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Empowering generation z accountants in the era of data complexity and open innovation: Nurturing big data analytics, diagnostic, and forensic accounting skills

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Journal of Open Innovation: Technology, Market, and complexity 2024年第2期10卷

作者： Imjai, Narinthon Swatdikun, Trairong Rungruang, Prasit Basiruddin, Rohaida Aujirapongpan, Somnuk Faculty of Management Science Nakhon Si Thammarat Rajabhat University Nakhon Si Thammarat Thailand School of Accountancy and Finance Walailak University Nakhon Si Thammarat Thailand Faculty of Management Science Songkhla Rajabhat University Songkhla Thailand Azman Hashim International Business School Universiti Teknologi Malaysia Kuala Lumpur Malaysia

This research fills a significant knowledge gap by exploring the development of Big data Analytics Skills (BAS), Diagnostic Skills (DS), and Forensic Accounting Skills (FAS) among Generation Z accounting students in Thailand. Employing a quantitative research approach, the study conducted an online survey of fourth-year accounting students from this generation, utilizing the convenience sampling technique. The investigation aimed to assess the impact of BAS and DS on FAS within the accounting student cohort. The sample consisted of 465 participants. Path analysis conducted using the SmartPLS program affirmed significant relationships: BAS significantly influences both DS (H1, β=0.817, p<0.001) and FAS (H2, β=0.391, p<0.001), while DS notably impacts FAS (H3, β=0.484, p<0.001), concurrently serving as a mediator between BAS and FAS (H4, β=0.395, p<0.001). These findings suggest the imperative need for curriculum development and tailored training programs aimed at enriching these skills among emerging accountants. Such skill enhancement is vital to equip them to grapple with the challenges inherent in the era of big data and to bolster their competence in the detection and prevention of financial fraud and deceptive practices. The study not only holds significance for the development of skills essential for future accountants but also makes substantial contributions towards enhancing transparency and accountability within the accounting and finance sector. Furthermore, it opens avenues for potential improvements in educational and training curricula in the field of accounting, ensuring alignment with the demands of the big data era and better preparing emerging accountants to effectively address the specter of potential fraud. © 2024 The Authors

关键词： Accounting Students Big data Analytics data complexity Diagnostic Skills Forensic Accounting Skills Open innovation

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Estimating data complexity and drift through a multiscale generalized impurity approach

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Journal of Computational Mathematics and data Science 2024年 12卷

作者： Costa, Diogo Rocha, Eugénio M. Ferreira, Nelson Department of Mechanical Engineering University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Center for Research & Development in Mathematics and Applications University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Mathematics University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Bosch Termotecnologia S.A. Aveiro Portugal

The quality of machine learning solutions, and of classifier models in general, depend largely on the performance of the chosen algorithm, and on the intrinsic characteristics of the input data. Although work has been extensive on the former of these aspects, the latter has received comparably less attention. In this paper, we introduce the Multiscale Impurity complexity Analysis (MICA) algorithm for the quantification of class separability and decision-boundary complexity of datasets. MICA is both model and dimensionality-independent and can provide a measure of separability based on regional impurity values. This makes it so that MICA is sensible to both global and local data conditions. We show MICA to be capable of properly describing class separability in a comprehensive set of both synthetic and real datasets and comparing it against other state-of-the-art methods. After establishing the robustness of the proposed method, alternative applications are discussed, including a streaming-data variant of MICA (MICA-S), that can be repurposed into a model-independent method for concept drift detection. © 2024 The Author(s)

关键词： Classification difficulty data complexity data drift data separability Machine learning

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Addressing data-complexity for Imbalanced data-sets: A Preliminary Study on the Use of Preprocessing for C4.5

Addressing Data-Complexity for Imbalanced Data-sets: A Preli...

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9th International Conference on Intelligent Systems Design and Applications

作者： Luengo, Julian Fernandez, Alberto Herrera, Francisco Garcia, Salvador Univ Granada Dept Comp Sci Granada Spain Univ Granada Dept AI Granada Spain Univ Jaen Dept Comp Sci Jaen Spain

ISBN: (纸本)9781424447350

In this work we analyse the behaviour of the C4.5 classification method with respect to a bunch of imbalanced data-sets. We consider the use of two metrics of data complexity known as "maximum Fishers discriminant ratio" and "nonlinearity of 1NN classifier", to analyse the effect of preprocessing (oversampling in this case) in order to deal with the imbalance problem. In order to do that, we analyse C4.5 over a wide range of imbalanced data-sets built from real data, and try to extract behaviour patterns from the results. We obtain rules that describe both good or bad behaviours of C4.5 in the case of using the original data-sets (absence of preprocessing) and when applying preprocessing. These rules allow us to determine the effect of the use of preprocessing and to predict the response of C4.5 to preprocessing from the data-set's complexity metrics prior to its application, and then establish when the preprocessing would be useful to.

关键词： C4.5 Classification data complexity Imbalanced data-sets Oversampling

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Agile combination of advanced booking models for short-term railway arrival forecasting1

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 141卷

作者： Tsai, Tsung-Hsien Natl Quemoy Univ Dept Tourism Management 1 Univ Rd Jinning Township 892 Kinmen Taiwan

Accurate forecasts are essential for allocating perishable resources. As arrivals are the accumulation of reservations, utilizing reservation records to construct forecasting models is novel but challenging. This study focuses on agilely combining individual alternatives via a complexity-based selecting procedure. Real records over a year from a railway corporation are collected to form booking curves. Eleven complexity indicators are proposed to describe the characteristics of booking curves and five advanced booking models are constructed. Then five machine learning classifiers are applied to map the relationships between complexity and model suggestion. The empirical results show promising outcomes when a booking point is distant from the service day and cautiously selecting forecasting alternatives for combination is critical to predictive accuracy. More specifically, the Support Vector Machine combination achieves a 10% performance improvement compared with the all-inclusive average method. Additionally, the proposed scheme can achieve equal or better performance compared with individual models.

关键词： Combination scheme data complexity Booking curves Advanced booking models Revenue management

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Indistinguishability in controlled query evaluation over prioritized description logic ontologies

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JOURNAL OF WEB SEMANTICS 2025年 84卷

作者： Cima, Gianluca Lembo, Domenico Marconi, Lorenzo Rosati, Riccardo Savo, Domenico Fabio Sapienza Univ Roma Rome Italy Univ Bergamo Bergamo Italy

In this paper we study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering over databases, knowledge bases, and ontologies. CQE is based on the notion of censor, which defines the answers to each query posed to the data/knowledge base. We investigate both semantic and computational properties of CQE in the context of OWL ontologies, and specifically in the description logic DL-LiteR, which underpins the OWL 2 QL profile. In our analysis, we focus on semantics of CQE based on censors (called optimal GA censors) that enjoy the so-called indistinguishability property, analyzing the trade-off between maximizing the amount of data disclosed by query answers and minimizing the computational cost of privacy-preserving query answering. We first study the data complexity of skeptical entailment of unions of conjunctive queries under all the optimal GA censors, showing that the computational cost of query answering in this setting is intractable. To overcome this computational issue, we then define a different semantics for CQE centered around the notion of intersection of all the optimal GA censors. We show that query answering over OWL 2 QL ontologies under the new intersection-based semantics for CQE enjoys tractability and is first-order rewritable, i.e. amenable to be implemented through SQL query rewriting techniques and the use of standard relational database systems;on the other hand, this approach shows limitations in terms of amount of data disclosed. To improve this aspect, we add preferences between ontology predicates to the CQE framework, and identify a semantics under which query answering over OWL 2 QL ontologies maintains the same computational properties of the intersection-based approach without preferences.

关键词： Description logics Ontologies Confidentiality preservation Query answering data complexity

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Out-of-distribution detection using normalizing flows on the data manifold

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APPLIED INTELLIGENCE 2025年第7期55卷 1-18页

作者： Razavi, Seyedeh Fatemeh Mehmanchi, Mohammadmahdi Hosseini, Reshad Tavassolipour, Mostafa Univ Tehran Coll Engn Sch ECE North Kargar St Tehran *** Iran

Using the intuition that out-of-distribution data have lower likelihoods, a common approach for out-of-distribution detection involves estimating the underlying data distribution. Normalizing flows are likelihood-based generative models providing a tractable density estimation via dimension-preserving invertible transformations. Conventional normalizing flows are prone to fail in out-of-distribution detection, because of the well-known curse of dimensionality problem of the likelihood-based models. To solve the problem of likelihood-based models, some works try to modify likelihood for example by incorporating a data complexity measure. We observed that these modifications are still insufficient. According to the manifold hypothesis, real-world data often lie on a low-dimensional manifold. Therefore, we proceed by estimating the density on a low-dimensional manifold and calculating a distance from the manifold as a measure for out-of-distribution detection. We propose a powerful criterion that combines this measure with the modified likelihood measure based on data complexity. Extensive experimental results show that incorporating manifold learning while accounting for the estimation of data complexity improves the out-of-distribution detection ability of normalizing flows. This improvement is achieved without modifying the model structure or using auxiliary out-of-distribution data during training.

关键词： data complexity Generative models Manifold learning Normalizing flows Out-of-distribution

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complexity of Secondary Scientific data Sources and Students' Argumentative Discourse

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INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 2010年第9期32卷 1207-1225页

作者： Kerlin, Steven C. McDonald, Scott P. Kelly, Gregory J. Penn State Univ Dept Curriculum & Instruct University Pk PA 16802 USA

This study examined the learning opportunities provided to students through the use of complex geological data supporting scientific inquiry. Through analysis of argumentative discourse in a high school Earth science classroom, uses of US Geological Survey (USGS) data were contrasted with uses of geoscience textbook data. To examine these differences, we conducted a review of frameworks for argumentation analysis, and through adaptation to the research foci of this study, developed a new analytic approach. Applying this approach to student classroom discourse revealed the affordances and constraints of both the complex USGS datasets and the filtered textbook data sources. The challenges of teaching with complex data are presented.

关键词： Argumentation Discourse Earth science education Inquiry-based teaching data complexity

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Preliminary Evaluation of Classification complexity Measures on Imbalanced data

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Chinese Intelligent Automation Conference (CIAC)

作者： Xing, Yan Cai, Hao Cai, Yanguang Hejlesen, Ole Toft, Egon Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Aalborg Univ Dept Hlth Sci & Technol Aalborg Denmark

ISBN: (纸本)9783642384660;9783642384653

Classification complexity measures play an important role in classifier selection and are primarily designed for balanced data. Focusing on binary classification, this paper proposes a novel methodology to evaluate their validity on imbalanced data. The twelve complexity measures composed by Ho are evaluated on synthetic imbalanced data sets with various probability distributions, various boundary shapes and various data skewness. The experimental results demonstrate that most of the complexity measures are statistically changeable as data skewness varies. They need to be revised and improved for imbalanced data.

关键词： data complexity Imbalanced data Classification

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Geometrical complexity of data Approximators

Geometrical Complexity of Data Approximators

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12th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Mirkes, Evgeny M. Zinovyev, Andrei Gorban, Alexander N. Univ Leicester Dept Math Leicester LE1 7RH Leics England Institut Curie F-75005 Paris France INSERM U900 Paris France Mines ParisTech Fontainebleau France

ISBN: (纸本)9783642386787

There are many methods developed to approximate a cloud of vectors embedded in high-dimensional space by simpler objects: starting from principal points and linear manifolds to self-organizing maps, neural gas, elastic maps, various types of principal curves and principal trees, and so on. For each type of approximators the measure of the approximator complexity was developed too. These measures are necessary to find the balance between accuracy and complexity and to define the optimal approximations of a given type. We propose a measure of complexity (geometrical complexity) which is applicable to approximators of several types and which allows comparing data approximations of different types.

关键词： data analysis Approximation algorithms data structures data complexity Model selection

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A Local complexity Based Combination Method for Decision Forests Trained with High-dimensional data

A Local Complexity Based Combination Method for Decision For...

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12th International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Campos, Yoisel Morell, Carlos Ferri, Francesc J. Univ Holguin Oscar Lucero Moya Dept Informat Holguin Cuba Univ Cent Marta Abeu Comp Sci Dept Santa Clara Cuba Univ Valencia Dept Dinformat Valencia Spain

ISBN: (纸本)9781467351171;9781467351188

Accurate machine learning with high-dimensional data is affected by phenomena known as the "curse" of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how this idea significantly outperforms the standard non-weighted average combination and also the renowned Classifier Local Accuracy competence estimate, while consuming significantly less time.

关键词： Multi-classifier Systems Decision Forests Classifier Competence Estimation data complexity High-dimensional data

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