Abstract

The purpose of this paper is twofold: (i) to investigate some of the main issues surrounding the classification of digital currencies, and (ii) to identify the accounting practices and standards tied to digital currencies. This paper discusses two different types of digital currencies, including: central bank digital currencies (CBDCs) and privately issued cryptocurrencies such as Bitcoin. The findings of this study suggest that current accounting standards do not precisely cover the accounting treatment of digital currencies, even though the estimated value of market capitalisation of cryptocurrency in 2022 was USD 200 billion. This conceptual paper identifies the imminent need for an accounting standard to provide guidance on the identification, classification, measurement, and presentation of digital currencies. In the interim, existing accounting standards can be amended to incorporate digital currencies to avoid inconsistent global accounting approaches.

Introduction

Digital currency is an unparalleled technological advancement that has received increasing attention from researchers, investors, financial institutions and regulators. It is a mode of exchange that does not have a physical or tangible basis and exists purely in an electronic form. It should not, however, be confused with electronic money such as an online bank account, which shows the amount of cash that is held in a specified account and is linked to a physical currency. Many businesses are now pursuing digital currency as part of their wider financial management system, and this should be appropriately recorded in their financial statements. Despite the rapid increase in the amount and frequency of digital currency transactions, there is no clear guidance from the International Accounting Standards Board (IASB). The classification of digital currencies is a significant issue, and lack of guidance from standard-setters affects the accounting treatment of digital currencies and disclosures in financial statements.

Current literature shows the lack of focus on accounting treatment of digital currencies. In this conceptual paper, we aim to address this literature gap by examining accounting classification and treatment of transactions involving digital currencies. In addition, the paper aims to discuss a number of potential risks associated with the use of digital currencies and lack of clear classifications. Key questions around the classification, reporting and disclosure of digital currencies are addressed in this paper, including: In what asset class should digital currencies be classified as? and, is there an existing accounting standard for digital currency within the current IFRS standards?

Summary and conclusion

Digital currencies represent a genuinely new development in the landscape of financial payments. The main issue is that there are currently no clear or universal accounting standards or classifications for digital currencies. It is critical that blockchain technology, regarded as the underlying technology of most digital currencies, is fully understood, in order to be able to create relevant accounting and taxation standards as well as regulatory frameworks. Blockchain could therefore positively impact business models due to its unique characteristics, which address a number of key business issues. In addition, it offers the possibility of ‘triple-entry accounting’, in which accounting entries are cryptographically sealed by a third entry and made accessible to all third parties. Furthermore, blockchain accounting can reduce errors in disclosure and earnings management, improve the reliability and timelines of accounting information, and accordingly mitigate information asymmetry.

Accounting standard setters seem to be struggling to cope with the exponential growth in digital assets, which has led to a lack of consensus among them about how to treat these assets. This demonstrates that there is an urgent need for clear guidance to be issued by the standard-setters, as this absence has hindered the development of a universal accounting practice for digital currencies and has thus posed a number of challenges for authorities in relation to the classification and measurement of digital currencies.

Abstract

In recent years artificial intelligence (AI) has been seen as a technology with tremendous potential for enabling companies to gain an operational and competitive advantage. However, despite the use of AI, businesses continue to face challenges and are unable to immediately realize performance gains. Furthermore, firms need to introduce robust AI systems and mitigate AI risks, which emphasizes the importance of creating suitable AI governance practices. This study, explores how AI governance is applied to promote the development of robust AI applications that do not introduce negative effects, based on a comparative case analysis of three firms in the energy sector. The study illustrates which practices are placed to produce knowledge that assists with decision making while at the same time overcoming barriers with recommended actions leading to desired outcomes. The study contributes by exploring the main dimensions relevant to AI’s governance in organizations and by uncovering the practices that underpin them.

Introduction

As businesses adopt Artificial Intelligence (AI), they are faced with new value propositions, but they also have to deal with new challenges, such as reducing the gap between intent and action(Amershi et al., 2019; Enholm et al., 2021; Mishra & Pani, 2020). Artificial intelligence has been perceived as a tool with which we can layer many different functions or as a solution to problems that are beyond the ability of traditional applications to solve. (Smuha, 2019). In order to gain a competitive advantage over their competitors (Raisch & Krakowski, 2021), businesses have implemented and deployed AI solutions to automate their processes, increase efficiency and reduce costs (Frank et al., 2019; Gregory et al., 2020). To achieve these goals, AI governance is essential. According to Butcher and Beridze (2019), AI governance “can be characterized as a variety of tools, solutions, and levers that influence AI development and applications”. Yet, further research is needed to better determine how AI Governance can be introduced into a company and whether AI governance can assist a company in achieving its objectives.

While AI has the potential to generate business value in terms of performance, productivity and effectiveness, it is not autonomous, as it works in concert with human capabilities (Zhang et al., 2021). Consequently, organizational capabilities are the results of combining and deploying multiple complementary resources within a firm to achieve competitive advantage (Mikalef & Gupta, 2021). When a firm optimizes its firm-level resources and adopts AI technological innovations, it can enhance its transformed projects’ business value which drives business value and impacts performance (Wamba-Taguimdje et al., 2020). Simultaneously, the AI algorithms can be considered performative in the sense that they assist in decision-making, the extent to which their use can form organizational processes, or even take autonomous decisions (Faraj et al., 2018; Grønsund & Aanestad, 2020) that leads to new organization capabilities through AI. The use of AI, for instance, could create more substantial customer acquisition or higher customer lifetime value and lower operating costs or reduce credit risk.

Discussion

In this study, we set out to explore the underlying activities that comprise an organization’s AI governance. Specifcally, we built on the prior distinction between structural, relational, and procedural dimensions of governance in order to understand how organizations are planning around their AI deployments. Through a multi-case study of three organizations that have been using AI for several years, we conducted a series of interviews with key respondents and identifed a set of activities that were relevant under each of the three dimensions, as well as challenges they faced during deployments of AI and how they managed to overcome them. Our analysis essentially points out the various obstacles that AI governance is oriented to overcoming, and the mechanisms employed to operationalize them.

Specifcally, we fnd that the obstacles that are identifed during the process of deploying AI are observable at diferent phases and concern diferent job roles. When it comes to difcult management responsibilities that a business owner must do, AI solutions can always provide a variety of responses and probabilities for each of these alternatives. However, AI lacks the ability to make decisions in specifc contexts. To make the ultimate decision, a business owner or manager must employ intuition to reconcile the choices provided by AI (Kar & Kushwaha, 2021). In addition, they span various levels of analysis, from the personal, such as fear of AI and reluctance of employees to adopt it, to organizational-level ones, such as organizational directives on how to comply with laws and regulations. What is more, the study reveals not only that AI governance is a multi-faceted issue for organizations but that it spans multiple levels, therefore requiring a structured approach when it is deployed. In addition, different concerns emerge at diferent phases of AI projects, so AI governance also encapsulates a temporal angle in its formation and deployment.

Abstract

The IoT market is continuously expanding, and security measures parallel to the growing industrialization has drawn the research community’s attention. While several works have focused on the different technologies used to protect IoT devices, this paper aims to narrow the focus to Blockchain and Artificial Intelligence (AI) solutions for security and privacy issues faced by these devices. These technologies address two major vulnerabilities with edge devices on IoT networks: centralization and weak edge nodes. Blockchain provides a distributed system that can help avoid a single-point failure and can be used to optimize battery life on low-level devices by load balancing. On the other hand, AI’s power to learn and adapt is critical to automate systems and make them intelligent enough to analyze collected data. The combination of these two technologies is capable of mitigating multiple challenges IoT architectures face currently. The paper discusses the latest major architectures that integrate Blockchain and AI to enhance security within the. This paper also presents the comprehensive take on the device security and drawbacks to these mechanisms. This work also addresses the future of security in IoT and suggests different areas that need further work to better comprehend how the combination of these technologies can assist in enhancing security.

INTRODUCTION

The Internet of Things (IoT) market, a network of devices that communicate and exchange data with each other, has expanded over the last decade. IoT devices provide different applications such as health care, smart homes, wearables, and smart cities. The advent of a wide range of devices requires security that matches its capabilities to ensure the safety of the system and network. The number and type of attacks carried out against IoT devices have increased exponentially. As the need for faster and more efficient devices increases, there is a lag in equal breakthroughs in IoT security. According to Business Insider’s 2020 IoT research, IoT devices are predicted to increase to 41 billion by 2027 [1].

In order to meet this challenge, we must focus on the security strategies that match IoT devices’ hardware and computational power to minimize internal or outsider attacks.

CONCLUSION

This paper defined the role that AI/ML and Blockchain have currently in IoT devices, explaining in detail how Blockchain technology works and how AI/ML is used to lessen the computational load on IoT devices. We then talked about Blockchain and AI/ML in IoT security and how having these technologies strengthens privacy, data integrity, and data processing. Four potential solutions were explored and analyzed to understand how they could help deficiencies in the current IoT network setup as well as challenges that were not addressed in the solutions. Finally, future lanes of research were brought up for researchers to study.

This paper presented the role that AI/ML and Blockchain have currently in IoT devices and how these two can work in tandem to lessen the computational load on IoT devices. The paper then discussed the scope of Blockchain and AI/ML in IoT security towards strengthening privacy, data integrity, and data processing. Then it addresses the vulnerabilities and feasibility of AI/ML and Blockchain towards IoT security. With several potential solutions, four major works were studied. The paper then progresses to analyze these works on several parameters, summarizing them based on their: 1) Feasibility, 2) Security Strength, 3) Applicability and 4) Robustness.

Abstract

     Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as ‘non-depressed’ or ‘depressed’. Moreover, three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.

Introduction

     Analyzing the presence of mood and psychological disorders through behavioral and linguistic cues from social media data remains a critical area of interdisciplinary research. In addition to these disorders, the last decade has seen exponentially increasing attempts to assess related symptomatology such as depressive disorders, self-harm, and severity of mental illness using non-clinical data (Bucci et al., 2019). Social media platforms and other online discussion forums have been particularly appealing to the research community for various research purposes (e.g., population-level mental health monitoring (Conway & O’Connor, 2016), personal traits detection (Marouf et al., 2020), cyberbullying spotting (Bozyiğit et al., 2021), etc.) because of the massive scale of data. This massive data flow has resulted from increasing rates of internet access and people spontaneously sharing their suffering, pain, and struggle anonymously on these platforms (Ofek et al., 2015). Recognizing the early symptoms of depressive disorder through a person’s language use can prevent many disastrous outcomes like self-harm, suicide, etc., and even help deploy effective treatment in proper time. Moreover, the outbreak of the COVID-19 pandemic is likely to have devastating impacts on the mental health of millions of individuals as lockdown in the affected areas has reported high rises in the incident rates of mood disorder, including acute stress disorder, post-traumatic stress disorder, generalized anxiety disorder, and overall sub-clinical mental health deterioration (Singh et al., 2020). The scope of mental health deterioration during the COVID-19 pandemic and the comprehensive nature of diagnosing depressive disorders have provided an unprecedented need to infer the mental states of individuals from all-inclusive resources. Recent studies have revealed that valuable insights into the impact of the pandemic on population-level mental health can be inferred from posts or comments on social media (Low et al., 2020).

Conclusions and future work

     This work introduced a new typology for diagnosing depression severities from social media texts, as well as a unique dataset of labeled tweets with a confidence score for each label. The dataset was constructed based on strong ground truths and clinical validation, and it is expected to help alleviate the scarcity of mental health data to some extent. The description of the process and challenges in creating such a dataset may motivate researchers to collect similar corpora of this scale from other social media and discussion forums. The experimental results indicated that existing state-of-the-art models often fail to understand the contextual undertone of the data samples. Developing a model that is capable of comprehending the subdued relationship and differences among depression severities can result in an even better understanding of human cognition. Moreover, analysis of the classification performance indicates that there is no distinct division of keywords among different depression severities. The same keyword might be used differently to express different emotions, rather it is more important to understand the context of the tweet to diagnose the severity of depression. Broader implications of this research may include personalizing and directing preventative and awareness messages by health professionals to the users in need.

Big data is an emerging torrent. We are held up in a Lake of data and its intensity is continuously increasing. With the fast growth of promising applications like social media, web, mobile services, and other applications across various organizations, there is a rapid growth of data. Thus arises the notion of ‘‘Big Data’’. Data analysis, querying, storage, retrieval organization and modeling are the fundamental challenges associated with it. These challenges are posed due to the fact that big data is complex in nature. In this paper, we address above issues and many more to realize the bottlenecks. But we believe that appropriate research in big data will lead to a new wave of advances that will revolutionize the market and the future analysis platforms, services as well as products and will tackle all the challenges.

Introduction

The data revolution has just begun. The term ‘Big Data’ is relatively new. It has started gaining momentum a decade ago which as a result has led to tremendous increase of data set size. It has started revolutionising commerce, science, medicine, finance and everyday life. Data creation is taking place at an extraordinary rate due to advances more or less in every field of science [1]. IoT, now also referred as the Internet of Everything connects new devices and new sources that generate mounds of data with every passing second [2]. More than 90% of the data in the world has been produced in the past 2 years only [3]. As per stats [4–6], every day we create 2.5 quintillion [a million raised to the power of five (1030)] bytes of data. However, the word ‘Big’ in big data does not only imply to size. Had it been referring to size only, the way out would have been quite effortless or simple. Instead big data is a broad term that describes the enormous volume of data sets, so large and complex that it becomes difficult to handle, process, analyse, manage, store and retrieve the data sets in a specified time frame using traditional methods. Thus big data may be defined as data that is ‘so huge’, ‘so fast’ and ‘so tough’ for present tools to process. Here ‘so big’ states that organisations must be able to deal with data on a peta byte level, ‘so fast’ indicates that the data needs to be processed very quickly, similarly ‘so hard’ indicates that data can not fit in the existing set of tools for processing and managing. Basically we can say that the big data differs from the traditional data in three ways—the amount of data (size), the rate of data creation and transmission (velocity) and the heterogeneity of data—structured, unstructured or semi-structured (variety).

1- Big Data science

2- Cloud computing for Big Data

3- Main requirements of Big Data analysis on Clouds

4- Big Data science frameworks

5- Discussion and open challenges

References

Abstract

Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data. The intersection of these trends is what is, nowadays, called Big Data Science. Big Data Science requires scalable architectures for storing and processing data. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting Big Data Science as a commodity service for data scientists. In addition, we analyze and classify the state-of-the-art of big data analytics frameworks, available today mostly on Clouds, based on their supported service models. Furthermore, we provide various insights about the latest ongoing developments and open challenges in this domain.

Big Data science

The continuous growth and integration of data storage, computation, digital devices and networking empowered a rich environment for the explosive growth of Big Data as well as the tools through which data is produced, shared, cured and analyzed [43]. In addition to the 4Vs (Volume, Velocity, Variety and Veracity), it is vital to consider an additional feature of Big Data that is Value. Value is obtained by analyzing Big Data and extracting from them hidden patterns, trends and knowledge models by using smart data analysis algorithms and techniques. Data science methods must be able to analyze Big Data and extract features we don’t know. Those learned features improve the value of data that will make it possible to better understand phenomena and behaviors, optimizing processes, and improving machine, business and scientific discovery. Therefore, we cannot look at Big Data Science without considering data analysis and machine learning as key steps for including value as a Big Data Science strategy. In practice, big data analytics tools enable data scientists to discover correlations and patterns via analyzing massive amounts of data from various sources that are of different types. Recently, Big Data science [3] has emerged as a modern and important data analysis discipline. It is considered as an amalgamation of classical disciplines such as statistics, artificial intelligence, mathematics and computer science with its sub-disciplines including database systems, machine learning and distributed systems. It combines existing approaches with the aim of turning abundantly available data into value for individuals, organizations, and society. The ultimate goal of data science techniques is to convert data into meaningful information. Both in business and in science, data science methods have shown to facilitate more robust decision making capabilities. In the last few years, we have witnessed a huge emergence of Big Data Science in various real-world applications such as business optimization, financial trading, healthcare data analytics and social network analysis, just to name but a few [43]. In particular, we can think of the relationship between Big Data and data science as being like the relationship between crude oil and an oil refinery.

The digitalization of the healthcare system is changing the way we practice medicine and conduct clinical research.1,2 The widespread implementation of electronic health records (EHRs) is paving the way for Big Data research and is bringing the world of data science to the patient’s bedside.2-4 Within the healthcare system, the intensive care unit (ICU) presents a particularly convincing case for using data science to improve patient care.5 For one, the evidence supporting many of the interventions performed in the ICU is rather scarce and practice variability is abundant.5,6 In addition, the complexity of critical illness makes the traditional reductionist approach to medical research insufficient, that is, single drug intervention trials or single pathway biomarker studies are unlikely to satisfy the clinical realities of the ICU.5 Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible.7,8 Moreover, the data required to do this are being generated and digitized in troves. EHRs, bedside monitors, medication pumps, and ventilators are continuously generating new minable data, and soon the advancement of modern molecular diagnostics will result in a deluge of “omics” data derived from the genome, transcriptome, microbiome, and a long list of other “-omes” (Figure 1). As Big Data and data science gradually infiltrate most aspects of clinical research and – ultimately– clinical care in the ICU, it is increasingly evident that intensivists should be familiar with the promise and perils of these approaches. In this paper, we will review the definitions, types of algorithms, applications, challenges, and future of Big Data and data science in critical care.

In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. Big data presents all the attributes of a buzz word, but it should be thoroughly investigated so that it can be decided whether big data is a mere trend or the premises of a true revolution for research and routine practice. Origins of the term “big data” are unclear and we propose here to go many years back in time to search for them. Techniques usually presented as specific to big data such as machine learning techniques are supposed to support the ambition of personalized, predictive and preventive medicines. These techniques are mostly far from being new and are more than 50 years old for the most ancient. On the other hand, several issues closely related to the properties of big data and inherited from other scientific fields such as artificial intelligence are often underestimated if not ignored. In this paper, we expose the most important of these issues. Finally, in a context of general enthusiasm about the big data phenomenon, forensic science is still awaiting for their position papers as well as for a comprehensive outline of what kind of contribution big data could bring to the field. The present situation calls for definitions and actions to rationally guide research and practice in big data. Here, we briefly present what is at stakes for forensic science if it wants to embrace the philosophy of the big data era while avoiding its main pitfalls.

1 – Is big data more than a buzz word and where does it come from? No one can determine where or when the use of the term “big data” originated (1). It exponentially spread and contaminated all scientific and non-scientific fields within the past decade.

1.1 Origins of big data: definitions and practices of big data in the past decade.

Big data is a vague and generic term that can encompass several distinct and non-exclusive properties. The origin of the big data concept is often attributed to a short technical report from the META group which is an American consulting firm, since become Gartner (2). This report was written by Doug Laney in 2001 and presented the challenge of the “3D Data management” which evolved into the 3Vs concept: Volume – Variety – Velocity. Other Vs have been proposed since then, such as Veracity. All Vs refer to data: the challenges that faces the information society are bound to great Volumes of Various and heterogeneous data to process in real-time (Velocity). Practically, there is no consensual definition of big data. Even if many recognized the 3Vs terminology, not all understand the same meaning for each V. Volume may be the most cited property of big data, maybe because echoing to the “big” part. What is Volume? If data are figured by a 2-dimensional table (columns for variables and lines for observations or patients) is a big Volume of data a table with many observations (lines) or with many variables (columns)? Or both? Baro and colleagues also suggested defining Volume as a combination of both these dimensions (3) (figure1). The distinction seems theoretic but has strong implications, since it is the number of variables that defines the dimensionality of data, and not the number of observations (see part 2 – the curse of dimensionality).

Abstract:

IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction between “everything”, IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytics tools are able to extract “sense” from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset.