Recognition and Evaluation of Data as Intangible Assets

Xiong, Feng; Xie, Maoyue; Zhao, Lingjuan; Li, Cheng; Xuan, Fujun

doi:10.1177/21582440221094600

Cited by 14 publications

(11 citation statements)

References 33 publications

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“…We fine-tune the pre-trained video swin transformer's weights on both RGB & DCT using 32-frame videos from the BBSI dataset. For implementation, we have used the MMaction2 toolbox [3], and the fine-tuned swin RGB and swin DCT networks serve as feature extractors, producing 1024-dimensional feature vectors.…”

Section: Video Swinmentioning

confidence: 99%

MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings

Madan,

Jain,

Sharma

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems. Furthermore, behavioral language cues are essential for active engagement in social agent-based user interactions. Despite the progress made in computer vision for tasks like head and body pose estimation, there is still a need to explore the detection of finer behaviors such as gesturing, grooming, or fumbling. This paper proposes a multiview attention fusion method named MAGIC-TBR that combines features extracted from videos and their corresponding Discrete Cosine Transform coefficients via a transformer-based approach. The experiments are conducted on the BBSI dataset and the results demonstrate the effectiveness of the proposed feature fusion with multiview attention. The code is available at: https://github.com/surbhimadan92/MAGIC-TBR CCS CONCEPTS• Computing methodologies → Machine learning; • Humancentered computing → Empirical studies in HCI.

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Section: Video Swinmentioning

confidence: 99%

MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings

Madan,

Jain,

Sharma

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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“…The cost of intangible assets is repaid through depreciation, unless otherwise established by PBU 14/2000. Depreciation of intangible assets is carried out by one of the three main methods for calculating depreciation: straight-line method; reducing balance method; method of writing off the cost in proportion to the volume of products (works) (Xiong et al, 2022). At the same time, there is also a need to conduct an impairment test of the asset, which consists in comparing the initial cost less depreciation charges with the recoverable amount.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It may be noted that the process is two-way interconnected: some consumers are embracing the new digital technologies -companies are adapting their operations to the new technologies -all consumers are adopting the new system of operation. However, the intermediary in this algorithm, on both sides, are IT companies offering digital solutions to both consumers and businesses (Xiong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Relevant Problems of Intangible Asset Impairment in The Russian IT Industry

Prodanova,

Tarasova,

Volobuev

et al. 2023

Br. J. Ed., Tech. Soc.

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Resumo: A indústria de TI está evoluindo rapidamente e começando a penetrar em todas as áreas da vida. As grandes empresas estão a formar ecossistemas que lhes permitem expandir e diversificar significativamente as suas fontes de rendimento. No caso da indústria de TI, o desenvolvimento da base de software para a implementação de tais ecossistemas depende diretamente de novos produtos de software, patentes, tecnologias e know-how, que na contabilidade moderna são maioritariamente classificados como ativos intangíveis. Assim, examinar o impacto de fatores externos e internos na imparidade deste tipo de ativos é uma questão de grande relevância na conjuntura atual. Este artigo se concentrará nos ativos intangíveis das principais empresas russas de TI, como Yandex e VK.Group. O estudo inclui uma análise da literatura relevante e do quadro regulamentar sobre imparidade de ativos intangíveis, bem como um exame da dinâmica do desempenho da demonstração da posição financeira do Yandex e do VK.Group relacionada com esta questão. A pesquisa pretende delinear as especificidades da redução ao valor recuperável de ativos intangíveis na indústria e as questões que surgem durante esse processo. Com base nos resultados, as empresas de TI ampliaram sua base de ativos devido à expansão ativa de serviços e fontes de renda. À medida que a Yandex se expandiu, a sua quota de ativos intangíveis aumentou, enquanto o VK.Group reduziu a sua quota de ativos intangíveis.

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“…In the last few years, accounting literature has dealt with the question of how Big Data impacts the tasks, roles, and activities in accounting (Bhimani and Willcocks, 2014;Cockcroft and Russell, 2018;Leitner-Hanetseder et al, 2021;Vasarhelyi et al, 2015;Warren et al, 2015), how it might improve financial decision making, or how financial reporting might take place (Warren et al, 2015). However, there is little research on factors that deals with how to recognize and even less on how to assess the increasingly important (potential) value of data within financial statements (Birch et al, 2021;Warren et al, 2015;Schwarz, 2020;Xiong et al, 2022). May this be true to the fact that data recognition and data assessment may need new, innovative and out-of-the-box research initiatives in the age of digitalization (Monteiro et al, 2020) as data are seemingly totally different from the previous assets recognized under IFRS?…”

Section: Introductionmentioning

confidence: 99%

AI-powered information and Big Data: current regulations and ways forward in IFRS reporting

Leitner-Hanetseder

Lehner²

2022

JAAR

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PurposeWith the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.Design/methodology/approachBased on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.FindingsAnalyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.Research limitations/implicationsScholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.Practical implicationsThe framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.Originality/valueThis article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.

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Recognition and Evaluation of Data as Intangible Assets

Cited by 14 publications

References 33 publications

MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings

MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings

Relevant Problems of Intangible Asset Impairment in The Russian IT Industry

AI-powered information and Big Data: current regulations and ways forward in IFRS reporting

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