Search results

The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.

Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.

Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.

Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.

The change of production methods, the industrial revolutions, technological developments, and digital transformation have affected almost all functions in the enterprises. Accounting and auditing areas are also quite affected by this transformation. Another important result of technology and digitalization is the rapid increase in errors, frauds, and irregularities. Enterprises are looking for new solutions and investigations against irregularities and frauds. Audits for errors, frauds, or irregularities are among the interests of forensic accounting. Many methods are used to identify errors and frauds in the forensic accounting. However, it is inevitable that digital technologies should be utilized in forensic accounting applications as a result of the rapid spread of automation and computer programs in enterprises within the framework of digitalized business activities. Hence, enterprises will be able to get more effective results through computer programs and artificial intelligence in terms of fraud audit in forensic accounting. Expert system applications use artificial intelligence to enable computer programs to behave just like people. One of the most widely used, most easily applicable, and most understandable types of expert system is rule-based expert system. The aim of this study is to determine the accounting fraud that may occur in enterprises within the framework of forensic accounting through rule-based expert systems. For this purpose, various applications have been implemented in a large-scale production enterprise through the use of rule-based expert systems for the determination of accounting fraud. Benford’s Law, risk levels, and various other criteria were used in the creation of expert systems. According to the results obtained from the study, it has been seen that by means of rule-based expert system applications, enterprises can better detect existing frauds and prevent further irregularities in the future. The study is important and it is expected that the study will contribute to the literature because it is shown in the study that the rule-based expert systems, applied in many fields under the title of social sciences, can also be applied in the field of forensic accounting and auditing.

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

This study aims to evaluate whether the Big-4’s commenting efforts influence the characteristics of Financial Accounting Standards Board’s (FASB’s) Final_Standards using the content of their comment letters. Whether auditors lobby standard-setters to help their clients or to help themselves and whether they are successful are questions highly relevant to issues of auditor independence and audit effectiveness.

Based on components of Mergenthaler (2009), this study develops a rules-based continuum change score to measure how much more (less) rules-based a Final_Standard is compared to its exposure draft to evaluate the influence of the Big-4 on the FASB’s standard-setting for 63 accounting standards.

The findings show that extensive comment letters and increased uncertainty language are associated with increases in the rules-based attributes included in Final_Standards. These results suggest the Big-4 prioritize a reduction in their own litigation risk over the possible preferences of their clients for less rigid standards. Moreover, the results are consistent with their comment letters influencing the FASB’s decision to include more rules-based attributes in Final_Standards.

This study develops a potential proxy for audit risk by assessing the changes in the rules-based characteristics of proposed accounting standards and using the content of the comment letters to evaluate whether the Big-4 accounting firms may influence the FASB’s Final_Standards. Overall, this study provides a unique perspective on the influence of constituents on the FASB’s standard-setting.

This paper presents a hybrid artificial intelligence (AI) system capable of integrating techniques of case‐based reasoning, rule induction and expert system, using them for knowledge acquisition and problem solving of selecting appropriate retaining wall systems at the project planning stage. The proposed hybrid system can eliminate the bottleneck of knowledge acquisition in developing a knowledge‐based system and improve the solution quality of the AI‐based system. Test results indicate that solutions generated by the proposed hybrid system are better than those generated by using a single technique.

The purpose of this paper is to use the framework of rule-based and relation-based governance to examine the evolution of governance environment in the East Asian region including China, South Korea and Taiwan.

Both qualitative and quantitative evidences are presented to demonstrate the paths these East Asian countries take in their transitions from relation-based governance to rule-based governance. Based on the framework, this analysis sheds light on the debate on whether East Asian economies will eventually move away from relation-based governance to rule-based societies.

The authors find that relation-based governance has helped East Asian countries achieve rapid economic growth in the early stages of their development. However, as the scale and scope of East Asian economies expand, continuing to rely on it may hinder their further development and therefore these countries should adopt a rule-based governance system in order to be efficient and competitive in the world market. While South Korea and Taiwan have made substantial progress in this transition, China has just embarked on the process.

This paper is among the first to systematically review the theories and evidence of the transition and the challenges East Asian countries face during the process.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

Purpose – The paper presents a content analysis of the 2009 Exposure Draft of Code of Ethics for Professional Accountants in China. It aims to investigate how equivalently the Chinese Institute of Certified Public Accountants (CICPA) adopts the International Federation of Accountants (IFAC) Code with certain adjustments due to specific national circumstances. The investigation is intended to highlight the principles-based conceptual framework approach to settlement of ethical standards and regulation for professional conduct.

Design/Methodology/Approach – Regarding the codes of ethics for professional accountants as a genre of discourse text, this paper applies a content analysis method to the investigation of how the newly revised Code of Ethics for Professional Accountants in China adopts the IFAC Code of the same type. Both semantic content and presentation format are considered in the content analysis.

Findings – This study puts forward the argument that even though CICPA claims to have equivalently adopted the principles-based conceptual framework of the IFAC ethical codification, the rigid legalistic presentation format might, however, deviate from the newly revised codification of CICPA from ethical principles to regulatory rules. Our findings prove a practical and nation-specific form of combining direct import and legal enhancement at a time when the Chinese accounting profession is on its way to converging with the IFAC Code of Ethics.

Research limitations/Implications – One limitation of the current study is the lack of information about the motivation of CICPA in adopting the principles-based conceptual framework approach to ethical codification, besides the pragmatic needs of global economic and business environments. Also, the current study focuses its comparison on IFAC and CICPA, without limited consideration of differences in cultural traits.

Practical implications – Content analysis results and conclusions of the study might render pragmatic the implications for future adoptions of the IFAC Code by various national or regional professional bodies.

Originality/Value – This paper proposes a content analysis, in terms of semantic units and legislative formats in ethical codification documents, to identify the principles-based conceptual framework approach in the IFAC and CICPA codes of ethics.

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms.