Reviewerly/exHarmony huggingface dataset:
Deep Learning in Detecting Financial Statement Fraud

The main aim of this study is to test the ability of a deep neural network (DNN) to detect and to predict financial statement fraud (FSF) and non-financial statement fraud (NFSF). This study also intended to reduce increasing financial losses due to FSF and to improve the reliability of financial statements (FS) for various business and investment decision-making purposes. A supervised DNN application is introduced in this study, accompanied by an end-to-end automated process to improve the overall efficiency in detecting and predicting FSF and NFSF, using only annual reports of public listed companies from various industries with different market capital sizes. This study report focused on the application of DNN as the tech tool which leverages the traditional FSF-detecting techniques (financial ratios and financial risk indicators) as the basis to attempt to detect and predict the FSF. The findings of this study indicated that the proposed DNN model can predict and detect both FSF and NFSF with an accuracy score of 89.11%.
Developing an Asset Management Plan for Your Future Engineering Organization

Utilities develop asset management plans that pertain to physical assets within their organization. However, are engineering and utility companies focusing upon those future individuals, or potential individuals, who are their key resource in developing such plans? This presentation takes the key components of an asset management plan and applies those to the organizational structure of the engineering firm or utility by coupling the findings of an internationally published research study, which highlights motivational traits and value systems of existing and incoming generations. Obviously to attract and retain the best and the brightest engineers and utility operating personnel, an environment must be developed and maintained in order for such individuals to be productive.
Bank Network Credit Model and Risk Management System Based on Big Data Technology

With the development and application of big data technology, the process of the traditional financial industry to the intelligent age has been further accelerated. As computer information technology continues to participate in social activities, many industries have increasingly strong demands for big data technology. For example, in the financial industry, big data technology can play a very helpful role in the transformation of traditional financial services and the innovation of business models. As an important area of financial business, the bank network credit business has a huge impact on the development of the entire financial industry. Many people know that the risk of bank online credit business is very high and it is particularly easy to cause non-performing assets. At this time, using big data technology and applying it to the risk management system can greatly improve the risk management ability of credit business. This chapter analyzes the current status of bank A’s credit risk and the problems in the credit risk management system, finds out the problems and causes of bank A’s credit risk management system, and applies big data technology to the bank’s online credit risk prevention system, internal evaluation, and identification system; network credit risk mitigation system has been improved and designed, a bank network credit risk management system based on big data technology has been created, bank A’s total deposit and loan changes and non-performing network loans have been analyzed in recent years, and corresponding conclusions have been drawn.
Predicting Financial Statement Fraud using Artificial Neural Networks

Financial statement fraud is ever-increasing within companies and is stirring chaos worldwide. The issue lies within a misguided tone at the top, which makes it difficult to identify fraud and takes a longer time to uncover. The costs associated with the deliberate management misrepresentation could lead to insolvency and affect the company itself, investors, stakeholders, as well as the economy as a whole. This research aims to construct a predictive model using a key technology used in the financial field, artificial neural networks (ANNs). The study uses data from 50 fraudulent companies and 150 non-fraudulent companies from the U.S. SEC, NYSE, LSE and Athex, where variables are computed based on the fraud risk indicators from ISA 240. A multi-layer perceptron feed-forward neural network with a back-propagation algorithm was utilized to construct the model. The results show the predictive accuracy of the ANN model at 93.3%.
Society Intellectualization Technologies for Eurasia

To ensure a rapid increase in the level of competences of the population and its compliance with the tasks of sustainable development in the 21st century, the article proposes foundations for the project aiming at the development and testing of technologies for the formation of an integrated intellectualization system. The focus of this research is aimed at identifying, analyzing, and evaluating the effectiveness of intellectualization technologies implemented by universities. The relevance and scientific novelty of the issues underlying this work are due to the growing need for the rapid intellectualization of society in the context of the deployment of integration projects and projects of development as well as security insurance in Eurasia.
A Heterogeneous Network Representation Learning Approach for Academic Behavior Prediction

Predicting authors’ academic behavior (e.g. co-authorship, citation) based on heterogeneous academic network can help scholars to grasp interesting research directions and participate in various co-operations. Most of the existing network representation methods use the structural and content features of nodes, but have not fully exploited the edges (relationships) between nodes (entities) and investigated the semantic compatibility of different edge types yet. To solve the above problems, a heterogeneous network representation learning method (HNEABP) is proposed to improve feature extraction and academic behavior prediction performance. HNEABP has three strengths: 1) capture rich neighbor information via balanced sampling and Skip-Gram, 2) apply knowledge graph embedding (KGE) technique to learn pairwise node information and to weight the importance of first-order neighbors, 3) solve the semantic incompatibility of edges based on KGE. Validation experiments on three academic network datasets show that HNEABP outperforms the popular network representation methods, which gives the credit to HNEABP for learning richer feature information effectively, so as to improve the performance of academic behavior prediction.
Multiscale Computational Materials Science

In this chapter we start with a discussion of a fundamental assumption on the nature of matter, namely the idea of particles as basic constituents and the consequences thereof. In Sects. 2.1–2.3 we focus on clarifying some terminology in computational multiscale modeling and learn about the concepts of “models” in science. Section 2.4 focuses on hierarchical modeling concepts in classical and quantum physics which is followed by discussing the standard model of elementary particle physics and the attempts of reducing all understanding of material behavior to the interactions of some basic constituents (particles). In Sect. 2.6 we introduce the basics of computer science such as recursive programming, the concepts of formal (computer) languages, finite automata, complexity theory and the Turing machine. The chapter ends with some reading suggestions and several problems as an offer for the ambitious reader.
BLOCK-OPTICS: An Efficient Density-Based Clustering Based on OPTICS

This paper proposes an efficient density-based clustering method based on OPTICS. Clustering is an important class of unsupervised learning methods that group data points based on similarity, and density-based clustering detects dense regions of data points as clusters. The ordering points to identify the clustering structure (OPTICS) is one of such methods that has been widely used due to its advantages over the predecessor DBSCAN. The computational cost of OPTICS is not too high ( $$O(n \log {n}$$ where n is the number of data points), but it still suffers from a long execution time when the input size is large and/or the number of dimensions is high. The reason is that the algorithm requires massive distance calculation for each data point against other points to calculate the density around it. To make it more efficient, we propose BLOCK-OPTICS, which allows us to perform OPTICS more efficiently by eliminating unnecessary distance calculations for high-density areas. The experimental results using a synthetic dataset and several real-world datasets show that BLOCK-OPTICS is much faster than the original OPTICS while maintaining the same results.
A Study on Feedback Content on Lesson Plans of Preservice Teachers: for the World Regional Geography Units of the Elementary Social Studies

Objectives The study analyzed feedback on lesson plans written by preservice teachers(PSTs) for the first time, identified problem patterns, and sought appropriate teaching methods for each type of feedback.&#x0D; Methods PSTs designed lesson plans for the world regional geography units of the elementary social studies. After reviewing them, a total of 475 cases of instructor feedback provided to PSTs were categorized by the thematic analysis method that classifies feedback content according to similar topics.&#x0D; Results In top categories, there were 5 types: understanding of achievement standards, curriculum reconstruction, introduction of instructional activities, body of instructional activities, and conclusion of instructional activities. There were 10 sub-types appeared frequently: guidance for teaching and learning activities, composition and planning of lessons, unit marking, recognition of the number system of achievement standards, detailed explanation methods, validity of learning topics, confirmation of prior lesson, description of learning objectives, playing time of video clip and allocation of time within lesson.&#x0D; Conclusions Various difficulties that PSTs may experience while preparing lesson plans and guidance methods in response to them were examined in detail. The professionalism of instructional design is a competency that is formed in the long term throughout the entire life cycle and career stage of a teacher. Even if the level of preparation and experience of PSTs is low, meaningful educational effects can be achieved if they are guided with goals and methods suitable for them.
Analysis of Social Perception of Early Childhood Teacher Competency through Big Data

Objectives The purpose of this study is to explore the direction of the competency of early childhood teachers by examining factors affecting the overall social perception and early childhood teacher competency of daycare center teacher competency, kindergarten teacher competency, and early childhood teacher competency through big data analysis.&#x0D; Methods The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. To this end, using the search terms for daycare center teacher competency, kindergarten teacher competency, and early childhood teacher competency, Naver blog, cafe, news, intellectual, academic information, web information and Daum blog, cafe, news, web information, Google news, Facebook and web documents were collected and analyzed. The collected data went through the first and second refining processes, and 50 major words were selected based on word frequency and semantic network analysis was performed.&#x0D; Results First, as a result of frequency analysis of keywords related to childcare center teacher competency, education showed the highest frequency, followed by childcare teachers, teachers, daycare centers, and support. As a result of centrality was education, and keyword with the highest mediation centrality was teacher. Second, as a result of frequency analysis of keywords related to kindertarten teacher compotencey, teachers showed the highest frequency, folowed by training, kindergarten, and education. As a result of centrality analysis, the keyworld with the highest connection centrality, proximity centrality, and mediation centrality was teacher. Third, as a result of frequency analysis of keywords related to the competency of early childhood teachers, education showed the highest frequency, followed by research, trainging, kindergarten, and teachers. As a result of the centrality analysis, the keyword with the highest connection centrality was pre-kindergarten teacher, and the keywrod with the highest proximity centrality and mediation centrality was research.&#x0D; Conclusions Through big data analysis, there was no difference in the perception of daycare center teachers and kindergarten teacher’s competency. It can be seen that the expectations for the role as a researcher are high.
A Study on the Actual and Future of Coding Education for Korean Preschooler: Based on LEGO Play

Objectives The purposes of this study, by grasping the current status of early childhood coding education, which is emerging as a global issue, we sought the direction in which early childhood education in Korea should move forward and suggested a new direction for talent education in preparation for the future society.&#x0D; Methods To this end, the current status of early childhood coding education and domestic early childhood coding education from the perspective of constructivism was identified, the role of Lego play was considered, and the direction of future early childhood education was investigated.&#x0D; Results To this end, previous studies have shown that domestic infant coding education is not an active subject for infants, and infant coding teaching tools are mainly focused on creative thinking, and teacher competence is important to induce infant interest.&#x0D; Conclusions The purpose of this study that Lego Play as a learner-centered play tool has the digital literacy skills required in the era of the 4th Industrial Revolution, and at the same time, it has a very important function as a convergence infant coding education that develops creative problem-solving skills through team interaction and cooperation.
An Analysis of Research Treads about Secondary STEAM in Korea: Focusing on Master's and Doctoral Dissertations

Objectives This study analyzed the trends of Korean research on secondary school STEAM learning targeting master’s and doctoral dissertations released for the last 10 years from 2012 to 2021 in order to present foundational material that can help study STEAM and conduct related education afterwards.&#x0D; Methods By using the database of KERIS, this author selected 176 master’s and doctoral dissertations related to STEAM and analyzed the trends of research in terms of study characteristics, topics, subjects, and methods. Concerning the study method, this research conducted frequency analysis and crossover analysis using Excel as part of literature review.&#x0D; Results First, according to the results of analyzing the characteristics of theses, regarding the years of publication, a lot of research was done from 2013 to 2019 as 145 theses (82.4%). Concerning universities releasing them, more theses were released by ‘Korea National University of Education’, ‘Yonsei University’, ‘Kongju National University’, and ‘Ewha Womans University’ in order. About degrees, 165 were ‘master’s theses’, and 11 were ‘doctoral dissertations’. Second, regarding the topics of research, 47 were about ‘development’, 36 were for ‘development and application’, 29 were about ‘influences and effects’, and 26 dealt with ‘analysis’, and so on. Third, about the subjects of research, 82 theses targeted ‘literatures’, 46 ‘middle school students’, and 34 ‘high school students’, and so on. Fourth, concerning study methods, 83 were ‘studies reviewing literatures’ and 79 were ‘quantitative research’.&#x0D; Conclusions Based on the above results of analyzing the major trends, this author concludes that research on secondary school STEAM learning deals with such topics as ‘development’ and ‘development and application’, targets literatures or middle/high school students, and adopts study methods like literature review or quantitative research. Follow-up research will have to be done systematically in a way to excavate diverse topics about secondary school STEAM learning and diversity the subjects and methods further.
Achieving an optimal group structure in a neural architecture search

The method proposed in this letter searches for an effective group structure of group convolutions in a convolutional neural network that can improve the classification accuracy. The model's group structure is obtained using an effective differential neural architecture search. Our code can be accessed at https://github.com/minercode625/grunas.git.
Boosting Adversarial Attack Transferability via Random Block Shuffle

An interesting property of deep convolutional neural networks is their weakness to adversarial examples, which can deceive the models with subtle perturbations. Though adversarial attack algorithms have accomplished excellent performance in the white-box scenario, they frequently display a low attack success rate in the black-box scenario. Various transformation-based attack methods are shown to be powerful to enhance the transferability of adversarial examples. In this work, several novel transformation-based attack methods that integrate with the Random Block Shuffle (RBS) and Ensemble Random Block Shuffle (ERBS) mechanisms are come up with to boost adversarial transferability. First of all, the RBS calculates the gradient of the shuffled input instead of the original input. It increases the diversity of adversarial perturbation’s gradient and makes the original input’s information more invisible for the model. Based on the RBS, the ERBS is proposed to decrease gradient variance and stabilize the update direction further, which integrates the gradient of transformed inputs. Moreover, by incorporating various gradient-based attack methods with transformation-based methods, the adversarial transferability could be additionally improved fundamentally and relieve the overfitting problem. Our best attack method arrives an average success rate of 85.5% on two normally trained models and two adversarially trained models, which outperforms existing baselines.
Sentiment Analysis of Developers’ Comments on GitHub Repository: A Study

The sentiments of developers play a major role in productivity, code quality, and satisfaction. The workload of the developers and their interest in a specific programming language affect the overall quality of the development process. Open source projects, where developers (or contributors) work based on their interest in contributing to the project apart of their routine work. In this paper, we are analysing the sentiments of the developers on GitHub while working on different open source projects. Our study mainly focuses on three aspects: (1) analysing the day of the week in which the comment was made by the developer, (2) emotions of the developer throughout the course of a project, and (3) emotions with different programming languages. The analysis was done by looking into the developer comments on issues, pull requests, and comments for the repository. Our results show that projects developed on Monday’s tend to more negative emotion. Additionally, comments written in issues have higher negative polarity in their sentimental content, and projects developed in Java and Python have more positive comments as compared to C and C++.
Augmented Reality-based Robot Control for Laparoscopic Surgery

Abstract Minimally invasive surgery is the standard for many abdominal interventions, with an increasing use of telemanipulated robots. As collaborative robots enter the field of medical interventions, their intuitive control needs to be addressed. Augmented reality can thereby support a surgeon by representing the surgical scene in a natural way. In this work, an augmented reality based robot control for laparoscopic cholecystectomy is presented. A user can interact with the virtual scene to clip the cystic duct and artery as well as to manipulate the deformable gallbladder. An evaluation was performed based on the SurgTLX and system usability scale.
Similarity-Based Graph Enhanced Text Representation Learning for Electronic Component Knowledge Graph Completion

In the electronic component supply chain system, manually built knowledge graph usually lacks the alternative relations among the electronic components. Prevalent graph embedding approaches exhibit strong capability in representing graph elements. However, it's difficult to generalize to never-seen elements due to the graph incompleteness, and the Laplacian-based convolution of GCN limits the information propagation to immediate neighbors. In contrast, the pre-trained encoder have stronger ability to extract semantic information. In this paper, we propose a hybrid encoding approach SiGeTR: Similarity-based Graph Enhanced Text Representation. Based on the approach of structural encoding, it incorporates the textual encoding which employs the text of triples in the graph and contextualized repre-sentations. Meanwhile, we propose to use node similarity based convolution matrices in the GCN to compute node embeddings. In experiments, our methods obtain state-of-the-art performance on the electronic components knowledge graph benchmark dataset and achieve significant results with low resources.
Self-Adaptive Knowledge Embedding for Large-Scale Electronic Component Knowledge Graph

Substitution of electronic components is an important research topic in the supply chain management of design and manufacture of electronic products. Previous studies mainly use simulation technology and case study, the system is complex and unable to comprehensively evaluate the different properties of components in each application environment. In this paper, we propose the Electronic Component Knowledge Graph (ECKG), which helps to discover knowledge from a large amount of data and assist in the substitution of electronic components. The ECKG integrates the electronic component data from different manufacturers and contains substitution relations labeled by domain expert experience. The ECKG contains two types of nodes: the central node is the representation of electronic components, and the peripheral node contains the attribute values that provides semantic support for the central node, which helps learning the structural knowledge. Moreover, we present the Self-adaptive Knowledge Embedding (SAKE) approach that integrates the semantic information of peripheral nodes into their corresponding central node. The SAKE is pre-trained on our large-scale ECKG with a knowledge-based attention mechanism to obtain the contextual representation of the central nodes. Experiment results show that SAKE outperforms other counterparts on the entity typing and link prediction tasks.
A Survey on Image Cryptosystems based on DNA Inspired Approaches

The image data are large in size with highly correlated adjacent pixels. Therefore, the conventional data encryption technology is inadequate to effectively deal with such kind of data for ensuring their security. Deoxyribonucleic Acid (DNA) encryption is a frontal encryption technology as DNA molecule has a large data capacity, vast parallelism and has low ultra power requirement. Over the years, different image cryptosystems have been proposed with the use of DNA based encoding/ decoding and its sequential operations. The present paper provides an indepth analysis of the research carried out by different authors in the last decade uptill now as reported in the literature. It also provides a pathway for further research in the area.