Information For Authors Research Articles

Analysis of intangible assets reporting standards and automation in KSA within an Islamic context – a case study

Purpose This paper aims to the significance of intangible assets in boosting financial credibility and accounting transparency in Saudi Arabia and other Islamic countries, aligning with Saudi Vision 2030. Moreover, it articulates the indispensable role of information and communication technology (ICT), including cutting-edge technologies such as artificial intelligence (AI) and blockchain, inefficaciously mitigating manual challenges while safeguarding the authenticity and reliability of financial information. Design/methodology/approach This study examines relevant literature on national and international standards to define the significance of intangible assets within conventional and Islamic Shari’ah principles in the Kingdom of Saudi Arabia (KSA). It delves into how automation technologies can improve accuracy and verifiability in reporting intangible assets while adhering to Islamic Shari’ah Compliance (ISC) principles, providing valuable insights into standardization and automation benefits. Findings The study highlights that ISC laws affect intangible asset reporting. Integrating conventional accounting principles with ISC is necessary to establish a unified international standard. Adopting ICT, AI and blockchain is crucial for automating and ensuring consistent, verifiable reporting of intangible assets. Research limitations/implications This case study highlights the importance of intangible assets in KSA, but its findings require validation through workshops with scholars, stakeholders and policymakers to ensure broader acceptance and applicability beyond the specific context. Practical implications This study provides guidance on using AI, blockchain and other ICT tools to ensure accurate financial reporting by validating and verifying intangible assets, benefiting stakeholders, policymakers, accountants, auditors and ISC professionals. Originality/value This study underscores the importance of reporting intangible assets according to Islamic principles in KSA, with relevance to other Islamic countries. Promoting International Financial Reporting Standards and Saudi Organization for Certified Public Accountants standards enhances accurate reporting and supports Saudi Vision 2030 goal of boosting global investment and trade.

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

Recent Progress in ECDSA and DSA Digital Signature Algorithms

Digital signatures are essential for verifying the authenticity and integrity of information, thus facilitating digital authentication. The Elliptic Curve Digital Signature Algorithm (ECDSA) and the Digital Signature Algorithm (DSA) play critical roles in the field of digital signatures. This paper explores the conceptual frameworks and mathematical underpinnings of these algorithms, providing an in-depth analysis of their advantages and disadvantages with a focus on computational time complexity and key length requirements. The distinct applications of ECDSA and DSA in various fields are examined, highlighting how the selection of an algorithm influences security and efficiency in different scenarios. This analysis also considers the algorithms' suitability for specific security applications, offering insights into their strategic deployment. By comparing these characteristics, the study aims to guide the selection of appropriate digital signature technology, tailored to meet operational needs and security specifications. This comparative approach not only elucidates the practical implications of each algorithm but also contributes to a broader understanding of their roles in securing digital transactions.

Դավիթ Դվնեցու վկայաբանությունը և նրա պատմագիտական արժեքը

The long-term Persian-Arabic rule had a profound impact on Armenian hagiographical literature. The persecutions and ideological pressures of the conquerors compelled the Armenian clergy to devise suitable solutions. In these circumstances, separate martyrdoms were created concerning the conversion of representatives of the conquered peoples. Surhan was also one of the Armenian heroes who was named David after being baptised. It can be recorded that the author of the Martyrdom fulfilled the order of the bishop of Amatunyatsʻ and the Mamikonian princes. This is confirmed by the relic symbols found in the Martyrdom, including the cross and the spear. David's Martyrdom, with its distinctive allegorical structure and plot, nevertheless conveys authentic information about the period of Arab invasions and dominions. The Islamic challenge compelled the author to present David's Martyrdom in a more convincing manner, showcasing novel religious plots and spatial perceptions. The Martyrdom contains numerous references that provide invaluable insights into the economic and political life of Armenia in the second half of the 7th century. The data on the pre-Islamic legal system, in particular the rule of Hudud (Ḥudūd Allāh [God's boundaries]), are also noteworthy.

The Silent Saboteur: The Impact and Management of Malicious Word-Of-Mouth in The Digital Age

In the digital era, the impact of malicious word-of-mouth on individuals and businesses is complex. Malicious online reviews and false information can spread quickly, causing serious damage to personal reputation and corporate branding. This article provides an in-depth analysis of the effects of malicious word-of-mouth on various aspects of the economy and society, and proposes effective strategies for response. Through specific case studies, such as the controversy surrounding the BOY LONDON brand, the public relations crisis of the Wahaha brand, the damage to Jiang Ping's personal image, and the "pink-haired girl" incident, this article illustrates how malicious word-of-mouth can erode consumer decision-making autonomy, pose threats to personal physical and mental health, and tarnish the image of brands and enterprises. To address these challenges, companies and governments must take proactive measures, such as monitoring and responding to negative information on the Internet to maintain a positive image and strengthening Internet regulation to ensure the authenticity and transparency of information and to prevent the spread of false information. At the same time, improving public media literacy helps people identify and resist the influence of malicious word-of-mouth.

Reproductive physiology of Indian pangolin (Manis crassicaudata) in Pothwar Plateau, Pakistan

Abstract The Indian pangolin (Manis crassicaudata) is an “endangered” mammal species that is restricted to four range countries of Asia. The ecology and breeding behavior of Indian pangolins is poorly understood due to a lack of scientific data. Due to heavy poaching and illegal killing, the species population has declined drastically in the last decade. Considering the fact that Indian pangolin has low reproductive output, it needs special conservation measures and authentic information about its breeding ecology. We estimated concentrations of three reproductive hormones from scats using ELISA kits in male and female pangolins inhabiting the Pothwar Plateau, Pakistan, to confirm its breeding pattern in its range. Testosterone levels were found elevated from May (0.59 ng/ml) until October (1.28 ng/ml). Oestradiol levels were raised from May (0.59 pg/ml) until December (2.64 pg/ml). Progesterone concentrations were relatively low from January until March but increased from April (4.85 pg/ml) through December (14.0 pg/ml) with highest concentrations (20.13 pg/ml) in September and October. Mean hormone (testosterone, oestradiol and progesterone) concentrations were elevated in winter compared to summer (p < 0.001). The current study is the first one to quantify hormonal levels in this species, and our findings establish the breeding pattern of Indian pangolin.

Augmenting Multimodal Content Representation with Transformers for Misinformation Detection

Information sharing on social media has become a common practice for people around the world. Since it is difficult to check user-generated content on social media, huge amounts of rumors and misinformation are being spread with authentic information. On the one hand, most of the social platforms identify rumors through manual fact-checking, which is very inefficient. On the other hand, with an emerging form of misinformation that contains inconsistent image–text pairs, it would be beneficial if we could compare the meaning of multimodal content within the same post for detecting image–text inconsistency. In this paper, we propose a novel approach to misinformation detection by multimodal feature fusion with transformers and credibility assessment with self-attention-based Bi-RNN networks. Firstly, captions are derived from images using an image captioning module to obtain their semantic descriptions. These are compared with surrounding text by fine-tuning transformers for consistency check in semantics. Then, to further aggregate sentiment features into text representation, we fine-tune a separate transformer for text sentiment classification, where the output is concatenated to augment text embeddings. Finally, Multi-Cell Bi-GRUs with self-attention are used to train the credibility assessment model for misinformation detection. From the experimental results on tweets, the best performance with an accuracy of 0.904 and an F1-score of 0.921 can be obtained when applying feature fusion of augmented embeddings with sentiment classification results. This shows the potential of the innovative way of applying transformers in our proposed approach to misinformation detection. Further investigation is needed to validate the performance on various types of multimodal discrepancies.

The impact of Blockchain adoption on supply chain performance: evidence from food industry

The food supply chain (FSC) constantly needs to address persistent challenges such as information asymmetry, low transparency, food quality and authenticity, and unnecessary waste. Blockchain is perceived as a promising solution to overcome these FSC challenges. Existing literature captures a conceptual understanding of various aspects of Blockchain for FSC, such as how the technology can enhance transparency, efficiency, and food authenticity. However, a quantitative assessment of the overall impact of Blockchain adoption on the FSC operational performance is still missing. This study combines empirical and analytical approaches to investigate the evident research gap. Under the lens of systems thinking and System Dynamics (SD) modelling perspective, the study collected questionnaire and interview data to develop different FSC models for evaluating the impact of Blockchain on key operational performance metrics. The findings indicated that Blockchain positively affects inventory level and lead time in the immediate term, and cost in the long term. The results also warn that forgoing inventory buffers can come with the cost-of-service level. This study provides quantitative evidence of the positive influence of Blockchain on the FSC. This research contributes by extending the understanding of Blockchain's implications on broader supply chain performance from a systems perspective.

Artificial intelligence tools and concepts give access to authenticity and quality information in Brazilian olive oil volatilome

Brazil is the world's second largest importer of olive oil and stared to produce its own oil quite recently in two distinct geographic areas. The production is still very small, and studies regarding oil composition are scarce. Determination of discriminant differences are useful to support appeals regarding origin indication and to detect fraud and adulteration with oils from other countries. In this work the Brazilian extra virgin olive oils (EVOOs) volatilome is explored by Artificial Intelligence (AI) tools developed on multiple headspace solid-phase microextraction (MHS-SPME) combined with comprehensive 2D gas chromatography-mass spectrometry and flame ionization detection (GC×GC-MS/FID) data. Using MHS-SPME, external standard calibration, and FID predicted relative response factors (RRF), the accurate quantification of 51 informative volatile compounds was carried out and the odorants responsible for key-positive sensory attributes were used to generate distinctive aroma blueprints aligned with the AI smelling based on sensomics. As authenticity and origin assessment decision-making tool, augmented visualization by computer vision was applied on volatilome 2D fingerprints. By this approach, extra-virgin olive oil samples (n=35), from two olive cultivars (Arbequina and Koroneiki) harvested in the main producing regions in Brazil (Rio Grande do Sul and Serra da Mantiqueira) in 2021 and 2022, were effectively discriminated and mislabeled products regarding their geographical origin were, for the first time, promptly identified.

Biometric CNN Model for Verification Based on Blockchain and Hyperparameter Optimization

Nowadays, fingerprints as biometrics are among the most popular means of identity verification for various applications. However, they are susceptible to theft, tampering, or alteration by attackers after storage. Hence, it is critical to guarantee the privacy of these fingerprint templates because standard privacy techniques are not secure enough. Additionally, fingerprint templates are verified using a deep learning model to distinguish between authentic and fake fingerprints, making them more protected and secure by storing them inside a blockchain, which has become the most common secure technique in recent years. This paper implements the proposed efficient and secure biometric system for verification based on blockchain technology and hyperparameter optimization. First, for the storing phase, each user’s authentic fingerprint template, private, and public keys are saved in the block and linked to the previous block in the chain by a hash function. If a hacker attempts to assault a fingerprint, all prior blocks must be changed. Second, for the authentication phase, the user logs in with his fingerprint and looks up the required template in the chain. If the required fingerprint exists, it creates a new block with the login details, and the number 1 is returned, which means that the authentication is valid; if it does not exist, it is verified to see if it is authentic or fake using the proposed biometric convolutional neural network (CNN). The proposed CNN uses the Grid Search (GS) algorithm to tune hyperparameters to distinguish between authentic and fake fingerprints. The SOCOFing dataset is used for evaluating our experiment. According to the experimental results, the proposed CNN model achieved the highest accuracy of 99.52%. As a result of the blockchain, our system can return authentication information after looking up the chain in 300 ms.

Secure and Transparent Craftwork Authentication and Transaction System: Integrating Digital Fingerprinting and Blockchain Technologies

This study proposes a method that enables craftsmen to define and apply the unique characteristics of their craftworks to distinguish between originals and imitations and to protect and trade their intellectual property rights. In the first step, a digital fingerprint that enables the authentication of the original craftworks was generated by applying hash functions that can digitize various attributes of the craftworks and create a unique ID. In the second step, a blockchain transaction system for the original authentication of the craftwork was developed by applying consortium blockchain technology. This system allows multiple craft-related organizations to participate together, and when a transaction occurs, a smart contract is created and stored on the blockchain, thereby enabling the tracking and management of transaction histories. Furthermore, a DApp was developed that enables buyers to verify the craftwork authentication and access detailed information by scanning the digital fingerprint (QR code) of the craftwork, which is integrated with the blockchain system. In the third step, the research results were evaluated through a satisfaction survey conducted with 121 participants and a usability evaluation with 10 craftsmen, both of which yielded positive feedback. This study successfully realizes a secure and transparent craftwork transaction system that guarantees both security and efficiency through the integration of digital fingerprinting and blockchain technologies.

Transformation in Beliefs and Religious Practices among Religious Followers in Present-Day Vietnam

Vietnam is a country with diverse religions. The transformation in beliefs and religious practices among religious followers in Vietnam is constantly influenced by global trends, the industrial revolution 4.0, the country’s achievements in renovation, international integration, and the Party and State’s religious policies. This context drives religious organizations to continually revive, develop both structurally and operationally, and diversify their activities. However, this dynamism also brings complexity and impacts various aspects of social life. This study sheds light on the current process of transformation in faith and religious practices among followers of different religions in Vietnam. Furthermore, it provides authentic information for relevant researchers to compare, contrast, and support scientific arguments. Ultimately, it offers reasonable grounds for Vietnam’s functional agencies to issue and enforce more effective policies related to religion.

From likes to luggage: The role of social media content in attracting tourists

The tourism industry has become increasingly information-driven where tourists need reliable information to make their travel decisions. Social media content creators have emerged as prominent figures in disseminating authentic and credible information about tourist destinations worldwide. However, scant attention has been paid to explore the role of social media content in influencing tourist inflows and tourist engagement with tourism-related content on social media. To fill this gap, the present study utilized three social media analytic tools: VidIQ, Tubebuddy, and Social Blade, to collect data pertaining to tourism-related content across six tourist destinations in the Arabian Gulf region. The results indicate that these six tourist destinations collectively attracted 47 million visitors in a single year while garnering 158 million views on YouTube. Our findings indicate that tourists’ consumption of tourism-related content on YouTube directly relates to the number of tourists visiting a destination. In addition, the present study also finds that destination popularity leads to higher number of video views on YouTube as well as higher numbers of tourists to the destination. Furthermore, the current study delineates the role of content creator characteristics in influencing user engagement with tourism-related content. The present study offers valuable insights for destination management organizations, tourism operators, tourism industry stakeholders, content creators, social media marketing managers and destination brand managers.

Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation

The recommendation system aims to recommend items to users by capturing their personalized interests. Traditional recommendation systems typically focus on modeling target behaviors between users and items. However, in practical application scenarios, various types of behaviors (e.g., click, favorite, purchase, etc.) occur between users and items. Despite recent efforts in modeling various behavior types, multi-behavior recommendation still faces two significant challenges. The first challenge is how to comprehensively capture the complex relationships between various types of behaviors, including their interest differences and interest commonalities. The second challenge is how to solve the sparsity of target behaviors while ensuring the authenticity of information from various types of behaviors. To address these issues, a multi-behavior recommendation framework based on Multi-View Multi-Behavior Interest Learning Network and Contrastive Learning (MMNCL) is proposed. This framework includes a multi-view multi-behavior interest learning module that consists of two submodules: the behavior difference aware submodule, which captures intra-behavior interests for each behavior type and the correlations between various types of behaviors, and the behavior commonality aware submodule, which captures the information of interest commonalities between various types of behaviors. Additionally, a multi-view contrastive learning module is proposed to conduct node self-discrimination, ensuring the authenticity of information integration among various types of behaviors, and facilitating an effective fusion of interest differences and interest commonalities. Experimental results on three real-world benchmark datasets demonstrate the effectiveness of MMNCL and its advantages over other state-of-the-art recommendation models. Our code is available at https://github.com/sujieyang/MMNCL.

ARTIFICIAL INTELLIGENCE IN ACCOUNTING: A BIBLIOMETRIC ANALYSIS

This study aims to analyze the scientific production on Artificial Intelligence (AI) in Accounting through a bibliometric study. In the search filters in the Web of Science database, the following keywords were used: "artificial intelligence" (Title) AND "accounting" (Title), resulting in 71 documents. The results showed a trend in the intersection between accounting and emerging technologies, such as artificial intelligence, big data and blockchain. Each set of research highlights different aspects and applications of these technologies, from accounting education and practice to auditing and digital transformation. This diversity suggests a dynamic and rapidly evolving field of research, where technological innovation plays a central role. Technology has significant impacts on accounting, and understanding these changes allows professionals in the field to be prepared for the ongoing transformations. In this sense, accounting will not cease to exist, but it will need to find its reason for existing to remain competitive in the face of new automated processes, acting mainly in the creation and authentication of information generated through automated processes, thus ensuring flexibility in decision-making.

Research on the decentralized application of blockchain technology in campus information security

In the digital information age, the traditional centralized storage model is vulnerable to security attacks, which leads to the spread of false information and difficulty in tracing. This study proposes a decentralized campus information security system using blockchain technology and builds a tamper-proof, traceable, and privacy-protected architecture through Ethereum and Inter Planetary File System (IPFS). The system uses zero-knowledge proof and homomorphic encryption technology to ensure privacy and uses IPFS as an off-chain storage mechanism to improve the scalability of the system and data access speed. Experimental results show that compared with traditional digital applications, the system performs well in ensuring the authenticity and security of information and effectively protects user privacy.

AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content

Amidst the burgeoning information age, the rapid development of artificial intelligence-generated content (AIGC) has brought forth challenges regarding information authenticity. The proliferation of distorted information significantly impacts users negatively. This study aims to systematically categorize distorted information within AIGC, delve into its internal characteristics, and provide theoretical guidance for its management. Utilizing ChatGPT as a case study, we conducted empirical content analysis on 243 instances of distorted information collected, comprising both questions and answers. Three coders meticulously interpreted each instance of distorted information, encoding error points based on a predefined coding scheme and categorizing them according to error type. Our objective was to refine and validate the distorted information category list derived from the review through multiple rounds of pre-coding and test coding, thereby yielding a comprehensive and clearly delineated category list of distorted information in AIGC. The findings identified 8 first-level error types: “Overfitting”; “Logic errors”; “Reasoning errors”; “Mathematical errors”; “Unfounded fabrication”; “Factual errors”; “Text output errors”; and “Other errors”, further subdivided into 31 second-level error types. This classification list not only lays a solid foundation for studying risks associated with AIGC but also holds significant practical implications for helping users identify distorted information and enabling developers to enhance the quality of AI-generated tools.

A Secure and Verifiable Blockchain-Based Framework for Personal Data Validation

The online services provided by the Service Provider (SP) have brought significant convenience to people’s lives. Nowadays, people have grown accustomed to obtaining diverse services via the Internet. However, some SP utilize or even tamper with personal data without the awareness or authorization of the Data Provider (DP), a practice that seriously undermines the authenticity of the DP’s authorization and the integrity of personal data. To address this issue, we propose a Verifiable Authorization Information Management Scheme (VAIMS). During the authorization process, the authorization information and personal data fingerprints will be uploaded to the blockchain for permanent record, and then the SP will store such authorization information and personal data. The DP generates corresponding authorization fingerprints based on the authorization information and stores them independently. Through the authorization information and authorization fingerprints on the chain, the DP can verify the authenticity of the authorization information stored by the SP at any time. Meanwhile, by leveraging the personal data fingerprint on the blockchain, the DP can check whether the personal data stored by the SP has been tampered with. Additionally, the scheme incorporates database technology to accelerate data query. We implemented a VAIMS prototype on Etherum, and experiments demonstrate that the scheme is effective.

Social media, misinformation and fake news in the pandemic: the dominant gaps and future research avenues

PurposeSocial networking sites have become breeding grounds for the spread of fake news and misinformation. At the height of the Covid-19 pandemic, the spread of fake news intensified, causing complications for health communicators by drowning authentic information from verifiable official sources. Looking at the impact of this growing phenomenon on people’s attitudes and behaviour during the pandemic, research in the area must be populated to help governments, supranational organisations, non-governmental organisations as well as civil society organisations to formulate policies to curb the menace. This study was therefore undertaken to unravel current gaps and future research avenues to empower academia in achieving the desired scholarly impact.Design/methodology/approachThe study adopted the systematic review approach, relying on 56 peer-reviewed articles on social media, fake news and misinformation in the Covid-19 pandemic.FindingsThe study found that the use of social media during the height of the pandemic led to unhelpful information creation and sharing behaviours such that people’s self-awareness reduced drastically, thereby impeding the fight against the pandemic. The study also established that Entertainment motives, Ignorance and Altruism motives were the dominant factors that influenced the spread of fake news. There was evidence of the marginalization of research on the subject matter from contexts such as Africa, South America and Oceania.Originality/valueThis study has established existing gaps in issues and evidence, methodology, theory and context and consequently discussed future research avenues for social media use and the spread of fake news. The study has also provided practical implications for both governmental and non-governmental organisations in curbing the phenomenon of fake news and misinformation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2022-0366.

Optimized data management with color multiplexing in QR codes

This study integrates colorimetry and computation by identifying their commonalities to develop a novel encryption system centered around color, specifically using QR codes. We propose an approach that multiplexes QR codes of varying colors, each containing distinct information. A key is generated to encapsulate user-specific data and identify the QR code with authentic information. We develop serverless architectures to facilitate rapid encryption and decryption processes. The system’s performance and efficiency are evaluated through two architectures: a sequential system implemented on Google Colab and a distributed system utilizing AWS Lambda serverless architecture. Metrics such as NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity) and key space analysis, indicative of the system’s robustness, are analyzed according to existing literature. In addition, the cost of this serverless technology is evaluated in comparison to cloud and local. Our findings demonstrate that the serverless architecture offers a viable and efficient solution for coding. The implications of this research extend across various sectors, including defense, healthcare, and everyday digital interactions, presenting a scalable and secure alternative for data encryption and communication.