt-Tuning for Stance Detection

Stance detection is an important task in opinion mining, which aims to determine whether the author of a text is in favor of, against, or neutral towards a specific target. By now, the scarcity of annotations is one of the remaining problems in stance detection. In this paper, we propose a Stance-Emotion joint Data Augmentation with Gradual Prompt-tuning (SEGP) model to address this problem. In order to generate more training samples, we propose an auxiliary sentence based Stance-Emotion joint Data Augmentation (SEDA) method, formulate data augmentation as a conditional masked language modeling task. We leverage different relations between stance and emotion to construct auxiliary sentences. SEDA generates augmented samples by predicting the masked words conditioned on both their context and auxiliary sentences. Furthermore, we propose a Gradual Prompt-tuning method to make better use of the augmented samples, which is a combination of prompt-tuning and curriculum learning. Specifically, the model starts by training on only original samples, then adds augmented samples as training progresses. Experimental results show that SEGP significantly outperforms the state-of-the-art approaches.
Infographics to Develop Graphical Literacy

Tools for easily creating infographics are widely available, both online and through statistics, mathematics, and other programs. Determining the appropriate graphs to produce for different kinds of data is an important skill for students at all levels to learn, as is determining the best graph for a specific audience. With the increased availability of data comes the increased expectation that researchers in all disciplines can effectively communicate their findings to a wide range of audiences. Experts in graphical design have defined aspects of “graphical excellence,” but the effectiveness of graphically portrayed information depends a great deal on the needs and abilities of the intended audience. To create effective graphs, students not only need to be familiar with tools for creating graphs, they also need to be familiar with the communication, cognitive, and aesthetic principles associated with infographic design.
SAP S/4HANA on Microsoft Azure – Concepts and Architecture

This chapter provides an introduction to the basic concepts and functionality of Microsoft Azure Cloud. The aim of the chapter is to provide a detailed introduction to cloud hosting and, in particular, to create a theoretical understanding of the various components and terminology. It thus forms the basis for the practical implementation of the exemplary system architecture in Chapter 7 . After a brief introduction to the history of Microsoft Azure, the governance structures are first explained, which enable the mapping of complex corporate structures such as responsibilities and approval processes in Azure. Aspects that are illuminated here concern account management by means of subscriptions and management groups, as well as role concepts and quotas. Finally, the main part of the chapter deals with the actual resource management. For this purpose, all terminologies and provisioning options relevant for SAP hosting are explained. The focus here is on provisioning using virtual computers; alternative offerings such as Hana Large Instances are also briefly addressed. Finally, the chapter concludes with a presentation of an S/4HANA reference architecture presented on the official Microsoft documentation. In particular, the interaction of the components described at the beginning of this chapter is discussed.
Research on technical support ability and collaborative planning algorithm in online examination

Starting from a typical online examination EBMG (Examination Behavior Model Graph), this paper obtains the relationship between the examination effectiveness index and the examinee scale through the probability data of the examiner’s examination response behavior, establishes the optimization model of the hardware structure by using the network maximum flow theory, and finds the relationship between the technical support ability and the examinee scale through the analysis of the model. Based on these two relationships, we find a collaborative planning algorithm, which transforms the relationship between the examinee scale and the technical support ability of the system under the same quality of service into the network flow diagram of online examination, and obtains the best cost-effective path of online examination system through the maximum flow algorithm.
Exchange Rate Developments of Cryptocurrencies Based on Event Study Analysis

As the cryptocurrency market dynamically evolves, important financial and economic issues arise. The main focus of the present research is on the price of cryptocurrencies. Following the exploration of the literature base, special emphasis was put on the comparison between the crypto market and markets for different asset classes (gold, stocks, foreign currency) and on the identification of connection points. Next, the article focuses on the period after 2020, and applies the event study methodology in order to establish, how the two cryptocurrencies with the highest market capitalization (Bitcoin and Ethereum) reacted to selected events. These events mainly encompassed hacker attacks aimed at the systems that form the basis of the operation of cryptocurrencies, and also certain steps regarding their regulation and application. Overall, it was established that hacker attacks did not have a significant effect on the exchange rates of the two examined cryptocurrencies. Effects of regulatory action on prices are mixed, however even significant effects can be regarded as short-lived.
Seeking optimal parameters for achieving a lightweight reservoir computing: A computational endeavor

&lt;abstract&gt;&lt;p&gt;Reservoir computing (RC) is a promising approach for &lt;italic&gt;model-free&lt;/italic&gt; prediction of complex nonlinear dynamical systems. Here, we reveal that the randomness in the parameter configurations of the RC has little influence on its short-term prediction accuracy of chaotic systems. This thus motivates us to articulate a new reservoir structure, called homogeneous reservoir computing (HRC). To further gain the optimal input scaling and spectral radius, we investigate the forecasting ability of the HRC with different parameters and find that there is an ellipse-like optimal region in the parameter space, which is completely beyond the area where the spectral radius is smaller than unity. Surprisingly, we find that this optimal region with better long-term forecasting ability can be accurately reflected by the contours of the $ l_{2} $-norm of the output matrix, which enables us to judge the quality of the parameter selection more directly and efficiently.&lt;/p&gt;&lt;/abstract&gt;
Navigation of Cart Follower Using Artificial Intelligence

Wheelchair users encountered difficulties in carrying their items. A person who uses a wheelchair will get easy to move around with a wheelchair cart follower should be implemented to track and follow the wheelchair automatically while maintaining wireless connect 40 cm with the wheelchair to avoid any unnecessary collision. This project is developed using the camera and ultrasonic sensor inputs as the input to artificial intelligence (AI) to achieve such specifications. In this project, the target object, the wheelchair, is illustrated using a frisbee, while a robot car model illustrates AI. An obstacle avoidance algorithm and an object tracking algorithm using AI are developed. The obstacle avoidance algorithm detects the distance between the target object and the cart follower and controls the cart follower to move backwards if the distance is less than 40 cm. Object tracking algorithm uses deep neural network (DNN) transfer learning in object detection application with pre-trained Single Shot Detector (SSD.) The model was developed to track the target object’s position and react accordingly once the target object moves. Based on this proposed system, the obstacle avoidance algorithm achieved 99.66% accuracy in distance ranging. In comparison, the object tracking algorithm provided a complete navigation system with 90% efficiency for the cart follower to track and follow the movement of the target object.
An Analysis on Speaking Materials of a Student Textbook

The main interest of this study was to know if the high school textbook "BRIGHT AN ENGLISH" is suitable for the K-13 curriculum.To carry out this study and obtain the results, the author used the method of description and content analysis.The data collection technique of choice was a document review.Furthermore, literature checklists are research tools.The data is described and analyzed according to the following steps: get data from BRIGHT AN ENGLISH and K-13 programs, establish criteria to match said documents in BRIGHT AN ENGLISH program with K-13program, match and describe describe and analyze the spoken material BRIGHT AN ENGLISH for the K-13 program.The results show that most of BRIGHT AN ENGLISH's oral practice materials are consistent with the K-13curriculum.BRIGHT AN ENGLISH is still relevant today and can be used as a reference for teachers.
МUSIC-MODEL OF ACADEMIC MOTIVATION OF UNIVERSITY STUDENTS: COURSE DESIGN

The article describes the model of learning motivation MUSIC, which teachers can use to develop their own training courses in the disciplines of higher education. Model author B.D. Jones included five components in the structure of the model of academic (learning) motivation, such as: student empowerment, usefulness, success, interest, care. The article describes the components of the model and presents methodological recommendations that teachers can take into account when developing their own system of motivating students in the educational process. A positive result of the creation of MUSIC was that the study of the model stimulates research in the field of higher education related to its structural components, proposals to improve the efficiency of the learning process at the university.
An Innovative Design of Decoder Circuit using Reversible Logic

Reversible logic has been widely used in the field of VLSI. In this paper, we introduce a method that enables the designers to implement this technique in their decoder circuits. In Logical Reversibility is not allowed for Fan-out and Feed-back. It is primarily used for applications in various sectors such as Computer Graphics, High Performance Computing, and Nanoscale. This paper presents a reversible logic circuit with respect to its energy consumption. It is mainly useful for minimizing the need for power supply. The comparison study on garbage output and quantum cost of gates is presented
Research on stock historical data based on Markowitz Model and Index Model

Because of the coexistence of return and risk, how to establish an optimal portfolio to maximize benefits and minimize risks has always been a problem for many investors.However, the existing literature rarely uses different venture capital portfolio models to compare and analyze the same group of stocks, to get which model is more conducive to investors' decision-making analysis.This paper explores the optimal portfolio of the stock portfolio under 5 constraints by using Index Model and Markowitz Model.The key finding taken from our results are as follows: Markowitz Model shows that the return of the optimal portfolio is 7.51% under the minimum variance of constraints 1, 2, and 3 separately, which is higher than the 7.15% from Index Model.In these cases, the sharp ratio from Markowitz Model is 68.54%, while Index Model shows 74.23%.Meanwhile, under the maximum sharp of constraints 1, 2, and 3, we get the returns are 14.01%, 16.56%, and 16.99% in Markowitz Model, correspondingly 12.07%, 12.87%, and 12.87% in Index Model.For Constraints 4 and 5, on the one hand, we get the returns from Markowitz Model always higher than the returns from Index Model.On the other hand, the sharp ratio calculated by Markowitz Model is lower than Index Models.By comparing the results, it is concluded that the Markowitz model is more applicable.
A New Formal Model Analysis of Deterrent to Brinkmanship and the Causes of Security Dilemma

How close does a nation come to the brink of war with an opponent during the process of crisis bargaining?The present study constructs a new formal model focused on a challenger's incentive to intensify military provocations against a defender that involves a trade-off between securing a more profitable bargaining outcome and increasing the risk of inadvertent war.This new model identifies the conditions under which the defender's threat of military coercion effectively deters the challenger from engaging in his payoff-maximizing level of military provocations (i.e., his preferred brinkmanship).In conclusion, a sufficiently strong provocation incentive on the part of the challenger makes the defender's implicit threat of coercion incredible, thus allowing the challenger to engage in his preferred brinkmanship.A presented numerical example further suggests that a security dilemma could arise because military buildup by the defender could weaken the deterrent effect on the challenger's military provocations.
Research on the Relationship Between User Attribute Context and Micro Implicit Interaction Behavior Based on Mobile Intelligent Terminal

Abstract User browsing behavior is an important kind of implicit feedback data reflecting users’ interests and preferences in the field of recommendation system. How to make full use of user browsing behavior data and combined with other context information to improve recommendation efficiency has become a research hotspot. This paper analyzes the user micro network implicit feedback behavior of mobile intelligent terminal, and studies the influence of user attribute context on user micro network implicit feedback behavior by using binary and multiple regression analysis. The results show that the user’s age attribute, regional attribute and occupation attribute are a kind of very important context information.
Using Convolutional Neural Networks for Fault Analysis and Alleviation in Accelerator Systems

Today, Neural Networks are the basis of breakthroughs in virtually every technical domain. Their application to accelerators has recently resulted in better performance and efficiency in these systems. At the same time, the increasing hardware failures due to the latest semiconductor technology need to be addressed. Since accelerator systems are often used to back time-critical applications such as self-driving cars or medical diagnosis applications, these hardware failures must be eliminated. Our research evaluates these failures from a systemic point of view. Based on our results, we find critical results for the system reliability enhancement, and we further put forth an efficient method to avoid these failures with minimal hardware overhead.
Human Activity Recognition Using Significant Skeletal Joints

The growing development in the sensory implementation has facilitated that the human activity can be used either as a tool for remote control of the device or as a tool for sophisticated human behaviour analysis. The prime contribution of the proposed system is to harness the potential of learning approach in order to carry out computational efficiency towards activity recognition process. A template for an activity recognition system is also provided in which the reliability of the process of recognition and system quality is preserved with a good balance. The research presents a condensed method of extraction of features from spatial and temporal features of event feeds that are further subject to the mechanism of machine learning to enhance the recognition accuracy. The importance of proposed study is reflected in the results, which, when trained using KNN, show higher accuracy performance. The proposed system demonstrated 10-15% of memory usage over 532 MB of digitized real-time event information with 0.5341 seconds of processing time consumption.
Fuzzy Keyword Search Over Encrypted Data in Cloud Computing: An Extensive Analysis

With the ever-increasing rate of growth and flexibility of cloud computing daily, more sensitive and insensitive data are being stored in the cloud. For the protection of sensitive identity information, information must be encrypted before distribution. Traditional search encryption schemes offer a variety of search methods for encrypted data but only support direct keyword searches. Inappropriate keyword searches for cloud storage systems, do not allow users to make spelling or similar formatting errors, and hence greatly reduces program usage. We analyzed fuzzy keyword search which would entertain typos when the user performs searching over files in the cloud which is kept in an encrypted manner. We plan to use base64 for encryption and decryption of files to be uploaded and AES encryption for N-gram keywords to be searched. A keyword or keyword phrase will be associated with each file; its N-grams will be encoded using AES. N-gram keywords generated using the user’s search input would be used to retrieve the file from cloud. File which has the highest relatable N-gram keywords when compared to N-gram keywords generated by the user’s search input will be retrieved using the concept of Jaccard Index.
发现4000多年前有古埃及文字的太湖浮雕陶柱 (The Relief Pottery Pillar with Ancient Egyptian Hierographs More Than 4000 Years Ago Was Found in Taihu Lake, China)

Chinese Abstract: 摘要：渔民从太湖打捞上来有古埃及文字和浮雕的陶柱，经科学仪器测年其年代为公元前2200年。对陶柱材质分析，判断陶柱应为本地制造，但刻有古埃及象形符号文字，大意描述某位王子乘船视察土地的情形。English Abstract: The pottery pillar with ancient Egyptian hieroglyphs and reliefs salvaged by fishermen from Taihu Lake are dated by scientific instruments to 2200 BC. According to the analysis of the material of the pottery pillar, it is considered that the pottery pillar should be made locally, but it is engraved with ancient Egyptian hieroglyphic symbols to describe the situation of a prince inspecting the land by boat.
Arabic Broken Plural Model Based on the Broken Pattern

Broken (or irregular) plurals present a significant issue in natural language processing. It is known that there is not a fixed and general rule assembling all the broken plural forms. Extracting the plurals or reducing them to their singular form is a task that affects the performance of information retrieval, annotation, tagging tasks, and many other NLP applications. This paper describes, on one hand, our own Arabic broken plural list and, on the other hand, the process of extracting the plural and its singular form using several machine learning classifiers. Obtained results show that the Random Forest classifier outperforms the other statistical classifiers with an accuracy of approximately 98%.
Contactless Access Control System Based on Voiceprint Recognition

In order to increase the security and convenience of access control system, a contact-free access control system based on voiceprint recognition is designed and has been tested for many times. This system takes the speaker’s voiceprint feature as the “key”, and it contains an Android-based voiceprint recognition APP—which is related to voiceprint registration, voiceprint verification, model addition or deletion and other functions—and a self-designed control system which communicates with the APP through Bluetooth technology. During functioning, a random 8-bit dynamic number was obtained from the cloud through the network API interface provided by IFLYTEK (Chinese information technology company) as the text password, and then the voiceprint information was uploaded to the cloud for identification, and the result was transmitted to the hardware control module through Bluetooth. By testing, the similarity between the speaker’s voiceprint information and the corresponding model is more than 97%, and the negative rate of recognition was 0%, with the average recognition time less than 0.4 s.
Correction to: Review of Cryptocurrencies Implementations in the Cloud Environment: Ethereum in the Cloud

This chapter was inadvertently published without updating the following error.
MODERN EDUCATION IN A CHANGING WORLD

The article deals with the problem of education in the context of a changing world. Trends in the development of modern education from the perspective of an anthropocentric approach are presented. The necessity of ensuring equal access of all children to education, the development of the principles of artificial intelligence in conditions of mixed education (online and offline) is emphasized.
SAP S/4HANA on AWS Elastic Compute Cloud – Deployment

SAP S/4HANA is SAP’s modern ERP solution that is powered by in-memory database HANA and is in the core of mission-critical digital business of most big enterprises across the globe. The HANA database which is in the heart of this digital business runs in its full potential when hosted on purpose-built hardware for harnessing its power. And this is one of the reasons SAP has the SAP Integration and Certification Center (SAP ICC) that tests and certifies Infrastructure-as-a-Service (IaaS) platforms, along with other certifications like the storage solution that supports SAP HANA. The list of certified and supported SAP HANA Hardware can be referenced at