Reviewerly/exHarmony huggingface dataset:
Design of a Financial Accounting Management System Based on a Computer Network

Financial management is one of the most core tasks of a social team and an important link related to the survival of enterprises and the economic development of the country. Therefore, financial management is regarded as a top priority from the national tax and financial system to enterprises and individuals. However, with the rapid development of social economy and the extremely active economic activities in China, especially the complicated capital flows and the rapid changes in the market, even if the financial system is upgraded many times, it is still difficult to meet the requirements of enterprises. In recent years, computer network technology has been more and more widely used in all aspects of social production and life. It can replace people to complete complex, tedious, repetitive, and consumptive work. Therefore, the research and development of financial accounting management system based on computer network technology have important production significance and social value. Firstly, this paper summarizes the development process and current situation of the financial management system, points out the difficulties existing in the current financial accounting management, and investigates the development and application status of computer network technology. Then, it studies the shortcomings of current financial work and the necessity and feasibility of applying computer technology to financial management. Finally, it analyzes the beneficial effects of computer network technology in financial management, designs the overall structure of the financial accounting management system based on computer network technology, studies the characteristics of each subsystem of the structure, points out the technical and institutional challenges faced by computer network technology, and gives corresponding suggestions. This paper is an applied research on the application of advanced computer network technology in financial accounting management, which helps the rapid development of China’s financial accounting management system and provides a combination idea for the application of computer network technology in other traditional industries.
From double-entry bookkeeping and ledger to blockchain technology: New frontiers for accounting information systems

The paper investigates the current state of studies on double-entry bookkeeping and ledgers in accounting through a bibliometric analysis covering the1990-2021period. The study allows an interpretation of accounting information systems as they evolve, due to the impetus of recent, disruptive information technology. The authors used a sample of 230 publications, collected from the Web of Sci-ence, and adopted VOSviewer software to illustrate different relational techniques: citation, co-citation, keyword co-occurrence, and bibliographic coupling analyses. The results highlight the emergence of recent research streams that are weakly connected to the foundation of modern accounting, albeit sharing the same roots with seminal historical accounting contributions in terms of trustworthiness, moral-ity, and communication. This study's main contribution, based on its findings, is a better understanding of the growing interest in double-entry bookkeeping and ledg-er, focusing on blockchain and its dimensions. Potentially, the study can shed light on the possible implications of new technologies for accounting and bookkeeping. The universality of accounting language is called upon to describe new "genealo-gies of calculation" by converging professional and academic efforts in a field that can benefit extensively from a transdisciplinary approach to research.
Cryptanalysis and improvement on two party quantum private comparison based on seven-qubit and eight-qubit states

Ji et al. [Mod. Phys. Lett. A 34, 1950229 (2019)] and Fan et al. [Mod. Phys. Lett. A 37, 2250026 (2022)] proposed two-party quantum private comparison (QPC) protocols based on 7-qubit and 8-qubit states, respectively. However, these two QPC protocols did not consider third-party (TP) attacks. Therefore, this study focuses on TP attacks and demonstrates that the property of a 7-qubit or 8-qubit entanglement state is useless in this attack situation; that is, the participant wastes resources to maintain the qubits. For the same assumption environment proposed by Ji et al. and Fan et al., this study proposes an improvement and simplification protocol that allows participants to reach the goal of private comparison by using only classical computation and the classical communication channel without any entanglement state.
Virtual reality supported trainings for lean education: conceptualization, design and evaluation of competency-oriented teaching-learning environments

Purpose The purpose of this approach is to structure and facilitate the development of competency-oriented virtual training scenarios. This approach should help to develop reproducible results. Design/methodology/approach This publication presents a procedure for the structured, competency-oriented design of virtual teaching–learning environments. The procedure is based on the competency-oriented design of learning factories according to Tisch and consists of a total of three phases with various substeps. The different steps of the method are presented by means of an application example and the generated scenario is evaluated by means of a competency measurement according to Glass regarding its suitability for teaching competencies. Findings In addition to an application example that introduces the approach, this publication presents concrete results from the research project. In addition to a review of the opportunities and limitations of virtual reality (VR) in continuing education, these also refer to possible, realizable requirements for the use of VR. In addition, results on the suitability of VR learning environments for competency development in the context of value streaming mapping training are presented. Originality/value This approach represents a substantial extension of existing concepts for the design of physical learning factory training for lean topics. It can help learning factory operators and other training providers to design competency- and user-oriented virtual training. VR offers a multitude of potentials in this context, but these can only be exploited through conscientious use of the technology.
Guided Task Planning Under Complex Constraints

Creating a plan, i.e., composing a sequence of items to achieve a task is inherently complex if done manually. This requires not only finding a sequence of relevant items but also understanding user requirements and incorporating them as constraints. For instance, in course planning, items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, items are points of interest (POIs) and constraints represent time and monetary budget, two user-specified requirements. Most importantly, a plan must comply with the ideal interleaving of items to achieve a goal such as enhancing students' skills towards the broader learning goal of an education program, or in the travel scenario, improving the overall user experience. We study the Task Planning Problem (TPP) with the goal of generating a sequence of items that optimizes multiple objectives while satisfying complex constraints. TPP is modeled as a Constrained Markov Decision Process, and we adapt weighted Reinforcement Learning to learn a policy that satisfies complex dependencies between items, user requirements, and satisfaction. We present a computational framework RL-Planner for TPP. RL-Planner requires minimal input from domain experts (academic advisors for courses, or travel agents for trips), yet produces personalized plans satisfying all constraints. We run extensive experiments on datasets from university programs and from travel agencies. We compare our solutions with plans drafted by human experts and with fully automated approaches. Our experiments corroborate that existing automated solutions are not suitable to solve TPP and that our plans are highly comparable to expensive handcrafted ones.
Research on E-government Information Security Based on Cloud Computing

As an important pillar of social informatization, e-government not only provides more convenient services for the public, but also effectively improves administrative efficiency. At the same time, the application of cloud computing technology also urgently requires the government to improve the level of digital construction. This paper proposes the concept of e-government based on cloud computing, analyze the possible hidden dangers that cloud computing brings to e-government in management, technology, and security, and build cloud computing e-government information security system from three aspects: cloud security management, cloud security technology, and cloud security assurance.
Review on FIR Filters Using Different Adders and Multipliers Based on Vedic Mathematics

A comparative study of Adders, multipliers and different combination of FIR Filters taken from different sources. FIR Filter designed by using different block which are Adders, Multipliers, and Delay elements. Digital multipliers are mostly used in designing new gadgets, and have many other applications in digital signal processing. FIR Filters are easy to design and are less power consuming. [1] This paper consist of several adders and multiplier like Ripple carry adder, Carry Look Ahead Adder, Han Carlson Adder, Dadda Multiplier, Booth Multiplier, and Vedic Multiplier etc. [2] The focus in this paper is on the parameter of adders, Multiplier and Fir Filters such as Power, LUT utilization, area and delay to find out an efficient FIR Filters with Vedic Mathematics concept. Among these multipliers Vedic Multiplier is the high speed and low power consuming multiplier.
HOST-ATS

We present HOST-ATS, a holistic system for the automatic selection and evaluation of soccer video thumbnails, which is composed of a dashboard-controlled machine learning (ML) pipeline, and a dynamic user survey. The ML pipeline uses logo detection, close-up shot detection, face detection, image quality prediction, and blur detection to automatically select thumbnails from soccer videos in near real-time, and can be configured via a graphical user interface. The web-based dynamic user survey can be employed to qualitatively evaluate the thumbnails selected by the pipeline. The survey is fully configurable and easy to update via continuous integration, allowing for the dynamic aggregation of participant responses to different sets of multimedia assets. We demonstrate the configuration and execution of the ML pipeline via the custom dashboard, and the agile (re-)deployment of the user survey via Firebase and Heroku cloud service integrations, where the audience can interact with configuration parameter updates in real-time. Our experience with HOST-ATS shows that an automatic thumbnail selection system can yield highly attractive highlight clips, and can be used in conjunction with existing soccer broadcast practices in real-time.
The Impact of Virtual Reality on Student Design Decisions

Benjamin H. George, Jessica Fernandez and Peter Summerlin Benjamin H. George, PhD, ASLA, is an associate professor in the Landscape Architecture & Environmental Planning Department at Utah State University. His work focuses on the integration of visualization technologies into the design process and the intersection of media and the landscape. He conducts research on the implementation and impact of online education with the design studio. Dr. George has collaborated with a number of leading technology firms, design firms, and botanical gardens. Dr. Jessica Fernandez is an assistant professor at the University of Georgia. Her research and teaching incorporate contemporary approaches to the creation and assessment of place through big data, VR/AR, and other emerging applications. As a licensed landscape architect, she has over a decade of experience working on a variety of nationwide award-winning projects. She owns ALPHA Design Studio, where she pursues innovative solutions in the design and building industry. Peter Summerlin is an associate professor in the Department of Landscape Architecture at Mississippi State University. His work explores contemporary technology as tools for design, discovery, communication, and collaboration in the design process. He is a licensed landscape architect and LEED Accredited Professional with practical experience in both the public and private sector.
Amplitude Reordering Accelerates the Adaptive Variational Quantum Eigensolver Algorithms

The variational quantum eigensolver (VQE) algorithm can simulate the chemical systems such as molecules in the noisy-intermediate-scale quantum devices and shows promising applications in quantum chemistry simulations. The accuracy and computational cost of the VQE simulations are determined by the underlying ansatz. Therefore, the most important issue is to generate a compact and accurate ansatz, which requires a shallower parametric quantum circuit and can achieve an acceptable accuracy. The newly developed adaptive algorithms (AAs) such as the adaptive derivative-assembled pseudo-Trotter VQE (ADAPT-VQE) can solve this issue via generating compact and accurate ansatzes. However, these AAs show very low computational efficiency because they require a large number of additional measurements. Here we propose an amplitude reordering (AR) strategy to accelerate the promising but expensive AAs by adding operators in a “batched” fashion in a way that their order is still quasi-optimal. We first introduce the AR method into ADAPT-VQE and build the AR-ADAPT-VQE algorithm. We then endow the energy-sorting VQE (ES-VQE) algorithm with the adaptive feature and introduce the AR into AES-VQE to form the AR-AES-VQE algorithm. To demonstrate the performance of these algorithms, we calculate the dissociation curves of three small molecules, LiH, linear BeH2, and linear H6, by using (AR-)ADAPT-VQE and (AR-)AES-VQE algorithms. It is found that all of the AR-equipped AAs (AR-AAs) can significantly reduce the number of iterations and subsequently accelerate the calculations with a speedup of up to more than ten times without the obvious loss of accuracy. The final ansatz generated by the AR-AAs not only avoids extra circuit depth but also maintains the computational accuracy; sometimes the AR-AAs even outperforms their original counterparts.
A Two-Stage SEM–Artificial Neural Network Analysis of Integrating Ethical and Quality Requirements in Accounting Digital Technologies

Digital technologies affect all areas and activities of society. Accounting is no exception to this trend, as organizational information system accounting increasingly integrates digital technologies. The paper aims to study the integration of ethical requirements with the quality requirements in implementing digital technologies based on artificial intelligence, blockchain, the internet of things, and cloud computing in financial and managerial accounting. This empirical study of 396 accountants from Romanian organizations involves investigating the influence of ethical and quality requirements of digital technologies on the perception of users’ satisfaction in financial and managerial accounting. Empirical research encompasses a quantitative approach using structural equation modeling and artificial neural network analysis in a two-stage procedure. Some of the existing ethical issues can be addressed by implementing new digital technologies but implementing these emerging technologies can generate other ethical and quality issues that accounting and IT professionals must address in a combined effort. The research results show that the ethical requirements that influence the perception of financial and managerial accounting are security and trust. Among the quality requirements, the most critical influence in the perception of accountants is reliability.
When a Chatbot Smiles at You: The Psychological Mechanism Underlying the Effects of Friendly Language Use by Product Recommendation Chatbots

Based on the computers are social actors theory and social presence theory, the current study investigates the psychological mechanism by which the use of friendly language by a personalized product recommendation chatbot influences product attitudes. Results indicated that the effect of the friendly chatbot on more positive product attitudes was sequentially mediated by social presence and user satisfaction. Previous experience with product recommendation chatbots was found to moderate the serial mediation effects. Furthermore, the current study found that a friendly chatbot led to higher rates of contact information disclosure by consumers. Theoretical and practical implications are discussed.
A Comparative Study of Classifying English News Articles Using Machine Learning Algorithms

Mainly news helps people to identify, what happened around them. News is imperative as a social gathering space as well, subsequently, daily papers either online or physical put an accentuation on news. Most people use news websites nowadays. Every newsreader has a preferable news category and not all readers are read all the categories of news. It is different from person to person. Therefore, the category of news is very important. It is very hard to get all the news from one news website. Sometimes it may not have the category who is searching for. To overcome these issues classification techniques are used to classify the news articles. This English news classification method aims to extract news articles from a variety of online news sites and classify them based on their domain categories such as business, health, politics, technology, and sports. The datasets are converted into machine trainable format by using Term Frequency-Inverse Document Frequency (TF-IDF) techniques. The different classification algorithms are applied to the training and testing dataset to create a model and then classify the news articles based on their relevant categories. The five individual classifications algorithms such as Decision Tree, Random Forest, Naïve Bayes, Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are applied. Among them, SVM shows better results. When combining these five algorithms using the Ensemble Learning method, it shows a better accuracy result 92.75% with the lowest error rate than individual algorithms to predict the news articles based on their category.
Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines

Intensive care unit (ICU) readmissions are associated with mortality and poor outcomes. To improve discharge decisions, machine learning (ML) could help to identify patients at risk of ICU readmission. However, as many models are black boxes, dangerous properties may remain unnoticed. Widely used post hoc explanation methods also have inherent limitations. Few studies are evaluating inherently interpretable ML models for health care and involve clinicians in inspecting the trained model.An inherently interpretable model for the prediction of 3 day ICU readmission was developed. We used explainable boosting machines that learn modular risk functions and which have already been shown to be suitable for the health care domain. We created a retrospective cohort of 15,589 ICU stays and 169 variables collected between 2006 and 2019 from the University Hospital Münster. A team of physicians inspected the model, checked the plausibility of each risk function, and removed problematic ones. We collected qualitative feedback during this process and analyzed the reasons for removing risk functions. The performance of the final explainable boosting machine was compared with a validated clinical score and three commonly used ML models. External validation was performed on the widely used Medical Information Mart for Intensive Care version IV database.The developed explainable boosting machine used 67 features and showed an area under the precision-recall curve of 0.119 ± 0.020 and an area under the receiver operating characteristic curve of 0.680 ± 0.025. It performed on par with state-of-the-art gradient boosting machines (0.123 ± 0.016, 0.665 ± 0.036) and outperformed the Simplified Acute Physiology Score II (0.084 ± 0.025, 0.607 ± 0.019), logistic regression (0.092 ± 0.026, 0.587 ± 0.016), and recurrent neural networks (0.095 ± 0.008, 0.594 ± 0.027). External validation confirmed that explainable boosting machines (0.221 ± 0.023, 0.760 ± 0.010) performed similarly to gradient boosting machines (0.232 ± 0.029, 0.772 ± 0.018). Evaluation of the model inspection showed that explainable boosting machines can be useful to detect and remove problematic risk functions.We developed an inherently interpretable ML model for 3 day ICU readmission prediction that reached the state-of-the-art performance of black box models. Our results suggest that for low- to medium-dimensional datasets that are common in health care, it is feasible to develop ML models that allow a high level of human control without sacrificing performance.
Review of Security Enhancement in IoT using Blockchain

Internet of Things or IoT is generally referred as the interconnection of smart/intelligent devices to collect data and make smart decisions. IoT will change the life of peoples in the coming years. IoT can be used to provide support in various fields e.g., Agriculture, health, facility management, industrial production control, product inventory, safety and security and so on. However, it is not easy to implement extensive security solutions due to technical limitations of IoT. Also, the standards meant for IoT networks are not mature enough to provide security. As a result, several incidents of security lapse affect IoT reliability. Blockchain technologies can be implemented in IoT devices to provide accuracy and integrity of data along with its limited hardware resources. In this paper, we explore different existing security solutions, future aspects, security challenges and their pros and cons as well as comparison and discussion of various well-known research works of security using Blockchain. This work will be helpful for new researchers to get detailed information of blockchain based security solutions and their limitations.