A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.

SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

The Effects of Competence and Auditor Training on Fraud Detection Within Multinational Companies in Sub-Saharan Africa

The aim of this study is to examine the qualities that auditors engaged in detecting potential fraud within multinational corporations in Sub-Saharan Africa should possess.To achieve this goal,a quantitative approach was used to develop and test a research model based on three theories:agency theory,attribution theory,and cognitive dissonance theory.Responses from a panel of two hundred and nine(209)auditors who conducted a legal audit mission in a Sub-Saharan multinational were analyzed using SmartPLS 3.3.3 software.The results emphasize the crucial importance of auditors’competence and continuous training in fraud detection.However,professional skepticism and time pressure were found to be non-significant in this context.This conclusion provides essential insights for auditors,highlighting the key qualities needed to effectively address fraud detection within multinational corporations in Sub-Saharan Africa.

Does the Internal Audit Function Play a Role in Mitigating Earnings Management?

This paper aims to investigate the effect of the characteristics of the internal audit function(IAF)on earnings management(EM)for a sample of 27 Tunisian listed companies.The authors employed the Correlated Panels Corrected Standard Errors model to estimate the regression equation.The results showed that EM is negatively associated with internal audit effectiveness,frequency of audit committee meetings with Chief Audit Executives(CAEs),the partial outsourcing of internal audit,and the firm’s size.Moreover,the authors found a positive relationship between EM and the use of IAF as a training ground for management and the private sector.This study is important to academics,regulators,and policymakers in introducing new governance reforms to strengthen the IAF as an important internal governance mechanism to reduce earnings management practices in emerging countries.The results also provide useful information for investors to examine the effect of internal audit characteristics on earnings management.

Innovation of Audit Business Management Models under the Conditions of Information Technology

This paper focuses on the innovation of audit business management in the information technology era.In the wave of digitalization,audit companies need to adjust their management mode to take advantage of the advantages of informatization.The key is to use digital tools to strengthen data analysis and reshape the audit process.This not only improves efficiency and accuracy but also improves the overall audit quality.The article also highlights the importance of developing a culture of continued auditor learning and skills development to ensure competitiveness in the digital age.At the end of the paper,the success of audit companies depends on innovation ability and the importance of combining management mode with technological innovation.

Analysis of the Impact of Big Data Technology on Environmental Pollution Control Audit

As China strives towards the second centenary goal,increasing attention is being paid to environmental pollution and other related issues.Concurrently,with the rapid development of big data technology,many big data solutions have been applied to environmental pollution control audits,exerting a significant impact.This paper presents the current situation of environmental pollution audits,summarizing the application of big data from the perspectives of both domestic and international research.In terms of data collection and data analysis for environmental pollution audits,cloud platform technology,and visualization technology are selected based on multiple data sources.The impact in the field of environmental pollution control audits is further analyzed.It is found that the environmental pollution audit cloud platform is not yet perfect,the technical skills of audit personnel are insufficient,and some technologies are not mature.Relevant suggestions are put forward to provide a reference for the future development of big data technology and its integration with environmental pollution control audits.

Audit审核在车辆质检工作中的使用方法研析

Audit审核起源于德国大众,是企业模拟用户对自己的产品质量进行内部监督的觉行为。通过Audit审核在车辆质检工作中的应用,企业可以更加全面地了解车辆的质量状况,及时发现并解决问题,提升车辆的整体质量水平,从而满足消费者的需求,增强市场竞争力。Audit审核是站在用户的角度对成品车辆进行质量检查/测量的方法(内部和外部),获得汽车制造厂的广泛应用。Audit审核是在与用户相对的汽车制造厂里进行的审核检验,只有站在用户角度提升产品质量,才能发挥Audit审核的最佳作用。

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

A GIS audit framework is necessary considering the diverse nature of GIS with regard to components, applications and industry. In practice, checklists are generated during the audit process based on specific objectives. There is no standardized list of items that can be used as a reference. The purpose of this study was to develop a GIS audit framework as a foundation for GIS audits. The framework provides that comprehensive approach to various GIS aspects during the audit process. The design builds on a developed conceptual framework where most significant categories of GIS audit parameters namely data quality, software utilization, GIS competency and procedures (work flows) were identified. The study adopted a reductive model approach to simplify the complexity associated with each category of GIS audit parameter. The resultant audit elements for each category are organized in a matrix that forms an integral part of the framework. The columns comprise audit goal, audit questions and audit subjects as indicators which are qualitatively measured. The rows comprise the parameters (data quality, software utilization, personnel competency and procedure (workflows)). To use the framework, an auditor only needs to create an audit checklist that consists of particular parameters and indicators from the framework depending on audit objective. As part of an on-going research, the next step will involve validating the framework through a mock testing process.

A Brief Discussion of Audit Risks and Their Causes

As a crucial supervisory department overseeing economic operations,the audit department plays a pivotal role in safeguarding the healthy development of the country’s economy,exposing corruption,and effectively maintaining social and economic peace and stability.The work of auditing involves detecting existing issues,providing remedies,and consistently contributing to national governance.However,as the market economic environment undergoes continuous changes,legal systems advance,and the audit environment becomes more complex,audit risks have gradually gained prominence.The prevention of audit risks has increasingly captured the attention of audit agencies at all levels.This necessitates audit institutions to continually adapt to new challenges,comprehend risks,and exert effective control over them.This article delves into the causes of audit risks,exploring ways to effectively identify and respond to these risks within a complex and ever-changing environment.The objective is to ensure the objectivity and accuracy of audit reports,allowing the audit function to truly serve as an“immune”system,protecting public interests.

Smart Contract Based DDoS Attack Traceability Audit Mechanism in Intelligent IoT

In this paper,we focus on providing data provenance auditing schemes for distributed denial of service(DDoS)defense in intelligent internet of things(IoT).To achieve effective DDoS defense,we introduce a two-layer collaborative blockchain framework to support data auditing.Specifically,using data scattered among intelligent IoT devices,switch gateways self-assemble a layer of blockchain in the local autonomous system(AS),and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle,to obtain a global security model.To optimize the processing delay of the security model,we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements.Since the flood of identity spoofing packets,it is difficult to solve the identity consistency of data with traditional detection methods,and accountability cannot be pursued afterwards.Thus,we proposed a Packet Traceback Telemetry(PTT)scheme,based on in-band telemetry,to solve the problem.Specifically,the PTT scheme is executed on the distributed switch side,the controller to schedule and select routing policies.Moreover,a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources.Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path,reduce the resource consumption compared with existing tracing scheme.Data tracing audit method has fine-grained detection and feasible performance.

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule‑based decision‑making model

A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis.This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies,which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment.To obtain this goal and inspired by a model ensemble,we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing,fuzzy set theory,and a multi-attribute decision making algorithm.The results display that the order of priority in improvement—(A)AI application strategy,(B)AI governance,(D)the human factor,and(C)data infrastructure and data quality—is based on the magnitude of their impact.This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.Highlights Artificial intelligence(AI)promotes the sustainability development of audit tasks.A fuzzy MRDM model extracts key factors from large amounts of data.Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors.An effective framework of AI-driven business audit is proposed in which“AI cognition of senior executives”is the most important criterion.

Assessment of the Implementation of Energy Conservation Opportunities Arising from Energy Audits;A Study of Four-Star and Five-Star Hotels in Nairobi Kenya

This study assesses the implementation of energy conservation opportunities in four-star and five-star hotels in Nairobi. The Covid-19 pandemic had a significant impact on the Hospitality Industry. Currently, there is a growing inclination to furnish guests with superior and sustainable services in an energy-efficient and eco-friendly way. Comprehensive research was conducted from energy audits gathered from the establishments and contracted auditing companies, on top of this, hotel staff were given digital questionnaires. To add to the data, the researcher surveyed the hotels with engineering managers. The Energy Audits found that all 10 hotels had adopted Energy Conservation Opportunities (ECOs). After further analysis, the mean adoption rate of Energy Conservation Opportunities (ECOs) during the past three years was 55.83%, which was below the aim of 100%. According to studies, hotel staff manages energy to cut costs. The researcher found that hotels use up a lot of energy. However, they have conservation potential, depending on government policies, costs, ease of implementation, and management commitment to sustainable practices. Essentially, Energy Conservation Opportunities (ECOs) reduce energy expenditures and boost reliable revenues, especially during high energy prices and uncertainty.

An Uneven Impact of Emerging Technologies on Taxpayers With Various Income Levels Audited by the IRS: Empirical Evidence From an AI Technology Application and Regression Models

This study aims to investigate the influence of emerging technology adoption on tax compliance, encompassing both the Internal Revenue Service’s (IRS) compliance audits and taxpayers’ compliance performance (collectively, tax compliance). We employed the Gradient Descent optimization algorithm, an artificial intelligence (AI) technology application, to scrutinize the connection between the quality of US tax filings and the development of emerging technology, among other contributing factors. Additionally, we utilized multiple linear regression to evaluate the relationships between dependent variables, specifically IRS audit rates and the no-change rate at different income levels,1 and several independent variables, including a proxy for emerging technology in the form of tax software. Our findings reveal that while emerging technology significantly impacts tax compliance within the IRS and taxpayers’ performance, its effects vary across income groups. Notably, emerging technology seems to confer greater advantages to higher-income individuals compared to their lower-income counterparts. These study results hold considerable policy implications for government decision-makers in promoting the adoption of emerging technology among lower-income taxpayers.

Linux下基于audit的文件操作溯源机制研究

随着网络通信技术的迅速发展,网络服务器系统相关安全研究越来越深入,如何溯源服务器系统中目标文件操作,成为文件系统安全的核心研究点。文章基于linux下audit系统,设计了一种文件操作溯源框架,分层解决文件操作监控、日志传输、暂存、提取、过滤、解析和存储,按需提取一定时间段内的一系列文件操作的核心日志数据信息,为服务器端文件安全提供溯源支持。

Exploring the Internal Audit Practices of Electric Power Enterprises in the Context of the New Economic Normal

In the current economic landscape characterized by a decelerating domestic economy,rising investments,and an extended project construction timeline,power generation enterprises must enhance their internal audit efforts to sustain their competitive edge in business development.Addressing systemic shortcomings methodically and practically can enhance the internal audit effectiveness within electric power enterprises and contribute to the steady enhancement of their production and operation performance.

The Construction and Implementation Path of the Audit System for Quality Control in the Big Data Era

In the era of big data,the construction and implementation of a quality control audit system are particularly crucial.This article delves into the impact of big data technology on quality control auditing,establishes a quality control auditing system in the big data era,and elucidates the pathway to realizing this system.Through the application of big data technology to quality control audits,there is an enhancement in audit efficiency,the attainment of more accurate risk assessment,and the provision of robust support for the sustainable development of enterprises.

基于“Classin+Audition”的校外“云”合唱教学实践探索

“云”合唱是信息技术迅猛发展下,“合唱+互联网”的音乐创作与传播新方式。电脑、耳机和摄像头机,或者更简便的智能手机,这些普通的家庭数字设备就可以支持学生参与“云”合唱的学习与实践。通过Classin平台与Audition音频编辑软件的功能,积极探索校外“云”合唱教学,满足了学生在线开展艺术实践的需求,应用数字化技术体验合作的快乐。

Enhancement of Audit Closed-Loop Management for Optimal Audit Results Transformation

The process of converting audit findings into actionable improvement measures within an enterprise is pivotal for achieving the“value-added”objective of internal audits and fostering sustainable business development.By elucidating the definition and significance of transforming internal audit results,this study underscores the imperative of applying and effectively converting these findings.Additionally,it seeks to streamline the value assessment framework for internal audit result transformation and delineates key factors that impede this transformation.Furthermore,this study explores strategies to bolster the closed-loop audit management system and outlines specific methods for enhancing the transformation of internal audit results within the enterprise,thereby contributing to its overall progress.

《酒精使用障碍筛查量表》(AUDIT)的测试

目的 :对酒精使用障碍筛查量表 (AUDIT)进行测试 ,以寻找中国人“危险及有害饮酒”的界线分值 ,以便及早在人群中用简便的方法筛查出“危险及有害饮酒者” ,并对他们进行早期干预。方法 :引入国外近年发展起来的酒精使用障碍筛查量表 (AUDIT) ,通过在社区测试 ,对其在中国适用性作用评价。结果 :AUDIT量表在中国长春地区部分人群中测试具有良好的信度 ;并测得男性危险及有害饮酒的界线分值为 7分 ,此时量表区分危险及有害饮酒的灵敏度和特异度均较高 ,分别为 99 7%和 90 %。AUDIT量表的重测信度也较满意。结果 :AUDIT值得在中国进行全国性测试后推广应用。

Quality in Independent Auditing:A Research on Auditors

The aim of this study is to determine the opinion related to the audit quality of independent auditors in Turkey.For this purpose,a study was carried out on the auditors operating in the TRC 1 region and having an auditor certificate.Questionnaire method was used in the research.In the evaluation of the data,frequency,mean,and standard deviation were used as descriptive statistical methods.In addition,t-test and one-way ANOVA test were used to compare the data between independent groups.Research results show no significant difference between the opinions of the independent auditors according to various variables.