Author: Latest Results

Cryptocurrency is new vogue: a reflection on money laundering prevention

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Abstract

It has been argued that with the increasing crypto economy and the liquidity of cryptocurrency globally, cryptocurrency could potentially serve as another vehicle for money laundering activities. Yet, only a handful of studies probe the emerging nexus between cryptocurrency and money laundering and the feasibility of anti-money laundering (AML) strategies presented by law enforcement and financial institutions from a criminologist’s perspective. Therefore, this study uses the literature on money laundering to analyze the features of cryptocurrency that account for its popularity. A money laundering triangle is presented that corresponds with the use of cryptocurrency from within a criminological framework. This study also suggests that in addition to domestic and international interagency cooperation across jurisdictions, future developments should target the characteristics of cryptocurrencies such as anonymity, decentralization and blockchain which could help make cryptocurrency usage less attractive to motivated offenders across money laundering stages.

Corporate governance from colonial Ceylon to post-civil war Sri Lanka

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Abstract

This paper examines the corporate governance mechanisms in Sri Lanka, a country that only a little more than a decade ago emerged from a 30-year long civil war. We focus on the evolution of Sri Lanka’s corporate governance from historical, sociocultural, and institutional perspectives. Taking Sri Lanka as a case where inclusiveness and reconciliation at the board level is important, we aim to diagnose the key corporate governance issues which we then broaden towards other South Asia developing countries and provide a research agenda. Many Sri Lankan firms suffer from typical Asian-style agency problems; conflicts of interest between (i) minority and majority shareholders, (ii) shareholders and debtholders, and (iii) shareholders and stakeholders. The most prevalent agency problem is the expropriation of minority shareholders’ rights through ownership pyramids, cross-holdings, or intermediate private firms. Although creditor protection laws limit the expropriation of debtholders’ rights, firms’ widespread use of political connections forces banks to grant cheap credit at favorable terms. Poor stakeholder management creates agency problems following window dressing of ESG disclosures, corporate opacity, and ethnicity-and gender-based discrimination in the workplace. This study shows that social norms and ethical values play a non-negligible role in the functioning of the corporate governance regulation and in corporate culture in Sri Lanka.

Corporate governance from colonial Ceylon to post-civil war Sri Lanka

Latest Results

Abstract

This paper examines the corporate governance mechanisms in Sri Lanka, a country that only a little more than a decade ago emerged from a 30-year long civil war. We focus on the evolution of Sri Lanka’s corporate governance from historical, sociocultural, and institutional perspectives. Taking Sri Lanka as a case where inclusiveness and reconciliation at the board level is important, we aim to diagnose the key corporate governance issues which we then broaden towards other South Asia developing countries and provide a research agenda. Many Sri Lankan firms suffer from typical Asian-style agency problems; conflicts of interest between (i) minority and majority shareholders, (ii) shareholders and debtholders, and (iii) shareholders and stakeholders. The most prevalent agency problem is the expropriation of minority shareholders’ rights through ownership pyramids, cross-holdings, or intermediate private firms. Although creditor protection laws limit the expropriation of debtholders’ rights, firms’ widespread use of political connections forces banks to grant cheap credit at favorable terms. Poor stakeholder management creates agency problems following window dressing of ESG disclosures, corporate opacity, and ethnicity-and gender-based discrimination in the workplace. This study shows that social norms and ethical values play a non-negligible role in the functioning of the corporate governance regulation and in corporate culture in Sri Lanka.

Equity-Oriented Learning Design: An Entangled Future

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Abstract

There is growing interest in the literature towards a more distributed, collaborative view of learning design that focuses on relationships and connection. In this paper, we propose a vision of learning design that is entangled and crosses boundaries, framed by an equity-oriented mindset that blurs and resists boundaries, and merges learning design with facilitation, external with internal communities, and academia with activism. Based on interviews with learning design practitioners across the world, and framed by theories of posthumanism and postdigital education, we share eight orientations that form a future and equity-oriented learning design pattern: awareness of context, matter matters, co-creating with humans and non-humans, relationality and connection, modelling vulnerability, the entanglement of the personal and political, and creating transformative spaces. We then use one of our responses to the advent of ChatGPT to show how these orientations have helped us in times of uncertainty and disruption, an agential cut that highlights the value of acknowledging the material-discursive relationships emerging in and through our work. As such, rather than focusing on conventional, static definitions and models, we are interested in knowledge-making processes that come into existence when we practise learning design and engage with each other and the world around us, and who we become in our relationships with others, both human and non-human, and the patterns that we form in this entanglement.

Biometric systems for identification and verification scenarios using spatial footsteps components

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Abstract

Humans are distinguished by their walking patterns; many approaches, including using various types of sensors, have been used to establish walking patterns as biometrics. By studying the distinguishing features of a person's footsteps, footstep recognition may be utilized in numerous security applications, such as managing access in protected locations or giving an additional layer of biometric verification for secure admittance into restricted regions. We proposed biometric systems for verifying and identifying a person by acquiring spatial foot pressure images from the values obtained from the piezoelectric sensors using the Swansea Foot Biometric Database, which contains 19,980 footstep signals from 127 users and is the most prominent open-source gait database available for footstep recognition. The images acquired are fed into the ConvNeXt model, which was trained using the transfer learning technique, using 16 stride footstep signals in each batch with an Adam optimizer and a learning rate of 0.0001, and using sparse categorical cross-entropy as the loss function. The proposed ConvNeXt model has been adjusted to acquire 512 feature vectors per image, and these feature vectors are used to train the logistic regression models. We propose two biometric systems. The first biometric system is based on training one logistic regression model as a classifier to identify 40 different users using 1600 signals for training, 6697 signals for validation, and 200 signals for evaluation. The second biometric system is based on training 40 logistic regression models, one for each user, to validate the user's authenticity, with a total number of 2363 training signals, 7077 validation signals, and 500 evaluation signals. Each of the 40 models has a 40-training signal per client and a different number of signals per imposter, a different number of signals for the validation that ranges between 8 and 650 signals, a 5-signal for an authenticated client, and a different number of signals per imposter for evaluation. Independent validation and evaluation sets are used to evaluate our systems. In the biometric identification system, we obtained an equal error rate of 15.30% and 21.72% for the validation and evaluation sets, while in the biometric verification system, we obtained an equal error rate of 6.97% and 10.25% for the validation and evaluation sets.

Biometric systems for identification and verification scenarios using spatial footsteps components

Latest Results

Abstract

Humans are distinguished by their walking patterns; many approaches, including using various types of sensors, have been used to establish walking patterns as biometrics. By studying the distinguishing features of a person's footsteps, footstep recognition may be utilized in numerous security applications, such as managing access in protected locations or giving an additional layer of biometric verification for secure admittance into restricted regions. We proposed biometric systems for verifying and identifying a person by acquiring spatial foot pressure images from the values obtained from the piezoelectric sensors using the Swansea Foot Biometric Database, which contains 19,980 footstep signals from 127 users and is the most prominent open-source gait database available for footstep recognition. The images acquired are fed into the ConvNeXt model, which was trained using the transfer learning technique, using 16 stride footstep signals in each batch with an Adam optimizer and a learning rate of 0.0001, and using sparse categorical cross-entropy as the loss function. The proposed ConvNeXt model has been adjusted to acquire 512 feature vectors per image, and these feature vectors are used to train the logistic regression models. We propose two biometric systems. The first biometric system is based on training one logistic regression model as a classifier to identify 40 different users using 1600 signals for training, 6697 signals for validation, and 200 signals for evaluation. The second biometric system is based on training 40 logistic regression models, one for each user, to validate the user's authenticity, with a total number of 2363 training signals, 7077 validation signals, and 500 evaluation signals. Each of the 40 models has a 40-training signal per client and a different number of signals per imposter, a different number of signals for the validation that ranges between 8 and 650 signals, a 5-signal for an authenticated client, and a different number of signals per imposter for evaluation. Independent validation and evaluation sets are used to evaluate our systems. In the biometric identification system, we obtained an equal error rate of 15.30% and 21.72% for the validation and evaluation sets, while in the biometric verification system, we obtained an equal error rate of 6.97% and 10.25% for the validation and evaluation sets.

A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification

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Abstract

Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field.

A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification

Latest Results

Abstract

Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field.

Assessing the leeway of state-led strategic communication abroad: a comparison of news coverage on Austria, Germany, and Switzerland in Arabic

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Abstract

Public diplomacy programmes with the goal to enhance a country’s reputation and image abroad have become wide-spread practice, also among small states with little geopolitical relevance. News media offer one of the most important platforms of their implementation. But do small states have the leeway to successfully implement their communication strategies on a global scale? Are media-based public diplomacy strategies even an option for those cases? This study assesses these questions based on international media resonance of states. Relating to the theoretical approach of country news value literature, a comparative research design is implemented. It analyses news coverage on the three German-speaking countries Germany, Austria, and Switzerland, performing a multi-level automated text analysis of 11,513 news media articles in Arabic. In accordance with existing empirical and theoretical contributions, it is shown that high-status states have more resonance-based leeway. Nevertheless, media resonance-based leeway of smaller states with lower status is caused differently, i.e. by their political, rather than their economic or military power.

The role of digital social innovations to address SDGs: A systematic review

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Abstract

The impact of the COVID-19 pandemic has increased the search for solutions to social problems associated with the Sustainable Development Goals (SDGs). Main actors are turning to Digital Social Innovations (DSIs), defined as collaborative innovations where enterprises, users and communities collaborate using digital technologies to promote solutions at scale and speed, connecting innovation, the social world and digital ecosystems to reach the 2030 Agenda. This study aims to identify how digital transformations and social innovations solve social problems and address SDGs. We conducted a systematic review based on a sample of 45 peer-reviewed articles published from 2010 to 2022, combining a bibliometric study and a content analysis focusing on opportunities and threats impacting these fields. We observed the spread and increasing use of technologies associated with all 17 SDGs, specially blockchain, IoT, artificial intelligence, and autonomous robots that are increasing their role and presence exponentially, completely changing the current way of doing things, offering a dramatic evolution in many different segments, such as health care, smart cities, agriculture, and the combat against poverty and inequalities. We identified many threats concerning ethics, especially with the increased use of public data, and concerns about the impacts on the labor force and the possible instability and impact it may cause in low skill/low pay jobs. We expect that our findings advance the concept of digital social innovations and the benefits of its adoption to promote social advancements.